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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Med.</journal-id>
<journal-title>Frontiers in Medicine</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Med.</abbrev-journal-title>
<issn pub-type="epub">2296-858X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmed.2024.1327746</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Medicine</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>COVID-19 mortality rate and its determinants in Ethiopia: a systematic review and meta-analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes"><name><surname>Wondmeneh</surname> <given-names>Temesgen Gebeyehu</given-names></name><xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1182435/overview"/>
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<role content-type="https://credit.niso.org/contributor-roles/resources/"/>
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<role content-type="https://credit.niso.org/contributor-roles/supervision/"/>
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<contrib contrib-type="author"><name><surname>Mohammed</surname> <given-names>Jemal Abdu</given-names></name>
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<aff><institution>Department of Public Health, College of Medical and Health Science, Samara University</institution>, <addr-line>Semera</addr-line>, <country>Ethiopia</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0001">
<p>Edited by: Shailendra Saxena, King George's Medical University, India</p>
</fn>
<fn fn-type="edited-by" id="fn0002">
<p>Reviewed by: Miguel &#x00C1;ngel Castro Villamor, Universidad de Valladolid, Spain</p>
<p>Max Carlos Ram&#x00ED;rez-Soto, University of San Mart&#x00ED;n de Porres, Peru</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Temesgen Gebeyehu Wondmeneh, <email>tomigeb2006@gmail.com</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>27</day>
<month>02</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1327746</elocation-id>
<history>
<date date-type="received">
<day>27</day>
<month>10</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>12</day>
<month>02</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Wondmeneh and Mohammed.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Wondmeneh and Mohammed</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec id="sec1">
<title>Background</title>
<p>The COVID-19 mortality rate continues to be high in low-income countries like Ethiopia as the new variant&#x2019;s transmission expands and the countries&#x2019; limited capacity to combat the disease causes severe outcomes, including deaths. The aim of this study is to determine the magnitude of the COVID-19 mortality rate and its determinants in Ethiopia.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>The main electronic databases searched were PubMed, CINAHL, Google Scholar, and African journals online. The included studies&#x2019; qualities were assessed independently using the Newcastle-Ottawa scale. The data was extracted in Microsoft Excel spreadsheet format. The pooled effect size and odds ratios with 95% confidence intervals across studies were determined using the random-effects model. I<sup>2</sup> is used to estimate the percentage of overall variation across studies due to heterogeneity. Egger&#x2019;s test and funnel plot were used to find the published bias. A subgroup analysis was conducted. The effect of a single study on the overall estimation was determined by sensitivity analysis.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>A total of 21 studies with 42,307 study participants were included in the final analysis. The pooled prevalence of COVID-19 mortality was 14.44% (95% CI: 10.35&#x2013;19.08%), with high significant heterogeneity (I<sup>2</sup>&#x2009;=&#x2009;98.92%, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001). The risk of mortality from COVID-19 disease was higher for patients with comorbidity (AHR&#x2009;=&#x2009;1.84, 95% CI: 1.13&#x2013;2.54) and cardiovascular disease (AHR&#x2009;=&#x2009;2, 95% CI: 1.09&#x2013;2.99) than their counterparts without these conditions.</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>A significant number of COVID-19 patients died in Ethiopia. COVID-19 patients with comorbidities, particularly those with cardiovascular disease, should receive special attention to reduce COVID-19 mortality.</p>
</sec>
<sec id="sec5">
<title>Systematic review registration</title>
<p><ext-link xlink:href="https://www.crd.york.ac.uk/PROSPERO/" ext-link-type="uri">https://www.crd.york.ac.uk/PROSPERO/</ext-link>, registration identifier (ID) CRD42020165740.</p>
</sec>
</abstract>
<kwd-group>
<kwd>COVID-19</kwd>
<kwd>mortality</kwd>
<kwd>Ethiopia</kwd>
<kwd>rate</kwd>
<kwd>risk factors</kwd>
</kwd-group>
<counts>
<fig-count count="9"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="58"/>
<page-count count="13"/>
<word-count count="7228"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Infectious Diseases: Pathogenesis and Therapy</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="sec6">
<title>Background</title>
<p>The Latin word &#x201C;corona,&#x201D; which means crown, is where the word &#x201C;coronavirus&#x201D; comes from. The name refers to the virus&#x2019; distinctive appearance under an electron microscope, which consists of rounded particles with a rim of protrusions like the solar corona (<xref ref-type="bibr" rid="ref1">1</xref>). The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for the extremely contagious disease known as coronavirus disease 2019 (COVID-19). Its catastrophic effects around the globe have resulted in more than 6 million deaths (<xref ref-type="bibr" rid="ref2">2</xref>). COVID-19 mortality was 6.3% in Brazil (<xref ref-type="bibr" rid="ref3">3</xref>) and 3.6% in China (<xref ref-type="bibr" rid="ref4">4</xref>). The WHO African Region (WHO AFR) accounted for 82.7% of cases and 76.2% of deaths around the globe. As of February 24, 2023, 10.8 million COVID-19 cases were reported in Africa, with 228,738 deaths (CFR: 2.1%) and 9.8 million recoveries (93.8%) (<xref ref-type="bibr" rid="ref5">5</xref>, <xref ref-type="bibr" rid="ref6">6</xref>). The incidence rate of mortality was 0.203 per 1,000 persons per day in the WHO African region (<xref ref-type="bibr" rid="ref7">7</xref>). In studies of sub-Saharan Africa, the pooled prevalence of mortality in COVID-19 patients was 4.8% (<xref ref-type="bibr" rid="ref8">8</xref>) and 2.4% (<xref ref-type="bibr" rid="ref9">9</xref>). In Kenya, 14% of patients with COVID-19 died (<xref ref-type="bibr" rid="ref10">10</xref>). In Ethiopia, the case fatality rate of COVID-19 ranges between 1 and 20% (<xref ref-type="bibr" rid="ref11">11</xref>) while the incidence of COVID-19 mortality was 9.13 per 1,000 person-days in Ethiopia (<xref ref-type="bibr" rid="ref12">12</xref>). The effectiveness of COVID-19 mitigation and patient