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<hr />
<div>{{study|moderator=Julle}}<br />
[[category:EBoDE]]<br />
<br />
==Introduction==<br />
<br />
Exposures to many environmental stressors are known to endanger human health. Negative impacts on health can range from mild psychological effects (e.g. noise annoyance), to effects on morbidity (such as asthma caused by exposure to air pollution), and to increased mortality (such as lung cancer provoked by radon exposure). Properly targeted and followed-up environmental health policies, such as the coal burning ban in Dublin (1990) and the smoking ban in public places in Rome (2005) have demonstrated significant and immediate population level reductions in deaths and diseases. In order to develop effective policy measures, quantitative information about the extent of health impacts of different environmental stressors is needed.<br />
<br />
As demonstrated by the examples above, health effects of environmental factors often vary considerably with regard to their severity, duration and magnitude. This makes it difficult to compare different (environmental) health effects and to set priorities in health policies or research programs. Public health policies generally aim to allocate resources effectively for maximum health benefits while avoiding undue interference with other societal functions and human activities. In order to develop such policies, it is necessary to know what ‘maximum health benefits’ are. Decades ago, such decisions tended to be made based on mortality statistics: which (environmental) factor causes most deaths? However, nowadays, most people get relatively old, and priority has shifted from quantity to quality of life. This has lead to the need to incorporate morbidity effects into public health decisions, and therefore to find a way of comparing dissimilar health effects.<br />
<br />
Such comparison and prioritisation of environmental health effects is made possible by expressing the diverging health effects in one unit: the environmental burden of disease (EBD). Environmental burden of disease figures express both mortality and morbidity effects in a population in one number. They quantify and summarize (environmental) health effects and can be used for:<br />
* Comparative evaluation of environmental burden of disease (“how bad is it?”)<br />
* Evaluation of the effectiveness of environmental policies (largest reduction of disease burden)<br />
* Estimation of the accumulation of exposures to environmental factors (for example in urban areas)<br />
* Communication of health risks<br />
<br />
An example of an integrated health measure that can be used to express the environmental burden of disease is the DALY (Disability Adjusted Life Years). DALYs combine information on quality and quantity of life. They give an indication of the (potential) number of healthy life years lost in a population due to premature mortality or morbidity, the latter being weighted for the severity of the disorder. The concept was first introduced by Murray and Lopez (1996) as part of the Global Burden of Disease study, which was launched by the World Bank. Since then, the World Health Organization (WHO) has endorsed the procedure, and the DALY approach has been used in various studies on a global, national and regional level.<br />
<br />
WHO collects a vast set of data on the global burden of disease. The first study quantified the health effects of more than 100 diseases for eight regions of the world in 1990 (Murray and Lopez, 1996). It generated comprehensive and internally consistent estimates of mortality and morbidity by age, gender and region. In a former WHO study, it was shown that almost a quarter of all disease worldwide was caused by environmental exposure (Prüss-Üstün and Corvalán, 2006). In industrial sub-regions this estimate was about 16% (15–18%). These fractions, however, are dependent on the conclusiveness of the included environmental factors and health effects. The WHO programme on quantifying environmental health impacts has addressed more than a dozen stressors <ref>The WHO programme[http://www.who.int/quantifying_ehimpacts/publications/en/]</ref>. In order to support further applications of the environmental burden of disease (EBD) assessments, a methodological guidance has been published by WHO (Prüss-Üstün et al., 2003) and was followed here too.<br />
<br />
In Europe, national environmental burden of disease (EBD) assessments are on-going in several countries. The work by RIVM was one of the first systematic European works in this area that utilized disability-adjusted life years (DALY) as a measure to compare the burden of different health outcomes related to the exposure of the population to environmental stressors (Hollander et al., 1999). The results highlighted that (i) a number of environmental stressors may cause chronic or acute diseases or death, (ii) a few top ranking stressors cause over 90% of the national EBD, and (iii) these top ranking stressors are not necessarily those that have drawn the most concern, regulatory action and/or preventive investment.<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
<br />
==Objectives==<br />
<br />
The EBoDE-project was set up in order to guide environmental health policy making in the six participating countries (Belgium, Finland, France, Germany, Italy and the Netherlands) and potentially beyond. From a policy perspective, these insights from the EBoDE-project can be useful to evaluate past policies and to gain insight in setting the policy priorities for the future. We have calculated the total EBD associated with the nine environmental stressors. The total EBD is not identical to the avoidable burden of disease, because some exposures are not realistically reducible to zero (e.g. fine particles). Also, our estimates do not take into account the costs of reducing the EBD. Thus, the results are only one input into the full process of developing cost-effective policies to achieve better environmental health.<br />
<br />
The objectives of the project were to update the available previous assessments, to focus on stressors relevant for the European region, to provide harmonized EBD assessments for participating countries, and to develop and make available the methodologies for further development and other countries.<br />
The specific objectives are to:<br />
• Provide harmonized environmental burden of disease (EBD) estimates for selected environmental stressors in the participating six countries;<br />
• Test the methodologies in a harmonized way across the countries.<br />
• Assess the comparability of the quantifications and ranking of the EBD<br />
• between countries<br />
• within countries<br />
• between environmental stressors;<br />
• Qualitative assessments of variation and uncertainty in the input parameters and results.<br />
<br />
Environmental burden of disease estimates have been calculated for:<br />
• nine environmental stressors: benzene, dioxins (including furans and dioxin-like PCBs), second-hand smoke, formaldehyde, lead, noise, ozone, particulate matter (PM) and radon;<br />
• six European countries: Belgium, Finland, France, Germany, Italy and the Netherlands;<br />
• the year 2004 (and some trend estimates for the year 2010).<br />
As outlined above, the EBoDE study was carried out in order to test the environmental burden of disease methodology in various countries. The results of the studies are intended to allow comparison of the disease burden between different environmental stressors and between countries. Consequently, the study does not to identify the ‘reduction potential’. Our estimates should therefore not be interpreted as the ‘avoidable burden of disease’: most risks cannot realistically be completely removed by any policy measures. For some exposures, however, the numbers may nonetheless be interpretable as reduction potential, eg for dioxins, formaldehyde, benzene, etc, as these exposures could potentially be completely eliminated.<ref name="EBoDe"/><br />
<br />
==Outline of this report==<br />
<br />
This report describes the methods, data and results of the EBoDE-project. Chapter 2 presents the methodology. The environmental stressors are introduced in Chapter 3, which also presents the data used (selected health endpoints, exposure data, exposure response functions). In Chapter 4, the results are presented and discussed. Chapter 5 gives information about uncertainties in the approach, and provides some alternative calculations using different input values. In Chapter 6 conclusions are drawn. The report ends with the references and two appendices: Appendix A presents country-specific results and Appendix B some considerations for using a life-table approach in EBD modelling.<ref name="EBoDe"/><br />
<br />
==Uncertainties and limitations==<br />
Assessment of uncertainties is essential in a comparison of quantitative estimates that are based on data from heterogeneous sources and slightly varying methods. Due to the wide range of data sources and models and the limited resources within the EBoDE project, systematic analysis of all uncertainties was not possible. However, we were able to assess a number of specific sources of uncertainties in more detail as part of the work, yielding some insights into the reliability of the overall assessment.<br />
The studied health impacts span approximately four orders of magnitude in size from few DALYs per million to almost 10 000 DALYs per million. The overall ranking of the environmental stressors seems to be rather robust against the relatively large uncertainties in individual estimates or methodological choices like discounting and age-weighing. However, some of the estimated ranges are overlapping. This concerns especially second hand smoke, radon and transportation noise that compete for the questionable honour of being the second most important environmental stressor in the participating countries. Among these stressors the differences are smaller than the corresponding uncertainties of the estimates.<br />
The health state of an individual person is the result of a complex mixture of genetic, environmental and behavioural factors. In a typical case of death, numerous factors play together. This means, for example, that a single death caused by a cardiovascular disease could be avoided by either reducing air pollution, or a better diet, or more physical activity. Therefore, if the individual attributable fractions are summed over a number of risk factors, a value over 100% may sometimes be found. For this and other reasons, it has been argued that death counts are not suitable for quantification of the impacts (Brunekreef et al., 2007). Therefore the authors recommend to mainly use aggregate population measures of health like DALYs, YLLs and YLDs.<br />
This chapter presents the quantitative results for selected sources of uncertainties and discusses the project limitations and author judgment of the reliability of the ranking.<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Transportation noise'''<br />
<br />
Burden of disease estimation for transportation noise is currently under active development. The estimates presented here were based on the only available international exposure data source, the first stage version of the European Noise Directive database (2007), which is not conclusive yet. Therefore it is clear that most of the exposures for transportation noise are underestimated. In some studies annoyance and cognitive impairment have been used as an additional health end-points for environmental noise. However, due to the selected more limited definition of ‘health’ as ICD-classified health states used in our assessment, annoyance and cognitive impairment were not included here. Only road, rail and air traffic exposures were included; many other sources also contribute to the noise exposures. Low exposures below the END data collection limits (50 and 55 dB) were not included. For these reasons it can be expected that when these limitations are solved, the impact estimates will increase.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Transport noise<br />
| Annoyance; cognitive impairment, tinnitus<br />
| Small proportion of target population is covered. Conversion between different noise metrics. Different samples. Different data estimation years<br />
| <br />
| Disability weight for sleep disturbance is uncertain. MI vs IHD<br />
| **<br />
| Underestimation due to uncovered populations and exclusion of low exposures, endpoints and noise sources<br />
|----<br />
|}<br />
<br />
See also:<br />
<br />
[[Health effects of Second-hand smoke in Europe]]<br />
<br />
[[Health effects of benzene in Europe]]<br />
<br />
[[Health effects of radon in Europe]]<br />
<br />
[[Health effects of ozone in Europe]]<br />
<br />
[[Health effects of dioxins in Europe]]<br />
<br />
[[Health effects of formaldehyde in Europe]]<br />
<br />
[[Health effects of lead in Europe]]<br />
<br />
[[Health effects of particulate matter in Europe]]<br />
<br />
==Conclusions and recommendations==<br />
<br />
Development of efficient environment and health policies and evaluation of their success requires quantitative information about environmental exposures and their health impacts. Disability adjusted life years (DALYs) can be used as an indicator for the environmental burden of disease by expressing both morbidity and mortality effects in one number. World Health Organization Global Burden of Disease and Environmental Burden of Disease programmes have developed methodologies for estimating environmental burden of disease. However, harmonized exposure data and established methods are still lacking for a large number of stressors that have relevance in the developed world. The current study aimed to test the available methods in six European countries using a harmonized approach. Nine stressors were selected that were considered relevant and interesting for Europe. The selection was intended to cover the most important environmental causes of public health impacts, but also to cover less important exposures that have had high significance in public debate or policy development.<br />
<br />
The results showed that the EBD methodology can be used to estimate the burden of disease in a harmonized way over a number of stressors and countries. The highest overall public health impact was estimated for ambient fine particles (PM2.5; annually 6000-9000 non-discounted DALYs per million in the six participating countries) followed by second-hand smoke (600-1200) transportation noise (500-1100), and radon (600-900). Lower impacts were estimated for dioxins and lead, followed by ozone, all containing also larger relative uncertainties. Lowest impacts were estimated for benzene and formaldehyde.<br />
<br />
Quantitative assessment of the various factors affecting the relative ranking of the stressors based on their health impact indicated that the ranking of non-overlapping estimates seems rather robust, even when the exact numbers contain variable amount of uncertainties. The scientific evidence on the causality and quantitative understanding of the exposure-response relationship was considered to have highest reliability for fine particles, second-hand smoke, radon and benzene. Medium uncertainties in the exposures and exposure response-relationships were identified for noise, lead and ozone. Quantitative results for dioxins and formaldehyde were considered most uncertain when evaluating the scientific evidence base.<br />
<br />
Differences in the representativity of the exposure data affect the comparability of estimates between the countries. Well comparable exposure data was available for particulate matter and ozone, followed by radon, second hand smoke, benzene, and dioxins. Lowest comparability was found for lead and formaldehyde. Transportation noise exposure data collection is well defined in the European Noise Directive (END), but the comparability of the data available from the first phase of data collection has not reached these standards yet. The comparability of estimates between the stressors is affected also by the selection of the health endpoints and the uncertainty in exposure response functions. It is unlikely that these differences in health response models could be solved in the near future.<br />
<br />
Environmental burden of disease estimates support meaningful policy evaluation and resource allocation. Besides, policy analysis also needs to account for the reduction potential of exposures, and other factors such as costs of policy measures and equity issues. The proposed methods for burden of disease estimation should be developed further to cover a larger range of environmental factors and health impacts and to include a systematic evaluation of uncertainties.<ref name="EBoDe"/><br />
<br />
<br />
==See also==<br />
<br />
*[[Abbreviations in EBoDE]]<br />
*[[Additional results of EBoDE by country|Additional results by country]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_dioxins_in_Europe&diff=21751Health effects of dioxins in Europe2011-06-13T12:22:07Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EBoDE]]<br />
<br />
[[File:Dioxinsdaly.png|thumb|400px|]]<br />
<br />
Dioxins (including furans and dioxin-like PCBs) are a group of polychlorinated organic compounds with the same toxic mechanism. They are by-products of various industrial processes and combustion activities and are considered to be highly toxic.<br />
<br />
Dioxins and dioxin-like PCBs are quantified by toxic equivalents (TEQs) representing the total toxicity compared to the most toxic compound, 2,3,7,8-Tetrachlorodibenzodioxin (TCDD). The power of toxicity is calculated with Toxic Equivalent Factors (TEFs), which allow the toxic potentials of each compound to be added up, in order to derive the TEQ of the mixture. Acute toxicity, leading for example to chlorakne or alteration of liver function, is only expected at very high doses. Long-term exposure to dioxins has been linked to effects on the immune system, the nervous system, the endocrine system and reproductive functions and is also known to cause tooth and bone defects, diabetes as well as several types of cancer (USEPA, 2003). The association between dioxins and cancer has been most consistent for non-Hodgkin’s lymphoma. IARC classified TCDD (2,3,7,8-Tetrachlorodibenzo-p-dioxin), as a “known human carcinogen” (IARC, 1997). All other dioxin-like compounds are classified as “likely to be carcinogenic to humans”.<br />
<br />
This group of chemicals is selected in EBoDE because of their high toxicity and potential troubling exposures through e.g. mothers milk.<br />
<ref name="EBoDe"></ref><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
In EBoDE, we have quantified the effect of exposure to dioxins and dioxin-like PCBs on cancer (all cancer types, mortality only). The non-fatal and non-cancer effects were not suited for health impact assessments due to difficulties in estimating the exposure-response relationships and the other input parameters necessary for estimating DALYs. Therefore, our estimates may underestimate the true dioxin-related burden of disease.<br />
<br />
Leino et al. (2008) assumed a linear exposure-response relationship for excess cancers associated with dioxin intake. They estimated the health risk for toxicity equivalent intake assuming additivity of the toxicity of the different types of dioxins and all cancer cases to be lethal.<br />
<br />
The EBoDE calculations use the Leino et al. (2008) approach, but the results have been corrected with an updated cancer slope factor 1×10-3 per pg/kg/d of dioxin intake of the U.S. Environmental Protection Agency (USEPA, 2003; NAS, 2006). The assumption that all cancers are lethal may lead to overestimation of the impacts.<br />
<br />
The health endpoints considered in this project for dioxins and the corresponding exposure-response functions are summarized in Table 3-19 in section 3.12. YLD estimates in the table are based on the attributable fraction derived from the ERF using method 2A (see Figure 2-1), which is applied to the total YLD for all cancers as represented in the WHO database.<br />
<ref name="EBoDe"></ref><br />
<br />
==Exposure data==<br />
<br />
Dioxins and dioxin-like PCBs are persistent and bio-accumulating. The main exposure route for these chemicals is animal fat in nutrition, which accounts for about 90% of all exposure. Other routes, such as inhalation, play a minor role.<br />
In order to estimate health effects related to dioxin exposure, daily intake data were needed. This intake depends on eating habits, age, gender, body weight and food consumption. Often, breast feeding contributes to the highest intake of dioxins for humans in their life. Dioxins have a long half life. Therefore the development of health effects in humans depends not only on the daily intake, but also on the body burden accumulated over years. On average, the daily intake of dioxins and dioxin-like PCBs decreases, while the body burden increases with age.<br />
<br />
The cancer slope factor is expressed for daily intake of adults. There are different ways to measure the daily intake, each with different limitations. Table 3-2 describes some different measurement methods and provides short information about their use and limitations.<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-2. Different ways to measure daily intake of dioxins and dioxin-like PCBs.<br />
!<br />
! Type of measurement<br />
! Type of use<br />
! Specific limitations and uncertainties<br />
|-----<br />
| A<br />
| Survey (questionnaire) on food consumption<br />
| Information on food consumption and about the content of dioxins in representative food samples allow modelling of daily intake<br />
| Results are modelled for an average population - food contamination and eating habits can differ on a large scale<br />
|-----<br />
| B<br />
| Total diet studies<br />
| The total diet in a population group over a certain time period and dioxin in this food or representative food samples are measured.<br />
| Results are only relevant for the investigated groups and not necessarily representative for the whole population, sampling period influence the results.<br />
|-----<br />
| C<br />
| Human biomonitoring Investigation of human milk or blood levels<br />
| Analyses of samples can show the body burden. Experimental scaling is used to convert observed biomonitoring results (blood) into daily intakes.<br />
| D-R function is based on daily intake. Human milk or blood samples are not widely available. Different fat content of the bodies influences the results.<br />
|}<br />
<br />
In addition, in all these studies different compounds can be measured:<br />
# Only dioxins and furans;<br />
# dioxins, furans; and dioxin-like PCBs<br />
# dioxins, furans and dioxin-like PCBs as well as all other dioxin-like compounds detected as dioxin-like activity, expressed as TEQ in Bioassays (e.g. CALLUX).<br />
<br />
In the EBoDE project, we have used national exposure data because there is no international comparable data source available. The different countries have used different methods to derive the daily intake values.<br />
<br />
Table 3-3 provides a summary of the data and sources for dioxin. The specific data used in this project are summarized in Table 3-21 in section 3.12.<br />
For the EBoDE project daily intake data are expressed as Toxic Equivalent (TEQ), estimated using the Toxic Equivalent Factors (TEFs) as provided by WHO (Van den Berg et al. 1998). Even though later TEFs exist (Van den Berg et al., 2006; http://www.who.int/ipcs/assessment/tef_update/en/), we used the results of the 1998 review, because most available data have been calculated using these TEFs.<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-3.: Summary of the sources of dioxin data. Explanation for A, B C see Table 3-2.<br />
! Countries<br />
! Population groups<br />
! Source<br />
! Sampling years<br />
! Compounds measured<br />
! Dioxin intake 2004 pg/kg bw/d<br />
|-----<br />
| Belgium (A)<br />
| female 18-44 y<br />
adults 50-65 y<br />
adults<br />
| Bilau 2008<br />
Bilau 2008<br />
Calculated mean<br />
| 2002–2006<br />
| Calux-all dioxin-like compounds<sup>1</sup><br />
| 2.1<br />
1.7<br />
1.9 (mean)<br />
|-----<br />
| Finland (A)<br />
| all<br />
| Kiviranta et al 2005<br />
| 2002<br />
| Dioxins+PCB<br />
| 1.5<br />
|-----<br />
| France (C)<br />
| 30–65 y<br />
| Fréry et al. 2006<br />
| 2004<br />
| Dioxins+PCB<br />
| 2.3<sup>2</sup><br />
|-----<br />
| Germany (A)<br />
| adults<br />
| Umweltbundesamt 2005<br />
| 2003<br />
| Dioxins+PCB<br />
| 2.0<br />
|-----<br />
| Italy (A)<br />
| 13–94 y<br />
| Fattore et al 2006<br />
| 1997–2003<br />
| Dioxins+PCB<br />
| 2.3<sup>3</sup><br />
|-----<br />
| Netherlands (A+B)<br />
| adults<br />
| De Mul 2008<br />
| 2004<br />
| Dioxins+PCB<br />
| 1.0<sup>4</sup><br />
|}<br />
<br />
<small><sup>1</sup> Belgium – Dioxin and all dioxin-like compounds are measured with Bioassay, only the sum of all dioxin-like compounds is given; the daily intake was calculated as mean of the 2 adult groups.<br />
<br />
<sup>2</sup> France – daily intake calculated based on blood concentration of 27.7 WHO-TEQ pg/g blood fat.<br />
<br />
<sup>3</sup> Italy – daily intake were calculated using, for most dioxin and DL-PCB concentration data, a database available from the European Commission (Gallani et al., 2004).<br />
<br />
<sup>4</sup> Netherlands – Values in the study were calculated using TEFs from 2005. For comparability, we have adjusted the values as presented by Mul et al (2008) by adapting the results to TEF 1998 adding 10%.</small><br />
<br />
We have only used data on the daily intake of adults. We have chosen to do so, because the daily intake differs substantially between different age groups. The highest intakes are calculated for breastfed babies (about 50 to 100 WHO-TEQ pg/kg bw/d). Children have a higher intake than adults because of the different proportion between body weight and food intake and their different food habits (children take more milk and dairy products). Since there are only very few data for children available, we have limited ourselves to adults.<br />
<br />
Due to the differences in measurement approach, it is difficult to compare dioxin intake numbers between countries. As a form of quality assurance, we have compared our daily intake estimates of dioxins and dioxin-like PCBs to international data on dioxins and PCBs in mother's milk (milk data from 2001–2003) as provided by WHO in the ENHIS-database (WHO, 2007a) and from Malisch and Leeuwen (2003). In principle, the ratio between the estimated daily intakes and the levels of mother’s milk should be roughly similar between countries. The ratios are presented in Table 3-4. As can be seen from this table, the ratios are relatively similar across the countries, except in the Netherlands, where the intake level seems to be somewhat lower than in the other countries in comparison with the mother’s milk levels. We have not corrected for this difference in the EBoDE calculations, as the causes for the difference are yet unknown.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Estimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-4. Comparison of dioxins and PCBs human milk (WHO, 2007a) and the estimated daily intakes (country-specific results – see Table 3-3.<br />
! Country<sup>a</sup><br />
! Human milk<br />
ng TEQ/kg fat<br />
! Daily intake<br />
pg TEQ/kg bw/d<br />
! Factor<br />
milk/intake<br />
|-----<br />
| Belgium<br />
| 29.5<br />
| 1.9<br />
| 16<br />
|-----<br />
| Finland<br />
| 15.3<br />
| 1.5<br />
| 10<br />
|-----<br />
| Germany<br />
| 26.2<br />
| 2.0<br />
| 13<br />
|-----<br />
| Italy<br />
| 29.0<br />
| 2.3<br />
| 13<br />
|-----<br />
| Netherlands<br />
| 29.8<br />
| 1.0<br />
| 30<br />
|}<br />
<br />
<small><sup>a</sup> France was not included in the WHO-milk study.</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Dioxins''' Our calculations were based on the same approach as applied earlier by Leino et al (2008), but we utilized an updated cancer slope factor that is approximately seven times higher than the one used by Leino et al. Leino et al. did the calculations for Finland only. The work presented here also updated the exposure estimates in order to allow for good international comparability, yet some differences between the national intake estimation methods remained.<ref name="EBoDe"></ref><br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Dioxins (plus furans and PCBs)<br />
| Effects on the immune, endocrine, reproductive and nervous system; tooth and bone defects. All cases of cancer assumed to be fatal.<br />
| Indirect exposure metrics. Different measurement methods. Daily intake of food depends on age, body weight and eating habits. Exposure varies within countries (from region to region)<br />
| Uncertain cancer slope factor. Assumed additivity of the toxicity of different types<br />
| UR method of calculating PAF results in overestimation because all cases are assumed to be fatal.<br />
| ***<br />
| Underestimation of non cancer effects, Overestimation of cancer effects (all lethal)<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_radon_in_Europe&diff=21750Health effects of radon in Europe2011-06-13T12:21:42Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
[[File:radondaily.jpg|thumb|400px|]]<br />
<br />
==About radon==<br />
<br />
Radon is a short-lived radioactive gas that occurs naturally in soils and rocks. It is generated by the radioactive decay of uranium. Indoor radon concentrations differ based on the characteristics of the geological substrates beneath houses and the use of different building materials.<br />
<br />
Exposure to radon can lead to lung cancer. Studies to estimate the risk of lung cancer associated with residential radon exposure have been conducted in many European countries (Lagarde et al. 1997, Bochicchio, 2005, 2008; Darby et al., 2005, 2006). Radon is classified by IARC as carcinogenic to humans (type 1, 1988) with genotoxic action. No safe level of exposure can be determined (WHO, 2000a). Besides lung cancer radon is not known to cause other health effects.<br />
<br />
Radon has a synergistic effect with smoking. Epidemiological evidence suggests that the risk of simultaneous exposure to both tobacco smoke and radon is more than additive but that it may be less than multiplicative.<br />
<ref name="EBoDe"></ref><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
Radon effects are usually presented as additional cases of lung cancer at a certain exposure (i.e. unit risk model). In order to account for the interaction with smoking, however, a relative risk model seems more appropriate. We therefore calculated results using both a unit risk model and a relative risk model (method 1A and 2A). The RR method (1A) is used in the final aggregate results. The radon UR model (UR=6.6E-07 (Bq m<sup>-3</sup>)-1, Darby et al., 2005) is used for comparison of UR and RR modelling approaches in Chapter 5.<br />
<br />
The relative risk model, as suggested by the meta-analysis of Darby et al. (2005), assumes the lung cancer risk from radon to be linearly proportional to the radon exposure, but also to the background lung cancer rate caused by tobacco smoking (and, to a lesser extent, by exposure to second-hand smoke, ambient air particulate matter and possibly some occupational exposures) (see Table 3-19 in section 3.12 for the RR values).<br />
<ref name="EBoDe"></ref><br />
<br />
==Exposure data==<br />
<br />
The soil uranium contents and respectively the residential radon concentrations vary significantly between the countries. Yet the differences within the countries are still far greater, and the indoor radon concentrations in individual buildings are essentially impossible to predict. Long-term average indoor radon concentrations, however, are relatively easy to measure and are therefore better known and comparable between the countries than those of any other indoor air contaminant.<br />
<br />
EBoDE uses the national residential radon exposure estimates as collected by the EU RadonMapping project (http://radonmapping.jrc.ec.europa.eu; country reports available from http://radonmapping.jrc.ec.europa.eu/index.php?id=37&no_cache=1&dlpath=National_Summary_Reports, accessed 11 June 2009). and the UNSCEAR 2000 Report, as presented in Table 3-14 and summarized in Table 3-21 in section 3.12. No further national data collection was conducted, but some additional international data sources were identified, notably from the WHO Radon project (IRP, 2010).<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Estimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-14. Radon concentrations in dwellings determined in indoor surveys (compiled from National Summary Reports at http://radonmapping.jrc.ec.europa.eu/ and UNSCEAR, 2000). The respective cancer risks are estimated from background lung cancer rates using both absolute and relative risk models.<br />
! Country<br />
! AM<br />
(Bq m<sup>-3</sup>)<br />
! GM<br />
(Bq m<sup>-3</sup>)<br />
! GSD<br />
! % (of people exposed) › ≥200 Bq m<sup>-3</sup><br />
! % (of people exposed) › ≥400 Bq m<sup>-3</sup><br />
! Max<br />
(Bq m<sup>-3</sup>)<br />
|-----<br />
| Belgium<br />
| 69<br />
| 76<br />
| 2.0<br />
| <br />
| 0.5<br />
| 4 500<br />
|-----<br />
| Finland<br />
| 120<br />
| 84<br />
| 2.1<br />
| 12.3<br />
| 3.6<br />
| 33 000<br />
|-----<br />
| France<br />
| 89<br />
| 53<br />
| 2.7<br />
| 8.5<br />
| 2.0<br />
| 4 964<br />
|-----<br />
| Germany<br />
| 50<br />
| 40<br />
| 1.9<br />
| 3.0<br />
| 1.0<br />
| 10 000<br />
|-----<br />
| Italy<br />
| 70<br />
| 52<br />
| 2.0<br />
| 4.1<br />
| 0.9<br />
| 1 036<br />
|-----<br />
| The Netherlands<br />
| 30<br />
| 25<br />
| 1.6<br />
| 0.3<br />
| 0.0<br />
| 382<br />
|}<br />
<br />
<small>AM: Arithmetic Mean; Bq: Becquerel; GM: Geometric Mean; GSD: Geometric Standard Deviation.</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Radon''' The exposure estimation and dose-response models are based on earlier international analysis conducted by Darby et al. (2006). In comparison with that the current work added estimation of the impacts in DALYs. Comparison of UR and RR models yielded similar results. The results using the RR approach, accounting for the national differences in the background rates of lung cancer, were selected for reporting.<br />
<ref name="EBoDe"></ref><br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Radon<br />
| No health endpoints excluded<br />
| Possible oversampling of geographical regions known problematic<br />
| <br />
| <br />
| *<br />
| No substantial error expected<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_Second-hand_smoke_in_Europe&diff=21749Health effects of Second-hand smoke in Europe2011-06-13T12:21:02Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Mori|stub=Yes}}<br />
[[category:EBoDE]]<br />
<br />
[[File:secondhandsmokedaily.png|thumb|400px|]]<br />
<br />
== Second-hand smoke ==<br />
<br />
=== About second-hand smoke ===<br />
<br />
Second-hand smoke (SHS; also called environmental tobacco smoke or passive smoking) is a known human carcinogen (IARC, 2004). Exposure to SHS has been shown to cause lung cancer, IHD (ischemic heart disease) sudden infant death syndrome, asthma, lower respiratory infections in young children, low birth weight, reduced pulmonary function among children, acute otitis media, and acute irritant symptoms (WHO, 1999; Californian EPA 2005; US Surgeon General 2006; IARC 2004, Jaakkola et al. 2003). Most evidence for SHS-related impacts is fairly consistent.<br />
<br />
SHS has been selected in our study because of its high public health impact, public concern and political interest. Policy measures to (further) reduce SHS exposure have been implemented in the recent past (e.g. the smoking ban) and further policy actions may be taken in the future. <br />
<ref name="EBoDe"></ref><br />
<br />
=== Selected health endpoints and exposure-response functions ===<br />
<br />
Out of the large number of health endpoints that SHS is associated with, we selected mortality and morbidity due to lung cancer and ischemic heart disease (IHD), morbidity due to onset of asthma (both in children and in adults), lower respiratory infections and acute otitis media. For the other health endpoints mentioned above, strong evidence is available, but the necessary disease statistics were lacking. <br />
<br />
For the SHS-related burden of disease calculations, we have followed the recent WHO methods on the global estimation of disease burden from SHS (Öberg et al. 2010). A summary of outcomes with their respective evidence levels is provided in Table 3-5. The exposure response functions are presented in Table 3-19. <br />
<br />
The selected exposure-response values are not gender-specific (e.g. exposure to male or female smoking spouse; exposure to paternal or maternal smoking). Instead, we used the mean relative risk for exposure to adults’ smoking. This choice was made in order to limit the sensitivity to gender-specific changes in smoking habits over time and across countries, and because not all exposure data were provided separately for men and women. <br />
<br />
The selected outcomes are being applied only to non-smokers, i.e. to the non-smoking disease burden. To that effect, the disease burden due to active smoking has been deduced from the total disease burden, by country (based on total disease burden and active smoking disease burden by country provided by WHO; update 2002 based on Ezzati et al. (2004)).<br />
<ref name="EBoDe"></ref><br />
<br />
<br />
{| border="1" cellpadding="5" cellspacing="0"<br />
|+ TABLE 3-5. Summary of recent reviews of health effects of second hand smoke (Adapted from: Öberg et al. 2010). <br />
|-<br />
| rowspan="2" | '''Health endpoint'''<br />
| rowspan="2" | '''Description'''<br />
| colspan="3" | '''Conclusion regarding the level of evidence (in 3 reports)'''<br />
|-<br />
| '''WHO (1999)'''<br />
| '''Californian EPA (2005)'''<br />
| '''U.S. Surgeon General (2006)'''<br />
|-<br />
| colspan="5" | '''Outcomes in children'''<br />
|-<br />
| Acute lower respiratory infection (ALRI)<br />
| Incidence of acute lower respiratory illnesses and hospitalizations<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Otitis media (middle ear infection)<br />
| Incidence of otitis media<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Asthma onset<br />
| Incidence of new cases<br />
| n<br />
| ***<br />
| **<br />
|-<br />
| colspan="5" | <br />
|-<br />
| colspan="5" | '''Outcomes in adults'''<br />
|-<br />
| Asthma induction<br />
| Adult-onset incident asthma<br />
| ***<br />
| **<br />
| n<br />
|-<br />
| Lung cancer<br />
| Incidence<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Ischemic heart disease (IHD)<br />
| Incidence of any ischemic heart disease<br />
| ***<br />
| ***<br />
| n<br />
|}<br />
<br />
<small>* = The evidence of causality is concluded to be “inconclusive”, “little”, “unclear” or “inadequate”. <br> ** = The evidence of causality is concluded to be “suggestive”, “some” or “may contribute”. <br> *** = The evidence of causality is concluded to be “sufficient” or “supportive”. <br> n = Not evaluated in the report. </small><br />
