Prevalence of diabetic foot ulcer and its association with duration of illness and residence in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Diabetic foot ulcer (DFU), devastating complications of diabetes mellitus, is a major public health problem, and one of the leading reasons for hospital admission, amputations, and even death among diabetic patients in Ethiopia. Despite its catastrophic health consequences, the national burden of diabetic foot ulcer remains unknown in Ethiopia. Hence, the objective of this systematic review and meta-analysis was to estimate the national prevalence of diabetic foot ulcer and investigate the association with duration of illness and patient residence among diabetic patients. Methods We searched PubMed, Google Scholar, Cochrane Library, CINAHL, EMBASE, and PsycINFO databases for studies of diabetic foot ulcers prevalence that published from conception up to June 30, 2019. Quality of each article was assessed using a modified version of the Newcastle-Ottawa Scale for cross-sectional studies. All statistical analyses were done using STATA version 14 software for Windows, and meta-analysis was carried out using a random-effects method. The pooled national prevalence of diabetic foot ulcers was presented using a forest plot. Results A total of 10 studies with 3,029 diabetic patients were included. The pooled national prevalence of diabetic foot ulcers among Ethiopian diabetic patients was 11.27% (95% CI 7.22, 15.31%, I 2 =94.6). Duration of illness (OR: 3.91, 95%CI 2.03, 7.52, I 2 =63.4%) and patients’ residence (OR: 3.40, 95%CI 2.09, 5.54, I 2 =0.0%) were significantly associated with a diabetic foot ulcer. Conclusion In Ethiopia, at least one out of ten diabetic patients had diabetic foot ulcers. Healthcare policymakers (FMoH) need to improve the standard of diabetic care and should design effective preventive strategies to improve health care delivery for people with diabetes and reduce the risk of foot ulceration.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".