clinical outcomes are influenced by the economy and medical resources. A study&#x2019;s findings revealed that the COVID-19 pandemic is likely to get worse in developing nations, making low-income countries more vulnerable if it is not controlled (<xref ref-type="bibr" rid="ref13">13</xref>). Cultural norms affect COVID-19-related mortality (<xref ref-type="bibr" rid="ref14">14</xref>). Individualism societies were favorably correlated with COVID-19 prevalence, mortality, and case fatality rates; in contrast, collectivism societies were adversely correlated with these characteristics. This correlation between individualism and the severity of the virus problem may be explained by the fact that social non-cooperativeness in individualistic societies decreases the effectiveness of non-pharmaceutical treatments intended to alleviate the virus problem (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref16">16</xref>). A high mortality rate from COVID-19 has been reported in patients with comorbidities (<xref ref-type="bibr" rid="ref17 ref18 ref19">17&#x2013;19</xref>). The risk of COVID-19 mortality was increased for those with advanced age, male gender, hypertension, diabetes mellitus, cardiovascular disease, and cancer (<xref ref-type="bibr" rid="ref20 ref21 ref22 ref23 ref24">20&#x2013;24</xref>). Patients with acute respiratory distress syndrome, which is a potentially fatal disease, were more likely to experience severe COVID-19 morbidity and mortality (<xref ref-type="bibr" rid="ref25">25</xref>). Hand washing, social distancing, and quarantine are the key strategies for controlling the spread of the COVID-19 disease in society (<xref ref-type="bibr" rid="ref26">26</xref>). Despite the extensive implementation of pandemic prevention and control measures in Ethiopia with the active participation of policymakers&#x2019; leadership, healthcare facilities encountered shortages of emergency care medical supplies, ventilators, medical equipment, oxygen supplies, and well-ventilated isolation rooms (<xref ref-type="bibr" rid="ref27">27</xref>). Various studies reported that the COVID-19 pandemic in low-income countries would continue to worsen, with potentially fatal results.</p>
<p>There was a systematic review of the incidence rate of COVID-19 mortality and its predictors in Ethiopia, published in 2023 (<xref ref-type="bibr" rid="ref12">12</xref>). This study only included studies with incidence rates without considering prevalence studies, which could result in the study&#x2019;s power being inadequate to estimate the mortality rate of COVID-19, as only a limited number of studies with an incidence rate were used to determine the mortality rate of COVID-19. However, the current systematic review conducted to estimate the pooled mortality rate of COVID-19 and its predictors increased the power of the study by including both prevalence and incidence studies of COVID-19 mortality compared to the previous study. In this context, a systematic review and meta-analysis was conducted to estimate the accurate burden of the COVID-19 mortality rate and its associated risk factors in Ethiopia since the current study improved its power.</p>
<sec id="sec7">
<title>Objectives</title>
<p>The first objective of this review was to determine the pooled prevalence of COVID-19 mortality among coronavirus 2-infected patients in Ethiopia. The second objective was to determine the pooled incidence rate of mortality among COVID-19 patients, which can be used to measure the risk of death from COVID-19 disease over some specific period of time that can aid in recognizing whether the condition is increasing, decreasing, or remaining static. Finally, this systematic review and meta-analysis identified risk factors associated with the COVID-19 mortality rate in Ethiopia. To prevent COVID-19 mortality, determining the magnitude of COVID-19 mortality and a thorough exploration and understanding of its risk factors were inputs for policymakers and programmers to establish prevention strategies that helped control the pandemic&#x2019;s adverse health effects or minimize its risk factors.</p>
</sec>
<sec id="sec8">
<title>Review question</title>
<p>
<list list-type="order">
<list-item>
<p>What is the pooled prevalence of mortality among COVID-19 patients in Ethiopia?</p>
</list-item>
<list-item>
<p>What is the pooled incidence rate of mortality among COVID-19 patients in Ethiopia?</p>
</list-item>
<list-item>
<p>What are the contributing factors to the mortality among COVID-19 patients in Ethiopia?</p>
</list-item>
</list>
</p>
</sec>
</sec>
<sec sec-type="methods" id="sec9">
<title>Methods</title>
<sec id="sec10">
<title>Protocol and registration</title>
<p>The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in this systematic review and meta-analysis (<xref ref-type="bibr" rid="ref28">28</xref>) (<xref ref-type="supplementary-material" rid="SM1">Supplementary material S1</xref>). The protocol has been published in PROSPERO with ID CRD42020165740.</p>
</sec>
<sec id="sec11">
<title>Searching strategies</title>
<p>Two authors (TGW and JAM) ran the search strategy across pertinent databases using an advanced search strategy that was designed, constructed with Boolean operators, and matched to accessible and available databases. Using the main databases of PubMed, CINAHL, Google Scholar, and African Journals Online, all pertinent published articles were pulled up. The following keywords were used to search all electronic databases: mortality, COVID-19, and Ethiopia. The first search strategies were conducted from May 19, 2023, to May 21, 2023, and then updated from January 22, 2024, to January 27, 2024. The comprehensive search strategies were found in the <xref ref-type="supplementary-material" rid="SM2">Supplementary material S2</xref>.</p>
</sec>
<sec id="sec12">
<title>Selection of studies for inclusion in the review</title>
<p>Articles found through various database searches were combined and exported to Endnote X8.1 software. Duplicate articles were eliminated using Endnote X8.1 software. TGW and JAM independently screened the selected articles for relevance to the review objective using their titles and abstracts. Then, the full texts of all articles deemed relevant in the initial screening were assessed independently by two reviewers (TGW and JAM) for their eligibility to be included in the final analysis. If there was disagreement between two authors, the third expert&#x2019;s colleague reached a consensus.</p>
</sec>
<sec id="sec13">
<title>Outcome measurement</title>
<p>In this study, the mortality rate was determined as the percentage of COVID-19 cases having a death outcome. Two parameters were necessary to calculate the COVID-19 mortality rate: the total number of confirmed COVID-19 patients and the number of deaths from COVID-19 cases. Based on this, the mortality rate was estimated by dividing the number of deaths from COVID-19 cases by the total number of confirmed COVID-19 patients (sample size).</p>