<br />
<br />
=== Exposure data ===<br />
Exposures to SHS and background risks vary by gender. Therefore, the data collection should account for differences in the exposures by gender. Some health effects are specific for children, so exposure data also had to be collected separately for children. Overall, the following exposure data are required for estimating the health impacts from SHS: <br />
# Percentage of children exposed to SHS (i.e. regularly exposed), OR percentage of children having at least one smoking parent <br />
# Percentage of non-smoking men exposed to SHS <br />
# Percentage of non-smoking women exposed to SHS <br />
<br />
For exposure data collection, we used data from national and international surveys as for example the Survey on Tobacco by the Gallup Organization for the European Commission (EC, 2009) or the European Community Respiratory Health Survey (Janson et al. 2006). The fieldwork for this study was conducted in December 2008 and over 26,500 randomly-selected citizens aged 15 years and over were interviewed in the 27 EU Member States and in Norway. The exposures for the six countries included in EBoDE <br />
are presented in Table 3-6. The “upper estimate” is used as the most realistic estimate, as this exposure description matches best the exposure definition used in epidemiological studies from which we derived our exposure-response functions. The lower estimates are provided in Table 3-6 for future sensitivity analysis. Table 3-21 in section 3.12 provides a summary of these data.<br />
<ref name="EBoDe"></ref><br />
<br />
{| {{prettytable}}<br />
|+ TABLE 3-6. Summary of European SHS exposure data for children and non-smoking adults.<br />
| rowspan="2" |<br />
! scope="col" colspan="2" |Children<br />
! scope="col" colspan="4" |Adults<br />
|-<br />
| '''[%]'''<br />
| '''Data year, reference'''<br />
| '''men [%]'''<br />
| '''women [%]'''<br />
| '''total [%]'''<br />
| '''Data year, reference'''<br />
|-<br />
| Belgium <small><sup>a)</sup></small><br />
| -<br />
| -<br />
| 59 <br> 34 <br> -<br />
| 48 <br> 32 <br> -<br />
| 53 <br> 33 <br> 25/30<small><sup>b)</sup></small><br />
| 1990–1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer2<small><sup>c)</sup></small> <br />
|-<br />
| Finland<br />
| 7<br />
| 1996, Lund<small><sup>3</sup></small><br />
| 14 <br> - <br> - <br />
| 13 <br> - <br> -<br />
| - <br> 15 <br> 6/14<small><sup>b)</sup></small><br />
| 2002, Jousilahti<small><sup>4</sup></small> <br> 2004, NPHI<small><sup>5</sup></small> <br> 2008, Eurobarometer<small><sup>2d)</sup></small><br />
|-<br />
| France<br />
| 23/33<small><sup>b)</sup></small><br />
| 2005, INPES<small><sup>6</sup></small><br />
| 38 <br> 23 <br> - <br> -<br />
| 46 <br> 30 <br> - <br> -<br />
| 42 <br> 26 <br> 13/21<small><sup>b)</sup></small> <br> 13/22<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2005, INPES<small><sup>6b)</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Germany<br />
| 24<br />
| 2003-2006, <br> GerES IV<small><sup>7</sup></small><br />
| 48 <br> 51 <br> 28 <br> -<br />
| 42 <br> 60 <br> 26 <br> -<br />
| 44 <br> - <br> 27 <br> 20/28<small><sup>b)</sup></small><br />
| 1990-1994 ECHRS I<small><sup>1</sup></small> <br> 1998, BGS<small><sup>8</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Italy<br />
| 50<br />
| 2001, <br> ICONA<small><sup>9</sup></small><br />
| 62 <br> 37 <br> -<br />
| 49 <br> 30 <br> -<br />
| 55 <br> 34 <br> 22/26<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Netherlands<br />
| 20/36<small><sup>b)</sup></small><br />
| 2000-2005, <br> RIVM<small><sup>10e)</sup></small><br />
| 68 <br> - <br> 45 <br> - <br> -<br />
| 67 <br> - <br> 33 <br> - <br> -<br />
| 67 <br> 30 <br> 39 <br> 18/40<small><sup>b)</sup></small> <br> 18/27<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 1998-2001, RIVM<small><sup>10</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2004-2007, RIVM<small><sup>10</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|}<br />
<small> NA: Adequate data not available <br><br />
NB: Additional national data are available for some countries, however, these did not match the description of regular exposure. <br><br />
Definitions used for lower and upper estimates: <br><br />
<sup>a)</sup> For Belgium, no data for children was found; estimate is calculated using mean of other countries.<br><br />
References: <sup>1</sup> Janson et al. 2006; <sup>2</sup> EC 2009; <sup>3</sup> Lund et al. 1998; <sup>4</sup> Jousilahti and Helakorpi 2002; <sup>5</sup> Finnish National Public Health Institute, 2004; <sup>6</sup> Institut National de Prévention et d’Education pour la Santé (INPES) 2005; <sup>7</sup> Conrad et al. 2008; <sup>8</sup> Schulze and Lampert 2006; <sup>9</sup> Tominz et al. 2005; <sup>10</sup> van Gelder et al. 2008. <br><br />
<sup>b)</sup> Lower/upper estimates; INPES: Lower estimate based on “regular” exposure; upper estimate based on exposure “from time to time”; <br><br />
Eurobarometer: Lower estimate based on daily exposure of more than one hour exposure at work and home exposure; upper estimate based on daily exposure of also less than one hour at work and home exposure. RIVM: ranges based on values provided by various studies. <br><br />
<sup>c)</sup> Exposure at home and at work supposed to be distributed equally. <br><br />
<sup>d)</sup> Finnish national data (NPHI) also provide survey results, but total exposure to SHS for non-smokers are more difficult to interpret. Therefore only the Eurobarometer data were taken into account here. <br><br />
<sup>e)</sup> The RIVM report contains data from various studies (e.g. Doetinchem, STIVORO, PIAMA) </small><br />
<br />
<br />
Available exposure data (Table 3-6) range across several years, and have been assessed with slightly differing <br />
definitions of exposures. In order to estimate exposure data for the target year (2004), exposures have been <br />
modelled on the basis of the survey data listed in Table 3-6 as follows: <br />
* Modelling was performed with total adult data, and men/women and children data were assumed to vary according to the same trends. <br />
* Power functions showed the highest correlations in most countries, and were therefore applied in all <br />
countries. No trend was apparent for Finland, therefore only the mean was applied. <br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref> <br />
<br />
Resulting trends are displayed in Figure 3-1, and estimated exposure data for 2004 in Table 3-7.<br />
<br />
[[Image:Percentage_of_adults_exposed_to_ETS.png|none|Pretty pixör!]]<br />
<small> FIGURE 3-2. Observed SHS exposure levels (markers) (% of non-smokers) for adults and corresponding modelled <br />
trends (lines) in the participating countries. </small><br />
<br />
<br />
{| {{prettytable}}<br />
|+ TABLE 3-7. Modelled exposure to SHS, in children and non-smoking adults in 2004.<br />
! scope="col" rowspan="2" |Year 2004<br />
! scope="col" colspan="2" |Children<br />
! scope="col" colspan="2" |Adults (total)<br />
! scope="col" colspan="2" |Women<br />
! scope="col" colspan="2" |Men<br />
|-<br />
! scope="col" |Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
|-<br />
| Belgium<br />
| NA<br />
| NA<br />
| 28<br />
| 32<br />
| 27<br />
| 31<br />
| 29<br />
| 33<br />
|-<br />
| Finland<br />
| 4<br />
| NA<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
|-<br />
| France<br />
| 23<br />
| 33<br />
| 17<br />
| 25<br />
| 20<br />
| 29<br />
| 15<br />
| 22<br />
|-<br />
| Germany<br />
| 24<br />
| NA<br />
| 26<br />
| 31<br />
| 25<br />
| 30<br />
| 27<br />
| 33<br />
|-<br />
| Italy<br />
| 40<br />
| NA<br />
| 26<br />
| 30<br />
| 23<br />
| 26<br />
| 29<br />
| 32<br />
|-<br />
| Netherlands<br />
| 20<br />
| 36<br />
| 22<br />
| 30<br />
| 19<br />
| 25<br />
| 26<br />
| 34<br />
|}<br />
<small> * Lower and upper estimates correspond to different computations of survey data. For example, the upper estimate corresponds to the <br />
inclusion of shorter durations of exposure from certain surveys. </small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Second hand smoke'''''(SHS)'':Our burden of disease calculation for SHS was based on a WHO model (Öberg et al., 2010). The exposure estimates were updated against available national and international data sources for the target year 2004, but otherwise the results are comparable with the WHO assessment. Other recent estimates of burden of disease for SHS were also available for Germany (Heidrich et al. 2007; Keil et al. 2005), which provided similar results as the current estimates.<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Second Hand Smoke<br />
| Sudden infant death syndrome; low birth weight; reduced pulmonary function among children; acute irritant symptoms.<br />
| Data from different years and consequent temporal interpolation. Differing definitions of exposures. Data gaps for some countries<br />
| ERF from earlier decades when questionnaire responses may have been less sensitive. Odds ratios used as RR estimates<br />
| Various assumptions made, e.g. smokers are not susceptible to SHS<br />
| *<br />
| Underestimation due to excluded endpoints. Potential overestimation due to increased questionnaire sensitivity<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Overview_of_the_EBoDE-project&diff=21748Overview of the EBoDE-project2011-06-13T12:19:15Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Julle}}<br />
[[category:EBoDE]]<br />
<br />
==Introduction==<br />
<br />
Exposures to many environmental stressors are known to endanger human health. Negative impacts on health can range from mild psychological effects (e.g. noise annoyance), to effects on morbidity (such as asthma caused by exposure to air pollution), and to increased mortality (such as lung cancer provoked by radon exposure). Properly targeted and followed-up environmental health policies, such as the coal burning ban in Dublin (1990) and the smoking ban in public places in Rome (2005) have demonstrated significant and immediate population level reductions in deaths and diseases. In order to develop effective policy measures, quantitative information about the extent of health impacts of different environmental stressors is needed.<br />
<br />
As demonstrated by the examples above, health effects of environmental factors often vary considerably with regard to their severity, duration and magnitude. This makes it difficult to compare different (environmental) health effects and to set priorities in health policies or research programs. Public health policies generally aim to allocate resources effectively for maximum health benefits while avoiding undue interference with other societal functions and human activities. In order to develop such policies, it is necessary to know what ‘maximum health benefits’ are. Decades ago, such decisions tended to be made based on mortality statistics: which (environmental) factor causes most deaths? However, nowadays, most people get relatively old, and priority has shifted from quantity to quality of life. This has lead to the need to incorporate morbidity effects into public health decisions, and therefore to find a way of comparing dissimilar health effects.<br />
<br />
Such comparison and prioritisation of environmental health effects is made possible by expressing the diverging health effects in one unit: the environmental burden of disease (EBD). Environmental burden of disease figures express both mortality and morbidity effects in a population in one number. They quantify and summarize (environmental) health effects and can be used for:<br />
* Comparative evaluation of environmental burden of disease (“how bad is it?”)<br />
* Evaluation of the effectiveness of environmental policies (largest reduction of disease burden)<br />
* Estimation of the accumulation of exposures to environmental factors (for example in urban areas)<br />
* Communication of health risks<br />
<br />
An example of an integrated health measure that can be used to express the environmental burden of disease is the DALY (Disability Adjusted Life Years). DALYs combine information on quality and quantity of life. They give an indication of the (potential) number of healthy life years lost in a population due to premature mortality or morbidity, the latter being weighted for the severity of the disorder. The concept was first introduced by Murray and Lopez (1996) as part of the Global Burden of Disease study, which was launched by the World Bank. Since then, the World Health Organization (WHO) has endorsed the procedure, and the DALY approach has been used in various studies on a global, national and regional level.<br />
<br />
WHO collects a vast set of data on the global burden of disease. The first study quantified the health effects of more than 100 diseases for eight regions of the world in 1990 (Murray and Lopez, 1996). It generated comprehensive and internally consistent estimates of mortality and morbidity by age, gender and region. In a former WHO study, it was shown that almost a quarter of all disease worldwide was caused by environmental exposure (Prüss-Üstün and Corvalán, 2006). In industrial sub-regions this estimate was about 16% (15–18%). These fractions, however, are dependent on the conclusiveness of the included environmental factors and health effects. The WHO programme on quantifying environmental health impacts has addressed more than a dozen stressors <ref>The WHO programme[http://www.who.int/quantifying_ehimpacts/publications/en/]</ref>. In order to support further applications of the environmental burden of disease (EBD) assessments, a methodological guidance has been published by WHO (Prüss-Üstün et al., 2003) and was followed here too.<br />
<br />
In Europe, national environmental burden of disease (EBD) assessments are on-going in several countries. The work by RIVM was one of the first systematic European works in this area that utilized disability-adjusted life years (DALY) as a measure to compare the burden of different health outcomes related to the exposure of the population to environmental stressors (Hollander et al., 1999). The results highlighted that (i) a number of environmental stressors may cause chronic or acute diseases or death, (ii) a few top ranking stressors cause over 90% of the national EBD, and (iii) these top ranking stressors are not necessarily those that have drawn the most concern, regulatory action and/or preventive investment.<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
<br />
==Objectives==<br />
<br />
The EBoDE-project was set up in order to guide environmental health policy making in the six participating countries (Belgium, Finland, France, Germany, Italy and the Netherlands) and potentially beyond. From a policy perspective, these insights from the EBoDE-project can be useful to evaluate past policies and to gain insight in setting the policy priorities for the future. We have calculated the total EBD associated with the nine environmental stressors. The total EBD is not identical to the avoidable burden of disease, because some exposures are not realistically reducible to zero (e.g. fine particles). Also, our estimates do not take into account the costs of reducing the EBD. Thus, the results are only one input into the full process of developing cost-effective policies to achieve better environmental health.<br />
<br />
The objectives of the project were to update the available previous assessments, to focus on stressors relevant for the European region, to provide harmonized EBD assessments for participating countries, and to develop and make available the methodologies for further development and other countries.<br />
The specific objectives are to:<br />
• Provide harmonized environmental burden of disease (EBD) estimates for selected environmental stressors in the participating six countries;<br />
• Test the methodologies in a harmonized way across the countries.<br />
• Assess the comparability of the quantifications and ranking of the EBD<br />
• between countries<br />
• within countries<br />
• between environmental stressors;<br />
• Qualitative assessments of variation and uncertainty in the input parameters and results.<br />
<br />
Environmental burden of disease estimates have been calculated for:<br />
• nine environmental stressors: benzene, dioxins (including furans and dioxin-like PCBs), second-hand smoke, formaldehyde, lead, noise, ozone, particulate matter (PM) and radon;<br />
• six European countries: Belgium, Finland, France, Germany, Italy and the Netherlands;<br />
• the year 2004 (and some trend estimates for the year 2010).<br />
As outlined above, the EBoDE study was carried out in order to test the environmental burden of disease methodology in various countries. The results of the studies are intended to allow comparison of the disease burden between different environmental stressors and between countries. Consequently, the study does not to identify the ‘reduction potential’. Our estimates should therefore not be interpreted as the ‘avoidable burden of disease’: most risks cannot realistically be completely removed by any policy measures. For some exposures, however, the numbers may nonetheless be interpretable as reduction potential, eg for dioxins, formaldehyde, benzene, etc, as these exposures could potentially be completely eliminated.<ref name="EBoDe"/><br />
<br />
==Outline of this report==<br />
<br />
This report describes the methods, data and results of the EBoDE-project. Chapter 2 presents the methodology. The environmental stressors are introduced in Chapter 3, which also presents the data used (selected health endpoints, exposure data, exposure response functions). In Chapter 4, the results are presented and discussed. Chapter 5 gives information about uncertainties in the approach, and provides some alternative calculations using different input values. In Chapter 6 conclusions are drawn. The report ends with the references and two appendices: Appendix A presents country-specific results and Appendix B some considerations for using a life-table approach in EBD modelling.<ref name="EBoDe"/><br />
<br />
==Uncertainties and limitations==<br />
Assessment of uncertainties is essential in a comparison of quantitative estimates that are based on data from heterogeneous sources and slightly varying methods. Due to the wide range of data sources and models and the limited resources within the EBoDE project, systematic analysis of all uncertainties was not possible. However, we were able to assess a number of specific sources of uncertainties in more detail as part of the work, yielding some insights into the reliability of the overall assessment.<br />
The studied health impacts span approximately four orders of magnitude in size from few DALYs per million to almost 10 000 DALYs per million. The overall ranking of the environmental stressors seems to be rather robust against the relatively large uncertainties in individual estimates or methodological choices like discounting and age-weighing. However, some of the estimated ranges are overlapping. This concerns especially second hand smoke, radon and transportation noise that compete for the questionable honour of being the second most important environmental stressor in the participating countries. Among these stressors the differences are smaller than the corresponding uncertainties of the estimates.<br />
The health state of an individual person is the result of a complex mixture of genetic, environmental and behavioural factors. In a typical case of death, numerous factors play together. This means, for example, that a single death caused by a cardiovascular disease could be avoided by either reducing air pollution, or a better diet, or more physical activity. Therefore, if the individual attributable fractions are summed over a number of risk factors, a value over 100% may sometimes be found. For this and other reasons, it has been argued that death counts are not suitable for quantification of the impacts (Brunekreef et al., 2007). Therefore the authors recommend to mainly use aggregate population measures of health like DALYs, YLLs and YLDs.<br />
This chapter presents the quantitative results for selected sources of uncertainties and discusses the project limitations and author judgment of the reliability of the ranking.<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Transportation noise'''<br />
<br />
Burden of disease estimation for transportation noise is currently under active development. The estimates presented here were based on the only available international exposure data source, the first stage version of the European Noise Directive database (2007), which is not conclusive yet. Therefore it is clear that most of the exposures for transportation noise are underestimated. In some studies annoyance and cognitive impairment have been used as an additional health end-points for environmental noise. However, due to the selected more limited definition of ‘health’ as ICD-classified health states used in our assessment, annoyance and cognitive impairment were not included here. Only road, rail and air traffic exposures were included; many other sources also contribute to the noise exposures. Low exposures below the END data collection limits (50 and 55 dB) were not included. For these reasons it can be expected that when these limitations are solved, the impact estimates will increase.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Transport noise<br />
| Annoyance; cognitive impairment, tinnitus<br />
| Small proportion of target population is covered. Conversion between different noise metrics. Different samples. Different data estimation years<br />
| <br />
| Disability weight for sleep disturbance is uncertain. MI vs IHD<br />
| **<br />
| Underestimation due to uncovered populations and exclusion of low exposures, endpoints and noise sources<br />
|----<br />
|}<br />
<br />
See also:<br />
<br />
[[Health effects of Second-hand smoke in Europe]]<br />
<br />
[[Health effects of benzene in Europe]]<br />
<br />
[[Health effects of radon in Europe]]<br />
<br />
[[Health effects of ozone in Europe|Health effects of PM and Ozone in Europe]]<br />
<br />
[[Health effects of dioxins in Europe]]<br />
<br />
[[Health effects of formaldehyde in Europe]]<br />
<br />
[[Health effects of lead in Europe]]<br />
<br />
==Conclusions and recommendations==<br />
<br />
Development of efficient environment and health policies and evaluation of their success requires quantitative information about environmental exposures and their health impacts. Disability adjusted life years (DALYs) can be used as an indicator for the environmental burden of disease by expressing both morbidity and mortality effects in one number. World Health Organization Global Burden of Disease and Environmental Burden of Disease programmes have developed methodologies for estimating environmental burden of disease. However, harmonized exposure data and established methods are still lacking for a large number of stressors that have relevance in the developed world. The current study aimed to test the available methods in six European countries using a harmonized approach. Nine stressors were selected that were considered relevant and interesting for Europe. The selection was intended to cover the most important environmental causes of public health impacts, but also to cover less important exposures that have had high significance in public debate or policy development.<br />
<br />
The results showed that the EBD methodology can be used to estimate the burden of disease in a harmonized way over a number of stressors and countries. The highest overall public health impact was estimated for ambient fine particles (PM2.5; annually 6000-9000 non-discounted DALYs per million in the six participating countries) followed by second-hand smoke (600-1200) transportation noise (500-1100), and radon (600-900). Lower impacts were estimated for dioxins and lead, followed by ozone, all containing also larger relative uncertainties. Lowest impacts were estimated for benzene and formaldehyde.<br />
<br />
Quantitative assessment of the various factors affecting the relative ranking of the stressors based on their health impact indicated that the ranking of non-overlapping estimates seems rather robust, even when the exact numbers contain variable amount of uncertainties. The scientific evidence on the causality and quantitative understanding of the exposure-response relationship was considered to have highest reliability for fine particles, second-hand smoke, radon and benzene. Medium uncertainties in the exposures and exposure response-relationships were identified for noise, lead and ozone. Quantitative results for dioxins and formaldehyde were considered most uncertain when evaluating the scientific evidence base.<br />
<br />
Differences in the representativity of the exposure data affect the comparability of estimates between the countries. Well comparable exposure data was available for particulate matter and ozone, followed by radon, second hand smoke, benzene, and dioxins. Lowest comparability was found for lead and formaldehyde. Transportation noise exposure data collection is well defined in the European Noise Directive (END), but the comparability of the data available from the first phase of data collection has not reached these standards yet. The comparability of estimates between the stressors is affected also by the selection of the health endpoints and the uncertainty in exposure response functions. It is unlikely that these differences in health response models could be solved in the near future.<br />
<br />
Environmental burden of disease estimates support meaningful policy evaluation and resource allocation. Besides, policy analysis also needs to account for the reduction potential of exposures, and other factors such as costs of policy measures and equity issues. The proposed methods for burden of disease estimation should be developed further to cover a larger range of environmental factors and health impacts and to include a systematic evaluation of uncertainties.<ref name="EBoDe"/><br />
<br />
<br />
==See also==<br />
<br />
*[[Abbreviations in EBoDE]]<br />
*[[Additional results of EBoDE by country|Additional results by country]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_Second-hand_smoke_in_Europe&diff=21747Health effects of Second-hand smoke in Europe2011-06-13T12:17:44Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Mori|stub=Yes}}<br />
[[category:EBoDE]]<br />
<br />
[[File:secondhandsmokedaily.png|thumb|400px|]]<br />
<br />
== Second-hand smoke ==<br />
<br />
=== About second-hand smoke ===<br />
<br />
Second-hand smoke (SHS; also called environmental tobacco smoke or passive smoking) is a known human carcinogen (IARC, 2004). Exposure to SHS has been shown to cause lung cancer, IHD (ischemic heart disease) sudden infant death syndrome, asthma, lower respiratory infections in young children, low birth weight, reduced pulmonary function among children, acute otitis media, and acute irritant symptoms (WHO, 1999; Californian EPA 2005; US Surgeon General 2006; IARC 2004, Jaakkola et al. 2003). Most evidence for SHS-related impacts is fairly consistent.<br />
<br />
SHS has been selected in our study because of its high public health impact, public concern and political interest. Policy measures to (further) reduce SHS exposure have been implemented in the recent past (e.g. the smoking ban) and further policy actions may be taken in the future. <br />
<ref name="EBoDe"></ref><br />
<br />
=== Selected health endpoints and exposure-response functions ===<br />
<br />
Out of the large number of health endpoints that SHS is associated with, we selected mortality and morbidity due to lung cancer and ischemic heart disease (IHD), morbidity due to onset of asthma (both in children and in adults), lower respiratory infections and acute otitis media. For the other health endpoints mentioned above, strong evidence is available, but the necessary disease statistics were lacking. <br />
<br />
For the SHS-related burden of disease calculations, we have followed the recent WHO methods on the global estimation of disease burden from SHS (Öberg et al. 2010). A summary of outcomes with their respective evidence levels is provided in Table 3-5. The exposure response functions are presented in Table 3-19. <br />
<br />
The selected exposure-response values are not gender-specific (e.g. exposure to male or female smoking spouse; exposure to paternal or maternal smoking). Instead, we used the mean relative risk for exposure to adults’ smoking. This choice was made in order to limit the sensitivity to gender-specific changes in smoking habits over time and across countries, and because not all exposure data were provided separately for men and women. <br />
<br />
The selected outcomes are being applied only to non-smokers, i.e. to the non-smoking disease burden. To that effect, the disease burden due to active smoking has been deduced from the total disease burden, by country (based on total disease burden and active smoking disease burden by country provided by WHO; update 2002 based on Ezzati et al. (2004)).<br />
<ref name="EBoDe"></ref><br />
<br />
<br />
{| border="1" cellpadding="5" cellspacing="0"<br />
|+ TABLE 3-5. Summary of recent reviews of health effects of second hand smoke (Adapted from: Öberg et al. 2010). <br />
|-<br />
| rowspan="2" | '''Health endpoint'''<br />
| rowspan="2" | '''Description'''<br />
| colspan="3" | '''Conclusion regarding the level of evidence (in 3 reports)'''<br />
|-<br />
| '''WHO (1999)'''<br />
| '''Californian EPA (2005)'''<br />
| '''U.S. Surgeon General (2006)'''<br />
|-<br />
| colspan="5" | '''Outcomes in children'''<br />
|-<br />
| Acute lower respiratory infection (ALRI)<br />
| Incidence of acute lower respiratory illnesses and hospitalizations<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Otitis media (middle ear infection)<br />
| Incidence of otitis media<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Asthma onset<br />
| Incidence of new cases<br />
| n<br />
| ***<br />
| **<br />
|-<br />
| colspan="5" | <br />
|-<br />
| colspan="5" | '''Outcomes in adults'''<br />
|-<br />
| Asthma induction<br />
| Adult-onset incident asthma<br />
| ***<br />
| **<br />
| n<br />
|-<br />
| Lung cancer<br />
| Incidence<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Ischemic heart disease (IHD)<br />
| Incidence of any ischemic heart disease<br />
| ***<br />
| ***<br />
| n<br />
|}<br />
<br />
<small>* = The evidence of causality is concluded to be “inconclusive”, “little”, “unclear” or “inadequate”. <br> ** = The evidence of causality is concluded to be “suggestive”, “some” or “may contribute”. <br> *** = The evidence of causality is concluded to be “sufficient” or “supportive”. <br> n = Not evaluated in the report. </small><br />
<br />
<br />
=== Exposure data ===<br />
Exposures to SHS and background risks vary by gender. Therefore, the data collection should account for differences in the exposures by gender. Some health effects are specific for children, so exposure data also had to be collected separately for children. Overall, the following exposure data are required for estimating the health impacts from SHS: <br />
# Percentage of children exposed to SHS (i.e. regularly exposed), OR percentage of children having at least one smoking parent <br />
# Percentage of non-smoking men exposed to SHS <br />
# Percentage of non-smoking women exposed to SHS <br />
<br />
For exposure data collection, we used data from national and international surveys as for example the Survey on Tobacco by the Gallup Organization for the European Commission (EC, 2009) or the European Community Respiratory Health Survey (Janson et al. 2006). The fieldwork for this study was conducted in December 2008 and over 26,500 randomly-selected citizens aged 15 years and over were interviewed in the 27 EU Member States and in Norway. The exposures for the six countries included in EBoDE <br />
are presented in Table 3-6. The “upper estimate” is used as the most realistic estimate, as this exposure description matches best the exposure definition used in epidemiological studies from which we derived our exposure-response functions. The lower estimates are provided in Table 3-6 for future sensitivity analysis. Table 3-21 in section 3.12 provides a summary of these data.<br />
<ref name="EBoDe"></ref><br />
<br />
{| {{prettytable}}<br />
|+ TABLE 3-6. Summary of European SHS exposure data for children and non-smoking adults.<br />
| rowspan="2" |<br />
! scope="col" colspan="2" |Children<br />
! scope="col" colspan="4" |Adults<br />
|-<br />
| '''[%]'''<br />
| '''Data year, reference'''<br />
| '''men [%]'''<br />
| '''women [%]'''<br />
| '''total [%]'''<br />
| '''Data year, reference'''<br />
|-<br />
| Belgium <small><sup>a)</sup></small><br />
| -<br />
| -<br />
| 59 <br> 34 <br> -<br />
| 48 <br> 32 <br> -<br />
| 53 <br> 33 <br> 25/30<small><sup>b)</sup></small><br />
| 1990–1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer2<small><sup>c)</sup></small> <br />
|-<br />
| Finland<br />
| 7<br />
| 1996, Lund<small><sup>3</sup></small><br />
| 14 <br> - <br> - <br />
| 13 <br> - <br> -<br />
| - <br> 15 <br> 6/14<small><sup>b)</sup></small><br />
| 2002, Jousilahti<small><sup>4</sup></small> <br> 2004, NPHI<small><sup>5</sup></small> <br> 2008, Eurobarometer<small><sup>2d)</sup></small><br />
|-<br />
| France<br />
| 23/33<small><sup>b)</sup></small><br />
| 2005, INPES<small><sup>6</sup></small><br />
| 38 <br> 23 <br> - <br> -<br />
| 46 <br> 30 <br> - <br> -<br />
| 42 <br> 26 <br> 13/21<small><sup>b)</sup></small> <br> 13/22<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2005, INPES<small><sup>6b)</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Germany<br />
| 24<br />
| 2003-2006, <br> GerES IV<small><sup>7</sup></small><br />
| 48 <br> 51 <br> 28 <br> -<br />
| 42 <br> 60 <br> 26 <br> -<br />
| 44 <br> - <br> 27 <br> 20/28<small><sup>b)</sup></small><br />
| 1990-1994 ECHRS I<small><sup>1</sup></small> <br> 1998, BGS<small><sup>8</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Italy<br />
| 50<br />
| 2001, <br> ICONA<small><sup>9</sup></small><br />
| 62 <br> 37 <br> -<br />
| 49 <br> 30 <br> -<br />
| 55 <br> 34 <br> 22/26<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Netherlands<br />
| 20/36<small><sup>b)</sup></small><br />
| 2000-2005, <br> RIVM<small><sup>10e)</sup></small><br />
| 68 <br> - <br> 45 <br> - <br> -<br />
| 67 <br> - <br> 33 <br> - <br> -<br />
| 67 <br> 30 <br> 39 <br> 18/40<small><sup>b)</sup></small> <br> 18/27<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 1998-2001, RIVM<small><sup>10</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2004-2007, RIVM<small><sup>10</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|}<br />
<small> NA: Adequate data not available <br><br />
NB: Additional national data are available for some countries, however, these did not match the description of regular exposure. <br><br />
Definitions used for lower and upper estimates: <br><br />
<sup>a)</sup> For Belgium, no data for children was found; estimate is calculated using mean of other countries.<br><br />
References: <sup>1</sup> Janson et al. 2006; <sup>2</sup> EC 2009; <sup>3</sup> Lund et al. 1998; <sup>4</sup> Jousilahti and Helakorpi 2002; <sup>5</sup> Finnish National Public Health Institute, 2004; <sup>6</sup> Institut National de Prévention et d’Education pour la Santé (INPES) 2005; <sup>7</sup> Conrad et al. 2008; <sup>8</sup> Schulze and Lampert 2006; <sup>9</sup> Tominz et al. 2005; <sup>10</sup> van Gelder et al. 2008. <br><br />
<sup>b)</sup> Lower/upper estimates; INPES: Lower estimate based on “regular” exposure; upper estimate based on exposure “from time to time”; <br><br />
Eurobarometer: Lower estimate based on daily exposure of more than one hour exposure at work and home exposure; upper estimate based on daily exposure of also less than one hour at work and home exposure. RIVM: ranges based on values provided by various studies. <br><br />
<sup>c)</sup> Exposure at home and at work supposed to be distributed equally. <br><br />
<sup>d)</sup> Finnish national data (NPHI) also provide survey results, but total exposure to SHS for non-smokers are more difficult to interpret. Therefore only the Eurobarometer data were taken into account here. <br><br />
<sup>e)</sup> The RIVM report contains data from various studies (e.g. Doetinchem, STIVORO, PIAMA) </small><br />
<br />
<br />
Available exposure data (Table 3-6) range across several years, and have been assessed with slightly differing <br />
definitions of exposures. In order to estimate exposure data for the target year (2004), exposures have been <br />
modelled on the basis of the survey data listed in Table 3-6 as follows: <br />
* Modelling was performed with total adult data, and men/women and children data were assumed to vary according to the same trends. <br />
* Power functions showed the highest correlations in most countries, and were therefore applied in all <br />
countries. No trend was apparent for Finland, therefore only the mean was applied. <br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref> <br />
<br />
Resulting trends are displayed in Figure 3-1, and estimated exposure data for 2004 in Table 3-7.<br />
<br />
[[Image:Percentage_of_adults_exposed_to_ETS.png|none|Pretty pixör!]]<br />
<small> FIGURE 3-2. Observed SHS exposure levels (markers) (% of non-smokers) for adults and corresponding modelled <br />
trends (lines) in the participating countries. </small><br />
<br />
<br />
{| {{prettytable}}<br />
|+ TABLE 3-7. Modelled exposure to SHS, in children and non-smoking adults in 2004.<br />
! scope="col" rowspan="2" |Year 2004<br />
! scope="col" colspan="2" |Children<br />
! scope="col" colspan="2" |Adults (total)<br />
! scope="col" colspan="2" |Women<br />
! scope="col" colspan="2" |Men<br />
|-<br />
! scope="col" |Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
|-<br />
| Belgium<br />
| NA<br />
| NA<br />
| 28<br />
| 32<br />
| 27<br />
| 31<br />
| 29<br />
| 33<br />
|-<br />
| Finland<br />
| 4<br />
| NA<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
|-<br />
| France<br />
| 23<br />
| 33<br />
| 17<br />
| 25<br />
| 20<br />
| 29<br />
| 15<br />
| 22<br />
|-<br />
| Germany<br />
| 24<br />
| NA<br />
| 26<br />
| 31<br />
| 25<br />
| 30<br />
| 27<br />
| 33<br />
|-<br />
| Italy<br />
| 40<br />
| NA<br />
| 26<br />
| 30<br />
| 23<br />
| 26<br />
| 29<br />
| 32<br />
|-<br />
| Netherlands<br />
| 20<br />
| 36<br />
| 22<br />
| 30<br />
| 19<br />
| 25<br />
| 26<br />
| 34<br />
|}<br />
<small> * Lower and upper estimates correspond to different computations of survey data. For example, the upper estimate corresponds to the <br />
