M J Succi1, S Y Lee, J A Alexander. 1. Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor 48109-2029, USA.
Abstract
OBJECTIVE: To examine the dynamic effects of competition and hospital market position on rural hospital closures. DATA SOURCE/STUDY SETTING: Analysis of all rural community hospitals operating between 1984 and 1991, with the exception of sole-provider hospitals. Data for the study are obtained from four sources: the AHA Annual Surveys of Hospitals, the HCFA Cost Reports, the Area Resource File, and a hospital address file constructed by Geographic Inc. DATA COLLECTION AND ANALYSIS: Variables are merged to construct pooled, time-series observations for study hospitals. Hospital closure is specified as a function of hospital market position, market level competition, and control variables. Discrete-time logistic regressions are used to test hypotheses. PRINCIPAL FINDINGS: Rural hospitals operating in markets with higher density had higher risk of closure. Rural hospitals that differentiated from others in the market on the basis of geographic distance, basic services, and high-tech services had lower risks of closure. Effects of market density on closure disappeared when market position was included in the model, indicating that differentiation in markets should be taken into account when evaluating the effects of competition on rural hospital closure. CONCLUSIONS: Our findings suggest that rural hospitals can reduce competitive pressures through differentiation and that accurate measures of competition in geographically defined market areas are critical for understanding competitive dynamics among rural hospitals.
OBJECTIVE: To examine the dynamic effects of competition and hospital market position on rural hospital closures. DATA SOURCE/STUDY SETTING: Analysis of all rural community hospitals operating between 1984 and 1991, with the exception of sole-provider hospitals. Data for the study are obtained from four sources: the AHA Annual Surveys of Hospitals, the HCFA Cost Reports, the Area Resource File, and a hospital address file constructed by Geographic Inc. DATA COLLECTION AND ANALYSIS: Variables are merged to construct pooled, time-series observations for study hospitals. Hospital closure is specified as a function of hospital market position, market level competition, and control variables. Discrete-time logistic regressions are used to test hypotheses. PRINCIPAL FINDINGS: Rural hospitals operating in markets with higher density had higher risk of closure. Rural hospitals that differentiated from others in the market on the basis of geographic distance, basic services, and high-tech services had lower risks of closure. Effects of market density on closure disappeared when market position was included in the model, indicating that differentiation in markets should be taken into account when evaluating the effects of competition on rural hospital closure. CONCLUSIONS: Our findings suggest that rural hospitals can reduce competitive pressures through differentiation and that accurate measures of competition in geographically defined market areas are critical for understanding competitive dynamics among rural hospitals.