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Incidence of recovery rate and predictors among hospitalized COVID- 19 infected patients in Ethiopia; a systemic review and meta-analysis

樱花视频 volume听25, Article听number:听1644 (2025) Cite this article

Abstract

Background

Despite global efforts to mitigate COVID-19 infection through vaccination and therapeutic interventions, morbidity and mortality rates continued at variable rates. Although mortality risk and clinical features of COVID-19 are well-documented, recovery patterns and prognostic factors post-admission remain inconclusive, particularly in resource-limited settings like Ethiopia. This systematic review and meta-analysis (SRM) aimed to estimate the pooled incidence rate of recovery and predictors among hospitalized COVID-19 patients in Ethiopia.

Methods

We searched (N鈥�=鈥�1,191) articles using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline from PubMed/MEDLINE (N鈥�=鈥�755), Scopus (N鈥�=鈥�137), Web of Science (N鈥�=鈥�84), Science Direct (N鈥�=鈥�148), Cochran (N鈥�=鈥�25), and Google Scholar searching (N鈥�=鈥�42) from December 2019 to February 2024. The data were extracted using a Microsoft Excel spreadsheet and exported to Stata TM听version 17.0 for further analysis. The Article quality was assessed using the Joanna Briggs Institute checklist. The pooled incidence rate of recovery was estimated using a weighted inverse variance random-effects meta-regression. Heterogeneity among studies was evaluated using the I2 statistic. Subgroup analyses and sensitivity tests were also conducted to explore publication bias. This file is registered in international Prospero with ID (CRD42024518569).

Result

Sixteen (N鈥�=鈥�16) published studies with 7,676 hospitalized COVID-19 patients were included in the final report. The mean age of participants ranged from 29 (卤鈥�17) to 57.5 (卤鈥�3) years, with male patients constituting the largest proportion of participants, 4,491(58.5%). During recovery screening, 6,304(82.21%) cases were discharged as improved, 159 (2.1%) attriters, and 818 (10.6%) died during inpatient treatment. The pooled incidence of recovery, mortality, and attrition rates were found to be 82.32% (95% CI: 78.81鈥�85.83; I2鈥�=鈥�94.8%), 14.3% (I2鈥�=鈥�98.45%), and 2.7% (I2鈥�=鈥�81.34%), respectively. Incidence of recovery rate varied across regions and epidemic phases, with the highest rate observed in Addis Ababa (89.94%, I2鈥�=鈥�78.33%) and the lowest reported in the Tigray region (59.7%, I2鈥�=鈥�0.0%). Across epidemic phases, the recovery rate was 88.05% (I2鈥�=鈥�29.56%) in Phase II, 84.09% (I2鈥�=鈥�97.57%) in Phase I, and 78.92% (I2鈥�=鈥�96.9%) in Phase III, respectively. Factors included being aged 15鈥�30听years (pooled OR鈥�=鈥�2.01), male sex (pooled OR鈥�=鈥�1.46), no dyspnea (pooled OR鈥�=鈥�2.4; I2鈥�=鈥�79%), and no baseline comorbidities (pooled OR鈥�=鈥�1.15; I2鈥�=鈥�89.3%) were predictors for recovery.

Conclusion听and recommendation

In Ethiopia, more than eight out of ten hospitalized COVID-19 patients recovered after inpatient treatment. However, the incidence of recovery rates varied significantly across epidemic phases, study settings, and regions. Factors including younger age, male sex, no dyspnea (shortness of breathing), and no underlying comorbidity heightened recovery. It is highly recommended those inpatients cares should focus on high-risk groups (older adults) and implement standardized treatment protocols in each study setting. Regions with lower recovery rates need aid in logistical support and training for healthcare providers.

