• View in gallery

    Study flow chart.

  • View in gallery

    Venn diagram showing overlapping between the main comorbidities among COVID-19 hospitalized patients. Patients with chronic kidney disease (CKD) (n = 7) and those with cancer (n = 4) were not included in the Venn diagram because of the limitation of the package for a maximum of seven comorbidities. Of the seven patients with CKD, three had concomitant hypertension and diabetes (n = 3), DM (n = 3), and HTN (n = 1). Among the four patients with cancer, one had concomitant heart disease.

  • View in gallery

    Cumulative hazard of death over time stratified by age-group. The steps in the graph indicate points at which patients died. Patients discharged were censored at time of discharge. The time axis extends to 80 days because that is the longest a patient stayed in hospital.

  • 1.

    World Health Organization, 2020. COVID-19 Dashboard. Available at: https://covid19.who.int/. Accessed September 20, 2020.

  • 2.

    World Health Organization, 2020. New WHO Estimates: Up to 190 000 People Could Die of COVID-19 in Africa if Not Controlled. Available at: https://www.afro.who.int/news/new-who-estimates-190-000-people-could-die-covid-19-africa-if-not-controlled. Accessed August 24, 2020.

    • Search Google Scholar
    • Export Citation
  • 3.

    Nachega JB, Seydi M, Zumla A, 2020. The late arrival of COVID-19 in Africa–mitigating pan-continental spread. Clin Infect Dis 71: 875878.

  • 4.

    Mehtar S, Preiser W, Lakhe NA, Bousso A, TamFum JM, Kallay O, Seydi M, Zumla A, Nachega JB, 2020. Limiting the spread of COVID-19 in Africa: one size mitigation strategies do not fit all countries. Lancet Glob Health 8: e881e883.

    • Search Google Scholar
    • Export Citation
  • 5.

    Ayoub HH, Chemaltelly H, Seedat S, Mumtaz GR, Makhoul M, Abu-Raddad LJ, 2020. Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide. PLoS One 15: e0237959.

    • Search Google Scholar
    • Export Citation
  • 6.

    Nachega JB 2020. From easing lockdowns to scaling-up community-based COVID-19 screening, testing, and contact tracing in Africa–shared approaches, innovations, and challenges to minimize morbidity and mortality. Clin Infect Dis ciaa695. doi: 10.1093/cid/ciaa695.

    • Search Google Scholar
    • Export Citation
  • 7.

    Hale T, Webster S, Petherick A, Phillips T, Kira B, 2020. Oxford COVID-19 Government Response Tracker, Blavatnik School of Government. Available at: https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker. Accessed August 24, 2020.

    • Search Google Scholar
    • Export Citation
  • 8.

    Massinga Loembé M, Tshangela A, Salyer SJ, Varma JK, Ouma AEO, Nkengasong JN, 2020. COVID-19 in Africa: the spread and response. Nat Med 26: 9991003.

    • Search Google Scholar
    • Export Citation
  • 9.

    Coker M, Folayan MO, Michelow IC, Oladokun RE, Torbunde N, Sam-Agudu NA, 2020. Things must not fall apart: the ripple effects of the COVID-19 pandemic on children in sub-saharan Africa. Pediatr Res (Epub ahead of print). doi: 10.1038/s41390-020-01174-y.

    • Search Google Scholar
    • Export Citation
  • 10.

    Amimo F, Lambert B, Magit A, 2020. What does the COVID-19 pandemic mean for HIV, tuberculosis, and malaria control? Trop Med Health 48: 32.

  • 11.

    World Health Organization, 2020. Clinical Management of COVID-19. Available at: https://www.who.int/publications/i/item/clinical-management-of-covid-19. Accessed August 16, 2020.

    • Search Google Scholar
    • Export Citation
  • 12.

    Democratic Republic of the Congo, Ministry of Health Secretary, 2020. COVID-19 National Case Management Protocol. Protocole de prise en Charge de l’épidémie a Coronavirus COVID-19. Kinshasa, Democratic Republic of Congo: Ministry of Public Health.

    • Search Google Scholar
    • Export Citation
  • 13.

    Chen H, Boutros PC, 2011. Venn diagram: a package for the generation of highly customizable Venn and Euler diagrams in R. BMC Bioinformatics 12: 35.

    • Search Google Scholar
    • Export Citation
  • 14.

    Guan WJ 2020. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 382: 17081720.

  • 15.

    Lescure FX 2020. Clinical and virological data of the first cases of COVID-19 in Europe: a case series. Lancet Infect Dis 20: 697706.

  • 16.

    Goyal P 2020. Clinical characteristics of COVID-19 in New York city. N Engl J Med 382: 23722374.

  • 17.

    Wald ER, Schmit KM, Gusland DY, 2020. A pediatric infectious disease perspective on COVID-19. Clin Infect Dis ciaa1095. doi: 10.1093/cid/ciaa1095.

    • Search Google Scholar
    • Export Citation
  • 18.

    Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC, 2020. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA 324: 782793.

    • Search Google Scholar
    • Export Citation
  • 19.

    Pan D 2020. The impact of ethnicity on clinical outcomes in COVID-19: a systematic review. EClinicalMedicine 23: 100404.

  • 20.

    Joseph NP 2020. Racial/ethnic disparities in disease severity on admission chest radiographs among patients admitted with confirmed COVID-19: a retrospective cohort study. Radiology 202602. doi: 10.1148/radiol.2020202602.

    • Search Google Scholar
    • Export Citation
  • 21.

    Parker A 2020. High HIV prevalence in an early cohort of hospital admissions with COVID-19 in Cape Town, South Africa. S Afr Med J (Epub ahead of print). Available at: http://www.samj.org.za/index.php/samj/article/view/13054/9454.

    • Search Google Scholar
    • Export Citation
  • 22.

    Horby P, Landrain M; RECOVERY Trial Press Release, 2020. Low- Cost Dexamethasone Reduces Death by Up to One Third in Hospitalised Patients with Severe Respiratory Complications of COVID-19. Available at: https://www.ox.ac.uk/news/2020-06-16-low-cost-dexamethasone-reduces-death-one-third-hospitalised-patients-severe. Accessed July 5, 2020.

    • Search Google Scholar
    • Export Citation
  • 23.

    Liu B, Li M, Zhou Z, Guan X, Xiang Y, 2020. Can we use interleukin-6 blockade for coronavirus disease 2019 (COVID-19)-induced cytokine release syndrome (CRS)? J Autoimmun 111: 102452.

    • Search Google Scholar
    • Export Citation
  • 24.

    Zhou F 2020. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395: 10541062.

    • Search Google Scholar
    • Export Citation
  • 25.

    Huang C 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395: 497506.

  • 26.

    Kahn SE, Hull RL, Utzschneider KM, 2006. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444: 840846.

  • 27.

    Ryan PM, Caplice NM, 2020. Is adipose tissue a reservoir for viral spread, immune activation and cytokine amplification in COVID‐19. Obesity 28: 11911194.

    • Search Google Scholar
    • Export Citation
  • 28.

    Cariou B 2020. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study. Diabetologia 63: 15001515.

    • Search Google Scholar
    • Export Citation
  • 29.

    Orioli L, Hermans MP, Thissen JP, Maiter D, Vandeleene B, Yombi JC, 2020. COVID-19 in diabetic patients: related risks and specifics of management. Ann Endocrinol (Paris) 81: 101109.

