Health impact of radon in Europe

This page is a knowledge crystal of subtype assessment. The page identifier is Op_en4712
Moderator:Teemu R (see all)
Citation of this page: Teemu Rintala, Jouni T. Tuomisto: Health impact of radon in Europe. Opasnet 2011. [1]. Accessed 23 Nov 2024.
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Question: Radon gas in homes is a major environmental health hazard causing lung cancer. Good building policies can reduce radon concentrations in indoor air in homes. What are the effects of different plausible building policies on radon in homes and consequently on lung cancer mortality in Europe between 2010 and 2050? Answer: There are currently 43000 (95 % CI 7186-104660) lung cancer deaths due to indoor radon in Europe. This number is likely to increase in the future due to decreased ventilation in aim to reduce energy consumption, if other measures are not taken. It is important to maintain good air exchange conditions and proper building insulation even when energy saving measures are taken. More should be known about practical conditions and potential to radon reduction, as well as about the knowledge level, preparedness, and practical hindrances to take such actions by building owners. Value of information analysis shows that this information is so valuable that putting resources in this research is clearly cost-effective. The ultimate aim should be to reduce energy consumption, greenhouse gas emissions, and indoor radon in a cost-effective and synergistic way.

Scope

In this study, the research question is the following: What are the effects of different building policies on radon in residential buildings, and consequently on lung cancer incidence in Europe? The building policies considered aim at reducing greenhouse gas emissions and thus mitigate climate change. We look specifically at years 2010, 2020, 2030, and 2050 in the European Union. The study is performed as an open assessment in the internet as a part of the so called Common Case Study of INTARESE and HEIMTSA projects. A technical objective was to test feasibility of web workspace and on-line modelling tools developed in the projects.

Boundaries

Boundaries, scenarios, intended users, and participants are the same as in the Common Case Study. In brief, the situation is assessed in EU-30 (the current 27 EU member states plus Norway, Iceland, and Switzerland) for the next forty years. Four scenarios are considered:

1. BAU: business as usual contains the implementation of already made decisions but no further actions;
2. ALL: all such policies are implemented that are required to reduce the total greenhouse gas emissions by 70 % by 2050;
3. INSULATION: only building insulation policies from ALL are implemented (ALL also contains policies to increase biomass use, but these are not implemented here);
4. RENOVATION: same as ALL except that ventilation is not improved in 50 % of those buildings that are insulated up to tighter standards (in other scenarios, insulation is always combined with improved ventilation).

Rationale

The assessment is based on a causal model presented in the figure. Each node in the graph (also called a variable in the model) are described in more detail elsewhere; only a summary of the model is presented here.

Policy estimation

European building policies described above are considered. The aim of the policies is to mitigate climate change by reducing greenhouse gas emissions from heating and cooling of buildings. In this sub-assessment, we do not consider greenhouse gas emissions or climate impacts, but only health impacts (specifically lung cancer) occurring as collateral damages or benefits. The purpose of the assessment is to estimate the impacts of each building policy and identify those policies that produce the best health outcomes.

Exposure estimation

The logic of the assessment is that the climate change mitigation policies considered affect air exchange rates in buildings. There is a simple negative association between air exchange and radon: air exchange removes radon from indoor air. However, there are many complicating factors which are not considered in this assessment. Radon comes to indoor air mainly from soil via gas leakages in the building ground floor. The most important omission in the assessment is that there are effective methods to prevent radon leakages into the building in the first place. Although these are typically cheap to implement during construction, they can be very costly if implemented in an existing building. Therefore, this first pass assessment simply assumes that such measures are not taken more in the future than what they have been taken so far, i.e. the radon emissions from soil into indoor air will remain at the current levels. Although this is a somewhat pessimistic assumption, the knowledge about radon and its mitigation have been around for decades, and the current situation is the result of radon policies that societies have been willing to implement in practice. Nation-wide radon estimates were obtained from EnVIE project and UNSCEAR 2000 report reviewed in this sub-assessment (http://en.opasnet.org/w/Radon_concentrations_in_European_residences). However, United Kingdom, Czech Republic, and Slovenia were rejected due to lack of data.

Health effect estimation

Lung cancer mortality (number of deaths due to lung cancer) attributable to indoor radon concentrations was chosen as the outcome of interest for two reasons. First, there is clear evidence about the causal association between indoor radon and lung cancer; second, lung cancer is the only known endpoint of radon exposure, and as a deathly disease, focusing on mortality can produce a reasonable estimate about the total magnitude of the problem.