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Introduction

Coronavirus disease 2019 (COVID- 19) is an infectious disease caused by Severe Acute Respiratory Syndrome Corona-virus 2 (SARS-CoV- 2) and remains a global concern [1, 2]. The disease had different epidemic phases and variant forms of strain infection [3, 4] and presented with diverse and nonspecific clinical presentations, ranging from mild flu-like symptoms to a fatal Acute Respiratory Distress Syndrome (ARDS) of viral pneumonia [5, 6]. In global literature, it is described that coughing [1, 7] and dyspnea [7, 8] influenza-like symptoms of fever, and myalgia [6, 9, 10] were the most prominent. However, the elevation of hematological biomarkers including C-reactive protein (CRP) [11, 12], interleukin- 6 [8, 12], platelets, [13, 14], eosinophil [12, 13], hemoglobin [2, 7], and albumin level [2, 14, 15] were proxy indicators for sever infectivity [11]. During the inpatient treatment of COVID- 19 cases, three physiological changes were reported mainly respiratory failure (hypoxemia, ARDS), circulatory collapse (hypotension, shock, arrhythmias, organ failures) [1, 11, 16], and physical changes (inflammation) [11, 13].

The COVID- 19 pandemic significantly challenged healthcare systems, especially during the second and third waves (late 2020 to mid- 2023), which strained long and short-term strains on the primary healthcare system [5, 8] attributed to post-admission mortality rates of 8.1%鈥�30% for ambulatory and 16%鈥�78% for critical cases [1, 17, 18]. Despite global efforts to combat COVID- 19 through pharmacological and non-pharmacological interventions, both primary and secondary sequels have persisted [19]. According to Worldometer data as of April 13, 2024, there were 704,753,890 confirmed cases worldwide, resulting in 7,010,681 deaths, and 675,619,811 recoveries were reported [20, 21].

In Ethiopia, three COVID- 19 epidemic surges have occurred since the first case was reported in March 2020 [22]. By mid-April 13, 2024, out of 501,157 confirmed cases, 7,574 deaths, and 488,171 recovery cases were reported, which makes the national level recovery rate of 4,148 per 1 million people [20, 21]. Based on Current evidence indicates that prognosis depends on baseline patient characteristics including comorbidities, and laboratory findings [23]. A Systemic review and meta-analysis (SRM) finding highlighted [5, 24, 25] that admission times, age of the patient, viral load, and comorbidities were proxy indicators for prolonged durations of viral shading [23]. A multinational systematic review of 57 studies found that admission rates and recovery proportions varied significantly across different populations and age groups [7]. On the other hand findings from China demonstrated that an inverse relationship between hospital admission and patient age was observed, lowest admission rate (1%) were cases 鈮も��20 years, and the highest 31.4%were aged 鈮モ��60 years [10, 26].

In Ethiopia, clinical features and mortality risks of COVID- 19 have been primarily quantified indifferent epidemic surges, with the pooled incidence of mortality rates of 14.44% in Phase I [17], and 9.13% in Phase II [27] with cases of fatality rate post admission 1鈥�20% [28]. However, the recovery rate among admitted patients remains inconclusive and poorly characterized, with significant variation across treatment settings and study regions [20, 27]. These review metrics are critical for evaluating effectiveness and evaluating tailored healthcare interventions, addressing regional disparities, and guiding future public health planning using aggregated data. Therefore this SRM aimed to estimate the pooled incidence rate and identify predictors for recovery admitted COVID- 19 infected patients in Ethiopia.

Methods

Study setting and methods

This systematic review and meta-analysis (SRM) includes articles published exclusively in Ethiopia, the Federal Democratic Republic with a government structure comprising previously nine regional states [14]. The country spans a total area of 1,100,000 km2 and is organized into regions and zonal clusters, which are further also segmented into district levels. Ethiopia has approximately 112 million populations and stands as Africa鈥檚 second-most populous country with 56,010,000 females& 56,069,000 males according to 2019 population projections [17, 27].

Protocol registration

To ensure methodological rigor, the study protocol was registered in PROSPERO (ID: CRD42024518569.

Data-searching-tools

The selected articles were reported following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines as outlined in (S1 Checklist PRISMA 2020) [29].

