Trends in disability and limitations among US adults aged 18-44 years, 2000-2018
Bibliographic record
Abstract
Understanding disability trends is critical for health care and social policy. Although trends in disability and limitations have been studied extensively among older and middle-aged adults, little is known about trends in younger Americans, despite their importance for current and future population health. We present the first comprehensive evidence on disability trends among US adults age 18-44. We analyze 20 measures of disability and limitations collected in the nationally representative National Health Interview Survey 2000-2018 (n = 261 505). Robust Poisson models estimate age- and sex-adjusted trends and their covariates. Over one-quarter (27.4%) reported at least 1 disability or limitation; the age-adjusted prevalence increased by 5% from 2000 to 2018. However, trends for specific disabilities and limitations varied tremendously. Activities of daily living and instrumental activities of daily living limitations, cognitive, and social disabilities increased steeply (by 65%-89% over the study period). Mobility limitations were generally unchanged or increased modestly. Hearing and "other" limitations decreased significantly (25%-48% decrease). The trends are only partly explained by education, health behaviors, chronic conditions, and other covariates. Disability trends research must not be limited to older adults. Researchers and policy makers interested in health care policy, planning, and caregiving should pay attention to disability trends among young adults in the United States.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".