Making underrepresented pain visible: Chronic pain disparities among immigrant adolescents in 30 European countries
Bibliographic record
Abstract
Chronic pain is prevalent among adolescents and is associated with significant impairments in physical, emotional, social, and academic functioning. Despite advances in pediatric pain research, including efforts to improve diagnosis and management, critical gaps remain in understanding pain disparities among subpopulations. Immigrant adolescents represent a particularly overlooked group. While Europe hosts more than one-third of the global migrant population, epidemiological data on chronic pain in immigrant adolescents remain scarce and inconsistent. This study examined chronic pain prevalence and differences between immigrant and non-immigrant adolescents in Europe. Cross-sectional data from the 2018 Health Behaviour in School-aged Children study (n=146,187), including nationally representative samples of 11-, 13-, and 15-year-olds from general schools in 30 countries, were used. Chronic pain, defined as daily pain for at least six months, was assessed via self-report. Cross-national age- and gender-standardized prevalences were calculated, and log-binomial regressions examined differences between immigrant and non-immigrant adolescents. Overall, 14% of immigrant adolescents reported chronic pain, compared to 12% of non-immigrants. Significant variations were observed across countries, with chronic pain rates ranging from 7% in Estonia to 31% in Bulgaria. Immigrant adolescents reported significantly higher chronic pain rates in eight countries, particularly in Central and Eastern Europe. This study shows that chronic pain is disproportionately prevalent among immigrant adolescents in several European countries. Policymakers should prioritize comprehensive integration programs, inclusive health care, and culturally responsive care to address inequities and mitigate the burden of chronic pain in this population. PERSPECTIVE: This study reveals significant disparities in chronic pain among immigrant adolescents living in Central and Eastern European countries. By identifying a vulnerable and underrepresented group, these findings underscore the need for inclusive health policies and offer a foundation for future research on sociocultural mechanisms contributing to pediatric chronic pain inequities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".