Prevalence and associated factors of prediabetes among primary health care attendees in the West bank of Palestine: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Prediabetes represents a major global health challenge with substantial implications for population health. This study aimed to determine the prevalence of prediabetes among attendees at Palestinian primary health care (PHC) centers and identify associated factors. METHODS: A cross-sectional study was conducted in PHC centers in the West Bank from February to June 2024. A total of 635 participants, representing both sexes, were recruited from six PHC centers, two from each of the three main regions of the West Bank, including one central and one peripheral center per region. Prediabetes was diagnosed through HbA1c testing. Associated factors were assessed through face-to-face interviews using the validated Canadian Diabetes Risk Questionnaire (CANRISK). RESULTS: The overall prevalence of prediabetes among PHC attendees was 13.7% (95% Confidence Interval (CI): 11.0-16.3%). Multivariable logistic regression analysis identified several factors significantly associated with prediabetes. These included central obesity (adjusted Odss Ratio (aOR) = 4.2; 95% CI: 1.3-13.9), male sex (aOR = 4.5; 95% CI: 2.1-9.7), older age (aOR = 24.1; 95% CI: 7.9-73.7), and a family history of diabetes (aOR = 4.3; 95% CI: 1.6-12.2). Additional significant variables included unemployment (aOR = 2.4; 95% CI: 1.15-4.9), physical inactivity (aOR = 2.1; 95% CI: 1.1-4.1), and irregular consumption of fruits and vegetables (aOR = 3.1; 95% CI: 1.9-5.7). CONCLUSION: The study reveals a significant prevalence of prediabetes among Palestinian PHC attendees, with central obesity, male sex, older age, and family history of diabetes emerging as key associated factors. We recommend that health policymakers integrate prediabetes detection into primary care, and clinicians prioritize lifestyle interventions for individuals at risk.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".