Abstract:
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Area and time-specific estimates of key health indicators are of great interest for health care and policy purposes. Such estimates provide the information needed to identify areas with increased risk, effectively allocate resources, and target interventions. Unfortunately, the sample size of data available at a granular space-time scale is often too small to provide reliable estimates and uncertainty intervals. While it is appealing to use space-time smoothing models, and many approaches have been suggested for this endeavor, it is rare for spatial models to incorporate the weighting scheme, leaving the analysis potentially subject to bias. We present Bayesian space-time models which incorporate the design based weights and individual-level covariates. This work is motivated by an effort to estimate rates of health indicators (e.g. diabetes, smoking) by health reporting areas in King County from the Behavioral Risk Factor Surveillance Survey. The computation times for the methods are short, and all approaches are implemented in R using currently available packages.
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