Risk factors for death in hospitalized COVID-19 patients in Africa: A systematic review and meta-analysis

Abstract

Background:

The coronavirus disease 2019 has quickly spread worldwide since it first appeared in Wuhan, China, in late 2019. The most affected country in Africa was South Africa. This study aimed to identify the risk factors for death in hospitalized COVID-19 patients in Africa.

Methods:

We conducted a systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We searched articles from the following database: PubMed, Embase, Cochrane Library, Medline, and COVID-19 Research Database. We used Google Scholar for gray literature. The language used in this article was English. The last search was conducted on January 15, 2023. Pooled HRs, or ORs, and 95% confidence intervals, were calculated separately to identify the risk factors for death in hospitalized COVID-19 patients. Heterogeneity was assessed by Cochran’s Q statistic and the I² test. The Egger test was used to assess publication bias. Subgroup analysis was performed to determine the source of heterogeneity. Data analysis was performed using Stata version 17. A P value < .05 was considered significant.

Results:

A total of 16,600 articles were obtained from the database search; finally, 16 articles met the inclusion criteria and were eligible for data extraction. The analysis revealed that the pooled prevalence of mortality in hospitalized COVID-19 patients was 13.9%. Advanced age was a significant risk factor for death in hospitalized COVID-19 patients, with the pooled coronavirus mortality HR and OR being 3.73 (95% CI: 2.27–5.19) and 1.04 (95% CI: 1.02–1.06), respectively. In addition, male gender (pOR 1.23; 95% CI: 1.07–1.40), patients with diabetes mellitus (DM) (pOR 1.26; 95% CI: 1.01–1.51), hypertension (HTN) (pOR 1.56; 95% CI: 1.27–1.85), chronic kidney disease (CKD) (pHR 5.43; 95% CI: 0.18–10.67), severe or critical conditions (pOR 9.04; 95% CI: 3.14–14.94) had a significantly increased risk of coronavirus-related mortality. The main limitations of the present study stem from the predominant use of published studies, which could introduce publication bias.

Conclusion:

According to this study, advanced age, male gender, hypertension, diabetes mellitus, chronic kidney disease, and severe or critical condition were clinical risk factors associated with death outcomes in hospitalized COVID-19 patients in Africa.

1. Introduction

Since its emergence in Wuhan, China, in late 2019, coronavirus disease-2019 (COVID-19) has rapidly become a public health concern worldwide.^[1] There were 684,973,424 confirmed total cases as of April 9, 2023, with 6,837,748 deaths and 657,760,269 recovered.^[2] The continent of Africa confirmed its first case of COVID-19 in Egypt on February 14, 2020.^[3] As of April 9, 2023, Africa recorded 12,813,850 confirmed cases and 258,672 deaths.^[2] The most affected country in Africa was South Africa with 4,075,101 cases.^[2] COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), a member of the Betacoronavirus genus.^[4] The virus spreads through both direct and indirect contact. Person-to-person transmissions are typically transmitted through droplets produced by coughing, sneezing, and talking. Indirect transmission occurs when a person touches a contaminated surface or object before touching their face.^[5] The clinical manifestations of COVID-19 disease differ from patient to patient. Fever, a dry cough, and tiredness are the most common infection symptoms.^[6] Less common symptoms include nausea or vomiting, muscular or joint pain, sore throat, loss of sense of smell or taste or both, nasal congestion, conjunctivitis, headache, various types of skin rashes, diarrhea, shivering, and dizziness.^[7] As the illness worsens, the patient will experience severe shortness of breath, low blood oxygen levels, lung destruction, and organ failure.^[8] The symptoms of COVID-19 infections, such as weariness, sputum, hemoptysis, dyspnea, and chest tightness, were found to be independent predictors of death in the study by Yang et al^[9] Although most COVID-19 patients are expected to have a good prognosis, older patients and those with comorbidities may have worse outcomes.^[10] Patients with chronic underlying illnesses are more likely to develop viral pneumonia, dyspnea, and hypoxemia within 1 week of disease onset, which can lead to respiratory or end-organ failure and even death.^[11] According to the 2022 World Health Organization (WHO) non-communicable disease progress monitor, non-communicable diseases account for 50% to 88% of deaths in African countries.^[12] The rising burden of non-communicable diseases will pressure treatment and care services and gravely threaten the health and lives of millions of people in Africa.^[12] Africans may be at greater risk of dying of COVID-19 due to the high prevalence of non-communicable diseases. Therefore, we aimed to identify the risk factors for death in hospitalized COVID-19 patients in Africa.

2. Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis were followed for planning, conducting, and reporting the current systematic review and meta-analysis.^[13] The protocol of this study is registered in PROSPERO with registration number: CRD42022375718 and was developed and followed in the process. This review was based on published data, it did not involve contact with participants, and therefore no ethical approval was required for it to be conducted. However, the present review is part of a large study approved by the University of KwaZulu-Natal/Biomedical Research Ethics Committee (UKZN/BREC) under reference number BREC/00005034/2022.

