期刊
ANNALS OF EPIDEMIOLOGY
卷 25, 期 5, 页码 329-335出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.annepidem.2015.02.007
关键词
Heart disease mortality; Spatiotemporal trends; Geographic patterns; Bayesian methods; Joinpoint regression; Generalized linear mixed models; Spatial analysis
资金
- Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health [K01HD074726]
Purpose: To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010. Methods: We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty. Results: The average local percent change in heart disease mortality from each method was -4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0-0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods. Conclusions: The similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates. (C) 2015 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据