    • Search Google Scholar
    • Export Citation
  • 30.

    Zhang YM, Zhang H, 2020. Genetic roadmap for kidney involvement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Clin J Am Soc Nephrol 15: 10441046.

    • Search Google Scholar
    • Export Citation
  • 31.

    Su H 2020. Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China. Kidney Int 98: 219227.

  • 32.

    Diao B 2020. Human kidney is a target for novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. medRxiv. Available at: https://doi.org/10.1101/2020.03.04.20031120.

    • Search Google Scholar
    • Export Citation
  • 33.

    Kissling S, Rotman S, Gerber C, Halfon M, Lamoth F, Comte D, Lhopitallier L, Sadallah S, Fakhouri F, 2020. Collapsing glomerulopathy in a COVID-19 patient. Kidney Int 98: 228231.

    • Search Google Scholar
    • Export Citation
  • 34.

    Alberici F 2020. Management of patients on dialysis and with kidney transplant during SARS-CoV-2 (COVID-19) pandemic in Brescia, Italy. Kidney Int Rep 5: 580585.

    • Search Google Scholar
    • Export Citation
  • 35.

    Hirsch JS Northwell COVID-19 Research Consortium, Northwell Nephrology COVID-19 Research Consortium, 2020. Acute kidney injury in patients hospitalized with COVID-19. Kidney Int 98: 209218.

    • Search Google Scholar
    • Export Citation
  • 36.

    Skipper CP 2020. Hydroxychloroquine in nonhospitalized adults with early COVID-19: a randomized trial. Ann Intern Med M20M4207. doi: 10.7326/M20-4207.

    • Search Google Scholar
    • Export Citation
  • 37.

    Horby P, Landray M, 2020. No Clinical Benefit from Use of Hydroxychloroquine in Hospitalised Patients with COVID-19. Press Release. Available at: https://www.recoverytrial.net/news/statement-from-the-chief-investigators-of-therandomised-evaluation-of-covid-19-therapy-recovery-trialon-hydroxychloroquine-5-june-2020-no-clinical-benefitfrom-use-of-hydroxychloroquine-in-hospitalised-patients-withcovid-19. Accessed July 5, 2020.

    • Search Google Scholar
    • Export Citation
  • 38.

    Cavalcanti AB 2020. Hydroxychloroquine with or without azithromycin in mild-to-moderate COVID-19. N Engl J Med (Epub ahead of print, 2020 July 23) doi: 10.1056/NEJMoa2019014.

    • Search Google Scholar
    • Export Citation
  • 39.

    Williamson E 2020. OpenSAFELY: Factors Associated with COVID-19-related Hospital Death in the Linked Electronic Health Records of 17 Million Adult NHS Patients. Available at: https://www.medrxiv.org/content/10.1101/2020.05.06.20092999v1. Accessed August 15, 2020.

    • Search Google Scholar
    • Export Citation
  • 40.

    Boulle A 2020. HIV and risk of COVID-19 death: a population cohort study from the western Cape province, South Africa. Clin Infect Dis ciaa1198. Available at: https://www.medrxiv.org/content/10.1101/2020.07.02.20145185v2.

    • Search Google Scholar
    • Export Citation
  • 41.

    Castagnoli R, Votto M, Licari A, Brambilla I, Bruno R, Perlini S, Rovida F, Baldanti F, Marseglia GL, 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children and adolescents: a systematic review. JAMA Pediatr (Epub ahead of print). doi: 10.1001/jamapediatrics.2020.1467.

    • Search Google Scholar
    • Export Citation
  • 42.

    Feldstein LR 2020. Multisystem inflammatory syndrome in U.S. children and adolescents. N Engl J Med 383: 334346.

  • 43.

    Mehta NS 2020. SARS-CoV-2 (COVID-19): what do we know about children? A systematic review. Clin Infect Dis ciaa556. doi: 10.1093/cid/ciaa556.

    • Search Google Scholar
    • Export Citation
  • 44.

    Webb K, Abraham DR, Faleye A, McCulloch M, Rabie H, Scott C; Cape Town MISC-Team, 2020. Multisystem inflammatory syndrome in children in South Africa. Lancet Child Adolesc Health 4: e38.

    • Search Google Scholar
    • Export Citation

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Clinical Characteristics and Outcomes of Patients Hospitalized for COVID-19 in Africa: Early Insights from the Democratic Republic of the Congo

View More View Less
  • 1 Department of Medicine, Centre for Infectious Diseases, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa;
  • 2 Department of Epidemiology and International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland;
  • 3 Department of Epidemiology, Infectious Diseases and Microbiology, Center for Global Health, University of Pittsburgh, Pittsburgh, Pennsylvania;
  • 4 Community Health Department, Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo;
  • 5 Epidemiological Surveillance Team, COVID-19 Response, Health Emergencies Program, World Health Organization, Kinshasa, Democratic Republic of the Congo;
  • 6 Department of Endocrinology and Nutrition, Cliniques Universitaires St-Luc, Brussels, Belgium;
  • 7 African Center of Biostatistics Excellence (ACBE), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa;
  • 8 Department of Pediatrics, Institute of Human Virology, University of Maryland School of Medicine, Baltimore, Maryland;
  • 9 International Research Center of Excellence, Institute of Human Virology Nigeria, Abuja, Nigeria;
  • 10 Department of Paediatrics, University of Cape Coast School of Medical Sciences, Cape Coast, Ghana;
  • 11 Department of Public Health, Centre Interdisciplinaire de Recherche en Ethnopharmacologie, Faculty of Medicine, Université Notre-Dame du Kasayi, Kananga, Democratic Republic of the Congo;
  • 12 Faculty of Public Health, Université Moderne de Kinkole, Kinshasa, Democratic Republic of the Congo;
  • 13 Department of Medical Microbiology and Virology, Faculty of Medicine, University of Kinshasa, National Institute of Biomedical Research (INRB), Kinshasa, Democratic Republic of the Congo;
  • 14 Direction Surveillance Épidémiologique (DSE), Direction Générale de Lutte contre la Maladie (DGLM), Ministère de la Santé Publique et Riposte COVID-19, Kinshasa, Democratic Republic of the Congo;
  • 15 Faculty of Medicine, University of Mbuji-Mayi, Mbuji-Mayi, Democratic Republic of the Congo;
  • 16 Elizabeth Glaser Pediatric AIDS Foundation, Washington, District of Columbia;
  • 17 Department of Real World & Advanced Analytics, Cytel, Vancouver, Canada;
  • 18 Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada;
  • 19 Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania;
  • 20 Division of Infection and Immunity, Centre for Clinical Microbiology, University College London, London, United Kingdom;
  • 21 National Institute for Health Research Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, United Kingdom;
  • 22 Department of Internal Medicine, School of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo

ABSTRACT

Little is known about the clinical features and outcomes of SARS-CoV-2 infection in Africa. We conducted a retrospective cohort study of patients hospitalized for COVID-19 between March 10, 2020 and July 31, 2020 at seven hospitals in Kinshasa, Democratic Republic of the Congo (DRC). Outcomes included clinical improvement within 30 days (primary) and in-hospital mortality (secondary). Of 766 confirmed COVID-19 cases, 500 (65.6%) were male, with a median (interquartile range [IQR]) age of 46 (34–58) years. One hundred ninety-one (25%) patients had severe/critical disease requiring admission in the intensive care unit (ICU). Six hundred twenty patients (80.9%) improved and were discharged within 30 days of admission. Overall in-hospital mortality was 13.2% (95% CI: 10.9–15.8), and almost 50% among those in the ICU. Independent risk factors for death were 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). In marginal structural model analysis, there was no statistically significant difference in odds of clinical improvement (adjusted odds ratio [aOR] = 1.53, 95% CI: 0.88–2.67, P = 0.132) nor risk of death (aOR = 0.65, 95% CI: 0.35–1.20) when comparing the use of chloroquine/azithromycin versus other treatments. In this DRC study, the high mortality among patients aged < 20 years and with severe/critical disease is of great concern, and requires further research for confirmation and targeted interventions.

