Estimating activity limitation in the noninstitutionalized population: a method for small areas.

Abstract

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. 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. 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. 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.