Factors affecting conversion rates to Medicaid among new admissions to nursing homes.
OBJECTIVE
This study examines conversion to Medicaid as a payment source among a cohort of newly admitted nursing home residents.


METHODS
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.


METHODS
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.


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.


RESULTS
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.
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Center for Gerontology and Health Care Research, Brown University, Providence, RI 02912.
Intrator
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Laliberte
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D000203:Activities of Daily Living; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual; D003663:Decision Trees; D004522:Educational Status; D005260:Female; D006302:Health Services Research; D006801:Humans; D007326:Institutionalization / Q000191:economics*; D007629:Kentucky; D008137:Longitudinal Studies; D008297:Male; D017533:Marital Status; D008484:Medicaid / Q000706:statistics & numerical data*; D008498:Medical Record Linkage; D006278:Medicare / Q000706:statistics & numerical data; D008923:Missouri; D009735:Nursing Homes / Q000191:economics* / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D012111:Residence Characteristics; D012959:Socioeconomic Factors; D013022:South Carolina; D016019:Survival Analysis; D013714:Tennessee; D014481:United States