Prevalence of diabetes mellitus among tuberculosis patients in Sub-Saharan Africa: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Tuberculosis and diabetes mellitus are significant global public health challenges. In Sub-Saharan Africa, study findings regarding prevalence of diabetes mellitus amongst tuberculosis patients have been inconsistent and highly variable. Therefore, this systematic review and meta-analysis estimates the overall prevalence of diabetes mellitus among tuberculosis patients in Sub-Saharan Africa. Four international databases (PubMed, Google Scholar, Science Direct and Cochrane Library) were systematically searched. We included all observational studies reporting the prevalence of DM among TB patients in Sub-Saharan Africa. All necessary data for this review were extracted using a standardized data extraction format by two authors (CT and AA1). STATA Version 14 statistical software was employed to conduct meta-analysis. The Cochrane Q test statistics and I 2 test were used to assess the heterogeneity of the studies. Finally, a random effects meta-analysis model was computed to estimate the pooled prevalence of diabetes mellitus in TB patients. Besides, subgroup analysis was done based on different factors. In the meta-analysis, sixteen studies fulfilled the inclusion criteria and were included. The findings of these 16 studies revealed that the pooled prevalence of diabetes mellitus among tuberculosis patients in Sub-Saharan Africa was 9.0% (95% CI: 6.0, 12.0%). The highest prevalence of diabetes mellitus among tuberculosis patients was found in Nigeria (15%), followed by Tanzania (11%), and then Ethiopia (10%). Besides, the prevalence of diabetes mellitus among HIV infected TB patients was (8.9%) which is slightly higher than HIV uninfected (7.7%) TB patients. Diabetes mellitus among tuberculosis patients in Sub-Saharan Africa was significantly high. Moreover, this study found that there was a high prevalence of DM among HIV infected than uninfected TB patients. It is strongly recommended to screen for DM among TB patients and special emphasis should be given for early screening of DM among TB/HIV co-infected patients.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".