Bridging the pulse: Exploring inequalities in diabetes and hypertension medication prescriptions in Spain’s immigrant and native communities
Bibliographic record
Abstract
Migrants often face barriers in accessing high quality healthcare, leading to unequal treatment. This research investigates the disparities in medication utilization for cardiovascular risk factors between immigrant and native-born populations in Spain. The study specifically examines differences in drug prescriptions for managing diabetes and hypertension, two key contributors to cardiovascular disease. We analyze administrative healthcare records to examine the probability of patients receiving prescriptions for antidiabetic and antihypertensive medications. Additionally, we assess the likelihood of patients undergoing tests to measure glycated hemoglobin levels and blood pressure, two crucial indicators for monitoring diabetes and hypertension management.The analysis is stratified across different levels of medical needs, by also controlling for individual socioeconomic status, physician diagnoses, biometric data and primary care centers fixed effects. The findings reveal that all immigrant groups have lower probabilities of being prescribed medications for diabetes and hypertension and this is especially true for people with higher levels of healthcare needs. These findings underscore the importance of addressing healthcare disparities to achieve more equitable outcomes for immigrant communities. • Study on migrant-native healthcare gaps in Spain, focusing on CVDs. • Immigrants less likely to get CVD meds vs. natives, needs-adjusted. • Immigrant health outcomes vary: better in US/Canada, mixed in Europe. • Examines diabetes/hypertension care disparities via health admin data. • Barriers: language, discrimination, culture, SES, systemic issues.
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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 | 0.001 | 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".