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Although reliable direct state and local estimates of the activity-limited population are frequently unavailable, regression-adjusted synthetic estimates can be made. Such estimates use multivariate methods to model activity limitation at the national level and then apply model-predicted probabilities to corresponding community-specific demographic data. Methods. using the 1989 National Health Interview Survey and the 1991 Area Resource File System, this study produced log-linear regression models that included person-level demographic and county-level contextual variables as predictors of activity limitation. Model-predicted rates were then multiplied by corresponding intercensal population data to generate state and local synthetic estimates of activity limitation. Results. Rates of activity limitation generally were found to increase with age and as the socioeconomic conditions of the county in which an individual resided worsened. Race and sex also tended to be statistically significant predictors of activity limitation. Conclusions. Activity limitation can be effectively modeled by age, sex, race, and community socioeconomic status. Synthetic estimates such as these are relatively simple to generate and can be useful for small-area planning in the absence of direct local estimates. [ABSTRACT FROM AUTHOR]

Health planning efforts for the population age 65 and over have been hampered continually by the lack of reliable estimates of the noninstitutionalized long-term care population. Until recently national estimates were virtually nonexistent, and reliable small area estimates remain unavailable. However, with the recent publication of several national surveys and the 1990 Census, 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 probabilities to corresponding state and county data. Using the 1984 National Health Interview Survey's Supplement on Aging and the 1986 Area Health Resources File System, we have produced log-linear regression models that include demographic and contextual variables as predictors of functional dependency among the noninstitutionalized population age 65 and over. Age, sex, race, and the percent of the 65 and over population who reside in poverty were found to be significant predictors of functional dependency. Applying these models to 1986 Medicare Enrollment Statistics, regression-adjusted synthetic estimates of two levels of functional dependency were produced for all states and--as examples of how the rates can be used to produce additional synthetic estimates--the largest county in each state. We also produced point estimates and standard errors for the national prevalence of functional dependency among the noninstitutionalized population age 65 and over. [ABSTRACT FROM AUTHOR]