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Comment & Response
March NaN, 2016

Nonlinear Exposure-Outcome Associations and Public Health Policy

Author Affiliations
  • 1Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
JAMA. 2016;315(12):1286-1287. doi:10.1001/jama.2015.18023

To the Editor Dr Chokshi and colleagues1 highlighted the challenges of nonlinear associations for effective public health interventions, including the potential harms caused by adopting the approach of shifting an entire exposure distribution.

These concerns are valid if the exposure-outcome relationship is causal and the J-shape of the association is a true representation of the causal relationship. If the association is not causal, then intervening on the exposure will have no effect, although it may divert resources from effective interventions. Furthermore, if the lower end of the apparent J-shape is biased and there is in fact a causal linear association, then their concerns are unwarranted. Key is the need to understand what types of bias could produce an association in one end of the distribution that is in the direction opposite to that in the rest of the distribution. Chokshi and colleagues highlighted the importance of reverse causality, in which existing (but unknown) disease at the time of exposure assessment influences its level and the outcome. For example, the observational J-shaped association of alcohol with coronary heart disease (CHD) has been attributed to patients with disease being more likely to quit drinking.

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