Comparison of estimation methods for creating small area rates of acute myocardial infarction among Medicare beneficiaries in California.
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.
Harvard Center for Population and Development Studies, Harvard University, 9 Bow St, Cambridge, MA 02138, USA. Electronic address: yasaitis@mail.med.upenn.edu.
Laura C
LC
Yasaitis
Department of Social and Behavioral Sciences, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA.
Mariana C
MC
Arcaya
Department of Social and Behavioral Sciences, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA.
S V
SV
Subramanian
D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D002140:California / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D006278:Medicare; D009010:Monte Carlo Method; D009203:Myocardial Infarction / Q000453:epidemiology*; D014481:United States
Local Disease Rates; Markov Chains; Medicare; Myocardial Infarction; Spatial Analysis