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Ioannis Karagiannis

Measures of Impact 18 th EPIET/EUPHEM Introductory Course September-October 2012 Lazareto, Menorca, Spain. Ioannis Karagiannis. Objectives. To define measures of impact To calculate the attributable risk among the exposed in the population Eventually, make sense of stuff. Scenario.

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Ioannis Karagiannis

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  1. Measures of Impact18th EPIET/EUPHEM Introductory CourseSeptember-October 2012Lazareto, Menorca, Spain Ioannis Karagiannis

  2. Objectives • To define measures of impact • To calculate the attributable risk • among the exposed • in the population • Eventually, make sense of stuff

  3. Scenario • You are in charge of health promotion “Preventing automobile-related deaths” • Limited budget  best reduction of deaths • Evidence: retrospective cohort study: “causes of automobile-related deaths”

  4. Relative Risks Best reduction of deaths? Prevent drink & drive? Prevent speeding?

  5. Relative Risks 0.000005 0.000001 0.50.1 Risk (exposed) Risk (unexposed) RR = 5.0

  6. Measures of Impact • Provide information about the public health impact of an exposure • Contribution of an exposure to the frequency of disease • Several concepts • Attributable risk (AR) • Attributable risk among exposed (AR%) • Attributable risk in the population (PAR) • Preventable fraction among exposed (PF)

  7. Attributable Risk (AR)(synonyms: Risk Difference) • Quantifies disease burden in exposed group attributable to exposure in absolute terms • AR = Re - Ru • Answers: • what is the risk attributed to the exposure? • what is the excess risk due to the exposure? • Only use if causality “exposure  outcome”

  8. Attributable Risk (AR) c c+d a a+b c c+d a a+b - Attributable Risk = Re – background risk Outcome no yes a+b = Re a b exposed not exposed c+d = Ru c d Attributable Risk = AR = Re - Ru

  9. Attributable Risk (AR) How high is the added risk of dying caused by the exposure “speeding“? Risk 0.05 Risk of death by speeding Added risk ? 0.01 Risk of death by driving below the speed limit 0.00 exposure: speeding

  10. AR (speeding) = 0.05 - 0.01 = 0.04 “speeding increases the risk of dying by 0.04. Four out of 100 speeding drivers will die in addition to normal (=background) because they drove too fast“. AR Speeding

  11. AR (drunk driving) = 0.15 - 0.01 = 0.14 “drunk driving increases the risk of dying by 0.14. Fourteen out of 100 drunk drivers die in addition to normal (background) death by driving because they were drunk while driving." AR Drunk driving

  12. Summary so far

  13. Attributable Risk Percent (AR%)(synonyms: Attributable Fraction) • Attributable risk expressed as a percentage of risk in the exposed population • Proportion of disease among the exposed which: • can be attributed to the exposure • could be prevented by eliminating the exposure • AR% looks at exposed population,not the total population

  14. Attributable Risk Percent (AR%) • Example speeding: What proportion of all speeding deaths (denominator) died because they drove too fast (numerator)? deaths caused by speeding deaths of all who drove too fast AR% = x 100

  15. Attributable Risk Percent (AR%) 1 Relative Risk = 1 - x 100 RR - 1 RR = x 100 RR > 1 Risk (exposed) - Risk (unexposed) Risk (exposed) x 100 AR% = Risk (exposed) Risk (unexposed) Risk (exposed) Risk (exposed) = - x 100

  16. AR% (speeding) = 80% “80% of all people who died while driving too fast, died because they drove too fast“. AR% Speeding drivers

  17. AR% (drunk driving) = 93% “93% of all people who died while being drunk, died because they were drunk“. AR% Drunk drivers

  18. Summary so far

  19. AR & AR% in Case-Control Studies Relative Risk - 1 Relative Risk AR% = x 100 • No direct risk estimates in case-control study • AR (risk difference) and AR% calculation IMPOSSIBLE!

