Regional mapping of incidence rates using spatial Bayesian models.
This study takes a statistical-modeling point of view to the assessment of health care services and procedures. The emphasis is on small-area prediction of incidence rates from spatially contiguous regions, although suitable modifications can also give doctor-level predictions. The main idea is to recognize the individuality of each region through a spatial Bayesian model for incidence rates. A noise component, because of location error and measurement error, is filtered out using empirical Bayes methods. The resulting smoothed predictors of incidence rates provide an accurate picture of the health care service or procedure under investigation.
Department of Statistics, Iowa State University, Ames 50011.
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Cressie
D001499:Bayes Theorem; D002585:Cesarean Section / Q000706:statistics & numerical data; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D015994:Incidence; D009657:North Carolina; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D017062:Small-Area Analysis; D014481:United States