Association Between Age and Severity at Disability Onset and All-Cause Mortality: Longitudinal Observational Study From the Health and Retirement Study
Bibliographic record
Abstract
Background: Disability is a global public health challenge, with its prevalence increasing, particularly among older adults, and it exerts a profound impact on both health outcomes and mortality rates. Objective: This study investigates the associations between age at disability onset, severity at disability onset, and all-cause mortality in community-dwelling adults. Methods: We analyzed data from waves 10 to 16 (2010-2023) of the Health and Retirement Study, a nationally representative longitudinal survey of US adults aged ≥51 years. Participants without disabilities in activities of daily living (ADLs) or instrumental activities of daily living (IADLs) from the Health and Retirement Study were followed biennially until December 31, 2023. During the follow-up period, 4500 participants developed ADL disability and 4260 developed IADL disability. For each case participant, a control participant matched for age (+1 to -1 y) and sex was randomly selected. Multivariable Cox proportional hazards models were used to assess hazard ratios (HRs) for all-cause mortality among participants with new-onset disabilities, stratified by age groups and severity at disability onset. Results: Over a median follow-up duration of 8.58 years, 1709 (37.98%) deaths occurred in the ADL group and 1832 (43%) deaths occurred in the IADL group. Individuals who developed ADL disability before the age of 55 years exhibited the highest all-cause mortality risk compared to matched controls (HR 3.12, 95% CI 1.85-5.26), which further increased with severe disability (HR 4.07, 95% CI 2.03-8.19). The mortality risk was inversely associated with age at onset. A parallel trend was identified in the IADL cohort. Notably, men demonstrated a significantly elevated mortality risk compared to women, emphasizing the need for gender-specific interventions. Conclusions: Early and severe disability onset significantly increases mortality risk, with men experiencing a disproportionately higher risk. Preventive strategies aimed at addressing early-onset and severe disability, with consideration of gender differences, are essential for improving long-term outcomes in affected populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".