Talk:Attributable risk

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Scientific disputes possibly related to attributable risk

Arch Toxicol 2009

• Slama et al (2007) Environ Health Perspect 115(9):1283-1292.
• Morfeld P (2009) Arch Toxicol 83:105-106.
• Slama et al (2009) Arch Toxicol 83:293-295.
• A plea for rigorous and honest science: false positive findings and biased presentations in epidemiological studies - Springer.[1],accessed 2016-05-12.
• Comment on Slama R, Cyrys J, Herbarth O, Wichmann H-E, Heinrich J. (2009) A further plea for rigorous science and explicit disclosure of potential conflicts of interest. Morfeld P. (2009) A plea for rigorous and honest science—false positive findings and biased presentations in epidemiological studies. Archives of Toxicology 83:105–106 - Springer.[2],accessed 2016-05-12.
• Comment on Slama R, Cyrys J, Herbarth O, Wichmann H-E, Heinrich J. saying: “The authors did not wish to reply, given Dr. Morfeld’s persistence in refusing to fill in the conflict of interest statement and in misleadingly quoting parts of the sentences of our publications” - Springer.[3],accessed 2016-05-12.

  ⇤--#: . A dispute about choosing new endpoints based on non-significance of analyses planned a priori. Not relevant discussion for attributable risk. --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: attack)

Inhalation Toxicology 2013

• (2013-01-01) "Reply to the “Letter to the Editor” by Morfeld et al.". Inhalation Toxicology 25 (1): 65–65. doi:10.3109/08958378.2012.753492. ISSN 0895-8378. Retrieved on 2016-05-12. 

  ⇤--#: . Dispute about microscopical slicing techniques of nanoparticles. Not relevant discussion for attributable risk. --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: attack)

Arch Toxicol 2013

• Commentary to Gebel 2012: a quantitative review should apply meta-analytical methods - Springer.[4],accessed 2016-05-12.

  ⇤--#: . A dispute about meta-analytic methods. Not relevant discussion for attributable risk. --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: attack)

J Occup Environ Med 2015

• Morfeld, Peter (2015-02). "Buchanich et al (2014): The ecologic fallacy may have severely biased the findings". Journal of Occupational and Environmental Medicine / American College of Occupational and Environmental Medicine 57 (2): –13. doi:10.1097/JOM.0000000000000381. ISSN 1536-5948. PMID 25654527. 
• (2015-02) "Response to Morfeld:". Journal of Occupational and Environmental Medicine 57 (2): –13-e14. doi:10.1097/JOM.0000000000000397. ISSN 1076-2752. Retrieved on 2016-05-12. 

  ⇤--#: . A dispute about interpreting the possibility of ecologic fallacy. Not relevant discussion for attributable risk. --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: attack)

J Occup Environ Med 2016

• Dell LD et al (2015) J Occup Environ Med. 57;984-997.
• Morfeld P
• (2016-01) "Authors' Response to Dr. Morfeld". Journal of Occupational and Environmental Medicine / American College of Occupational and Environmental Medicine 58 (1): –23. doi:10.1097/JOM.0000000000000618. ISSN 1536-5948. PMID 26716858. 

  ⇤--#: . Dispute about multiple testing and consequent false positives. Not relevant discussion for attributable risk. --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: attack)

Particle and Fiber Toxicology 2016

• Hartwig A (2014) MAK Value Documentation 2012, 2014.
• Morfeld et al (2015) Part Fibre Toxicol 12(1):3.
• Hartwig a (Part Fibre Toxicol 2015
• (2016-01-08) "Response to the Reply on behalf of the ‘Permanent Senate Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area’ (MAK Commission) by Andrea Hartwig Karlsruhe Institute of Technology (KIT)". Particle and Fibre Toxicology 13. doi:10.1186/s12989-015-0112-6. ISSN 1743-8977. PMID 26746196. Retrieved on 2016-05-12. 

  ⇤--#: . A dispute about extrapolating particle deposition from rats to humans. Not relevant for attributable fraction. --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: attack)

Int Arch Occup Environ Health 2016

• Möhner M (2015) Int Arch Occup Environ Health.
• Morfeld P (2015) Int Arch Occup Environ Health.
• Möhner, Matthias (2016-02-22). "Response to the letter to the editor from Morfeld". International Archives of Occupational and Environmental Health: 1–2. doi:10.1007/s00420-016-1121-y. ISSN 1432-1246 0340-0131, 1432-1246. Retrieved on 2016-05-12. 