2.1. Eligibility criteria

2.1.1. Inclusion criteria.

• •Participants laboratory confirmed COVID-19 patients.
• •Studies reporting on the risk factors for death in hospitalized COVID-19 patients.
• •Studies published from 2020 to 2022.
• •Observational studies including multivariate analysis.
• •Peer-reviewed English language publications.
• •Studies conducting in the African region.

2.1.2. Exclusion criteria.

• •Review articles, and studies without full text available.
• •Cases reports, commentaries, errata, protocols, reports, and letters to the editor.

According to the PECO framework, inclusion, and exclusion criteria were listed in Table 1.

Table 1.

Inclusion and exclusion criteria.

PECO acronym	Inclusion criteria	Exclusion criteria
P-Participant/ Population	Hospitalized COVID-19 Patients in Africa	Hospitalized COVID-19 Patients in other continents.
E-Exposure	Laboratory confirmed COVID-19	Other previous pandemics
C-Control	Not applicable	Not applicable
O-Outcome	Risk factors of hospitalized patients who died from COVID-19 in Africa from 2020 to 2022	–

COVID-19 = coronavirus disease-2019.

2.2. Search strategy for identifying relevant studies

We searched from the following database: PubMed, Embase, Cochrane Library, Medline, and COVID-19 Research Database to identify relevant studies. We used Google Scholar to search for gray literature. The language used in this article was English. The last search was conducted on January 15, 2023. We used the following medical subject headings (MeSH) and non-MeSH terms in our search strategy: “COVID-19” or “SARS-Cov-2” or “2019-nCoV” or “novel coronavirus” or “severe acute respiratory syndrome coronavirus 2”) AND (“death” or “mortality” or “survival” or “fatal outcome”) AND (“Sub-Saharan Africa” or “Africa” or Southern Africa” or “Central Africa” or “West Africa” or “East Africa” or “Northern Africa.” This search strategy was adapted for a possible extension to other databases, and we manually searched reference lists from relevant studies. All literature searches were downloaded into an EndNote library to facilitate the screening process of articles from databases. All details regarding the search strategy were reported in the supplemental file, http://links.lww.com/MD/J524.

2.3. Quality assessment

The methodological items for the non-randomized studies (MINORS) list were used to assess the methodological quality of the studies included in this meta-analysis. Two independent reviewers (M.R.G. and T.M.) assessed all the included studies. A MINORS item gets a score of 0 if it was not reported, a score of 1 if it was reported but was not adequate, and a score of 2 if it was reported and adequate. If the overall MINORS score was 17 or more, the study was of high quality, and if the total score was < 17, the study was of low quality.^[14,15]

2.4. Data collection

Two reviewers independently screened all the titles and abstracts based on predefined inclusion criteria. The review author team retrieved the full texts of all included abstracts. Two review authors independently screened all the full-text publications, and disagreements were resolved by involving a third author. The literature selection process and reasons for exclusion and inclusion criteria were documented by a Preferred Reporting Items for Systematic Review and Meta-Analysis flow diagram (Fig. 1).^[16]

Figure 1.

PRISMA flow diagram.

2.5. Data extraction

The following information was extracted from each eligible article based on the preformed format: first author, years of publication, country, study design, sample size, percentage of women, mean/median age, percentage of risk factors, effect estimates (hazard ratio (HR) or OR), and adjusted risk factors, mortality rate.

2.6. Data synthesis and analysis

The original articles were used to extract essential information, such as study design and effect estimates. We used published peer-reviewed ORs or HRs (along with 95% CIs) to investigate the relationship between COVID-19 death and risk factors. To address the risk of death in COVID-19 patients, pooled HRs, or ORs, and 95% confidence intervals (CIs) were calculated separately. Cochran’s Q statistic and I² test were used to assess heterogeneity. A fixed-effects model was adopted if no significant heterogeneity was observed (I² ≤ 50%, P > .1); otherwise, a random-effects model was applied.^[17] The Egger test was used to assess publication bias.^[18] Subgroup analysis was performed to determine the source of heterogeneity. Stata version 17 was used to perform data analysis. A P value < .05 was considered significant.

3. Results

3.1. Study selection

After applying the inclusion and exclusion criteria, only 16 studies remained from the original 16,600 articles retained for the search (Fig. 1).