Search strategy; an extensive article search was carried out from PubMed/MEDLINE (N鈥�= 755), Scopus (N鈥�= 137), Web of Science (N鈥�= 84), Science Direct (N鈥�= 148), Cochran (N鈥�= 25), and Google Scholar searching (N鈥�= 42). We included studies published from 2019 at the end of February 2024. For instance one of the PubMed database searches using Boolean operators included the following:"Recovery rate"OR"Survival Rate"OR"Viral Clearance"AND"Predictors"OR"Determinants"OR"Factors"AND"Hospitalized"OR"Inpatient treatment"AND"Ethiopia". This approach utilized free-text keywords as well as MeSH terms. The refined searching string incorporated both free-text and Mesh Terms as ("COVID- 19"[MeSH Terms] OR"COVID- 19"[Text Word]) AND ("Ethiopia"[MeSH Terms] OR"Ethiopia"[Text Word]) AND ("incidence density rate"[MeSH Terms] OR"incidence density rate"[Text Word] OR"predictors of recovery"[MeSH Terms] OR"predictors of recovery"[Text Word] OR"hospitalized"[MeSH Terms] OR"hospitalized"[Text Word]. An example of search engine approaches in PubMed is shown (((((((recovery from COVID- 19 [Text Word]) OR (cured rate from COVID- 19 disease [Text Word])) OR (SARS-CoV- 2 infected [MeSH Terms])) AND (rate [Text Word])) OR (incidence [Text Word])) OR (Recovery [Text Word])) OR (predictors [Text Word])) OR (patient with COVID- 19 [MeSH Terms])) AND (Ethiopia [MeSH Terms])) OR (Ethiopia [Text Word])".

Eligibility criteria; inclusion criteria

All cohort studies estimated recovery rate among COVID- 19 infected patients and started treatment in Ethiopia". We considered published studies with cohort designing without language and time restriction versions. The eligibility criteria for given articles are established on a logical grid structure that considers"Context, Condition, and Population (COCOPop)."which framework is narrated as (1) Condition: COVID- 19 infection; recovery from COVID- 19, including articles reporting the rate and predictors of recovery among hospitalized COVID- 19 patients. (2) Context: Published articles should focus on individuals admitted to public health institutions with laboratory-confirmed COVID- 19 infection and received treatment in ICU.(3)Study Population: Admitted COVID- 19 patients undergoing inpatient treatment either public or private health institutions.

Exclusion criteria

Articles lacking abstracts, publication dates, journal/author names, inaccessible full text, and editorial comments were excluded.

Study population

This SRM examined admitted COVID- 19 patients undergoing inpatient treatment at public health institutions. The study followed a rigorous screening process for article inclusion, accepting publications in any language and restricting study designs to cohort studies, as these are best suited for calculating recovery rate incidence. Articles were excluded if they lacked abstracts, or publication dates, had inaccessible full texts, or were editorial comments.

Outcome ascertainment

This SRM had two main objectives, which included estimating the recovery rate after initiating inpatient treatment for admitted COVID- 19 patients. The recovery rate was calculated by extracting effect sizes from each study, organizing the data into an Excel spreadsheet, and then dividing by the total person-days of risk observation using the Pro-Command in STATA version 17. The second objective was identifying predictors for recovery after aggregating associated factors from each eligible study using weighted inverse random-effects meta-regression.

Operational words

COVID- 19 disease status [30]; the disease status of diagnosed COVID- 19 cases was classified based on the management and treatment guidelines of Ethiopia as follows:"1. Mild Disease: Patients with mild symptoms not needing hospitalization can be managed at home or in isolation facilities for specific supportive care and are not admitted to the ICU. 2. Complicated COVID- 19 cases; Patients exhibit symptoms such as fever, malaise, cough, upper respiratory symptoms, and possibly lower respiratory symptoms with potential chest X-ray infiltrates while maintaining sufficient oxygenation on room air, making them eligible for inpatient treatment and supportive care. 3. Severe, and complicated cases; Patients who have developed complications such as hypoxia (SPO2 <鈥�93% on atmospheric air or PaO2:FiO2 <鈥�300 mmHg), tachypnea in respiratory distress (RR >鈥�30 breaths/minute), and 鈮モ��50% involvement on chest imaging [31]. 4. Critical and end-stage COVID- 19 cases; Patients with severe respiratory distress including acute respiratory distress syndrome (ARDS), septic shock, and multiple organ failure and critically needed inpatient care at the intensive care unit (ICU). During inpatient treatment, they may need mechanical ventilation, exhibit tachypnea (respiratory rate >鈥�30 breaths/minute), and may have 鈮モ��50% lung failure in chest imaging [1, 11, 26]. Recovery/viral shading/RNA-negative Conversion) rate: This denotes the resolution of symptoms and/or signs as determined by the respective clinician, irrespective of biochemical factors, especially when the Reverse Transcription Polymerase Chain Reaction (RT-PCR) re-test confirms viral genomic negativity [32, 33]. COVID- 19 Epidemic phases in Ethiopia [3, 30, 34]; Ethiopia experienced a notable surge in COVID- 19 cases, resulting in heightened testing, strain on healthcare facilities, and the implementation of restrictions. The COVID- 19 epidemic phases were classified into three stages until the end of February 2024. However, reporting and updating of daily pandemic data are continued.