The current epidemiological literature contains plausible exposure-response functions for the association of indoor radon and lung cancer. The current exposure-response estimate is 1.16 (risk ratio RR) for lung cancer mortality per 100 Bq/m³ radon concentration increase (Darby 2004 and 2005). Linear no-threshold exposure-response function was assumed for the whole population in each country.

Lung cancer mortality due to radon depends also on the background mortality of lung cancer and the population size. Population size differs by country and also in time; data from the Common Case Study was used also in this sub-assessment. The same lung cancer background mortality is assumed for the whole Europe: 58.2 cases/100000 person-years (Globocan 2008). The main exposures causing lung cancer are known fairly well: smoking, asbestos, radon, and smoke from any source. Many of these exposures are decreasing at some rate in Europe. However, for simplicity we assume no change in the background risk of lung cancer, and this somewhat overestimates the impacts of radon and also the impacts of policies on lung cancer in the future.

Policy evaluation

Finally, the lung cancer mortalities under each policy scenario are compared and the optimum scenario is found. It should be noted, however, that this sub-assessment only has a very narrow view on all impacts of the policies and therefore it cannot be used as an ultimate guidance for policy selection. Instead, this sub-assessment gives important information for the Common Case Study as a whole, which may produce such overall conclusions.

Impact estimation

For overall conclusions, it is crucial that the impacts observed in a sub-assessment can be compared with other impacts observed in other sub-assessment. To this aim, we expressed the outcome using two alternative summary indicators: disability-adjusted life years (DALY) and euros (€). DALYs are computed by multiplying the number of cases of a disease with a respective disability or severity weight and the duration of the disease. The idea is to measure the overall healthy years that are lost due to several diseases. The disability weight (estimated by WHO) for lung cancer is 0.146. We assume that each case of lung cancer causes a period of 2 to 36 months under disease, and a life expectancy loss of 1 to 15 years.

Lung cancer is a rather deadly disease and few patient that get a lung cancer diagnosis will actually be cured. Therefore, lung cancer mortality covers most DALYs involved, especially when the years with disease before death are included. However, there is also a fraction of patients that eventually die from something else, and their DALYs are not included in an assessment about mortality cases only. Therefore, we used slightly higher estimates for life expectancy loss to compensate for the non-mortality cases. We thought this was more reliable than trying to count lung cancer morbidity separately and then end up with double counting problems.

The costs of diseases include direct costs of treatment, indirect costs due to loss of productivity (absence from work), and willingness of a person to pay extra to avoid the disease. Because the monetary estimation of impacts is not the main objective in this sub-assessment, we do not go through this laborious path. Instead, we simply assume that the DALY estimate also provides a reasonable indicator of all monetary costs of the asthma cases. Thus, we multiply the DALY estimate with an estimate of willingness to pay to avoid a loss of one healthy life year. This has typically been in the order of 30000 - 60000 euros per saved life year. This results in a preliminary estimate of monetary impact, which can be used in comparisons in other parts of the Common Case Study and the value of information analysis (see below).

A methodological objective was a proof of concept for running assessment models via open internet interface. Therefore, the model development, data storage, and model runs were all performed in Opasnet using R software and Opasnet Base. The main page of the sub-assessment is http://en.opasnet.org/w/Health_impact_of_radon_in_Europe .

Analyses

Two analyses were performed in the sub-assessment. First, the main analysis was the optimisation of the health impact across different policy options as described before. Second, a value of information analysis was performed based on the monetary impact estimates.

Value of information is a statistical method that estimates the largest sum of money a decision maker should be willing to pay to be able to reduce uncertainty in the decision before actually making the decision. The analysis is based on the idea that even if one of the options seemed to be the best based on the expected value of impact, it is possible that, due to uncertainties described in the decision model, some other option could actually be the best. The decision maker would be better off, if she could do more research, reduce the uncertainty and actually find out whether an alternative indeed turns out to be better. The beauty of value of information analysis is that it can be performed before the decision, but more importantly, before any further research is done. If the value of information analysis shows low value, the decision maker can decide now with only a low probability of regret afterwards. On the other hand, if it shows high value, the decision-maker would be better off if she postponed the actual decision and put effort in further research and analysis (assuming that such research is feasible).