inclusion of shorter durations of exposure from certain surveys. </small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Second hand smoke'''''(SHS)'':Our burden of disease calculation for SHS was based on a WHO model (Öberg et al., 2010). The exposure estimates were updated against available national and international data sources for the target year 2004, but otherwise the results are comparable with the WHO assessment. Other recent estimates of burden of disease for SHS were also available for Germany (Heidrich et al. 2007; Keil et al. 2005), which provided similar results as the current estimates.<br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Second Hand Smoke<br />
| Sudden infant death syndrome; low birth weight; reduced pulmonary function among children; acute irritant symptoms.<br />
| Data from different years and consequent temporal interpolation. Differing definitions of exposures. Data gaps for some countries<br />
| ERF from earlier decades when questionnaire responses may have been less sensitive. Odds ratios used as RR estimates<br />
| Various assumptions made, e.g. smokers are not susceptible to SHS<br />
| *<br />
| Underestimation due to excluded endpoints. Potential overestimation due to increased questionnaire sensitivity<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_lead_in_Europe&diff=21746Health effects of lead in Europe2011-06-13T12:17:06Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
[[File:Leaddaily.png|thumb|400px|]]<br />
<br />
==About lead==<br />
<br />
Lead is present in the environment due to former application of lead in gasoline, leaded drinking water pipes, and use of lead in paints and other housing materials. Exposures to lead originate from various sources including air, drinking water, food stuff as well as surfaces and consumer products.<br />
<br />
Lead is one of the most studied environmental pollutants and has been associated with a large number of health implications (WHO, 2007b). Exposure to lead may cause, amongst other things, kidney damage, miscarriages, effects of the nervous system, declined fertility, alterations in growth and endocrine function, and behavioural disruptions (Hauser et al. 2008; Lanphear et al., 2005; Selevan et al. 2003). Lead is a known neurotoxic pollutant affecting the development of the central nervous system of children and consequently their intelligence. Effects on attention, behaviour disorders and hearing-threshold changes have been described as particularly important (Needleman 1990, WHO/IPCS 1995). Lead exposures have also been shown to be associated with increased blood pressure and risk of hypertension in (female) adults (Nash et al. 2003). Correlations with low lead levels have been reported for the attention deficit hyperactivity disorder (ADHD) (Braun et al., 2006). In addition, there is evidence showing that lead may cause cancer. Lead has been loosely linked with cancers of the lung and stomach. IARC (2006b) rated lead and inorganic lead compounds as probably carcinogenic to humans (Group 2A). Current studies suggest that there is no “safe” level of lead exposure.<br />
<br />
Most of the health endpoints are significant at much higher exposure levels that are found in European population today. Exposure to lead has significantly decreased for many countries in the last two decades, especially since the phasing out of leaded gasoline and the replacement of leaded water pipes. For example, Figure 3-3 shows the reduction of internal exposure to lead in humans in German students between the 1980s and now (German Environmental Specimen Bank [Umweltprobenbank des Bundes], data available online at www.umweltprobenbank.de). Indeed, lead has been the success story in environmental policies, but the follow-up in exposure data in the general population is poor.<br />
<ref name="EBoDe"></ref><br />
<br />
[[Image:Blood_Lead_Level_in_German_Students.png|none|Blood-Pb in German Students 1981-2009|FIGURE 3-4. Blood-Pb in German Students (1981–2009, geometric mean in μg/l, sampling location: city of Münster)]]<br />
<br />
<small>FIGURE 3-4. Blood-Pb in German Students (1981–2009, geometric mean in μg/l, sampling location: city of Münster)</small><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
The EBoDE project focuses on two endpoints that have been shown to be relevant at current exposure levels: mild mental retardation (due to IQ loss) and hypertensive disease (due to rise in systolic blood pressure). For the other health endpoints, i. a., no empirically sound exposure-response-relationships are available. Therefore, our results may underestimate the actual EBD of lead exposure in Europe. The extent of this underestimation cannot be quantified sufficiently.<br />
<br />
The hypothesis of an effect threshold was rejected in several studies (Téllez-Rojo et al. 2006, Binns et al. 2007, Chiodo et al. 2004, Kordas et al. 2006). There is strong evidence for an association between B-Pb (blood lead) and negative effects on neuropsychological parameters at levels lower than 100 μg/l (Walkowiak et al., 1998; Canfield et al., 2004; Carta et al., 2005). Therefore, extending the dose-response curve to the range below 100 μg/l is possible. Lanphear et al. (2005) proposed a log-linear model for this curve.<br />
<br />
Findings on lead’s effects on the central nervous system in the low-dose range are available from longitudinal and cross-sectional studies (Lanphear et al., 2005). These studies showed B-Pb and decrease in IQ points with B-Pb in children. The WHO model for IQ loss was recently updated to consider B-Pb levels above 24 μg/l. It has to be taken into account, however, that no threshold for mental retardation has been confirmed, yet. The exposure/response-function (ERF) in the WHO model is:<br />
<br />
<math> IQloss = \frac{(B_{Pb} - 24)}{20} </math> (Lanphear et al., 2005; see also Table 3-19 in section 3.12).<br />
<br />
The population distribution of IQ is as defined as N(100;15). When the IQ falls below a diagnostic threshold, IQ loss is defined as mild mental retardation, which is the health endpoint used in this study. This threshold is set at 70 IQ points. We calculate the number of cases of mild mental retardation by estimating how many individuals in the target age group (children 0-4 years) exceed the diagnostic thresholds due to the lead exposure.<br />
<br />
Several longitudinal studies have examined associations of blood pressure change or hypertension incidence in relation to lead concentration in blood or bone. Glenn et al. (2006) concluded that systolic blood pressure is associated both with acute changes in the blood lead level as well as with long-term cumulative exposure. Blood lead levels can increase in women over the menopause, as lead is released from bone. This may increase women’s risk of high blood pressure.<br />
<br />
The current WHO model for increased systolic blood pressure in adults aged 20–79 years assumes a linear relationship between 50-200 μg/l (increase of 1.25 mmHg for males and 0.8 mmHg for females per increase of 50 μg/l B-Pb). Above 200 μg/l, an increase of 3.75 mmHg for males and 2.4 mmHg for females per increase of 50 μg/l B-Pb is assumed. The model does not account for aggravating effects of increased blood lead levels during the menopause.<br />
<br />
The ERF for mean increase in the systolic blood (mmHg) in the WHO model is (B-Pb >50 μg/l) (Fewtrell et al, 2003):<br />
<br />
<math> \Delta mmHg = \frac{(B_{Pb} - 50)}{40} </math><br />
<br />
The calculation of the numbers of cases of hypertensive disease is similar to the calculations for mild mental retardation. The population distribution of systolic blood pressure is defined as N(135, 15). When exposure exceeds the diagnostic threshold, of 140 mmHg, the increase in blood pressure is defined as hypertensive disease. We calculate how many individuals in the target age group (>15 year olds) exceed the diagnostic threshold due to the lead exposure.<br />
<ref name="EBoDe"></ref><br />
<br />
==Exposure data==<br />
<br />
It is not easy to estimate lead exposure levels, because population exposure measurements are not regularly conducted, and because of the decreasing trends in lead concentrations which are not fully known. The most reliable way to account for all different possible exposure routes is to measure the body burden of lead. The commonly used exposure metric for such measurement is the blood lead level (B-Pb, whole blood, μg/l).<br />
<br />
For the application of the WHO model for IQ loss, distributions of B-Pb (defined by percentiles) are necessary, stratified by specific age groups. This means that data are needed about different fractions of the population that are exposed to certain categories of B-Pb levels. No coherent international data sources were identified for lead. Hence, data from individual studies conducted in all participating countries were used. The year in which these studies were conducted differs between countries and in some cases the limited temporal coverage prohibited trend estimation. In these cases the most recent data have been used. It is clear that the limited temporal representativity of the lead exposure data poses a significant source of uncertainty. Due to well established lowering trends for lead this is expected to cause mainly unknown overestimation of exposures and effects.<br />
<br />
The data are presented in Table 3-9 below and summarized in Table 3-21 in section 3.12. As shown in Table 3-9, lead data have been measured in different age groups in the different countries. Data from the German Environmental Survey (GerES) show that age is an important influencing factor for B-Pb levels in humans. As there is virtually no evidence for a significant reduction in B-Pb levels since the year 2000, the difference in age groups is assumed to be one of the most important sources of uncertainties when comparing the different countries. Unfortunately, B-Pb data are not sufficient to correct the country data for age.<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-9. Lead data (μg/l) for different countries, measured in different age groups and years, used in the lognormal simulation to yield the required distributional parameters.<br />
! rowspan="2" scope="col" width="115" | Country<br />
! colspan="3" scope="col" width="175" | Estimates (2004)<br />
! rowspan="2" scope="col" width="115" | Age group<br />
! rowspan="2" scope="col" width="115" | Year<br />
|-----<br />
! AM<br />
! GM<br />
! SD<br />
|-----<br />
| Belgium<br />
| 22<br />
| <br />
| 16<br />
| 14-15<br />
| 2000-06<br />
|-----<br />
| Finland<br />
| 16<br />
| <br />
| 11<br />
| Adults<br />
| 2004<br />
|-----<br />
| France<br />
| <br />
| 26<br />
| 18<br />
| 18-74<br />
| 2006-07<br />
|-----<br />
| Germany<br />
| 22<br />
| <br />
| 16<br />
| 20-29<br />
| 2004<br />
|-----<br />
| Italy<br />
| 39<br />
| <br />
| 24<br />
| 18-64<br />
| 2000<br />
|-----<br />
| Netherlands<br />
| <br />
| 19<br />
| 11<br />
| 1-6<br />
| 2005<br />
|}<br />
<br />
<small>AM: Arithmetic Mean; GM: Geometrical Mean; SD: Standard Deviation (estimated using coefficient of variation).</small><br />
<br />
As indicated above, both of the exposure-response models used apply a threshold level (50 μg l-1 and 24 μg l-1). Therefore, it is necessary to assess the fraction of the population being exposed to levels higher than these threshold levels. A probabilistic simulation model was used to calculate the fraction of the population exceeding the threshold using mean and standard deviation data and assuming lognormal distributions. Standard deviations were estimated for the simulation using a coefficient of variation estimated from the Finnish data.<br />
<ref name="EBoDe"></ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-10. Population distributions of blood lead levels used in the simulation of threshold exeedances assuming log-normal distribution.<br />
! <br />
! scope="col" width="50" | Country<br />
! scope="col" width="50" | BE<br />
! scope="col" width="50" | FI<br />
! scope="col" width="50" | FR<br />
! scope="col" width="50" | DE<br />
! scope="col" width="50" | IT<br />
! scope="col" width="50" | NL<br />
|-----<br />
| rowspan="3" | Adults<br />
| mean<br />
| 22.0<br />
| 16.0<br />
| 25.0<br />
| 22.0<br />
| 39.0<br />
| 19.0<br />
|-----<br />
| SD<br />
| 15.6<br />
| 11.4<br />
| 17.8<br />
| 15.6<br />
| 27.7<br />
| 13.5<br />
|-----<br />
| CV<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
|-----<br />
| rowspan="3" | Children<br />
| mean<br />
| 22.0<br />
| 16.0<br />
| 25.0<br />
| 22.0<br />
| 39.0<br />
| 19.0<br />
|-----<br />
| SD<br />
| 15.6<br />
| 11.4<br />
| 17.8<br />
| 15.6<br />
| 27.7<br />
| 13.5<br />
|-----<br />
| CV<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
|}<br />
<br />
<small>SD: Standard deviation, CV: coefficient of variation.</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Lead''':<br />
The calculation focused on mild mental retardation and hypertensive disease only. WHO EBD estimates (Fewtrell et al., 2003) include cerebro-vascular and other cardiovascular diseases besides hypertensive disease; therefore the current estimates for lead are slightly lower than the WHO estimates.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Estimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Lead<br />
| Other cardiovascular diseases than hypertensive disease; kidney damage; miscarriages; other effects of the nervous system; declined fertility; alterations in growth and endocrine function; behavioural disruptions; hearing-threshold changes; hyperkinetic syndrome; lung and stomach cancers. MMR: proxy for all lost IQ points<br />
| Differences in study year. Differences in studied age group. Incomplete data, temporal extrapolation and poorly known exposure trends<br />
| Threshold level. Shape of ERF<br />
| Evidence limited at prevailing low exposure levels. Estimation of threshold exceedances<br />
| **<br />
| Underestimation due to excluded end-points<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_formaldehyde_in_Europe&diff=21745Health effects of formaldehyde in Europe2011-06-13T12:16:32Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
[[File:Formaldehydedaly.png|thumb|400px|]]<br />
<br />
==About formaldehyde==<br />
<br />
Formaldehyde is a high-production volume chemical widely used in building materials, industrial processes and wide range of products. Formaldehyde is widely present both indoors and outdoors, but it reaches high levels mostly indoors. It is used in the production of several building materials and household products, or it can be a by-product of combustion. The high volatility of the compound can lead to high formaldehyde levels in indoor spaces.<br />
<br />
Predominant acute symptoms of formaldehyde exposure in humans are irritation of the eyes, nose and throat and aggravation of asthma symptoms (WHO, 2000a). A number of studies point to formaldehyde as an important indoor irritant associated with respiratory illness. A relationship between asthma-like symptoms and indoor concentrations of formaldehyde has been reported, as well as between exposure to formaldehyde emitted from indoor paint and asthma. Repeated exposures are not associated with more severe effects or lowering of the threshold concentration. Consequently, short-term concentrations are predictive of the effects also after long-term exposure.<br />
<br />
Exposure to formaldehyde has also been associated with development of cancer. Convincing evidence exists of high concentrations of formaldehyde being capable of inducing nasal cancer in rats and possibly in mice and genotoxic effects in a variety of in vitro and in vivo systems. Sinonasal cancer in humans has also been associated with high formaldehyde exposures in occupational industrial settings (ranging from 2 to 6 mg m-3) (WHO, 2000a). Based on this, IARC has recently classified formaldehyde as carcinogen group 1 (IARC, 2006a).<br />
<br />
Formaldehyde was included in EBoDE due to its high toxicological potential, economic significance and related political concern.<br />
<ref name="EBoDe"></ref><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
In the EBoDE study, only the development of asthma in toddlers has been included. Sinonasal cancer was not included, because the WHO Air Quality Guidelines working group (WHO, 2000a) as well as recent update of the reviews for the development of WHO Guidelines for indoor air quality (WHO, 2010b) concluded that there is no epidemiological or toxicological evidence that formaldehyde would be associated with sinonasal cancer at levels below 1 mg/m3. The WHO Guidelines for Indoor Air Quality use eye irritation as the main health end-point associated with formaldehyde; however, due to difficulties in estimating a burden of disease from irritation this endpoint was not included in our calculations.<br />
<br />
Association with asthma is suggested by the systematic review by McGwin et al., 2010, even though evidence has not been consistent across all the studies (e.g. Krzyzanowski et al, 1990). We selected childhood asthma as the endpoint for formaldehyde, but due to the inconsistencies in the scientific evidence the estimates calculated here should be considered preliminary and to be confirmed by future research. In order to estimate formaldehyde-related asthma, we used the exposure-response function as reported by Rumchev et al. (2002). They studied a cohort of 88 children in Perth, Australia. For every 10 μg m-3 increase in formaldehyde exposure in bedrooms, they found an increase of 3% in the risk of having asthma (OR=1.03, 95% CI 1.02–1.04). Based on a reanalysis of their data over reported exposure categories and rescaling for 1 μg m-3, the relative risk used in our calculation is 1.0167 (see also Table 3-19 in section 3.12). Asthma effects were calculated for children (<3 years). A similar association may potentially exist for older children and adults, but due to the lack of evidence such relationship was not modelled. This may lead to underestimation of the true formaldehyde-related burden of disease.<br />
<br />
A threshold level for effects was applied. The original study by Rumchev reported elevated risks starting from exposures of 60 μg m-3. When their data were plotted in order to derive the relative risk, the threshold could be even as low as 40 μg m-3. However, the Rumchev study was criticized for confounding factors. WHO (2000a, 2010b) indicated that the lowest concentration that has been associated with nose and throat irritation in exposed workers after short-term exposure is 0.1 mg m-3, although some individuals can sense the presence of formaldehyde at lower concentrations. To prevent significant sensory irritation in the general population, an air quality guideline value of 0.1 mg m-3 as a 30-minute average was recommended as the WHO Guideline (WHO, 2010b). This is the threshold value that we used in our calculations. Since this is an order of magnitude lower than the presumed threshold for cytotoxic damage to the nasal mucosa, there is a negligible risk of upper respiratory tract cancer in humans below this threshold. As part of the uncertainty analysis, we compared alternative threshold models for cancer (threshold levels of 40, 60 and 100 μg m-3) and asthma, see section 5.2.<br />
<ref name="EBoDe"></ref><br />
<br />
==Exposure data==<br />
<br />
Inhalation is the dominant pathway for formaldehyde exposure in humans. The relevant exposure metric is the residential indoor air level (μg/m-3). As indicated above, both of the exposure-response models used apply a threshold level (100 μg m-3). Therefore, it is necessary to assess the fraction of the population being exposed to levels higher than this threshold level. A probabilistic simulation model was used to calculate the fraction of the population exceeding the threshold using mean and standard deviation data and assuming lognormal distributions. No international exposure data sources were identified for formaldehyde, so data have been collected from heterogeneous national sources.<br />
<br />
For Belgium, Germany and the Netherlands, only mean exposures were available, without information about the variability. For these three countries, the exposure distributions were based on the data from the other countries (estimated coefficient of variation: 0.6).<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-8. Population distributions of residential formaldehyde concentrations.<br />
! Country<br />
! mean<br />
μg m<sup>-3</sup><br />
! sd<br />
μg m<sup>-3</sup><br />
! References<br />
|-----<br />
| Belgium<br />
| 24.0<br />
| 14.4<sup>1</sup><br />
| Swaans et al,. 2008<br />
|-----<br />
| Finland<br />
| 41.6<br />
| 22.4<br />
| Jurvelin et al, 2001<br />
|-----<br />
| France<br />
| 23.0<br />
| 14.0<br />
| OQAI, 2006<br />
|-----<br />
| Germany<br />
| 26.0<br />
| 15.6<sup>1</sup><br />
| Umweltbundesamt, 2008<br />
|-----<br />
| Italy<br />
| 16.0<br />
| 8.0<br />
| Lovreglio et al, 2009<br />
|-----<br />
| Netherlands<br />
| 13.0<br />
| 7.8<sup>1</sup><br />
| Dongen,van & Vos, 2008<br />
|}<br />
<br />
<small><sup>1</sup> Mean coefficient of variation of the countries with data on variability used for estimation.</small><br />
<br />
Exposure data for formaldehyde are presented in Table 3-21 in section 3.12.<br />
<br />
The mean formaldehyde indoor concentrations vary from 13 μg m-3 in the Netherlands to about 42 μg m-3 in Finland. In Finland formaldehyde exposure levels are higher than in many other developed countries due to the construction materials used and the relatively tightly sealed building envelopes. As shown in Figure 3-3, approximately 42% of population is exposed to levels above 40 μg m-3 and 2 % above 100 μg m-3.<br />
<br />
[[Image:Estimated_formaldehyde_exposure_Finland.png|none|Estimated formaldehyde exposure|FIGURE 3-3. Estimated formaldehyde exposure distribution in Finland.]]<br />
<br />
<small>FIGURE 3-3. Estimated formaldehyde exposure distribution in Finland.</small><br />
<br />
Data comparability is compromised for formaldehyde by the differences in population sampling. In France, Germany and the Netherlands, data measurements are representative for country-wide exposure. However, in other countries, measurements have only been carried out in a few cities or were based on a smaller subset of houses.<br />
<ref name="EBoDe"></ref><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Formaldehyde'''<br />
<br />
No international burden of disease study utilizing DALYs for formaldehyde was identified. WHO Guidelines for Indoor Air Quality used eye irritation as the main health end-point in setting a safe exposure level. However eye irritation cannot be directly used as a health end-point in burden of disease calculation because no disability weight exists and therefore was not accounted for here. Scientific evidence on the association between formaldehyde and childhood asthma is not considered sufficiently consistent yet; thus the results presented here must be taken as provisional estimates of the magnitude of the health impacts, to be confirmed by future studies.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Formaldehyde<br />
| Acute symptoms; nasopharyngeal and sinonasal cancers.<br />
| Data from different years. Population representativity varies. For some countries limited national coverage. Limitation in technique to detect peak exposures<br />
| Shape of ERF. Threshold level. Partly inconclusive evidence for the endpoint. ERF from &lt;3 yr olds; potential effects at older ages not accounted for<br />
| Simulation of threshold exceedances. Selection of age groups<br />
| ***<br />
| Underestimation, mainly due to exclusion of = 3year olds but also not accounting for eye irritation<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_dioxins_in_Europe&diff=21744Health effects of dioxins in Europe2011-06-13T12:15:17Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EBoDE]]<br />
<br />
[[File:Dioxinsdaly.png|thumb|400px|]]<br />
<br />
Dioxins (including furans and dioxin-like PCBs) are a group of polychlorinated organic compounds with the same toxic mechanism. They are by-products of various industrial processes and combustion activities and are considered to be highly toxic.<br />
<br />
Dioxins and dioxin-like PCBs are quantified by toxic equivalents (TEQs) representing the total toxicity compared to the most toxic compound, 2,3,7,8-Tetrachlorodibenzodioxin (TCDD). The power of toxicity is calculated with Toxic Equivalent Factors (TEFs), which allow the toxic potentials of each compound to be added up, in order to derive the TEQ of the mixture. Acute toxicity, leading for example to chlorakne or alteration of liver function, is only expected at very high doses. Long-term exposure to dioxins has been linked to effects on the immune system, the nervous system, the endocrine system and reproductive functions and is also known to cause tooth and bone defects, diabetes as well as several types of cancer (USEPA, 2003). The association between dioxins and cancer has been most consistent for non-Hodgkin’s lymphoma. IARC classified TCDD (2,3,7,8-Tetrachlorodibenzo-p-dioxin), as a “known human carcinogen” (IARC, 1997). All other dioxin-like compounds are classified as “likely to be carcinogenic to humans”.<br />
<br />
This group of chemicals is selected in EBoDE because of their high toxicity and potential troubling exposures through e.g. mothers milk.<br />
<ref name="EBoDe"></ref><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
In EBoDE, we have quantified the effect of exposure to dioxins and dioxin-like PCBs on cancer (all cancer types, mortality only). The non-fatal and non-cancer effects were not suited for health impact assessments due to difficulties in estimating the exposure-response relationships and the other input parameters necessary for estimating DALYs. Therefore, our estimates may underestimate the true dioxin-related burden of disease.<br />
<br />
Leino et al. (2008) assumed a linear exposure-response relationship for excess cancers associated with dioxin intake. They estimated the health risk for toxicity equivalent intake assuming additivity of the toxicity of the different types of dioxins and all cancer cases to be lethal.<br />
<br />
The EBoDE calculations use the Leino et al. (2008) approach, but the results have been corrected with an updated cancer slope factor 1×10-3 per pg/kg/d of dioxin intake of the U.S. Environmental Protection Agency (USEPA, 2003; NAS, 2006). The assumption that all cancers are lethal may lead to overestimation of the impacts.<br />
<br />
The health endpoints considered in this project for dioxins and the corresponding exposure-response functions are summarized in Table 3-19 in section 3.12. YLD estimates in the table are based on the attributable fraction derived from the ERF using method 2A (see Figure 2-1), which is applied to the total YLD for all cancers as represented in the WHO database.<br />
<ref name="EBoDe"></ref><br />
<br />
==Exposure data==<br />
<br />
Dioxins and dioxin-like PCBs are persistent and bio-accumulating. The main exposure route for these chemicals is animal fat in nutrition, which accounts for about 90% of all exposure. Other routes, such as inhalation, play a minor role.<br />
In order to estimate health effects related to dioxin exposure, daily intake data were needed. This intake depends on eating habits, age, gender, body weight and food consumption. Often, breast feeding contributes to the highest intake of dioxins for humans in their life. Dioxins have a long half life. Therefore the development of health effects in humans depends not only on the daily intake, but also on the body burden accumulated over years. On average, the daily intake of dioxins and dioxin-like PCBs decreases, while the body burden increases with age.<br />
<br />
The cancer slope factor is expressed for daily intake of adults. There are different ways to measure the daily intake, each with different limitations. Table 3-2 describes some different measurement methods and provides short information about their use and limitations.<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-2. Different ways to measure daily intake of dioxins and dioxin-like PCBs.<br />
!<br />
! Type of measurement<br />
! Type of use<br />
! Specific limitations and uncertainties<br />
|-----<br />
| A<br />
| Survey (questionnaire) on food consumption<br />
| Information on food consumption and about the content of dioxins in representative food samples allow modelling of daily intake<br />
| Results are modelled for an average population - food contamination and eating habits can differ on a large scale<br />
|-----<br />
| B<br />
| Total diet studies<br />
| The total diet in a population group over a certain time period and dioxin in this food or representative food samples are measured.<br />
| Results are only relevant for the investigated groups and not necessarily representative for the whole population, sampling period influence the results.<br />
|-----<br />
| C<br />
| Human biomonitoring Investigation of human milk or blood levels<br />
| Analyses of samples can show the body burden. Experimental scaling is used to convert observed biomonitoring results (blood) into daily intakes.<br />
| D-R function is based on daily intake. Human milk or blood samples are not widely available. Different fat content of the bodies influences the results.<br />
|}<br />
<br />
In addition, in all these studies different compounds can be measured:<br />
# Only dioxins and furans;<br />
# dioxins, furans; and dioxin-like PCBs<br />
# dioxins, furans and dioxin-like PCBs as well as all other dioxin-like compounds detected as dioxin-like activity, expressed as TEQ in Bioassays (e.g. CALLUX).<br />
<br />
In the EBoDE project, we have used national exposure data because there is no international comparable data source available. The different countries have used different methods to derive the daily intake values.<br />
<br />
Table 3-3 provides a summary of the data and sources for dioxin. The specific data used in this project are summarized in Table 3-21 in section 3.12.<br />
For the EBoDE project daily intake data are expressed as Toxic Equivalent (TEQ), estimated using the Toxic Equivalent Factors (TEFs) as provided by WHO (Van den Berg et al. 1998). Even though later TEFs exist (Van den Berg et al., 2006; http://www.who.int/ipcs/assessment/tef_update/en/), we used the results of the 1998 review, because most available data have been calculated using these TEFs.<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-3.: Summary of the sources of dioxin data. Explanation for A, B C see Table 3-2.<br />
! Countries<br />
! Population groups<br />
! Source<br />
! Sampling years<br />
! Compounds measured<br />
! Dioxin intake 2004 pg/kg bw/d<br />
|-----<br />
| Belgium (A)<br />
| female 18-44 y<br />
adults 50-65 y<br />
adults<br />
| Bilau 2008<br />
Bilau 2008<br />
Calculated mean<br />
| 2002–2006<br />
| Calux-all dioxin-like compounds<sup>1</sup><br />
| 2.1<br />
1.7<br />
1.9 (mean)<br />
|-----<br />
| Finland (A)<br />
| all<br />
| Kiviranta et al 2005<br />
| 2002<br />
| Dioxins+PCB<br />
| 1.5<br />
|-----<br />
| France (C)<br />
| 30–65 y<br />
| Fréry et al. 2006<br />
| 2004<br />
| Dioxins+PCB<br />
| 2.3<sup>2</sup><br />
|-----<br />
| Germany (A)<br />
| adults<br />
| Umweltbundesamt 2005<br />
| 2003<br />
| Dioxins+PCB<br />
| 2.0<br />
|-----<br />
| Italy (A)<br />
| 13–94 y<br />
| Fattore et al 2006<br />
| 1997–2003<br />
| Dioxins+PCB<br />
| 2.3<sup>3</sup><br />
|-----<br />
| Netherlands (A+B)<br />
| adults<br />
| De Mul 2008<br />
| 2004<br />
| Dioxins+PCB<br />
| 1.0<sup>4</sup><br />
|}<br />
<br />
<small><sup>1</sup> Belgium – Dioxin and all dioxin-like compounds are measured with Bioassay, only the sum of all dioxin-like compounds is given; the daily intake was calculated as mean of the 2 adult groups.<br />
<br />
<sup>2</sup> France – daily intake calculated based on blood concentration of 27.7 WHO-TEQ pg/g blood fat.<br />
<br />
<sup>3</sup> Italy – daily intake were calculated using, for most dioxin and DL-PCB concentration data, a database available from the European Commission (Gallani et al., 2004).<br />
<br />
<sup>4</sup> Netherlands – Values in the study were calculated using TEFs from 2005. For comparability, we have adjusted the values as presented by Mul et al (2008) by adapting the results to TEF 1998 adding 10%.</small><br />
<br />
We have only used data on the daily intake of adults. We have chosen to do so, because the daily intake differs substantially between different age groups. The highest intakes are calculated for breastfed babies (about 50 to 100 WHO-TEQ pg/kg bw/d). Children have a higher intake than adults because of the different proportion between body weight and food intake and their different food habits (children take more milk and dairy products). Since there are only very few data for children available, we have limited ourselves to adults.<br />
<br />
Due to the differences in measurement approach, it is difficult to compare dioxin intake numbers between countries. As a form of quality assurance, we have compared our daily intake estimates of dioxins and dioxin-like PCBs to international data on dioxins and PCBs in mother's milk (milk data from 2001–2003) as provided by WHO in the ENHIS-database (WHO, 2007a) and from Malisch and Leeuwen (2003). In principle, the ratio between the estimated daily intakes and the levels of mother’s milk should be roughly similar between countries. The ratios are presented in Table 3-4. As can be seen from this table, the ratios are relatively similar across the countries, except in the Netherlands, where the intake level seems to be somewhat lower than in the other countries in comparison with the mother’s milk levels. We have not corrected for this difference in the EBoDE calculations, as the causes for the difference are yet unknown.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Estimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-4. Comparison of dioxins and PCBs human milk (WHO, 2007a) and the estimated daily intakes (country-specific results – see Table 3-3.<br />
! Country<sup>a</sup><br />
! Human milk<br />
ng TEQ/kg fat<br />
! Daily intake<br />
pg TEQ/kg bw/d<br />
! Factor<br />
milk/intake<br />
|-----<br />
| Belgium<br />
| 29.5<br />
| 1.9<br />
| 16<br />
|-----<br />
| Finland<br />
| 15.3<br />
| 1.5<br />
| 10<br />
|-----<br />
| Germany<br />
| 26.2<br />
| 2.0<br />
| 13<br />
|-----<br />
| Italy<br />
| 29.0<br />
| 2.3<br />
| 13<br />
|-----<br />
| Netherlands<br />
| 29.8<br />
| 1.0<br />
| 30<br />
|}<br />
<br />
<small><sup>a</sup> France was not included in the WHO-milk study.</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Dioxins''' Our calculations were based on the same approach as applied earlier by Leino et al (2008), but we utilized an updated cancer slope factor that is approximately seven times higher than the one used by Leino et al. Leino et al. did the calculations for Finland only. The work presented here also updated the exposure estimates in order to allow for good international comparability, yet some differences between the national intake estimation methods remained.<br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Dioxins (plus furans and PCBs)<br />
| Effects on the immune, endocrine, reproductive and nervous system; tooth and bone defects. All cases of cancer assumed to be fatal.<br />
| Indirect exposure metrics. Different measurement methods. Daily intake of food depends on age, body weight and eating habits. Exposure varies within countries (from region to region)<br />
| Uncertain cancer slope factor. Assumed additivity of the toxicity of different types<br />
| UR method of calculating PAF results in overestimation because all cases are assumed to be fatal.<br />
| ***<br />
| Underestimation of non cancer effects, Overestimation of cancer effects (all lethal)<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_particulate_matter_in_Europe&diff=21743Health effects of particulate matter in Europe2011-06-13T12:14:06Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
[[File:PM2.5daily.png|thumb|400px|]]<br />
<br />
==About particulate matter==<br />
<br />
Exposure to Particulate matter (PM) has been associated with both respiratory and cardiovascular effects and total non-violent mortality (Pope and Dockery, 2006, WHO, 2006a,b) and it is the most thoroughly internationally reviewed environmental pollutant during the last decade. PM was selected in EBoDE due to its high public health impact, economic significance (industry, transport) and political concern. Particulate matter is a complex mixture of components from natural and anthropogenic sources and is partly created in chemical and physical processes in the atmosphere from gaseous primary components like sulphur dioxide, nitrogen oxides, ammonia, and volatile organic compounds. The health implications of the particulate matter components have been extensively studied, but still the most convincing epidemiological evidence associates PM2.5 mass concentrations with the health impacts (Pope & Dockery, 2006).<br />
<ref name="EBoDe"></ref><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
For PM (and ozone) we followed the health impact assessment approach as laid out in the Clean Air For Europe (CAFE) project and based on WHO European Centre for Environment and Health and CLTRAP Task Force on Health consultations (Hurley et al. 2005, WHO, 2006a, b). PM2.5 and PM10 both serve as indicators of a complex mixture of physically and chemically heterogeneous composition. In the EBoDE calculations, we calculated burden of disease related both to PM10 and to PM2.5 exposure. Due to the overlap between these two indicators, in the aggregate results only the results for PM2.5 are included. For PM2.5, we calculated the burden of disease for cardiopulmonary mortality, lung cancer mortality, total non-violent mortality, chronic bronchitis and restricted activity days (RAD; defined by Hurley et al., 2005). Due to the overlap between the different mortality endpoints, we included only cause specific mortality in the aggregate results. For PM10, lower respiratory symptoms (LRS) and new cases of chronic bronchitis were included.<br />
<br />
For mortality, we used the relative risks as provided by Pope (Pope et al., 2002; WHO, 2006a,b). For morbidity, relative risks are based on the thorough review made for the CAFE estimates by Hurley et al. (2005) and WHO (2006b). The health endpoints and corresponding exposure-response functions are summarized in Table 3-19 in section 3.12.<br />
<ref name="EBoDe"></ref><br />
<br />
==Exposure data==<br />
<br />
Annual population weighed mean ambient concentrations of PM2.5 and PM10 were estimated, similarly to the values for ozone, by the European Topic Centre on Air and Climate Change (ETC/ACC) using geographical modelling. Population density data were based on JRC data. For further details, see the ozone section. Exposure values are presented in Table 3-13 and summarized in Table 3-21 in section 3.12.<br />
<br />
The calculations involve no reference concentration for estimating the PM effects, so all PM-related morbidity and mortality are included in the burden of disease estimates. This is in contrast to, for example, the CAFE calculations, in which only the impacts of European anthropogenic emissions were estimated. The EBoDE calculations include the contribution to PM from natural sources and sources outside Europe.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-13. National population weighted averages of ambient PM levels in 2005 for the target countries (de Leeuw and Horalek, 2009).<br />