  1. 1.

    First Wave (Phase 1); this outbreak lasted from December 2019 to December 2020.

  2. 2.

    Phase 2: This phase encompasses from the beginning of January 2021 to May 2022.

  3. 3.

    Phase 3: This phase extends from the end of May 2022 to February 2024.

Quality assessment and appraisal procedures

In this study, three authors (FKB, TKB, and TT) extracted articles and evaluated their quality based on study eligibility criteria. In cases of disagreement or uncertainty during data extraction and duplicate removal, the third author (GTB), facilitated and resolved the issues through discussions. The reviewers collectively assessed full-text articles, including titles, abstracts, and full content, using a Microsoft Excel spreadsheet for detailed extraction. Two independent reviewers used JBI checklists to assess each article's quality. The quality assessment process involved two authors FKB and TT for independently extracting and evaluating. This included verifying publication details and resolving discrepancies through discussion based on study titles and abstracts. Key information like principal investigators, publication years, study period, setting, population, and sample size was documented in an Excel sheet. Finally, all authors, including FKB, TKB, and TT assessed each article's bias using the JBI (Joanna Briggs Institute) using cohort design of Critical Appraisal Checklist for the quality of eligible articles.

Data extraction, analysis, and biases assessment

EndNote version 8.1 was utilized to export and compile all identified and potentially relevant citations of published articles. Duplications were eliminated during the screening and selection processes. Reviewers evaluated abstracts before advancing to full-text articles, which were assessed based on specific criteria for inclusion of a given article. Data were modified in a Microsoft Excel spreadsheet, and after cleaning and adjustments, STATA version 17 was utilized for further data analysis. Descriptive statistics and weighted inverse variance random-effects meta-regression were employed to estimate the pooled recovery rate post-admission in COVID- 19 infections. The presence of heterogeneity among studies was assessed using Cochran's Q test and quantified with I squared test (I 2) statistic. Potential sources of heterogeneity among studies were assessed using subgroup analyses.

Publication biases assessment

Publication bias was assessed both qualitatively through visual inspection of funnel plots test, and quantitative regression to detect the effect on pooled estimations [35, 36].

Result

Screening of included studies

A total of 1,191 studies were retrieved from PubMed/MEDLINE (N鈥�= 755), Scopus (N鈥�= 137), Web of Science (N鈥�= 84), Science-Direct (N鈥�= 148), Cochran (N鈥�= 25), and Google Scholar searching (N鈥�= 42). After careful screening of articles, 934 were excluded due to duplication. The remaining 257 studies were further assessed and 181 were removed by Titles (N鈥�= 152) and Abstract readings (N鈥�= 29). Moreover, 76 publications were screened for final eligibility. Of these 60 excluded due to, unclear data collection methods (n鈥�= 29), unmet outcomes of interest (n鈥�= 15), inaccessible full texts (n鈥�= 9), and (n鈥�= 6) removed due to qualitative reports. Finally, 16 studies [2, 18, 32, 33, 37,38,39,40,41,42,43,44,45,46,47,48] met the final inclusion criteria of met-analysis as described (Fig. 1).

