Conclusion Epidemiology and what matters most Epidemiology matters: a new introduction to methodological foundations Chapter 14

Epidemiology Matters – Chapter 14 Seven steps of an epidemiological study Balancing comparability and external validity Small effects, big implications Consequentialist epidemiology implications Causal explanation versus intervention Summary Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 14 Seven steps of an epidemiological study Balancing comparability and external validity Small effects, big implications Consequentialist epidemiology implications Causal explanation versus intervention Summary Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 1 Seven steps Define the population of interest Conceptualize and create measures of exposures and health indicators Take a sample of the population Estimate measures of association between exposures and health indicators of interest Rigorously evaluate whether the association observed suggests a causal association Assess the evidence for causes working together, i.e., interaction Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

Epidemiology Matters – Chapter 14 Seven steps of an epidemiological study Balancing comparability and external validity Small effects, big implications Consequentialist epidemiology implications Causal explanation versus intervention Summary Epidemiology Matters – Chapter 14

Comparability and external validity All epidemiologic studies should be conducted with a clear intent to improve the health of populations However no one study can stand alone without an evidence base, no one study will settle a causal question, no one study will be the last word on any issue Epidemiology Matters – Chapter 14

Comparability and external validity Comparability: achieving within study sample ensures causal effect estimate(s) are internally valid Chapter 10: Randomization, matching, and stratification are foundational approaches to achieve comparability of study sample Epidemiology Matters – Chapter 14

Comparability and external validity External validity: extent to which our findings are generalizable to a base population. This requires an understanding of factors that together are involved in producing a causal estimate Chapter 7: most causes of disease do not act in isolation, i.e., interaction Chapter 11: assess interaction in data - evident when risk of disease among exposed to two potential causes > additive effect of each cause Chapter 12: relation between exposure and health indicator is externally valid to another population to the extent that interacting causes with exposure are distributed similarly Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 14 Seven steps of an epidemiological study Balancing comparability and external validity Small effects, big implications Consequentialist epidemiology implications Causal explanation versus intervention Summary Epidemiology Matters – Chapter 14

Small effects, big implications Does the causal effect obtained in a study have consequence for the populations in which burden of disease is greatest? Are the effect estimates obtained in study translatable to actual cases of illness and disease potentially prevented by intervention? To answer: compare effect estimate magnitude to prevalence of exposures of interest; small magnitude of effect may translate to large public health benefits Epidemiology Matters – Chapter 14

Small effects, big implications example Question: intervening to prevent occurrence of disease in Farrlandia, an overall population risk of 6/100, over 5 years Two exposures associated with disease: Exposure A associated with increased risk ratio of 1.2 disease onset Exposure B associated with 5-fold increase disease risk Which exposure should we invest public health time and money in preventing? Answer may depend on the prevalence of these exposures Epidemiology Matters – Chapter 14

Small effects, big implications example Exposure A Exposure B Interpretation: Exposure A has prevalence of 80% (800/1000). A risk ratio of 1.2 and 5 year risk of 6%. Exposure to A caused 45 cases. Interpretation: Exposure B has prevalence of 5%. A risk ratio of 5.0 and 5 year risk of 6%. Exposure to B caused 12 cases. Even though Exposure A has weaker overall effect on disease compared with Exposure B , it is responsible for almost four times disease more because it is more prevalent in population Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 14 Seven steps of an epidemiological study Balancing comparability and external validity Small effects, big implications Consequentialist epidemiology implications Causal explanation versus intervention Summary Epidemiology Matters – Chapter 14

Consequentialist epidemiology The ultimate purpose of epidemiology, the quantitative science of public health, is to understand the causes of human disease and improve health of the populations where the burden of disease is greatest Health is not distributed equally across populations, a consequentialist epidemiologists engages in science beyond local borders Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 14 Implications To study under 5 mortality in US Sample the population (Chapter 4) Measure potential causes of interest (Chapter 5) Estimate associations of effect of potential causes on child mortality (Chapter 6) Assess associations for internal validity (Chapter 8) Assess interaction (Chapter 11) Consider the conditions for external validity across populations (Chapter 12) An epidemiology of consequence makes sure to study child mortality in resource poor versus resource rich settings Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 14 Seven steps of an epidemiological study Balancing comparability and external validity Small effects, big implications Consequentialist epidemiology implications Causal explanation versus intervention Summary Epidemiology Matters – Chapter 14

Causal explanation and interventions Effects of causes are not necessarily equal to the effects of interventions on those causes Epidemiologic studies can isolate specific effects of exposures by creating comparable exposed and unexposed groups However, exposures cannot be removed in isolation, resulting in alterations to changing distribution of component causes once causes are manipulated This can have unintended consequences including increasing another adverse outcome Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 14 Seven steps of an epidemiological study Balancing comparability and external validity Small effects, big implications Consequentialist epidemiology implications Causal explanation versus intervention Summary Epidemiology Matters – Chapter 14

Epidemiology Matters – Chapter 1 Seven steps Define the population of interest Conceptualize and create measures of exposures and health indicators Take a sample of the population Estimate measures of association between exposures and health indicators of interest Rigorously evaluate whether the association observed suggests a causal association Assess the evidence for causes working together, i.e., interaction Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

Epidemiology Matters – Chapter 1 epidemiologymatters.org Epidemiology Matters – Chapter 1