3.2. Characteristics of included studies.

Out of 60 reviewed articles, 16 were eligible for data extraction (Table 2). All 16 articles were conducted in Africa, 1^[19] of them in Nigeria, 2^[20,34] in Egypt, 2^[21,30] in Ethiopia, 1^[22] in South Africa, 2^[23,24] in Uganda, 4 in DRC,^{[25,26,32,33]} 1^[27] in Ghana, 2^[28,29] in Guinea, 1^[29] in Burkina Faso, 1^[31] in Niger. Of these, 5^{[19,25,27,32,33]} articles were published in 2020, 4^{[28,29,31,34]} in 2021, and 7^{[20–24,26,30]} in 2022. The total sample size in the present systematic review and meta-analysis was 12,297 participants, predominantly men. The proportion of diabetes mellitus (DM) ranged from 6.8% to 75.32%, hypertension (HTN) from 9.5% to 72%, cardiovascular disease from 3.45% to 83.48%, chronic lung disease from 2% to 58.70%, chronic kidney disease (CKD) from 0.6% to 20.83%, and HIV from 0.3% to 13.64%. Most of the studies included in the meta-analysis were retrospective cohort studies. The highest mortality rate was reported in South Africa, with 65.8%,^[22] and the lowest was in Nigeria, with 4%. The total number of deaths was 1718, and the overall prevalence of deceased patients was 13.9%. The patients included in the meta-analysis were admitted to hospitals.

Table 2.

Characteristics of included studies.

Authors and year of publication	Country	Study design	Time period of study	Sample size	Female (%)	Age [median, IQR, mean (SD)]	Number of deaths	Mortality rate (%)	CVD (%)	HTN (%)	DM (%)	CLD (%)	CKD (%)	HIV (%)
1. Abayomi et al 2020[19]	Nigeria	Retrospective cohort study	Submit on September 2, 2020	2184	34.2	43 ± 16	87	4	–	16.7	6.8	–	0.6	0.3
2. AbdelG haffar et al 2022[20]	Egypt	Retrospective cohort study	April–July 2020	3712	51.1	–	900	24.2	8.1	29.7	31.1	–	4.1	–
3. Abebe et al 2022[21]	Ethiopia	Retrospective cohort study	May7–October 28, 2020	925	30	30(25–44)	56	6.1	42.2	–	25.0	16.4	4.3	5.2
4. Allwood et al 2022[22]	South Africa	Prospective study	November 5, 2020–30 April 2021	82	67	46	54	65.8	–	48	41	2	–	11
5. Apiyo et al 2022[23]	Uganda	Retrospective cohort study	June 2020–September 2021	160	36.5	45 (37–57)	18	11.3	–	26.9	16.9	–	–	1.9
6. Baguma et al 2022[24]	Uganda	Retrospective cohort study	March 2020–October 2021	664	–	–	32	4.8	–	–	–	–	–	–
7. Bepouka et al 2020[25]	DRC	Retrospective cohort study	March 23–June 15, 2020	141	32.6	49.6 ± 16.5	41	29.1	4.3	23.4	17.0	2.8	–	–
8. Bepouka et al 2022[26]	DRC	Retrospective cohort study	March 2020–May 2021	222	28.45	70(64–74)	97	43.7	3.45	52.51	27.49	–	–	–
9. Boateng et al 2020[27]	Ghana	Prospective study	June 1–July 27 2020	25	44	59.3 ± 20.6	4	16	24	72	36	–	–	–
10. Donamou et al 2021[28]	Guinea	Retrospective cohort study	March 12–July 12, 2020	140	21	58 ± 14	35	25	–	–	–	–	–	–
11. Jaspard et al 2021[29]	Burkina Faso and Guinea	Prospective cohort study	March 1–November 12, 2020 (Burkina) April 1–November 4 (Guinea)	1805	36.0	41(30–57)	90	5	–	21	12	–	–	–
12. Kaso et al 2022[30]	Ethiopia	Retrospective cohort study	July 1, 2020–March 5, 2021	422	38.6	41.06 ± 20.61	47	11.14	6.9	9.5	17.1	11.4	2.1	2.8
13. Katoto et al 2021[31]	Niger	Retrospective cohort study	March 19–November 17, 2020	729	28.12	40(30–61)	85	11.66	83.48	–	75.32	58.70	20.83	13.64
14. Matangila et al 2020[32]	DRC	Retrospective cohort study	March 11–July 22, 2020	160	49	54(38–64)	32	20	7	34	19	3	–	–
15. Nachega et al 2020[33]	DRC	Retrospective cohort study	March 10–July 31, 2020	766	34.4	46(34–58)	101	13.2	3.9	25.4	14.0	3.4	0.9	1.6
16. Nassar et al 2021[34]	Egypt	Retrospective cohort study	April 28–July 29, 2020	160	32.5	60 ± 14	39	24.4	20.6	55.6	45.6	10.6	9.4	–
Total	NA	NA	NA	12297	NA	NA	1718	NA	NA	NA	NA	NA	NA	NA

CKD = chronic kidney disease, CLD = chronic lung diseases, CVD = cardiovascular diseases, DM = diabetes mellitus, DRC = the Democratic Republic of the Congo, HIV = human immunodeficiency virus, HTN = hypertension, IQR = interquartile range, NA = not applicable, SD = standard deviation.