Fig. 1
figure 1

Article selection of PRISMA flow diagnram for recovery from COVID- 19 infection in Ethiopia

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Description of the included studies

This studies was conducted using eight Ethiopian regions studies reports; including Amhara (N鈥�= 3) [18, 40, 41], Addis Ababa (N鈥�= 3) [2, 37, 38], Oromia (N鈥�= 3) [32, 44, 45], central Ethiopia regions (N鈥�= 2) [39, 43], Benishangul Gumuz (N鈥�= 1) [42], Tigray (N鈥�= 1) [33], Sidam (N鈥�= 1) [46, 48] and South West (N鈥�= 1) [47] regions. Equal proportions of studies were conducted on Phase I [2, 32, 33, 37, 38, 44, 47], and Phase III [18, 39, 41, 42, 45] of epidemic surges. However, the remaining four articles were conducted (N鈥�= 1) [38, 40, 43, 46, 48]conducted during Phase II of epidemic surges. Almost all studies employed a retrospective cohort [32, 33, 37, 39,40,41,42,43,44,45,46,47,48] except three of the studies used a prospective cohort [2, 18, 38]. Majorities of studies conducted at hospital set-ups [32, 37,38,39, 41, 45,46,47,48]; except six [2, 33, 40, 41, 43, 45] studies were in COVID- 19 diagnosing and treatment centers. This review included a sample size ranging from 139 cases in Tigray [33] to largest number of admitted patient 1,345 in Addis Ababa [2]. The largest and smallest mortality proportions of cases reported 80/202(36.6%) in Amhara [49] and 13/276(4.7%) in Oromia regions. Male patients constituted majority of admitted cases, accounting for 58.5%, (4491/7676) vs. 41.5%, (3185/7676) with female. The included studies had mean follow-up periods ranging from 5 (卤 2.2) to 19 (卤 9.5) days. A large proportion of recovered cases was reported 89.5% in Addis Ababa [2],& 77.24% small number of recoveries was reported in Benishangul [42] regions. Thirty-three percent of 2576(33.6%) of admitted patients had baseline comorbidities as described in (Table 1).

Table 1 Descriptive characteristics of included study in this systematics review and meta-analysis for treatment recovery of admitted COVID-19 patients
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Pooled Incidence rate of recovery from COVID- 19 infection

This review comprised 16 individual studies with 7676 hospitalized COVID- 19 infected patients.

During recovery screening, 6,304(82.21%) cases discharged as improved, 159 (2.1%) attrite, and 818 (10.6%) died during inpatient treatment. The pooled incidence of recovery rate was found 82.32% (95% CI: 78.81鈥�85.83; I2鈥�= 94.8%), 14.3% (I2鈥�= 98.45%), and 2.7% (I2鈥�= 81.34%), as described in (Fig. 2).

Fig. 2
figure 2

Forest plot of polled recovery rate of admitted patient with COVID- 19 infection in Ethiopia

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Pooled mortality rate among studies reported

In the final review, 16 eligibel studies [18, 22, 32, 33, 38,39,40,41,42,43,44, 46,47,48, 50, 51] reported died cases during inpatent treatments. After we aggregated each effect size from each study, the pooled incidence of the mortality rate was estimated at 14.3%(95%CI;9.16 to 19.43%, I2 =鈥�98.45) as shown (Fig. 3).

Fig. 3
figure 3

Forest plot of polled mortality rate among admitted COVID- 19 patients in Ethiopia

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Pooled attrition rate among studies reported

Out of the total, eight [22, 32, 40, 45, 46, 51] studies reported patient self-discharge during inpatient COVID- 19 treatment. The random effect variance regression reported, that the pooled attrition rate was estimated at 2.7%(95%CI; 1.63 to 3.76, I2 =鈥�81.34%) as shown (Fig. 4).

Fig. 4
figure 4

Forest plot of polled attrition rate among studies reported self-discharge from treatment setting

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Subgroup analysis among studies

During the final report, the highest heterogeneity among studies was observed (I2鈥�= 94.5%). To assess the source of heterogeneity, subgroup analyses were conducted using epidemic phases, study region, and study setting. Accordingly, a recovery rate was higher for patients admitted at epidemic Phase II surges, which is 88.05%(95%; 74.93 to 93.25, I2 =鈥�29.56%) compared with rate reported during Phase I, 84.09%(95%CI: 74.93 to 93.25, I2 =鈥�97.57%) and phases III 78.92%(95%CI: 71.81 to 86.03, I2 =鈥�96.9%) as shown (Fig. 5). Likewise, the COVID- 19 recovery rate was higher for patients treated in hospital settings (87.15%; 95% CI: 77.1鈥�90.99; I2鈥�= 97.5%) compared with treated cases in COVID- 19 diagnosing and treatment centers (76.98%; 95% CI: 68.48鈥�85.48; I2鈥�= 98.5%) as shown (Fig. 6).

