A Multivariate Space–Time Model for Analysing County Level Heart Disease Death Rates by Race and Sex. Issue 1 (22nd February 2017)

• Record Type: Journal Article
• Authors: Quick, Harrison; Waller, Lance A.; Casper, Michele
• Is Part Of: Journal of the Royal Statistical Society. Volume 67:Issue 1(2018:Jan.)
• Page Start: 291
• Page End: 304
• Publication Date: 2017-02-22
• Subjects: Bayesian methods -- Heart disease -- Non-separable models -- Racial disparities in health -- Sex disparities in health -- Spatiotemporal data analysis; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12215
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: Although death rates from heart disease have declined sharply over the past 50 years, the rate of decline varies by location, race and sex. Despite these declines, heart disease continues to be the leading cause of death in the USA. We propose a non-separable multivariate spatiotemporal Bayesian model to obtain a clearer picture of the temporally varying trends in county level heart disease death rates for men and women of different races in the USA. After verifying the effectiveness of our model via simulation, we apply our model to a data set of over 230000 county level heart disease death rates.