3.3. Quality assessment

According to the MINORS tool, all the articles included in the meta-analysis were of high quality, as indicated in Table 3.

Table 3.

Quality assessment.

Authors and year of publication	MINORS
1. Abayomi et al[19]	17
2. AbdelGhaffar et al[20]	20
3. Abebe et al[21]	18
4. Allwood et al[22]	17
5. Apiyo et al[23]	17
6. Baguma et al[24]	17
7. Bepouka et al[25]	18
8. Bepouka et al[26]	18
9. Boateng et al[27]	17
10. Donamou et al[28]	17
11. Jaspard et al[29]	17
12. Kaso et al[30]	18
13. Katoto et al[31]	18
14. Matangila et al[32]	17
15. Nachega et al[33]	17
16. Nassar et al[34]	17

MINORS = the methodological items for nonrandomized studies.

3.4. Analysis of adjusted estimates

Sixteen studies provided adjusted data on COVID-19 infection-related mortality^[19–34] (Table 4). In 5 studies, a multivariate Cox regression model and, in 11, a multivariate logistic regression model, were applied. Advanced age,^{[19–21,23–25,29–33]} male gender,^[19,20,29] female gender,^[24] hypertension,^[20,23,29] diabetes mellitus,^[20,23,24] obesity,^[33] chronic kidney disease,^[20,30,33] cardiovascular disease,^[21,34] and HIV/AIDS^[30] were considered as predictors of death in multivariate analysis. Dyspnea,^[19,25] weakness,^[19,21] and cough^[19] were symptoms linked to an increased risk of death. Patients admitted in critical or severe conditions also had an increased risk of death.^[19,25,29] The biological parameters associated with an increased risk of death were elevated D-dimer^[22] and AST.^[32]

Table 4.

Risk factors for increased risk for death in studies using regression models.

