Thirty-Day Hospital Readmission for Medicaid Enrollees with Schizophrenia: The Role of Local Health Care Systems


Alisa B Busch, Arnold M Epstein, Thomas G McGuire, Sharon-Lise T Normand, and Richard G Frank. 2015. “Thirty-Day Hospital Readmission for Medicaid Enrollees with Schizophrenia: The Role of Local Health Care Systems.” J Ment Health Policy Econ, 18, 3, Pp. 115-24.


BACKGROUND: Examining health care system characteristics possibly associated with 30-day readmission may reveal opportunities to improve healthcare quality as well as reduce costs. AIMS OF THE STUDY: Examine the relationship between 30-day mental health readmission for persons with schizophrenia and county-level community treatment characteristics. METHODS: Observational study of 18 state Medicaid programs (N=274 counties, representing 103,967 enrollees with schizophrenia 28,083 of whom received more than 1 mental health hospitalization) using Medicaid administrative and United States Area Health Resource File data from 2005. Medicaid is a federal-state program and major health insurance provider for low income and disabled individuals, and the predominant provider of insurance for individuals with schizophrenia. The Area Health Resource File provides county-level estimates of providers. We first fit a regression model examining the relationship between 30-day mental health readmission and enrollee characteristics (e.g., demographics, substance use disorder [SUD], and general medical comorbidity) from which we created a county-level demographic and comorbidity case-mix adjuster. The case-mix adjuster was included in a second regression model examining the relationship between 30-day readmission and county-level factors: (i) quality (antipsychotic/visit continuity, post-hospital follow-up); (ii) mental health hospitalization (length of stay, admission rates); and (iii) treatment capacity (e.g., population-based estimates of outpatient providers/clinics). We calculated predicted probabilities of readmission for significant patient and county-level variables. RESULTS: Higher county rates of mental health visits within 7-days post-hospitalization were associated with lower readmission probabilities (e.g., county rates of 7-day follow up of 55% versus 85%, readmission predicted probability (PP) [95%CI]=16.1% [15.8%-16.4%] versus 13.3% [12.9%-13.6%]). In contrast, higher county rates of mental health hospitalization were associated with higher readmission probabilities (e.g., country admission rates 10% versus 30%, readmission predicted probability=11.3% [11.0%-11.6%] versus 16.7% [16.4%-17.0%]). Although not our primary focus, enrollee comorbidity was associated with higher predicted probability of 30-day mental health readmission: PP [95%CI] for enrollees with SUD=23.9% [21.5%-26.3%] versus 14.7% [13.9%-15.4%] for those without; PP [95% CI] for those with=three chronic medical conditions=25.1% [22.1%-28.2%] versus none=17.7% [16.3%-19.1]. DISCUSSION: County rates of hospitalization and 7-day follow-up post hospital discharge were associated with readmission, along with patient SUD and general medical comorbidity. This observational design limits causal inference and utilization patterns may have changed since 2005. However, overall funding for U.S. Medicaid programs remained constant since 2005, reducing the likelihood significant changes. Last, our inability to identify community capacity variables associated with readmission may reflect imprecision of some variables as measured in the Area Health Resource File. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE AND FOR HEALTH POLICIES: Healthcare policy and programming to reduce 30-day mental health readmissions should focus on county-level factors that contribute to hospitalization in general and improving transitions to community care, as well as patient comorbidity. IMPLICATIONS FOR FURTHER RESEARCH: Given the likely importance of local care systems, to reduce readmission future research is needed to refine community-level capacity variables that are associated with reduced readmissions; and to evaluate models of care coordination in this population.