! rowspan="2" scope="col" width="100" | Country<br />
! colspan="2" scope="col" width="200" | Concentrations<br />
|-----<br />
| PM<sub>10</sub><br />
(μg m<sup>-3</sup>)<br />
| PM<sub>2.5</sub><br />
(μg m<sup>-3</sup>)<br />
|-----<br />
| Belgium<br />
| 28.9<br />
| 18.7<br />
|-----<br />
| Finland<br />
| 13.3<br />
| 9.1<br />
|-----<br />
| France<br />
| 19.1<br />
| 12.3<br />
|-----<br />
| Germany<br />
| 22.1<br />
| 16.0<br />
|-----<br />
| Italy<br />
| 32.7<br />
| 19.6<br />
|-----<br />
| Netherlands<br />
| 29.1<br />
| 18.7<br />
|}<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''PM and ozone'''<br />
<br />
The methodology developed in Clean Air for Europe -project (CAFE) (Hurley et al., 2005) was applied using updated exposure estimates. The updated exposures are based on ambient air quality monitoring data that contain, besides the anthropogenic components that CAFE focused on, also natural sources of PM<sub>2.5</sub>. The spatial resolution of the updated model is 25 times higher (grid size 10x 10 km² instead of 50x50 km²). Compared to the CAFE estimates the current work adds estimation of the impacts in DALYs. The WHO Environmental Burden of Disease programme uses a non-linear exposure-response function (Ostro, 2004) that at higher exposures yields lower impacts than the linear CAFE model. WHO also sets a threshold level at 7.5 μg m<sup>-3</sup>.<ref name="EBoDe"></ref><br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Particulate matter<br />
| Morbidity outcomes evaluated using the CAFE simplifications<br />
| Total PM (not just anthropogenic emissions)<br />
| Potential threshold level<br />
| Unit risk simplifications for morbidity outcomes<br />
| *<br />
| No substantial error expected or overestimation due to inclusion of natural background<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_ozone_in_Europe&diff=21742Health effects of ozone in Europe2011-06-13T12:11:04Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
[[File:Ozonedaily.png|thumb|400px|]]<br />
<br />
==About ozone==<br />
<br />
Ozone in the lower atmosphere (or tropospheric ozone) is not emitted directly, but is formed in the atmosphere in photochemical reactions from anthropogenic and natural emissions of precursor components involving mostly volatile organic compounds (VOCs) and nitrogen oxides (mainly NO and NO<sub>2</sub>). These substances react to form ozone under the influence of sunlight. Ozone is highly reactive and therefore other air pollutants also easily consume the ozone present in the air. Therefore, the highest ozone levels are typically found in background regions and levels in urban areas are generally lower than in the countryside.<br />
<br />
Exposure to ozone can lead to a variety of respiratory health effects, such as coughing, throat irritation and reduced lung function. In addition, it can worsen bronchitis, emphysema, and asthma (WHO, 2006a). Ozone levels are increasing over time, and are cause for political concern.<br />
<ref name="EBoDe"></ref><br />
<br />
===Selected health endpoints and exposure-response functions===<br />
<br />
For ozone, as well as for PM (see section 3.9), we followed the health impact assessment approach as laid out in the Clean Air For Europe (CAFE) project and based on WHO European Centre for Environment and Health and CLTRAP Task Force on Health consultations. Health effects that are taken into consideration include total non-violent mortality, minor restricted activity days (MRADs), and cough and lower respiratory symptoms (LRS) in children aged 5–14 years. The choice of these endpoints was guided by Cost Benefit Analysis as carried out in the CAFE project (Hurley et al, 2005, WHO 2008). The health endpoints considered and the corresponding exposure-response functions are summarized in Table 3-19 in section 3.12.<br />
<ref name="EBoDe"></ref><br />
<br />
===Exposure data===<br />
<br />
The exposure metric used for ozone calculations is the sum of ozone maximum 8-h levels above 35 ppb, called SOMO35 (WHO, 2008). SOMO35 (expressed in μg m<sup>-3</sup> × hours) is the sum of the maximum daily 8-hour concentrations that are exceeding 35 ppb (70 μg m<sup>-3</sup>) for each day in the calendar year, i.e. e.g. a daily level of 100 μg m<sup>-3</sup> would contribute 30 to the SOMO35 calculation. Regardless of the name referring to the ppb unit of measurement, the values are expressed as mass concentrations (μg m<sup>-3</sup>).<br />
<br />
For ozone (as well as for PM, see section 3.9), exposures were estimated by the European Topic Centre on Air and Climate Change (ETC/ACC) using AirBase data and air quality maps (SOMO35) (de Leeuw & Horalek, 2009). The European Environment Agency (EEA) has recently published an evaluation of new monitoring-based methods to estimate population weighted spatial distributions of ambient PM and ozone levels (EEA, 2009). These methods are based on interpolated maps using 10×10 km spatial resolution and using observed concentrations from national monitoring networks as primary data source. These are combined with regional chemistry transport modelling (CTM) and other supplemental data sources to improve estimates in observation-sparse areas. Maps for rural and urban areas were created separately and were subsequently merged. This approach aims to provide an objective method for dealing with the differences found between the rural and urban interpolated concentration fields in most areas of Europe (EEA, 2009). It is different from the earlier Clean Air for Europe (CAFE) work, which relied on modelling as its primary source of information and uses monitoring only to calibrate the European Monitoring and Evaluation Programme (EMEP) chemical transportation model. The modelling approach is better suitable for prospective scenario analyses, while the monitoring based approach may be considered more reliable for retrospective analyses.<br />
<br />
The air quality maps were prepared for 2005 with interpolation methodology using co-kriging of observed concentrations using additional spatial information (EMEP model results, meteorological data, altitude, population density map). The year 2005 instead of 2004 was chosen as the modelling year by EEA for practical purposes. Description of the maps is given by Horálek et al (2007) and de Leeuw and Horalek (2009). A brief introduction to AirBase and a description of the state of and recent trends in European air quality is presented by Mol et al (2009).<br />
<br />
Population weighted ambient ozone concentrations were calculated using population data for year 2005. The population density map (resolution 10x10 km) is based on the detailed population map prepared by JRC (reference year 2002, see Horalek et al., 2008 for further description of this dataset). The population density map for 2005 is made by scaling the 2002-reference map using the 2005/2002 ratio of national population numbers. Within a country the same age distribution is assumed in all grid cells.<br />
Resulting population-weighted ozone exposure values for the participating countries are shown in Table 3-12 and are also summarized in Table 3-21 in section 3.12. The geographical distribution of the SOMO35 levels in Europe is shown in Figure 3-5.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-12.: National population weighted averages of ambient ozone levels (SOMO35) in 2005 for the six EBoDE countries (de Leeuw and Horalek, 2009).<br />
! scope="col" width="150" | Country<br />
! scope="col" width="150" | SOMO35<br />
(μg m<sup>-3</sup>)<br />
|-----<br />
| Belgium<br />
| 2 787<br />
|-----<br />
| Germany<br />
| 4 164<br />
|-----<br />
| Finland<br />
| 2 580<br />
|-----<br />
| France<br />
| 4 756<br />
|-----<br />
| Italy<br />
| 8 134<br />
|-----<br />
| Netherlands<br />
| 1 920<br />
|}<br />
<br />
[[Image:Ozone_SOMO35_levels_Europe_2005_EBoDE.png|none|Ozone SOMO35-levels in Europe in 2005]]<br />
<br />
<small>FIGURE 3-5. Ozone SOMO35-levels in Europe in 2005 (EEA, 2009).</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''PM and ozone'''<br />
<br />
The methodology developed in Clean Air for Europe -project (CAFE) (Hurley et al., 2005) was applied using updated exposure estimates. The updated exposures are based on ambient air quality monitoring data that contain, besides the anthropogenic components that CAFE focused on, also natural sources of PM<sub>2.5</sub>. The spatial resolution of the updated model is 25 times higher (grid size 10x 10 km² instead of 50x50 km²). Compared to the CAFE estimates the current work adds estimation of the impacts in DALYs. The WHO Environmental Burden of Disease programme uses a non-linear exposure-response function (Ostro, 2004) that at higher exposures yields lower impacts than the linear CAFE model. WHO also sets a threshold level at 7.5 μg m<sup>-3</sup>.<ref name="EBoDe"></ref><br />
<br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Ozone<br />
| Possible long-term effects<br />
| Spatial interpolation. Impact of urban areas<br />
| <br />
| YLL not known<br />
| **<br />
| Overestimation (YLL set to 12 months). Underestimation due to exclusion of potential long-term effects<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_radon_in_Europe&diff=21741Health effects of radon in Europe2011-06-13T12:09:46Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
[[File:radondaily.jpg|thumb|400px|]]<br />
<br />
==About radon==<br />
<br />
Radon is a short-lived radioactive gas that occurs naturally in soils and rocks. It is generated by the radioactive decay of uranium. Indoor radon concentrations differ based on the characteristics of the geological substrates beneath houses and the use of different building materials.<br />
<br />
Exposure to radon can lead to lung cancer. Studies to estimate the risk of lung cancer associated with residential radon exposure have been conducted in many European countries (Lagarde et al. 1997, Bochicchio, 2005, 2008; Darby et al., 2005, 2006). Radon is classified by IARC as carcinogenic to humans (type 1, 1988) with genotoxic action. No safe level of exposure can be determined (WHO, 2000a). Besides lung cancer radon is not known to cause other health effects.<br />
<br />
Radon has a synergistic effect with smoking. Epidemiological evidence suggests that the risk of simultaneous exposure to both tobacco smoke and radon is more than additive but that it may be less than multiplicative.<br />
<ref name="EBoDe"></ref><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
Radon effects are usually presented as additional cases of lung cancer at a certain exposure (i.e. unit risk model). In order to account for the interaction with smoking, however, a relative risk model seems more appropriate. We therefore calculated results using both a unit risk model and a relative risk model (method 1A and 2A). The RR method (1A) is used in the final aggregate results. The radon UR model (UR=6.6E-07 (Bq m<sup>-3</sup>)-1, Darby et al., 2005) is used for comparison of UR and RR modelling approaches in Chapter 5.<br />
<br />
The relative risk model, as suggested by the meta-analysis of Darby et al. (2005), assumes the lung cancer risk from radon to be linearly proportional to the radon exposure, but also to the background lung cancer rate caused by tobacco smoking (and, to a lesser extent, by exposure to second-hand smoke, ambient air particulate matter and possibly some occupational exposures) (see Table 3-19 in section 3.12 for the RR values).<br />
<ref name="EBoDe"></ref><br />
<br />
==Exposure data==<br />
<br />
The soil uranium contents and respectively the residential radon concentrations vary significantly between the countries. Yet the differences within the countries are still far greater, and the indoor radon concentrations in individual buildings are essentially impossible to predict. Long-term average indoor radon concentrations, however, are relatively easy to measure and are therefore better known and comparable between the countries than those of any other indoor air contaminant.<br />
<br />
EBoDE uses the national residential radon exposure estimates as collected by the EU RadonMapping project (http://radonmapping.jrc.ec.europa.eu; country reports available from http://radonmapping.jrc.ec.europa.eu/index.php?id=37&no_cache=1&dlpath=National_Summary_Reports, accessed 11 June 2009). and the UNSCEAR 2000 Report, as presented in Table 3-14 and summarized in Table 3-21 in section 3.12. No further national data collection was conducted, but some additional international data sources were identified, notably from the WHO Radon project (IRP, 2010).<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Estimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-14. Radon concentrations in dwellings determined in indoor surveys (compiled from National Summary Reports at http://radonmapping.jrc.ec.europa.eu/ and UNSCEAR, 2000). The respective cancer risks are estimated from background lung cancer rates using both absolute and relative risk models.<br />
! Country<br />
! AM<br />
(Bq m<sup>-3</sup>)<br />
! GM<br />
(Bq m<sup>-3</sup>)<br />
! GSD<br />
! % (of people exposed) › ≥200 Bq m<sup>-3</sup><br />
! % (of people exposed) › ≥400 Bq m<sup>-3</sup><br />
! Max<br />
(Bq m<sup>-3</sup>)<br />
|-----<br />
| Belgium<br />
| 69<br />
| 76<br />
| 2.0<br />
| <br />
| 0.5<br />
| 4 500<br />
|-----<br />
| Finland<br />
| 120<br />
| 84<br />
| 2.1<br />
| 12.3<br />
| 3.6<br />
| 33 000<br />
|-----<br />
| France<br />
| 89<br />
| 53<br />
| 2.7<br />
| 8.5<br />
| 2.0<br />
| 4 964<br />
|-----<br />
| Germany<br />
| 50<br />
| 40<br />
| 1.9<br />
| 3.0<br />
| 1.0<br />
| 10 000<br />
|-----<br />
| Italy<br />
| 70<br />
| 52<br />
| 2.0<br />
| 4.1<br />
| 0.9<br />
| 1 036<br />
|-----<br />
| The Netherlands<br />
| 30<br />
| 25<br />
| 1.6<br />
| 0.3<br />
| 0.0<br />
| 382<br />
|}<br />
<br />
<small>AM: Arithmetic Mean; Bq: Becquerel; GM: Geometric Mean; GSD: Geometric Standard Deviation.</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Radon''' The exposure estimation and dose-response models are based on earlier international analysis conducted by Darby et al. (2006). In comparison with that the current work added estimation of the impacts in DALYs. Comparison of UR and RR models yielded similar results. The results using the RR approach, accounting for the national differences in the background rates of lung cancer, were selected for reporting.<br />
<br />
{| {{prettytable}}<br />
| <br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Radon<br />
| No health endpoints excluded<br />
| Possible oversampling of geographical regions known problematic<br />
| <br />
| <br />
| *<br />
| No substantial error expected<br />
|----<br />
|}<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Overview_of_the_EBoDE-project&diff=21730Overview of the EBoDE-project2011-06-10T09:11:04Z<p>Iiro: /* Uncertainties per stressor and comparison with other studies */</p>
<hr />
<div>{{study|moderator=Julle}}<br />
[[category:EBoDE]]<br />
<br />
==Introduction==<br />
<br />
Exposures to many environmental stressors are known to endanger human health. Negative impacts on health can range from mild psychological effects (e.g. noise annoyance), to effects on morbidity (such as asthma caused by exposure to air pollution), and to increased mortality (such as lung cancer provoked by radon exposure). Properly targeted and followed-up environmental health policies, such as the coal burning ban in Dublin (1990) and the smoking ban in public places in Rome (2005) have demonstrated significant and immediate population level reductions in deaths and diseases. In order to develop effective policy measures, quantitative information about the extent of health impacts of different environmental stressors is needed.<br />
<br />
As demonstrated by the examples above, health effects of environmental factors often vary considerably with regard to their severity, duration and magnitude. This makes it difficult to compare different (environmental) health effects and to set priorities in health policies or research programs. Public health policies generally aim to allocate resources effectively for maximum health benefits while avoiding undue interference with other societal functions and human activities. In order to develop such policies, it is necessary to know what ‘maximum health benefits’ are. Decades ago, such decisions tended to be made based on mortality statistics: which (environmental) factor causes most deaths? However, nowadays, most people get relatively old, and priority has shifted from quantity to quality of life. This has lead to the need to incorporate morbidity effects into public health decisions, and therefore to find a way of comparing dissimilar health effects.<br />
<br />
Such comparison and prioritisation of environmental health effects is made possible by expressing the diverging health effects in one unit: the environmental burden of disease (EBD). Environmental burden of disease figures express both mortality and morbidity effects in a population in one number. They quantify and summarize (environmental) health effects and can be used for:<br />
* Comparative evaluation of environmental burden of disease (“how bad is it?”)<br />
* Evaluation of the effectiveness of environmental policies (largest reduction of disease burden)<br />
* Estimation of the accumulation of exposures to environmental factors (for example in urban areas)<br />
* Communication of health risks<br />
<br />
An example of an integrated health measure that can be used to express the environmental burden of disease is the DALY (Disability Adjusted Life Years). DALYs combine information on quality and quantity of life. They give an indication of the (potential) number of healthy life years lost in a population due to premature mortality or morbidity, the latter being weighted for the severity of the disorder. The concept was first introduced by Murray and Lopez (1996) as part of the Global Burden of Disease study, which was launched by the World Bank. Since then, the World Health Organization (WHO) has endorsed the procedure, and the DALY approach has been used in various studies on a global, national and regional level.<br />
<br />
WHO collects a vast set of data on the global burden of disease. The first study quantified the health effects of more than 100 diseases for eight regions of the world in 1990 (Murray and Lopez, 1996). It generated comprehensive and internally consistent estimates of mortality and morbidity by age, gender and region. In a former WHO study, it was shown that almost a quarter of all disease worldwide was caused by environmental exposure (Prüss-Üstün and Corvalán, 2006). In industrial sub-regions this estimate was about 16% (15–18%). These fractions, however, are dependent on the conclusiveness of the included environmental factors and health effects. The WHO programme on quantifying environmental health impacts has addressed more than a dozen stressors <ref>The WHO programme[http://www.who.int/quantifying_ehimpacts/publications/en/]</ref>. In order to support further applications of the environmental burden of disease (EBD) assessments, a methodological guidance has been published by WHO (Prüss-Üstün et al., 2003) and was followed here too.<br />
<br />
In Europe, national environmental burden of disease (EBD) assessments are on-going in several countries. The work by RIVM was one of the first systematic European works in this area that utilized disability-adjusted life years (DALY) as a measure to compare the burden of different health outcomes related to the exposure of the population to environmental stressors (Hollander et al., 1999). The results highlighted that (i) a number of environmental stressors may cause chronic or acute diseases or death, (ii) a few top ranking stressors cause over 90% of the national EBD, and (iii) these top ranking stressors are not necessarily those that have drawn the most concern, regulatory action and/or preventive investment.<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
<br />
==Objectives==<br />
<br />
The EBoDE-project was set up in order to guide environmental health policy making in the six participating countries (Belgium, Finland, France, Germany, Italy and the Netherlands) and potentially beyond. From a policy perspective, these insights from the EBoDE-project can be useful to evaluate past policies and to gain insight in setting the policy priorities for the future. We have calculated the total EBD associated with the nine environmental stressors. The total EBD is not identical to the avoidable burden of disease, because some exposures are not realistically reducible to zero (e.g. fine particles). Also, our estimates do not take into account the costs of reducing the EBD. Thus, the results are only one input into the full process of developing cost-effective policies to achieve better environmental health.<br />
<br />
The objectives of the project were to update the available previous assessments, to focus on stressors relevant for the European region, to provide harmonized EBD assessments for participating countries, and to develop and make available the methodologies for further development and other countries.<br />
The specific objectives are to:<br />
• Provide harmonized environmental burden of disease (EBD) estimates for selected environmental stressors in the participating six countries;<br />
• Test the methodologies in a harmonized way across the countries.<br />
• Assess the comparability of the quantifications and ranking of the EBD<br />
• between countries<br />
• within countries<br />
• between environmental stressors;<br />
• Qualitative assessments of variation and uncertainty in the input parameters and results.<br />
<br />
Environmental burden of disease estimates have been calculated for:<br />
• nine environmental stressors: benzene, dioxins (including furans and dioxin-like PCBs), second-hand smoke, formaldehyde, lead, noise, ozone, particulate matter (PM) and radon;<br />
• six European countries: Belgium, Finland, France, Germany, Italy and the Netherlands;<br />
• the year 2004 (and some trend estimates for the year 2010).<br />
As outlined above, the EBoDE study was carried out in order to test the environmental burden of disease methodology in various countries. The results of the studies are intended to allow comparison of the disease burden between different environmental stressors and between countries. Consequently, the study does not to identify the ‘reduction potential’. Our estimates should therefore not be interpreted as the ‘avoidable burden of disease’: most risks cannot realistically be completely removed by any policy measures. For some exposures, however, the numbers may nonetheless be interpretable as reduction potential, eg for dioxins, formaldehyde, benzene, etc, as these exposures could potentially be completely eliminated.<ref name="EBoDe"/><br />
<br />
==Outline of this report==<br />
<br />
This report describes the methods, data and results of the EBoDE-project. Chapter 2 presents the methodology. The environmental stressors are introduced in Chapter 3, which also presents the data used (selected health endpoints, exposure data, exposure response functions). In Chapter 4, the results are presented and discussed. Chapter 5 gives information about uncertainties in the approach, and provides some alternative calculations using different input values. In Chapter 6 conclusions are drawn. The report ends with the references and two appendices: Appendix A presents country-specific results and Appendix B some considerations for using a life-table approach in EBD modelling.<ref name="EBoDe"/><br />
<br />
==Uncertainties and limitations==<br />
Assessment of uncertainties is essential in a comparison of quantitative estimates that are based on data from heterogeneous sources and slightly varying methods. Due to the wide range of data sources and models and the limited resources within the EBoDE project, systematic analysis of all uncertainties was not possible. However, we were able to assess a number of specific sources of uncertainties in more detail as part of the work, yielding some insights into the reliability of the overall assessment.<br />
The studied health impacts span approximately four orders of magnitude in size from few DALYs per million to almost 10 000 DALYs per million. The overall ranking of the environmental stressors seems to be rather robust against the relatively large uncertainties in individual estimates or methodological choices like discounting and age-weighing. However, some of the estimated ranges are overlapping. This concerns especially second hand smoke, radon and transportation noise that compete for the questionable honour of being the second most important environmental stressor in the participating countries. Among these stressors the differences are smaller than the corresponding uncertainties of the estimates.<br />
The health state of an individual person is the result of a complex mixture of genetic, environmental and behavioural factors. In a typical case of death, numerous factors play together. This means, for example, that a single death caused by a cardiovascular disease could be avoided by either reducing air pollution, or a better diet, or more physical activity. Therefore, if the individual attributable fractions are summed over a number of risk factors, a value over 100% may sometimes be found. For this and other reasons, it has been argued that death counts are not suitable for quantification of the impacts (Brunekreef et al., 2007). Therefore the authors recommend to mainly use aggregate population measures of health like DALYs, YLLs and YLDs.<br />
This chapter presents the quantitative results for selected sources of uncertainties and discusses the project limitations and author judgment of the reliability of the ranking.<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Transportation noise'''<br />
<br />
Burden of disease estimation for transportation noise is currently under active development. The estimates presented here were based on the only available international exposure data source, the first stage version of the European Noise Directive database (2007), which is not conclusive yet. Therefore it is clear that most of the exposures for transportation noise are underestimated. In some studies annoyance and cognitive impairment have been used as an additional health end-points for environmental noise. However, due to the selected more limited definition of ‘health’ as ICD-classified health states used in our assessment, annoyance and cognitive impairment were not included here. Only road, rail and air traffic exposures were included; many other sources also contribute to the noise exposures. Low exposures below the END data collection limits (50 and 55 dB) were not included. For these reasons it can be expected that when these limitations are solved, the impact estimates will increase.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
See also:<br />
[[Health effects of Second-hand smoke in Europe]]<br />
<br />
[[Health effects of benzene in Europe]]<br />
<br />
[[Health effects of radon in Europe]]<br />
<br />
[[Health effects of ozone in Europe|Health effects of PM and Ozone in Europe]]<br />
<br />
[[Health effects of dioxins in Europe]]<br />
<br />
[[Health effects of formaldehyde in Europe]]<br />
<br />
[[Health effects of lead in Europe]]<br />
<br />
==Conclusions and recommendations==<br />
<br />
Development of efficient environment and health policies and evaluation of their success requires quantitative information about environmental exposures and their health impacts. Disability adjusted life years (DALYs) can be used as an indicator for the environmental burden of disease by expressing both morbidity and mortality effects in one number. World Health Organization Global Burden of Disease and Environmental Burden of Disease programmes have developed methodologies for estimating environmental burden of disease. However, harmonized exposure data and established methods are still lacking for a large number of stressors that have relevance in the developed world. The current study aimed to test the available methods in six European countries using a harmonized approach. Nine stressors were selected that were considered relevant and interesting for Europe. The selection was intended to cover the most important environmental causes of public health impacts, but also to cover less important exposures that have had high significance in public debate or policy development.<br />
<br />
The results showed that the EBD methodology can be used to estimate the burden of disease in a harmonized way over a number of stressors and countries. The highest overall public health impact was estimated for ambient fine particles (PM2.5; annually 6000-9000 non-discounted DALYs per million in the six participating countries) followed by second-hand smoke (600-1200) transportation noise (500-1100), and radon (600-900). Lower impacts were estimated for dioxins and lead, followed by ozone, all containing also larger relative uncertainties. Lowest impacts were estimated for benzene and formaldehyde.<br />
<br />
Quantitative assessment of the various factors affecting the relative ranking of the stressors based on their health impact indicated that the ranking of non-overlapping estimates seems rather robust, even when the exact numbers contain variable amount of uncertainties. The scientific evidence on the causality and quantitative understanding of the exposure-response relationship was considered to have highest reliability for fine particles, second-hand smoke, radon and benzene. Medium uncertainties in the exposures and exposure response-relationships were identified for noise, lead and ozone. Quantitative results for dioxins and formaldehyde were considered most uncertain when evaluating the scientific evidence base.<br />
<br />
Differences in the representativity of the exposure data affect the comparability of estimates between the countries. Well comparable exposure data was available for particulate matter and ozone, followed by radon, second hand smoke, benzene, and dioxins. Lowest comparability was found for lead and formaldehyde. Transportation noise exposure data collection is well defined in the European Noise Directive (END), but the comparability of the data available from the first phase of data collection has not reached these standards yet. The comparability of estimates between the stressors is affected also by the selection of the health endpoints and the uncertainty in exposure response functions. It is unlikely that these differences in health response models could be solved in the near future.<br />
<br />
Environmental burden of disease estimates support meaningful policy evaluation and resource allocation. Besides, policy analysis also needs to account for the reduction potential of exposures, and other factors such as costs of policy measures and equity issues. The proposed methods for burden of disease estimation should be developed further to cover a larger range of environmental factors and health impacts and to include a systematic evaluation of uncertainties.<ref name="EBoDe"/><br />
<br />
<br />
==See also==<br />
<br />
*[[Abbreviations in EBoDE]]<br />
*[[Additional results of EBoDE by country|Additional results by country]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_Second-hand_smoke_in_Europe&diff=21716Health effects of Second-hand smoke in Europe2011-06-08T09:17:38Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Mori|stub=Yes}}<br />
[[category:EBoDE]]<br />
<br />
[[File:secondhandsmokedaily.png|thumb|400px|]]<br />
<br />
== Second-hand smoke ==<br />
<br />
=== About second-hand smoke ===<br />
<br />
Second-hand smoke (SHS; also called environmental tobacco smoke or passive smoking) is a known human carcinogen (IARC, 2004). Exposure to SHS has been shown to cause lung cancer, IHD (ischemic heart disease) sudden infant death syndrome, asthma, lower respiratory infections in young children, low birth weight, reduced pulmonary function among children, acute otitis media, and acute irritant symptoms (WHO, 1999; Californian EPA 2005; US Surgeon General 2006; IARC 2004, Jaakkola et al. 2003). Most evidence for SHS-related impacts is fairly consistent.<br />
<br />
SHS has been selected in our study because of its high public health impact, public concern and political interest. Policy measures to (further) reduce SHS exposure have been implemented in the recent past (e.g. the smoking ban) and further policy actions may be taken in the future. <br />
<ref name="EBoDe"></ref><br />
<br />
=== Selected health endpoints and exposure-response functions ===<br />
<br />
Out of the large number of health endpoints that SHS is associated with, we selected mortality and morbidity due to lung cancer and ischemic heart disease (IHD), morbidity due to onset of asthma (both in children and in adults), lower respiratory infections and acute otitis media. For the other health endpoints mentioned above, strong evidence is available, but the necessary disease statistics were lacking. <br />
<br />
For the SHS-related burden of disease calculations, we have followed the recent WHO methods on the global estimation of disease burden from SHS (Öberg et al. 2010). A summary of outcomes with their respective evidence levels is provided in Table 3-5. The exposure response functions are presented in Table 3-19. <br />
<br />
The selected exposure-response values are not gender-specific (e.g. exposure to male or female smoking spouse; exposure to paternal or maternal smoking). Instead, we used the mean relative risk for exposure to adults’ smoking. This choice was made in order to limit the sensitivity to gender-specific changes in smoking habits over time and across countries, and because not all exposure data were provided separately for men and women. <br />
<br />
The selected outcomes are being applied only to non-smokers, i.e. to the non-smoking disease burden. To that effect, the disease burden due to active smoking has been deduced from the total disease burden, by country (based on total disease burden and active smoking disease burden by country provided by WHO; update 2002 based on Ezzati et al. (2004)).<br />
<ref name="EBoDe"></ref><br />
<br />
<br />
{| border="1" cellpadding="5" cellspacing="0"<br />
|+ TABLE 3-5. Summary of recent reviews of health effects of second hand smoke (Adapted from: Öberg et al. 2010). <br />
|-<br />
| rowspan="2" | '''Health endpoint'''<br />
| rowspan="2" | '''Description'''<br />
| colspan="3" | '''Conclusion regarding the level of evidence (in 3 reports)'''<br />
|-<br />
| '''WHO (1999)'''<br />
| '''Californian EPA (2005)'''<br />
| '''U.S. Surgeon General (2006)'''<br />
|-<br />
| colspan="5" | '''Outcomes in children'''<br />
|-<br />
| Acute lower respiratory infection (ALRI)<br />
| Incidence of acute lower respiratory illnesses and hospitalizations<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Otitis media (middle ear infection)<br />
| Incidence of otitis media<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Asthma onset<br />
| Incidence of new cases<br />
| n<br />
| ***<br />
| **<br />
|-<br />
| colspan="5" | <br />
|-<br />
| colspan="5" | '''Outcomes in adults'''<br />
|-<br />
| Asthma induction<br />
| Adult-onset incident asthma<br />
| ***<br />
| **<br />
| n<br />
|-<br />
| Lung cancer<br />
| Incidence<br />
| ***<br />
| ***<br />
| ***<br />
|-<br />
| Ischemic heart disease (IHD)<br />
| Incidence of any ischemic heart disease<br />
| ***<br />
| ***<br />
| n<br />
|}<br />
<br />
<small>* = The evidence of causality is concluded to be “inconclusive”, “little”, “unclear” or “inadequate”. <br> ** = The evidence of causality is concluded to be “suggestive”, “some” or “may contribute”. <br> *** = The evidence of causality is concluded to be “sufficient” or “supportive”. <br> n = Not evaluated in the report. </small><br />
<br />
<br />
=== Exposure data ===<br />
Exposures to SHS and background risks vary by gender. Therefore, the data collection should account for differences in the exposures by gender. Some health effects are specific for children, so exposure data also had to be collected separately for children. Overall, the following exposure data are required for estimating the health impacts from SHS: <br />
# Percentage of children exposed to SHS (i.e. regularly exposed), OR percentage of children having at least one smoking parent <br />
# Percentage of non-smoking men exposed to SHS <br />
# Percentage of non-smoking women exposed to SHS <br />
<br />
For exposure data collection, we used data from national and international surveys as for example the Survey on Tobacco by the Gallup Organization for the European Commission (EC, 2009) or the European Community Respiratory Health Survey (Janson et al. 2006). The fieldwork for this study was conducted in December 2008 and over 26,500 randomly-selected citizens aged 15 years and over were interviewed in the 27 EU Member States and in Norway. The exposures for the six countries included in EBoDE <br />
are presented in Table 3-6. The “upper estimate” is used as the most realistic estimate, as this exposure description matches best the exposure definition used in epidemiological studies from which we derived our exposure-response functions. The lower estimates are provided in Table 3-6 for future sensitivity analysis. Table 3-21 in section 3.12 provides a summary of these data.<br />
<ref name="EBoDe"></ref><br />
<br />
{| {{prettytable}}<br />
|+ TABLE 3-6. Summary of European SHS exposure data for children and non-smoking adults.<br />
| rowspan="2" |<br />
! scope="col" colspan="2" |Children<br />
! scope="col" colspan="4" |Adults<br />
|-<br />
| '''[%]'''<br />
| '''Data year, reference'''<br />
| '''men [%]'''<br />
| '''women [%]'''<br />
| '''total [%]'''<br />
| '''Data year, reference'''<br />
|-<br />
| Belgium <small><sup>a)</sup></small><br />
| -<br />
| -<br />
| 59 <br> 34 <br> -<br />
| 48 <br> 32 <br> -<br />
| 53 <br> 33 <br> 25/30<small><sup>b)</sup></small><br />
| 1990–1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer2<small><sup>c)</sup></small> <br />
|-<br />
| Finland<br />
| 7<br />
| 1996, Lund<small><sup>3</sup></small><br />
| 14 <br> - <br> - <br />
| 13 <br> - <br> -<br />
| - <br> 15 <br> 6/14<small><sup>b)</sup></small><br />
| 2002, Jousilahti<small><sup>4</sup></small> <br> 2004, NPHI<small><sup>5</sup></small> <br> 2008, Eurobarometer<small><sup>2d)</sup></small><br />
|-<br />
| France<br />
| 23/33<small><sup>b)</sup></small><br />
| 2005, INPES<small><sup>6</sup></small><br />
| 38 <br> 23 <br> - <br> -<br />
| 46 <br> 30 <br> - <br> -<br />
| 42 <br> 26 <br> 13/21<small><sup>b)</sup></small> <br> 13/22<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2005, INPES<small><sup>6b)</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Germany<br />
| 24<br />
| 2003-2006, <br> GerES IV<small><sup>7</sup></small><br />
| 48 <br> 51 <br> 28 <br> -<br />
| 42 <br> 60 <br> 26 <br> -<br />
