Geographic Variation of Procedure Utilization

A Hierarchical Model Approach

Datos Bibliográficos

ID9100342
AutoresConstantine Gatsonis (0000-0001-6846-4520, Harvard University, autor de correspondencia), Sharon‐Lise T Normand (0000-0001-7027-4769, Harvard University, autor de correspondencia), Sharon-Lise Normand, Chuanhai Liu (0000-0003-1919-7280), Carl Morris, Carl N Morris (Harvard University, autor de correspondencia)
Año1993
Volumen31
Númerosupplement
PáginasYS54-YS59
Fecha de publicación1993-05-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-199305001-00008
PMID8492586
OpenAlexW1968476081
IdiomaEN
Citas recibidas10
Referencias citadas5

In this study, an abbreviated introduction to hierarchical statistical models for quantifying and explaining variations in the utilization of medical care is presented. The illustrative example was derived from an analysis of interstate variation in coronary angiography utilization for Medicare patients with a recent acute myocardial infarction. The hierarchical model distinguished within-from between-states variation: the former was modeled via a separate logistic regression for each state, with age and sex as the independent variables, while the latter was modeled via a multivariate normal distribution for the coefficients of the state-specific logistic models. Alternative computation approaches were compared and model fit was assessed. Estimates of the distribution of state rates of angiography for an average patient and for age-by-sex strata were obtained. The results showed substantial interstate variation in angiography utilization, but only moderate interstate variation in the effects of age and sex on the decision to perform angiography. This analytic approach allows substantially more detailed results than those by standardization, and accounts for sample size differences between units of aggregation. The next major step in the analysis would be to derive smoothed estimates of the individual state logistic models by pooling data across states. The analysis can also be extended to incorporate other patient characteristics, such as race and comorbidity, and state characteristics, such as geographic location and availability of the procedure

Environmental health · Geographic variation · Population · Statistics · Variation (astronomy) · Advanced Statistical Process Monitoring · Computer Science · Mathematics · Medicine · Soil Geostatistics and Mapping · Statistical Methods and Bayesian Inference