COSTS AND EFFECTS OF ARKANSAS\u27 LONG-TERM CARE DEMONSTRATION PROJECT (MEDICAID, CHANELLING, COMMUNITY ASSESSMENT)

The purpose of this study was to assess the impact of the Arkansas Long-Term Care Demonstration Project upon Arkansas\u27 Medicaid expenditures and upon the clients it serves. A Retrospective Medicaid expenditure study component used analyses of variance techniques to test for the Project\u27s effects upon aggregated expenditures for 28 demonstration and control counties representing 25 percent of the State\u27s population over four years, 1979-1982.^ A second approach to the study question utilized a 1982 prospective sample of 458 demonstration and control clients from the same 28 counties. The disability level or need for care of each patient was established a priori. The extent to which an individual\u27s variation in Medicaid utilization and costs was explained by patient need, presence or absence of the channeling project\u27s placement decision or some other patient characteristic was examined by multiple regression analysis. Long-term and acute care Medicaid, Medicare, third party, self-pay and the grand total of all Medicaid claims were analyzed for project effects and explanatory relationships.^ The main project effect was to increase personal care costs without reducing nursing home or acute care costs (Prospective Study). Expansion of clients appeared to occur in personal care (Prospective Study) and minimum care nursing home (Retrospective Study) for the project areas. Cost-shifting between Medicaid and Medicare in the project areas and two different patterns of utilization in the North and South projects tended to offset each other such that no differences in total costs between the project areas and demonstration areas occurred. The project was significant ((beta) = .22, p \u3c .001) only for personal care costs. The explanatory power of this personal care regression model (R(\u272) = .36) was comparable to other reported health services utilization models. Other variables (Medicare buy-in, level of disability, Social Security Supplemental Income (SSI), net monthly income, North/South areas and age) explained more variation in the other twelve cost regression models.