Composite risk of cardiovascular disease comorbidity in people living with diabetes in Africa.
Bibliographic record
Abstract
Aim: Despite the high likelihood of comorbidity from shared risk factors, the cardiovascular disease (CVD) risk, based on the combined presence of key risk factors, in people with diabetes mellitus is poorly understood. Thus, this study quantifies the composite CVD risk and its association with sociodemographic factors in people living with diabetes mellitus in Africa. Methods: Using the WHO STEPwise approach to noncommunicable diseases risk factor Surveillance (STEPS) data and generalized linear mixed models, we analyzed CVD risk in 4738 nonpregnant adults with diabetes in Africa. The composite CVD risk was measured with the STEPS-CARDIO index, which includes smoking, overweight/obesity, hypertension, physical activity, and diet. Results: The mean STEPS-CARDIO score ranged from 2 to 3.22, signifying that, on average, people with diabetes in the included countries had a medium risk of developing CVD. Higher CVD risk was observed with increasing age [odds ratio (OR): 1.03, 95% confidence interval (CI): 1.02-1.04], and among women compared with men (OR: 1.20, 95% CI: 1.05-1.38). Other factors - education, marital status, occupation, and household size - were also generally associated with elevated CVD risk, but being a student (compared to being employed) was associated with lower risk (OR: 0.51, 95% CI: 0.31-0.86). Additional analyses suggested that this protective association was partly age-driven, as most students were younger than 30 years. Conclusion: Findings highlight gendered and social differences in CVD risk among people with diabetes mellitus in Africa, underscoring the need for tailored preventive interventions.
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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 | 0.001 |
| 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".