INTRODUCTION

SARS-CoV-2 infection and COVID-19 arrived later in sub-Saharan Africa (SSA) than in most other regions of the world. As of August 26, 2020, there were 1,014,834 cases and 20,787 deaths (2.1% case fatality rate [CFR]) in the WHO African Region.1 The high numbers of cases and deaths expected in SSA have not been witnessed to date, despite relatively weak health systems and other barriers limiting comprehensive implementation of public health interventions.2 Several explanations have been hypothesized for this unexpected finding, including early lockdowns, low SARS-CoV-2 testing capacity, a younger population, and concomitant cross-immunity from parasitic diseases and other circulating coronaviruses.36

The Democratic Republic of the Congo (DRC) confirmed its first COVID-19 case on March 10, 2020 and within 2 weeks declared a state of emergency that included travel bans, lockdowns, widespread testing, and quarantine.6 As of August 26, 2020, the DRC has reported 9,891 COVID-19 cases and 251 deaths (2.5% CFR), with the capital city Kinshasa being the epicenter. With increased testing, more COVID-19 cases are being reported in SSA,35 but data on sociodemographic/clinical characteristics and outcomes among hospitalized patients are still scanty. It is important to ascertain whether features of COVID-19 in Africa differ from those in non-African countries.7,8 Furthermore, in SSA, there are little data on the prevalence of SARS-CoV-2 coinfection or comorbidity with noncommunicable diseases (NCDs) (e.g., hypertension, diabetes, and obesity) and communicable diseases (e.g., HIV, tuberculosis [TB], and malaria), which may influence COVID-19 presentations and outcomes.9,10 We aimed to describe clinical characteristics, laboratory features, and outcomes of hospitalized patients with COVID-19 in DRC and to differentiate them from other non-African populations.

METHODS

Study design, study population, and criteria for hospital admission.

We conducted a cohort analysis using routinely collected data from the DRC Ministry of Health’s COVID-19 Multi-Sectoral Response Committee database, spanning March 10, 2020–July 31, 2020. All COVID-19 patients admitted at the seven largest health facilities in Kinshasa (one private, two faith-based Catholic, and four public) were eligible for inclusion. Patients were staged according to the WHO COVID-19 clinical categories of mild, moderate, severe, and critical disease (Supplemental Table 1).11 The decision to hospitalize patients was based on signs or symptoms of moderate/severe disease, comorbidities, pregnancy, or the development of complications in cases initially managed at home.

Predictors and outcomes variables.

Using standardized data collection forms, we extracted sociodemographic, clinical (including comorbidities), laboratory, COVID-19 treatment, and current medication data. Outcomes of interest were clinical improvement within 30 days (primary) and in-hospital mortality (secondary).

SARS-CoV-2 RT-PCR testing.

Oropharyngeal or nasal samples were processed at the Virology Laboratory of the National Institute for Biomedical Research in Kinshasa. Samples were tested for SARS-CoV-2 RNA by either BGI RT-PCR using the ABI 7500 Fast Applied Biosystems instrument (Thermo Fisher Scientific, Waltham, MA) or Xpert Xpress SARS-CoV-2 using the GeneXpert platform (Cepheid, Sunnyvale, CA), following the manufacturers’ instructions.

Case management procedures.

On admission, a detailed history, physical examination, including pulse oximetry, was performed. Self-reported HIV and TB status was further corroborated on admission with a review and confirmation of documented medical record information on relevant medications and/or care for these coinfections. The same was carried out for patients self-reporting NCD comorbidities (e.g., hypertension and diabetes). Patients were treated with symptom management, supplemental oxygen, and compassionate treatment protocols according to national guidelines in effect at the time.12 Mild cases were treated with hydroxychloroquine (HCQ)/chloroquine (CQ) + azithromycin (AZ), and moderate cases were treated with HCQ/CQ + AZ (Option 1) or lopinavir/ritonavir (LPV/r) (Option 2) + enoxaparin (prophylactic low–molecular weight heparin).10 Severe cases were treated with HCQ/CQ + AZ + third-generation cephalosporin + enoxaparin and assisted ventilation (Option 1), or remdesivir + third-generation cephalosporin + enoxaparin + vitamin C and assisted ventilation (Option 2), or HCQ/CQ + (LPV/r) + third-generation cephalosporin + enoxaparin + dexamethasone and assisted ventilation (Option 3).12 As of August 24, 2020, remdesivir has not yet been licensed in the DRC, and not all patients received all indicated treatments because of lack of availability.

Statistical analysis.

We summarized baseline demographic and clinical characteristics using frequencies and proportions by clinical stage at presentation. Continuous variables were summarized using medians (IQR). Chi-square tests were used to compare proportions and Wilcoxon rank-sum tests to compare medians between mild/moderate and severe/critical cases. COVID-19 symptom resolution was assessed by comparing proportions of patients with symptoms at day 1 (day of admission) versus 10 days later, using the chi-square test for marginal homogeneity. Laboratory values were compared at day 1 and day 10 using the Wilcoxon signed-rank test.

We estimated the proportion of patients with clinical improvement, stratified by baseline demographic and clinical characteristics. Factors associated with clinical improvement at P-value < 0.1 in unadjusted univariable logistic regression were included in a multivariable logistic regression model to identify independent factors associated with clinical improvement. The strength of the association was expressed as adjusted odds ratios and accompanying 95% CIs. Similarly, we estimated the hazard of death stratified by baseline characteristics and identified factors independently associated with death using Cox regression. The final regression model was performed after the proportionality of hazards assumption was confirmed by a nonsignificant global test and Schoenfeld residuals with horizontal tendency, as well as the presence of parallelism in the In[In(S(t))] plot.

We summarized the strength of association between factors and death using adjusted hazard ratios and associated 95% CIs. We used a marginal structural model (MSM) based on inverse probability of treatment weighting (IPTW) to assess the efficacy of the CQ + AZ combination versus other therapy, with death as the outcome. Gender, age, WHO stage of disease at admission, hypertension, diabetes mellitus, asthma/chronic obstructive pulmonary disease (COPD), heart disease, chronic kidney disease (CKD), HIV, TB, obesity, and cancer were included in the treatment model as potential confounders. All analyses were performed using Stata software version 16.1 (College Station, TX). The Venn diagram illustrations of comorbidities and their combination were completed in R Studio Version 1.3.959, May 2020 (R Studio Inc., Boston, MA).13

Regulatory approvals.

The study was approved by the University of Kinshasa School of Public Health’s Ethics Committee (N°ESP/CE/114/2020 – July 17, 2020); the Institutional Review Board of the University of Pittsburgh, PA (STUDY20080174); and the DRC’s National COVID-19 Multi-Sectorial Response Committee and National Institute of Biomedical Research.

RESULTS

Sociodemographic and clinical characteristics of hospitalized confirmed cases.

