American Journal of Epidemiology Advance Access originally published online on March 6, 2009
American Journal of Epidemiology 2009 169(9):1140-1147; doi:10.1093/aje/kwp015
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PRACTICE OF EPIDEMIOLOGY |
Estimating the Effects of Potential Public Health Interventions on Population Disease Burden: A Step-by-Step Illustration of Causal Inference Methods
Correspondence to Dr. Jennifer Ahern, School of Public Health, University of California, Berkeley, 101 Haviland Hall, Berkeley, CA 94720-7358 (e-mail: jahern{at}berkeley.edu).
Received for publication March 26, 2008. Accepted for publication January 13, 2009.
Causal inference methods allow estimation of the effects of potential public health interventions on the population burden of disease. Motivated by calls for epidemiologic research to be presented in ways that are more informative for intervention, the authors present a didactic discussion of the steps required to estimate the population effect of a potential intervention using an imputation-based causal inference method and discuss the assumptions of and limitations to its use. An analysis of neighborhood smoking norms and individual smoking behavior is used as an illustration. The implementation steps include the following: 1) modeling the adjusted exposure and outcome association, 2) imputing the outcome probability for each individual while manipulating the exposure by "setting" it to different values, 3) averaging these probabilities across the population, and 4) bootstrapping confidence intervals. Imputed probabilities represent counterfactual estimates of the population smoking prevalence if neighborhood smoking norms could be manipulated through intervention. The degree to which temporal ordering, randomization, stability, and experimental treatment assignment assumptions are met in the illustrative example is discussed, along with ways that future studies could be designed to better meet the assumptions. With this approach, the potential effects of an intervention targeting neighborhoods, individuals, or other units can be estimated.
causality; intervention studies; methods; population; residence characteristics; smoking; social environment
Abbreviations: GEE, generalized estimating equation; OR, odds ratio