Sex differences in chronic kidney disease prevalence in Asia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Previous reports on the prevalence of chronic kidney disease (CKD) in Asia have suggested important sex disparities but have been inconsistent in nature. We sought to synthesize available sex-disaggregated CKD prevalence data in Asia to quantify sex disparities in the region. Methods: We systematically searched MEDLINE and Embase for observational studies involving ≥500 adults who reported sex-disaggregated CKD prevalence data in any of the 26 countries in East, Southeast and South Asia. For each study we calculated the female:male prevalence ratio (PR), with a ratio >1 indicating a higher female prevalence. For each country, log-transformed PRs were pooled using random effects meta-analysis. These were then combined using a fixed effects model, weighting by population size, to estimate a pooled PR for each of East, Southeast and South Asia and Asia overall. Results: Sex-disaggregated data were available from 171 cohorts, spanning 15 countries and comprising 2 550 169 females and 2 595 299 males. Most studies (75.4%) came from East Asia (China, Taiwan, Japan and South Korea). Across Asia, CKD prevalence was higher in females {pooled prevalence 13.0% [95% confidence interval (CI) 11.3-14.9]} compared with males [pooled prevalence 12.1% (95% CI 10.3-14.1)], with a pooled PR of 1.07 (95% CI 0.99-1.17). Substantial heterogeneity was observed between countries. The pooled PRs for East, Southeast and South Asia were 1.11 (95% CI 1.02-1.21), 1.09 (0.88-1.36) and 1.03 (0.87-1.22), respectively. Conclusions: Current evidence suggests considerable between-country and -region heterogeneity in the female:male PR of CKD. However, there remains a large part of the region where data on sex-specific CKD prevalence are absent or limited. Country-level assessment of the differential burden of CKD in females and males is needed to define locally relevant policies that address the needs of both sexes.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.003 | 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".