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Often, a community becomes alarmed when high rates of cancer are noticed, andresidents suspect that the cancer cases could be caused by a known source ofhazard. In response, the CDC recommends that departments of health perform astandardized incidence ratio (SIR) analysis to determine whether the observedcancer incidence is higher than expected. This approach has several limitationsthat are well documented in the literature. In this paper we propose a novelcausal inference approach to cancer cluster investigations, rooted in thepotential outcomes framework. Assuming that a source of hazard representing apotential cause of increased cancer rates in the community is identified apriori, we introduce a new estimand called the causal SIR (cSIR). The cSIR is aratio defined as the expected cancer incidence in the exposed populationdivided by the expected cancer incidence under the (counterfactual) scenario ofno exposure. To estimate the cSIR we need to overcome two main challenges: 1)identify unexposed populations that are as similar as possible to the exposedone to inform estimation under the counterfactual scenario of no exposure, and2) make inference on cancer incidence in these unexposed populations usingpublicly available data that are often available at a much higher level ofspatial aggregation than what is desired. We overcome the first challenge byrelying on matching. We overcome the second challenge by developing a Bayesianhierarchical model that borrows information from other sources to impute cancerincidence at the desired finer level of spatial aggregation. We apply ourproposed approach to determine whether trichloroethylene vapor exposure hascaused increased cancer incidence in Endicott, NY.

A causal inference framework for cancer cluster investigations using publicly available data