Authors	Setting	Regression model	Significant risk factors (effect estimate, 95% CI)
1. Abayomi et al[19]	Nine treatment centers in Lagos state, Southwest Nigeria	Multivariable logistic regression models	The most significant symptom predictor of COVID-19 death was difficulty in breathing (OR: 19.26, 95% CI 10.95–33.88), followed by weakness (OR: 3.04, 95% CI 1.44–6.42), Cough (OR: 1.87, 95% CI 1.04–3.37%). Let us also point out that advanced age (OR: 1.04, 95% CI 1.02–1.06), males gender (OR: 2.21, 95% CI 1.06–4.58), and admission at critical or severe stage (OR: 153.47, 95% CI 30.74–766.35) were a predictor of death
2. AbdelGhaffar et al[20]	Six hospitals affiliated to the General Organization For Teaching Hospitals and Institutes (GOTHI) in Egypt	Multivariate logistic regression model (forward stepwise selection)	Age > 60 years (OR: 2.83, 95% CI 2.38–3.35, P = .001), male gender (OR: 1.211, 95% CI 1.1–1.43, P = .025), diabetes mellitus (OR: 1.25, 95% CI 1.034–1.53, P = .022), hypertension (OR: 1.51, 95% CI 1.243–1.84; P = .001), and chronic renal insufficiency (OR: 3.398, 95% CI 2.45–4.71, P = .001) were independent predictors for mortality among admitted patients.
3. Abebe et al [21]	Six COVID-19 isolation and treatment centers namely Mekelle, Maichew, Axum, Adigrat, Shire and Humera in Ethiopia.	Multivariable binary logistic regression	The odds of mortality was higher for patients who had cardiovascular diseases (AOR = 2.49, 95% CI: 1.03–6.03), shortness of breath (AOR = 9.71, 95% CI: 4.73–19.93) and body weakness (AOR = 3.04, 95% CI: 1.50–6.18). Moreover, the estimated odds of mortality significantly increased with patient’s age.
4. Allwood et al[22]	Tygerberg Hospital, a 1380-bed tertiary hospital in Cape Town, South Africa	Cox’s proportional hazards model	An elevated D-dimer level was associated with increased risk of death in the ICU [HR 1.05, 95% confidence interval (CI) 1.00–1.11]
5. Apiyo et al[23]	Single-center, at Case Hospital, Kampala, Uganda.	A multivariable logistic regression model	Factors strongly associated with all-cause mortality were as follows: age >50 years (odds ratio [OR]: 8.6, 95% confidence interval [CI]: 1.1–69.2, and P = .042), having at least 1 comorbidity (OR: 3.2, 95% CI: 1.1–8.9, and P = .029), hypertension (OR: 3.2, 95% CI: 1.2–8.6, and P = .024), diabetes mellitus (OR: 2.9, 95% CI: 1.0–8.5, and P = .056), and oxygen saturation < 92% (OR: 5.1, 95% CI: 1.8–14.4, and P = .002)
6. Baguma et al[24]	Gulu Regional Referral Hospital in Northern Uganda	Multivariable logistical regression analysis.	The independent factors associated with mortality among the COVID-19 patients were females AOR = 0.220, 95% CI: 0.059–0.827; P = .030; Diabetes mellitus AOR = 9.014, 95% CI: 1.726–47.067; P = .010; Ages of 50 years and above AOR = 2.725, 95% CI: 1.187–6.258; P = .018; tiredness AOR = 0.059, 95% CI: 0.009–0.371; P < .001; general body aches and pains AOR = 0.066, 95% CI: 0.007–0.605; P = .020; loss of speech and movement AOR = 0.134, 95% CI: 0.270–0.660; P = .010 and other co-morbidities AOR = 6.860, 95% CI: 1.309–35.957; P = .020.
7. Bepouka et al[25]	The Kinshasa University Hospital, a large regional hospital in DRC.	Multivariate Cox model	Age between 40 and 59 years [adjusted Hazard Ratio (aHR): 4.07; 95% CI: 1.16–8.30], age at least 60 years (aHR: 6.65; 95% CI: 1.48–8.88), severe or critical COVID-19 (aHR: 14.05; 95% CI: 6.3–15.67) and presence of dyspnea (aHR: 5.67; 95% CI: 1.46-21.98) were independently and significantly associated with the risk of death
8. Bepouka et al[26]	Kinshasa University Hospital, a big regional hospital in DRC	Cox regression models	Low oxygen saturation of < 90% (aHR 1.69; 95% CI [1.03–2.77]; P = .038) was an independent predictor of mortality.
9. Boateng et al[27]	Treatment center of the University Hospital, Kumasi, Ghana	Multivariable logistic regression analysis.	Increasing age and high systolic blood pressure in unadjusted but no factors in multivariate analysis
10. Donamou et al[28]	Intensive Care Unit of the COVID Treatment Center of Donka National Hospital, in Guinea.	multivariate logistic regression analysis	Acute Respiratory Distress Syndrome (ARDS) (OR = 6.33, 95% CI [1.66–29]; P = .007), a Brescia score ≥ 2 (OR = 5.8, 95% CI [1.7–19.2]; P = .004) and admission delay (OR = 5.6, 95% CI [1.8–17.5]; P = .003).
11. Jaspard et al[29]	Hospitals in Burkina Faso and Guinea	Multivariable logistic regression	In multivariable analysis, the risk of death was higher in men (aOR 2.0, 95% CI 1.1–3.6), people aged ≥ 60 years (aOR 2.9, 95% CI 1.7–4.8), admission at severe stage (aOR 9.0 95% CI 5.0–16.8) and those with chronic hypertension (aOR 2.1, 95% CI 1.2–3.4).
12. Kaso et al [30]	Bokoji Hospital COVID-19 treatment center, in Ethiopia	Cox regression analysis	Patients that age between 31 and 45 years (aHR = 2.55; 95% CI: 1.03–6.34), older than 46 years (aHR = 2.59; ; 95% CI: 1.27–5.30), chronic obstructive pulmonary disease (aHR = 4.60, 95% CI: 2.37–8.91), Chronic kidney disease (aHR = 5.58, 95% CI: 1.70–18.37), HIV/AIDS (aHR = 3.66, 95% CI: 1.20–11.10), admission to the Intensive care unit (aHR = 7.44, 95% CI: 1.82–30.42), and being on intranasal oxygen care (aHR = 6.27, 95% CI: 2.75–4.30) were independent risk factors increasing risk of death from COVID-19 disease than their counterparts.
13. Katoto et al[31]	Hospitals from all 8 political regions (Niamey, Agadez, Diffa, Dosso, Maradi, Tahaoua, Tillaberi and Zinder) in Niger	Cox regression analysis	Comorbidity (adjusted hazards ratio [aHR] 2.04; 95% CI: 2.38–6.35), shortness of breath at baseline (aHR 2.04; 95% CI: 2.38–6.35) and being 60 years or older (aHR 3.32; 95% CI: 1.88–5.89) increased the risk of COVID-19 mortality 2- to 5-folds
14. Matangila et al[32]	Clinique Ngaliema, a public hospital in Kinshasa, DRC	Multivariate logistic regression models	OR: Older age: 1.06 (1.0–1.11), lower SpO2: 0.94 (0.90–0.98), higher heart rate: 1.06 (1.02–1.11), elevated AST: 1.02 (1.01–1.03)
15. Nachega et al [33]	7 largest health facility in Kinshasa, DRC	Cox regression	Age < 20 years (adjusted hazard ratio [aHR] = 6.62, 95% CI: 1.85–23.64), 40–59 years (aHR = 4.45, 95% CI: 1.83–10.79), and ≥ 60 years (aHR = 13.63, 95% CI: 5.70–32.60) compared with those aged 20–39 years, with obesity (aHR = 2.30, 95% CI: 1.24–4.27), and with chronic kidney disease (aHR = 5.33, 95% CI: 1.85–15.35)
16. Nassar et al[34]	2 ICUs of Cairo University Hospitals in Egypt	Forward stepwise multivariable logistic regression analysis	Ischemic heart disease (OR: 13.04, 95% CI: 3.66−46.43, P < .001), history of smoking (OR: 5.28, 95% CI: 1.19–23.41, P = .029), and the occurrence of bacterial pneumonia during the ICU stay (OR: 4.87, 95% CI: 1.67−14.24, P = .004) were independently associated with a higher risk of in-hospital death.

