Caribbean Women Face Higher Obesity and Diabetes Amid Socioeconomic Struggles
Bibliographic record
Abstract
Background Women in the Caribbean region experience significant health disparities shaped by intersecting medical and socioeconomic challenges. High rates of obesity, diabetes, and maternal mortality have been observed alongside persistent inequalities in development, employment, and food access. This study aimed to assess gender-based disparities in obesity, diabetes, reproductive health, and socioeconomic conditions across Caribbean countries, comparing outcomes to North America to identify structural drivers of women's health inequities. Methods We analyzed regional and gender-based trends in health and social outcomes across up to 30 Caribbean countries. Publicly available data from 2019 to 2022 were compiled to assess noncommunicable disease prevalence, reproductive health indicators, and key economic metrics. Caribbean nations were compared to the United States and Canada to contextualize findings. Results Women in the Caribbean had higher obesity prevalence and a greater proportion of diabetes-related deaths compared to men. The region also reported elevated maternal and infant mortality, lower inequality-adjusted development scores, and wider gender gaps in unemployment. Food insecurity affected more than 40% of the population in several countries. Adolescent fertility and mortality rates were also higher in the Caribbean than in North America. Multivariate analyses revealed strong associations between chronic disease outcomes and structural indicators such as healthcare access and economic inequality. Conclusion Caribbean women face compounding health risks driven by overlapping medical, economic, and social vulnerabilities. These disparities highlight the need for coordinated regional strategies that go beyond behavioral health to address the broader structural determinants of health and gender equity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".