Disaggregating Activities of Daily Living Limitations for Predicting Nursing Home Admission
Objective: To examine whether disaggregated activities of daily living (ADL) limitations better predict the risk of nursing home admission compared to conventionally used ADL disability counts. Data Sources: We used panel data from the Health and Retirement Study (HRS) for years 1998–2010. The HRS i...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2015
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| Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/4832 https://ink.library.smu.edu.sg/context/lkcsb_research/article/5831/viewcontent/hesr0050_0560_pv.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Objective: To examine whether disaggregated activities of daily living (ADL) limitations better predict the risk of nursing home admission compared to conventionally used ADL disability counts. Data Sources: We used panel data from the Health and Retirement Study (HRS) for years 1998–2010. The HRS is a nationally representative survey of adults older than 50 years (n = 18,801). Study Design: We fitted Cox regressions in a continuous time survival model with age at first nursing home admission as the outcome. Time-varying ADL disability types were the key explanatory variables. Principal Findings: Of the six ADL limitations, bathing difficulty emerged as the strongest predictor of subsequent nursing home placement across cohorts. Eating and dressing limitations were also influential in driving admissions among more recent cohorts. Using simple ADL counts for analysis yielded similar adjusted R2s; however, the amount of explained variance doubled when we allowed the ADL disability measures to time-vary rather than remain static. Conclusions: Looking beyond simple ADL counts can provide health professionals insights into which specific disability types trigger long-term nursing home use. Functional disabilities measured closer in time carry more prognostic power than static measures. |
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