Fig. 5
figure 5

Subgroup analysis by COVID- 19 epidemic phases and treatment recovery of patient

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Fig. 6
figure 6

Subgroup analysis by Study-Setting and treatment recovery of COVDI- 19 infected patient

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In addition, studies conducted in prospective cohorts reported lower COVID- 19 patient recovery rates compared to retrospective cohorts as described in 87.5% (95% CI: 76.03 to 99.14, I2鈥�= 96.23%) VS. 91.7% (95% CI: 88.51 to 94.96, I2鈥�= 93.1%) as described in (Fig. 7).

Fig. 7
figure 7

Subgroup analysis by Study Design and treatment recovery of COVDI- 19 infected patients

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The regional disparities for treatment recovery rate were evident, with Addis Ababa had the highest reports (89.94%; 95% CI: 86.13鈥�93.75; I2鈥�= 78.33%), followed by South West Ethiopia Region (88.8%; 95% CI: 84.52鈥�92.4; I2鈥�= 0.00%), Oromia Region (87.48%; 95% CI: 82.36鈥�90.36; I2鈥�= 83.9%), and Central Ethiopia Region (83.6%; 95% CI: 72.5鈥�94.6; I2鈥�= 92.5%). However, lower recovery rates were described in Amhara (77.46%; 95% CI: 65.3鈥�90.3; I2鈥�= 88.12%), Benishangul Gumuz (77.23%; 95% CI: 72.51鈥�81.71; I2鈥�= 0.0%), and Sidama (76.96%; 95% CI: 71.33鈥�82.6; I2鈥�= 88.12%) regions, while Tigray region recorded the lowest recovery rate (59.7%; 95% CI: 51.5鈥�67.5; I2鈥�= 0.0%) compared with Addis Ababa (Fig. 8).

Fig. 8
figure 8

Subgroup analysis by study regions and treatment recovery of COVDI- 19 infected patients

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Publication Bias鈥�

The funnel plot analysis indicated asymmetry funnel plot was observed in (Fig. 9). However, further regression using Egger's test (P鈥�= 0.199) and Begg's test (P鈥�= 0.241) indicated no significant evidence of publication bias and evidenced with rated as'high'inGRADE assessment.

Fig. 9
figure 9

Funnels plot for publication biases assessment for treatment recovery from COVID- 19 patients

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A leave-one-out sensitivity analysis

The final results of a leave-one-out sensitivity analysis indicated no individual study significantly affected the overall pooled estimate, which confirmed the robustness of final reports and no evidence of influential outliers of studies for pooled estimation as described in (Fig. 10).

Fig. 10
figure 10

A leaves one out sensitivity analysis for detection of outliers鈥�

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Predictors for recovery post admission in COVID- 19 infection

This systematic review analyzed adjusted odds ratios from primary studies to identify predictors of recovery from COVID- 19 hospitalization, focusing on socio-demographic, clinical, and immunological factors. However, age, gender, and absence of comorbidity during admission increased the recovery rate. Accordingly, male patients had twice increased the likelihood of recovery rate compared with female patients (pooled Odds Ratio =鈥�1.46, 95% CI: 1.14鈥�1.88; I2鈥�= 47.2%). Likewise, patients aged 15鈥�30 years (pooled OR =鈥�2.01, 95% CI: 1.41鈥�2.86; I2鈥�= 33.3%), and no comorbidity other than COVID- 19 infection (Pooled OR =鈥�1.15 (95%CI: 1.12 to 1.79, I2 =鈥�89.3%) and absence of dyspnea at admission) (Pooled OR =鈥�2.4; (95%CI: 1.68鈥�17.4, I2 =鈥�79%) were predictors for recovery compared with counter groups as presented (Table 2). Table 2; Pooled estimates of predictors for treatment recovery of admitted COVID- 19 patient in Ethiopia.

