Prevalence and risk factors for diabetes mellitus among adults in Ghana: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence and risk factors for diabetes in Ghana vary from location to location, yet no study has been conducted to quantitatively synthesize the available studies. METHODS: The authors conducted a comprehensive literature search in Medline (PubMed), Embase, Cinahl, Web of Science, Scopus, African journals and grey literature. Two reviewers screened the articles for relevance, identified and selected studies based on inclusion and exclusion criteria. Methodological quality was independently assessed, using two validated assessment-of-bias tools. We explored study heterogeneity and performed a publication bias check. RESULTS: This search yielded a total of 624 articles, of which 17 studies were finally selected for the qualitative review and 12 studies included in the meta-analysis. The overall prevalence of diabetes mellitus among adult Ghanaians was high at 6.46% (95% CI: 4.66-8.26%) based on the inverse-variance random-effects model. Significant risk factors associated with diabetes were a family history of diabetes (OR: 3.46, 95% CI: 2.50-4.78), physical inactivity (OR: 3.06, 95% CI: 1.66-5.64) and age ≥40 years (OR: 2.36, 95% CI: 1.77-3.16). CONCLUSION: Studies with high methodological quality provided sufficient evidence about diabetes prevalence among adults and the associated significant risk factors in Ghana.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".