| 44 <br> - <br> 27 <br> 20/28<small><sup>b)</sup></small><br />
| 1990-1994 ECHRS I<small><sup>1</sup></small> <br> 1998, BGS<small><sup>8</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Italy<br />
| 50<br />
| 2001, <br> ICONA<small><sup>9</sup></small><br />
| 62 <br> 37 <br> -<br />
| 49 <br> 30 <br> -<br />
| 55 <br> 34 <br> 22/26<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|-<br />
| Netherlands<br />
| 20/36<small><sup>b)</sup></small><br />
| 2000-2005, <br> RIVM<small><sup>10e)</sup></small><br />
| 68 <br> - <br> 45 <br> - <br> -<br />
| 67 <br> - <br> 33 <br> - <br> -<br />
| 67 <br> 30 <br> 39 <br> 18/40<small><sup>b)</sup></small> <br> 18/27<small><sup>b)</sup></small><br />
| 1990-1994, ECHRS I<small><sup>1</sup></small> <br> 1998-2001, RIVM<small><sup>10</sup></small> <br> 2002, ECRHS II<small><sup>1</sup></small> <br> 2004-2007, RIVM<small><sup>10</sup></small> <br> 2008, Eurobarometer<small><sup>2</sup></small><br />
|}<br />
<small> NA: Adequate data not available <br><br />
NB: Additional national data are available for some countries, however, these did not match the description of regular exposure. <br><br />
Definitions used for lower and upper estimates: <br><br />
<sup>a)</sup> For Belgium, no data for children was found; estimate is calculated using mean of other countries.<br><br />
References: <sup>1</sup> Janson et al. 2006; <sup>2</sup> EC 2009; <sup>3</sup> Lund et al. 1998; <sup>4</sup> Jousilahti and Helakorpi 2002; <sup>5</sup> Finnish National Public Health Institute, 2004; <sup>6</sup> Institut National de Prévention et d’Education pour la Santé (INPES) 2005; <sup>7</sup> Conrad et al. 2008; <sup>8</sup> Schulze and Lampert 2006; <sup>9</sup> Tominz et al. 2005; <sup>10</sup> van Gelder et al. 2008. <br><br />
<sup>b)</sup> Lower/upper estimates; INPES: Lower estimate based on “regular” exposure; upper estimate based on exposure “from time to time”; <br><br />
Eurobarometer: Lower estimate based on daily exposure of more than one hour exposure at work and home exposure; upper estimate based on daily exposure of also less than one hour at work and home exposure. RIVM: ranges based on values provided by various studies. <br><br />
<sup>c)</sup> Exposure at home and at work supposed to be distributed equally. <br><br />
<sup>d)</sup> Finnish national data (NPHI) also provide survey results, but total exposure to SHS for non-smokers are more difficult to interpret. Therefore only the Eurobarometer data were taken into account here. <br><br />
<sup>e)</sup> The RIVM report contains data from various studies (e.g. Doetinchem, STIVORO, PIAMA) </small><br />
<br />
<br />
Available exposure data (Table 3-6) range across several years, and have been assessed with slightly differing <br />
definitions of exposures. In order to estimate exposure data for the target year (2004), exposures have been <br />
modelled on the basis of the survey data listed in Table 3-6 as follows: <br />
* Modelling was performed with total adult data, and men/women and children data were assumed to vary according to the same trends. <br />
* Power functions showed the highest correlations in most countries, and were therefore applied in all <br />
countries. No trend was apparent for Finland, therefore only the mean was applied. <br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref> <br />
<br />
Resulting trends are displayed in Figure 3-1, and estimated exposure data for 2004 in Table 3-7.<br />
<br />
[[Image:Percentage_of_adults_exposed_to_ETS.png|none|Pretty pixör!]]<br />
<small> FIGURE 3-2. Observed SHS exposure levels (markers) (% of non-smokers) for adults and corresponding modelled <br />
trends (lines) in the participating countries. </small><br />
<br />
<br />
{| {{prettytable}}<br />
|+ TABLE 3-7. Modelled exposure to SHS, in children and non-smoking adults in 2004.<br />
! scope="col" rowspan="2" |Year 2004<br />
! scope="col" colspan="2" |Children<br />
! scope="col" colspan="2" |Adults (total)<br />
! scope="col" colspan="2" |Women<br />
! scope="col" colspan="2" |Men<br />
|-<br />
! scope="col" |Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
! scope="col" | Lower*<br>[%]<br />
! scope="col" | Upper*<br>[%]<br />
|-<br />
| Belgium<br />
| NA<br />
| NA<br />
| 28<br />
| 32<br />
| 27<br />
| 31<br />
| 29<br />
| 33<br />
|-<br />
| Finland<br />
| 4<br />
| NA<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
| 14<br />
|-<br />
| France<br />
| 23<br />
| 33<br />
| 17<br />
| 25<br />
| 20<br />
| 29<br />
| 15<br />
| 22<br />
|-<br />
| Germany<br />
| 24<br />
| NA<br />
| 26<br />
| 31<br />
| 25<br />
| 30<br />
| 27<br />
| 33<br />
|-<br />
| Italy<br />
| 40<br />
| NA<br />
| 26<br />
| 30<br />
| 23<br />
| 26<br />
| 29<br />
| 32<br />
|-<br />
| Netherlands<br />
| 20<br />
| 36<br />
| 22<br />
| 30<br />
| 19<br />
| 25<br />
| 26<br />
| 34<br />
|}<br />
<small> * Lower and upper estimates correspond to different computations of survey data. For example, the upper estimate corresponds to the <br />
inclusion of shorter durations of exposure from certain surveys. </small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Second hand smoke'''''(SHS)'':Our burden of disease calculation for SHS was based on a WHO model (Öberg et al., 2010). The exposure estimates were updated against available national and international data sources for the target year 2004, but otherwise the results are comparable with the WHO assessment. Other recent estimates of burden of disease for SHS were also available for Germany (Heidrich et al. 2007; Keil et al. 2005), which provided similar results as the current estimates.<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_ozone_in_Europe&diff=21616Health effects of ozone in Europe2011-06-07T09:13:46Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
==About ozone==<br />
<br />
Ozone in the lower atmosphere (or tropospheric ozone) is not emitted directly, but is formed in the atmosphere in photochemical reactions from anthropogenic and natural emissions of precursor components involving mostly volatile organic compounds (VOCs) and nitrogen oxides (mainly NO and NO<sub>2</sub>). These substances react to form ozone under the influence of sunlight. Ozone is highly reactive and therefore other air pollutants also easily consume the ozone present in the air. Therefore, the highest ozone levels are typically found in background regions and levels in urban areas are generally lower than in the countryside.<br />
<br />
Exposure to ozone can lead to a variety of respiratory health effects, such as coughing, throat irritation and reduced lung function. In addition, it can worsen bronchitis, emphysema, and asthma (WHO, 2006a). Ozone levels are increasing over time, and are cause for political concern.<br />
<ref name="EBoDe"></ref><br />
<br />
===Selected health endpoints and exposure-response functions===<br />
<br />
For ozone, as well as for PM (see section 3.9), we followed the health impact assessment approach as laid out in the Clean Air For Europe (CAFE) project and based on WHO European Centre for Environment and Health and CLTRAP Task Force on Health consultations. Health effects that are taken into consideration include total non-violent mortality, minor restricted activity days (MRADs), and cough and lower respiratory symptoms (LRS) in children aged 5–14 years. The choice of these endpoints was guided by Cost Benefit Analysis as carried out in the CAFE project (Hurley et al, 2005, WHO 2008). The health endpoints considered and the corresponding exposure-response functions are summarized in Table 3-19 in section 3.12.<br />
<ref name="EBoDe"></ref><br />
<br />
===Exposure data===<br />
<br />
The exposure metric used for ozone calculations is the sum of ozone maximum 8-h levels above 35 ppb, called SOMO35 (WHO, 2008). SOMO35 (expressed in μg m<sup>-3</sup> × hours) is the sum of the maximum daily 8-hour concentrations that are exceeding 35 ppb (70 μg m<sup>-3</sup>) for each day in the calendar year, i.e. e.g. a daily level of 100 μg m<sup>-3</sup> would contribute 30 to the SOMO35 calculation. Regardless of the name referring to the ppb unit of measurement, the values are expressed as mass concentrations (μg m<sup>-3</sup>).<br />
<br />
For ozone (as well as for PM, see section 3.9), exposures were estimated by the European Topic Centre on Air and Climate Change (ETC/ACC) using AirBase data and air quality maps (SOMO35) (de Leeuw & Horalek, 2009). The European Environment Agency (EEA) has recently published an evaluation of new monitoring-based methods to estimate population weighted spatial distributions of ambient PM and ozone levels (EEA, 2009). These methods are based on interpolated maps using 10×10 km spatial resolution and using observed concentrations from national monitoring networks as primary data source. These are combined with regional chemistry transport modelling (CTM) and other supplemental data sources to improve estimates in observation-sparse areas. Maps for rural and urban areas were created separately and were subsequently merged. This approach aims to provide an objective method for dealing with the differences found between the rural and urban interpolated concentration fields in most areas of Europe (EEA, 2009). It is different from the earlier Clean Air for Europe (CAFE) work, which relied on modelling as its primary source of information and uses monitoring only to calibrate the European Monitoring and Evaluation Programme (EMEP) chemical transportation model. The modelling approach is better suitable for prospective scenario analyses, while the monitoring based approach may be considered more reliable for retrospective analyses.<br />
<br />
The air quality maps were prepared for 2005 with interpolation methodology using co-kriging of observed concentrations using additional spatial information (EMEP model results, meteorological data, altitude, population density map). The year 2005 instead of 2004 was chosen as the modelling year by EEA for practical purposes. Description of the maps is given by Horálek et al (2007) and de Leeuw and Horalek (2009). A brief introduction to AirBase and a description of the state of and recent trends in European air quality is presented by Mol et al (2009).<br />
<br />
Population weighted ambient ozone concentrations were calculated using population data for year 2005. The population density map (resolution 10x10 km) is based on the detailed population map prepared by JRC (reference year 2002, see Horalek et al., 2008 for further description of this dataset). The population density map for 2005 is made by scaling the 2002-reference map using the 2005/2002 ratio of national population numbers. Within a country the same age distribution is assumed in all grid cells.<br />
Resulting population-weighted ozone exposure values for the participating countries are shown in Table 3-12 and are also summarized in Table 3-21 in section 3.12. The geographical distribution of the SOMO35 levels in Europe is shown in Figure 3-5.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-12.: National population weighted averages of ambient ozone levels (SOMO35) in 2005 for the six EBoDE countries (de Leeuw and Horalek, 2009).<br />
! scope="col" width="150" | Country<br />
! scope="col" width="150" | SOMO35<br />
(μg m<sup>-3</sup>)<br />
|-----<br />
| Belgium<br />
| 2 787<br />
|-----<br />
| Germany<br />
| 4 164<br />
|-----<br />
| Finland<br />
| 2 580<br />
|-----<br />
| France<br />
| 4 756<br />
|-----<br />
| Italy<br />
| 8 134<br />
|-----<br />
| Netherlands<br />
| 1 920<br />
|}<br />
<br />
[[Image:Ozone_SOMO35_levels_Europe_2005_EBoDE.png|none|Ozone SOMO35-levels in Europe in 2005]]<br />
<br />
<small>FIGURE 3-5. Ozone SOMO35-levels in Europe in 2005 (EEA, 2009).</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''PM and ozone'''<br />
<br />
The methodology developed in Clean Air for Europe -project (CAFE) (Hurley et al., 2005) was applied using updated exposure estimates. The updated exposures are based on ambient air quality monitoring data that contain, besides the anthropogenic components that CAFE focused on, also natural sources of PM<sub>2.5</sub>. The spatial resolution of the updated model is 25 times higher (grid size 10x 10 km² instead of 50x50 km²). Compared to the CAFE estimates the current work adds estimation of the impacts in DALYs. The WHO Environmental Burden of Disease programme uses a non-linear exposure-response function (Ostro, 2004) that at higher exposures yields lower impacts than the linear CAFE model. WHO also sets a threshold level at 7.5 μg m<sup>-3</sup>.<ref name="EBoDe"></ref><br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_benzene_in_Europe&diff=21604Health effects of benzene in Europe2011-06-07T08:46:17Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Mori|}}<br />
<br />
== Benzene ==<br />
<br />
<br />
=== About benzene ===<br />
<br />
Benzene is an organic chemical compound that was added to gasoline in the past. The use of benzene as an additive in gasoline is now limited, but it is still used by industry in the production of for example drugs and plastics. In addition, cigarette smoke contains some benzene. <br />
<br />
Inhalation is the major route of human exposure to benzene. However, exposure may also occur through oral absorption or by dermal exposure (primarily in workplace settings). Exposure to benzene- contaminated water can cause inhalation and dermal absorption in the general population (e.g. when having a shower), but this does not occur often (US Department of Health, 2007). <br />
<br />
The genotoxicity of benzene has been extensively studied. Benzene is a known carcinogen for which no safe level of exposure can be recommended. The most significant adverse effects from prolonged exposure to benzene are haematotoxicity, genotoxicity and carcinogenicity (IARC group 1 carcinogen) (IARC 1982, 1987). Chronic benzene exposure can result in bone marrow depression expressed as leukopenia, anaemia and/or thrombocytopenia, which can in turn lead to pancytopenia and aplastic anaemia (WHO, 2000b). <br />
Increased mortality from leukaemia has repeatedly been demonstrated in workers occupationally exposed (Arp et al 1983, IARC 1982, Decouflé et al 1983, Bond et al 1986, McCraw, 1985, Yin 1987, Paxton et al. 1994a, b). There are also studies that using proxies of benzene exposure indicate an increased risk of leukaemia in children, but conclusions are not definitive (Weng et al, 2009, Brosselin et al, 2009, Whitworth et al 2008, Gunier et al 2008, Steffen et al, 2004, Crosignani et al, 2004, Pearson et al, 2000, Nordlinder et al, 1997). <br />
<br />
Benzene was selected in the EBoDE project because it may pose high individual risks and is still of global concern. Even though policies in Europe have already greatly reduced environmental benzene exposure, it is still identified as a concern (e.g. the INDEX project identified benzene as high priority stressor (Koistinen et al., 2008, Kotzias et al., 2005); European air quality directive 2008/50/EC; setting of WHO guidelines for indoor air quality (WHO, 2010b)). <br />
<br />
<br />
=== Selected health endpoints and exposure-response functions ===<br />
<br />
Benzene effects were estimated for leukaemia, including morbidity and mortality. Other proposed health endpoints were not included, because they only occur at high exposure levels, typical of occupational settings. We used the exposure response function as recommended by the WHO Air Quality Guidelines (WHO, 2000b) (see Table 3-19 in section 3.12). WHO uses the 1984 risk calculation of Crump (1984), in which the geometric mean of the range of estimates of the excess lifetime risk of leukaemia at an air concentration of 1 µg/m3 is estimated to be 6 × 10-6 (unit risk). This estimate falls within the range of the risk estimate that is used by the US EPA (2.2 x 10-6 to 7.8 x 10-6 per µg m-3). This unit risk is applied to the whole population, including children. Specific estimates that have been supplied for children could not be used, because the underlying studies often use proxies of exposure (petrol station density, traffic density, etc.) instead of actual benzene exposure levels. <br />
<br />
The estimated number of leukaemia cases were used to calculate the population attributable fraction using method 2A. <br />
<br />
<br />
=== Exposure data ===<br />
<br />
Benzene exposures are best described by residential indoor air levels (µg m-3). Besides being affected by benzene levels in outdoor air, indoor levels may be raised especially by indoor smoking and potentially the storage and use of fuels e.g. in case of attached garages and storage rooms. <br />
<br />
Benzene is a regulated ambient pollutant and therefore outdoor monitoring is required by the European Union. Benzene measurements are included in the AirBase database (European Environment Agency, AirBase, 2009).<br />
<br />
Benzene exposure is estimated from national indoor levels, supplemented with outdoor levels. Different national data demonstrate that benzene exposure concentrations vary from 0.9 µg m-3 in the Netherlands to 2.9 µg m-3 in Italy. The data used in this project are summarized in Table 3-21 in section 3.12. <br />
<br />
The confidence levels of the exposure data cannot be directly compared, because the measurements are based on different time periods. Data from the Netherlands and France reflect a 1 week average exposure, while Italian and Finnish data are based on 2 day measurements. <br />
<br />
Sources of uncertainty in exposure data include differences in sampling selection. In France, data reflect a large number of dwellings, while in other countries data are limited to a smaller number of monitored houses. In addition, the presence or absence of tobacco smoke in indoor environments is not always reported, making comparison more difficult. This at least partly explains the higher levels in Finland, where benzene from smoking was included. In Italy, levels are likely to be higher because of the large number of two-stroke engines used there, which emit a lot of benzene.<br />
<br />
<br />
{|{{prettytable}}<br />
|+TABLE 3-1. Characteristics of benzene indoor concentration measurements. <br />
! Country<br />
! Including benzene from smoking<br />
! Sample size<br />
! Time periods of measurements<br />
|-----<br />
| Belgium <br />
| Yes<br />
| 85 houses and 25 day-care centers<br />
| <br />
|-----<br />
| Finland<br />
| Yes<br />
| random; 20 adults<br />
| 2 day average<br />
|-----<br />
| France<br />
| Yes<br />
| 567 residences<br />
| 1 week average<br />
|-----<br />
| Germany<br />
| Yes<br />
| 1790 subjects<br />
| <br />
|-----<br />
| Italy<br />
| Yes<br />
| 50 subjects<br />
| 2 day average<br />
|-----<br />
| Netherlands<br />
| Yes<br />
| 1240 dwellings<br />
| 1 week average<br />
|}<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Benzene'''<br />
No international burden of disease study utilizing DALYs for benzene was identified. Some studies using exposure proxies like proximity of gasoline stations have studies health impacts with inconsistent results.<br />
Dioxins. Our calculations were based on the same approach as applied earlier by Leino et al (2008), but we utilized an updated cancer slope factor that is approximately seven times higher than the one used by Leino et al. Leino et al. did the calculations for Finland only. The work presented here also updated the exposure estimates in order to allow for good international comparability, yet some differences between the national intake estimation methods remained.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
{| {{prettytable}}<br />
|<br />
| Excluded health endpoints and related assumptions<br />
| Exposure data<br />
| Exposure response function<br />
| Calculation method<br />
| Level of overall uncertainty a)<br />
| Likely over- or underestimation b)<br />
|----<br />
| Benzene<br />
| Anaemia; genotoxicity; other blood cancers than leukaemia; leukaemia morbidity; effects on the immune, endocrine and nervous system; acute effects. All cases of leukaemia assumed to be fatal<br />
| Population representativity varies. Differences in number of dwellings. Different types of measurements (indoor/outdoor; in – or excluding SHS, etc). Sampling times differ<br />
| No specific relationships for children used (i.e. same UR used for all ages)<br />
| UR method of calculating PAF leads to overestimation because all cases are assumed to be fatal.<br />
| *<br />
| Underestimation due to excluded health endpoints, but overestimation due to UR method<br />
|----<br />
|}<br />
[[Category:EBoDE]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Overview_of_the_EBoDE-project&diff=21591Overview of the EBoDE-project2011-06-07T08:17:47Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Julle}}<br />
[[category:EBoDE]]<br />
<br />
==Introduction==<br />
<br />
Exposures to many environmental stressors are known to endanger human health. Negative impacts on health can range from mild psychological effects (e.g. noise annoyance), to effects on morbidity (such as asthma caused by exposure to air pollution), and to increased mortality (such as lung cancer provoked by radon exposure). Properly targeted and followed-up environmental health policies, such as the coal burning ban in Dublin (1990) and the smoking ban in public places in Rome (2005) have demonstrated significant and immediate population level reductions in deaths and diseases. In order to develop effective policy measures, quantitative information about the extent of health impacts of different environmental stressors is needed.<br />
<br />
As demonstrated by the examples above, health effects of environmental factors often vary considerably with regard to their severity, duration and magnitude. This makes it difficult to compare different (environmental) health effects and to set priorities in health policies or research programs. Public health policies generally aim to allocate resources effectively for maximum health benefits while avoiding undue interference with other societal functions and human activities. In order to develop such policies, it is necessary to know what ‘maximum health benefits’ are. Decades ago, such decisions tended to be made based on mortality statistics: which (environmental) factor causes most deaths? However, nowadays, most people get relatively old, and priority has shifted from quantity to quality of life. This has lead to the need to incorporate morbidity effects into public health decisions, and therefore to find a way of comparing dissimilar health effects.<br />
<br />
Such comparison and prioritisation of environmental health effects is made possible by expressing the diverging health effects in one unit: the environmental burden of disease (EBD). Environmental burden of disease figures express both mortality and morbidity effects in a population in one number. They quantify and summarize (environmental) health effects and can be used for:<br />
* Comparative evaluation of environmental burden of disease (“how bad is it?”)<br />
* Evaluation of the effectiveness of environmental policies (largest reduction of disease burden)<br />
* Estimation of the accumulation of exposures to environmental factors (for example in urban areas)<br />
* Communication of health risks<br />
<br />
An example of an integrated health measure that can be used to express the environmental burden of disease is the DALY (Disability Adjusted Life Years). DALYs combine information on quality and quantity of life. They give an indication of the (potential) number of healthy life years lost in a population due to premature mortality or morbidity, the latter being weighted for the severity of the disorder. The concept was first introduced by Murray and Lopez (1996) as part of the Global Burden of Disease study, which was launched by the World Bank. Since then, the World Health Organization (WHO) has endorsed the procedure, and the DALY approach has been used in various studies on a global, national and regional level.<br />
<br />
WHO collects a vast set of data on the global burden of disease. The first study quantified the health effects of more than 100 diseases for eight regions of the world in 1990 (Murray and Lopez, 1996). It generated comprehensive and internally consistent estimates of mortality and morbidity by age, gender and region. In a former WHO study, it was shown that almost a quarter of all disease worldwide was caused by environmental exposure (Prüss-Üstün and Corvalán, 2006). In industrial sub-regions this estimate was about 16% (15–18%). These fractions, however, are dependent on the conclusiveness of the included environmental factors and health effects. The WHO programme on quantifying environmental health impacts has addressed more than a dozen stressors <ref>The WHO programme[http://www.who.int/quantifying_ehimpacts/publications/en/]</ref>. In order to support further applications of the environmental burden of disease (EBD) assessments, a methodological guidance has been published by WHO (Prüss-Üstün et al., 2003) and was followed here too.<br />
<br />
In Europe, national environmental burden of disease (EBD) assessments are on-going in several countries. The work by RIVM was one of the first systematic European works in this area that utilized disability-adjusted life years (DALY) as a measure to compare the burden of different health outcomes related to the exposure of the population to environmental stressors (Hollander et al., 1999). The results highlighted that (i) a number of environmental stressors may cause chronic or acute diseases or death, (ii) a few top ranking stressors cause over 90% of the national EBD, and (iii) these top ranking stressors are not necessarily those that have drawn the most concern, regulatory action and/or preventive investment.<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
<br />
==Objectives==<br />
<br />
The EBoDE-project was set up in order to guide environmental health policy making in the six participating countries (Belgium, Finland, France, Germany, Italy and the Netherlands) and potentially beyond. From a policy perspective, these insights from the EBoDE-project can be useful to evaluate past policies and to gain insight in setting the policy priorities for the future. We have calculated the total EBD associated with the nine environmental stressors. The total EBD is not identical to the avoidable burden of disease, because some exposures are not realistically reducible to zero (e.g. fine particles). Also, our estimates do not take into account the costs of reducing the EBD. Thus, the results are only one input into the full process of developing cost-effective policies to achieve better environmental health.<br />
<br />
The objectives of the project were to update the available previous assessments, to focus on stressors relevant for the European region, to provide harmonized EBD assessments for participating countries, and to develop and make available the methodologies for further development and other countries.<br />
The specific objectives are to:<br />
• Provide harmonized environmental burden of disease (EBD) estimates for selected environmental stressors in the participating six countries;<br />
• Test the methodologies in a harmonized way across the countries.<br />
• Assess the comparability of the quantifications and ranking of the EBD<br />
• between countries<br />
• within countries<br />
• between environmental stressors;<br />
• Qualitative assessments of variation and uncertainty in the input parameters and results.<br />
<br />
Environmental burden of disease estimates have been calculated for:<br />
• nine environmental stressors: benzene, dioxins (including furans and dioxin-like PCBs), second-hand smoke, formaldehyde, lead, noise, ozone, particulate matter (PM) and radon;<br />
• six European countries: Belgium, Finland, France, Germany, Italy and the Netherlands;<br />
• the year 2004 (and some trend estimates for the year 2010).<br />
As outlined above, the EBoDE study was carried out in order to test the environmental burden of disease methodology in various countries. The results of the studies are intended to allow comparison of the disease burden between different environmental stressors and between countries. Consequently, the study does not to identify the ‘reduction potential’. Our estimates should therefore not be interpreted as the ‘avoidable burden of disease’: most risks cannot realistically be completely removed by any policy measures. For some exposures, however, the numbers may nonetheless be interpretable as reduction potential, eg for dioxins, formaldehyde, benzene, etc, as these exposures could potentially be completely eliminated.<ref name="EBoDe"/><br />
<br />
==Outline of this report==<br />
<br />
This report describes the methods, data and results of the EBoDE-project. Chapter 2 presents the methodology. The environmental stressors are introduced in Chapter 3, which also presents the data used (selected health endpoints, exposure data, exposure response functions). In Chapter 4, the results are presented and discussed. Chapter 5 gives information about uncertainties in the approach, and provides some alternative calculations using different input values. In Chapter 6 conclusions are drawn. The report ends with the references and two appendices: Appendix A presents country-specific results and Appendix B some considerations for using a life-table approach in EBD modelling.<ref name="EBoDe"/><br />
<br />
==Uncertainties and limitations==<br />
Assessment of uncertainties is essential in a comparison of quantitative estimates that are based on data from heterogeneous sources and slightly varying methods. Due to the wide range of data sources and models and the limited resources within the EBoDE project, systematic analysis of all uncertainties was not possible. However, we were able to assess a number of specific sources of uncertainties in more detail as part of the work, yielding some insights into the reliability of the overall assessment.<br />
The studied health impacts span approximately four orders of magnitude in size from few DALYs per million to almost 10 000 DALYs per million. The overall ranking of the environmental stressors seems to be rather robust against the relatively large uncertainties in individual estimates or methodological choices like discounting and age-weighing. However, some of the estimated ranges are overlapping. This concerns especially second hand smoke, radon and transportation noise that compete for the questionable honour of being the second most important environmental stressor in the participating countries. Among these stressors the differences are smaller than the corresponding uncertainties of the estimates.<br />
The health state of an individual person is the result of a complex mixture of genetic, environmental and behavioural factors. In a typical case of death, numerous factors play together. This means, for example, that a single death caused by a cardiovascular disease could be avoided by either reducing air pollution, or a better diet, or more physical activity. Therefore, if the individual attributable fractions are summed over a number of risk factors, a value over 100% may sometimes be found. For this and other reasons, it has been argued that death counts are not suitable for quantification of the impacts (Brunekreef et al., 2007). Therefore the authors recommend to mainly use aggregate population measures of health like DALYs, YLLs and YLDs.<br />
This chapter presents the quantitative results for selected sources of uncertainties and discusses the project limitations and author judgment of the reliability of the ranking.<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''SHS'''<br />
<br />
Our burden of disease calculation for SHS was based on a WHO model (Öberg et al., 2010). The exposure estimates were updated against available national and international data sources for the target year 2004, but otherwise the results are comparable with the WHO assessment. Other recent estimates of burden of disease for SHS were also available for Germany (Heidrich et al. 2007; Keil et al. 2005), which provided similar results as the current estimates.<br />
<br />
'''Transportation noise'''<br />
<br />
Burden of disease estimation for transportation noise is currently under active development. The estimates presented here were based on the only available international exposure data source, the first stage version of the European Noise Directive database (2007), which is not conclusive yet. Therefore it is clear that most of the exposures for transportation noise are underestimated. In some studies annoyance and cognitive impairment have been used as an additional health end-points for environmental noise. However, due to the selected more limited definition of ‘health’ as ICD-classified health states used in our assessment, annoyance and cognitive impairment were not included here. Only road, rail and air traffic exposures were included; many other sources also contribute to the noise exposures. Low exposures below the END data collection limits (50 and 55 dB) were not included. For these reasons it can be expected that when these limitations are solved, the impact estimates will increase.<br />
<br />
'''PM and ozone'''<br />
<br />
The methodology developed in Clean Air for Europe -project (CAFE) (Hurley et al., 2005) was applied using updated exposure estimates. The updated exposures are based on ambient air quality monitoring data that contain, besides the anthropogenic components that CAFE focused on, also natural sources of PM2.5. The spatial resolution of the updated model is 25 times higher (grid size 10x 10 km² instead of 50x50 km²). Compared to the CAFE estimates the current work adds estimation of the impacts in DALYs. The WHO Environmental Burden of Disease programme uses a non-linear exposure-response function (Ostro, 2004) that at higher exposures yields lower impacts than the linear CAFE model. WHO also sets a threshold level at 7.5 μg m-3.<br />
<br />
<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_benzene_in_Europe&diff=21575Health effects of benzene in Europe2011-06-07T07:54:56Z<p>Iiro: moved Benzene to Health effects of benzene in Europe: Wrong title</p>
<hr />
<div>{{study|moderator=Mori|}}<br />
<br />
== Benzene ==<br />
<br />
<br />
=== About benzene ===<br />
<br />
Benzene is an organic chemical compound that was added to gasoline in the past. The use of benzene as an additive in gasoline is now limited, but it is still used by industry in the production of for example drugs and plastics. In addition, cigarette smoke contains some benzene. <br />
<br />
Inhalation is the major route of human exposure to benzene. However, exposure may also occur through oral absorption or by dermal exposure (primarily in workplace settings). Exposure to benzene- contaminated water can cause inhalation and dermal absorption in the general population (e.g. when having a shower), but this does not occur often (US Department of Health, 2007). <br />
<br />
The genotoxicity of benzene has been extensively studied. Benzene is a known carcinogen for which no safe level of exposure can be recommended. The most significant adverse effects from prolonged exposure to benzene are haematotoxicity, genotoxicity and carcinogenicity (IARC group 1 carcinogen) (IARC 1982, 1987). Chronic benzene exposure can result in bone marrow depression expressed as leukopenia, anaemia and/or thrombocytopenia, which can in turn lead to pancytopenia and aplastic anaemia (WHO, 2000b). <br />
Increased mortality from leukaemia has repeatedly been demonstrated in workers occupationally exposed (Arp et al 1983, IARC 1982, Decouflé et al 1983, Bond et al 1986, McCraw, 1985, Yin 1987, Paxton et al. 1994a, b). There are also studies that using proxies of benzene exposure indicate an increased risk of leukaemia in children, but conclusions are not definitive (Weng et al, 2009, Brosselin et al, 2009, Whitworth et al 2008, Gunier et al 2008, Steffen et al, 2004, Crosignani et al, 2004, Pearson et al, 2000, Nordlinder et al, 1997). <br />
<br />
Benzene was selected in the EBoDE project because it may pose high individual risks and is still of global concern. Even though policies in Europe have already greatly reduced environmental benzene exposure, it is still identified as a concern (e.g. the INDEX project identified benzene as high priority stressor (Koistinen et al., 2008, Kotzias et al., 2005); European air quality directive 2008/50/EC; setting of WHO guidelines for indoor air quality (WHO, 2010b)). <br />
<br />
<br />
=== Selected health endpoints and exposure-response functions ===<br />
<br />
Benzene effects were estimated for leukaemia, including morbidity and mortality. Other proposed health endpoints were not included, because they only occur at high exposure levels, typical of occupational settings. We used the exposure response function as recommended by the WHO Air Quality Guidelines (WHO, 2000b) (see Table 3-19 in section 3.12). WHO uses the 1984 risk calculation of Crump (1984), in which the geometric mean of the range of estimates of the excess lifetime risk of leukaemia at an air concentration of 1 µg/m3 is estimated to be 6 × 10-6 (unit risk). This estimate falls within the range of the risk estimate that is used by the US EPA (2.2 x 10-6 to 7.8 x 10-6 per µg m-3). This unit risk is applied to the whole population, including children. Specific estimates that have been supplied for children could not be used, because the underlying studies often use proxies of exposure (petrol station density, traffic density, etc.) instead of actual benzene exposure levels. <br />
<br />
The estimated number of leukaemia cases were used to calculate the population attributable fraction using method 2A. <br />
<br />
<br />
=== Exposure data ===<br />
<br />
Benzene exposures are best described by residential indoor air levels (µg m-3). Besides being affected by benzene levels in outdoor air, indoor levels may be raised especially by indoor smoking and potentially the storage and use of fuels e.g. in case of attached garages and storage rooms. <br />
<br />
Benzene is a regulated ambient pollutant and therefore outdoor monitoring is required by the European Union. Benzene measurements are included in the AirBase database (European Environment Agency, AirBase, 2009).<br />
<br />
Benzene exposure is estimated from national indoor levels, supplemented with outdoor levels. Different national data demonstrate that benzene exposure concentrations vary from 0.9 µg m-3 in the Netherlands to 2.9 µg m-3 in Italy. The data used in this project are summarized in Table 3-21 in section 3.12. <br />
<br />
The confidence levels of the exposure data cannot be directly compared, because the measurements are based on different time periods. Data from the Netherlands and France reflect a 1 week average exposure, while Italian and Finnish data are based on 2 day measurements. <br />
<br />
