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Community-Level Correlates of Hospitalizations for Persons With Schizophrenia

Dr. Fortney is affiliated with the Department of Veterans Affairs South Central Mental Illness Research, Education and Clinical Center, North Little Rock, Arkansas, and the Department of Psychiatry, University of Arkansas for Medical Sciences, 4224 Shuffield Dr., Little Rock, AR 72205 (e-mail: fortneyjohnc{at}uams.edu). Dr. Xu is with the Institute for Health Research, Kaiser Permanente Colorado, Denver. When this research was conducted, Ms. Dong was with the Mental Health Program, Western Interstate Commission for Higher Education, Boulder, Colorado.

OBJECTIVES: Hospitalizations are likely to depend on both individual-level and community-level risk factors. This study identified community-level correlates of hospitalization for persons with schizophrenia and identified counties with high hospitalization rates. METHODS: Data from 811 counties in 14 states were collected from the Agency for Healthcare Research and Quality, U.S. Census Bureau, U.S. Department of Agriculture, and the Health Resources and Services Administration. The dependent variable was specified as the county-level hospitalization rates for 2000, adjusted for age and sex, as determined from the Statewide Inpatient Database. To control for spatial autocorrelation, a Bayesian-Poisson conditional autoregressive model was used to measure the correlation between community-level factors and standardized hospitalization rates. Spatially smoothed standardized hospitalization rates were examined to identify counties with high rates. RESULTS: There were 1.6 schizophrenia-related hospitalizations per 1,000 residents. Community-level correlates significantly predicting higher rates of hospitalization included percentage of residents in poverty (relative incidence rate [RIR]=1.03), percentage unemployed (RIR=1.04), hospital beds per 1,000 (RIR=1.04), social workers per 1,000 (RIR=1.09), and market penetration rate of health maintenance organizations (RIR=1.00) (p<.05). Community-level correlates significantly predicting lower rates of hospitalization included percentage of uninsured residents (RIR=.99), housing stress (RIR=.91), rural location (RIR for the most rural counties=.57), and a farm-dependent economy (RIR=.75) (p<.05). A quarter (25%) of the counties had higher than expected hospitalization rates, and spatial clustering was evident. CONCLUSIONS: Results should be of interest to health plans seeking to control high-cost hospitalizations. Although improved availability of specialty mental health outpatient treatment could prevent hospitalizations for schizophrenia, the study indicated no correlation between access to specialists and hospitalization rates.

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