Small Area Estimation of Dependency: Final Report. ABSTRACT

01/13/1989

Health planning efforts for the elderly have been hampered by the lack of reliable estimates of the noninstitutionalized long-term care population. Until recently national estimates were virtually nonexistent, and reliable local estimates remain unavailable. With the recent publication of several national surveys, however, synthetic estimates can be made for states and counties by using multivariate methods to model functional dependency at the national level, and then applying the predicted probability to corresponding state and county demographic and contextual data. Using the 1984 National Health Interview Survey's Supplement on Aging and the 1986 Area Health Resources File System, we produced log-linear regression models that included demographic and contextual variables as predictors of functional dependency among the noninstitutionalized elderly. We found race, sex, age, and the percent of the elderly population in the community who reside in poverty to be significant predictors of functional dependency. Applying these models to 1986 Medicare Enrollment Statistics we produced estimates of two levels of functional dependency for all states and a sample of counties.

*** *** *** *** ***

While a substantial portion of long-term care planning occurs at the state and local level, many of the rigorous and authoritative population surveys provide prevalence data on the community-based long-term care population which is reliable only for national estimates. Health planning efforts for the elderly have been hampered by the lack of reliable data for making population-based estimates at subnational levels.

This paper presents log-linear regression models that can be used to produce regression-adjusted synthetic estimates of the elderly community-based long-term care population. We present state estimates, as well as estimates for a sample of counties.

View full report

Preview
Download

"smareaes.pdf" (pdf, 2.82Mb)

Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®