Sources of uncertainty in exposure data include differences in sampling selection. In France, data reflect a large number of dwellings, while in other countries data are limited to a smaller number of monitored houses. In addition, the presence or absence of tobacco smoke in indoor environments is not always reported, making comparison more difficult. This at least partly explains the higher levels in Finland, where benzene from smoking was included. In Italy, levels are likely to be higher because of the large number of two-stroke engines used there, which emit a lot of benzene.<br />
<br />
<br />
{|{{prettytable}}<br />
|+TABLE 3-1. Characteristics of benzene indoor concentration measurements. <br />
! Country<br />
! Including benzene from smoking<br />
! Sample size<br />
! Time periods of measurements<br />
|-----<br />
| Belgium <br />
| Yes<br />
| 85 houses and 25 day-care centers<br />
| <br />
|-----<br />
| Finland<br />
| Yes<br />
| random; 20 adults<br />
| 2 day average<br />
|-----<br />
| France<br />
| Yes<br />
| 567 residences<br />
| 1 week average<br />
|-----<br />
| Germany<br />
| Yes<br />
| 1790 subjects<br />
| <br />
|-----<br />
| Italy<br />
| Yes<br />
| 50 subjects<br />
| 2 day average<br />
|-----<br />
| Netherlands<br />
| Yes<br />
| 1240 dwellings<br />
| 1 week average<br />
|}<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Benzene'''<br />
No international burden of disease study utilizing DALYs for benzene was identified. Some studies using exposure proxies like proximity of gasoline stations have studies health impacts with inconsistent results.<br />
Dioxins. Our calculations were based on the same approach as applied earlier by Leino et al (2008), but we utilized an updated cancer slope factor that is approximately seven times higher than the one used by Leino et al. Leino et al. did the calculations for Finland only. The work presented here also updated the exposure estimates in order to allow for good international comparability, yet some differences between the national intake estimation methods remained.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
[[Category:EBoDE]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Benzene&diff=21576Benzene2011-06-07T07:54:56Z<p>Iiro: moved Benzene to Health effects of benzene in Europe: Wrong title</p>
<hr />
<div>#REDIRECT [[Health effects of benzene in Europe]]</div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_lead_in_Europe&diff=21566Health effects of lead in Europe2011-06-07T07:43:13Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
==About lead==<br />
<br />
Lead is present in the environment due to former application of lead in gasoline, leaded drinking water pipes, and use of lead in paints and other housing materials. Exposures to lead originate from various sources including air, drinking water, food stuff as well as surfaces and consumer products.<br />
<br />
Lead is one of the most studied environmental pollutants and has been associated with a large number of health implications (WHO, 2007b). Exposure to lead may cause, amongst other things, kidney damage, miscarriages, effects of the nervous system, declined fertility, alterations in growth and endocrine function, and behavioural disruptions (Hauser et al. 2008; Lanphear et al., 2005; Selevan et al. 2003). Lead is a known neurotoxic pollutant affecting the development of the central nervous system of children and consequently their intelligence. Effects on attention, behaviour disorders and hearing-threshold changes have been described as particularly important (Needleman 1990, WHO/IPCS 1995). Lead exposures have also been shown to be associated with increased blood pressure and risk of hypertension in (female) adults (Nash et al. 2003). Correlations with low lead levels have been reported for the attention deficit hyperactivity disorder (ADHD) (Braun et al., 2006). In addition, there is evidence showing that lead may cause cancer. Lead has been loosely linked with cancers of the lung and stomach. IARC (2006b) rated lead and inorganic lead compounds as probably carcinogenic to humans (Group 2A). Current studies suggest that there is no “safe” level of lead exposure.<br />
<br />
Most of the health endpoints are significant at much higher exposure levels that are found in European population today. Exposure to lead has significantly decreased for many countries in the last two decades, especially since the phasing out of leaded gasoline and the replacement of leaded water pipes. For example, Figure 3-3 shows the reduction of internal exposure to lead in humans in German students between the 1980s and now (German Environmental Specimen Bank [Umweltprobenbank des Bundes], data available online at www.umweltprobenbank.de). Indeed, lead has been the success story in environmental policies, but the follow-up in exposure data in the general population is poor.<br />
<br />
[[Image:Blood_Lead_Level_in_German_Students.png|none|Blood-Pb in German Students 1981-2009|FIGURE 3-4. Blood-Pb in German Students (1981–2009, geometric mean in μg/l, sampling location: city of Münster)]]<br />
<br />
<small>FIGURE 3-4. Blood-Pb in German Students (1981–2009, geometric mean in μg/l, sampling location: city of Münster)</small><br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
The EBoDE project focuses on two endpoints that have been shown to be relevant at current exposure levels: mild mental retardation (due to IQ loss) and hypertensive disease (due to rise in systolic blood pressure). For the other health endpoints, i. a., no empirically sound exposure-response-relationships are available. Therefore, our results may underestimate the actual EBD of lead exposure in Europe. The extent of this underestimation cannot be quantified sufficiently.<br />
<br />
The hypothesis of an effect threshold was rejected in several studies (Téllez-Rojo et al. 2006, Binns et al. 2007, Chiodo et al. 2004, Kordas et al. 2006). There is strong evidence for an association between B-Pb (blood lead) and negative effects on neuropsychological parameters at levels lower than 100 μg/l (Walkowiak et al., 1998; Canfield et al., 2004; Carta et al., 2005). Therefore, extending the dose-response curve to the range below 100 μg/l is possible. Lanphear et al. (2005) proposed a log-linear model for this curve.<br />
<br />
Findings on lead’s effects on the central nervous system in the low-dose range are available from longitudinal and cross-sectional studies (Lanphear et al., 2005). These studies showed B-Pb and decrease in IQ points with B-Pb in children. The WHO model for IQ loss was recently updated to consider B-Pb levels above 24 μg/l. It has to be taken into account, however, that no threshold for mental retardation has been confirmed, yet. The exposure/response-function (ERF) in the WHO model is:<br />
<br />
<math> IQloss = \frac{(B_{Pb} - 24)}{20} </math> (Lanphear et al., 2005; see also Table 3-19 in section 3.12).<br />
<br />
The population distribution of IQ is as defined as N(100;15). When the IQ falls below a diagnostic threshold, IQ loss is defined as mild mental retardation, which is the health endpoint used in this study. This threshold is set at 70 IQ points. We calculate the number of cases of mild mental retardation by estimating how many individuals in the target age group (children 0-4 years) exceed the diagnostic thresholds due to the lead exposure.<br />
<br />
Several longitudinal studies have examined associations of blood pressure change or hypertension incidence in relation to lead concentration in blood or bone. Glenn et al. (2006) concluded that systolic blood pressure is associated both with acute changes in the blood lead level as well as with long-term cumulative exposure. Blood lead levels can increase in women over the menopause, as lead is released from bone. This may increase women’s risk of high blood pressure.<br />
<br />
The current WHO model for increased systolic blood pressure in adults aged 20–79 years assumes a linear relationship between 50-200 μg/l (increase of 1.25 mmHg for males and 0.8 mmHg for females per increase of 50 μg/l B-Pb). Above 200 μg/l, an increase of 3.75 mmHg for males and 2.4 mmHg for females per increase of 50 μg/l B-Pb is assumed. The model does not account for aggravating effects of increased blood lead levels during the menopause.<br />
<br />
The ERF for mean increase in the systolic blood (mmHg) in the WHO model is (B-Pb >50 μg/l) (Fewtrell et al, 2003):<br />
<br />
<math> \Delta mmHg = \frac{(B_{Pb} - 50)}{40} </math><br />
<br />
The calculation of the numbers of cases of hypertensive disease is similar to the calculations for mild mental retardation. The population distribution of systolic blood pressure is defined as N(135, 15). When exposure exceeds the diagnostic threshold, of 140 mmHg, the increase in blood pressure is defined as hypertensive disease. We calculate how many individuals in the target age group (>15 year olds) exceed the diagnostic threshold due to the lead exposure.<br />
<br />
==Exposure data==<br />
<br />
It is not easy to estimate lead exposure levels, because population exposure measurements are not regularly conducted, and because of the decreasing trends in lead concentrations which are not fully known. The most reliable way to account for all different possible exposure routes is to measure the body burden of lead. The commonly used exposure metric for such measurement is the blood lead level (B-Pb, whole blood, μg/l).<br />
<br />
For the application of the WHO model for IQ loss, distributions of B-Pb (defined by percentiles) are necessary, stratified by specific age groups. This means that data are needed about different fractions of the population that are exposed to certain categories of B-Pb levels. No coherent international data sources were identified for lead. Hence, data from individual studies conducted in all participating countries were used. The year in which these studies were conducted differs between countries and in some cases the limited temporal coverage prohibited trend estimation. In these cases the most recent data have been used. It is clear that the limited temporal representativity of the lead exposure data poses a significant source of uncertainty. Due to well established lowering trends for lead this is expected to cause mainly unknown overestimation of exposures and effects.<br />
<br />
The data are presented in Table 3-9 below and summarized in Table 3-21 in section 3.12. As shown in Table 3-9, lead data have been measured in different age groups in the different countries. Data from the German Environmental Survey (GerES) show that age is an important influencing factor for B-Pb levels in humans. As there is virtually no evidence for a significant reduction in B-Pb levels since the year 2000, the difference in age groups is assumed to be one of the most important sources of uncertainties when comparing the different countries. Unfortunately, B-Pb data are not sufficient to correct the country data for age.<br />
<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-9. Lead data (μg/l) for different countries, measured in different age groups and years, used in the lognormal simulation to yield the required distributional parameters.<br />
! rowspan="2" scope="col" width="115" | Country<br />
! colspan="3" scope="col" width="175" | Estimates (2004)<br />
! rowspan="2" scope="col" width="115" | Age group<br />
! rowspan="2" scope="col" width="115" | Year<br />
|-----<br />
! AM<br />
! GM<br />
! SD<br />
|-----<br />
| Belgium<br />
| 22<br />
| <br />
| 16<br />
| 14-15<br />
| 2000-06<br />
|-----<br />
| Finland<br />
| 16<br />
| <br />
| 11<br />
| Adults<br />
| 2004<br />
|-----<br />
| France<br />
| <br />
| 26<br />
| 18<br />
| 18-74<br />
| 2006-07<br />
|-----<br />
| Germany<br />
| 22<br />
| <br />
| 16<br />
| 20-29<br />
| 2004<br />
|-----<br />
| Italy<br />
| 39<br />
| <br />
| 24<br />
| 18-64<br />
| 2000<br />
|-----<br />
| Netherlands<br />
| <br />
| 19<br />
| 11<br />
| 1-6<br />
| 2005<br />
|}<br />
<br />
<small>AM: Arithmetic Mean; GM: Geometrical Mean; SD: Standard Deviation (estimated using coefficient of variation).</small><br />
<br />
As indicated above, both of the exposure-response models used apply a threshold level (50 μg l-1 and 24 μg l-1). Therefore, it is necessary to assess the fraction of the population being exposed to levels higher than these threshold levels. A probabilistic simulation model was used to calculate the fraction of the population exceeding the threshold using mean and standard deviation data and assuming lognormal distributions. Standard deviations were estimated for the simulation using a coefficient of variation estimated from the Finnish data.<br />
<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-10. Population distributions of blood lead levels used in the simulation of threshold exeedances assuming log-normal distribution.<br />
! <br />
! scope="col" width="50" | Country<br />
! scope="col" width="50" | BE<br />
! scope="col" width="50" | FI<br />
! scope="col" width="50" | FR<br />
! scope="col" width="50" | DE<br />
! scope="col" width="50" | IT<br />
! scope="col" width="50" | NL<br />
|-----<br />
| rowspan="3" | Adults<br />
| mean<br />
| 22.0<br />
| 16.0<br />
| 25.0<br />
| 22.0<br />
| 39.0<br />
| 19.0<br />
|-----<br />
| SD<br />
| 15.6<br />
| 11.4<br />
| 17.8<br />
| 15.6<br />
| 27.7<br />
| 13.5<br />
|-----<br />
| CV<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
|-----<br />
| rowspan="3" | Children<br />
| mean<br />
| 22.0<br />
| 16.0<br />
| 25.0<br />
| 22.0<br />
| 39.0<br />
| 19.0<br />
|-----<br />
| SD<br />
| 15.6<br />
| 11.4<br />
| 17.8<br />
| 15.6<br />
| 27.7<br />
| 13.5<br />
|-----<br />
| CV<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
| 0.71<br />
|}<br />
<br />
<small>SD: Standard deviation, CV: coefficient of variation.</small><br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Lead''':<br />
The calculation focused on mild mental retardation and hypertensive disease only. WHO EBD estimates (Fewtrell et al., 2003) include cerebro-vascular and other cardiovascular diseases besides hypertensive disease; therefore the current estimates for lead are slightly lower than the WHO estimates.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Radon&diff=21563Radon2011-06-07T07:33:54Z<p>Iiro: </p>
<hr />
<div>[[heande:Radon]]<br />
[[Category:Indoor air]]<br />
[[Category:Radon]]<br />
[[Category:Pollutants]]<br />
{{encyclopedia|moderator=Jouni}}<br />
<br />
==What is Radon==<br />
<br />
Radon is a colourless, odourless, radioactive gas. It comes from the radioactive decay of radium, which in turn comes from the radioactive decay of uranium. Uranium acts as a permanent source of radon and is found in small quantities in all soils and rocks, although the amount varies from place to place. It is particularly prevalent in granite areas but not exclusively so. Radon levels vary not only between different parts of the country but even between neighbouring buildings.<br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
Radon in the soil and rocks mixes with air and rises to the surface where it is quickly diluted in the atmosphere. <br />
Concentrations in the open air are very low. However, radon concentration in soil-gas can be very high, typically from less than 10 000 to 100 000 Bq/m3. Entry of this radon-bearing air into living spaces is the main reason for elevated indoor radon concentrations. Mineral building materials also emit radon. Radon that enters enclosed spaces, such as buildings, can reach relatively high concentrations in some circumstances.<br />
<br />
When radon decays it forms tiny radioactive particles called radon daughters which may be breathed into the lungs. If formed in air, these particles may be inhaled and some will be deposited in the lungs. The radiation emitted by them as they decay can give a high dose to lung tissues and damage them. Being exposed to radon and its decay products increases the risk of developing lung cancer. In addition, smoking and exposure to radon are known to work together to greatly increase the risk of developing lung cancer. It is important however to confirm that whilst radon causes lung cancer the majority of lung cancer risk is caused by smoking. <br />
<br />
In addition to the risk from radon in air it is now recognised that some private water supplies contain levels of radon which should also be controlled. However, it is important to recognise that radon in water presents a far smaller health hazard than radon in air, both in term of the numbers of people exposed to high levels, and in terms of the risks to the most exposed individuals. Tap water supplied by public utilities is usually treated and poses no risk to the user. However it is advisable to have water from private bore holes in radon affected areas tested, and if necessary treated.<br />
<br />
==Risk==<br />
<br />
Radon is classified by International Agency for the Research on Cancer as known human carcinogen (IARC Group 1). Radon is second only to tobacco smoking as a cause of lung cancer. Two types of cancer risk estimations have been applied for ionising radiation, including lung cancer from α-radiation of radon and radon daughters. Absolute risk estimation assumes the risk to be a product of radon exposure level and dose/response, and it is usually expressed as a unit risk, i.e. lifetime probability per lifetime exposure. For radon the lung cancer unit risk estimate is 3-6*10-5 Bq/m3 (Pershagen et al. 1994). Relative risk estimation assumes that the additional cancer risk of radon depends on the background lung cancer levels. Because the background level depends strongly on tobaccos smoking, consequentially the additional lung cancer risk caused by radon also depends on smoking. Epidemiological evidence gives support to the relative risk model. For every additional Bq/m3 the lung cancer increases by 0.14% of its background incidence. Multiplying this relative unit risk by the European population weighed average radon concentration of 65 Bq/m3 results in an estimate that radon in the indoor air accounts for about 9 % of all lung cancer cases and consequently about 2 % of all cancer in Europe. (Darby et al. 2005). Besides lung cancer radon is not known to cause other health effects.<br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
Total estimated number of annual lung cancer incidence attributable to radon exposure in EU-Europe (plus Albania, Croatia, Switzerland and Norway) is about 21 000. This makes radon second to only tobacco as a cause of lung cancer. Direct comparison between the countries is not possible, because lung cancer incidence depends almost linearly on the total population. It is, however, interesting to compare the <br />
<br />
==Environmental and Occupational Guidelines and Standards==<br />
<br />
Radon concentrations in the ambient air vary significantly in time and space, typically around the order of magnitude of 10 Bq/m3. Similar levels would be desirable but are not achievable in the indoor air. WHO Air Quality Guidelines (2000) does not recommend any guideline value for radon, but suggests that remedial measures should be considered for buildings where the radon progeny concentrations exceed 100 Bq/m3 as an annual average. <br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
National indoor air radon guidelines are rather similar across Europe. The guideline values and respectively the preventive actions have gradually become stricter over the past decades. Differences, therefore, depend mainly on the year when the guideline came into effect. The Finnish regulation here is give as an example: Current national radon guideline value (action value) for older buildings is 400 Bq/m3 and design criterion for all new buildings is 200 Bq/m3. 400 Bq/m3 is also set as an action value for all workplaces and as a limit value for all schools and day care centres. <br />
* Radiation Act (592/1991) chapter 12 Natural radiation, section 45-49 latest amendment 22.12.2005<br />
* Radiation Decree (1512/1991) chapter 7 Natural radiation, section 26-28 (pursuant to the Radiation Act); latest amendment 29.12.2005/1264<br />
* Ministry for Social Affairs and Health Order on the Upper Limits for Radon Concentration in Places of Residence (944/1992) (pursuant to Radiation Act section 48 and Radiation Decree)<br />
<br />
<br />
==Discussion==<br />
<br />
Of all indoor air contaminants radon is the most unpredictable. Even at extremely high concentrations it is not detectable by the senses, it is of natural origin and penetrates into the building from the ground underneath. In spite of these obstacles, and thanks to large randomised surveys and harmonised monitoring methods, the levels of radon as well as its large (country averages) and small (building statistics) scale distributions are probably better known and more reliably comparable between the different regions of Europe than those of any other indoor air contaminant. Table 3.5.1.1 demonstrates that there are fivefold differences between the country averages and that the maximum levels may exceed country median values by more than three orders of magnitude. Distribution of the exposure to and risk of radon within the population is the most skewed of all common indoor air contaminants. <br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
Because the radon level in any existing or new building is still quite difficult to estimate without actual measurement, most of the buildings with radon levels that exceed the guideline values are still unknown to the owners, occupants and national authorities, and, thus, outside of any remedial programmes. Pointing out all buildings which do not meet the guideline values would require monitoring of almost every building, renovating all detected non-compliance buildings would require convincing millions of building owners and occupants of the necessity of the work and costs, and finally, actually accomplishing these tasks would still reduce the lung cancer risks of radon only marginally, because most of the radon induced lung cancers are caused by indoor air radon concentrations which do meet the current guidelines. <br />
<br />
These facts clearly point out that the most effective radon mitigation policies will focus on new buildings and buildings undergoing major renovations, and would aim at reducing all indoor radon levels, also those that are otherwise well below, e.g., 200 or even 100 Bq/m3.<br />
<br />
==See also==<br />
<br />
* [[Radon]]<br />
* [[ERF for long-term indoor exposure to radon and lung cancer]]<br />
* [http://rem.jrc.ec.europa.eu/RemWeb/Publications/EUR_RADON.pdf An Overview of Radon Surveys in Europe]. Joint Research Centre, 2005. ISBN 92-79-01066-2 <br />
* In [[Heande]] (password-protected)<br />
** [[:heande:Indoor air quality & its impact on man|Indoor air quality & its impact on man]]<br />
** [[:heande:Radon|Radon]]<br />
** [[:heande:Indoor air|Indoor air]]<br />
** [[:heande:Radon sisäilma annos-vaste|Radon sisäilma annos-vaste]]<br />
** [[:heande:Radon sisäilma altistus Suomi|Radon sisäilma altistus Suomi]]<br />
** [[:heande:Radon ja pitkäikäiset nuklidit porakaivo, kokonaissyöpäkuolemat annos-vaste|Radon ja pitkäikäiset nuklidit porakaivo]]<br />
** [[:heande:Radon ja pitkäikäiset nuklidit porakaivo, efektiivinen annos|Radon ja pitkäikäiset nuklidit porakaivo]]<br />
** [[:heande:Radon ja pitkäikäiset nuklidit porakaivovedessä|Radon ja pitkäikäiset nuklidit porakaivovedessä]]<br />
<br />
==References==<br />
<br />
<references/><br />
<br />
==Keywords==<br />
<br />
radon, indoor air, air pollutant, uranium, lung cancer<br />
<br />
==Related files==<br />
<br />
{{mfiles}}</div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Radon&diff=21561Radon2011-06-07T07:29:16Z<p>Iiro: </p>
<hr />
<div>[[heande:Radon]]<br />
[[Category:Indoor air]]<br />
[[Category:Radon]]<br />
[[Category:Pollutants]]<br />
{{encyclopedia|moderator=Jouni}}<br />
<br />
==What is Radon==<br />
<br />
Radon is a colourless, odourless, radioactive gas. It comes from the radioactive decay of radium, which in turn comes from the radioactive decay of uranium. Uranium acts as a permanent source of radon and is found in small quantities in all soils and rocks, although the amount varies from place to place. It is particularly prevalent in granite areas but not exclusively so. Radon levels vary not only between different parts of the country but even between neighbouring buildings.<br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
Radon in the soil and rocks mixes with air and rises to the surface where it is quickly diluted in the atmosphere. <br />
Concentrations in the open air are very low. However, radon concentration in soil-gas can be very high, typically from less than 10 000 to 100 000 Bq/m3. Entry of this radon-bearing air into living spaces is the main reason for elevated indoor radon concentrations. Mineral building materials also emit radon. Radon that enters enclosed spaces, such as buildings, can reach relatively high concentrations in some circumstances.<br />
<br />
When radon decays it forms tiny radioactive particles called radon daughters which may be breathed into the lungs. If formed in air, these particles may be inhaled and some will be deposited in the lungs. The radiation emitted by them as they decay can give a high dose to lung tissues and damage them. Being exposed to radon and its decay products increases the risk of developing lung cancer. In addition, smoking and exposure to radon are known to work together to greatly increase the risk of developing lung cancer. It is important however to confirm that whilst radon causes lung cancer the majority of lung cancer risk is caused by smoking. <br />
<br />
In addition to the risk from radon in air it is now recognised that some private water supplies contain levels of radon which should also be controlled. However, it is important to recognise that radon in water presents a far smaller health hazard than radon in air, both in term of the numbers of people exposed to high levels, and in terms of the risks to the most exposed individuals. Tap water supplied by public utilities is usually treated and poses no risk to the user. However it is advisable to have water from private bore holes in radon affected areas tested, and if necessary treated.<br />
<br />
==Risk==<br />
<br />
Radon is classified by International Agency for the Research on Cancer as known human carcinogen (IARC Group 1). Radon is second only to tobacco smoking as a cause of lung cancer. Two types of cancer risk estimations have been applied for ionising radiation, including lung cancer from α-radiation of radon and radon daughters. Absolute risk estimation assumes the risk to be a product of radon exposure level and dose/response, and it is usually expressed as a unit risk, i.e. lifetime probability per lifetime exposure. For radon the lung cancer unit risk estimate is 3-6*10-5 Bq/m3 (Pershagen et al. 1994). Relative risk estimation assumes that the additional cancer risk of radon depends on the background lung cancer levels. Because the background level depends strongly on tobaccos smoking, consequentially the additional lung cancer risk caused by radon also depends on smoking. Epidemiological evidence gives support to the relative risk model. For every additional Bq/m3 the lung cancer increases by 0.14% of its background incidence. Multiplying this relative unit risk by the European population weighed average radon concentration of 65 Bq/m3 results in an estimate that radon in the indoor air accounts for about 9 % of all lung cancer cases and consequently about 2 % of all cancer in Europe. (Darby et al. 2005). Besides lung cancer radon is not known to cause other health effects.<br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
Total estimated number of annual lung cancer incidence attributable to radon exposure in EU-Europe (plus Albania, Croatia, Switzerland and Norway) is about 21 000. This makes radon second to only tobacco as a cause of lung cancer. Direct comparison between the countries is not possible, because lung cancer incidence depends almost linearly on the total population. It is, however, interesting to compare the <br />
<br />
==Environmental and Occupational Guidelines and Standards==<br />
<br />
Radon concentrations in the ambient air vary significantly in time and space, typically around the order of magnitude of 10 Bq/m3. Similar levels would be desirable but are not achievable in the indoor air. WHO Air Quality Guidelines (2000) does not recommend any guideline value for radon, but suggests that remedial measures should be considered for buildings where the radon progeny concentrations exceed 100 Bq/m3 as an annual average. <br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
National indoor air radon guidelines are rather similar across Europe. The guideline values and respectively the preventive actions have gradually become stricter over the past decades. Differences, therefore, depend mainly on the year when the guideline came into effect. The Finnish regulation here is give as an example: Current national radon guideline value (action value) for older buildings is 400 Bq/m3 and design criterion for all new buildings is 200 Bq/m3. 400 Bq/m3 is also set as an action value for all workplaces and as a limit value for all schools and day care centres. <br />
* Radiation Act (592/1991) chapter 12 Natural radiation, section 45-49 latest amendment 22.12.2005<br />
* Radiation Decree (1512/1991) chapter 7 Natural radiation, section 26-28 (pursuant to the Radiation Act); latest amendment 29.12.2005/1264<br />
* Ministry for Social Affairs and Health Order on the Upper Limits for Radon Concentration in Places of Residence (944/1992) (pursuant to Radiation Act section 48 and Radiation Decree)<br />
<br />
<br />
==Discussion==<br />
<br />
Of all indoor air contaminants radon is the most unpredictable. Even at extremely high concentrations it is not detectable by the senses, it is of natural origin and penetrates into the building from the ground underneath. In spite of these obstacles, and thanks to large randomised surveys and harmonised monitoring methods, the levels of radon as well as its large (country averages) and small (building statistics) scale distributions are probably better known and more reliably comparable between the different regions of Europe than those of any other indoor air contaminant. Table 3.5.1.1 demonstrates that there are fivefold differences between the country averages and that the maximum levels may exceed country median values by more than three orders of magnitude. Distribution of the exposure to and risk of radon within the population is the most skewed of all common indoor air contaminants. <br />
<ref name="enviewp2"> EnVIE: Indoor Air Pollution Exposure. EnVIE project (Co-ordination Action on Indoor Air Quality and Health Effects; Project no. SSPE-CT-2004-502671) Deliverable 2.1 (WP2 Technical Report). KTL, Kuopio, 2008. [http://paginas.fe.up.pt/~envie/documents/finalreports/Final%20Reports%20Publishable/EnVIE%20WP2%20Final%20Report.pdf (on project website)] [http://heande.opasnet.org/heande/extensions/mfiles/mf_getfile.php?anon=true&docid=3318&fileid=3318&filename=EnVIE%20WP2%20Final%20Report.pdf (on Heande website)]</ref><br />
<br />
Because the radon level in any existing or new building is still quite difficult to estimate without actual measurement, most of the buildings with radon levels that exceed the guideline values are still unknown to the owners, occupants and national authorities, and, thus, outside of any remedial programmes. Pointing out all buildings which do not meet the guideline values would require monitoring of almost every building, renovating all detected non-compliance buildings would require convincing millions of building owners and occupants of the necessity of the work and costs, and finally, actually accomplishing these tasks would still reduce the lung cancer risks of radon only marginally, because most of the radon induced lung cancers are caused by indoor air radon concentrations which do meet the current guidelines. <br />
<br />
These facts clearly point out that the most effective radon mitigation policies will focus on new buildings and buildings undergoing major renovations, and would aim at reducing all indoor radon levels, also those that are otherwise well below, e.g., 200 or even 100 Bq/m3.<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Radon'''<br />
<br />
The exposure estimation and dose-response models are based on earlier international analysis conducted by Darby et al. (2006). In comparison with that the current work added estimation of the impacts in DALYs. Comparison of UR and RR models yielded similar results. The results using the RR approach, accounting for the national differences in the background rates of lung cancer, were selected for reporting.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
==See also==<br />
<br />
* [[Radon]]<br />
* [[ERF for long-term indoor exposure to radon and lung cancer]]<br />
* [http://rem.jrc.ec.europa.eu/RemWeb/Publications/EUR_RADON.pdf An Overview of Radon Surveys in Europe]. Joint Research Centre, 2005. ISBN 92-79-01066-2 <br />
* In [[Heande]] (password-protected)<br />
** [[:heande:Indoor air quality & its impact on man|Indoor air quality & its impact on man]]<br />
** [[:heande:Radon|Radon]]<br />
** [[:heande:Indoor air|Indoor air]]<br />
** [[:heande:Radon sisäilma annos-vaste|Radon sisäilma annos-vaste]]<br />
** [[:heande:Radon sisäilma altistus Suomi|Radon sisäilma altistus Suomi]]<br />
** [[:heande:Radon ja pitkäikäiset nuklidit porakaivo, kokonaissyöpäkuolemat annos-vaste|Radon ja pitkäikäiset nuklidit porakaivo]]<br />
** [[:heande:Radon ja pitkäikäiset nuklidit porakaivo, efektiivinen annos|Radon ja pitkäikäiset nuklidit porakaivo]]<br />
** [[:heande:Radon ja pitkäikäiset nuklidit porakaivovedessä|Radon ja pitkäikäiset nuklidit porakaivovedessä]]<br />
<br />
==References==<br />
<br />
<references/><br />
<br />
==Keywords==<br />
<br />
radon, indoor air, air pollutant, uranium, lung cancer<br />
<br />
==Related files==<br />
<br />
{{mfiles}}</div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_formaldehyde_in_Europe&diff=21559Health effects of formaldehyde in Europe2011-06-07T07:23:22Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Pauli|stub=Yes}}<br />
[[Category:EDoBE]]<br />
<br />
==About formaldehyde==<br />
<br />
Formaldehyde is a high-production volume chemical widely used in building materials, industrial processes and wide range of products. Formaldehyde is widely present both indoors and outdoors, but it reaches high levels mostly indoors. It is used in the production of several building materials and household products, or it can be a by-product of combustion. The high volatility of the compound can lead to high formaldehyde levels in indoor spaces.<br />
<br />
Predominant acute symptoms of formaldehyde exposure in humans are irritation of the eyes, nose and throat and aggravation of asthma symptoms (WHO, 2000a). A number of studies point to formaldehyde as an important indoor irritant associated with respiratory illness. A relationship between asthma-like symptoms and indoor concentrations of formaldehyde has been reported, as well as between exposure to formaldehyde emitted from indoor paint and asthma. Repeated exposures are not associated with more severe effects or lowering of the threshold concentration. Consequently, short-term concentrations are predictive of the effects also after long-term exposure.<br />
<br />
Exposure to formaldehyde has also been associated with development of cancer. Convincing evidence exists of high concentrations of formaldehyde being capable of inducing nasal cancer in rats and possibly in mice and genotoxic effects in a variety of in vitro and in vivo systems. Sinonasal cancer in humans has also been associated with high formaldehyde exposures in occupational industrial settings (ranging from 2 to 6 mg m-3) (WHO, 2000a). Based on this, IARC has recently classified formaldehyde as carcinogen group 1 (IARC, 2006a).<br />
<br />
Formaldehyde was included in EBoDE due to its high toxicological potential, economic significance and related political concern.<br />
<br />
==Selected health endpoints and exposure-response functions==<br />
<br />
In the EBoDE study, only the development of asthma in toddlers has been included. Sinonasal cancer was not included, because the WHO Air Quality Guidelines working group (WHO, 2000a) as well as recent update of the reviews for the development of WHO Guidelines for indoor air quality (WHO, 2010b) concluded that there is no epidemiological or toxicological evidence that formaldehyde would be associated with sinonasal cancer at levels below 1 mg/m3. The WHO Guidelines for Indoor Air Quality use eye irritation as the main health end-point associated with formaldehyde; however, due to difficulties in estimating a burden of disease from irritation this endpoint was not included in our calculations.<br />
<br />
Association with asthma is suggested by the systematic review by McGwin et al., 2010, even though evidence has not been consistent across all the studies (e.g. Krzyzanowski et al, 1990). We selected childhood asthma as the endpoint for formaldehyde, but due to the inconsistencies in the scientific evidence the estimates calculated here should be considered preliminary and to be confirmed by future research. In order to estimate formaldehyde-related asthma, we used the exposure-response function as reported by Rumchev et al. (2002). They studied a cohort of 88 children in Perth, Australia. For every 10 μg m-3 increase in formaldehyde exposure in bedrooms, they found an increase of 3% in the risk of having asthma (OR=1.03, 95% CI 1.02–1.04). Based on a reanalysis of their data over reported exposure categories and rescaling for 1 μg m-3, the relative risk used in our calculation is 1.0167 (see also Table 3-19 in section 3.12). Asthma effects were calculated for children (<3 years). A similar association may potentially exist for older children and adults, but due to the lack of evidence such relationship was not modelled. This may lead to underestimation of the true formaldehyde-related burden of disease.<br />
<br />
A threshold level for effects was applied. The original study by Rumchev reported elevated risks starting from exposures of 60 μg m-3. When their data were plotted in order to derive the relative risk, the threshold could be even as low as 40 μg m-3. However, the Rumchev study was criticized for confounding factors. WHO (2000a, 2010b) indicated that the lowest concentration that has been associated with nose and throat irritation in exposed workers after short-term exposure is 0.1 mg m-3, although some individuals can sense the presence of formaldehyde at lower concentrations. To prevent significant sensory irritation in the general population, an air quality guideline value of 0.1 mg m-3 as a 30-minute average was recommended as the WHO Guideline (WHO, 2010b). This is the threshold value that we used in our calculations. Since this is an order of magnitude lower than the presumed threshold for cytotoxic damage to the nasal mucosa, there is a negligible risk of upper respiratory tract cancer in humans below this threshold. As part of the uncertainty analysis, we compared alternative threshold models for cancer (threshold levels of 40, 60 and 100 μg m-3) and asthma, see section 5.2.<br />
<br />
==Exposure data==<br />
<br />