  ----#: . Dispute about biases estimating SMR. May be relevant for attributable risk. The original paper and Morfeld's comment should be studied. --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: comment)

Int J Public Health 2016

• Morfeld, Peter (2016-04-26). "Quantifying the health impacts of ambient air pollutants: methodological errors must be avoided". International Journal of Public Health. doi:10.1007/s00038-015-0766-8. ISSN 1661-8564. PMID 27117686. 
• Héroux et al. Response to “Quantifying the health impacts of ambient air pollutants: methodological errors must be avoided”. International Journal of Public Health, pp 1-2.First online: 26 April 2016 doi:10.1007/s00038-016-0808-x [5]

  ←--#: . This is clearly relevant. Morfeld argues that excess cases do not equal premature cases. "Lim and colleagues (Lim et al 2012) relied exclusively on excess case statistics which do not allow to 'calculate the proportion of deaths or disease burden caused by specific risk factors'. Calculations of years of life lost due to exposure potientially suffer from similar problems (Morfeld 2004)." --Jouni (talk) 13:13, 13 May 2016 (UTC) (type: truth; paradigms: science: defence)

Response to Morfeld and Erren Int J Public Health

Marie-Eve Héroux, Bert Brunekreef, H. Ross Anderson, Richard Atkinson, Aaron Cohen, Francesco Forastiere, Fintan Hurley, Klea Katsouyanni, Daniel Krewski, Michal Krzyzanowski, Nino Künzli, Inga Mills, Xavier Querol, Bart Ostro, Heather Walton. Response to “Quantifying the health impacts of ambient air pollutants: methodological errors must be avoided”. International Journal of Public Health, pp 1-2.First online: 26 April 2016 doi:10.1007/s00038-016-0808-x [6]

Letter to the Editor

Response to “Quantifying the health impacts of ambient air pollutants: methodological errors must be avoided”

We thank Morfeld and Erren for their interest in our recent publication on “Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project” (Héroux et al. 2015). Morfeld and Erren claim that there are potential problems with the statistical approach used in our paper to measure the impact on mortality from air pollution. In fact, they state that “Greenland showed that a calculation based on RR estimates, as performed in the EU research project, does estimate excess cases numbers—but it does not estimate the number of premature cases or etiological cases” (Greenland 1999).

Close reading of the Greenland (1999) paper reveals that he distinguishes three categories of cases occurring in the exposed, observed over a certain period of time: A0, cases which would have occurred anyway even in the absence of exposure—these would typically be estimated from the number of cases occurring in an unexposed control population; A1, cases that would have occurred anyway but were accelerated by exposure; and A2, cases which would not have occurred, ever, without exposure. The word ‘premature’ does not exist in Greenland’s paper, but we consider ‘premature’ and ‘accelerated’ to be the same here. What we usually call the attributable fraction among the exposed is equivalent to the attributable risk (RR−1)/RR which in Greenland’s paper is denoted as the etiologic fraction, (A1 + A2)/(A0 + A1 + A2). And then, etiologic cases are A1 + A2, and excess cases are A2. So, contrary to what Morfield and Erren write, the calculation as performed in our paper estimates etiologic cases (if we follow Greenland’s notation) and not excess cases. After all, in our epidemiology we cannot easily distinguish the excess cases from the accelerated cases.

But let us now take this one step further. Really, the distinction between excess cases and accelerated cases only makes sense for morbidity endpoints or for cause-specific mortality. One can envisage that some of the smokers who developed heart disease over some period of time would have developed it anyway, even in the absence of smoking, after the period of observation. We can only estimate this number A1 when we have observations of heart disease incidence in controls over a more extended period of time. Similarly, some of the smokers dying from heart disease during the period of observation might have died from heart disease anyway, but after a longer period of time. Note that the excess deaths due to heart disease A2, which would never have occurred in the smokers if they had not smoked, necessarily need to be compensated among the controls by an increase in deaths due to some other cause, as in the end, everyone dies. But for total mortality—which is where the bulk of our project’s burden estimates are based on—there are no excess cases (everybody dies in the end); so the estimates based on RR actually correctly estimate the ‘accelerated’ = ‘premature’ cases because the etiologic cases are now equivalent to the accelerated cases, in the absence of excess cases.

Interestingly, this was already described by Greenland in his example of total mortality among the A bomb survivors: “One might object that the extreme structure just described is unrealistic. In reality, however, this extremity is exactly what one should expect if the outcome under study is total mortality in a cohort followed for its entire lifetime, such as the cohort of atomic bomb survivors in Japan. Here, everyone experiences the outcome (death), so there are no “all-or-none” cases, yet everyone may also experience damage and consequent loss of years of life (even if only minor and stress related) owing to the exposure.”

