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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA134294-07
Application #
8754131
Study Section
Special Emphasis Panel (ZCA1-RPRB-2)
Project Start
Project End
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
7
Fiscal Year
2014
Total Cost
$145,592
Indirect Cost
$54,060
Name
Harvard University
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Bobb, Jennifer F; Claus Henn, Birgit; Valeri, Linda et al. (2018) Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. Environ Health 17:67
Chen, Han; Cade, Brian E; Gleason, Kevin J et al. (2018) Multiethnic Meta-Analysis Identifies RAI1 as a Possible Obstructive Sleep Apnea-related Quantitative Trait Locus in Men. Am J Respir Cell Mol Biol 58:391-401
Pierce, Brandon L; Kraft, Peter; Zhang, Chenan (2018) Mendelian randomization studies of cancer risk: a literature review. Curr Epidemiol Rep 5:184-196
Barfield, Richard; Feng, Helian; Gusev, Alexander et al. (2018) Transcriptome-wide association studies accounting for colocalization using Egger regression. Genet Epidemiol 42:418-433
Liu, Zhonghua; Lin, Xihong (2018) Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 74:165-175
Emilsson, Louise; García-Albéniz, Xabier; Logan, Roger W et al. (2018) Examining Bias in Studies of Statin Treatment and Survival in Patients With Cancer. JAMA Oncol 4:63-70
Sun, Ryan; Carroll, Raymond J; Christiani, David C et al. (2018) Testing for gene-environment interaction under exposure misspecification. Biometrics 74:653-662
Antonelli, Joseph; Cefalu, Matthew; Palmer, Nathan et al. (2018) Doubly robust matching estimators for high dimensional confounding adjustment. Biometrics :
Wilson, Ander; Zigler, Corwin M; Patel, Chirag J et al. (2018) Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression. Biometrics 74:1034-1044
Ritchie, Marylyn D; Davis, Joe R; Aschard, Hugues et al. (2017) Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186:771-777

Showing the most recent 10 out of 192 publications