Result

Results

Lung cancer cases due to radon in Europe: {{#opasnet_base_link:Op_en4715}}

• Results for the Biomass scenario are wrong and the scenario is perhaps irrelevant because biomass usage does not affect air exchange rates which this assessment is concerned with, so it should be ignored.

The impacts of European building policies on lung cancer due to indoor radon.

Result distributions

Value of information analysis results

Lung cancer cases in Europe due to indoor radon in residences (mean and 95% confidence interval).

	Year
Policy	2010	2020	2030	2050
BAU	43074 (7186-104660)	51801 (8934-129303)	58716 (9427-155621)	63718 (10407-178566)
All	NA	52660 (8892-130780)	68086 (10544-180827)	81022 (11983-235695)
Insulation	NA	NA	NA	80149 (11898-228747)
Renovation	NA	NA	NA	92783 (13365-275851)

Lung cancer DALYs in Europe due to indoor radon in residences (mean and 95% confidence interval).

	Year
Policy	2010	2020	2030	2050
BAU	358244 (54193-940205)	427824 (65203-1156026)	483005 (70921-1335438)	524154 (80215-1545369)
All	NA	433106 (65625-1140190)	562607 (78874-1614252)	663170 (89720-2045648)
Insulation	NA	NA	NA	664141 (88638-2108941)
Renovation	NA	NA	NA	773105 (107764-2392599)

Lung cancer monetary impact (based on DALYs) in Europe due to indoor radon in residences (mean and 95% confidence interval). Unit: M€

	Year
Policy	2010	2020	2030	2050
BAU	16147 (2519-42699)	19250 (3039-53378)	21770 (3121-60868)	23585 (3590-70455)
All	NA	19464 (3009-53226)	25219 (3682-71977)	29715 (3973-94741)
Insulation	NA	NA	NA	29877 (4049-93146)
Renovation	NA	NA	NA	34810 (4748-106804)

Lung cancer cases attributable to indoor radon in residences in Europe, year 2010.

Country of observation	Mean	SD
Austria	1071	1202
Belgium	885	872
Bulgaria	297	338
Switzerland	1540	2764
Cyprus	9	12
Germany	4843	4207
Denmark	373	400
Estonia	218	245
Spain	8460	18071
Finland	812	802
France	8320	12045
Greece	813	880
Hungary	1617	2161
Ireland	552	636
Italy	4952	4444
Lithuania	236	277
Luxembourg	69	64
Latvia	226	294
Malta	43	50
Netherlands	523	350
Norway	575	673
Poland	2264	2151
Portugal	1140	1214
Romania	1236	1313
Sweden	1375	1600
Slovakia	626	678
Total	43074

Conclusions

There are currently 43000 (95 % CI 7200 - 105000) lung cancer deaths per year due to indoor radon in Europe. This number is likely to increase in the future due to decreased ventilation in aim to reduce energy consumption, if other measures are not taken. It is important to maintain good air exchange even when energy saving measures are taken.

The value of information analysis shows that further information is worth about 5 to 10 billion euros. This is ten times higher than with dampness and asthma. In this assessment, all of the value goes into the most critical issue, namely the impacts of air exchange rate. However, in many cases the main problem is air leakage through the ground floor structures and lack of preventive measures (such as sub-floor ventilation), which contributes to radon entering indoor air in the first place. Therefore, focussing on air exchange only may not be the optimal solution. Instead, understanding is needed about why buildings are still often constructed in an unoptimal way and how high radon levels could be cost-effectively reduced in existing buildings.

A thorough examination of current knowledge was not done in this assessment. It seems obvious that collecting and organising existing information is a cost-effective way to reduce uncertainty in this issue, because that would cost only a small fraction of the value of that information according to this assessment.

Radon is invisible and odourless gas and impossible to detect with senses. It is technically easy to measure and it could therefore be routinely measured in all apartments. However, the building owners are typically unaware of the problem and may therefore ignore it. For example, in Finland, where radon concentrations are relatively high, majority of residents are unaware of the radon levels of their place of residence (Turunen et al. 2010 http://www.ehjournal.net/content/9/1/69). Even if the problem is acknowledged, there may be insufficient expertise or resources to repair the problem. Further on, the building industry has varying expertise to build radon-effective buildings.

In conclusion, more should be known about reasons why and how radon-effective building construction and maintenance practices are or are not implemented. Radon is an important environmental health issue that should be considered when climate-friendly building policies (especially those that affect indoor air conditions) are implemnented.