This is exactly the point made by Brunekreef et al. (2007) and we note that this paper was literally and favorably quoted in a paper mentioned in support of the letter (Erren and Morfeld 2011).

The final point to stress here is that the RRs for total mortality and air pollution in our project were all derived from cohort studies in which the denominator for the number of observed cases is not the number of persons exposed or unexposed, but the person years of observation. This is, of course, for the precise reason mentioned by Greenland: if one follows a cohort until extinction, the proportion of deaths is 1 in the exposed and the unexposed alike. The RRs used in our project therefore essentially estimate the ratio of life expectancies in exposed vs. unexposed over the observation period, as the period of observation is censored at time of death and thus shorter among the exposed (who die sooner) than among the unexposed. When applied to a life table, as some of us have shown already many years ago (Brunekreef 1997; Miller and Hurley 2003), one estimates years of life lost, a major component of the Disability-Adjusted Life Years or DALYs which form the core of the GBD analyses which Morfield and Erren also disqualify as an ‘error’. As is well known, the GBD estimates are also expressed as numbers of deaths attributed to certain risk factors, and these are typically denoted as ‘premature’ deaths precisely because there is no such thing as avoidable or excess deaths when it comes to total mortality.

Therefore, in contrast to Morfeld and Erren’s assertion, our project recommendations do properly take into account methodological considerations with respect to quantification of mortality impacts of air pollution.

References

• Brunekreef B (1997) Air pollution and life expectancy: is there a relation? Occup Environ Med 54:781–784. doi:10.​1136/​oem.​54.​11.​781
• Brunekreef B, Miller BG, Hurley JF (2007) The brave new world of lives sacrificed and saved, deaths attributed and avoided. Epidemiology 18(6):785–788
• Erren TC, Morfeld P (2011) Attributing the burden of cancer at work: three areas of concern when examining the example of shift-work. Epidemiol Perspect Innov 8:4. [7]
• Greenland S (1999) Relation of probability of causation to relative risk and doubling dose: a methodologic error that has become a social problem. Am J Public Health 89:1166–1169
• Héroux ME et al (2015) Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project. Int J Public Health 60:619–6272 doi:10.1007/s00038-015-0690-y [8]
• Miller B, Hurley JF (2003) Life table methods for quantitative impact assessments in chronic mortality. J Epidemiol Community Health 57:200–206. doi:10.​1136/​jech.​57.​3.​200

Copyright information © The Author(s) 2016

This is an open access article distributed under the terms of the Creative Commons Attribution IGO License ([9]), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any reproduction of this article there should not be any suggestion that WHO or this article endorse any specific organization or products. The use of the WHO logo is not permitted. This notice should be preserved along with the article’s original URL.

Greenland 1999

Greenland S. Relation of probability of causation to rellative risk and doubling dose: a methologic error that has become a social problem. Am J Public Health 1999 (89) 8: 1166-69.

"When an effect of exposure is to accelerate the time at which disease occurs, the rate fraction RF = (IR - 1)/IR will tend to understate the probability of causation because it does not fully account for the acceleration of disease occurrence. In particular, and contrary to common perceptions, a rate fraction of 50% (or, equivalently, a rate ratio of 2) does not correspond to a 50% probability of causation. This discrepancy between the rate fraction and the probability of causation has been overlooked by various experts in the legal as well as the scientific community, even though it undermines the rationale for a number of current legal standards. Furthermore, we should expect this discrepancy to vary with background risk factors, so that any global assessment of the discrepancy cannot provide assurances about the discrepancies within subgroups."

----#: . Interestingly, Greenland is worried that probability of causation is underestimated when using rate fraction. --Jouni (talk) 08:34, 13 May 2016 (UTC) (type: truth; paradigms: science: comment)

