Objective: We aimed to develop an algorithm for the identification of basic Activities of Daily Living (ADL)-dependency in health insurance databases.
Study design and setting: We used the AMI (Aging Multidisciplinary Investigation) population-based cohort including both individual face-to-face assessment of ADL-dependency and merged health insurance data. The health insurance factors associated with ADL-dependency were identified using a LASSO logistic regression model in 1000 bootstrap samples. An external validation on a 1/97 representative sample of the French Health Insurance general population of Affiliates has been performed.
Results: Among 995 participants of the AMI cohort aged ≥ 65y, 114 (11.5%) were ADL-dependent according to neuropsychologists individual assessments. The final algorithm developed included: age, sex, four drug classes (dopaminergic antiparkinson drugs, antidepressants, antidiabetic agents, lipid modifying agents), three type of medical devices (medical bed, patient lifter, incontinence equipment), four medical acts (GP's consultations at home, daily and non-daily nursing at home, transport by ambulance) and four long-term diseases (stroke, heart failure, coronary heart disease, Alzheimer and other dementia). Applying this algorithm, the estimated prevalence of ADL-dependency was 12.3% in AMI and 9.5% in the validation sample.
Conclusion: This study proposes a useful algorithm to identify ADL-dependency in the health insurance data.
Keywords: Activities of daily living; Aged; Cohort study; Dependency; Health insurance data; Pharmacoepidemiology.
Copyright © 2021. Published by Elsevier Inc.