This application seeks support to develop methods for the analysis of spatio-temporal area data. These methods aim to: identify trends in space and/or time; detect """"""""hot"""""""" or """"""""cold"""""""" spots of risk; assess delivery of interventions; detect populations who are subject to health disparities; and generate hypotheses concerning possible risk factors. Aggregated spatio-temporal disease data present unique statistical challenges, in particular one must account for: spatial and temporal dependence; the inherent instability of rare events; errors in numerators and denominators; and problems due to aggregation (which if not addressed may lead to """"""""ecological bias""""""""). This application addresses each of these issues, and has specific aims: 1. To develop and apply spatio-temporal models, in particular to carry out surveillance. 2. To develop a framework for analysis of the association between aggregated health outcomes and environmental exposures (in air, water, or soil) measured at point locations. 3. To develop efficient designs for combining ecological- and individual-level data to provide a stronger analytic basis for ecological studies. The methods developed will be genetically applicable to non-infectious diseases and will be illustrated using a range of data including publicly-available U.S. cancer incidence and mortality data, and Washington State small-area cancer incidence data. Developed methods will be made available to researchers via implementation within freely-available software.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA095994-04
Application #
7487082
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Lewis, Denise
Project Start
2005-09-30
Project End
2011-07-31
Budget Start
2008-08-01
Budget End
2011-07-31
Support Year
4
Fiscal Year
2008
Total Cost
$186,379
Indirect Cost
Name
University of Washington
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Cohen-Cline, Hannah; Beresford, Shirley A A; Barrington, Wendy Elizabeth et al. (2018) Associations between neighbourhood characteristics and depression: a twin study. J Epidemiol Community Health 72:202-207
Fisher, Leigh; Wakefield, Jon; Bauer, Cici et al. (2017) Time series modeling of pathogen-specific disease probabilities with subsampled data. Biometrics 73:283-293
Liang, Peter S; Mayer, Jonathan D; Wakefield, Jon et al. (2017) Temporal Trends in Geographic and Sociodemographic Disparities in Colorectal Cancer Among Medicare Patients, 1973-2010. J Rural Health 33:361-370
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
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
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
Wakefield, Jon; Simpson, Daniel; Godwin, Jessica (2016) Comment: Getting into Space with a Weight Problem. J Am Stat Assoc 111:1111-1118
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

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