Table 2 Pooled estimates of predictors for treatment recovery of admitted COVID- 19 patient in Ethiopia
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Discussion

In the final review of 16 studies, 7,676 confirmed COVID- 19 patients were screened for recovery from eight Ethiopian regions. Of the total, 6,304(82.1%) cases quit as recovered, 159 (2.07%) self-discharged from the center and 818 (10.6%) died. This made the pooled incidence rate of recovery post-admission with COVID- 19 infection 82.32% (95% CI: 78.81, 85.83; I2鈥�= 94.81%). This report is higher than the previous population level aggregated finding of 76.6% in Ethiopia [14], 81.7% in Spain [52], and 52.4% in six Sub-Saharan African Countries [53]. The differences in the findings may be due to the variations in the study population, study period, and disparities in socioeconomic among included participants. However, the final report is lower than 94.6% of population-level recovery descriptions in China [54]. The disparity likely results from differences in healthcare infrastructure between China and Ethiopia during the COVID- 19 surge. Ethiopia鈥檚 response to epidemics includes approximately 35 treatment centers, compared to China鈥檚 1,500 +. Ethiopia has about 1 doctor per 10,000 people, while China has 3 per 1,000. Additionally, Ethiopia has 0.3 ICU beds per 100,000 people, compared to over 3 in China. This disparity is reflected in inpatient death rates: 1.7% in Ethiopia versus 0.4% in China [14, 55].

In this review, significant regional variation in recovery rates was observed. Addis Ababa reported the highest recovery rate (89.94%), followed by South West (88.8%), Oromia (87.48%; Central Ethiopia (83.6%), Amhara (77.46%), Benishangul Gumuz (77.23%;), and Sidama (76.96%) regions. In contrast, Tigray Region recorded the lowest recovery rate (59.7%) compared with Addis Ababa. This might be related to issues of escalated conflicts in Ethiopia, particularly in the Tigray War (2020鈥�2022) with the central government, severely undermining regional healthcare capacity during epidemics. This is expressed in the majority of health facilities that had 30% damaged physical structures in main conflict sites (Tigray, Amhara, and Afara). Additionally, the exodus of trained medical personnel and health care providers during war correlated with a lower incidence of recovery, as evidenced by WHO surveillance data [56, 57].

In the current review, predictors of recovery were identified, accordingly being male cases increased the likelihood of early recovery compared with females. This finding is consistent with study results reported in Addis Ababa [49, 58] and Iran [59]. This may be related to the effects of sex-related hormonal influence for females on delayed COVID- 19 symptom resolution. A study finding from 73 countries from 2020 to 2021 for effects of sex different risk of cases fatality rate indicated, that female immune response was inflicted by steroid hormones (e.g., steroid/sex hormones) for admitted patients and delayed viral genomic negative conversion time. Which directly increased inpatient deaths compared with male patients [60]. In contrast to this, in our recovery screening reported, admission rates were increased for male patients and accounted for the largest proportions compared with counter-female admitted patients (Male 58.5%, N鈥�= 4,491) Vs. (females (41.5%, N鈥�= 3,185).

Consistent with previous studies finding [61, 62] an inverse association between increased patient ages and viral clearance rate, our study reported, being patients aged 15鈥�30 years twice increased the likelihood of recovery post admissions compared with older age(鈮� 60 years). This might be related to low physiological corpulence for age-related lung function, and caused severe hypoxia (SpO鈧� <鈥�90%) in older cases compared with young cases. This is supported by findings in Ethiopia that hospitalized patients had a higher death risk at age >鈥�60 (22.4%) compared to those <鈥�40 years (3.1%) [63].

The current review reported, that the absence of baseline comorbidities, other than SARS-CoV- 2 infection, increased twice the likelihood of recovery rate. This is consistent with systematic review findings in Greece [64], Portugal [23], and Kurdistan [65]. The presence of comorbidity with COVID- 19 infection declined innate immunity, particularly white blood cell counts, which engulfed foreign antigens including the virus. Thought thus, the risk of prolonging viral clearance rate decreased when declining the number of white blood cells and prolonged recovery time. On the other hand, the study finding in Australia, [66], Gambia [19], patients with chronic comorbidity had varied mortality risk [66] including obstructive pulmonary disease (AOR =鈥�4.9), and severe obesity (AOR =鈥�3.2) had prominent mortality risk [66].