  20. AR & AR% in Case-Control Studies No direct risk estimates in case-control study AR (risk difference) and AR% calculation IMPOSSIBLE? If odds ratio approximates relative risk, then Relative Risk - 1 Relative Risk AR% = x 100 Odds Ratio - 1 Odds Ratio AR% = x 100

  21. Population Attributable Risk (PAR%) • Proportion of cases in the total population attributable to the exposure • Proportion of disease in the total population that could be prevented if we could eliminate the risk factor • Determines exposures relevant to public health in community • Only use if causality “exposure  outcome”

  22. Population Attributable Risk (PAR%) • Example speeding: What proportion of all people who died (denominator) died because they drove too fast (numerator)? deaths caused by speeding total deaths in the population PAR% = x 100

  23. Population Attributable Risk (PAR%) p (RR - 1) p (RR - 1) +1 PAR% = x 100 p = proportion of population exposed PAR% = p(cases) x AR% p(cases) = proportion of cases exposed Risk (total pop) - Risk (unexposed) Risk (total pop) PAR% = x 100

  24. PAR(%) according to the relative riskfor various level of exposure frequency among cases 100.0% Pe 10% 90.0% Pe 25% Pe 50% 80.0% Pe 75% Pe 100% (AFe) 70.0% 60.0% Population attributable fraction 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 1 2 3 4 5 6 7 8 9 10 Relative risks

  25. 0.018 - 0.01 0.018 = 0.44 = 44% PAR% Speeding risk in unexposed risk in total population Risk (total) - Risk (not exposed) Risk(total) = PAR% =

  26. PAR% Speeding dead alive Risk 100/2000 = 0.05 2000 100 1900 speeding 8000 80 7920 80/8000 = 0.01 not speeding 180 9820 10000 Attributable Risk (AR) = 0.05 - 0.01 = 0.04 AR Risk(exposed) AR% = x 100 = (0.04/0.05) x 100 = 80% p(cases)= % cases exposed = 100/180 = 0.55 PAR% = pc x AR% = 0.55 x 80% = 44%

  27. 0.018 - 0.014 0.018 = 0.22 = 22% PAR% Drunk driving risk in unexposed risk in total population Risk (total) - Risk (unexposed) Risk(total) = PAR% =

  28. Summary • Best reduction of deaths? • Prevent drinking or speeding?

  29. PAR% in Case-Control Studies • proportion of controls exposed ≈ proportion of population exposed

  30. Summary

  31. Take-home message • There is more death and disability from frequent exposure to lower risks than to rare exposures to higher risks • Examples: • More people die from marginally elevated blood pressure (common) than from seriously elevated blood pressure (uncommon) • More people acquire HCV from unsafe injection (common exposure, lower risk) than from unsafe blood products (rare exposure, high risk)

  32. Preventable fraction (PF) • Exposure associated with decreased risk • Where RR < 1, exposure is protective • Proportion of cases that would have occurred if exposure hadn’t been present

  33. Preventable fraction (PF) • RR < 1  protective exposure (protective factor) • Proportion of cases that were prevented because of the exposure Risk (unexposed) - Risk (exposed) Risk (unexposed) PF = Risk (unexposed) Risk (exposed) Risk (unexposed) Risk (unexposed) PF = - PF = 1 - Relative Risk

  34. 600/300,000 - 100/200,000 600/300,000 = 0.75 Preventable Fraction (PF)Vaccine efficacy Risk (unexposed) - Risk (exposed) Risk (unexposed) PF = PF =

  35. Preventable Fraction (PF)Vaccine efficacy • How many people would have been ill without the vaccine? • 200/100,000 cases of unvaccinated • In population of 200,000 we expect 400 cases • Only 100 cases occurred; 300 cases were prevented (by vaccine) • 300/400 = 75% of hypothetical cases were prevented

  36. True or false? • The relative risk of lung cancer and smoking is 9 • Therefore, if nobody smoked, the incidence of lung cancer would be nine times lower than it currently is  FalseMeasures of association are not measures of impact.The prevalence of smoking in the population also matters!

  37. True or false? • 90% of patients with lung cancer are smokers • Therefore, if nobody smoked, the incidence of lung cancer would be reduced by 90%  FalseThe proportion of a disease that may be explained by a specific exposure does not depend on the proportion of cases exposed. It also depends on the strength of the association (90% of patients with lung cancer also eat fresh salad for lunch every day)

  38. Thank you 

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