Prevalence of Human Brucellosis in Ethiopia: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Brucellosis is a significant public health problem in Ethiopia with a high prevalence in both animals and humans. The disease is mainly transmitted through the consumption of unpasteurized dairy products, direct contact with infected animals, and inhalation of contaminated aerosols. The prevalence of the disease varies across different regions of Ethiopia, with the highest rates reported in areas with high livestock populations and low levels of public health awareness, which causes human suffering and huge economic losses. Several epidemiological studies have been conducted; however, reports have shown discrepant results. Therefore, this study aimed to understand the current disease burden, incidence, variation, and research output/data. Therefore, this systematic review and meta-analysis aimed to generate representative data on the prevalence of human brucellosis in Ethiopia. Methods: - We searched several databases, including PubMed, ScienceDirect, African Journals Online, Embase, and Google Scholar, for articles published in English between September 2007 and August 2022. The Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument and Newcastle-Ottawa scale were used for data extraction and quality assessment, respectively. We used statistical software (Stata version 14) and random-effects model for meta-analysis at 95% confidence interval (CI). This study included all references and articles that reported the prevalence of human brucellosis in Ethiopia, and extracted data from 26 studies covering 5,718 human serum samples. The Complement Fixation Test (CFT) was used as a confirmatory serological test to diagnose human brucellosis. Result: - The results suggest that the seroprevalence of human brucellosis in Ethiopia is relatively high, with an overall pooled prevalence of 14.0% (95% CI: 10.23, 18.12). The highest prevalence was observed in the Afar region, with a prevalence of 24.21%), whereas the lowest prevalence was reported in the Oromia region, with a prevalence of 7.75%). The study also found a high degree of heterogeneity, with an I2 value of 97.64%, which suggesting that there were significant differences in the prevalence of human brucellosis across different regions and over time. The detected heterogeneity was attributed to political regions and publication years, indicating that there may be differences in the prevalence of human brucellosis among regions over time. Furthermore, this study found that the prevalence of human brucellosis shows an increasing trend from year to year. This suggests that the prevalence of human brucellosis may be increasing in Ethiopia; however, further research is required to confirm this trend. Conclusion: Human brucellosis is a significant health concern in Ethiopia, and further research is required to prevent and control this disease. The higher prevalence of the disease than previously reported in meta-analyses highlights the urgent need for action by responsible bodies, such as the Ministry of Health, to develop an appropriate strategy to address this problem. Further research is necessary to identify the risk factors associated with human brucellosis, so that effective prevention and control measures can be implemented. It is essential to prioritize the prevention and control of this disease to ensure the health and well-being of the population. Prospero registration number: - CRD42022350237
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".