Prevalence of and risk factors for diabetes mellitus in the school-attending adolescent population of the United Arab Emirates: a large cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: The prevalence of diabetes has reportedly increased among adolescents in low-income and middle-income countries of the Middle East and may be linked to social, demographic and economic contextual factors. This study aimed: (1) to estimate the prevalence of self-reported diagnosis of diabetes in the adolescent population of the United Arab Emirates (UAE); (2) to assess differences in the prevalence based on gender and (3) to identify other characteristics of those with diabetes including parental marital status, smoking/illegal drug use, quality of life and nationality. DESIGN: A secondary data analysis was performed on data from the National Study of Population Health in the UAE, conducted between 2007 and 2009. SETTING: Large cross-sectional population-based survey study. PARTICIPANTS: Survey was administered to a stratified random sample of 151 public and private schools from the UAE, across 7 emirates. 6365 school-attending adolescents (12-22 years; mean=16 years) participated. OUTCOMES: Multivariable logistic regression analysis was used to examine the relationships between diabetes diagnosis and characteristics of participants after adjusting for confounding from other predictors. RESULTS: The overall prevalence of self-reported diabetes was 0.9% (95% CI 0.7% to 1.2%) and was higher in males 1.5% (95% CI 1.0% to 2.1%) than females 0.5% (95% CI 0.3% to 0.8%), (p<0.001). Children of parents who were not currently married had more than twice the odds of self-reporting diabetes (p=0.031) compared with those with married parents. Adolescents who reported ever smoking/using illegal drugs had more than three times the odds of diabetes (p<0.001). CONCLUSION: We found a positive association between certain characteristics of adolescents and their diabetes status, including male gender, parental marital status and smoking/illegal drug use. The high prevalence of smoking/illegal drug use among those reporting a diagnosis of diabetes suggests the need for behavioural and mental health interventions for adolescents with diabetes, as well as strong parental support and involvement.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".