Chronic Pain in Older Adults: Prevalence, Risk Factors, and Consequences
Bibliographic record
Abstract
Abstract Over 100 million Americans experience chronic pain, and older adults are disproportionately affected. This symposium presents four studies using large population-based samples of older adults from three different countries to examine the prevalence, risk factors, consequences, and management of chronic pain. Using data from two generations of Framingham Heart Study participants, Felson identified a secular increase in widespread pain prevalence measured when both cohorts were in their 70s. This rising prevalence provides strong context for Limani’s examination of chronic pain’s effect on successful aging in the Canadian Longitudinal Study on Aging. Limani found that chronic pain significantly lowers respondents’ perceptions of their physical, psychological, and social wellbeing, with lower income and higher pain severity exacerbating these effects. Discerning safe and effective chronic pain management in older adults is a core research priority, particularly around opioid use. Milani investigated the interplay between sex, race/ethnicity, and cognitive impairment on self-reported opioid use from the Health and Retirement Study (HRS). After integrating Medicare Claims data, Milani will additionally explore parallel patterns of opioid prescription. Finally, Huang highlights the enduring health impact of early-life trauma using data from the Vietnam Health and Aging Study: early-life exposure to the Vietnam War increased the likelihood of later-life chronic pain. These studies leverage data from well-powered surveys to document trends in widespread pain prevalence, person-level consequences of chronic pain, disparities in opioid use, and the long-term health consequences of traumatic early-life experiences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".