The Geography of Pain in the United States and Canada
Bibliographic record
Abstract
Pain epidemiologists have, thus far, devoted scant attention to geospatial analyses of pain. Both cross-national and, especially, subnational variation in pain have been understudied, even though geographic comparisons could shed light on social factors that increase or mitigate pain. This study presents the first comparative analysis of pain in the U.S. and Canada, comparing the countries in aggregate, while also analyzing variation across states and provinces. Analyses are based on cross-sectional data collected in 2020 from U.S. and Canadian adults 18 years and older (N = 4,113). The focal pain measure is a product of pain frequency and pain interference. We use decomposition and regression analyses to link socioeconomic characteristics and pain, and inverse-distance weighting spatial interpolation to map pain levels. We find significantly and substantially higher pain in the U.S. than in Canada. The difference is partly linked to Americans' worse economic conditions. Additionally, we find significant pain variability within the U.S. and Canada. U.S. states in the Deep South, Appalachia, and parts of the West stand out as pain 'hotspots' with particularly high pain levels. Overall, our findings identify areas with a high need for pain prevention and management; they also urge further scholarship on geographic factors as important covariates in population pain. PERSPECTIVE: This study documents the high pain burden in the U.S. versus Canada, and points to states in the Deep South, Appalachia, and parts of the West as having particularly high pain burden. The findings identify geographic areas with a high need for pain prevention and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".