The Annals of Applied Statistics

Multivariate spatiotemporal modeling of age-specific stroke mortality

Harrison Quick, Lance A. Waller, and Michele Casper

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Geographic patterns in stroke mortality have been studied as far back as the 1960s when a region of the southeastern United States became known as the “stroke belt” due to its unusually high rates. While stroke mortality rates are known to increase exponentially with age, an investigation of spatiotemporal trends by age group at the county level is daunting due to the preponderance of small population sizes and/or few stroke events by age group. In this paper, we implement a multivariate space–time conditional autoregressive model to investigate age-specific trends in county-level stroke mortality rates from 1973 to 2013. In addition to reinforcing existing claims in the literature, this work reveals that geographic disparities in the reduction of stroke mortality rates vary by age. More importantly, this work indicates that the geographic disparity between the “stroke belt” and the rest of the nation is not only persisting, but may in fact be worsening.

Article information

Ann. Appl. Stat. Volume 11, Number 4 (2017), 2165-2177.

Received: August 2016
Revised: June 2017
First available in Project Euclid: 28 December 2017

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Age disparities in health Bayesian methods geographic disparities in health nonseparable models small area analysis


Quick, Harrison; Waller, Lance A.; Casper, Michele. Multivariate spatiotemporal modeling of age-specific stroke mortality. Ann. Appl. Stat. 11 (2017), no. 4, 2165--2177. doi:10.1214/17-AOAS1068.

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Supplemental materials

  • Supplement to “Multivariate spatiotemporal modeling of age-specific stroke mortality”. Appendix A contains the details of our Markov chain Monte Carlo (MCMC) algorithm and a description of the preprocessing smoothing approach used on our two covariates. Appendix B contains a supplemental discussion (and additional figures) related to the analysis of the stroke mortality data.