Inhalation is the dominant pathway for formaldehyde exposure in humans. The relevant exposure metric is the residential indoor air level (μg/m-3). As indicated above, both of the exposure-response models used apply a threshold level (100 μg m-3). Therefore, it is necessary to assess the fraction of the population being exposed to levels higher than this threshold level. A probabilistic simulation model was used to calculate the fraction of the population exceeding the threshold using mean and standard deviation data and assuming lognormal distributions. No international exposure data sources were identified for formaldehyde, so data have been collected from heterogeneous national sources.<br />
<br />
For Belgium, Germany and the Netherlands, only mean exposures were available, without information about the variability. For these three countries, the exposure distributions were based on the data from the other countries (estimated coefficient of variation: 0.6).<br />
<br />
{|{{prettytable}}<br />
|+ TABLE 3-8. Population distributions of residential formaldehyde concentrations.<br />
! Country<br />
! mean<br />
μg m<sup>-3</sup><br />
! sd<br />
μg m<sup>-3</sup><br />
! References<br />
|-----<br />
| Belgium<br />
| 24.0<br />
| 14.4<sup>1</sup><br />
| Swaans et al,. 2008<br />
|-----<br />
| Finland<br />
| 41.6<br />
| 22.4<br />
| Jurvelin et al, 2001<br />
|-----<br />
| France<br />
| 23.0<br />
| 14.0<br />
| OQAI, 2006<br />
|-----<br />
| Germany<br />
| 26.0<br />
| 15.6<sup>1</sup><br />
| Umweltbundesamt, 2008<br />
|-----<br />
| Italy<br />
| 16.0<br />
| 8.0<br />
| Lovreglio et al, 2009<br />
|-----<br />
| Netherlands<br />
| 13.0<br />
| 7.8<sup>1</sup><br />
| Dongen,van & Vos, 2008<br />
|}<br />
<br />
<sup>1</sup> Mean coefficient of variation of the countries with data on variability used for estimation.<br />
<br />
Exposure data for formaldehyde are presented in Table 3-21 in section 3.12.<br />
<br />
The mean formaldehyde indoor concentrations vary from 13 μg m-3 in the Netherlands to about 42 μg m-3 in Finland. In Finland formaldehyde exposure levels are higher than in many other developed countries due to the construction materials used and the relatively tightly sealed building envelopes. As shown in Figure 3-3, approximately 42% of population is exposed to levels above 40 μg m-3 and 2 % above 100 μg m-3.<br />
<br />
[[Image:Estimated_formaldehyde_exposure_Finland.png|none|Estimated formaldehyde exposure|FIGURE 3-3. Estimated formaldehyde exposure distribution in Finland.]]<br />
<br />
Data comparability is compromised for formaldehyde by the differences in population sampling. In France, Germany and the Netherlands, data measurements are representative for country-wide exposure. However, in other countries, measurements have only been carried out in a few cities or were based on a smaller subset of houses.<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Formaldehyde'''<br />
<br />
No international burden of disease study utilizing DALYs for formaldehyde was identified. WHO Guidelines for Indoor Air Quality used eye irritation as the main health end-point in setting a safe exposure level. However eye irritation cannot be directly used as a health end-point in burden of disease calculation because no disability weight exists and therefore was not accounted for here. Scientific evidence on the association between formaldehyde and childhood asthma is not considered sufficiently consistent yet; thus the results presented here must be taken as provisional estimates of the magnitude of the health impacts, to be confirmed by future studies.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_benzene_in_Europe&diff=21556Health effects of benzene in Europe2011-06-07T07:16:09Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Mori|}}<br />
<br />
== Benzene ==<br />
<br />
<br />
=== About benzene ===<br />
<br />
Benzene is an organic chemical compound that was added to gasoline in the past. The use of benzene as an additive in gasoline is now limited, but it is still used by industry in the production of for example drugs and plastics. In addition, cigarette smoke contains some benzene. <br />
<br />
Inhalation is the major route of human exposure to benzene. However, exposure may also occur through oral absorption or by dermal exposure (primarily in workplace settings). Exposure to benzene- contaminated water can cause inhalation and dermal absorption in the general population (e.g. when having a shower), but this does not occur often (US Department of Health, 2007). <br />
<br />
The genotoxicity of benzene has been extensively studied. Benzene is a known carcinogen for which no safe level of exposure can be recommended. The most significant adverse effects from prolonged exposure to benzene are haematotoxicity, genotoxicity and carcinogenicity (IARC group 1 carcinogen) (IARC 1982, 1987). Chronic benzene exposure can result in bone marrow depression expressed as leukopenia, anaemia and/or thrombocytopenia, which can in turn lead to pancytopenia and aplastic anaemia (WHO, 2000b). <br />
Increased mortality from leukaemia has repeatedly been demonstrated in workers occupationally exposed (Arp et al 1983, IARC 1982, Decouflé et al 1983, Bond et al 1986, McCraw, 1985, Yin 1987, Paxton et al. 1994a, b). There are also studies that using proxies of benzene exposure indicate an increased risk of leukaemia in children, but conclusions are not definitive (Weng et al, 2009, Brosselin et al, 2009, Whitworth et al 2008, Gunier et al 2008, Steffen et al, 2004, Crosignani et al, 2004, Pearson et al, 2000, Nordlinder et al, 1997). <br />
<br />
Benzene was selected in the EBoDE project because it may pose high individual risks and is still of global concern. Even though policies in Europe have already greatly reduced environmental benzene exposure, it is still identified as a concern (e.g. the INDEX project identified benzene as high priority stressor (Koistinen et al., 2008, Kotzias et al., 2005); European air quality directive 2008/50/EC; setting of WHO guidelines for indoor air quality (WHO, 2010b)). <br />
<br />
<br />
=== Selected health endpoints and exposure-response functions ===<br />
<br />
Benzene effects were estimated for leukaemia, including morbidity and mortality. Other proposed health endpoints were not included, because they only occur at high exposure levels, typical of occupational settings. We used the exposure response function as recommended by the WHO Air Quality Guidelines (WHO, 2000b) (see Table 3-19 in section 3.12). WHO uses the 1984 risk calculation of Crump (1984), in which the geometric mean of the range of estimates of the excess lifetime risk of leukaemia at an air concentration of 1 µg/m3 is estimated to be 6 × 10-6 (unit risk). This estimate falls within the range of the risk estimate that is used by the US EPA (2.2 x 10-6 to 7.8 x 10-6 per µg m-3). This unit risk is applied to the whole population, including children. Specific estimates that have been supplied for children could not be used, because the underlying studies often use proxies of exposure (petrol station density, traffic density, etc.) instead of actual benzene exposure levels. <br />
<br />
The estimated number of leukaemia cases were used to calculate the population attributable fraction using method 2A. <br />
<br />
<br />
=== Exposure data ===<br />
<br />
Benzene exposures are best described by residential indoor air levels (µg m-3). Besides being affected by benzene levels in outdoor air, indoor levels may be raised especially by indoor smoking and potentially the storage and use of fuels e.g. in case of attached garages and storage rooms. <br />
<br />
Benzene is a regulated ambient pollutant and therefore outdoor monitoring is required by the European Union. Benzene measurements are included in the AirBase database (European Environment Agency, AirBase, 2009).<br />
<br />
Benzene exposure is estimated from national indoor levels, supplemented with outdoor levels. Different national data demonstrate that benzene exposure concentrations vary from 0.9 µg m-3 in the Netherlands to 2.9 µg m-3 in Italy. The data used in this project are summarized in Table 3-21 in section 3.12. <br />
<br />
The confidence levels of the exposure data cannot be directly compared, because the measurements are based on different time periods. Data from the Netherlands and France reflect a 1 week average exposure, while Italian and Finnish data are based on 2 day measurements. <br />
<br />
Sources of uncertainty in exposure data include differences in sampling selection. In France, data reflect a large number of dwellings, while in other countries data are limited to a smaller number of monitored houses. In addition, the presence or absence of tobacco smoke in indoor environments is not always reported, making comparison more difficult. This at least partly explains the higher levels in Finland, where benzene from smoking was included. In Italy, levels are likely to be higher because of the large number of two-stroke engines used there, which emit a lot of benzene.<br />
<br />
<br />
{|{{prettytable}}<br />
|+TABLE 3-1. Characteristics of benzene indoor concentration measurements. <br />
! Country<br />
! Including benzene from smoking<br />
! Sample size<br />
! Time periods of measurements<br />
|-----<br />
| Belgium <br />
| Yes<br />
| 85 houses and 25 day-care centers<br />
| <br />
|-----<br />
| Finland<br />
| Yes<br />
| random; 20 adults<br />
| 2 day average<br />
|-----<br />
| France<br />
| Yes<br />
| 567 residences<br />
| 1 week average<br />
|-----<br />
| Germany<br />
| Yes<br />
| 1790 subjects<br />
| <br />
|-----<br />
| Italy<br />
| Yes<br />
| 50 subjects<br />
| 2 day average<br />
|-----<br />
| Netherlands<br />
| Yes<br />
| 1240 dwellings<br />
| 1 week average<br />
|}<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
'''Benzene'''<br />
No international burden of disease study utilizing DALYs for benzene was identified. Some studies using exposure proxies like proximity of gasoline stations have studies health impacts with inconsistent results.<br />
Dioxins. Our calculations were based on the same approach as applied earlier by Leino et al (2008), but we utilized an updated cancer slope factor that is approximately seven times higher than the one used by Leino et al. Leino et al. did the calculations for Finland only. The work presented here also updated the exposure estimates in order to allow for good international comparability, yet some differences between the national intake estimation methods remained.<br />
<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
[[Category:EBoDE]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Health_effects_of_benzene_in_Europe&diff=21536Health effects of benzene in Europe2011-06-06T12:00:07Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Mori|}}<br />
<br />
== Benzene ==<br />
<br />
<br />
=== About benzene ===<br />
<br />
Benzene is an organic chemical compound that was added to gasoline in the past. The use of benzene as an additive in gasoline is now limited, but it is still used by industry in the production of for example drugs and plastics. In addition, cigarette smoke contains some benzene. <br />
<br />
Inhalation is the major route of human exposure to benzene. However, exposure may also occur through oral absorption or by dermal exposure (primarily in workplace settings). Exposure to benzene- contaminated water can cause inhalation and dermal absorption in the general population (e.g. when having a shower), but this does not occur often (US Department of Health, 2007). <br />
<br />
The genotoxicity of benzene has been extensively studied. Benzene is a known carcinogen for which no safe level of exposure can be recommended. The most significant adverse effects from prolonged exposure to benzene are haematotoxicity, genotoxicity and carcinogenicity (IARC group 1 carcinogen) (IARC 1982, 1987). Chronic benzene exposure can result in bone marrow depression expressed as leukopenia, anaemia and/or thrombocytopenia, which can in turn lead to pancytopenia and aplastic anaemia (WHO, 2000b). <br />
Increased mortality from leukaemia has repeatedly been demonstrated in workers occupationally exposed (Arp et al 1983, IARC 1982, Decouflé et al 1983, Bond et al 1986, McCraw, 1985, Yin 1987, Paxton et al. 1994a, b). There are also studies that using proxies of benzene exposure indicate an increased risk of leukaemia in children, but conclusions are not definitive (Weng et al, 2009, Brosselin et al, 2009, Whitworth et al 2008, Gunier et al 2008, Steffen et al, 2004, Crosignani et al, 2004, Pearson et al, 2000, Nordlinder et al, 1997). <br />
<br />
Benzene was selected in the EBoDE project because it may pose high individual risks and is still of global concern. Even though policies in Europe have already greatly reduced environmental benzene exposure, it is still identified as a concern (e.g. the INDEX project identified benzene as high priority stressor (Koistinen et al., 2008, Kotzias et al., 2005); European air quality directive 2008/50/EC; setting of WHO guidelines for indoor air quality (WHO, 2010b)). <br />
<br />
<br />
=== Selected health endpoints and exposure-response functions ===<br />
<br />
Benzene effects were estimated for leukaemia, including morbidity and mortality. Other proposed health endpoints were not included, because they only occur at high exposure levels, typical of occupational settings. We used the exposure response function as recommended by the WHO Air Quality Guidelines (WHO, 2000b) (see Table 3-19 in section 3.12). WHO uses the 1984 risk calculation of Crump (1984), in which the geometric mean of the range of estimates of the excess lifetime risk of leukaemia at an air concentration of 1 µg/m3 is estimated to be 6 × 10-6 (unit risk). This estimate falls within the range of the risk estimate that is used by the US EPA (2.2 x 10-6 to 7.8 x 10-6 per µg m-3). This unit risk is applied to the whole population, including children. Specific estimates that have been supplied for children could not be used, because the underlying studies often use proxies of exposure (petrol station density, traffic density, etc.) instead of actual benzene exposure levels. <br />
<br />
The estimated number of leukaemia cases were used to calculate the population attributable fraction using method 2A. <br />
<br />
<br />
=== Exposure data ===<br />
<br />
Benzene exposures are best described by residential indoor air levels (µg m-3). Besides being affected by benzene levels in outdoor air, indoor levels may be raised especially by indoor smoking and potentially the storage and use of fuels e.g. in case of attached garages and storage rooms. <br />
<br />
Benzene is a regulated ambient pollutant and therefore outdoor monitoring is required by the European Union. Benzene measurements are included in the AirBase database (European Environment Agency, AirBase, 2009).<br />
<br />
Benzene exposure is estimated from national indoor levels, supplemented with outdoor levels. Different national data demonstrate that benzene exposure concentrations vary from 0.9 µg m-3 in the Netherlands to 2.9 µg m-3 in Italy. The data used in this project are summarized in Table 3-21 in section 3.12. <br />
<br />
The confidence levels of the exposure data cannot be directly compared, because the measurements are based on different time periods. Data from the Netherlands and France reflect a 1 week average exposure, while Italian and Finnish data are based on 2 day measurements. <br />
<br />
Sources of uncertainty in exposure data include differences in sampling selection. In France, data reflect a large number of dwellings, while in other countries data are limited to a smaller number of monitored houses. In addition, the presence or absence of tobacco smoke in indoor environments is not always reported, making comparison more difficult. This at least partly explains the higher levels in Finland, where benzene from smoking was included. In Italy, levels are likely to be higher because of the large number of two-stroke engines used there, which emit a lot of benzene.<br />
<br />
<br />
{|{{prettytable}}<br />
|+TABLE 3-1. Characteristics of benzene indoor concentration measurements. <br />
! Country<br />
! Including benzene from smoking<br />
! Sample size<br />
! Time periods of measurements<br />
|-----<br />
| Belgium <br />
| Yes<br />
| 85 houses and 25 day-care centers<br />
| <br />
|-----<br />
| Finland<br />
| Yes<br />
| random; 20 adults<br />
| 2 day average<br />
|-----<br />
| France<br />
| Yes<br />
| 567 residences<br />
| 1 week average<br />
|-----<br />
| Germany<br />
| Yes<br />
| 1790 subjects<br />
| <br />
|-----<br />
| Italy<br />
| Yes<br />
| 50 subjects<br />
| 2 day average<br />
|-----<br />
| Netherlands<br />
| Yes<br />
| 1240 dwellings<br />
| 1 week average<br />
|}<br />
<br />
==Uncertainties per stressor and comparison with other studies==<br />
<br />
''A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.''<br />
<br />
No international burden of disease study utilizing DALYs for benzene was identified. Some studies using exposure proxies like proximity of gasoline stations have studies health impacts with inconsistent results.<br />
Dioxins. Our calculations were based on the same approach as applied earlier by Leino et al (2008), but we utilized an updated cancer slope factor that is approximately seven times higher than the one used by Leino et al. Leino et al. did the calculations for Finland only. The work presented here also updated the exposure estimates in order to allow for good international comparability, yet some differences between the national intake estimation methods remained.<br />
<br />
[[Category:EBoDE]]</div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Overview_of_the_EBoDE-project&diff=21535Overview of the EBoDE-project2011-06-06T11:59:59Z<p>Iiro: </p>
<hr />
<div>{{study|moderator=Julle}}<br />
[[category:EBoDE]]<br />
<br />
==Introduction==<br />
<br />
Exposures to many environmental stressors are known to endanger human health. Negative impacts on health can range from mild psychological effects (e.g. noise annoyance), to effects on morbidity (such as asthma caused by exposure to air pollution), and to increased mortality (such as lung cancer provoked by radon exposure). Properly targeted and followed-up environmental health policies, such as the coal burning ban in Dublin (1990) and the smoking ban in public places in Rome (2005) have demonstrated significant and immediate population level reductions in deaths and diseases. In order to develop effective policy measures, quantitative information about the extent of health impacts of different environmental stressors is needed.<br />
<br />
As demonstrated by the examples above, health effects of environmental factors often vary considerably with regard to their severity, duration and magnitude. This makes it difficult to compare different (environmental) health effects and to set priorities in health policies or research programs. Public health policies generally aim to allocate resources effectively for maximum health benefits while avoiding undue interference with other societal functions and human activities. In order to develop such policies, it is necessary to know what ‘maximum health benefits’ are. Decades ago, such decisions tended to be made based on mortality statistics: which (environmental) factor causes most deaths? However, nowadays, most people get relatively old, and priority has shifted from quantity to quality of life. This has lead to the need to incorporate morbidity effects into public health decisions, and therefore to find a way of comparing dissimilar health effects.<br />
<br />
Such comparison and prioritisation of environmental health effects is made possible by expressing the diverging health effects in one unit: the environmental burden of disease (EBD). Environmental burden of disease figures express both mortality and morbidity effects in a population in one number. They quantify and summarize (environmental) health effects and can be used for:<br />
* Comparative evaluation of environmental burden of disease (“how bad is it?”)<br />
* Evaluation of the effectiveness of environmental policies (largest reduction of disease burden)<br />
* Estimation of the accumulation of exposures to environmental factors (for example in urban areas)<br />
* Communication of health risks<br />
<br />
An example of an integrated health measure that can be used to express the environmental burden of disease is the DALY (Disability Adjusted Life Years). DALYs combine information on quality and quantity of life. They give an indication of the (potential) number of healthy life years lost in a population due to premature mortality or morbidity, the latter being weighted for the severity of the disorder. The concept was first introduced by Murray and Lopez (1996) as part of the Global Burden of Disease study, which was launched by the World Bank. Since then, the World Health Organization (WHO) has endorsed the procedure, and the DALY approach has been used in various studies on a global, national and regional level.<br />
<br />
WHO collects a vast set of data on the global burden of disease. The first study quantified the health effects of more than 100 diseases for eight regions of the world in 1990 (Murray and Lopez, 1996). It generated comprehensive and internally consistent estimates of mortality and morbidity by age, gender and region. In a former WHO study, it was shown that almost a quarter of all disease worldwide was caused by environmental exposure (Prüss-Üstün and Corvalán, 2006). In industrial sub-regions this estimate was about 16% (15–18%). These fractions, however, are dependent on the conclusiveness of the included environmental factors and health effects. The WHO programme on quantifying environmental health impacts has addressed more than a dozen stressors <ref>The WHO programme[http://www.who.int/quantifying_ehimpacts/publications/en/]</ref>. In order to support further applications of the environmental burden of disease (EBD) assessments, a methodological guidance has been published by WHO (Prüss-Üstün et al., 2003) and was followed here too.<br />
<br />
In Europe, national environmental burden of disease (EBD) assessments are on-going in several countries. The work by RIVM was one of the first systematic European works in this area that utilized disability-adjusted life years (DALY) as a measure to compare the burden of different health outcomes related to the exposure of the population to environmental stressors (Hollander et al., 1999). The results highlighted that (i) a number of environmental stressors may cause chronic or acute diseases or death, (ii) a few top ranking stressors cause over 90% of the national EBD, and (iii) these top ranking stressors are not necessarily those that have drawn the most concern, regulatory action and/or preventive investment.<ref name="EBoDe">Otto Hänninen, Anne Knol: European Perspectives on Environmental Burden of Disease: Esimates for Nine Stressors in Six European Countries, <br />
Authors and National Institute for Health and Welfare (THL), Report 1/2011 [http://www.thl.fi/thl-client/pdfs/b75f6999-e7c4-4550-a939-3bccb19e41c1]</ref><br />
<br />
<br />
==Objectives==<br />
<br />
The EBoDE-project was set up in order to guide environmental health policy making in the six participating countries (Belgium, Finland, France, Germany, Italy and the Netherlands) and potentially beyond. From a policy perspective, these insights from the EBoDE-project can be useful to evaluate past policies and to gain insight in setting the policy priorities for the future. We have calculated the total EBD associated with the nine environmental stressors. The total EBD is not identical to the avoidable burden of disease, because some exposures are not realistically reducible to zero (e.g. fine particles). Also, our estimates do not take into account the costs of reducing the EBD. Thus, the results are only one input into the full process of developing cost-effective policies to achieve better environmental health.<br />
<br />
The objectives of the project were to update the available previous assessments, to focus on stressors relevant for the European region, to provide harmonized EBD assessments for participating countries, and to develop and make available the methodologies for further development and other countries.<br />
The specific objectives are to:<br />
• Provide harmonized environmental burden of disease (EBD) estimates for selected environmental stressors in the participating six countries;<br />
• Test the methodologies in a harmonized way across the countries.<br />
• Assess the comparability of the quantifications and ranking of the EBD<br />
• between countries<br />
• within countries<br />
• between environmental stressors;<br />
• Qualitative assessments of variation and uncertainty in the input parameters and results.<br />
<br />
Environmental burden of disease estimates have been calculated for:<br />
• nine environmental stressors: benzene, dioxins (including furans and dioxin-like PCBs), second-hand smoke, formaldehyde, lead, noise, ozone, particulate matter (PM) and radon;<br />
• six European countries: Belgium, Finland, France, Germany, Italy and the Netherlands;<br />
• the year 2004 (and some trend estimates for the year 2010).<br />
As outlined above, the EBoDE study was carried out in order to test the environmental burden of disease methodology in various countries. The results of the studies are intended to allow comparison of the disease burden between different environmental stressors and between countries. Consequently, the study does not to identify the ‘reduction potential’. Our estimates should therefore not be interpreted as the ‘avoidable burden of disease’: most risks cannot realistically be completely removed by any policy measures. For some exposures, however, the numbers may nonetheless be interpretable as reduction potential, eg for dioxins, formaldehyde, benzene, etc, as these exposures could potentially be completely eliminated.<ref name="EBoDe"/><br />
<br />
==Outline of this report==<br />
<br />
This report describes the methods, data and results of the EBoDE-project. Chapter 2 presents the methodology. The environmental stressors are introduced in Chapter 3, which also presents the data used (selected health endpoints, exposure data, exposure response functions). In Chapter 4, the results are presented and discussed. Chapter 5 gives information about uncertainties in the approach, and provides some alternative calculations using different input values. In Chapter 6 conclusions are drawn. The report ends with the references and two appendices: Appendix A presents country-specific results and Appendix B some considerations for using a life-table approach in EBD modelling.<ref name="EBoDe"/><br />
<br />
==Uncertainties and limitations==<br />
Assessment of uncertainties is essential in a comparison of quantitative estimates that are based on data from heterogeneous sources and slightly varying methods. Due to the wide range of data sources and models and the limited resources within the EBoDE project, systematic analysis of all uncertainties was not possible. However, we were able to assess a number of specific sources of uncertainties in more detail as part of the work, yielding some insights into the reliability of the overall assessment.<br />
The studied health impacts span approximately four orders of magnitude in size from few DALYs per million to almost 10 000 DALYs per million. The overall ranking of the environmental stressors seems to be rather robust against the relatively large uncertainties in individual estimates or methodological choices like discounting and age-weighing. However, some of the estimated ranges are overlapping. This concerns especially second hand smoke, radon and transportation noise that compete for the questionable honour of being the second most important environmental stressor in the participating countries. Among these stressors the differences are smaller than the corresponding uncertainties of the estimates.<br />
The health state of an individual person is the result of a complex mixture of genetic, environmental and behavioural factors. In a typical case of death, numerous factors play together. This means, for example, that a single death caused by a cardiovascular disease could be avoided by either reducing air pollution, or a better diet, or more physical activity. Therefore, if the individual attributable fractions are summed over a number of risk factors, a value over 100% may sometimes be found. For this and other reasons, it has been argued that death counts are not suitable for quantification of the impacts (Brunekreef et al., 2007). Therefore the authors recommend to mainly use aggregate population measures of health like DALYs, YLLs and YLDs.<br />
This chapter presents the quantitative results for selected sources of uncertainties and discusses the project limitations and author judgment of the reliability of the ranking.<br />
<br />
''Uncertainties per stressor and comparison with other studies''<br />
<br />
A list of the most important sources of uncertainty for each stressor in the EBoDE calculations is provided in Table 5-1. Some of these are further explained below. In addition, we will compare our estimates to results of a selection of similar studies. Comparison of different studies on environmental burden of disease helps to understand the role of various methodological and strategic selections made in each study, like the selection of stressors or health endpoints.<br />
<br />
'''Benzene'''<br />
<br />
No international burden of disease study utilizing DALYs for benzene was identified. Some studies using exposure proxies like proximity of gasoline stations have studies health impacts with inconsistent results.<br />
Dioxins. Our calculations were based on the same approach as applied earlier by Leino et al (2008), but we utilized an updated cancer slope factor that is approximately seven times higher than the one used by Leino et al. Leino et al. did the calculations for Finland only. The work presented here also updated the exposure estimates in order to allow for good international comparability, yet some differences between the national intake estimation methods remained.<br />
<br />
'''SHS'''<br />
<br />
Our burden of disease calculation for SHS was based on a WHO model (Öberg et al., 2010). The exposure estimates were updated against available national and international data sources for the target year 2004, but otherwise the results are comparable with the WHO assessment. Other recent estimates of burden of disease for SHS were also available for Germany (Heidrich et al. 2007; Keil et al. 2005), which provided similar results as the current estimates.<br />
<br />
'''Formaldehyde'''<br />
<br />
No international burden of disease study utilizing DALYs for formaldehyde was identified. WHO Guidelines for Indoor Air Quality used eye irritation as the main health end-point in setting a safe exposure level. However eye irritation cannot be directly used as a health end-point in burden of disease calculation because no disability weight exists and therefore was not accounted for here. Scientific evidence on the association between formaldehyde and childhood asthma is not considered sufficiently consistent yet; thus the results presented here must be taken as provisional estimates of the magnitude of the health impacts, to be confirmed by future studies.<br />
<br />
'''Lead'''<br />
<br />
The calculation focused on mild mental retardation and hypertensive disease only. WHO EBD estimates (Fewtrell et al., 2003) include cerebro-vascular and other cardiovascular diseases besides hypertensive disease; therefore the current estimates for lead are slightly lower than the WHO estimates.<br />
<br />
'''Transportation noise'''<br />
<br />
Burden of disease estimation for transportation noise is currently under active development. The estimates presented here were based on the only available international exposure data source, the first stage version of the European Noise Directive database (2007), which is not conclusive yet. Therefore it is clear that most of the exposures for transportation noise are underestimated. In some studies annoyance and cognitive impairment have been used as an additional health end-points for environmental noise. However, due to the selected more limited definition of ‘health’ as ICD-classified health states used in our assessment, annoyance and cognitive impairment were not included here. Only road, rail and air traffic exposures were included; many other sources also contribute to the noise exposures. Low exposures below the END data collection limits (50 and 55 dB) were not included. For these reasons it can be expected that when these limitations are solved, the impact estimates will increase.<br />
<br />
'''PM and ozone'''<br />
<br />
The methodology developed in Clean Air for Europe -project (CAFE) (Hurley et al., 2005) was applied using updated exposure estimates. The updated exposures are based on ambient air quality monitoring data that contain, besides the anthropogenic components that CAFE focused on, also natural sources of PM2.5. The spatial resolution of the updated model is 25 times higher (grid size 10x 10 km² instead of 50x50 km²). Compared to the CAFE estimates the current work adds estimation of the impacts in DALYs. The WHO Environmental Burden of Disease programme uses a non-linear exposure-response function (Ostro, 2004) that at higher exposures yields lower impacts than the linear CAFE model. WHO also sets a threshold level at 7.5 μg m-3.<br />
<br />
'''Radon'''<br />
<br />
The exposure estimation and dose-response models are based on earlier international analysis conducted by Darby et al. (2006). In comparison with that the current work added estimation of the impacts in DALYs. Comparison of UR and RR models yielded similar results. The results using the RR approach, accounting for the national differences in the background rates of lung cancer, were selected for reporting.<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Polychlorinated_biphenyl&diff=21320Polychlorinated biphenyl2011-06-01T07:31:23Z<p>Iiro: </p>
<hr />
<div>{{encyclopedia|moderator=Henrik}} <br />
'''Polychlorinated biphenyls''' (PCB-compounds): a group of oily<br />
stable chemicals, which are mixtures of many congeners. They are very poorly water soluble and lipophilic (see<br />
''[[PCB#Physicochemical properties|PCB – physicochemical properties]]''), and therefore accumulate in lipids<br />
(fats) of living organisms (see ''[[PCB#Environmental persistence|PCB – environmental persistence]]''), and<br />
bioaccumulate in trophic levels (see ''[[PCB#Biomagnification|PCB – biomagnification]]''). They<br />
contain small amounts (1 to 40 mg/kg) of PCDFs as impurities (see<br />
''[[PCB#Contaminants|PCB – contaminants]]'').[[category:Dioxin synopsis]]<br />
<ref>Jouko Tuomisto, Terttu Vartiainen and Jouni T. Tuomisto: Dioxin synopsis. Report. National Institute for Health and Welfare (THL), ISSN 1798-0089 ; 14/2011 [http://www.thl.fi/thl-client/pdfs/81322e2c-e9b6-4003-bb13-995dcd1b68cb]</ref><br />
(For detailed information, see International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993,<ref>International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993 [http://bit.ly/fubDjv]</ref>; Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149,<ref>Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149 [http://pubmed.gov/8037844]</ref>).<br />
<br />
== Acute toxicity ==<br />
<br />
toxicity occurring after a single dose within a few weeks. It is generally low, but it depends on the mixture of congeners, because dioxin-like (non-ortho, see ortho-PCBs) PCBs are much more toxic than other congeners. Their toxicity resembles that of dioxins (see PCDD/F - acute toxicity). <br />
<br />
== Analysis ==<br />
<br />
measurement of concentration of a compound in a sample. PCBs can be analysed by gas chromatography by using electron capture detector. This is a fairly widely available method, but if absolute accuracy and congener-specific analysis is needed, gas chromatography-mass spectrometry (see this) may be needed. This is a very expensive method not available in many laboratories in Europe. <br />
<br />
== Biomagnification ==<br />
<br />
property of PCB compounds to concentrate from one trophic level to the next (see also biomagnification). Many PCBs are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Therefore they concentrate along the food chain, and species at the "top" of the food chain (such as seals or eagles) are in special danger. <br />
<br />
== Carcinogenicity ==<br />
<br />
capacity of PCBs to cause cancer. A number of long-term carcinogenicity studies have been carried out in mice and rats. Interpretation is complicated by the lack of information of minor impurities, especially PCDFs. Some tested mixtures were free of PCDFs. In many of these studies hepatocellular adenomas and/or carcinomas (tumours of the liver) were found although the increase was not always significant. PCB mixtures are considered non-genotoxic, PCBs do not cause mutations or chromosomal damage. Therefore rodent tumourigenicity is considered to be of epigenetic nature (promoting rather than initiating effect, see mutagenicity, promoters). IARC classifies PCBs as probable human carcinogens on the basis of animal data. Remarkable caution is needed in extrapolating the available animal data to humans. None of the available epidemiological studies provide conclusive evidence of an association between PCB exposure and increased cancer mortality. (For more information, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Chemical structure ==<br />
<br />
see chemical structures. <br />
<br />
== Contaminants ==<br />
<br />
by-products found unintentionally in PCB products. Technical PCBs contain a number of various chlorinated byproducts, e.g. about 40% chlorobenzenes, a few percent chloronaftalenes, and also small amounts of PCDDs and PCDFs (93% PCDFs and 7% PCDDs of the total PCDD/F content in the PCB product which caused the Yusho Rice Oil accident, pentaPCBs, tetraPCBs and hexaPCBs dominating). PCDFs have been detected up to 40 mg/kg (ΣPCDF) in PCBs. Because commercial products were not sold according to composition but according to their physical properties, there may be large variations both between preparations and between lots of a preparation. <br />
<br />
== Disposal of ==<br />
<br />
PCB oils cannot be burned in usual conditions, because they burn poorly and evaporate to the environment along with their PCDD/F impurities. PCDFs may also be formed during PCB burning. Therefore PCBs are considered problem waste, which must be incinerated by a well-controlled process in a high-quality waste incinerator at the temperature of 1000 - 1200 °C, and with an effective fly-ash filtering system (see also incineration). <br />
<br />
== Elimination ==<br />
<br />
