There may be no part of epidemiology more central to public health practice than the evaluation of causal claims about exposures that keep us healthy, make us sick, and help us get better again. Some causal claims are sufficiently self-evident that we learn them as toddlers: hot stoves cause “owwies”. But causal questions in public health are much more complex, and to approach them we need to learn to ‘think like an epidemiologist’.
This requires us to think critically about causal claims as we develop our skills in applying the logic of causal inference. When faced with a causal claim most epidemiologists immediately question the source of data, the appropriateness of the measures, and the soundness of the analysis underlying the causal claim. Epidemiology is firmly grounded in the scientific method, but the components of the scientific method have been modified for use outside the laboratory, as applied to large groups of ‘free range’ humans. Learning these adaptations can be challenging.
This case introduces causal critical appraisal using, as an example, the claim that orchestra conductors live longer than members of other occupational groups because they are conductors. It is a suitable introductory case because it does not require subject matter expertise in theories of longevity or causes of death. Learners progress from basic to higherlevel concepts, beginning by recalling parts of the scientific method (e.g. control groups), and thinking about how each might be applied to this causal question. A mid-level objective is evaluating the appropriateness of the outcome measure, which requires understanding how average age at death is a poor measure compared to average life expectancy at birth, which in turn is less appropriate than average life expectancy at the age people typically become orchestra conductors. The case concludes by introducing confounding and confirmation bias.
To get learners to start ‘thinking like an epidemiologist’ about:
1. The epidemiological application of the scientific method.
2. Causal claims and the logic of causal inferences.
3. Source and appropriateness of data and measures.
Case Study Questions
Nine questions suitable for individual or group work are included in the Case Note. For example, “How would you apply the scientific method to thinking about a causal question?”
causation, causal claims, causal mechanism, life expectancy, average life expectancy (at birth; at age X), average age at death, bias (measurement/ascertainment; sampling; confirmation), scientific method (randomization/random assignment; blinding/masking; biological plausibility; control group)
Speechley, M. (2016). The Case of the Long-Lived Orchestra Conductors. in: Terry, A.L. & John-Baptiste, A. [eds] Western Public Health Casebook 2016. London, ON: Public Health Casebook Publishing.