OBJECTIVES: To investigate change in hospital utilization in a population and to discuss analytical strategies using large administrative databases, focusing on variations in rates of different types of hospital utilization by income quintile neighborhoods. DATA SOURCES: Hospital discharge abstracts from Manitoba Health, used to study the changes in utilization rates over eight fiscal years (1989-1996). STUDY DESIGN: We test the hypotheses that health reform has changed utilization rates, that utilization rates differ significantly across income quintiles (defined by the relative affluence of neighborhood of residence), and that these variations have been maintained over time. Our approach uses generalized estimating equations to produce robust and consistent results for studying rates of recurrent and nonrecurrent events longitudinally. DATA EXTRACTION METHODS: Rates of individuals hospitalized, hospital discharges, days of hospitalization, and hospitalization for different types of medical conditions and surgical procedures are generated for the period April 1, 1989 through March 31, 1997 for residents of Winnipeg, Manitoba. Data are grouped according to the individual's age, gender, and neighborhood of residence on April 1 of each of the eight fiscal years for the rate calculations. Neighborhood of residence and the 1991 Canadian Census public use database are used to assign individuals to income quintiles. PRINCIPAL FINDINGS: The substitution of outpatient surgery for inhospital surgery accounted for much of the change in hospital utilization over the 1989-1996 period. Health care reform did not have a significant effect on the utilization gradient already observed across socioeconomic groups. Health reform markedly accelerated declines in in-hospital utilization. CONCLUSIONS: Grouping the data with key characteristics intact facilitates the statistical analysis of utilization measures previously difficult to study. Such analyses of variations across time and space based on parametric models allows adjustment for continuous covariates and is more efficient than the traditional nonparametric approach using standardized rates.
OBJECTIVES: To investigate change in hospital utilization in a population and to discuss analytical strategies using large administrative databases, focusing on variations in rates of different types of hospital utilization by income quintile neighborhoods. DATA SOURCES: Hospital discharge abstracts from Manitoba Health, used to study the changes in utilization rates over eight fiscal years (1989-1996). STUDY DESIGN: We test the hypotheses that health reform has changed utilization rates, that utilization rates differ significantly across income quintiles (defined by the relative affluence of neighborhood of residence), and that these variations have been maintained over time. Our approach uses generalized estimating equations to produce robust and consistent results for studying rates of recurrent and nonrecurrent events longitudinally. DATA EXTRACTION METHODS: Rates of individuals hospitalized, hospital discharges, days of hospitalization, and hospitalization for different types of medical conditions and surgical procedures are generated for the period April 1, 1989 through March 31, 1997 for residents of Winnipeg, Manitoba. Data are grouped according to the individual's age, gender, and neighborhood of residence on April 1 of each of the eight fiscal years for the rate calculations. Neighborhood of residence and the 1991 Canadian Census public use database are used to assign individuals to income quintiles. PRINCIPAL FINDINGS: The substitution of outpatient surgery for inhospital surgery accounted for much of the change in hospital utilization over the 1989-1996 period. Health care reform did not have a significant effect on the utilization gradient already observed across socioeconomic groups. Health reform markedly accelerated declines in in-hospital utilization. CONCLUSIONS: Grouping the data with key characteristics intact facilitates the statistical analysis of utilization measures previously difficult to study. Such analyses of variations across time and space based on parametric models allows adjustment for continuous covariates and is more efficient than the traditional nonparametric approach using standardized rates.