V Mor1, O Intrator, L Laliberte. 1. Center for Gerontology and Health Care Research, Brown University, Providence, RI 02912.
Abstract
OBJECTIVE: This study examines conversion to Medicaid as a payment source among a cohort of newly admitted nursing home residents. DATA SOURCE: The longitudinal data used came from regular assessments of residents in the National Health Corporation's 43 for-profit nursing homes in Missouri, Kentucky, South Carolina, and Tennessee. This information system tracked all residents who were discharged, providing a comprehensive record that may have spanned multiple admissions. STUDY DESIGN: Using survival analysis methods, Cox regression, and survival trees, we contrasted the effect of state, initial payment source, education, age, and functional status on the rate of spend-down to Medicaid. DATA EXTRACTION METHODS: New-admission cohorts were created by linking an admission record for a newly admitted resident with all subsequent assessments and follow-up records to ascertain the precise dates of any payment source changes and other discharge transitions. PRINCIPAL FINDINGS: For the 1,849 individuals who were admitted as self-payers and who were still in the nursing home at the end of one year, there is a 19 percent probability of converting to Medicaid. All analytic methods revealed that education, age, and state of residence were predictive of spend-down among residents who were admitted as self-payers. CONCLUSIONS: Our results confirm the effect of education as an SES indicator and state as a proxy for Medicaid policy on spend-down. Future research should model the effects and duration of intervening hospitalizations and other transitions on Medicaid spend-down among new admissions.
OBJECTIVE: This study examines conversion to Medicaid as a payment source among a cohort of newly admitted nursing home residents. DATA SOURCE: The longitudinal data used came from regular assessments of residents in the National Health Corporation's 43 for-profit nursing homes in Missouri, Kentucky, South Carolina, and Tennessee. This information system tracked all residents who were discharged, providing a comprehensive record that may have spanned multiple admissions. STUDY DESIGN: Using survival analysis methods, Cox regression, and survival trees, we contrasted the effect of state, initial payment source, education, age, and functional status on the rate of spend-down to Medicaid. DATA EXTRACTION METHODS: New-admission cohorts were created by linking an admission record for a newly admitted resident with all subsequent assessments and follow-up records to ascertain the precise dates of any payment source changes and other discharge transitions. PRINCIPAL FINDINGS: For the 1,849 individuals who were admitted as self-payers and who were still in the nursing home at the end of one year, there is a 19 percent probability of converting to Medicaid. All analytic methods revealed that education, age, and state of residence were predictive of spend-down among residents who were admitted as self-payers. CONCLUSIONS: Our results confirm the effect of education as an SES indicator and state as a proxy for Medicaid policy on spend-down. Future research should model the effects and duration of intervening hospitalizations and other transitions on Medicaid spend-down among new admissions.