Interaction between sex and rurality on the prevalence of diabetes in Guyana: a nationally representative study
Bibliographic record
Abstract
INTRODUCTION: Diabetes prevalence has never been measured in Guyana. We conducted a nationally representative cross-sectional study to estimate the prevalence of diabetes and pre-diabetes, and the association between sex and diabetes. RESEARCH DESIGN AND METHODS: and fasting blood glucose testing. We estimated the prevalence of diabetes and pre-diabetes and measured the association between sex and diabetes prevalence using logistic regression to compute adjusted ORs. RESULTS: We included 805 adults (511 women, 294 men, mean age 41.8 (SD 14.4) years). The national prevalence of diabetes was 18.1% (95% CI: 15.4% to 20.8%), with higher rates among women (21.4%, 95% CI: 18.0% to 24.7%) than men (15.1%, 95% CI: 10.9% to 19.3%). Sex-specific diabetes prevalence varied significantly across urban and rural areas (p=0.002 for interaction). In rural areas, diabetes was twice as common among women (24.1%, 95% CI: 20.1% to 28.2%) compared with men (11.8%, 95% CI: 7.7% to 15.9%). After adjusting for prespecified covariates, rural women had double the odds of diabetes compared with rural men (OR 2.1, 95% CI: 1.20 to 3.82). This prevalence pattern was reversed in urban areas (diabetes prevalence, women: 13.9%, 95% CI: 8.7% to 19.0%; men: 22.0%, 95% CI: 12.9% to 31.1%), with urban women having half the odds of diabetes compared with urban men (OR 0.4, 95% CI: 0.20 to 0.99). We estimated that nearly one-third of women and over a quarter of men had diabetes or pre-diabetes. CONCLUSIONS: The burden of diabetes in Guyana is considerably higher than previously estimated, with an unexpectedly high prevalence among women-particularly in rural areas.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".