Abstract:
:Creating local population health measures from administrative data would be useful for health policy and public health monitoring purposes. While a wide range of options--from simple spatial smoothers to model-based methods--for estimating such rates exists, there are relatively few side-by-side comparisons, especially not with real-world data. In this paper, we compare methods for creating local estimates of acute myocardial infarction rates from Medicare claims data. A Bayesian Monte Carlo Markov Chain estimator that incorporated spatial and local random effects performed best, followed by a method-of-moments spatial Empirical Bayes estimator. As the former is more complicated and time-consuming, spatial linear Empirical Bayes methods may represent a good alternative for non-specialist investigators.
journal_name
Health Placejournal_title
Health & placeauthors
Yasaitis LC,Arcaya MC,Subramanian SVdoi
10.1016/j.healthplace.2015.08.003subject
Has Abstractpub_date
2015-09-01 00:00:00pages
95-104eissn
1353-8292issn
1873-2054pii
S1353-8292(15)00112-4journal_volume
35pub_type
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