Hospital closures--Florida

This dissertation utilized population ecology theory to examine a previously untested population: Florida hospitals. It examined hospital closures in Florida between 1965 and 1995. The statistical technique of survival analysis was used to analyze the data developed for the study. The population ecology literature argues that at the macro level, organizations experience various liabilities to their continued survival. This study examined four of these in the Florida hospital population: size, age, density at closure and density at founding. That is, it looked at the extent to which organizations that were small, mid-size, young, in organizationally dense areas, or established in organizationally dense areas experienced a high risk for mortality compared to other hospitals. The data set for the age and size of Florida hospitals was created using information contained in the 1965 through 1995 annual reports of the American Hospital Association. Hospital locations were geocoded on a map of the state. Density was determined by counting the number of hospitals within a five mile radius of each geocoded location. Two of the propositions suggested in the literature, the liability of age and the liability of density at founding, were confirmed by the survival analysis of the Florida data. Newer hospitals and hospitals founded in organizationally dense areas (five or more hospitals within a five mile radius) experienced a significantly greater incidence of closure than did those which were older or founded in less dense areas. Small hospitals (<100 beds) were also shown to be significantly more likely to close than large ones (>300 beds), as were hospitals located in organizationally sparse areas (no other hospitals in 5 mile radius) compared to those in organizationally dense areas (>5 hospitals in 5 mile radius). The study concluded with an examination of the policy implications of the results. It argued for the addition of a macro, population ecology perspective of hospital closures to existing analytical techniques in order to more fully understand the major risk factors confronting hospitals.