Abstract
ObjectivesThe University of Wisconsin Population Health Institute has published the County Health Rankings since 2010. These rankings use population-based data to highlight health outcomes and the multiple determinants of these outcomes and to encourage in-depth health assessment for all United States counties. A significant methodological limitation, however, is the uncertainty of rank estimates, particularly for small counties. To address this challenge, we explore the use of longitudinal and pooled outcome data in hierarchical Bayesian models to generate county ranks with greater precision.MethodsIn our models we used pooled outcome data for three measure groups: (1) Poor physical and poor mental health days; (2) percent of births with low birth weight and fair or poor health prevalence; and (3) age-specific mortality rates for nine age groups. We used the fixed and random effects components of these models to generate posterior samples of rates for each measure. We also used time-series data in longitudinal random effects models for age-specific mortality. Based on the posterior samples from these models, we estimate ranks and rank quartiles for each measure, as well as the probability of a county ranking in its assigned quartile. Rank quartile probabilities for univariate, joint outcome, and/or longitudinal models were compared to assess improvements in rank precision.ResultsThe joint outcome model for poor physical and poor mental health days resulted in improved rank precision, as did the longitudinal model for age-specific mortality rates. Rank precision for low birth weight births and fair/poor health prevalence based on the univariate and joint outcome models were equivalent.ConclusionIncorporating longitudinal or pooled outcome data may improve rank certainty, depending on characteristics of the measures selected. For measures with different determinants, joint modeling neither improved nor degraded rank precision. This approach suggests a simple way to use existing information to improve the precision of small-area measures of population health.
Highlights
The County Health Rankings, first published in 2010 by the University of Wisconsin Population Health Institute, provide population health measures for most United States counties
The joint outcome model for poor physical and poor mental health days resulted in improved rank precision, as did the longitudinal model for age-specific mortality rates
Rank precision for low birth weight births and fair/poor health prevalence based on the univariate and joint outcome models were equivalent
Summary
The County Health Rankings, first published in 2010 by the University of Wisconsin Population Health Institute, provide population health measures for most United States counties. The Rankings are designed to direct media and policy-maker attention toward the multiple determinants of health and encourage in-depth community health assessment [1]. One challenge to the Rankings is its reliance on small-area estimates, which are commonly affected by small sample sizes, large standard errors, and statistical outliers. These features of small-area estimates lead to uncertainty regarding the health of counties—especially for small counties—in the Rankings. Hierarchical Bayesian models, can be used to improve small-area estimates of health-related measures and the resulting ranks. The benefits of Bayesian estimates are well-known: they draw in extreme values that are often statistical artifacts due to data sparsity by using information from related units [2]. Bayesian estimates allow us to estimate more adequately uncertainty in performance across units—a critical feature when comparing (or ranking) entities [3]
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