COVID-19 Case Fatality Rate and Factors Contributing to Mortality in Ethiopia: A Systematic Review of Current Evidence
Bibliographic record
Abstract
Derara Girma,1 Hiwot Dejene,1 Leta Adugna,1 Mengistu Tesema,1 Mukemil Awol2 1Public Health Department, College of Health Sciences, Salale University, Fitche, Ethiopia; 2Department of Midwifery, College of Health Sciences, Salale University, Fiche, EthiopiaCorrespondence: Derara Girma, Email deraragirma24@gmail.comBackground: The ongoing novel coronavirus disease 2019 (COVID-19) is triggering significant morbidity and mortality due to its contagious nature and absence of definitive management. In Ethiopia, despite a number of primary studies have been conducted to estimate the case fatality rate (CFR) of COVID-19, no review study has attempted to summarize the findings to better understand the nature of pandemics and the virulence of the disease.Objective: To summarize the CFR of COVID-19 and factors contributing to mortality in Ethiopia.Methods: PRISMA guideline was followed. PubMed, Science Direct, CINAHL, SCOPUS, Hinari, and Google Scholar were systematically searched using pre-specified keywords. Observational studies ie, cohort, cross-sectional, and case-control studies were included. The Newcastle-Ottawa scale adapted for observational studies was used to assess the quality of included studies. CFR was defined as the proportion of COVID-19 cases with the outcome of death within a given period. Factors contributing to COVID-19 mortality at p-value < 0.05 were described narratively from the eligible articles.Results: A total of 13 observational studies were included in this study. Consequently, this review confirmed the CFR of COVID-19 in Ethiopia ranges between 1– 20%. Additionally, comorbid conditions, older age group, male sex, substance use, clinical manifestations (abnormal oxygen saturation level, atypical lymphocyte count, fever, and shortness of breath), disease severity, and history of surgery/trauma increased the likelihood of death from COVID-19 death.Conclusion: This study shows that the range of CFR of COVID-19 in Ethiopia is almost equivalent to other countries, despite the country’s low testing capacity and case detection rate in reference to its total population. Comorbid diseases, older age group, male sex, cigarette smoking, alcohol drinking, clinical manifestations and disease severity, and history of surgery/trauma were factors contributing to COVID-19 mortality in Ethiopia. Therefore, given the alarming global situation and rapidly evolving large-scale pandemics, urgent interdisciplinary interventions should be implemented in those vulnerable groups to lessen the risk of mortality. Furthermore, the CFR of COVID-19 should be estimated from all treatment and rehabilitation centers in the country, as underestimation could be linked to a lack of preparedness and mitigation. A large set of prospective studies are also compulsory to better understand the CFR of COVID-19 in Ethiopia.Keywords: case fatality rate, COVID-19, coronavirus, mortality, Ethiopia
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".