National income inequality and adolescent chronic pain: a time series analysis of 29 countries
Bibliographic record
Abstract
ABSTRACT: Chronic pain is a major public health concern in adolescence. It is not evenly distributed across the population, with higher prevalence among adolescents from lower socioeconomic status (SES) backgrounds. Evidence suggests that countries with greater income inequality report worse adolescent health and wider socioeconomic health disparities. However, international research on the role of national income inequality in shaping adolescent chronic pain remains limited. In this study, we examined whether national income inequality was associated with adolescent chronic pain prevalence and chronic pain-related socioeconomic disparities, and whether changes in income inequality over time corresponded to changes in these outcomes. Data were drawn from adolescents across 29 Western countries/regions that participated in 5 waves (2002, 2006, 2010, 2014, and 2018) of the Health Behaviour in School-aged Children surveys (pooled n = 826,563). Chronic pain and SES data were aggregated to create a country-level slope index of inequality and then combined with country-level national income inequality data. Prais-Winsten time-series regression models with panel-corrected standard errors were conducted. Results showed that higher national income inequality was associated with a higher prevalence of chronic pain (B = 0.303; P < 0.001) and greater chronic pain-related socioeconomic disparities (B = 0.003; P < 0.001). In addition, changes in national income inequality over successive survey years were associated with changes in chronic pain-related socioeconomic disparities (B = 0.004; P = 0.014). The study findings highlight the need for policies addressing structural income inequality and for integrating socioeconomic factors, social support, and community resources into interventions and treatments to reduce chronic pain prevalence and eliminate related disparities, especially among adolescents from lower SES backgrounds.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".