Hypertension in Syrian refugees: prevalence, awareness, and access to care in Denmark and Lebanon
Bibliographic record
Abstract
Background: Hypertension significantly contributes to global morbidity and mortality rates. Refugees are often exposed to significant risk factors that increase their chances of developing hypertension; however, the prevalence of hypertension among resettled refugees remains understudied. This study estimates and compares the prevalence of hypertension, levels of awareness, and access to care between two groups of Syrian refugees residing in humanitarian settings in Lebanon and Denmark. Methods: We analyzed cross-sectional data collected from refugee camps in Lebanon and asylum centers in Denmark. Cluster-randomized sampling was conducted between January 2016 and December 2019. We present the estimates of hypertension prevalence, psychosocial stress, and lifestyle factors by strata, along with prevalence differences adjusted for confounding using propensity score weighting. Multiple imputation was employed to address missing data, and 95% bootstrap confidence intervals were reported. Results: Of the 712 participants, 113 were from Denmark and 599 from Lebanon. Among them, 68% were women, and the median (IQR) age was 34 years (19). The self-reported prevalence of hypertension was 20% in Lebanon and 11% in Denmark. The prevalence of measured stage 2 hypertension was 23% in Lebanon and 24% in Denmark. The confounder-adjusted and multiple-imputed prevalence of hypertension was 5.0 (95% CI: -4.7; 16.1) percentage points higher in Denmark than in Lebanon. Among those with a known diagnosis of hypertension, only 1.6% in Lebanon and none in Denmark were currently taking medications for hypertension. The median (IQR) number of days since last seen by a medical professional was 8 days (48) in Lebanon and 8 days (21) in Denmark. The mean body mass index (BMI) was 27.0 (SD = 6.15) in Lebanon and 26.2 in Denmark (SD = 6.53). In Denmark, 50.5% reported smoking, and 23.6% reported drinking alcohol, while in Lebanon, the figures were 27.4 and 0.5%, respectively. Conclusion: Hypertension prevalence was found to be high among refugees, even in high-income countries (HICs) such as Denmark. Lack of access to medications in relation to self-reported prevalence was observed in both Lebanon and Denmark. Further investigation into psychosocial stress in humanitarian settings as a contributing factor to hypertension disparities in this population is crucial.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".