</sec>
<sec id="sec14">
<title>Eligible criteria</title>
<p>The PICO technique, which primarily uses condition, context, and population (CoCoPop) questions, was employed by the authors to determine the inclusion and exclusion criteria for this systematic review and meta-analysis. Study subjects must be COVID-19 patients with laboratory confirmation. Studies must record their outcomes as COVID-19 mortality or death, or the availability of enough data to determine the mortality (the total number of confirmed COVID-19 patients along with the number of COVID-19 death cases). Studies must be published in English, and there are no restrictions on the inclusion of studies based on publication type. A study must be conducted in Ethiopia. Studies with case reports, review articles, commentary, letters to the editor, and studies without full text were excluded. Studies with poor methodology or those that failed to clearly report mortality or death as the outcome or its risk factors were also excluded.</p>
</sec>
<sec id="sec15">
<title>Data extraction</title>
<p>TGW and JAM independently extracted all necessary data from the original articles. Disagreements were resolved by consensus. The data were extracted as a summary table in Microsoft Excel using a standardized data extraction format. For each included study, the first author&#x2019;s name, the publication year, the study period, region, study design, the total number of confirmed COVID-19 patients, and the number of COVID-19-dead cases (<xref ref-type="supplementary-material" rid="SM3">Supplementary material S3</xref>).</p>
</sec>
<sec id="sec16">
<title>Appraisal of the included studies&#x2019; quality</title>
<p>The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to verify scientific validity. The quality of each included study was assessed independently by two investigators (TGW and JAM) using a standardized set of criteria from the Newcastle-Ottawa Scale (NOS) (<xref ref-type="bibr" rid="ref29">29</xref>). Any disagreements were resolved at the invitation of a third expert. Three domain categories were included in the Newcastle-Ottawa Quality Assessment Form for Cohort Studies: selection (representativeness of the exposed cohort, selection of the non-exposed cohort, and ascertainment of exposure and outcome of interest not present at the start of the study), comparability, and outcome (assessment of outcome, long enough follow-up for outcome occurrence, and adequate follow-up of cohorts). The quality scores of the included studies were classified as low quality (&#x003C;50%), medium quality (50&#x2013;69%), and high quality (&#x2265;70%). Finally, articles scoring at least half (50%) of the total score were considered of good quality and included in the meta-analysis.</p>
</sec>
<sec id="sec17">
<title>Statistical analysis</title>
<p>All the relevant data was extracted from the included studies using a Microsoft Excel spreadsheet, which was then imported to STATA software version 15 for further analysis (<xref ref-type="bibr" rid="ref30">30</xref>). A random-effects model was applied due to the expected heterogeneity (<xref ref-type="bibr" rid="ref31">31</xref>). I<sup>2</sup> is used to estimate the percentage of overall variation across studies due to heterogeneity. I<sup>2</sup> equals 25, 50, and 75%, which were regarded as low, moderate, and high levels of heterogeneity, respectively (<xref ref-type="bibr" rid="ref32">32</xref>). When heterogeneity was greater than 50% (based on the I<sup>2</sup> statistic), the random-effects model was applied. Subgroup analyses were carried out to identify potential heterogeneity moderators in cases of substantial heterogeneity (<xref ref-type="bibr" rid="ref33">33</xref>). A funnel plot and the Egger regression test were employed to assess publication bias (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>). The trim-and-fill technique was used to correct publication bias (<xref ref-type="bibr" rid="ref36">36</xref>). Sensitivity analysis was carried out by omitting each study and computing the <italic>p</italic> values of the studies that remained (<xref ref-type="bibr" rid="ref37">37</xref>). The pooled effect size was also computed by the metan command using each factor&#x2019;s adjusted odd ratio (AOR) to determine the association between mortality and its risk factors. Tables and forest plots with a 95% CI were then used to summarize the findings of each selected article.</p>
</sec>
</sec>
<sec sec-type="results" id="sec18">
<title>Results</title>
<sec id="sec19">
<title>Search results</title>
<p>Electronic databases brought up 610 articles on COVID-19 mortality during the initial search. Two hundred eighty-five were removed as a result of duplication, and 269 were excluded after title and abstract screening because they did not relate to the study&#x2019;s objective. Fifty-six articles were reviewed for full text; 35 of them were excluded for a number of reasons, including the fact that 31 of the studies focused only on the recovery time of COVID-19 and its predictors, three of them were systematic reviews, and one did not provide an outcome of interest. The final analysis included 21 studies that met the eligibility criteria. The specific screening procedures are depicted in a PRISMA flow chart (<xref ref-type="fig" rid="fig1">Figure 1</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>PRISMA flow chart for the selection of studies for systematic review.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g001.tif"/>
</fig>
</sec>
<sec id="sec20">
<title>Characteristics of eligible studies</title>
<p>A total of 21 studies with 42,307 study participants were included in the final analysis. Out of a total of 21 eligible studies, one was a prospective cohort study (<xref ref-type="bibr" rid="ref38">38</xref>), and the remaining 20 were retrospective cohort studies. Ten studies reported both the magnitude of COVID-19 mortality and its determinants; 11 studies reported only the magnitude of mortality. In this study, there were six regions and one administrative city (Addis Ababa). Most studies were conducted in the Oromia region (<xref ref-type="bibr" rid="ref38 ref39 ref40 ref41 ref42 ref43 ref44">38&#x2013;44</xref>). In the Amhara region (<xref ref-type="bibr" rid="ref45 ref46 ref47">45&#x2013;47</xref>) and the Southern Nation Nationality of People (SNNP) (<xref ref-type="bibr" rid="ref48 ref49 ref50 ref51">48&#x2013;51</xref>), three and four studies were conducted, respectively. Addis Ababa (<xref ref-type="bibr" rid="ref52 ref53 ref54">52&#x2013;54</xref>) and Harari (<xref ref-type="bibr" rid="ref55">55</xref>, <xref ref-type="bibr" rid="ref56">56</xref>), had three and two studies, respectively. Tigray (<xref ref-type="bibr" rid="ref57">57</xref>) and Benshangul Gumz (<xref ref-type="bibr" rid="ref58">58</xref>) each only had one study. The smallest and largest sample sizes observed in the Tigray (<xref ref-type="bibr" rid="ref57">57</xref>) and Amhara regions (<xref ref-type="bibr" rid="ref45">45</xref>) were 139 and 28,533, respectively. The lowest magnitude of COVID-19 mortality (1.2%) was recorded in the Oromia region (<xref ref-type="bibr" rid="ref41">41</xref>), while the highest magnitude (63.3%) was recorded in Addis Ababa (<xref ref-type="bibr" rid="ref53">53</xref>). The maximum and minimum incidence rates of COVID-19 mortality were 56.7 per 1,000 persons per day (<xref ref-type="bibr" rid="ref53">53</xref>) and 4.7 per 1,000 persons per day (<xref ref-type="bibr" rid="ref46">46</xref>), respectively. The majority of studies (61.9%) were conducted between 2020 and 2021 (<xref ref-type="table" rid="tab1">Table 1</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Characteristics of eligible studies (<italic>n</italic>&#x2009;=&#x2009;21).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">ID</th>
