Background: A simple method of identifying elders at high risk for activity of daily living (ADL) dependence could facilitate essential research and implementation of cost-effective clinical care programs.
Objective: We used a nationally representative sample of 9446 older adults free from ADL dependence in 2006 to develop simple models for predicting ADL dependence at 2008 follow-up and to compare the models to the most predictive published model. Candidate predictor variables were those of published models that could be obtained from interview or medical record data.
Methods: Variable selection was performed using logistic regression with backward elimination in a two-third random sample (n=6233) and validated in a one-third random sample (n=3213). Model fit was determined using the c-statistic and evaluated vis-a-vis our replication of a published model.
Results: At 2-year follow-up, 8.0% and 7.3% of initially independent persons were ADL dependent in the development and validation samples, respectively. The best fitting, simple model consisted of age and number of hospitalizations in past 2 years, plus diagnoses of diabetes, chronic lung disease, congestive heart failure, stroke, and arthritis. This model had a c-statistic of 0.74 in the validation sample. A model of just age and number of hospitalizations achieved a c-statistic of 0.71. These compared with a c-statistic of 0.79 for the published model. Sensitivity analyses demonstrated model robustness.
Conclusions: Models based on a widely available data achieve very good validity for predicting ADL dependence. Future work will assess the validity of these models using medical record data.
*Indiana University Center for Aging Research
†Regenstrief Institute Inc
‡Department of Medicine, Division of General Internal Medicine and Geriatrics
§Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN
Supported by National Institute on Aging Grants P30 AG024967 and R01 AG031222 to the IU Roybal Center.
The authors declare no conflict of interest.
Reprints: Daniel O. Clark, PhD, Regenstrief Institute Inc., 410 West 10th St, HS 2000, Indianapolis, IN 46202. E-mail: daniclar@iupui.