process of discharging PCB out of the body. Elimination of chemicals out of the body is usually based on two mechanisms, excretion (such as in urine or faeces) or metabolism (chemical breakdown, often in the liver). Only water soluble materials can be excreted in the kidneys to urine, and PCBs as lipid soluble and poorly water soluble chemicals cannot be excreted practically at all as such. Metabolism tries to make them more water soluble, but especially higher chlorinated PCBs (see PCB - physicochemical properties) are metabolised very poorly, and therefore cannot be effectively excreted even with the help of metabolism. Therefore they accumulate in body fats, and their half-life (see this) may be even several years. <br />
<br />
== Environmental persistence ==<br />
<br />
ability of PCBs to continue existence in the environment. The stability of PCBs is a technical advantage, but it also means that they are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Neither soil microbes nor animals are able to break down effectively the highly chlorinated PCBs (see also ortho-PCBs). This causes very slow elimination (see PCB - elimination). Because some PCBs are more persistent than others are, the spectrum of congeners in the environment, animals and humans is never quite identical to that in the original commercial product. In water, PCBs are adsorbed on sediments and organic matter. This decreases the rate of volatilisation, but also slows down the degradation. <br />
<br />
== Half-life ==<br />
<br />
time needed to decrease the amount of PCBs to one-half. There is no systematic information on the half-life of all PCB congeners in humans. The half-lives of the most toxic non-ortho PCBs have been estimated to vary from 0.1 to 13 years. Somewhat fragmentary information suggests that the half-lives of PCBs are on the average around one year (with much variation to each direction). See also half-life. <br />
<br />
== Physicochemical properties ==<br />
<br />
All PCBs are lipophilic (soluble in fats and oils) and practically insoluble in water, but lipophilicity increases by increasing rate of chlorination (see PCB - chemical structure). Technical mixtures are mobile to viscous oils depending on the rate of chlorination, and their boiling point varies from 300 to 400 °C. They resist high temperatures and oxidising conditions without breaking down. Their electrical conductivity is very low which made them suitable cooling liquids for electrical equipment. <br />
<br />
== Sources ==<br />
<br />
Emissions. PCBs were manufactured from 1930 to 1970s or 1980s (varying in different countries), and the total production was in excess of a million tonnes. PCBs are still manufactured e.g. in Russia. They have spread to the environment in accidents (such as transformer fires or leaks), from volatilisation of waste landfills and incineration of mixed municipal waste (e.g. plastic materials). The virtually universal distribution of PCBs suggests transport in air. Human exposure. Food is the major source for human exposure to PCBs and dioxins, especially fatty foods: dairy products (butter, cheese, fatty milk), meat, egg, and fish. Some subgroups within the society (e.g., nursing babies and people consuming plenty of fish) may be highly exposed to these compounds and are thus at greater risk. Daily intake of PCBs is a few µg per person. PCB concentrations have been screened in two WHO international studies, and in Central Europe the concentrations have decreased in breast milk from 400-800 µg/kg (sum of six marker PCBs [see this] in milk fat) to 200-400 µg/kg from 1987 to 1993. The decrease in environmental concentrations is partly due to prohibition of the use of PCBs in Europe, partly due to improved incineration technology (see also PCDD/F - sources). <br />
<br />
== Toxicity in humans ==<br />
<br />
This is difficult to evaluate, because the exposure has usually been to a mixture of different congeners and also impurities such as PCDFs. Occupational exposure may be to different congeners than exposure of general public through food, because some congeners are more easily degraded in the environment than others. In occupational conditions skin rashes, itching, irritation of the conjunctivae, pigmentation of fingers and nails, chloracne, liver problems and neurological and unspecific psychological symptoms have been seen. In Yusho and Yu-Cheng incidents (see these) also various skin and nail problems were seen, as well as liver enlargement and immunological problems. In children of Yusho and Yu-Cheng patients skin problems, oedematous eyes, dentition at birth and lowered birth weight were seen, among others. Total exposures in these cases have been estimated at 600 to 1,800 mg per person (ΣPCB). The daily intake of PCBs in general population in most industrialised countries is of the order of some micrograms per person (ΣPCB). Such levels have not been associated with disease. (For a review, see Safe, Crit. Rev. Toxicol. 1994:24:87-149; for detailed evaluation, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Trade names ==<br />
<br />
Many companies in several countries have manufactured PCBs. The trade names include Apirolio, Aroclor, Clophen, Fenchlor, Kanechlor, Phenoclor, Pyralene, Pyranol, Pyroclor, Santotherm FR, and Sovol. Sometimes the trade name indicates the degree of chlorination, e.g. Aroclor 1254 contains 54&nbsp;% of chlorine, 12 indicates the number of carbon atoms. <br />
<br />
== Use ==<br />
<br />
PCBs have been used since 1930 because of their stability and low flammability (see PCB - physicochemical properties) as insulating materials in electrical equipment (electrical capacitors, transformers), as plasticizers (softening materials) in plastic products, and for a variety of other industrial purposes (in gas-transmission turbines, vacuum pumps, hydraulic fluids, adhesives, fire retardants, wax extenders, lubricants, cutting oils, oils in heat exchangers etc.). The total production was in excess of a million tonnes. Common trademarks included Aroclor, Clophen, and Kanechlor (see PCB - trade names).<br />
[[category:Dioxin synopsis]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Polychlorinated_biphenyl&diff=21319Polychlorinated biphenyl2011-06-01T07:28:27Z<p>Iiro: </p>
<hr />
<div>{{encyclopedia|moderator=Henrik}} <br />
'''Polychlorinated biphenyls''' (PCB-compounds): a group of oily<br />
stable chemicals, which are mixtures of many congeners. They are very poorly water soluble and lipophilic (see<br />
''[[PCB#Physicochemical properties|PCB – physicochemical properties]]''), and therefore accumulate in lipids<br />
(fats) of living organisms (see ''[[PCB]] – environmental persistence''), and<br />
bioaccumulate in trophic levels (see ''[[PCB]] – biomagnification''). They<br />
contain small amounts (1 to 40 mg/kg) of PCDFs as impurities (see<br />
''[[PCB]] – contaminants'').[[category:Dioxin synopsis]]<br />
<ref>Jouko Tuomisto, Terttu Vartiainen and Jouni T. Tuomisto: Dioxin synopsis. Report. National Institute for Health and Welfare (THL), ISSN 1798-0089 ; 14/2011 [http://www.thl.fi/thl-client/pdfs/81322e2c-e9b6-4003-bb13-995dcd1b68cb]</ref><br />
(For detailed information, see International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993,<ref>International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993 [http://bit.ly/fubDjv]</ref>; Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149,<ref>Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149 [http://pubmed.gov/8037844]</ref>).<br />
<br />
== Acute toxicity ==<br />
<br />
toxicity occurring after a single dose within a few weeks. It is generally low, but it depends on the mixture of congeners, because dioxin-like (non-ortho, see ortho-PCBs) PCBs are much more toxic than other congeners. Their toxicity resembles that of dioxins (see PCDD/F - acute toxicity). <br />
<br />
== Analysis ==<br />
<br />
measurement of concentration of a compound in a sample. PCBs can be analysed by gas chromatography by using electron capture detector. This is a fairly widely available method, but if absolute accuracy and congener-specific analysis is needed, gas chromatography-mass spectrometry (see this) may be needed. This is a very expensive method not available in many laboratories in Europe. <br />
<br />
== Biomagnification ==<br />
<br />
property of PCB compounds to concentrate from one trophic level to the next (see also biomagnification). Many PCBs are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Therefore they concentrate along the food chain, and species at the "top" of the food chain (such as seals or eagles) are in special danger. <br />
<br />
== Carcinogenicity ==<br />
<br />
capacity of PCBs to cause cancer. A number of long-term carcinogenicity studies have been carried out in mice and rats. Interpretation is complicated by the lack of information of minor impurities, especially PCDFs. Some tested mixtures were free of PCDFs. In many of these studies hepatocellular adenomas and/or carcinomas (tumours of the liver) were found although the increase was not always significant. PCB mixtures are considered non-genotoxic, PCBs do not cause mutations or chromosomal damage. Therefore rodent tumourigenicity is considered to be of epigenetic nature (promoting rather than initiating effect, see mutagenicity, promoters). IARC classifies PCBs as probable human carcinogens on the basis of animal data. Remarkable caution is needed in extrapolating the available animal data to humans. None of the available epidemiological studies provide conclusive evidence of an association between PCB exposure and increased cancer mortality. (For more information, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Chemical structure ==<br />
<br />
see chemical structures. <br />
<br />
== Contaminants ==<br />
<br />
by-products found unintentionally in PCB products. Technical PCBs contain a number of various chlorinated byproducts, e.g. about 40% chlorobenzenes, a few percent chloronaftalenes, and also small amounts of PCDDs and PCDFs (93% PCDFs and 7% PCDDs of the total PCDD/F content in the PCB product which caused the Yusho Rice Oil accident, pentaPCBs, tetraPCBs and hexaPCBs dominating). PCDFs have been detected up to 40 mg/kg (ΣPCDF) in PCBs. Because commercial products were not sold according to composition but according to their physical properties, there may be large variations both between preparations and between lots of a preparation. <br />
<br />
== Disposal of ==<br />
<br />
PCB oils cannot be burned in usual conditions, because they burn poorly and evaporate to the environment along with their PCDD/F impurities. PCDFs may also be formed during PCB burning. Therefore PCBs are considered problem waste, which must be incinerated by a well-controlled process in a high-quality waste incinerator at the temperature of 1000 - 1200 °C, and with an effective fly-ash filtering system (see also incineration). <br />
<br />
== Elimination ==<br />
<br />
process of discharging PCB out of the body. Elimination of chemicals out of the body is usually based on two mechanisms, excretion (such as in urine or faeces) or metabolism (chemical breakdown, often in the liver). Only water soluble materials can be excreted in the kidneys to urine, and PCBs as lipid soluble and poorly water soluble chemicals cannot be excreted practically at all as such. Metabolism tries to make them more water soluble, but especially higher chlorinated PCBs (see PCB - physicochemical properties) are metabolised very poorly, and therefore cannot be effectively excreted even with the help of metabolism. Therefore they accumulate in body fats, and their half-life (see this) may be even several years. <br />
<br />
== Environmental persistence ==<br />
<br />
ability of PCBs to continue existence in the environment. The stability of PCBs is a technical advantage, but it also means that they are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Neither soil microbes nor animals are able to break down effectively the highly chlorinated PCBs (see also ortho-PCBs). This causes very slow elimination (see PCB - elimination). Because some PCBs are more persistent than others are, the spectrum of congeners in the environment, animals and humans is never quite identical to that in the original commercial product. In water, PCBs are adsorbed on sediments and organic matter. This decreases the rate of volatilisation, but also slows down the degradation. <br />
<br />
== Half-life ==<br />
<br />
time needed to decrease the amount of PCBs to one-half. There is no systematic information on the half-life of all PCB congeners in humans. The half-lives of the most toxic non-ortho PCBs have been estimated to vary from 0.1 to 13 years. Somewhat fragmentary information suggests that the half-lives of PCBs are on the average around one year (with much variation to each direction). See also half-life. <br />
<br />
== Physicochemical properties ==<br />
<br />
All PCBs are lipophilic (soluble in fats and oils) and practically insoluble in water, but lipophilicity increases by increasing rate of chlorination (see PCB - chemical structure). Technical mixtures are mobile to viscous oils depending on the rate of chlorination, and their boiling point varies from 300 to 400 °C. They resist high temperatures and oxidising conditions without breaking down. Their electrical conductivity is very low which made them suitable cooling liquids for electrical equipment. <br />
<br />
== Sources ==<br />
<br />
Emissions. PCBs were manufactured from 1930 to 1970s or 1980s (varying in different countries), and the total production was in excess of a million tonnes. PCBs are still manufactured e.g. in Russia. They have spread to the environment in accidents (such as transformer fires or leaks), from volatilisation of waste landfills and incineration of mixed municipal waste (e.g. plastic materials). The virtually universal distribution of PCBs suggests transport in air. Human exposure. Food is the major source for human exposure to PCBs and dioxins, especially fatty foods: dairy products (butter, cheese, fatty milk), meat, egg, and fish. Some subgroups within the society (e.g., nursing babies and people consuming plenty of fish) may be highly exposed to these compounds and are thus at greater risk. Daily intake of PCBs is a few µg per person. PCB concentrations have been screened in two WHO international studies, and in Central Europe the concentrations have decreased in breast milk from 400-800 µg/kg (sum of six marker PCBs [see this] in milk fat) to 200-400 µg/kg from 1987 to 1993. The decrease in environmental concentrations is partly due to prohibition of the use of PCBs in Europe, partly due to improved incineration technology (see also PCDD/F - sources). <br />
<br />
== Toxicity in humans ==<br />
<br />
This is difficult to evaluate, because the exposure has usually been to a mixture of different congeners and also impurities such as PCDFs. Occupational exposure may be to different congeners than exposure of general public through food, because some congeners are more easily degraded in the environment than others. In occupational conditions skin rashes, itching, irritation of the conjunctivae, pigmentation of fingers and nails, chloracne, liver problems and neurological and unspecific psychological symptoms have been seen. In Yusho and Yu-Cheng incidents (see these) also various skin and nail problems were seen, as well as liver enlargement and immunological problems. In children of Yusho and Yu-Cheng patients skin problems, oedematous eyes, dentition at birth and lowered birth weight were seen, among others. Total exposures in these cases have been estimated at 600 to 1,800 mg per person (ΣPCB). The daily intake of PCBs in general population in most industrialised countries is of the order of some micrograms per person (ΣPCB). Such levels have not been associated with disease. (For a review, see Safe, Crit. Rev. Toxicol. 1994:24:87-149; for detailed evaluation, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Trade names ==<br />
<br />
Many companies in several countries have manufactured PCBs. The trade names include Apirolio, Aroclor, Clophen, Fenchlor, Kanechlor, Phenoclor, Pyralene, Pyranol, Pyroclor, Santotherm FR, and Sovol. Sometimes the trade name indicates the degree of chlorination, e.g. Aroclor 1254 contains 54&nbsp;% of chlorine, 12 indicates the number of carbon atoms. <br />
<br />
== Use ==<br />
<br />
PCBs have been used since 1930 because of their stability and low flammability (see PCB - physicochemical properties) as insulating materials in electrical equipment (electrical capacitors, transformers), as plasticizers (softening materials) in plastic products, and for a variety of other industrial purposes (in gas-transmission turbines, vacuum pumps, hydraulic fluids, adhesives, fire retardants, wax extenders, lubricants, cutting oils, oils in heat exchangers etc.). The total production was in excess of a million tonnes. Common trademarks included Aroclor, Clophen, and Kanechlor (see PCB - trade names).<br />
[[category:Dioxin synopsis]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Polychlorinated_biphenyl&diff=21318Polychlorinated biphenyl2011-06-01T07:27:44Z<p>Iiro: </p>
<hr />
<div>{{encyclopedia|moderator=Henrik}} <br />
'''Polychlorinated biphenyls''' (PCB-compounds): a group of oily<br />
stable chemicals, which are mixtures of many congeners. They are very poorly water soluble and lipophilic (see<br />
''[[PCB#physicochemical properties|PCB – physicochemical properties]]''), and therefore accumulate in lipids<br />
(fats) of living organisms (see ''[[PCB]] – environmental persistence''), and<br />
bioaccumulate in trophic levels (see ''[[PCB]] – biomagnification''). They<br />
contain small amounts (1 to 40 mg/kg) of PCDFs as impurities (see<br />
''[[PCB]] – contaminants'').[[category:Dioxin synopsis]]<br />
<ref>Jouko Tuomisto, Terttu Vartiainen and Jouni T. Tuomisto: Dioxin synopsis. Report. National Institute for Health and Welfare (THL), ISSN 1798-0089 ; 14/2011 [http://www.thl.fi/thl-client/pdfs/81322e2c-e9b6-4003-bb13-995dcd1b68cb]</ref><br />
(For detailed information, see International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993,<ref>International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993 [http://bit.ly/fubDjv]</ref>; Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149,<ref>Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149 [http://pubmed.gov/8037844]</ref>).<br />
<br />
== Acute toxicity ==<br />
<br />
toxicity occurring after a single dose within a few weeks. It is generally low, but it depends on the mixture of congeners, because dioxin-like (non-ortho, see ortho-PCBs) PCBs are much more toxic than other congeners. Their toxicity resembles that of dioxins (see PCDD/F - acute toxicity). <br />
<br />
== Analysis ==<br />
<br />
measurement of concentration of a compound in a sample. PCBs can be analysed by gas chromatography by using electron capture detector. This is a fairly widely available method, but if absolute accuracy and congener-specific analysis is needed, gas chromatography-mass spectrometry (see this) may be needed. This is a very expensive method not available in many laboratories in Europe. <br />
<br />
== Biomagnification ==<br />
<br />
property of PCB compounds to concentrate from one trophic level to the next (see also biomagnification). Many PCBs are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Therefore they concentrate along the food chain, and species at the "top" of the food chain (such as seals or eagles) are in special danger. <br />
<br />
== Carcinogenicity ==<br />
<br />
capacity of PCBs to cause cancer. A number of long-term carcinogenicity studies have been carried out in mice and rats. Interpretation is complicated by the lack of information of minor impurities, especially PCDFs. Some tested mixtures were free of PCDFs. In many of these studies hepatocellular adenomas and/or carcinomas (tumours of the liver) were found although the increase was not always significant. PCB mixtures are considered non-genotoxic, PCBs do not cause mutations or chromosomal damage. Therefore rodent tumourigenicity is considered to be of epigenetic nature (promoting rather than initiating effect, see mutagenicity, promoters). IARC classifies PCBs as probable human carcinogens on the basis of animal data. Remarkable caution is needed in extrapolating the available animal data to humans. None of the available epidemiological studies provide conclusive evidence of an association between PCB exposure and increased cancer mortality. (For more information, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Chemical structure ==<br />
<br />
see chemical structures. <br />
<br />
== Contaminants ==<br />
<br />
by-products found unintentionally in PCB products. Technical PCBs contain a number of various chlorinated byproducts, e.g. about 40% chlorobenzenes, a few percent chloronaftalenes, and also small amounts of PCDDs and PCDFs (93% PCDFs and 7% PCDDs of the total PCDD/F content in the PCB product which caused the Yusho Rice Oil accident, pentaPCBs, tetraPCBs and hexaPCBs dominating). PCDFs have been detected up to 40 mg/kg (ΣPCDF) in PCBs. Because commercial products were not sold according to composition but according to their physical properties, there may be large variations both between preparations and between lots of a preparation. <br />
<br />
== Disposal of ==<br />
<br />
PCB oils cannot be burned in usual conditions, because they burn poorly and evaporate to the environment along with their PCDD/F impurities. PCDFs may also be formed during PCB burning. Therefore PCBs are considered problem waste, which must be incinerated by a well-controlled process in a high-quality waste incinerator at the temperature of 1000 - 1200 °C, and with an effective fly-ash filtering system (see also incineration). <br />
<br />
== Elimination ==<br />
<br />
process of discharging PCB out of the body. Elimination of chemicals out of the body is usually based on two mechanisms, excretion (such as in urine or faeces) or metabolism (chemical breakdown, often in the liver). Only water soluble materials can be excreted in the kidneys to urine, and PCBs as lipid soluble and poorly water soluble chemicals cannot be excreted practically at all as such. Metabolism tries to make them more water soluble, but especially higher chlorinated PCBs (see PCB - physicochemical properties) are metabolised very poorly, and therefore cannot be effectively excreted even with the help of metabolism. Therefore they accumulate in body fats, and their half-life (see this) may be even several years. <br />
<br />
== Environmental persistence ==<br />
<br />
ability of PCBs to continue existence in the environment. The stability of PCBs is a technical advantage, but it also means that they are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Neither soil microbes nor animals are able to break down effectively the highly chlorinated PCBs (see also ortho-PCBs). This causes very slow elimination (see PCB - elimination). Because some PCBs are more persistent than others are, the spectrum of congeners in the environment, animals and humans is never quite identical to that in the original commercial product. In water, PCBs are adsorbed on sediments and organic matter. This decreases the rate of volatilisation, but also slows down the degradation. <br />
<br />
== Half-life ==<br />
<br />
time needed to decrease the amount of PCBs to one-half. There is no systematic information on the half-life of all PCB congeners in humans. The half-lives of the most toxic non-ortho PCBs have been estimated to vary from 0.1 to 13 years. Somewhat fragmentary information suggests that the half-lives of PCBs are on the average around one year (with much variation to each direction). See also half-life. <br />
<br />
== Physicochemical properties ==<br />
<br />
All PCBs are lipophilic (soluble in fats and oils) and practically insoluble in water, but lipophilicity increases by increasing rate of chlorination (see PCB - chemical structure). Technical mixtures are mobile to viscous oils depending on the rate of chlorination, and their boiling point varies from 300 to 400 °C. They resist high temperatures and oxidising conditions without breaking down. Their electrical conductivity is very low which made them suitable cooling liquids for electrical equipment. <br />
<br />
== Sources ==<br />
<br />
Emissions. PCBs were manufactured from 1930 to 1970s or 1980s (varying in different countries), and the total production was in excess of a million tonnes. PCBs are still manufactured e.g. in Russia. They have spread to the environment in accidents (such as transformer fires or leaks), from volatilisation of waste landfills and incineration of mixed municipal waste (e.g. plastic materials). The virtually universal distribution of PCBs suggests transport in air. Human exposure. Food is the major source for human exposure to PCBs and dioxins, especially fatty foods: dairy products (butter, cheese, fatty milk), meat, egg, and fish. Some subgroups within the society (e.g., nursing babies and people consuming plenty of fish) may be highly exposed to these compounds and are thus at greater risk. Daily intake of PCBs is a few µg per person. PCB concentrations have been screened in two WHO international studies, and in Central Europe the concentrations have decreased in breast milk from 400-800 µg/kg (sum of six marker PCBs [see this] in milk fat) to 200-400 µg/kg from 1987 to 1993. The decrease in environmental concentrations is partly due to prohibition of the use of PCBs in Europe, partly due to improved incineration technology (see also PCDD/F - sources). <br />
<br />
== Toxicity in humans ==<br />
<br />
This is difficult to evaluate, because the exposure has usually been to a mixture of different congeners and also impurities such as PCDFs. Occupational exposure may be to different congeners than exposure of general public through food, because some congeners are more easily degraded in the environment than others. In occupational conditions skin rashes, itching, irritation of the conjunctivae, pigmentation of fingers and nails, chloracne, liver problems and neurological and unspecific psychological symptoms have been seen. In Yusho and Yu-Cheng incidents (see these) also various skin and nail problems were seen, as well as liver enlargement and immunological problems. In children of Yusho and Yu-Cheng patients skin problems, oedematous eyes, dentition at birth and lowered birth weight were seen, among others. Total exposures in these cases have been estimated at 600 to 1,800 mg per person (ΣPCB). The daily intake of PCBs in general population in most industrialised countries is of the order of some micrograms per person (ΣPCB). Such levels have not been associated with disease. (For a review, see Safe, Crit. Rev. Toxicol. 1994:24:87-149; for detailed evaluation, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Trade names ==<br />
<br />
Many companies in several countries have manufactured PCBs. The trade names include Apirolio, Aroclor, Clophen, Fenchlor, Kanechlor, Phenoclor, Pyralene, Pyranol, Pyroclor, Santotherm FR, and Sovol. Sometimes the trade name indicates the degree of chlorination, e.g. Aroclor 1254 contains 54&nbsp;% of chlorine, 12 indicates the number of carbon atoms. <br />
<br />
== Use ==<br />
<br />
PCBs have been used since 1930 because of their stability and low flammability (see PCB - physicochemical properties) as insulating materials in electrical equipment (electrical capacitors, transformers), as plasticizers (softening materials) in plastic products, and for a variety of other industrial purposes (in gas-transmission turbines, vacuum pumps, hydraulic fluids, adhesives, fire retardants, wax extenders, lubricants, cutting oils, oils in heat exchangers etc.). The total production was in excess of a million tonnes. Common trademarks included Aroclor, Clophen, and Kanechlor (see PCB - trade names).<br />
[[category:Dioxin synopsis]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Polychlorinated_biphenyl&diff=21316Polychlorinated biphenyl2011-06-01T07:18:59Z<p>Iiro: </p>
<hr />
<div>{{encyclopedia|moderator=Henrik}} <br />
'''Polychlorinated biphenyls''' (PCB-compounds): a group of oily<br />
stable chemicals, which are mixtures of many congeners. They are very poorly water soluble and lipophilic (see<br />
''[[PCB – physicochemical properties|#physicochemical properties]]''), and therefore accumulate in lipids<br />
(fats) of living organisms (see ''[[PCB]] – environmental persistence''), and<br />
bioaccumulate in trophic levels (see ''[[PCB]] – biomagnification''). They<br />
contain small amounts (1 to 40 mg/kg) of PCDFs as impurities (see<br />
''[[PCB]] – contaminants'').[[category:Dioxin synopsis]]<br />
<ref>Jouko Tuomisto, Terttu Vartiainen and Jouni T. Tuomisto: Dioxin synopsis. Report. National Institute for Health and Welfare (THL), ISSN 1798-0089 ; 14/2011 [http://www.thl.fi/thl-client/pdfs/81322e2c-e9b6-4003-bb13-995dcd1b68cb]</ref><br />
(For detailed information, see International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993,<ref>International<br />
Programme on Chemical Safety, Environmental Health Criteria 140,<br />
WHO, Geneva, 1993 [http://bit.ly/fubDjv]</ref>; Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149,<ref>Safe, Crit. Rev. Toxicol.<br />
1994:24:87-149 [http://pubmed.gov/8037844]</ref>).<br />
<br />
== Acute toxicity ==<br />
<br />
toxicity occurring after a single dose within a few weeks. It is generally low, but it depends on the mixture of congeners, because dioxin-like (non-ortho, see ortho-PCBs) PCBs are much more toxic than other congeners. Their toxicity resembles that of dioxins (see PCDD/F - acute toxicity). <br />
<br />
== Analysis ==<br />
<br />
measurement of concentration of a compound in a sample. PCBs can be analysed by gas chromatography by using electron capture detector. This is a fairly widely available method, but if absolute accuracy and congener-specific analysis is needed, gas chromatography-mass spectrometry (see this) may be needed. This is a very expensive method not available in many laboratories in Europe. <br />
<br />
== Biomagnification ==<br />
<br />
property of PCB compounds to concentrate from one trophic level to the next (see also biomagnification). Many PCBs are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Therefore they concentrate along the food chain, and species at the "top" of the food chain (such as seals or eagles) are in special danger. <br />
<br />
== Carcinogenicity ==<br />
<br />
capacity of PCBs to cause cancer. A number of long-term carcinogenicity studies have been carried out in mice and rats. Interpretation is complicated by the lack of information of minor impurities, especially PCDFs. Some tested mixtures were free of PCDFs. In many of these studies hepatocellular adenomas and/or carcinomas (tumours of the liver) were found although the increase was not always significant. PCB mixtures are considered non-genotoxic, PCBs do not cause mutations or chromosomal damage. Therefore rodent tumourigenicity is considered to be of epigenetic nature (promoting rather than initiating effect, see mutagenicity, promoters). IARC classifies PCBs as probable human carcinogens on the basis of animal data. Remarkable caution is needed in extrapolating the available animal data to humans. None of the available epidemiological studies provide conclusive evidence of an association between PCB exposure and increased cancer mortality. (For more information, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Chemical structure ==<br />
<br />
see chemical structures. <br />
<br />
== Contaminants ==<br />
<br />
by-products found unintentionally in PCB products. Technical PCBs contain a number of various chlorinated byproducts, e.g. about 40% chlorobenzenes, a few percent chloronaftalenes, and also small amounts of PCDDs and PCDFs (93% PCDFs and 7% PCDDs of the total PCDD/F content in the PCB product which caused the Yusho Rice Oil accident, pentaPCBs, tetraPCBs and hexaPCBs dominating). PCDFs have been detected up to 40 mg/kg (ΣPCDF) in PCBs. Because commercial products were not sold according to composition but according to their physical properties, there may be large variations both between preparations and between lots of a preparation. <br />
<br />
== Disposal of ==<br />
<br />
PCB oils cannot be burned in usual conditions, because they burn poorly and evaporate to the environment along with their PCDD/F impurities. PCDFs may also be formed during PCB burning. Therefore PCBs are considered problem waste, which must be incinerated by a well-controlled process in a high-quality waste incinerator at the temperature of 1000 - 1200 °C, and with an effective fly-ash filtering system (see also incineration). <br />
<br />
== Elimination ==<br />
<br />
process of discharging PCB out of the body. Elimination of chemicals out of the body is usually based on two mechanisms, excretion (such as in urine or faeces) or metabolism (chemical breakdown, often in the liver). Only water soluble materials can be excreted in the kidneys to urine, and PCBs as lipid soluble and poorly water soluble chemicals cannot be excreted practically at all as such. Metabolism tries to make them more water soluble, but especially higher chlorinated PCBs (see PCB - physicochemical properties) are metabolised very poorly, and therefore cannot be effectively excreted even with the help of metabolism. Therefore they accumulate in body fats, and their half-life (see this) may be even several years. <br />
<br />
== Environmental persistence ==<br />
<br />
ability of PCBs to continue existence in the environment. The stability of PCBs is a technical advantage, but it also means that they are extremely persistent in the environment. Increase in chlorination (see PCB - physicochemical properties) increases both stability and lipophilicity. Neither soil microbes nor animals are able to break down effectively the highly chlorinated PCBs (see also ortho-PCBs). This causes very slow elimination (see PCB - elimination). Because some PCBs are more persistent than others are, the spectrum of congeners in the environment, animals and humans is never quite identical to that in the original commercial product. In water, PCBs are adsorbed on sediments and organic matter. This decreases the rate of volatilisation, but also slows down the degradation. <br />
<br />
== Half-life ==<br />
<br />
time needed to decrease the amount of PCBs to one-half. There is no systematic information on the half-life of all PCB congeners in humans. The half-lives of the most toxic non-ortho PCBs have been estimated to vary from 0.1 to 13 years. Somewhat fragmentary information suggests that the half-lives of PCBs are on the average around one year (with much variation to each direction). See also half-life. <br />
<br />
== Physicochemical properties ==<br />
<br />
All PCBs are lipophilic (soluble in fats and oils) and practically insoluble in water, but lipophilicity increases by increasing rate of chlorination (see PCB - chemical structure). Technical mixtures are mobile to viscous oils depending on the rate of chlorination, and their boiling point varies from 300 to 400 °C. They resist high temperatures and oxidising conditions without breaking down. Their electrical conductivity is very low which made them suitable cooling liquids for electrical equipment. <br />
<br />
== Sources ==<br />
<br />
Emissions. PCBs were manufactured from 1930 to 1970s or 1980s (varying in different countries), and the total production was in excess of a million tonnes. PCBs are still manufactured e.g. in Russia. They have spread to the environment in accidents (such as transformer fires or leaks), from volatilisation of waste landfills and incineration of mixed municipal waste (e.g. plastic materials). The virtually universal distribution of PCBs suggests transport in air. Human exposure. Food is the major source for human exposure to PCBs and dioxins, especially fatty foods: dairy products (butter, cheese, fatty milk), meat, egg, and fish. Some subgroups within the society (e.g., nursing babies and people consuming plenty of fish) may be highly exposed to these compounds and are thus at greater risk. Daily intake of PCBs is a few µg per person. PCB concentrations have been screened in two WHO international studies, and in Central Europe the concentrations have decreased in breast milk from 400-800 µg/kg (sum of six marker PCBs [see this] in milk fat) to 200-400 µg/kg from 1987 to 1993. The decrease in environmental concentrations is partly due to prohibition of the use of PCBs in Europe, partly due to improved incineration technology (see also PCDD/F - sources). <br />
<br />
== Toxicity in humans ==<br />
<br />
This is difficult to evaluate, because the exposure has usually been to a mixture of different congeners and also impurities such as PCDFs. Occupational exposure may be to different congeners than exposure of general public through food, because some congeners are more easily degraded in the environment than others. In occupational conditions skin rashes, itching, irritation of the conjunctivae, pigmentation of fingers and nails, chloracne, liver problems and neurological and unspecific psychological symptoms have been seen. In Yusho and Yu-Cheng incidents (see these) also various skin and nail problems were seen, as well as liver enlargement and immunological problems. In children of Yusho and Yu-Cheng patients skin problems, oedematous eyes, dentition at birth and lowered birth weight were seen, among others. Total exposures in these cases have been estimated at 600 to 1,800 mg per person (ΣPCB). The daily intake of PCBs in general population in most industrialised countries is of the order of some micrograms per person (ΣPCB). Such levels have not been associated with disease. (For a review, see Safe, Crit. Rev. Toxicol. 1994:24:87-149; for detailed evaluation, see International Programme on Chemical Safety, Environmental Health Criteria 140, WHO, Geneva, 1993). <br />
<br />
== Trade names ==<br />
<br />
Many companies in several countries have manufactured PCBs. The trade names include Apirolio, Aroclor, Clophen, Fenchlor, Kanechlor, Phenoclor, Pyralene, Pyranol, Pyroclor, Santotherm FR, and Sovol. Sometimes the trade name indicates the degree of chlorination, e.g. Aroclor 1254 contains 54&nbsp;% of chlorine, 12 indicates the number of carbon atoms. <br />
<br />
== Use ==<br />
<br />
PCBs have been used since 1930 because of their stability and low flammability (see PCB - physicochemical properties) as insulating materials in electrical equipment (electrical capacitors, transformers), as plasticizers (softening materials) in plastic products, and for a variety of other industrial purposes (in gas-transmission turbines, vacuum pumps, hydraulic fluids, adhesives, fire retardants, wax extenders, lubricants, cutting oils, oils in heat exchangers etc.). The total production was in excess of a million tonnes. Common trademarks included Aroclor, Clophen, and Kanechlor (see PCB - trade names).<br />
[[category:Dioxin synopsis]]<br />
<br />
==References==<br />
<references/></div>Iirohttp://en.opasnet.org/en-opwiki/index.php?title=Variable:Policy_options_about_dealing_with_CO2_emissions&diff=21314Variable:Policy options about dealing with CO2 emissions2011-06-01T06:52:17Z<p>Iiro: Redirected page to Carbon pie approach</p>
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