<th align="left" valign="top">Author, publication year</th>
<th align="left" valign="top">Region</th>
<th align="left" valign="top">Study design</th>
<th align="center" valign="top">Study period</th>
<th align="center" valign="top">Sample size (<italic>N</italic>)</th>
<th align="center" valign="top">Cases</th>
<th align="center" valign="top"><italic>P</italic> (%)</th>
<th align="center" valign="top">IR per PD</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td align="left" valign="top">Kaso et al. (<xref ref-type="bibr" rid="ref39">39</xref>), 2022</td>
<td align="left" valign="top">Oromia</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">308</td>
<td align="center" valign="top">50</td>
<td align="center" valign="top">16.2%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>2</td>
<td align="left" valign="top">Kaso et al. (<xref ref-type="bibr" rid="ref40">40</xref>), 2022</td>
<td align="left" valign="top">Oromia</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">422</td>
<td align="center" valign="top">47</td>
<td align="center" valign="top">11.13%</td>
<td align="center" valign="top">6.35 per 1,000</td>
</tr>
<tr>
<td>3</td>
<td align="left" valign="top">Birhanu et al. (<xref ref-type="bibr" rid="ref55">55</xref>), 2022</td>
<td align="left" valign="top">Harari</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">355</td>
<td align="center" valign="top">96</td>
<td align="center" valign="top">27.04%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>4</td>
<td align="left" valign="top">Dessie et al. (<xref ref-type="bibr" rid="ref45">45</xref>), 2022</td>
<td align="left" valign="top">Amhara</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">28,533</td>
<td align="center" valign="top">2,873</td>
<td align="center" valign="top">10.07%</td>
<td align="center" valign="top">11.78 per 1,000</td>
</tr>
<tr>
<td>5</td>
<td align="left" valign="top">Mengist et al. (<xref ref-type="bibr" rid="ref46">46</xref>), 2022</td>
<td align="left" valign="top">Amhara</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">522</td>
<td align="center" valign="top">29</td>
<td align="center" valign="top">5.56%</td>
<td align="center" valign="top">4.7 per 1,000</td>
</tr>
<tr>
<td>6</td>
<td align="left" valign="top">Tamiru et al. (<xref ref-type="bibr" rid="ref47">47</xref>), 2023</td>
<td align="left" valign="top">Amhara</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">452</td>
<td align="center" valign="top">37</td>
<td align="center" valign="top">8.2%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>7</td>
<td align="left" valign="top">Getahun et al. (<xref ref-type="bibr" rid="ref54">54</xref>), 2023</td>
<td align="left" valign="top">Addis Ababa</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2021</td>
<td align="center" valign="top">393</td>
<td align="center" valign="top">32</td>
<td align="center" valign="top">8.1%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>8</td>
<td align="left" valign="top">Habtewold et al. (<xref ref-type="bibr" rid="ref38">38</xref>), 2022</td>
<td align="left" valign="top">Oromia</td>
<td align="left" valign="top">Prospective</td>
<td align="center" valign="top">2021</td>
<td align="center" valign="top">852</td>
<td align="center" valign="top">97</td>
<td align="center" valign="top">11.4%</td>
<td align="center" valign="top">9.9 per 1,000</td>
</tr>
<tr>
<td>9</td>
<td align="left" valign="top">Gudina et al. (<xref ref-type="bibr" rid="ref41">41</xref>), 2021</td>
<td align="left" valign="top">Oromia</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020</td>
<td align="center" valign="top">4,398</td>
<td align="center" valign="top">52</td>
<td align="center" valign="top">1.2%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>10</td>
<td align="left" valign="top">Kebede et al. (<xref ref-type="bibr" rid="ref58">58</xref>), 2022</td>
<td align="left" valign="top">Benishangul Gumuz</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020</td>
<td align="center" valign="top">288</td>
<td align="center" valign="top">50</td>
<td align="center" valign="top">17.4%</td>
<td align="center" valign="top">1.8 per 100</td>
</tr>
<tr>
<td>11</td>
<td align="left" valign="top">Ayana et al. (<xref ref-type="bibr" rid="ref56">56</xref>), 2021</td>
<td align="left" valign="top">Harari</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">531</td>
<td align="center" valign="top">101</td>
<td align="center" valign="top">19.02%</td>
<td align="center" valign="top">16.2 per 1,000</td>
</tr>
<tr>
<td>12</td>
<td align="left" valign="top">Churiso et al. (<xref ref-type="bibr" rid="ref48">48</xref>), 2022</td>
<td align="left" valign="top">SNNP</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">220</td>
<td align="center" valign="top">49</td>
<td align="center" valign="top">22.3%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>13</td>
<td align="left" valign="top">Nega et al. (<xref ref-type="bibr" rid="ref53">53</xref>), 2022</td>
<td align="left" valign="top">Addis Ababa</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">496</td>
<td align="center" valign="top">314</td>
<td align="center" valign="top">63.3%</td>
<td align="center" valign="top">56.7 per 1,000</td>
</tr>
<tr>
<td>14</td>
<td align="left" valign="top">Abebe et al. (<xref ref-type="bibr" rid="ref57">57</xref>), 2022</td>
<td align="left" valign="top">Tigray</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020</td>
<td align="center" valign="top">139</td>
<td align="center" valign="top">56</td>
<td align="center" valign="top">40.3%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>15</td>
<td align="left" valign="top">Lemma Tirore et al. (<xref ref-type="bibr" rid="ref49">49</xref>), 2022</td>
<td align="left" valign="top">SNNP</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">845</td>
<td align="center" valign="top">70</td>
<td align="center" valign="top">8.3%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>16</td>
<td align="left" valign="top">Misganaw et al. (<xref ref-type="bibr" rid="ref50">50</xref>), 2023</td>