R code for detailed analysis

• This code features R functions described on Opasnet Base Connection for R and Operating intelligently with multidimensional arrays in R.

DALY calculations

Disability weight for lung cancer (DALY): + Show code- Hide code library(OpasnetUtils) library(ggplot2) cancer <- opbase.data("Op_en4715") #, exclude = 48823) if(DALY=='Default'){ DALY <- opbase.data("Op_en2307") DALY <- DALY[DALY$Diagnosis=="Cancers -- Preterminal, Trachea, bronchus and lung",] DALY <- DALY[DALY[,3]=="60+",5]} n <- max(cancer$obs) YLL <- runif(n,1,15) YLD <- runif(n,2,36)/12*DALY cancer[,8] <- cancer[,8]*(YLL+YLD) meandaly <- tapply(cancer$Result,cancer[, c("Country", "Policy", "Year")], mean) meandaly <- as.data.frame(as.table(meandaly)) meandaly <- tapply(meandaly$Freq, meandaly[, c("Policy", "Year")], sum) meandaly plot1 <- as.data.frame(as.table(meandaly)) plot1 <- ggplot(plot1[plot1[,"Freq"]!=0,], aes(Year, weight=Freq, fill=Policy)) + geom_bar(position="dodge") + scale_x_discrete("Year") + scale_y_continuous("DALYs in 2050") plot1 cancer <- cancer[cancer$Year=="2050",] cancer <- tapply(cancer$Result, cancer[, c("obs", "Policy")], sum) cancer <- as.data.frame(as.table(cancer)) cancer <- cancer[is.na(cancer[,"Freq"])==FALSE,] plot4 <- ggplot(cancer, aes(x=Freq, y=..density.., fill=Policy)) + geom_density(adjust=4) + scale_x_continuous(expression("DALYs"), limits=c(-1000000, 5000000)) + scale_y_continuous("Density") plot4

