Poor glycemic control and its predictors among people living with diabetes in low- and middle-income countries: a systematic review and meta-analysis
Bibliographic record
Abstract
Variability in blood glucose remains a challenge in diabetic management. Therefore, this review aimed to estimate the overall poor glycemic control and identify its predictors among people living with diabetes in low- and middle-income countries (LMICs). The authors searched articles in PubMed, Embase, OVID, CINAHL Plus, Cochrane Library, PsychInfo, Google, and Google Scholar. The search results were exported to the Rayyan software to check their eligibility. The Newcastle–Ottawa scale was used to assess the study quality. Stata version 17 was used for analysis. A random effect model was computed. Heterogeneity was assessed by the Cochrane Q test and I-squared (I2). The funnel plot asymmetry test and/or Egger’s regression test (p < 0.05) were used to detect the publication bias. Then it was treated by the trim and fill analysis. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the reference number CRD42023430175. In total, forty-nine articles were used. Of which forty-five articles with 15,981 participants were used for pooled prevalence estimation. The pooled prevalence of poor glycemic control among people living with diabetes in LMICs was found to be 69.06% (95% CI: 65.66–72.46), I2 = 96.1%, p < 0.001). Alcohol intake (AOR = 2.07: 95% CI: 1.27–3.36), poor adherence to dietary recommendations (AOR = 3.16, 95% CI: 1.13–8.85), poor adherence to anti-diabetic medication (AOR = 2.85, 95% CI: 1.04 -7.85), diabetic complications (AOR = 1.37, 95% CI: 1.00–1.88), and co-morbid conditions (AOR = 1.98, 95% CI: 1.28–30.07) were found to be predictors of poor glycemic control. The pooled prevalence of poor glycemic control was significantly high in LMICs. Drinking alcohol, poor adherence to dietary recommendations, poor adherence to anti-diabetic medication, diabetes complications, and co-morbid conditions were found to be the determinants of poor glycemic control among people living with diabetes. Tight glycemic control strategies have been implemented to achieve optimal blood glucose. Further research on the regional and contextual factors influencing glycemic control would be recommended.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.009 | 0.010 |
| 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".