Increases in BMI and chronic pain for US adults in midlife, 1992 to 2016
Bibliographic record
Abstract
Recent unprecedented increases in mortality and morbidity during midlife are often ascribed to rising despair in the US population. An alternative and less often examined explanation is that these trends reflect, at least in part, the lagged effects of the obesity epidemic. Adults in midlife today are more likely to live with obesity and have a greater cumulative exposure to excess adiposity during their lifetime than any previous generation. Prior work has demonstrated a link between obesity and mortality risk at midlife, but the mechanisms remain unclear. Pain may represent one important pathway linking obesity to mortality trends. Pain is a debilitating condition that has increased significantly over recent decades and is associated with both morbidity and mortality, including suicide and opioid-related mortality. Evidence suggests obesity and pain may be linked, but there is little evidence of an association at the population level. In this paper, we examine to what extent increases in overweight and obesity explain the rising trends in chronic pain observed among middle-aged adults in the US from 1992 to 2016. We assess trends in both mild/moderate nonlimiting pain and severe and/or limiting pain. In doing so, we draw attention to one mechanism through which overweight/obesity may have contributed to recent population health trends. Our analysis found that increases in BMI from 1992 to 2016 may account for up to 20% of the upward trend in mild/moderate nonlimiting pain and 32% of the trend in severe and/or limiting pain for women, and 10% and 19% of the trends respectively for men.
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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 | 0.001 | 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".