Of 852 confirmed COVID-19 cases admitted, we analyzed 766 (89.9%) with complete information (Figure 1). Baseline sociodemographic characteristics and clinical stage did not differ between patients who were excluded and included in the analysis (Supplemental Table 2). Table 1 summarizes patient characteristics at admission by disease severity. The median (IQR) age was 46 (34–58) years, with 23.3% aged ≥ 60 years. Thirty-four (4.5%) patients were < 20 years, with a median age (IQR) of 14.5 (7–18) years and 11 (32%) younger than 10 years. Five hundred (65.3%) patients were male. Among the 262 females admitted, 12 (4.6%) were pregnant. At admission, 468 (61.1%) patients had mild, 107 (14.0%) moderate, 164 (21.4%) severe, and 27 (3.5%) critical disease. All 191 patients with severe or critical disease (25% of total) were admitted to the intensive care unit (ICU). Among 510 patients with SpO2 measurements, 38.2% had SpO2 ≤ 90% on room air. Four of the 34 children presented with severe or critical disease. Compared with those with mild/moderate COVID-19, severe/critical patients had higher median (IQR) C-reactive protein: 60 mg/dL (48.0–192.0) versus 24.0 mg/dL (2.6–54.0), respectively (P = 0.010), and median (IQR) D-dimer levels (ng/mL): 342.5 (246.0–443.0) versus 6.9 (2.8–100.0), respectively (P = 0.011).

Figure 1.
Figure 1.

Study flow chart.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-1240

Table 1

Demographics and clinical and laboratory characteristics (N = 766)

CharacteristicAll patients (n = 766)Severe patients (severe and critical) (n = 191)Non-severe patients (mild and moderate) (n = 575)P-value
Age median (years) (IQR)46 (34–58)58 (44–66)42 (32–54)< 0.001
Age-group (years), n (%)
 < 2034 (4.5)4 (2.1)30 (5.2)< 0.001
 20–39248 (32.5)31 (16.2)217 (37.9)
 40–59303 (39.7)67 (35.1)236 (41.3)
 ≥ 60178 (23.3)89 (46.6)89 (15.6)
Gender, n (%)
 Male500 (65.6)135 (71.1)365 (63.8)0.078
 Female262 (34.4)55 (28.9)207 (36.2)
 Missing413
Hypertension, n (%)
 Yes194 (25.4)87 (45.6)107 (18.7)< 0.001
 No570 (74.6)104 (54.4)466 (81.3)
 Missing202
Heart disease, n (%)
 Yes30 (3.9)21 (11.1)9 (1.6)< 0.001
 No733 (96.1)169 (88.9)564 (98.4)
 Missing312
Obesity, n (%)
 Yes39 (5.1)22 (11.5)17 (3.0)< 0.001
 No725 (94.9)169 (88.5)556 (97.0)
 Missing202
Diabetes, n (%)
 Yes107 (14.0)60 (31.6)47 (8.2)< 0.001
 No656 (86.0)130 (68.4)526 (91.8)
Missing312
Asthma/chronic obstructive pulmonary disease, n (%)
 Yes26 (3.4)12 (6.3)14 (2.4)0.011
 No738 (96.6)179 (93.7)559 (97.6)
 Missing202
Chronic kidney disease, n (%)
 Yes7 (0.9)3 (1.6)4 (0.7)0.375
 No759 (99.1)188 (98.4)571 (99.3)
Cancer, n (%)
 Yes5 (0.6)3 (1.6)2 (0.4)0.102
 No761 (99.4)188 (98.4)573 (99.6)
Pregnancy among females, n (%)
 Yes12 (4.6)3 (5.4)9 (4.4)0.720
 No250 (95.4)52 (94.6)198 (95.6)
SpO2, n (%)
 < 90%195 (38.2)166 (92.2)29 (8.8)< 0.001
 ≥ 90%315 (61.8)14 (7.8)301 (91.2)
 Missing25611245
HIV positive, n (%)
 Yes12 (1.6)3 (1.6)9 (1.6)1.000
 No752 (98.4)188 (98.4)564 (98.4)
 Missing202
Current tuberculosis, n (%)
 Yes19 (2.5)4 (2.1)15 (2.6)0.795
 No745 (97.5)187 (97.9)558 (97.4)
 Missing202
SpO2 (median, IQR), N89.0 (85–98) 51079 (66–87) 18098(95–99) 330< 0.001
Blood glucose (median, IQR) (mg/dL), N105 (23–182) 3325 (14.5–167.5) 16131 (103–182) 170.031
Serum C-reactive protein (median, IQR) (mg/dL), N32 (3.3–60) 3760 (48–192) 724 (2.6–54) 300.010
Serum potassium (median, IQR) (mEq/L), N3.9 (3.4–4.3) 174.3 (2.9–4.8) 33.9 (3.4–4.0) 140.488
Blood urea nitrogen, median (mg/dL), N32.5 (21.0–52.0) 4649.7 (41.0–63.0) 1423.1 (20.0–42.2) 320.002
Serum creatinine, (mg/dL), N1.0 (0.9–1.2) 481.2 (1.0–2.0) 131.0 (0.8–1.1) 350.008
Plasma D-dimer (median, IQR) (ng/mL), N183 (6.87–349) 11342.5 (246–443) 66.9 (2.8–100) 50.011
Electrocardiogram, n (%)
 Normal15 (20.6)1 (2.7)14 (38.9)< 0.001
 Abnormal58 (79.4)36 (97.3)22 (61.1)
 Missing693154539
Chloroquine + azithromycin630 (86.8)152 (80.8)478 (88.8)0.005
 Other*96 (13.2)36 (19.2)60 (11.2)
 Missing2424

Third-generation cephalosporin and/or amoxicillin +clavulanic acid and/or lopinavir/ritonavir and/or dexamethasone and/or azithromycin.

Among 764 patients with baseline comorbidity information, 264 (34.6%) reported at least one comorbidity, with 128 (48.5%) having more than one comorbidity (Figure 2). The most prevalent comorbidities were hypertension (25.4%) and diabetes (14.0%). Self-reported prevalence of obesity was 5.1%, heart disease 3.9%, asthma/COPD 3.4%, CKD 0.9%, active TB 2.5%, and HIV 1.6%. Patients with severe/critical disease were older and had a higher prevalence of hypertension, heart disease, obesity, diabetes, asthma/COPD, and poorer SpO2 levels than those with mild/moderate disease (Table 1). The majority of patients (n = 630, 86.8%) were treated with CQ/AZ. Eighteen patients of 545 (3.1%) on CQ/AZ versus 1/67 (1.5%) on other regimens reported at least one side effect (P = 0.70), including pruritus, skin rash, gastrointestinal upset, palpitations, or bradycardia. Overall, 620 patients (80.9%) improved and were discharged within 30 days; 101 (13.2%) died, and 20 (2.6%) were transferred to home care. Median hospital stay among recovered patients was 13 (IQR: 9–17) days. Of the 12 pregnant women, three presented with severe/critical disease and nine had mild/moderate disease. Five had comorbidities (one hypertension and obesity, one asthma, and three TB). All 12 pregnant women recovered and were discharged in 30 days. Four children (11.8%) died; they were 16-, 17-, 17-, and 19-year-olds. Three of four had severe/critical disease (severe pneumonia), and one had moderate disease (pneumonia) at admission.

Figure 2.
Figure 2.