Full model

+ Show code- Hide code library(OpasnetBaseUtils) library(ggplot2) cancer <- op_baseGetData("opasnet_base", "Op_en4715", exclude = 48823) array <- DataframeToArray(cancer) array <- array[,,,c(2,1,3,4),,] ##### Cases ##### means <- apply(array, c(2,3,4), mean, na.rm=TRUE) means <- apply(means, c(2,3), sum, na.rm=TRUE) plot1 <- as.data.frame(as.table(means)) plot1 <- ggplot(plot1[plot1[,"Freq"]!=0,], aes(Year, weight=Freq, fill=Policy)) + geom_bar(position="dodge") + scale_x_discrete("Year") + scale_y_continuous("Cases") plot1 ci <- apply(apply(array, c(1,3,4), sum, na.rm=TRUE), c(2,3), quantile, probs=c(0.025,0.975)) final1 <- means final1[,] <- paste(round(means), " (", round(ci[1,,]), "-", round(ci[2,,]), ")", sep="") final1[c(2:4,7:8,11:12)] <- NA final1 ##### DALYs ##### DALY <- array(NA, dim = c(dim(array), 3), dimnames = dimnames(array)) DALY[,,,,1] <- array DALY[,,,,2] <- runif(dim(array)[1],2,36)/12 DALY[,,,,3] <- runif(dim(array)[1],1,15) DALY <- DALY[,,,,1]*0.146*DALY[,,,,2]+DALY[,,,,1]*DALY[,,,,3] means <- apply(DALY, c(2,3,4), mean, na.rm=TRUE) means <- apply(means, c(2,3), sum, na.rm=TRUE) plot2 <- as.data.frame(as.table(means)) plot2 <- ggplot(plot2[plot2[,"Freq"]!=0,], aes(Year, weight=Freq, fill=Policy)) + geom_bar(position="dodge") + scale_x_discrete("Year") + scale_y_continuous("DALYs") plot2 ci <- apply(apply(DALY, c(1,3,4), sum, na.rm=TRUE), c(2,3), quantile, probs=c(0.025,0.975)) final2 <- means final2[,] <- paste(round(means), " (", round(ci[1,,]), "-", round(ci[2,,]), ")", sep="") final2[c(2:4,7:8,11:12)] <- NA final2 ##### Cost ##### mpdaly <- op_baseGetData("opasnet_base", "Op_en4858") cost <- IntArray(mpdaly, DALY, "DALYs") cost <- data.frame(cost[,c("obs","Country","Policy","Year")], Result=cost[,"Result"]*cost[,"DALYs"]) cost <- DataframeToArray(cost) cost <- cost[,,c(2,1,3,4),] means <- apply(cost, c(2,3,4), mean, na.rm=TRUE) means <- apply(means, c(2,3), sum, na.rm=TRUE)/10^6 plot3 <- as.data.frame(as.table(means)) plot3 <- ggplot(plot3[plot3[,"Freq"]!=0,], aes(Year, weight=Freq, fill=Policy)) + geom_bar(position="dodge") + scale_x_discrete("Year") + scale_y_continuous("Cost (M€)") plot3 ci <- apply(apply(cost, c(1,3,4), sum, na.rm=TRUE), c(2,3), quantile, probs=c(0.025,0.975))/10^6 final3 <- means final3[,] <- paste(round(means), " (", round(ci[1,,]), "-", round(ci[2,,]), ")", sep="") final3[c(2:4,7:8,11:12)] <- NA final3 ##### Probability density plot ##### costdf <- as.data.frame(as.table(apply(cost, c(1,3,4), sum)/1e9)) costdf <- costdf[is.na(costdf[,"Freq"])==FALSE,] plot4 <- ggplot(costdf, aes(x=Freq, y=..density.., fill=Policy)) + geom_density(alpha=0.2, adjust=4) + scale_x_continuous(expression("Cost ("*10^9*"€)"), limits = c(-50,300)) + scale_y_continuous("Density") + facet_wrap(~Year) plot4 ##### Expected Value of Perfect Information ##### evpi <- (apply(apply(cost, c(2,3,4), mean, na.rm=TRUE), c(1,3), min, na.rm=TRUE) - apply(apply(cost, c(1,2,4), min, na.rm=TRUE), c(2,3), mean, na.rm=TRUE))/1e6 plot5 <- as.data.frame(as.table(apply(evpi, 2, sum))) plot5 <- ggplot(plot5, aes(Var1, weight=Freq)) + geom_bar(position="dodge") + scale_x_discrete("Year") + scale_y_continuous("Value of perfect information (M€)") plot5 ##### Expected Value of Partial Perfect Information ##### #Same as that of perfect information, because of only one decision-dependent uncertain variable ae <- op_baseGetData("opasnet_base", "Erac2499") aer <- DataframeToArray(ae) aer <- aer[,,c(2,1,4,5),] dropnonmax <- function(x) { x[x<max(x, na.rm = TRUE)] <- NA return(x) } aer <- apply(aer, c(1,2,4), dropnonmax) aer <- as.data.frame(as.table(aer)) aer <- aer[,c(2,3,1,4,5)] colnames(aer)[3] <- "Policy" aer <- aer[is.na(aer[,"Freq"])==FALSE,] aer <- IntArray(aer, cost, "Cost") aer <- DataframeToArray(aer[,c("obs","Country","Year","Cost")],"Cost") test2 <- (apply(apply(cost, c(2,3,4), mean, na.rm=TRUE), c(1,3), min, na.rm=TRUE) - apply(aer, c(2,3), mean))/1e6 plot6 <- as.data.frame(as.table(apply(test2, 2, sum))) plot6 <- ggplot(plot6, aes(Var1, weight=Freq)) + geom_bar(position="dodge") + scale_x_discrete("Year") + scale_y_continuous("Value of perfect information (M€)") plot6 test2==evpi #Test if the values are the same test2-evpi # It seems that for some reason aer evppi is slightly smaller than evpi, # although it should be the only variable that varies in the "Policy" dimension

See also

• Radon
• ERF for long-term indoor exposure to radon and lung cancer
• An Overview of Radon Surveys in Europe. Joint Research Centre, 2005. ISBN 92-79-01066-2
• In Heande (password-protected)
• heande:HI:Radon Indoor Air Case
  • Indoor air quality & its impact on man
  • Radon
  • Indoor air
  • Radon sisäilma annos-vaste
  • Radon sisäilma altistus Suomi
  • Radon ja pitkäikäiset nuklidit porakaivo
  • Radon ja pitkäikäiset nuklidit porakaivo
  • Radon ja pitkäikäiset nuklidit porakaivovedessä

Keywords

Radon, indoor air, lung cancer, Europe

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