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Attribution of disease (and injury) to causes: binary harmful exposures

Attribution of disease (and injury) to causes: binary harmful exposures. John Powles Jwp11@cam.ac.uk. Political salience of attribution claims. Trends in death-certification rates for liver cirrhosis, 1950 -2000 Source: Leon et al Lancet, 2006, 367: 52-6. What is actually being claimed?

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Attribution of disease (and injury) to causes: binary harmful exposures

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  1. Attribution of disease (and injury) to causes: binary harmful exposures John Powles Jwp11@cam.ac.uk

  2. Political salience of attribution claims

  3. Trends in death-certification rates for liver cirrhosis, 1950 -2000 Source: Leon et al Lancet, 2006, 367: 52-6

  4. What is actually being claimed? How might one arrive at estimates such as these? What ‘inputs’ would be needed? What assumptions might need to be made?

  5. Attribution of disease or injury to exposure • Categorical ie ‘by definition’ • Eg 100% of ‘alcoholic liver disease’ is attributed to alcohol • Quantitative

  6. Quantitative attribution:Starting with the simplest case • Harmful exposure • Two exposure levels • Exposed • Non-exposed

  7. Existing knowledge of exposure/risk associations (RRs) for relevant diseases Distribution of exposure in the population (prop’n exposed) Attributable risk calculation

  8. Existing knowledge of exposure/risk associations (RRs) for relevant diseases Distribution of exposure in the population (prop’n exposed) Attributable risk calculation Proportion of diseases (and injuries) attributable

  9. Existing knowledge of exposure/risk associations (RRs) for relevant diseases Distribution of exposure in the population (prop’n exposed) Attributable risk calculation Proportion of diseases (and injuries) attributable Proportion of treatment costs attributable

  10. Existing knowledge of exposure/risk associations (RRs) for relevant diseases Distribution of exposure in the population (prop’n exposed) Attributable risk calculation Proportion of diseases (and injuries) attributable Proportion of treatment costs attributable Compared to what?

  11. What is actually being claimed? How might one arrive at estimates such as these? What ‘inputs’ would be needed? What assumptions might need to be made?

  12. Definitions of attributable risk General idea (at the individual level) The expected reduction in risk from a sustained shift to a (specified) more favourable exposure level So, for a harmful, binary exposure (eg smoking vs not smoking) The expected reduction in risk if not exposed.

  13. Symmetrical with the counterfactual theory of causation Causation: ‘This lung cancer was caused by smoking’ Under the counterfactual circumstance of this individual never having smoked, this lung cancer would not have occurred (or would have occurred later) Attribution: Under the counterfactual circumstance of this individual never having smoked we would expect his or her risk of lung cancer to be reduced by eg 95%

  14. In (exposed) individuals The amount by which we would expect the risk of the disease(s) of interest to be reduced under the more favourable counterfactual exposure level Usually expressed as a fraction of the risk at the less favourable exposure level eg The fraction by which we would expect the lung cancer risk in smokers to be reduced if (counterfactually) they had never smoked (‘Attributable fraction in the exposed’) Note heuristic / hypothetical nature of attribution claims

  15. Attributable fraction in the exposed (AF) (uncon-founded) risk in smokers (RR) For a harmful binary exposure (eg smoking vs non-smoking) Taking risk in non-exposed as 1 unit of risk (unconfounded) risk in exposed = (RR x 1) units of risk Attributable risk in exposed = RR-1 units of risk Attributable fraction in the exposed Attrib to smoking risk in non-smokers (=1) Not attrib to smoking

  16. In populations – the population attributable fraction (PAF) The fraction by which the incidence of the disease(s) of interest would be expected to be reduced under the more favourable counterfactual exposure distribution Usually expressed as a fraction of the risk at the less favourable exposure distribution eg (For a harmful binary exposure such as smoking/non-smoking) The fraction of lung cancer incidence that would be avoided if (counterfactually) the prevalence of smoking was 0%

  17. Calculating PAF: As a fraction of a fraction Fraction of cases in smokers attribut-able to smoking For a harmful binary exposure (eg smoking vs non-smoking) Population Attributable Fraction (PAF) = fraction of all cases occurring in the exposed x attributable fraction in the exposed So If 95% of lung cancer occurs among smokers and 95% of the cases in smokers are attributable to smoking, then PAF = 95% of 95% = 90% (approx) Ie 90% of the cases in the total population are attributable to smoking Attrib to smoking Fraction of cases occurring in smokers Not attrib. to smoking

  18. Calculating PAF: formula – graphical representation Risk (scaled to 1 in unexposed) RR 1 p 1-p 1 Proportion exposed (population size scaled to 1)

  19. Protective exposures (RR<1): 2 approaches 1. Use formula for ‘PAF’ Result will be negative. Can be used to estimate ‘prevented cases’ (even though it is a ratio but no longer interpretable as a fraction*) * Because the numerator (prevented cases) is no longer included in the denominator (actual cases)

  20. Protective exposures (RR<1): 2 approaches 2. Calculate ‘prevented fraction’ Uses the larger risk (or number of cases) that would have occurred in the absence of the exposure as the denominator Interpretable as a fraction May be calculated for the exposed And as Population prevented fraction

  21. Prevented fraction in the exposed Eg Prevented fraction of heart attacks among frequent light drinkers (RR=0.75) = 1 - RR = 0.25 Interpretation Frequent light drinkers have their risk of heart attack reduced by ¼ compared to what it would be if they were non-drinkers

  22. Population prevented fraction Risk (scaled to 1 in unexposed) 1 RR Eg if 33% of the population are moderate drinkers with an RR for death from IHD of 0.75, then the PPF is about 8% 1-p 1 p Proportion exposed

  23. Contexts of risk attribution • In a (single) epidemiological study What do these findings imply in terms of cases attributable to the exposure (Most common exposition in epi texts) • Public health assessment What does the totality of current evidence on the exposure/risk relationship imply in terms of cases attributable to the exposure

  24. Summary • Attribution claims are often at the centre of public health policy discussions • Such claims involve a comparison with an alternative (‘counterfactual’) level of exposure (and involve hypothetical reasoning – what the risk would have been if...) • Risk may be attributed categorically or quantitatively • The AF in the exposed is RR-1/RR • PAFs can be calculated as ‘fractions of fractions’ (AF x fraction occurring in exposed) or by formula • The formula can be recalled via a simple graphical representation of attributable and total cases. • Protective exposures need modified approach

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