Venn diagram showing overlapping between the main comorbidities among COVID-19 hospitalized patients. Patients with chronic kidney disease (CKD) (n = 7) and those with cancer (n = 4) were not included in the Venn diagram because of the limitation of the package for a maximum of seven comorbidities. Of the seven patients with CKD, three had concomitant hypertension and diabetes (n = 3), DM (n = 3), and HTN (n = 1). Among the four patients with cancer, one had concomitant heart disease.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-1240

Clinical and respiratory parameters at days 1 and 10 of hospitalization.

Supplemental Table 3 presents the changes in markers of infection among patients with assessments at day 1 and day 10. The proportion reporting headaches (23.2% versus 0.6%, respectively, P < 0.001), fever (39.8% versus 0.3%, P < 0.001), cough (40.9% versus 1.5%, P < 0.001), sore throat (12.0% versus 0.8%, P < 0.001), rhinorrhea (9.3% versus 0.1%, P < 0.001), and dyspnea (37.8% versus 1.0%, P < 0.001) decreased markedly between day 1 and day 10. Median oxygen saturation levels significantly improved from 89% to 98% over the same time interval.

Factors associated with clinical improvement.

The adjusted model for clinical improvement included age-group, the presence of comorbidities (hypertension, heart disease, diabetes, obesity, CKD, and cancer) and treatment received. Table 2 shows factors independently associated with clinical improvement during the observation time. Patients aged 20–39 years (aOR = 9.40, 95% CI: 4.78–18.52) or 40–59 years (aOR = 2.64, 95% CI: 1.64–4.26) were more likely to improve than patients ≥ 60 years. Patients with obesity (aOR = 0.27, 95% CI: 0.12–0.59) were less likely to improve than nonobese patients. Adjusting for treatment with CQ/AZ and age-group, the odds of clinical improvement among patients with severe/critical COVID-19 was 87% lower than among that with mild/moderate disease (aOR = 0.13, 95% CI: 0.08–0.20). In MSM analysis, there was no statistically significant difference in odds of clinical improvement (aOR = 1.53, 95% CI: 0.88–2.67, P = 0.132) when comparing the use of CQ/AZ versus other treatment regimens.

Table 2

Logistic regression of factors associated with clinical improvement within 30 days (N = 766)

CharacteristicImproved, n (%)Unadjusted odds ratio (95% CI)Adjusted odds ratio (95% CI)P-value
Gender
 Female (n = 262)211 (80.5)1
 Male (n = 500)406 (81.2)1.04 (0.71–1.52)
Age-group (years)
 < 20 (n = 34)29 (85.3)3.58 (1.32–9.71)2.98 (1.05–8.49)0.041
 20–39 (n = 248)233 (94.0)9.60 (5.25–17.55)9.40 (4.77–18.52)< 0.001
 40–59 (n = 303)246 (81.9)2.67 (1.76–4.05)2.64 (1.64–4.26)< 0.001
 ≥ 60 (n = 178)110 (61.8)11
Clinical stage at presentation
 Mild or moderate (n = 575)525 (91.3)1
 Severe or critical (n = 191)95 (49.7)0.09 (0.06–0.14)
Hypertension
 No (n = 570)480 (84.2)11
 Yes (n = 194)139 (71.6)0.47 (0.32–0.70)1.28 (0.76–2.18)0.356
Heart disease
 No (n = 733)600 (81.9)11
 Yes (n = 30)18 (60.0)0.33 (0.16–0.71)0.81 (0.32–2.03)0.656
Diabetes
 No (n = 656)547 (83.4)11
 Yes (n = 107)71 (66.4)0.39 (0.25–0.62)0.76 (0.43–1.35)0.351
Obesity
 No (n = 725)600 (82.8)11
 Yes (n = 39)19 (48.7)0.20 (0.10–0.38)0.27 (0.12–0.59)0.001
Asthma/chronic obstructive pulmonary disease
 No (n = 738)600 (81.3)1
 Yes (n = 26)19 (73.1)0.62 (0.26–1.51)
Chronic kidney disease
 No (n = 759)617 (81.3)11
 Yes (n = 7)3 (42.9)0.17 (0.04–0.78)0.22 (0.04–1.08)0.063
Cancer
 No (n = 761)618 (81.2)11
 Yes (n = 5)2 (40)0.15 (0.02–0.93)0.38 (0.06–2.50)0.313
HIV
 No (n = 752)611 (81.2)1
 Yes (n = 12)8 (66.7)0.46 (0.14–1.55)
Current tuberculosis
 No (n = 745)604 (81.1)1
 Yes (n = 19)15 (79.0)0.88 (0.29–2.68)
Chloroquine/azithromycin-based treatment vs. other
 No (n = 96)62 (64.6)11
 Yes (n = 630)526 (83.5)2.77 (1.74–4.43)3.62 (2.12–6.16)< 0.001
Received oxygen
 No (n = 330)307 (93.0)1
 Yes (n = 245)137 (55.9)0.10 (0.06–0.16)

Factors associated with in-hospital mortality.

Overall, in-hospital mortality was 13.2% (95% CI: 10.9–15.8). The median time between admission and death was 4 days (IQR: 2–5). There were no significant gender differences in mortality (13.0% females versus 13.4% males). More patients aged ≥ 60 years (32.0%) died, compared with those < 60 years (7.5%) (P < 0.001) (Table 3, Figure 3). In-hospital mortality was greater among patients with severe/critical disease than patients with mild/moderate disease (45.0% versus 2.6%, respectively, P < 0.001). Patients < 20 years (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) had significantly higher hazards of death than those aged 20–39 years. Significantly more patients with comorbidities died than those without comorbidities. Among the four children who died, one had diabetes and hypertension and the rest had no comorbidities. Mortality among patients with diabetes was greater than nondiabetics (27.1% versus 10.8%, respectively, P < 0.001). More obese versus nonobese patients died (43.6% versus 11.4%, P < 0.001). The hazard of death among obese patients was more than double than that for nonobese patients (aHR = 2.30, 95% CI: 1.24–4.27). Compared with those without CKD, patients with CKD were at a higher risk of death (57.1% versus 12.8, P < 0.001), with a more than 5-fold increase in the hazard of death (aHR = 5.33, 95% CI: 1.85–15.35). Patients who received CQ/AZ had significantly lower mortality than those who did not receive these drugs (11.0% versus 29.2%, respectively, P < 0.001). Mortality in patients receiving supplemental oxygen was greater than that among those who did not (37.6% versus 2.1%, respectively, P < 0.001). Patients who received CQ/AZ had a 74% reduction in hazard of death compared with those who did not receive CQ/AZ (aHR = 0.26, 95% CI: 0.16–0.42). However, in MSM analysis, there was no statistically significant difference in risk of death (aOR = 0.65, 95% CI: 0.35–1.20, P = 0.166) when comparing use of CQ/AZ versus other treatment regimens.

Figure 3.
Figure 3.

