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

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  • Rachel C. Nethery
  • Yue Yang
  • Anna J. Brown
  • Francesca Dominici

Abstract

Often, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the US Centers for Disease Control and Prevention recommend that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach has several limitations that are well documented in the existing literature. We propose a novel causal inference framework for cancer cluster investigations, rooted in the potential outcomes framework. Assuming that a source of hazard representing a potential cause of increased cancer rates in the community is identified a priori, we focus our approach on a causal inference estimand which we call the causal SIR. The causal SIR is a ratio defined as the expected cancer incidence in the exposed population divided by the expected cancer incidence for the same population under the (counterfactual) scenario of no exposure. To estimate the causal SIR we need to overcome two main challenges: first, we must identify unexposed populations that are as similar as possible to the exposed population to inform estimation of the expected cancer incidence under the counterfactual scenario of no exposure, and, second, publicly available data on cancer incidence for these unexposed populations are often available at a much higher level of spatial aggregation (e.g. county) than what is desired (e.g. census block group). We overcome the first challenge by relying on matching. We overcome the second challenge by building a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired level of spatial aggregation. In simulations, our statistical approach was shown to provide dramatically improved results, i.e. less bias and better coverage, than the current approach to SIR analyses. We apply our proposed approach to investigate whether trichloroethylene vapour exposure has caused increased cancer incidence in Endicott, New York.

Suggested Citation

  • Rachel C. Nethery & Yue Yang & Anna J. Brown & Francesca Dominici, 2020. "A causal inference framework for cancer cluster investigations using publicly available data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1253-1272, June.
  • Handle: RePEc:bla:jorssa:v:183:y:2020:i:3:p:1253-1272
    DOI: 10.1111/rssa.12567
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    References listed on IDEAS

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    1. G. Datta & M. Ghosh & R. Steorts & J. Maples, 2011. "Bayesian benchmarking with applications to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 574-588, November.
    2. Gotway C.A. & Young L.J., 2002. "Combining Incompatible Spatial Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 632-648, June.
    3. Iacus, Stefano M. & King, Gary & Porro, Giuseppe, 2011. "Multivariate Matching Methods That Are Monotonic Imbalance Bounding," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 345-361.
    4. Wakefield J. C & Morris S. E, 2001. "The Bayesian Modeling of Disease Risk in Relation to a Point Source," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 77-91, March.
    5. Shu Yang & Guido W. Imbens & Zhanglin Cui & Douglas E. Faries & Zbigniew Kadziola, 2016. "Propensity score matching and subclassification in observational studies with multi‐level treatments," Biometrics, The International Biometric Society, vol. 72(4), pages 1055-1065, December.
    6. Peter Diggle & Sara Morris & Paul Elliott & Gavin Shaddick, 1997. "Regression Modelling of Disease Risk in Relation to Point Sources," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 491-505, September.
    7. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    8. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
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    Cited by:

    1. Sharifah Saffinas Syed Soffian & Azmawati Mohammed Nawi & Rozita Hod & Huan-Keat Chan & Muhammad Radzi Abu Hassan, 2021. "Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review," IJERPH, MDPI, vol. 18(19), pages 1-20, October.

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