aHR = adjusted hazard ratio, aOR = adjusted odds ratio, ARDS Acute respiratory distress syndrome, aRR = adjusted risk ratio, AST = aspartate aminotransferase, CI = confidence interval, DRC = the Democratic Republic of the Congo, HIV = human immunodeficiency virus, ICU = intensive care unit, OR = odds ratio, sp O2 = oxygen saturation.

3.5. Risk factors for death

An important risk factor for death was advanced age, and the pooled HR and OR for coronavirus mortality were 3.73 (95% CI: 2.27–5.19) and 1.04 (95% CI: 1.02–1.06), respectively (Table 5; Figures 2 and 3). Three studies found that male patients had a significantly increased risk of COVID-19-related mortality (pOR 1.23; 95% CI:1.07–1.40). Patients with DM (pOR 1.26; 95% CI: 1.01–1.51), HTN (pOR 1.56; 95% CI: 1.27–1.85), CKD (pHR 5.43; 95% CI: 0.18–10.67), severe or critical conditions (pOR 9.04; 95% CI: 3.14–14.94) had a significantly increased risk of coronavirus-related mortality (Table 5; Figs. 4–8).

Table 5.

Results of subgroup analysis based on factors related to coronavirus mortality in terms of demographics, clinical characteristics, and comorbidities.

Risk factors	Effect measures	Numbers of study	Effect size (95% CI)	Heterogeneity	Egger’s test	P
				I2 value
Advanced age	pHR	4	3.73(2.27–5.19)	44.4	0.145	.109
	pOR	7	1.04(1.02–1.06)	93.1	0.000	.000
Male	pHR	0	–	–	–	–
	pOR	3	1.23(1.07–1.40)	25.9	0.259	NA
Female	pHR	0	–	–	–	–
	pOR	1	0.220(0.059–0.827)	–	–	–
DM	pHR	0	–	–	–	–
	pOR	3	1.26(1.01–1.51)	0.0	0.552	NA
HTN	pHR	0	–	–	–	–
	pOR	3	1.56(1.27–1.85)	0.0	0.409	NA
CKD	pHR	2	5.43(0.18–10.67)	0.0	0.964	NA
	pOR	1	3.398(2.45–4.71)	–	–	–
Severe or critical condition	pHR	1	14.05 (6.3–15.67)	–	–	–
	pOR	2	9.04(3.14–14.94)	0.0	0.441	NA

CKD = chronic kidney disease, DM = diabetes mellitus, HTN = hypertension, NA = not available, pHR = pooled hazard ratio, pOR = pooled odds ratio.

Figure 2.

Forest plot of studies using hazard ratio and showing the estimate for advanced age on COVID-19 mortality. COVID-19 = coronavirus disease-2019.

Figure 3.

Forest plot of studies using odds ratio and showing the estimate for advanced age on COVID-19 mortality. COVID-19 = coronavirus disease-2019.

Figure 4.

Forest plot of studies using odds ratio and showing the estimate for HTN on COVID-19 mortality. COVID-19 = coronavirus disease-2019, HTN = hypertension.

Figure 8.

Forest plot of studies using odds ratio and showing the estimate for male gender on COVID-19 mortality. COVID-19 = coronavirus disease-2019.

3.6. Heterogeneity and publication bias

Subgroup analysis was performed to determine the source of heterogeneity. Pooled OR statistics for advanced age showed heterogeneity between the studies considered (I² = 93.1) (Table 5). Egger’s test was performed to assess publication bias (Table 5).

4. Discussion

This study aimed to identify the risk factors for death in hospitalized COVID-19 patients in Africa. Advanced age, male gender, hypertension, diabetes mellitus, chronic kidney disease, and severe or critical conditions were clinical risk factors associated with death outcomes in hospitalized COVID-19 patients.

In our study, the mortality prevalence was 13.9%, which is higher than the death rate reported by the WHO. Another study reached similar conclusions to ours, reporting a combined mortality rate from COVID-19 of 20% in the US, Europe, and China.^[34] This is because the included patients in our meta-analysis were hospitalized, and some of the included studies had very high proportions of severe and critical patients. Although nearly all the included studies were carried out in the first wave, when these variants were not yet publicly reported, the introduction of variants of concern may also have an impact on case numbers and death.

