Presentation on theme: "Measures of Impact 18 th EPIET/EUPHEM Introductory Course September-October 2012 Lazareto, Menorca, Spain Ioannis Karagiannis."— Presentation transcript:
Measures of Impact 18 th EPIET/EUPHEM Introductory Course September-October 2012 Lazareto, 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? Relative Risk Driving too fast5 Driving while drunk11
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 = R e - R u 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) AR = R e - R u Outcome a cd yes no exposed not exposed b Attributable Risk = a a+b c c+d a+ba+b c+dc+d a a+b c c+d - Attributable Risk = R e – background risk = R e = R u
9 Attributable Risk (AR) Risk Risk of death by speeding Risk of death by driving below the speed limit How high is the added risk of dying caused by the exposure speeding? Added risk ? exposure: speeding 0.00
10 AR Speeding Outcome DeadAlive Risk Risk Ratio Risk Difference Speeding Yes No AR (speeding) = = 0.04 speeding increases the risk of dying by Four out of 100 speeding drivers will die in addition to normal (=background) because they drove too fast.
11 AR Drunk driving Outcome DeadAlive Risk Risk Ratio Risk Difference Drunk Driving Yes No AR (drunk driving) = = 0.14 drunk driving increases the risk of dying by Fourteen out of 100 drunk drivers die in addition to normal (background) death by driving because they were drunk while driving."
12 Summary so far MeasureSpeedingDrunk driving Relative Risk511 Attributable Risk
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
16 AR% Speeding drivers Outcome DeadAlive RiskAR% Speeding Yes = 80% No AR% (speeding) = 80% 80% of all people who died while driving too fast, died because they drove too fast.
17 AR% Drunk drivers Outcome DeadAlive RiskAR% Drunk Driving Yes = 93% No AR% (drunk driving) = 93% 93% of all people who died while being drunk, died because they were drunk.
18 Summary so far MeasureSpeedingDrunk driving Relative Risk511 Attributable Risk Attributable Risk%80%93%
19 AR & AR% in Case-Control Studies No direct risk estimates in case-control study -AR (risk difference) and AR% calculation IMPOSSIBLE! Relative Risk - 1 Relative Risk AR%= x 100
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%) Risk (total pop) - Risk (unexposed) Risk (total pop) x 100 PAR% = p (RR - 1) p (RR - 1) +1 x 100 PAR% = p = proportion of population exposed PAR% = p(cases) x AR% p(cases) = proportion of cases exposed
PAR(%) according to the relative risk for various level of exposure frequency among cases 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% Relative risks Population attributable fraction Pe 10% Pe 25% Pe 50% Pe 75% Pe 100% (AFe)
25 PAR% Speeding Outcome DeadAlive Risk Speeding Yes No Risk (total) - Risk (not exposed) Risk(total) PAR% = = = = 44% risk in total population risk in unexposed
26 PAR% Speeding deadalive speeding not speeding 1900 Risk 100/2000 = /8000 = 0.01 Attributable Risk (AR) = = 0.04 AR Risk(exposed) AR% =x 100 = (0.04/0.05) x 100 = 80% p(cases) = % cases exposed = 100/180 = 0.55 PAR% = p c x AR% = 0.55 x 80% = 44%
27 PAR% Drunk driving Outcome DeadAlive Risk Drunk Driving Yes No Risk (total) - Risk (unexposed) Risk(total) PAR% = = = = 22% risk in total population risk in unexposed
28 Summary MeasureSpeedingDrunk driving Relative Risk511 Attributable Risk Attributable Risk%80%93% Pop. attributable risk%44%22% % drivers with risk factor in population 20%3% Best reduction of deaths? Prevent drinking or speeding?
29 PAR% in Case-Control Studies proportion of controls exposed proportion of population exposed PAR% = P controls – (OR – 1) x 100 P controls (OR – 1) + 1 P controls = Proportion of controls exposed PAR% =P cases ( OR – 1 ) x 100 OR Where P cases = proportion cases exposed
30 Summary MeasureMeaningQuestion answered RR, OR Strength of association (between exposure and outcome) Is the exposure associated with the risk of getting ill/ the outcome? AR Excess risk of exposed (in absolute terms) What is the difference in risk between exposed and not exposed? AR% Proportion of risk of exposed attributed to exposure, potential prevention of exposed What proportion of those who are exposed and ill is likely due to the exposure? PAR% Proportion of risk of population attributed to exposure, potential prevention of population, Public Health relevance What proportion of those who are ill in the population is likely due to the exposure?
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 hadnt been present
33 RR < 1 protective exposure (protective factor) Proportion of cases that were prevented because of the exposure Risk (unexposed) - Risk (exposed) Risk (unexposed) Preventable fraction (PF) PF = Risk (unexposed)Risk (exposed) Risk (unexposed) Risk (unexposed) PF = - PF = 1 - Relative Risk
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
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 False Measures of association are not measures of impact. The prevalence of smoking in the population also matters!
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% False The 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)