Socio-demographic and clinical characteristics of diabetes mellitus in rural Rwanda: Time to contextualize the interventions? A cross-sectional study
Bibliographic record
Abstract
Abstract Aim Existing prevention and treatment strategies target traditional risk factors for diabetes, yet this approach might not always be appropriate in some populations. This study aims to assess the socio-demographic and clinical characteristics of diabetes in rural Rwanda. Methods A cross-sectional, clinic-based study was conducted in which individuals with diabetes mellitus were consecutively recruited from April 2015 to April 2016. Demographic and clinical data were collected from patient interviews, medical files and physical examinations. Chi-square tests and T-tests were used to detect disproportionate numbers and means between rural and urban residents. Results A total of 472 participants were recruited, including 295 women and 315 rural residents. Compared to urban residents, rural residents had lower levels of education, more low-income work and limited access to running water and electricity. Diabetes was diagnosed at a younger age in rural residents (mean age 32±18 vs 41±17 years; p < 0.001). Physical inactivity, family history of diabetes and obesity were significantly less prevalent in rural than in urban individuals (44% vs 66%, 14.9% vs 28.7% and 27.6% vs 54.1%, respectively; p < 0.001). The frequency of fruit and vegetable consumption was lower in rural than in urban participants. High waist circumference was more prevalent in urban than in rural women and men (75.3% vs 45.5% and 30% vs 6%, respectively; p< 0.001). History of childhood under-nutrition was more frequent in rural than in urban individuals (22.5% vs 6.4%; p< 0.001). Conclusions Characteristics of people with diabetes in rural Rwanda appear to differ from those of individuals with diabetes in urban settings, suggesting that prevention strategies should differ between settings.
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".