Conclusion Epidemiology and what matters most

ConclusionEpidemiology and what

matters most

Epidemiology matters: a new introduction to methodological foundations

Chapter 14

Epidemiology Matters – Chapter 14

1. Seven steps of an epidemiological study

2. Balancing comparability and external validity

3. Small effects, big implications

4. Consequentialist epidemiology implications

5. Causal explanation versus intervention

6. Summary







6. Summary

4Epidemiology Matters – Chapter 1

Seven steps

1. Define the population of interest

2. Conceptualize and create measures of exposures and health indicators

3. Take a sample of the population

4. Estimate measures of association between exposures and health indicators of

interest

5. Rigorously evaluate whether the association observed suggests a causal

association

6. Assess the evidence for causes working together, i.e., interaction

7. Assess the extent to which the result matters, is externally valid, to other

populations







6. Summary


Comparability and external validity

All epidemiologic studies should be conducted with a clear intent to improve the health of populationsHowever 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



Comparability: achieving within study sample ensures causal effect estimate(s) are internally validChapter 10: Randomization, matching, and stratification are foundational approaches to achieve comparability of study sample



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







6. Summary


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


Small effects, big implicationsexample

Question: intervening to prevent occurrence of disease in Farrlandia, an overall population risk of 6/100, over 5 yearsTwo exposures associated with disease:

Exposure A associated with increased risk ratio of 1.2 disease onset

Exposure B associated with 5-fold increase disease riskWhich exposure should we invest public health time and money in preventing? Answer may depend on the prevalence of these exposures


Small effects, big implicationsexample

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







6. Summary


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


ImplicationsTo 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







6. Summary


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 groupsHowever, exposures cannot be removed in isolation, resulting in alterations to changing distribution of component causes once causes are manipulatedThis can have unintended consequences including increasing another adverse outcome







6. Summary


Seven steps

1. Define the population of interest

2. Conceptualize and create measures of exposures and health indicators

3. Take a sample of the population

4. Estimate measures of association between exposures and health indicators of

interest

5. Rigorously evaluate whether the association observed suggests a causal

association

6. Assess the evidence for causes working together, i.e., interaction

7. Assess the extent to which the result matters, is externally valid, to other

populations


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Conclusion Epidemiology and what matters most