Talk:Attributable risk

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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.

• 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.

Structured argumentation

How to read discussions

Fact discussion: .
Opening statement: Attributable fraction should be used in assessing a disease fraction caused by air pollution. It is calculated with the formula AR = (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: Attributable 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: ⇤--#: . Attributable 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) ←--#: . By using AR to estimate the number of premature deaths attributable to air pollution it is implicitly assumed that the ‘etiologic fraction’ is identical to the ‘attributable fraction’.V1 --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ←--#: . Etiologic fraction should be used instead. --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: defence) ←--#: . 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) ----#: . 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) ←--#: . 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) ----#: . 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 AR 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) ←--#: . Attributable 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) ⇤--#: . 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) ----#: . 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) ⇤--#: . 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) ----#: . Attributable fraction is rejected unless this fairly weak attack is accepted. --Jouni (talk) 14:45, 23 March 2016 (UTC) (type: truth; paradigms: science: comment) ←--#: . 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) ----#: . Is this the same as attributable fraction? --Heta (talk) 12:31, 16 March 2016 (UTC) (type: truth; paradigms: science: comment) ⇤--#: . 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) ⇤--#: . 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) ----#: . 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) ⇤--#: . 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) ⇤--#: . 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) ⇤--#: . 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) ←--#: . 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) ←--#: . 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) ----#: . 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)