Economic Barriers to Pain Management: Disparities in Medication Access among Europeans Over 50
Bibliographic record
Abstract
Abstract Objectives Joint pain is a widespread phenomenon among older adults with significant costs to individuals and healthcare systems. This study investigates whether economic factors-specifically, the ability to pay and supplementary insurance-are associated with access to pain medication among Europeans aged 50 and older. Methods Data were obtained from the Survey of Health, Aging and Retirement in Europe (SHARE), comprising 64,281 individuals aged 50+ from nineteen European countries and Israel. Results One-third of respondents reported joint pain, with similar prevalence across age groups but higher rates among women. Among those reporting pain, 21.5% experienced mild pain, 52.9% moderate pain, and 26% severe pain. Back pain was most common (64.3%), followed by knee (44.2%) and hip pain (23.5%). Approximately half of the individuals with joint pain were not taking medication for pain management. Multivariate logistic regression revealed that medication use was significantly higher among those who were male, younger than 59, had higher education levels, reported the ability to cope economically, and possessed supplementary insurance. Notably, when controlling for economic factors, the likelihood of taking pain medication decreased with increasing age. Discussion Our findings demonstrate significant economic inequity in pain medication access among older Europeans. Despite joint pain being a major public health concern, financial barriers prevent many from accessing appropriate pain management. The strong association between income status and pain medication use highlights a critical healthcare disparity that requires policy intervention. While pain management approaches are multifaceted, ensuring equitable access to prescription medications is essential for comprehensive pain care and represents an important target for reducing health inequities among aging populations. Key messages • Economic factors create significant disparities in pain medication access among older Europeans, requiring targeted policy interventions. • Healthcare systems must address financial barriers to pain medication as an essential component of equitable pain management for aging populations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".