Cumulative hazard of death over time stratified by age-group. The steps in the graph indicate points at which patients died. Patients discharged were censored at time of discharge. The time axis extends to 80 days because that is the longest a patient stayed in hospital.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-1240

Table 3

Cox regression of factors associated with hazard of death (N = 766)

CharacteristicDied, n (%)Unadjusted hazards ratio (95% CI)Adjusted hazards ratio (95% CI)*P-value
Gender
 Female (n = 262)34 (13.0)1
 Male (n = 500)67 (13.4)1.03 (0.68–1.56)
Age-group (years)
 < 20 (n = 34)4 (11.8)5.10 (1.44–18.09)6.62 (1.85–23.65)0.004
 20–39 (n = 248)6 (2.4)11
 40–59 (n = 303)34 (11.2)4.62 (1.94–11.01)4.45 (1.83–10.79)0.001
 ≥ 60 (n = 178)57 (32.0)14.85 (6.40–34.46)13.63 (5.70–32.60)< 0.001
Clinical stage at admission
 Mild or moderate (n = 575)15 (2.6)1
 Severe or critical (n = 191)86 (45.0)20.84 (12.02–36.14)
Hypertension
 No (n = 570)56 (9.8)11
 Yes (n = 194)44 (22.7)2.32 (1.56–3.45)1.00 (0.62–1.61)0.986
Heart disease
 No (n = 733)89 (12.1)11
 Yes (n = 30)11 (36.7)3.52 (1.88–6.60)1.40 (0.68–2.88)0.364
Diabetes
 No (n = 656)71 (10.8)11
 Yes (n = 107)29 (27.1)2.53 (1.64–3.91)1.10 (0.66–1.81)0.720
Obesity
 No (n = 725)83 (11.4)11
 Yes (n = 39)17 (43.6)3.87 (2.86–6.56)2.30 (1.24–4.27)0.009
Asthma/chronic obstructive pulmonary disease
 No (n = 738)96 (13.0)1
 Yes (n = 26)4 (15.4)1.27 (0.46–3.45)
Chronic kidney disease
 No (n = 759)97 (12.8)11
 Yes (n = 7)4 (57.1)5.33 (1.96–14.52)5.33 (1.85–15.35)0.002
Cancer
 No (n = 761)99 (13.0)1
 Yes (n = 5)2 (40.0)3.90 (0.96–15.82)
HIV
 No (n = 752)98 (13.0)1
 Yes (n = 12)2 (16.7)1.23 (0.30–4.99)
Current tuberculosis
 No (n = 745)98 (13.2)1
 Yes (n = 19)2 (10.5)0.73 (0.18–2.98)
Chloroquine/azithromycin–based treatment
 No (n = 96)28 (29.2)11
 Yes (n = 630)69 (11.0)0.33 (0.21–0.52)0.26 (0.16–0.42)< 0.001
Received oxygen
 No (n = 330)7 (2.1)1
 Yes (n = 245)92 (37.6)21.88 (10.14–47.25)

DISCUSSION

This study is among the first to report clinical characteristics and outcomes of hospitalized COVID-19 patients in an African country. In this hospitalized Congolese cohort, ∼4.5% of patients were children < 20 years, which is similar to studies from China,14 Europe,15 and the United States16 that have reported between 1% and 5% of infections in children. Given that SARS-CoV-2 testing is more frequently prompted by symptoms and children typically have asymptomatic or mild infection, the frequency of SARS-CoV-2 infection in Congolese children is likely to be higher than 5%.17 Similar to Asian and Western cohorts, we observed male gender preponderance and previously reported presenting symptoms, including cough, fever, dyspnea, headache, sore throat, and rhinorrhea.18 Age and cardiometabolic comorbidities were associated with more severe forms of COVID-19 at admission and a higher risk of death. Unlike other reports, anosmia and dysgeusia were not documented in our cohort. Not surprisingly, patients admitted with severe COVID-19 were more likely to require oxygen therapy; these patients also differed from those with milder COVID-19 in terms of higher levels of inflammatory markers.

In-hospital mortality was 13.2% in our study population. Global estimates of in-hospital mortality from COVID-19 range between 15% and 20%, with up to 40% of hospitalized patients requiring intensive care.18 In Western countries, people of African descent and other racial minorities are at increased risk of worse clinical outcomes.19 In a recent U.S. cohort, age and proportion of inpatients with comorbidities were higher than our those in the Congolese cohort: mean age: 54 versus 48 years; hypertension: 44% versus 30%; diabetes: 39% versus 16%; obesity: 35% versus 3.8%; respectively.20 Furthermore, our overall in-hospital mortality rate (13.2%) may have been influenced by hospitalization of patients with mild disease who may been admitted because of inadequate care and isolation at home due to overcrowding and/or poverty. However, in-hospital mortality was greater among patients with severe/critical disease than among patients those with mild/moderate disease (45.0% versus 2.6%, respectively, P < 0.001), which is higher than Western reports20 but similar to the ∼50% mortality of patients requiring admission to the ICU in a South African cohort.21 Of note, dexamethasone has recently been shown to reduce mortality by one-third among seriously ill COVID-19 patients requiring oxygen or respiratory support. The drug was introduced in the DRC’s national COVID-19 treatment guidelines12 only from July 2020 (last month of our study period), soon after the U.K. Recovery Trial Press Release.22 Therefore, further evaluation is needed to ascertain whether in-ICU mortality will decrease with the use of dexamethasone in the DRC.

Among the comorbidities evaluated, hypertension and diabetes were clearly associated with more severe presentation and poorer prognosis for COVID-19. This is in line with findings published from China, the United States, and Europe.1416 These two comorbidities were strongly co-prevalent in our cohort, with 38% of hypertensive patients being diabetic, and 70% of diabetics being hypertensive. More importantly, despite the overall prevalence of self-reported obesity being low (potentially conservative bias due to underestimation), obesity was a significant independent predictor of mortality. Early studies suggest that cytokine release is central to the development of COVID-19–related respiratory distress,20 that interleukin-6 (IL-6) is produced by multiple cells including adipocytes,23,24 and that IL-6 levels are elevated in obese individuals.25,26 Furthermore, adipose tissue has been hypothesized to be a site for SARS-CoV-2 replication and shedding.27

In the French Coronavirus SARS-CoV-2 and Diabetes Outcomes (CORONADO) study, among diabetic inpatients with COVID-19, body mass index and poor long-term glucose control were independently associated with mechanical ventilation and/or death.28 Several arguments suggest that there is no causal link between severe pneumonia and chronic hyperglycemia and that the overrepresentation of diabetic patients with COVID-19 in ICUs indirectly reflects the impact of obesity.29 Furthermore, higher hemoglobin A1c (HbA1c) at admission does not appear to worsen COVID-19 prognosis in type II diabetes.30 For this study, there were no HbA1c data available; thus, we were unable to analyze its potential association with COVID-19 outcomes. We also found that CKD was an independent risk factor for mortality, as reported from outside Africa.3134 Patients presenting with SARS-CoV-2 infection have shown varying degrees of renal dysfunction, including a high incidence of acute kidney injury.32,35 A recent study reported that the human kidney may be a unique target for SARS-CoV-2 because it expresses angiotensin-converting enzyme-2 surface receptors.32,33,35

There was no significant difference in mortality when comparing CQ/AZ versus other regimens by MSM analysis. Our data do not conclusively exclude a CQ/AZ treatment effect, given the lack of a comparator arm, and nonrandom treatment allocation. However, recently published placebo-controlled trials from the United States,36 the United Kingdom,37 and Brazil38 have shown no effect of CQ or CQ/AZ on COVID-19 mortality. There is an urgent need for rigorous evaluation of other promising and scalable cost-effective therapeutic options for the DRC and other African countries.

