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Med Care ; 40(6): 500-9, 2002 Jun.
Article in English | MEDLINE | ID: mdl-12021676

ABSTRACT

BACKGROUND: There have been few studies of the extent to which differences in the pool of patients being managed might account for geographic variations in treatment rates. OBJECTIVE: For two cardiac procedures, cardiac catheterization and revascularization, we evaluate the hypothesis that differences in "the percentage of patients for whom the procedure is appropriate" is a factor explaining variations in use rates among those hospitalized with coronary heart disease (CHD). RESEARCH DESIGN: Based on hospital utilization patterns in Massachusetts in 1990, we created 70 small geographic areas. Using 1992 Massachusetts Peer Review Organization data, areas were ranked from highest to lowest based on (empirical-Bayes-adjusted) hospitalization rates for each procedure. One thousand seven hundred four cases from 43 hospitals were sampled, roughly half each from high and low use areas. Half had a procedure and half were candidates for the same procedure but did not have it. For each procedure, medical records were reviewed to determine whether the procedure was (or, for those not having it, would have been) appropriate, based on criteria developed using a modified Delphi approach. RESULTS: Among those having either procedure, appropriateness rates were similar in high and low rate areas (P = 0.59 for catheterization and P = 0.30 for revascularization). However, among candidates for either procedure who did not have it, appropriateness for performing the procedure was greater in high-rate areas (41.4% vs. 32.1%, P = 0.05 for catheterization; 71.2% vs. 57.2%, P = 0.003, for revascularization). CONCLUSION: Among those hospitalized with CHD, appropriateness rates for two cardiac procedures are higher in areas with higher use rates.


Subject(s)
Cardiac Catheterization/statistics & numerical data , Coronary Disease/therapy , Myocardial Revascularization/statistics & numerical data , Patient Selection , Aged , Hospitalization/statistics & numerical data , Humans , Massachusetts/epidemiology , Regional Health Planning/methods
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