<td align="left" valign="top">SNNP</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">1,032</td>
<td align="center" valign="top">128</td>
<td align="center" valign="top">12.4%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>17</td>
<td align="left" valign="top">Tsegaye et al. (<xref ref-type="bibr" rid="ref42">42</xref>), 2022</td>
<td align="left" valign="top">Oromia</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2022</td>
<td align="center" valign="top">300</td>
<td align="center" valign="top">13</td>
<td align="center" valign="top">4.3%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>18</td>
<td align="left" valign="top">Tolossa et al. (<xref ref-type="bibr" rid="ref43">43</xref>), 2021</td>
<td align="left" valign="top">Oromia</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020</td>
<td align="center" valign="top">263</td>
<td align="center" valign="top">15</td>
<td align="center" valign="top">5.7%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td>19</td>
<td align="left" valign="top">Tolossa et al. (<xref ref-type="bibr" rid="ref44">44</xref>), 2022</td>
<td align="left" valign="top">Oromia</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">318</td>
<td align="center" valign="top">51</td>
<td align="center" valign="top">16.04%</td>
<td align="center" valign="top">14.1 per 1,000</td>
</tr>
<tr>
<td>20</td>
<td align="left" valign="top">Atamenta et al. (<xref ref-type="bibr" rid="ref52">52</xref>), 2023</td>
<td align="left" valign="top">Addis Ababa</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020</td>
<td align="center" valign="top">602</td>
<td align="center" valign="top">87</td>
<td align="center" valign="top">14.5%</td>
<td align="center" valign="top">10.7 per 1,000</td>
</tr>
<tr>
<td>21</td>
<td align="left" valign="top">Fantaw et al. (<xref ref-type="bibr" rid="ref51">51</xref>), 2023</td>
<td align="left" valign="top">SNNP</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">2020&#x2013;2022</td>
<td align="center" valign="top">1,038</td>
<td align="center" valign="top">181</td>
<td align="center" valign="top">17.4%</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>PD, person day; IR, incidence rate; Dash (&#x2212;) indicates the data is not available or has not been reported. SNNP, Southern Nation Nationality of People; P, prevalence.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec21">
<title>Quality assessment of included studies</title>
<p>Two authors independently assessed the quality of the included papers using New Castle-Ottawa for cohort studies. For the prospective cohort study, a total of nine scores were used. A total of seven scores were used for retrospective cohort studies since two quality assessments&#x2014;length of follow-up and adequate (completeness) of follow-up&#x2014;were ignored because they are not applicable for retrospective cohorts. There was only one prospective cohort study that scored a perfect nine (100%). In a retrospective cohort study, 10 included studies scored 100%, while the other ten scored 71.4%. In general, all the included studies had high quality. The two authors&#x2019; mutually agreed-upon assessment of the quality of each included study is presented in the <xref ref-type="supplementary-material" rid="SM4">Supplementary material S4</xref>.</p>
</sec>
<sec id="sec22">
<title>The pooled prevalence of COVID-19 mortality</title>
<p>In this systematic review and meta-analysis, 21 eligible studies were included to determine the pooled prevalence of COVID-19 mortality. A total of 42,307 COVID-19 patients were included; of them, 4,428 died. The pooled prevalence of COVID-19 mortality was 14.44% (95% CI: 10.35&#x2013;19.08%), with high significant heterogeneity (I<sup>2</sup>&#x2009;=&#x2009;98.92%, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001) (<xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>The pooled prevalence of COVlD-19 mortality.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g002.tif"/>
</fig>
</sec>
<sec id="sec23">
<title>The pooled incidence rate of COVID-19 mortality</title>
<p>The incidence rate of COVID-19 mortality was available in nine studies; the other studies could not report the incidence rate. Thus, these nine studies were eligible to estimate the pooled incidence rate of COVID-19 mortality among the 21 included studies. The pooled incidence rate of COVID-19 mortality was reported as 1,000 persons per day. In this systematic review and meta-analysis, the pooled incidence rate of COVID-19 mortality was 13.68 (95% CI: 8.16&#x2013;19.2) per 1,000 persons per day. There is significant heterogeneity among the included studies (I<sup>2</sup>&#x2009;=&#x2009;84.87%, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001) (<xref ref-type="fig" rid="fig3">Figure 3</xref>).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>The pooled incidence rate of COVID-19 mortality per 1000 persons per day.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g003.tif"/>
</fig>
</sec>
<sec id="sec24">
<title>Subgroup analysis of the prevalence of COVID-19 mortality</title>
<p>The highest prevalence of COVID-19 mortality rate was observed in the Tigray region (40.3, 95% CI: 32.5&#x2013;48.6%), followed by Addis Ababa (25.9, 95% CI: 2.3&#x2013;62.6%), with the absence of heterogeneity. The highest significant level of heterogeneity occurred in the Oromia region (I<sup>2</sup> =&#x2009;98.4%, <italic>p</italic> &#x003C;&#x2009;0.001) and southern nation nationality of peoples (SNNP) (I<sup>2</sup> =&#x2009;93.9%, <italic>p</italic> &#x003C;&#x2009;0.001). The prevalence of COVID-19 mortality rate was found to be 14.6% (95% CI: 10.3&#x2013;19.5%) in retrospective studies, with significant heterogeneity (I<sup>2</sup> =&#x2009;99%, <italic>p</italic> &#x003C;&#x2009;0.001). The maximum prevalence of COVID-19 mortality (16.8, 95% CI: 11.1&#x2013;23.5%) was observed in studies conducted between 2020 and 2021, with significant heterogeneity (I<sup>2</sup> =&#x2009;98.7%, <italic>p</italic> &#x003C;&#x2009;0.001). In sample sizes of less than 422, the prevalence of COVID-19 mortality was 16.1% (95% CI: 10&#x2013;23.2%), with high heterogeneity (I<sup>2</sup> =&#x2009;95.4%, <italic>p</italic> &#x003C;&#x2009;0.001) (<xref ref-type="table" rid="tab2">Table 2</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Subgroup analysis of COVID-19 mortality in Ethiopia (<italic>n</italic>&#x2009;=&#x2009;21).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="left" valign="top">Categories</th>
<th align="center" valign="top">Included study</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Mortality rate (95%CI)</th>
<th align="center" valign="top">Heterogeneity (I<sup>2</sup>, <italic>p</italic>-value)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="7">Region</td>
<td align="left" valign="top">Oromia</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">6,861</td>
