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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

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

15. 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

16. 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

17. 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

18. 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

19. 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

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