Immigrant status and likelihood of opioid treatment. Lessons from Spain’s National Health Service
Bibliographic record
Abstract
This study investigates opioid prescription patterns among immigrants and native populations in Spain, using novel patient health records from the Base de Datos Clínicos de Atención Primaria (BDCAP). We examined two subsets of data from 2017 and 2018, specifically targeting individuals diagnosed with musculoskeletal (MSK) issues and new cancer diagnoses, as these conditions frequently involve pain management. Our empirical analysis involved estimating a series of linear and count data models to explore the relationship between regions of origin, socioeconomic factors, and the probability of opioid use, controlling for a rich set of health conditions, and primary care centers fixed effects. Despite previously documented healthcare inequities, Spain demonstrates no major differences in opioid prescriptions between immigrants and natives, highlighting the effectiveness of its National Health Service (NHS). This contrasts sharply with the opioid crises in the United States and Canada. The absence of significant disparities underscores the importance of comprehensive healthcare systems and stringent regulations on opioid prescribing practices, as observed in European guidelines. Policy implications include the need to maintain and strengthen public healthcare systems to ensure equitable access to essential medications like opioids and to continue monitoring and regulating opioid prescribing practices to safeguard public health.
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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 | 0.000 | 0.000 |
| 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".