<td align="center" valign="top">8.5% (3.18&#x2013;15.9%)</td>
<td align="center" valign="top">98.4%, <italic>p</italic> &#x003C;&#x2009;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Amhara</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">29,507</td>
<td align="center" valign="top">8% (5.4&#x2013;11.1%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top">SNNP</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">3,135</td>
<td align="center" valign="top">14.5% (9.7&#x2013;20.04%)</td>
<td align="center" valign="top">93.9%, <italic>p</italic> &#x003C;&#x2009;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Addis Ababa</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">1,491</td>
<td align="center" valign="top">25.9% (2.3&#x2013;62.6%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top">Harari</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">886</td>
<td align="center" valign="top">22.1% (19.4&#x2013;24.9%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top">Tigray</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">139</td>
<td align="center" valign="top">40.3% (32.5&#x2013;48.6%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top">Benishangul Gumuz</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">288</td>
<td align="center" valign="top">17.4% (13.4&#x2013;22.2%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Study design</td>
<td align="left" valign="top">Retrospective</td>
<td align="center" valign="top">20</td>
<td align="center" valign="top">41,455</td>
<td align="center" valign="top">14.6% (10.3&#x2013;19.5%)</td>
<td align="center" valign="top">99%, <italic>p</italic> &#x003C;&#x2009;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Prospective</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">852</td>
<td align="center" valign="top">11.4% (9.4&#x2013;13.7%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Study period</td>
<td align="left" valign="top">2020</td>
<td align="center" valign="top">5</td>
<td align="center" valign="top">5,690</td>
<td align="center" valign="top">13.0% (3.04&#x2013;28.3%)</td>
<td align="center" valign="top">99.04%, <italic>p</italic> &#x003C;&#x2009;0.001</td>
</tr>
<tr>
<td align="left" valign="top">2020&#x2013;2021</td>
<td align="center" valign="top">12</td>
<td align="center" valign="top">34,034</td>
<td align="center" valign="top">16.8% (11.1&#x2013;23.5%)</td>
<td align="center" valign="top">98.7%, <italic>p</italic> &#x003C;&#x2009;0.001</td>
</tr>
<tr>
<td align="left" valign="top">2021</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">1,245</td>
<td align="center" valign="top">10.3% (8.7&#x2013;12.1%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top">2020&#x2013;2022</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">1,338</td>
<td align="center" valign="top">13.8% (12&#x2013;15.8%)</td>
<td align="center" valign="top">&#x2013;</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Sample size</td>
<td align="left" valign="top">&#x003C; 422</td>
<td align="center" valign="top">9</td>
<td align="center" valign="top">2,584</td>
<td align="center" valign="top">16.1% (10&#x2013;23.2%)</td>
<td align="center" valign="top">95.4%, <italic>p</italic> &#x003C;&#x2009;0.001</td>
</tr>
<tr>
<td align="left" valign="top">&#x2265; 422</td>
<td align="center" valign="top">12</td>
<td align="center" valign="top">39,723</td>
<td align="center" valign="top">13.3% (8.2&#x2013;19.5%)</td>
<td align="center" valign="top">99.3%, <italic>p</italic> &#x003C;&#x2009;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>SNNP, Southern Nation Nationality of People.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec25">
<title>Publication bias</title>
<p>In this meta-analysis, Egger&#x2019;s test was employed to check for the existence of publication bias among the 21 included studies. Egger&#x2019;s test for a regression intercept obtained a <italic>p</italic>-value of 0.102, demonstrating no evidence of publication bias. There is possible evidence of publication bias in the funnel plot for <xref ref-type="fig" rid="fig4">Figure 4</xref>.</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Funnel plot for publication bias for the pooled prevalence of COVID-19 morality.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g004.tif"/>
</fig>
</sec>
<sec id="sec26">
<title>Sensitivity analysis</title>
<p>A sensitivity analysis was carried out to see if any small study effects influenced the pooled effect size among included studies (<italic>n</italic>&#x2009;=&#x2009;21). According to the leave-one-out sensitivity analysis, there were not any detectable differences (<xref ref-type="fig" rid="fig5">Figure 5</xref>). The current meta-analysis&#x2019;s findings appear to be fairly consistent.</p>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Sensitivity analysis for pooled prevalence of COVID-19 morality.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g005.tif"/>
</fig>
</sec>
<sec id="sec27">
<title>Determinants of COVID-19 mortality rate</title>
<p>For this systematic review, comorbidity, hypertension, cardiovascular disease, and kidney disease were eligible factors to conduct a meta-analysis. The risk of dying for patients with comorbidity and cardiovascular disease was significantly higher than that for those without conditions. However, those patients with kidney disease and hypertension did not have a significant difference in the COVID-19 mortality rate compared with their counterparts.</p>
</sec>
<sec id="sec28">
<title>The risk of COVID-19 mortality among patients with comorbidity</title>
<p>To check the risk of COVID-19 mortality for patients with comorbidity, four studies (<xref ref-type="bibr" rid="ref44">44</xref>, <xref ref-type="bibr" rid="ref45">45</xref>, <xref ref-type="bibr" rid="ref52">52</xref>, <xref ref-type="bibr" rid="ref58">58</xref>) were found; three of them (<xref ref-type="bibr" rid="ref45">45</xref>, <xref ref-type="bibr" rid="ref52">52</xref>, <xref ref-type="bibr" rid="ref58">58</xref>) revealed that the risk of COVID-19 mortality was statistically significant, and one (<xref ref-type="bibr" rid="ref44">44</xref>) did not have a significant association. This meta-analysis showed that the hazard of death from COVID-19 disease for patients with comorbidity was 1.84 times higher than that of patients without comorbidity (AHR&#x2009;=&#x2009;1.84, 95% CI: 1.13&#x2013;2.54), with moderately insignificant heterogeneity (I<sup>2</sup>&#x2009;=&#x2009;29.3%, <italic>p</italic>&#x2009;=&#x2009;0.236) (<xref ref-type="fig" rid="fig6">Figure 6</xref>).</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>The association between COVID-19 morality and comorbidity.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g006.tif"/>
</fig>
</sec>
<sec id="sec29">
<title>The risk of COVID-19 mortality for patients with hypertension</title>
