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Estimating activity limitation in the noninstitutionalized population: a method for small areas
J E Lafata1, G G Koch, W G Weissert
1Center for Health System Studies, Henry Ford Health System, Detroit, MI 48202-3450.
American Journal of Public Health
|November 1, 1994
Summary
Synthetic estimates using regression models can effectively determine activity limitation across communities. These models utilize demographic and socioeconomic data, proving useful for small-area planning when direct local estimates are unavailable.
Area of Science:
- Public Health
- Biostatistics
- Sociology
Background:
- Direct local estimates of the activity-limited population are often unavailable.
- Synthetic estimates offer a viable alternative for assessing population needs.
- Multivariate regression methods can model activity limitation at a national level.
Purpose of the Study:
- To develop and validate a method for generating state and local synthetic estimates of activity limitation.
- To identify key demographic and socioeconomic predictors of activity limitation.
Main Methods:
- Utilized the 1989 National Health Interview Survey and 1991 Area Resource File System.
- Developed log-linear regression models incorporating person-level and county-level variables.
- Applied model-predicted probabilities to intercensal population data for synthetic estimation.
Main Results:
- Activity limitation rates increase with age and worsen with declining county socioeconomic status.
- Race and sex were statistically significant predictors of activity limitation.
- The models effectively predicted activity limitation based on selected variables.
Conclusions:
- Activity limitation is significantly influenced by age, sex, race, and community socioeconomic status.
- Synthetic estimates are a practical and cost-effective tool for small-area health planning.
- This approach provides valuable data in the absence of direct local estimates.