Our findings showed no association of HIV and/or TB with baseline COVID-19 disease severity or prognosis. However, definitive conclusions cannot be made because of low prevalence of HIV (1.6%) and active TB (2.2%) in our cohort. A population-based U.K. study found that HIV-positive individuals had more than double the risk of COVID-19–related mortality than HIV-uninfected individuals after controlling for known confounding factors.39 Similarly, a retrospective analysis of > 20,000 South African adults with COVID-19 showed that HIV was associated with a doubling of COVID-19 mortality risk, although this may be an overestimate because of residual confounders.40 Larger SSA cohort studies are required to further define the epidemiological, clinical, and risk relationships among the overlapping epidemics of COVID-19, HIV, TB, and malaria.

Of great concern is that children and adolescents < 20 years had a CFR of 11.8% and were nearly seven times more likely to die than patients aged 20–39 years. By contrast, studies mostly from China report CFRs of < 1% among both symptomatic and asymptomatic, but mostly hospitalized children.41,42 A recent U.S. study reported a CFR of 2% among children hospitalized with COVID-19).43 All four pediatric deaths in our DRC cohort occurred among older children aged 16–19 years. Three of the four had severe/critical disease, and one had moderate disease at admission; also, three of these four cases had no underlying comorbidity. Webb et al.44 recently reported on 23 South African children with MIS-C, among whom 52% required ICU admission primarily because of cardiac dysfunction. There were no deaths reported in this South African cohort; all the children survived. Multisystem inflammatory syndrome was not specifically reported in our DRC pediatric cohort, but it may not have been recognized. Our study’s small number of children and the possibility of unmeasured confounding factors, such as the availability of necessary equipment, quality, and scope of pediatric intensive care, preclude concrete conclusions about excess COVID-19–related mortality among children and adolescents in the DRC. This calls for larger, robust investigations of COVID-19 outcomes among hospitalized children in SSA.

Our study has some limitations. Approximately 10% of patients had missing data on outcomes of interest and were not included in our analysis. However, these patients were comparable with those included with respect to sociodemographic characteristics and COVID-19 clinical stage. In addition, we were not able to compare clinical characteristics between hospitalized COVID-19 patients and outpatients. Finally, given the low prevalence of self-reported HIV and/or TB status, we cannot speculate on the impact of these conditions on COVID-19 outcomes. Strengths of our study include a robust sample size of hospitalized patients in SSA from where little information on COVID-19 has been reported. We also provide data on 34 children, a population for whom there are even less COVID-19 data available from SSA.9 Finally, the use of robust statistical methods such as MSM and IPTW creates more balanced comparisons between treatment groups, similar to those that would be found in a randomized clinical trial.

CONCLUSION

In this study, hospitalized patients in SSA with COVID-19 had a somewhat lower overall in-hospital mortality than hospitalized patients in non-African regions, but mortality in those with severe or critical disease was almost 50%. Age-groups at high risk and comorbidities associated with death were similar between our cohort and those from prior studies in Asia, Europe, and North America. Although our study provides insights into COVID-19 manifestations in Africa, more data are needed from countries across this region. Large-cohort observational studies are required to better define the epidemiology and factors affecting COVID-19 outcomes and the relationships between the overlapping epidemics of COVID-19, HIV, TB, and malaria among both young and older populations. Rigorous evaluations of promising, scalable, and cost-effective therapeutics and vaccines are needed globally.

Supplemental tables

ACKNOWLEDGMENTS

We acknowledge critical review by John L. Johnson, Case Western Reserve University, Cleveland, OH; Todd Brown, Johns Hopkins University, Baltimore, MD; Michael Reid, University of California, San Francisco, CA; Christina Wyatt, Durham, NC; Peter Kilmarx, NIH/FIC, Bethesda, MD; Wolfgang Preiser and Eric Decloedt, both from Stellenbosch University, Cape Town, South Africa; Andre-Pascal Kengne, South African Medical Research Council (SAMRC), Cape Town, South Africa; and Rodney Ehrlich, and Mary-Ann Davies, University of Cape Town, Cape Town, South Africa. Publication charges for this article were waived due to the ongoing pandemic of COVID-19.

REFERENCES

  • 1.

    World Health Organization, 2020. COVID-19 Dashboard. Available at: https://covid19.who.int/. Accessed September 20, 2020.

  • 2.

    World Health Organization, 2020. New WHO Estimates: Up to 190 000 People Could Die of COVID-19 in Africa if Not Controlled. Available at: https://www.afro.who.int/news/new-who-estimates-190-000-people-could-die-covid-19-africa-if-not-controlled. Accessed August 24, 2020.

    • Search Google Scholar
    • Export Citation
  • 3.

    Nachega JB, Seydi M, Zumla A, 2020. The late arrival of COVID-19 in Africa–mitigating pan-continental spread. Clin Infect Dis 71: 875878.

  • 4.

    Mehtar S, Preiser W, Lakhe NA, Bousso A, TamFum JM, Kallay O, Seydi M, Zumla A, Nachega JB, 2020. Limiting the spread of COVID-19 in Africa: one size mitigation strategies do not fit all countries. Lancet Glob Health 8: e881e883.

    • Search Google Scholar
    • Export Citation
  • 5.

    Ayoub HH, Chemaltelly H, Seedat S, Mumtaz GR, Makhoul M, Abu-Raddad LJ, 2020. Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide. PLoS One 15: e0237959.

    • Search Google Scholar
    • Export Citation
  • 6.

    Nachega JB 2020. From easing lockdowns to scaling-up community-based COVID-19 screening, testing, and contact tracing in Africa–shared approaches, innovations, and challenges to minimize morbidity and mortality. Clin Infect Dis ciaa695. doi: 10.1093/cid/ciaa695.

    • Search Google Scholar
    • Export Citation
  • 7.

    Hale T, Webster S, Petherick A, Phillips T, Kira B, 2020. Oxford COVID-19 Government Response Tracker, Blavatnik School of Government. Available at: https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker. Accessed August 24, 2020.

    • Search Google Scholar
    • Export Citation
  • 8.

    Massinga Loembé M, Tshangela A, Salyer SJ, Varma JK, Ouma AEO, Nkengasong JN, 2020. COVID-19 in Africa: the spread and response. Nat Med 26: 9991003.

    • Search Google Scholar
    • Export Citation
  • 9.

    Coker M, Folayan MO, Michelow IC, Oladokun RE, Torbunde N, Sam-Agudu NA, 2020. Things must not fall apart: the ripple effects of the COVID-19 pandemic on children in sub-saharan Africa. Pediatr Res (Epub ahead of print). doi: 10.1038/s41390-020-01174-y.

    • Search Google Scholar
    • Export Citation
  • 10.

    Amimo F, Lambert B, Magit A, 2020. What does the COVID-19 pandemic mean for HIV, tuberculosis, and malaria control? Trop Med Health 48: 32.

  • 11.

    World Health Organization, 2020. Clinical Management of COVID-19. Available at: https://www.who.int/publications/i/item/clinical-management-of-covid-19. Accessed August 16, 2020.

    • Search Google Scholar
    • Export Citation
  • 12.

    Democratic Republic of the Congo, Ministry of Health Secretary, 2020. COVID-19 National Case Management Protocol. Protocole de prise en Charge de l’épidémie a Coronavirus COVID-19. Kinshasa, Democratic Republic of Congo: Ministry of Public Health.

    • Search Google Scholar
    • Export Citation
  • 13.

    Chen H, Boutros PC, 2011. Venn diagram: a package for the generation of highly customizable Venn and Euler diagrams in R. BMC Bioinformatics 12: 35.