<p>Three studies (<xref ref-type="bibr" rid="ref46">46</xref>, <xref ref-type="bibr" rid="ref52">52</xref>, <xref ref-type="bibr" rid="ref53">53</xref>) were used to examine the relationship between hypertension and COVID-19 mortality. One study (<xref ref-type="bibr" rid="ref46">46</xref>) showed a significant association, whereas the other two studies (<xref ref-type="bibr" rid="ref52">52</xref>, <xref ref-type="bibr" rid="ref53">53</xref>) revealed no significant association. In the current meta-analysis, the risk of COVID-19 mortality did not differ significantly between patients with and without hypertension (AHR&#x2009;=&#x2009;0.83, 95% CI: 0.58&#x2013;1.09) in the absence of heterogeneity (I<sup>2</sup>&#x2009;=&#x2009;0.0) (<xref ref-type="fig" rid="fig7">Figure 7</xref>).</p>
<fig position="float" id="fig7">
<label>Figure 7</label>
<caption>
<p>The association between COVID-19 morality and hypertension.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g007.tif"/>
</fig>
</sec>
<sec id="sec30">
<title>The risk of COVID-19 mortality for patients with cardiovascular disease</title>
<p>The risk of COVID-19 mortality rate for patients with cardiovascular disease was examined in a total of three studies (<xref ref-type="bibr" rid="ref40">40</xref>, <xref ref-type="bibr" rid="ref46">46</xref>, <xref ref-type="bibr" rid="ref52">52</xref>). Two studies (<xref ref-type="bibr" rid="ref46">46</xref>, <xref ref-type="bibr" rid="ref52">52</xref>) found a significant association, while another study (<xref ref-type="bibr" rid="ref40">40</xref>) found no significant association. The findings of this systematic review and meta-analysis showed that the hazard of mortality from COVID-19 disease for patients with cardiovascular disease was two times higher than that of those who did not (AHR&#x2009;=&#x2009;2, 95% CI: 1.09&#x2013;2.91) with no heterogeneity (I<sup>2</sup>&#x2009;=&#x2009;0.0) (<xref ref-type="fig" rid="fig8">Figure 8</xref>).</p>
<fig position="float" id="fig8">
<label>Figure 8</label>
<caption>
<p>The association between COVID-19 morality and cardiovascular disease.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g008.tif"/>
</fig>
</sec>
<sec id="sec31">
<title>The risk of COVID-19 mortality for kidney disease patients</title>
<p>Three studies (<xref ref-type="bibr" rid="ref40">40</xref>, <xref ref-type="bibr" rid="ref52">52</xref>, <xref ref-type="bibr" rid="ref53">53</xref>) were used to assess the risk of COVID-19 mortality for patients with kidney disease. One study (<xref ref-type="bibr" rid="ref40">40</xref>) indicated a significant relationship, but two studies (<xref ref-type="bibr" rid="ref52">52</xref>, <xref ref-type="bibr" rid="ref53">53</xref>) reported no significant difference in COVID-19 mortality between patients with and without kidney disease. The result of the meta-analysis revealed that the risk of mortality from COVID-19 disease did not significantly differ between those patients with and without kidney disease (AHR&#x2009;=&#x2009;1.08; 95% CI: 0.71&#x2013;1.45). Low heterogeneity was reported (I<sup>2</sup>&#x2009;=&#x2009;2.2%, <italic>p</italic>&#x2009;=&#x2009;0.36) (<xref ref-type="fig" rid="fig9">Figure 9</xref>).</p>
<fig position="float" id="fig9">
<label>Figure 9</label>
<caption>
<p>The association between COVID-19 morality and kidney disease.</p>
</caption>
<graphic xlink:href="fmed-11-1327746-g009.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec32">
<title>Discussion</title>
<p>In order to limit the COVID-19 pandemic and its adverse outcomes on health, such as the severity of the illness and case fatality rate, evidence-based studies and coordinated preparedness and response efforts at national, regional, and global levels are required. This study serves to determine the magnitude of COVID-19 deaths and explore its risk factors in Ethiopia, aiming to establish preventive strategies. In this meta-analysis, the pooled prevalence of COVID-19 mortality was 14.66% (95% CI: 10.35&#x2013;19.08%). This magnitude of the finding is significantly higher than studies conducted in Brazil (<xref ref-type="bibr" rid="ref3">3</xref>) and China (<xref ref-type="bibr" rid="ref4">4</xref>). This variation may be explained by the disparities in medical resources (<xref ref-type="bibr" rid="ref13">13</xref>), including shortages of emergency care supplies, ventilators, equipment, oxygen supply, and well-ventilated isolation rooms in Ethiopia (<xref ref-type="bibr" rid="ref27">27</xref>). Moreover, the current pooled mortality rate of COVID-19 is substantially higher than that of the WHO Africa region (<xref ref-type="bibr" rid="ref5">5</xref>, <xref ref-type="bibr" rid="ref6">6</xref>), and studies conducted in sub-Saharan Africa (<xref ref-type="bibr" rid="ref8">8</xref>, <xref ref-type="bibr" rid="ref9">9</xref>). According to the global report, COVID-19 mortality rates in Ethiopia are relatively low, which is probably underreported. However, it is comparable with studies conducted in Kenya (<xref ref-type="bibr" rid="ref10">10</xref>) and Ethiopia (<xref ref-type="bibr" rid="ref11">11</xref>). The difference may be related to a difference in cultural norms (<xref ref-type="bibr" rid="ref14">14</xref>), i.e., countries with more individualistic societies may be affected more by COVID-19 mortality and case fatalities. These facts are associated with the fact that social lack of cooperation in individualistic societies decreases the effectiveness of non-pharmacological treatments intended to alleviate the disease (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref16">16</xref>). In this meta-analysis, the pooled incidence rate of COVID-19 mortality was 13.68 (95% CI: 8.2&#x2013;19.2) per 1,000 persons per day. This finding is much higher than the WHO Africa region, which reported 0.0203 per 1,000 persons per day (<xref ref-type="bibr" rid="ref7">7</xref>), and a systematic study in Ethiopia that reported 9.13 per 1,000 person-days (<xref ref-type="bibr" rid="ref12">12</xref>). This indicates that COVID-19 mortality is still rising. This can be a result of a shortage of late detection of cases, ventilators, oxygen, and other medical supplies and equipment in Ethiopia (<xref ref-type="bibr" rid="ref27">27</xref>). Another explanation for the difference in mortality might be that the majority of the study participants were in the older age group (<xref ref-type="bibr" rid="ref20 ref21 ref22 ref23 ref24">20&#x2013;24</xref>) and had comorbidities (<xref ref-type="bibr" rid="ref17 ref18 ref19">17&#x2013;19</xref>) in Ethiopia. Another factor contributing to the difference between the incidence rate of COVID-19 mortality in the current study and a previous systematic review and meta-analysis study (<xref ref-type="bibr" rid="ref12">12</xref>) was the inclusion of additional studies in the current systematic review and meta-analysis study, which increased the study&#x2019;s sample size and helped in detecting differences more accurately. The findings of this study suggest that the Ethiopian Ministry of Health should