Prevalence and antimicrobial susceptibility level of typhoid fever in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
Typhoid fever continues to be a health challenge in low-and middle-income countries where access to clean water and sanitation infrastructure is scarce. The non-confirmatory diagnostic method continues to hinder effective diagnosis and treatment, ensuring in a high antimicrobial resistance. This systematic review and meta-analysis aimed to estimate the pooled prevalence and antimicrobial susceptibility level of typhoid fever in Ethiopia. The review was designed based on the condition-context-population review approach. Fifteen eligible articles were identified from PubMed, Google Scholar, and Science Direct databases. Risk of bias and quality of studies were assessed using the Joanna Briggs Institute’s appraisal criteria. Heterogeneity was assessed using Cochran’s Q test and I2 statistics. The review protocol was registered in PROSPERO (registration number CRD42021224478). The estimated pooled prevalence of typhoid fever from blood and stool culture diagnosis was 3% (95% CI: 2%–4%, p < 0.01) (I2 = 82.25) and Widal test examination 33% (95% CI: 22%–44%) (I2 = 99.14). The sub-group analyses identified a lower detection of typhoid fever of 2% (95% CI: 1%–3%) among febrile patients compared to typhoid suspected cases of 6% (95% CI: 2%–9%). The stool culture test identified was twofold higher, value of 4% (95% CI: 2%-7%) salmonella S. Typhi infection than blood culture test of 2% (95% CI: 1%–4%). The antimicrobial susceptibility of salmonella S. Typhi for antibiotics was 94%, 80% and 65% for ceftriaxone, ciprofloxacin, and gentamycin respectively. Low susceptibility of salmonella S. Typhi isolates against nalidixic acid 22% (95% CI: 2%–46%) and chloramphenicol 11% (95% CI: 2%–20%) were observed. The diagnosis of typhoid fever was under or overestimated depending on the diagnostic modality. The Widal test which identified as nonreliable has long been used in Ethiopia for the diagnosis of salmonella S. Typhi causing high diagnosis uncertainties. Antimicrobial susceptibility of salmonella S. Typhi was low for most nationally recommended antibiotics. Ethiopian Food and Drug Authority must strengthen its continued monitoring and enhanced national antimicrobial surveillance system using the best available state-of-the-art technology and or tools to inform the rising resistance of salmonella S. Typhi towards the prescription of standard antibiotics. Finally, it is crucial to develop an evidence-based clinical decision-making support system for the diagnosis, empiric treatment and prevention of antimicrobial resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".