This is a proposal to develop statistical methods for the effective analysis of social health disparities spanning the cancer continuum. Our proposed methods are motivated by and applied to investigation of US disparities in breast cancer incidence and survival, although the methods have wide applicability in other cancers. In spatio-temporal analyses of trends in cancer incidence, one source of uncertainty that can potentially have large impacts on analysis conclusions is small area intercensal population count uncertainty. Another important source of uncertainty in large cancer registries is the lack of individual-level data. This source of uncertainty yields conservative estimates ofthe annual costs of socioeconomic disparities, and better methods are needed to accurately estimate and ultimately reduce such disparities. Finally, popular methods for assessing the interplay of race/ethnicity and socioeconomic status on cancer disparities have the potential to yield severely biased inferences. To address these sources of uncertainty in the spatio- temporal analysis of cancer health disparities, we propose to develop new statistical methods and apply them to data from the Surveillance, Epidemiology, and End Results (SEER) database linked with data from the National Longitudinal Mortality Survey (NLMS).
Specific aims ofthe project are to develop, evaluate, and implement (1) novel methods to incorporate intercensal population count uncertainty in the spatio-temporal analysis of cancer incidence data;(2) novel methods to integrate area- and individual-level data on socioeconomic position (SEP) to quantify socioeconomic disparities in cancer survival, accurately;(3) a causal inference framework to disentangle the roles of SEP and race in cancer survival disparities;and (4) fast algorithms and user-friendly open-source software for implementation ofthe developed methods. We will provide first-of-its-kind knowledge concerning the joint and individual impacts of race/ethnlcity, area and individual-level SEP on breast cancer incidence, mortality, and survival. Our newly developed methodology and resulting knowledge will contribute to key goals in NCI's and NIH's Strategic Plans to reduce and ultimately eliminate health disparities.

Public Health Relevance

This project proposes to develop statistical methods for the spatio-temporal assessment of social disparities in cancer. The resulting methods will be applied to yield new evidence on the interplay between racial/ethnic and socioeconomic disparities in breast cancer incidence and survival. The project will make software available so that the developed methods are broadly applicable for routine analyses of other cancer surveillance data.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1-RPRB-2)
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Harvard University
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Valeri, Linda; Reese, Sarah L; Zhao, Shanshan et al. (2017) Misclassified exposure in epigenetic mediation analyses. Does DNA methylation mediate effects of smoking on birthweight? Epigenomics 9:253-265
Chipman, J; Braun, D (2017) Simpson's paradox in the integrated discrimination improvement. Stat Med 36:4468-4481
Wilson, Ander; Chiu, Yueh-Hsiu Mathilda; Hsu, Hsiao-Hsien Leon et al. (2017) Bayesian distributed lag interaction models to identify perinatal windows of vulnerability in children's health. Biostatistics 18:537-552
García-Albéniz, Xabier; Hsu, John; Hernán, Miguel A (2017) The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening. Eur J Epidemiol 32:495-500
Krieger, Nancy; Feldman, Justin M; Waterman, Pamela D et al. (2017) Local Residential Segregation Matters: Stronger Association of Census Tract Compared to Conventional City-Level Measures with Fatal and Non-Fatal Assaults (Total and Firearm Related), Using the Index of Concentration at the Extremes (ICE) for Racial, Econ J Urban Health 94:244-258
Barnett, Ian; Mukherjee, Rajarshi; Lin, Xihong (2017) The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies. J Am Stat Assoc 112:64-76
Lee, Kyu Ha; Tadesse, Mahlet G; Baccarelli, Andrea A et al. (2017) Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation. Biometrics 73:232-241
Asafu-Adjei, Josephine; Mahlet, G Tadesse; Coull, Brent et al. (2017) Bayesian Variable Selection Methods for Matched Case-Control Studies. Int J Biostat 13:
Di, Qian; Wang, Yan; Zanobetti, Antonella et al. (2017) Air Pollution and Mortality in the Medicare Population. N Engl J Med 376:2513-2522
Cefalu, Matthew; Dominici, Francesca; Arvold, Nils et al. (2017) Model averaged double robust estimation. Biometrics 73:410-421

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