Prevalence of diabetes and prediabetes in South Asian countries: a systematic review and meta-analysis
Bibliographic record
Abstract
South Asia is observing an epidemiological transition from communicable to non-communicable diseases where diabetes is an important marker. In this study, we estimate the overall prevalence of diabetes and prediabetes in South Asian countries. A systematic literature review and meta-analysis is performed to estimate the prevalence of diabetes and prediabetes in Bangladesh, India, Nepal, Pakistan, Sri Lanka, Bhutan, the Maldives, and Afghanistan using studies based on only the nationally representative surveys and published from 2012 until June 2024. The quality of the included articles was assessed using the Newcastle–Ottawa Scale. Both random-effect (Der Simonian-Laird inverse variance) and fixed-effect models were used to perform meta-analyses followed by meta-regression. We identified 64 studies for diabetes and 14 studies for prediabetes, covering a total of 4,613,487 and 156,407 participants, respectively. Overall, the pooled prevalence of diabetes and prediabetes was 8.56% (95% CI 5.73–11.91; I2 = 99.99%) and 18.99% (95% CI 12.74–26.6; I2 = 99.87%), respectively, with high heterogeneity observed among the studies based on random-effect models. We also found that the prevalence of diabetes identified by clinical methods was higher than the self-reported measures. The analyses revealed that the prevalence of diabetes and prediabetes in South Asia throughout the study period is significantly elevated. This necessitates the establishment of comprehensive guidelines for South Asians to mitigate the escalating prevalence of diabetes and prediabetes.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| 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) | 0.002 | 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".