Choosing the right fraction

How to read discussions

Fact discussion: .
Opening statement: Excess fraction should be used in assessing a disease fraction caused by air pollution. It is calculated with the formula XF = (RR-1)/RR, where RR is the risk ratio (risk with exposure divided by risk without exposure). The alternative is to calculate etiologic fraction EF. It cannot be estimated directly from RR, but it is always between (RR-1)/[RRRR/(RR-1)] and 1. Closing statement: Excess fraction should be used to calculate health impacts when the interest is on population impact in two counterfactual exposure situations. In contrast, etiologic fraction should be used when the interest is either probability of causation (in e.g. legal cases) or fraction of cases advanced in time due to exposure (i.e., premature cases). (Resolved, i.e., a closing statement has been found and updated to the main page.)
Argumentation: ⇤--#1: . Excess fraction cannot be used to estimate probability of causation or fraction of cases advanced in time due to exposure (i.e., premature cases). --Jouni (talk) 14:45, 23 March 2016 (UTC) (type: truth; paradigms: science: attack) ←--#2: . By using excess fraction to estimate the number of premature deaths attributable to air pollution it is implicitly assumed that the ‘etiologic fraction’ is identical to the ‘excess fraction’.V1 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ←--#3: . Etiologic fraction should be used instead. --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ←--#4: . In the absence of a biological model of the disease process, although the exact value of the etiologic fraction cannot be computed, bounds on the number of premature deaths attributable to exposure can be determined.V3 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ----#5: . Estimation of the etiologic fraction is fraught with difficulty. Typically it cannot be identified without invoking strong biological assumptions.V2 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: comment) ←--#6: . Robins and Greenland (1989) proposed replacing (RR-1)/RR by a factor f, and proved that f is bounded by (RR-1)/[RRRR/(RR-1)] and 1. V4 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ----#7: . At the levels of RR typical of ambient air pollution the largest possible upward bias in an estimate of the etiologic fraction derived using the excess fraction equation in lieu of f would be a factor of between 2 and 2.5.V5 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: comment) ←--#8: . Excess fraction can be used to estimate population impact (burden of disease) in two counterfactual exposure situations. --Jouni (talk) 14:45, 23 March 2016 (UTC) (type: truth; paradigms: science: defence) ⇤--#9: . The validity of the estimates of excess deaths developed using formula (1) may be compromised by the use of RR values which have been adjusted for confounders through the use of Cox proportional hazards models.V8 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: attack) ----#10: . Does this mean that RR values should NOT have adjusted? This needs more scrutiny. --Jouni (talk) 07:00, 24 March 2016 (UTC) (type: truth; paradigms: science: comment) ⇤--#11: . Anything in science may be wrong and compromised. Therefore, more specific argument must be provided why this particular approach is more likely to be compromised. --Jouni (talk) 07:00, 24 March 2016 (UTC) (type: truth; paradigms: science: attack) ----#12: . Excess fraction is rejected unless this fairly weak attack is accepted. --Jouni (talk) 14:45, 23 March 2016 (UTC) (type: truth; paradigms: science: comment) ←--#13: . The excess fraction can be estimated using the formula without invoking strong biological assumptions.V6 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ----#14: . Is this the same as attributable fraction? --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: comment) ⇤--#15: . All members of a closed sub-cohort being 70 years old in 2010 (birth cohort, all born in 1940) will have died until 2050, even if all air pollution exposures would have been eliminated. Thus, the percentage of deaths until 2050 is 100% in this birth cohort even if there were no air pollution. According to formula (1), however, excess deaths will occur in this sub-cohort if exposed to air pollution in every year between 2010 and 2050. It follows that more people will die in the exposed birth cohort according to formula (1) between 2010 and 2050 than have ever lived in 2010. V12 --Heta (talk) 12:26, 17 March 2016 (UTC) (type: truth; paradigms: science: attack) ⇤--#16: . This argument is true, but potentially misleading. This paradoxical result would only occur if the approach was applied to calculate “excess deaths” in each year between 2010 and 2050 without properly adjusting the age distribution to reflect the reduced mortality at younger ages expected to flow from improvements in air quality. V14,V15 --Heta (talk) 12:26, 17 March 2016 (UTC) (type: truth; paradigms: science: attack) ----#17: . If the results in Lelieveld et al. 2015 had been characterized as ‘excess deaths’ instead of as ‘premature deaths attributable to air pollution’ much of the confusion that led to concern about our use of the formula could have been avoided.V7 --Heta (talk) 12:40, 16 March 2016 (UTC) (type: truth; paradigms: science: comment) ⇤--#18: . It would be preferable to report the results using an outcome measure, such as change in life expectancy, which explicitly reflects the impact of pollution on the timing of death.V9 --Heta (talk) 12:54, 16 March 2016 (UTC) (type: truth; paradigms: science: attack) ⇤--#19: . Which health indicator to choose is a related issue but not relevant in this argumentation, where the number of cases related to exposure has already been chosen as the indicator. --Jouni (talk) 14:45, 23 March 2016 (UTC) (type: truth; paradigms: science: attack) ⇤--#20: . Simple outcome measures, like ‘excess deaths,’ are commonly used in environmental policy analysis – where the societal benefits of various policies are estimated as the product of the value of statistical life and the number of ‘lives saved (NAS, 2002).’V11 --Heta (talk) 12:54, 16 March 2016 (UTC) (type: truth; paradigms: science: attack) ←--#21: . By doing so it might have avoided, to a large extent, the issues inherent in the interpretation of ‘premature deaths attributable to air pollution.’V10 --Heta (talk) 12:54, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ←--#22: . Effect measures which recognize the inevitability of death, such as “reduction in life expectancy” or “disability adjusted life years lost,” are clearly more readily interpretable. V13 --Heta (talk) 12:26, 17 March 2016 (UTC) (type: truth; paradigms: science: defence) ----#23: . For many purposes, measures such as "years of life lost“, "quality- (or disability-) adjusted years of life lost" are preferable.V16 --Heta (talk) 12:26, 17 March 2016 (UTC) (type: truth; paradigms: science: comment)

