A global study of pain prevalence across 52 countries: examining the role of country-level contextual factors
Bibliographic record
Abstract
ABSTRACT: There is wide variation in population-level pain prevalence estimates in studies of survey data around the world. The role of country-level social, economic, and political contextual factors in explaining this variation has not been adequately examined. We estimated the prevalence of unspecified pain in adults aged 25+ years across 52 countries using data from the World Health Survey 2002 to 2004. Combining data sources and estimating multilevel regressions, we compared country-level pain prevalence and explored which country-level contextual factors explain cross-country variations in prevalence, accounting for individual-level demographic factors. The overall weighted age- and sex-standardized prevalence of pain across countries was estimated to be 27.5%, with significant variation across countries (ranging from 9.9% to 50.3%). Women, older persons, and rural residents were significantly more likely to report pain. Five country-level variables had robust and significant associations with pain prevalence: the Gini Index, population density, the Gender Inequality Index, life expectancy, and global region. The model including Gender Inequality Index explained the most cross-country variance. However, even when accounting for country-level variables, some variation in pain prevalence remains, suggesting a complex interaction between personal, local, economic, and political impacts, as well as inherent differences in language, interpretations of health, and other difficult to assess cultural idiosyncrasies. The results give new insight into the high prevalence of pain around the world and its demonstrated association with macrofactors, particularly income and gender inequalities, providing justification for regarding pain as a global health priority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".