Consistent with previous studies reported in Greece [64], and the USA [67]; in this review, the absence of dyspnea at admission was twofold increased the likelihood of recovery. This may result from reduced tidal volume and impaired oxygen exchange in the lung; particular surfactant dysfunction related to SARS-CoV- 2 infection creates an edematous alveolar environment. This indirectly reduces blood oxygenation and causes the incidence of acute respiratory distress (ARDS). Findings from Germany [68] and Italy [69] supported that ARDS reduces blood oxygenation by 20鈥�30% and impacts on RNA negativity conversion rate [66].

Strengths and limitations of the studies

This SRM reported several strengths including more than three reviewers and the use of multiple databases used for searching. The absence of comprehensive demographic data and clinical variables is not mainly elaborated which can significantly influence the generalizability of the overall results. An important limitation is nine articles in full text is/are in-accessible for eligibility selection and all studies depend on cohort design, which also affected the overall estimation of the final report.

Conclusion and recommendation

In Ethiopia, eight out of ten hospitalized COVID- 19 patients recovered after inpatient treatment. However, the incidence of recovery rates varied significantly across epidemic phases, study settings, and regions. Factors including younger age, male sex, no dyspnea (shortness of breathing), and no underlying comorbidity heightened recovery. It is highly recommended that inpatient care should focus on high-risk groups (older adults,) and implementing standardized treatment protocols in each study setting. Regions with lower recovery rates need aid in logistical support and training for healthcare providers.

Implications for actions; the study highlights regional variations in COVID- 19 recovery rates across each treatment center. Which called emphasizing critical care and prioritizing tailored treatment for critical cases during admission. Stakeholders, including the government and healthcare workers, are urged to focus on allocating resources for timely care and monitoring recovery trends within each admission ward daily. It is crucial to consider patients'ages, and genders, as predictors for recovery in intensive care, necessitating the development of tailored treatment strategies and resource allocation for critically ill patients.

Data availability

All data generated for this study are submitted with the manuscript and for further information, please contact the corresponding author for non-commercial research purposes

Abbreviations

AHR:

Adjusted hazard ratio

SRM:

Systemic review and meta-analysis

RT-PCR:

Reverse Transcription Polymerase Chain Reaction

COVID- 19:

Short for Coronavirus Disease 2019, is an infectious disease caused by the novel coronavirus SARS-CoV-2

SARS-CoV- 2:

(Severe Acute Respiratory Syndrome Coronavirus 2) is the specific coronavirus strain responsible for causing COVID-19

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Acknowledgements

Acknowledgment I extend my deepest gratitude to my wife, Mrs. Emiye (Marwa) Bewuket, for her unwavering support throughout this process. Her dedication was especially invaluable during the challenging times of caring for our beloved daughter, Herani Fassikaw, during her critical efforts in writing this manuscript.

Funding

The author(s) declare no financial support for research from any organization.

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Authors and Affiliations

  1. College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia

    Fassikaw Kebede Bizuneh听&听Getaye Tizazu Biwota

  2. Australian National University, Canberra, Australia

    Tsheten Tsheten

  3. College of Social Science, Bahir Dar University, Bahir Dar, Ethiopia

    Tsehay Kebede Bizuneh

Authors
  1. Fassikaw Kebede Bizuneh

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  2. Getaye Tizazu Biwota

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  3. Tsheten Tsheten

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  4. Tsehay Kebede Bizuneh

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"FKB =conceptualized, designed, and wrote the original draft of the manuscript, as well as participated in writing, editing, and investigation. TT =contributed to writing, article searching editing, and full manuscript. TKB =had a formal analysis and methodology. GTB =assisted with data curation and software-related tasks. All authors reviewed and approved the final version of the manuscript, contributed to the article, approved the submitted version, and reviewed the manuscript".

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Correspondence to Fassikaw Kebede Bizuneh.

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Bizuneh, F.K., Biwota, G.T., Tsheten, T. et al. Incidence of recovery rate and predictors among hospitalized COVID- 19 infected patients in Ethiopia; a systemic review and meta-analysis. 樱花视频 25, 1644 (2025). https://doi.org/10.1186/s12889-025-22841-x

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  • DOI: https://doi.org/10.1186/s12889-025-22841-x

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樱花视频

ISSN: 1471-2458

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