Hidden Wounds: Prevalence of Chronic Wounds in Asia, A Systematic Review and Meta‑Analysis
Bibliographic record
Abstract
Background: Chronic wounds are a persistent and costly health issue globally, with particularly high burdens in low- and middle-income countries. Despite increasing awareness, comprehensive regional estimates of chronic wound prevalence across Asia remain limited and inconsistent. Purpose: This study aimed to estimate the pooled prevalence of chronic wounds in Asian populations and identify key moderators associated with their distribution and burden, including comorbidities, wound types, and geographic or healthcare-related factors. Methods: A systematic review and meta-analysis were conducted according to PRISMA guidelines. Searches of PubMed, Embase, Scopus, and Web of Science identified 46 eligible observational studies published through 2024. Random-effects models were used to estimate pooled prevalence. Subgroup and meta-regression analyses explored the influence of demographic, clinical, and geographic moderators. Risk of bias was assessed using the Newcastle-Ottawa Scale. Results: A total of 46 studies comprising 6,425 chronic wound cases were included in the analysis. The pooled prevalence of chronic wounds was 32.1% (95% CI: 25.9–39.1), with substantial heterogeneity (I² = 98.8%). Diabetic foot ulcers were the most common wound type. Prevalence was significantly higher in developing countries (34.8%), in Southeast Asia (50.8%), and in hospital-based settings (32.6%). Smoking (β = 0.049; p = 0.019) and malnutrition (β = 0.047; p < 0.001) were significantly associated with increased prevalence, while serum albumin was inversely associated (β = –4.308; p < 0.001). Conclusions: Chronic wounds are highly prevalent across Asian populations, particularly in resource-limited settings. Socioeconomic context, nutritional deficiencies, and modifiable lifestyle factors contribute substantially to this burden. These findings support the need for standardized wound surveillance, targeted prevention strategies, and improved nutritional and behavioral interventions, especially in developing health systems. Future studies should adopt prospective designs and harmonized definitions to strengthen regional and global wound epidemiology.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| 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".