Does Pain Explain Trends in Disability? An Analysis of Middle-Aged and Older U.S. Adults, 2002–2018
Bibliographic record
Abstract
OBJECTIVES: This article investigates the role of pain in disability trends in the United States, within the context of recent unfavorable disability trends and the concurrent rise in pain. METHODS: We conducted a 2-part analysis using National Health Interview Survey data from 2002 to 2018 for U.S. adults aged 45-84. First, we assessed how changes in the prevalence of 5 site-specific types of pain (headaches/migraines, joint, low back, neck, and facial/jaw pain) associated with disability trends. Second, we used self-reported causes of disability and examined whether there has been a change in the proportion of individuals who attribute their disability to 1 of 5 chronic or acute painful conditions. RESULTS: The 5 site-specific types of pain, individually and collectively, were significantly associated with increases in disability. If site-specific chronic pain had not increased during the study period, the trend for functional limitations would have been 40% lower, and that for activity limitations would have shown a slight decline instead of an increase. Attributions of functional limitations to painful conditions increased by 23% during the 2002-2018 period, representing an additional 9.82 million Americans experiencing pain-attributable disability. Arthritis/rheumatism, back/neck problems, and other musculoskeletal/connective conditions were the primary sources of pain-related disability. DISCUSSION: Our research provides the first systematic, national examination of how pain is contributing to disability trends in the United States. The findings have implications for disability reduction policies and shed light on the far-reaching consequences of pain for overall population health.
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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 | 0.003 |
| 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) | 0.001 | 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".