Our study revealed that advanced age was a significant risk factor for death in hospitalized COVID-19 patients, with the pooled coronavirus mortality HR and OR being 3.73 (95% CI: 2.27–5.19) and 1.04 (95% CI: 1.02–1.06), respectively (Table 5; Figs. 2 and 3). Other studies have found advanced age associated with death in COVID-19.^[35–37] One probable explanation is that aging changes the functioning of T cells (CD4+, CD8+) and B cells, which are immunological cells.^[38] It is believed that immunosenescence, or the remodeling of the immune system, and the risk of immunopathology in aged patients with decreased B and T lymphocyte functions are the leading causes of older patients’ susceptibility to severe COVID-19 disease and death.^[39,40] Impaired type-1 interferon (IFN) response is associated with age-related changes in innate and adaptive immunity. Furthermore, some SARS-CoV-2 nonstructural proteins suppress the type-1 IFN response, which inhibits CD8+ T-cell response to viral infection.^[41] Elderly patients may be more vulnerable to COVID-19 because of age-related declines in denovo T-cell response and/or the effects of underlying diseases, particularly chronic viral infections like cytomegalovirus (CMV).^[39] The cause is still unknown, however older COVID-19 convalescent plasma donors were reported to have greater titers of SARS-CoV-2-specific IgG and neutralizing antibodies than younger donors.^[39,42,43] Inflammaging, chronic low-grade inflammatory phenotype (CLIP), persistent viral infection, such as CMV, and other potential factors, such as smoking, decreased sex steroid secretion, and accumulated adipose tissue, cause an unbalanced pro-inflammatory milieu in elderly adults, which amplifies additional inflammatory responses upon SARS-CoV-2 infection and intensifies cytokine storm. It also affects the expression of ACE-2 and viral entry.^[39,44] Moreover, an increased type 2 cytokine production results in a lowered ability to stop virus replication and prolongs pro-inflammatory reactions, which can conduct in death. Additionally, this group may be at an increased risk of death due to high levels of angiotensin-converting enzyme genes in the heart and lungs.^[35,45–47]

Male gender was associated with a high risk for mortality according to our findings (pOR 1.23; 95% CI: 1.07–1.40) (Table 5; Fig. 8). Similarly, other studies from China, Italy, Denmark, and the USA also showed higher COVID-19-related mortalities in the male gender.^[48–52] Many prior meta-analyses corroborate the disputed claims regarding the male gender.^[35–38] Explanations from the literature state that, sex chromosome-related immune responses, distinct lifestyles that are more prevalent in males (alcohol, smoking, lower rates of handwashing, obesity), and comorbidities may all be contributing factors.^[51] The T-cell (CD8+, CD4+) and B-cell count in the adaptive immune system are lower in males than in females.^[38,53,54] Toll-like receptor-7 (TLR7) and carcinoembryonic antigen 2 (CEA2) are differentially expressed on the X chromosome, which gives females an edge in immunological regulation.^[55,56] According to research, healthy males, diabetic males, and males with chronic kidney disease have higher circulating ACE-2 than females.^[57] Type-1 angiotensin II receptor (AT2R) is down-regulated by estrogens, which also control renin activity. Genes coding for ACE-2 and AT2R are positioned on the X chromosome, indicating a potential for higher expression in females.^[57] After ovariectomy in females, ACE-2 activity increased and reduced after orchiectomy in males.^[57] Comparing the prostate to other body tissues, TMPRSS2 expression is noticeably higher in the prostate. It seems that TMPRSS2 transcription is regulated by androgenic ligands and an androgen receptor binding element in the promoter. This may explain the cause of the increased COVID-19 mortality rate in males.^[58] Many immune-related genes on the X chromosome have varying levels of expression in female immune cells, which can affect immunological response. Oestradiol improves T cell responses, neutrophil count, cytokine and antibody production, somatic hyper-mutation, and class switching. On the other hand, the immune system is suppressed by testosterone.^[59]

HTN was retained as a risk factor for the death of COVID-19 patients (pOR 1.56; 95% CI: 1.27–1.85) (Table 5; Fig. 4). Our results were in line with previous meta-analyses^[34,35,38] and in contrast with other meta-analyses.^[37,60] According to the literature, both endothelial dysfunction and Renin-Angiotensin System (RAS) imbalance have been linked to the severe COVID-19 hypertension outcome. Higher levels of Ang II, chemokines, and cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor (TNF-), have all been linked to endothelial dysfunction and a proinflammatory state in hypertension.^[61,62] When the conventional RAS axis (ACE/Ang II/AT1R) is active and the alternative axis (ACE2/Ang 1-7/Mas) is downregulated, the COVID-19 result is poor.^[61,63] A pro-inflammatory state is also promoted by RAS imbalance, one of the primary pathophysiological processes of COVID-19.^[64]