Meaning of premature

How to read discussions

Fact discussion: .
Opening statement: Premature mortality is about any death that is advanced in time because of a particular exposure. Similarly, premature case is a case that would occur later or not at all if there was no exposure. Closing statement: There are different schools here and there can be two different interpretations. The first is according to the statement. The second says that a death must be advanced substantially to be denoted as premature. (Resolved, i.e., a closing statement has been found and updated to the main page.)
Argumentation: ←--#: . This concept is important in e.g. the court, and therefore it must have a clear name. Premature is a good descriptive term for that. --Jouni (talk) 07:59, 8 April 2016 (UTC) (type: truth; paradigms: science: defence) ⇤--#: . Premature mortality should be used only about deaths that are advanced in time substantially. A few day's difference is not important. --Jouni (talk) 07:59, 8 April 2016 (UTC) (type: truth; paradigms: science: attack) ⇤--#: . Premature deaths is already widely used about all premature deaths, and people using the word for one meaning cannot prevent the usage for another meaning. --Jouni (talk) 07:59, 8 April 2016 (UTC) (type: truth; paradigms: science: attack) ⇤--#: . It should be noted that in e.g. air pollution literature, premature death means a substantial advancement of death. Air pollutants may cause deaths in terminal patients that would have died anyway within a few days. So there are at least two different interpretations. --Jouni (talk) 07:59, 8 April 2016 (UTC) (type: truth; paradigms: science: attack)

Abstract to ISEE, Rome 2016

Discussion rules as a method to resolve scientific disputes

Jouni T. Tuomisto, John S. Evans, Arja Asikainen, Pauli Ordén

Introduction: In the science-policy interface, we need better tools to synthesise discussions. We tested whether freely expressed discussions can be synthesised into resolutions using a few simple rules. We aimed at understanding key issues, not at mutual agreement of participants.

Methods: We studied two case studies about controversial topics and reorganised the information produced by participants. The topic was defined as research questions, and all content was evaluated against capability to answer the questions. The content was summarised into statments and, if possible, organised hierarchically so that statements attacked or defended one another. Statements not backed up by data were given little weight.

Results: The first case was a scientific dispute about how to estimate attributable deaths of air pollution in Lelieveld et al., Nature 2015: 525(7569):367-71. Discussion between the authors and critics was reorganised to identify and clarify the essence of the dispute. The information structure produced by the rules showed that the main dispute was about whether excess fraction or etiologic fraction should have been used. In the second case, we reorganised open web discussion about security risks caused by irregular immigrants in Finland in 2015. The discussion was held on a website coinciding with a national TV discussion. Most participants talked about their personal experience, but a few provided links to scientific studies and statistics, providing material for evidence-based discussion almost real-time.

Conclusions: Disputes about even heated and controversial topics can be clarified, understood or even resolved by using a set of rules for participation and information synthesis. Complex topics, openness, or large number of lay people participation did not hamper the process. Such rules should be tested in resolving scientific disputes on a large scale. If successful, the use of science in the society could benefit from practices of open collaboration.

• Structured discussion on attributable risk of air pollution
• Primary topic: Health impact assessment and participatory epidemiology
• Secondary topic: Policy and public health
• Presentation type: Oral or poster, no preference
• Do the findings in this presentation, when combined with previous evidence, support new policy?
  • Yes. Open collaboration and structured discussions could be used in resolving scientific disputes and improving the use of scientific information in the society.
• No financial conflicts of interest to declare
• All funding and employment resources:
  • Tuomisto JT, Asikainen A were employed full time by National Institute for Health and Welfare (THL). They and Ordén P were also funded by VN-TEAS-funded project Yhtäköyttä from the Prime Minister's Office, Finland.
  • Evans JS was funded by Harvard School of Public Health and Cyprus University of Technology.