Prevalence of chronic kidney disease stages 3–5 in low- and middle-income countries in Asia: A systematic review and meta-analysis
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a major public health problem in low- and middle-income countries (LMICs). Although CKD prevalence has been rapidly increasing in LMICs, particularly in Asia, quantitative studies on the current epidemiology of CKD in this region are limited. This study aimed to identify the prevalence of CKD stages 3-5 in LMICs in Asia, by subregion, country economy classification, identification of CKD, traditional and non-traditional risk factors. A systematic review and meta-analysis of observational studies was conducted through a literature search of seven electronic databases and grey literature search published until November 2021. The Newcastle-Ottawa quality assessment scale (NOS) was used to assess the risk of bias of each study. A random-effects model was used to estimate pooled prevalence. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42019120519). Of 4,548 potentially relevant records, 110 studies with moderate and high quality were included with 4,760,147 subjects. The average prevalence (95% CI) of CKD stages 3-5 in 14 LMICs in Asia was 11.2% (9.3-13.2%). The prevalence of CKD stages 3-5 was varied among subregions and country economic classification. CKD prevalence was 8.6% (7.2-10.2%) in east Asia, 12.0% (7.7-17.0%) in south-east Asia, 13.1% (8.7-18.2%) in western Asia, and 13.5% (9.5-18.0%) in south Asia. CKD prevalence was 9.8% (8.3-11.5%) in upper-middle-income countries and 13.8% (9.9-18.3%) in lower-middle-income countries. Prevalence of CKD stage 3-5 in LMICs in Asia is comparable to global prevalence. High level of heterogeneity was observed. Study of factors and interventions that lead to the delay of CKD progression is needed.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.009 | 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".