DM has been identified as an independent risk factor for death according to our meta-analysis with (pOR 1.26; 95% CI: 1.01–1.51) (Table 5; Fig. 5). Likewise, other systematic reviews reported that diabetes is a determinant of the severity and mortality of COVID-19 patients.^[65,66] Diabetes also increases the severity of COVID-19 and the death rate.^[67] Due to their predisposition for developing Acute Respiratory Distress Syndrome (ARDS), patients with diabetes and COVID-19 frequently require invasive ventilation care and intensive care unit (ICU).^[65] Developing DM-related comorbidities, such as ischemic heart disease and chronic renal insufficiency, is associated with a higher COVID-19 death rate.^[68] Patients who have both diabetes mellitus and COVID-19 experience aggravation of inflammation, immune system impairment, and disturbed glucose homeostasis. Because of these conditions, oxidative stress, cytokine release, and endothelial dysfunction increase and lead to increased liability for thromboembolism and organ damage. All these elements lead to a worsening of COVID-19, its quick progression to cardiac and respiratory failure, and therefore higher mortality.^[68] According to some studies, hyperglycemia secondary to Diabetes mellitus leads to immune dysfunction by impairing humoral and cellular functions and the antioxidant systems. Moreover, they revealed that diabetic patients were more susceptible to nosocomial infections.^[69,70] These elements could have increased the risk of death in diabetic patients with COVID-19.

Figure 5.

Forest plot of studies using odds ratio and showing the estimate for DM on COVID-19 mortality. COVID-19 = coronavirus disease-2019, DM = diabetes mellitus.

CKD was reported as a risk factor for death of COVID-19 patients (pHR 5.43; 95% CI: 0.18–10.67) (Table 5; Fig. 6) in our study. This was in contrast with 2 previous meta-analyses^[38,60] and in line with 2 other previous meta-analyses.^[34,36] This may be because patients with CKD have higher levels of pro-inflammatory cytokines. The resulting increased oxidative stress drives an inflammatory immune response. Viruses and bacteria that cause lung infections can be more common in patients with weakened immune systems.^[71,72]

Figure 6.

Forest plot of studies using hazard ratio and showing the estimate for CKD on COVID-19 mortality. CKD = chronic kidney disease, COVID-19 = coronavirus disease-2019.

Our study found that severe or critical condition was a predictor of death in COVID-19 patients (pOR 9.04; 95% CI: 3.14–14.94) (Table 5; Fig. 7). A similar result had been reported by a previous meta-analysis.^[60] Therefore, McCullough et al suggested that effective home management of COVID-19 may be crucial in lowering late presentations and mortality.^[73] To induce the necessary behavioral change in a pandemic like this, targeted and persistent public sensitization on the implications of the late presentation should be considered. In our meta-analysis, studies were conducted in the hospitals and were, therefore, not done at the early stages of the disease. Patients waited until becoming very ill to have testing done for COVID-19 in the hospital, even in wealthy nations.^[73] Public health warnings should strongly encourage people to seek care early, and policies for the efficient management of COVID-19 at home should be implemented to stop COVID-19 mortality. In this study, similar to other meta-analyses, some limitations should be noted. Our study included heterogeneity in some subgroup analyses that could be explained by the difference in patient population and disease severity. Some of the included studies had small sample sizes, which is one of the potential variables affecting COVID-19 mortality. Because of the inherent limitations of the observational study, the causal link between risk variables and poor outcomes in COVID-19 patients cannot be confirmed. Consequently, well-designed research with more significant sample sizes is required for verification.

Figure 7.

Forest plot of studies using odds ratio and showing the estimate for Severe or critical conditions on COVID-19 mortality. COVID-19 = coronavirus disease-2019.

In conclusion, this study demonstrates that advanced age, male gender, hypertension, diabetes mellitus, chronic kidney disease, and severe or critical condition were clinical risk factors associated with death outcomes in COVID-19 hospitalized patients in Africa. When addressing the expected prognosis of patients with COVID-19, clinicians and other healthcare professionals should consider these variables and act appropriately.

Acknowledgments

The authors would like to acknowledge and thank the Nelson R Mandela School of the College of Health Sciences, University of KwaZulu-Natal, for the support.

Author contributions

Conceptualization: Manimani Riziki Ghislain, Nombulelo Magula.

Data curation: Manimani Riziki Ghislain, Willy Tambwe Muzumbukilwa, Nombulelo Magula.

Formal analysis: Manimani Riziki Ghislain.

Methodology: Manimani Riziki Ghislain, Nombulelo Magula.

Project administration: Manimani Riziki Ghislain, Nombulelo Magula.

Software: Manimani Riziki Ghislain.

Supervision: Nombulelo Magula.

Validation: Nombulelo Magula.

Visualization: Manimani Riziki Ghislain, Nombulelo Magula.

Writing – original draft: Manimani Riziki Ghislain, Nombulelo Magula.

Writing – review & editing: Manimani Riziki Ghislain, Willy Tambwe Muzumbukilwa, Nombulelo Magula.

Supplementary Material

medi-102-e34405-s001.pdf ^{(195.7KB, pdf)}