Chronic pain prevalence and trends in urban, suburban, and rural areas among American adults aged 55+, 1998–2022
Bibliographic record
Abstract
Rural-urban health disparities in the United States are large and persistent, yet most surveillance efforts focus on mortality and disability. Monitoring rural-urban trends in pain, a major but overlooked indicator of population health, remains understudied. Given changes in demographics and resources of urban, suburban, and rural areas since the turn of the 21st century, which may have altered place-based differences in pain prevalence. Using nationally representative data from the Health and Retirement Study of 35,230 adults aged 55 and older (n = 206,600 person-wave observations), we estimated pain prevalence and trends across urban, suburban, and rural areas from 1998 to 2022. We assessed variation by age, sex/gender, race and ethnicity, and census region. Over 24 years, pain prevalence increased by 70 % (Prevalence Ratio [PR] = 1.70, 95 % Confidence Interval [CI]: 1.64, 1.75) and was consistently highest in rural areas and lowest in urban areas. However, pain prevalence rose most sharply in suburbs as compared to both rural and urban areas (suburban and time interaction vs. rural areas: PR = 1.08, 95 % CI: 1.00, 1.17). Suburban pain prevalence was similar to urban levels in 1998 but converged with that of rural levels by 2022. Stratified analyses revealed broadly similar patterns across demographic and regional groups, with particularly rapid increases among suburban populations in the South. These findings highlight nationwide increases in chronic pain, with suburban areas emerging as new "hotspots" alongside rural areas. Given that pain is a leading cause of disability and functional decline, monitoring place-based trends is essential for addressing this growing public health concern.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".