TRENDS IN PAIN PREVALENCE AMONG OLDER ADULTS IN THE UNITED STATES: 1992 TO 2012
Bibliographic record
Abstract
This analysis will first examine population trends in pain prevalence amongst older Americans across a twenty-year period and second assess the degree to which these trends are explained by changing socio-demographic composition and health characteristics. Pain is an important indicator of overall health among older adults and is related to many physical and psychosomatic conditions and disorders. While specific sources and proximate causes are not easy to identify, the negative consequences of pain for functioanl health are evident. Consequently, understanding trends in pain allows insight into changes in quality of life. Moreover, despite increased literature on trends in disability and other functional health disorders, few studies have monitored whether pain trends correspond with these. This study relies on over 150,000 observations from the Health and Retirement Study. This is an ideal data source since pain items have been measured consistently across most survey waves from 1992 to 2012. Deconstructing pain into mild, moderate and severe forms, the paper first evaluates trends in any and severity of pain over time. Preliminary results indicate rising trends for both males and females. Using binary and ordered logistic regression, the analysis then examines predictors of these rising trends. The conclusion will compare trends and predictors to the much more established literature on disability. In sum, despite presence of pain items in several national level surveys, little research has monitored trends over time, and as an indicator of population health, pain has been virtually ignored. The current study fills this gap.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".