give more attention to reducing the mortality of COVID-19 diseases through early detection of the disease (before severe complications) and appropriate management by providing adequate treatments and assisted instruments. The COVID-19 mortality rate was highest in the Tigray region (40.3%), followed by Addis Ababa (25.9%), while Amhara (8%) and Oromia (8.5%) had the lowest COVID-19 mortality in comparable percentages. This variation may be due to the fact that studies in regions with the highest COVID-19 mortality rates were done among critically ill COVID-19 patients, whose risk of death is increased. The sample size is the other possible variation for the differences in outcome occurrence because, in most of the region, a small number of studies with varying sample sizes were undertaken, which in turn indicates that the sample size is insufficient to detect the outcome. The COVID-19 mortality rate was slightly higher in retrospective studies with significant heterogeneity compared to a prospective study without heterogeneity. The variation may be due to the fact that the lack of heterogeneity in a prospective study implies that the outcome may not be detected precisely, possibly as a result of chance (small sample size). The COVID-19 mortality rate was 13% in 2020, 16.8% in 2020&#x2013;2021, 10.3% in 2021, and 13.3% in 2020&#x2013;2022. This indicates that from the early pandemic era (2020) to 2020&#x2013;2021, the death rate rose, and it decreased from 2021 to 2022. The reduction in the COVID-19 case fatality rate could be attributed to the COVID-19 pandemic&#x2019;s decline in 2021 as a result of the boosting of the COVID-19 vaccine and therapies. The decrease in mortality may also be attributed to the active implementation of pandemic prevention and control policies in Ethiopia (<xref ref-type="bibr" rid="ref27">27</xref>), such as hand washing, social distance, and quarantine, which served to reduce disease transmission (<xref ref-type="bibr" rid="ref26">26</xref>). The pooled prevalence of COVID-19 mortality was 13.3% in sample sizes greater than or equal to 422, while it was 16.1% in sample sizes less than 422. The existence of study participants with various comorbidities in these study groups could be the reason for this discrepancy.</p>
<p>The hazard of death from COVID-19 was greater for patients with comorbidity than for those without comorbidity. This evidence is consistent with the previous studies&#x2019; findings (<xref ref-type="bibr" rid="ref17 ref18 ref19">17&#x2013;19</xref>). Patients with cardiovascular disease had a higher risk of COVID-19 death than patients without this condition. These findings are in line with those from past studies (<xref ref-type="bibr" rid="ref20 ref21 ref22 ref23 ref24">20&#x2013;24</xref>). The increased risk of COVID-19 death for patients with comorbidities, including cardiovascular disease, shows the epidemiological effect of these diseases on people with chronic diseases. The reasons may be that the numerous pathophysiologic effects of chronic diseases, including cardiovascular disease, are significant for the outcomes of an infectious disease. For instance, inflammatory cytokines like IL-1&#x03B2; and TNF&#x03B1; may be released as a result of autoimmunity, which contributes to a chronic inflammatory state. Patients with kidney disease and hypertension did not differ significantly from those without kidney disease and hypertension in terms of their risk of COVID-19 mortality. The absence of a statistically significant difference may be attributable to the studies&#x2019; inclusion of a small number of patients with kidney disease and hypertension, which made it challenging to detect differences due to the effect of chance.</p>
<p>The limitation of this study is that it did not include all regions found in Ethiopia. The other limitation of this study is that the majority of the included studies were retrospective, which relies on records with limited variables that affect data validity and makes it challenging to control all confounding factors. There was a high level of heterogeneity across studies. There are heterogeneities even after subgroup analysis in studies conducted in the Oromia region and southern nation nationality of people, in studies using retrospective study designs, in the study period (2020 and 2020&#x2013;2021), and in study sample size (sample size&#x003C;422 vs. &#x2265;422). The occurrence of high heterogeneity may be caused by variations in the socio-demographic characteristics of study participants, the presence of comorbidity among study participants, and studies conducted on critically ill patients. Only one or a few studies were carried out across the majority of the region, which implies the sample size is inadequate to detect the outcome. Even though the study had shortcomings, it actually contributed to the development of evidence-based healthcare that helped reduce COVID-19 mortality.</p>
</sec>
<sec sec-type="conclusions" id="sec33">
<title>Conclusion</title>
<p>Substantial numbers of COVID-19 patients died in Ethiopia. The hazard of COVID-19 mortality for patients with comorbidities and cardiovascular disease was higher than that of patients without these diseases. For these COVID-19 mortality-prone patients, intensive surveillance, patient monitoring, and early medical intervention should be required. The distribution of the COVID-19 vaccination should be boosted, especially for people with comorbid conditions like cardiovascular disease.</p>
</sec>
<sec sec-type="data-availability" id="sec34">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="sec38">Supplementary material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="author-contributions" id="sec35">
<title>Author contributions</title>
<p>TGW: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. JAM: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec36">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<ack>
<p>We would like to express our gratitude to all of the primary authors of the studies included in this systematic review and meta-analysis.</p>
</ack>
<sec sec-type="COI-statement" id="sec37">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="sec100" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec38">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fmed.2024.1327746/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fmed.2024.1327746/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_2.DOCX" id="SM2" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_3.DOCX" id="SM3" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_4.DOCX" id="SM4" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<title>Abbreviations</title>
<fn fn-type="abbr"><p>COVID-19, coronavirus disease 2019; WHO, world health organization; AOR, adjusted odd ratio; AHR, adjusted hazard ratio; CI, confidence interval; Fig, figure.</p></fn></fn-group>
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