Association between acculturation, obesity and cardiovascular risk factors among male South Asian migrants in the United Arab Emirates – a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Approximately 65% of the United Arab Emirates (UAE) population are economic migrants from the low- and middle-income countries of South Asia. Emerging evidence suggests that expatriate populations from low or middle-income countries that migrate to high-income countries acculturate their lifestyle with the obesogenic behaviours of the host country. Previous research has focussed on migrant populations in the United States. The objective of this study was to assess the prevalence of obesity and explore the relationship between years of residency (surrogate measure for acculturation) and obesity among South Asian (from India, Pakistan and Bangladesh) male immigrants residing in the UAE. METHODS: A random sample of 1375 males was recruited from a mandatory residency visa health screening centre in Abu Dhabi (UAE). Employing a cross-sectional design, participants completed an interviewer-led adapted version of the World Health Organisation STEPS questionnaire, and anthropometric and blood pressure measurements were collected. Glycated haemoglobin (HbA1c) was measured in a random sub-sample (n = 100). Logistic regression was used to determine risk factors for being classified as obese, and to assess the relationship between years of residency and adiposity. RESULTS: The overall prevalence of body mass index-derived overweight and obesity estimates and waist-to-hip-derived central obesity rates was 615 (44.7%) and 917 (66.7%) males, respectively. Hypertension was present in 419 (30.5%) of the sample and diabetes in 9 (9.0%) of the sub-sample. Living in the UAE for six to 10 years or more than 10 years was independently associated with being classified with central obesity (adjusted odds ratio [AOR] 1.63 95% confidence intervals [CI] 1.13 - 2.35, p < 0.008; AOR 1.95 95% CI 1.26 - 3.01, p < 0.002; respectively) compared to residing in the UAE for one to five years. CONCLUSIONS: Our study revealed a high prevalence of overweight, central obesity and hypertension amongst a young South Asian male migrant population in the UAE. Study findings suggest a diminished 'Healthy Migrant Effect' with increased years of residency possibly due to greater acculturation and a transition in lifestyle behaviours. Health initiatives targeting the maintenance of a healthy body size, coupled with regular assessments of glucose control and blood pressure are urgently required in this population.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".