A major public health aim is to provide accurate estimates and predictions of disease burden at the area level;this endeavor is vital for prevention strategies. This proposal describes methods for modeling spatially and temporally indexed health data. The first two aims concern infectious disease data and the third aim small-area estimation (SAE) based on complex survey data. Despite the widespread collection of infectious disease data in time and space there are important gaps in statistical methodology for the analysis of such data. Specifically, there are both a limited number of modeling options and a lack of software implementations for those that are available. Hence, the emphasis in this project is on practically applicable methods that will be made available within freely-available software, based on modern Bayesian smoothing models. With respect to infectious disease data, a flexible spline- based model is proposed. The methods will be developed in the context of a number of developing world infectious disease applications including hand, foot and mouth disease and tuberculosis. For SAE the proposed approach combines design-based estimation techniques with spatial smoothing priors, to produce estimates with both low bias and low variance. These models will be applied to data from the Behavioral Risk Factor Surveillance System (BRFSS) and to infant mortality and HIV data from the Tanzania demographic and health survey (DHS), in order to answer important public health questions.

Public Health Relevance

The modeling of area-level disease counts in space and time has a number of important public health uses. In the developing world, vital registration systems often do not have full coverage, and estimates of infant deaths or HIV prevalence by area must be based on a variety of data sources;in general, the prediction of areas in which disease burden is likely to be high is an important public health endeavor since it allows appropriate action to be taken, such as the concentration of resources in those areas. The methods to be developed in this proposal focus on the modeling of infectious disease counts and on small area estimation, with smoothing over space and time being a key ingredient in both ventures, and with an emphasis on implementable methods.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Zhu, Li
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University of Washington
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Koepke, Amanda A; Longini Jr, Ira M; Halloran, M Elizabeth et al. (2016) PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH. Ann Appl Stat 10:575-595
Bauer, Cici; Wakefield, Jon; Rue, HÃ¥vard et al. (2016) Bayesian penalized spline models for the analysis of spatio-temporal count data. Stat Med 35:1848-65
Liang, Peter S; Mayer, Jonathan D; Wakefield, Jon et al. (2016) Temporal Trends in Geographic and Sociodemographic Disparities in Colorectal Cancer Among Medicare Patients, 1973-2010. J Rural Health :
Kim, Albert Y; Wakefield, Jon (2016) A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound. Epidemiology 27:347-55
Fisher, Leigh; Wakefield, Jon; Bauer, Cici et al. (2016) Time series modeling of pathogen-specific disease probabilities with subsampled data. Biometrics :
Song, Lin; Mercer, Laina; Wakefield, Jon et al. (2016) Using Small-Area Estimation to Calculate the Prevalence of Smoking by Subcounty Geographic Areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013. Prev Chronic Dis 13:E59
Mercer, Laina D; Wakefield, Jon; Pantazis, Athena et al. (2015) Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality. Ann Appl Stat 9:1889-1905
Smith, Theresa R; Wakefield, Jon; Dobra, Adrian (2015) Restricted Covariance Priors with Applications in Spatial Statistics. Bayesian Anal 10:965-990
Ross, Michelle; Wakefield, Jon (2015) Bayesian hierarchical models for smoothing in two-phase studies, with application to small area estimation. J R Stat Soc Ser A Stat Soc 178:1009-1023
Wakefield, Jonathan; Kim, Albert (2013) A Bayesian model for cluster detection. Biostatistics 14:752-65

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