    • Search Google Scholar
    • Export Citation
  • 14.

    Guan WJ 2020. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 382: 17081720.

  • 15.

    Lescure FX 2020. Clinical and virological data of the first cases of COVID-19 in Europe: a case series. Lancet Infect Dis 20: 697706.

  • 16.

    Goyal P 2020. Clinical characteristics of COVID-19 in New York city. N Engl J Med 382: 23722374.

  • 17.

    Wald ER, Schmit KM, Gusland DY, 2020. A pediatric infectious disease perspective on COVID-19. Clin Infect Dis ciaa1095. doi: 10.1093/cid/ciaa1095.

    • Search Google Scholar
    • Export Citation
  • 18.

    Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC, 2020. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA 324: 782793.

    • Search Google Scholar
    • Export Citation
  • 19.

    Pan D 2020. The impact of ethnicity on clinical outcomes in COVID-19: a systematic review. EClinicalMedicine 23: 100404.

  • 20.

    Joseph NP 2020. Racial/ethnic disparities in disease severity on admission chest radiographs among patients admitted with confirmed COVID-19: a retrospective cohort study. Radiology 202602. doi: 10.1148/radiol.2020202602.

    • Search Google Scholar
    • Export Citation
  • 21.

    Parker A 2020. High HIV prevalence in an early cohort of hospital admissions with COVID-19 in Cape Town, South Africa. S Afr Med J (Epub ahead of print). Available at: http://www.samj.org.za/index.php/samj/article/view/13054/9454.

    • Search Google Scholar
    • Export Citation
  • 22.

    Horby P, Landrain M; RECOVERY Trial Press Release, 2020. Low- Cost Dexamethasone Reduces Death by Up to One Third in Hospitalised Patients with Severe Respiratory Complications of COVID-19. Available at: https://www.ox.ac.uk/news/2020-06-16-low-cost-dexamethasone-reduces-death-one-third-hospitalised-patients-severe. Accessed July 5, 2020.

    • Search Google Scholar
    • Export Citation
  • 23.

    Liu B, Li M, Zhou Z, Guan X, Xiang Y, 2020. Can we use interleukin-6 blockade for coronavirus disease 2019 (COVID-19)-induced cytokine release syndrome (CRS)? J Autoimmun 111: 102452.

    • Search Google Scholar
    • Export Citation
  • 24.

    Zhou F 2020. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395: 10541062.

    • Search Google Scholar
    • Export Citation
  • 25.

    Huang C 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395: 497506.

  • 26.

    Kahn SE, Hull RL, Utzschneider KM, 2006. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444: 840846.

  • 27.

    Ryan PM, Caplice NM, 2020. Is adipose tissue a reservoir for viral spread, immune activation and cytokine amplification in COVID‐19. Obesity 28: 11911194.

    • Search Google Scholar
    • Export Citation
  • 28.

    Cariou B 2020. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study. Diabetologia 63: 15001515.

    • Search Google Scholar
    • Export Citation
  • 29.

    Orioli L, Hermans MP, Thissen JP, Maiter D, Vandeleene B, Yombi JC, 2020. COVID-19 in diabetic patients: related risks and specifics of management. Ann Endocrinol (Paris) 81: 101109.

    • Search Google Scholar
    • Export Citation
  • 30.

    Zhang YM, Zhang H, 2020. Genetic roadmap for kidney involvement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Clin J Am Soc Nephrol 15: 10441046.

    • Search Google Scholar
    • Export Citation
  • 31.

    Su H 2020. Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China. Kidney Int 98: 219227.

  • 32.

    Diao B 2020. Human kidney is a target for novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. medRxiv. Available at: https://doi.org/10.1101/2020.03.04.20031120.

    • Search Google Scholar
    • Export Citation
  • 33.

    Kissling S, Rotman S, Gerber C, Halfon M, Lamoth F, Comte D, Lhopitallier L, Sadallah S, Fakhouri F, 2020. Collapsing glomerulopathy in a COVID-19 patient. Kidney Int 98: 228231.

    • Search Google Scholar
    • Export Citation
  • 34.

    Alberici F 2020. Management of patients on dialysis and with kidney transplant during SARS-CoV-2 (COVID-19) pandemic in Brescia, Italy. Kidney Int Rep 5: 580585.

    • Search Google Scholar
    • Export Citation
  • 35.

    Hirsch JS Northwell COVID-19 Research Consortium, Northwell Nephrology COVID-19 Research Consortium, 2020. Acute kidney injury in patients hospitalized with COVID-19. Kidney Int 98: 209218.

    • Search Google Scholar
    • Export Citation
  • 36.

    Skipper CP 2020. Hydroxychloroquine in nonhospitalized adults with early COVID-19: a randomized trial. Ann Intern Med M20M4207. doi: 10.7326/M20-4207.

    • Search Google Scholar
    • Export Citation
  • 37.

    Horby P, Landray M, 2020. No Clinical Benefit from Use of Hydroxychloroquine in Hospitalised Patients with COVID-19. Press Release. Available at: https://www.recoverytrial.net/news/statement-from-the-chief-investigators-of-therandomised-evaluation-of-covid-19-therapy-recovery-trialon-hydroxychloroquine-5-june-2020-no-clinical-benefitfrom-use-of-hydroxychloroquine-in-hospitalised-patients-withcovid-19. Accessed July 5, 2020.

    • Search Google Scholar
    • Export Citation
  • 38.

    Cavalcanti AB 2020. Hydroxychloroquine with or without azithromycin in mild-to-moderate COVID-19. N Engl J Med (Epub ahead of print, 2020 July 23) doi: 10.1056/NEJMoa2019014.

    • Search Google Scholar
    • Export Citation
  • 39.

    Williamson E 2020. OpenSAFELY: Factors Associated with COVID-19-related Hospital Death in the Linked Electronic Health Records of 17 Million Adult NHS Patients. Available at: https://www.medrxiv.org/content/10.1101/2020.05.06.20092999v1. Accessed August 15, 2020.

    • Search Google Scholar
    • Export Citation
  • 40.

    Boulle A 2020. HIV and risk of COVID-19 death: a population cohort study from the western Cape province, South Africa. Clin Infect Dis ciaa1198. Available at: https://www.medrxiv.org/content/10.1101/2020.07.02.20145185v2.

    • Search Google Scholar
    • Export Citation
  • 41.

    Castagnoli R, Votto M, Licari A, Brambilla I, Bruno R, Perlini S, Rovida F, Baldanti F, Marseglia GL, 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children and adolescents: a systematic review. JAMA Pediatr (Epub ahead of print). doi: 10.1001/jamapediatrics.2020.1467.

    • Search Google Scholar
    • Export Citation
  • 42.

    Feldstein LR 2020. Multisystem inflammatory syndrome in U.S. children and adolescents. N Engl J Med 383: 334346.

  • 43.

    Mehta NS 2020. SARS-CoV-2 (COVID-19): what do we know about children? A systematic review. Clin Infect Dis ciaa556. doi: 10.1093/cid/ciaa556.

    • Search Google Scholar
    • Export Citation
  • 44.

    Webb K, Abraham DR, Faleye A, McCulloch M, Rabie H, Scott C; Cape Town MISC-Team, 2020. Multisystem inflammatory syndrome in children in South Africa. Lancet Child Adolesc Health 4: e38.

    • Search Google Scholar
    • Export Citation

Author Notes

Address correspondence to Jean B. Nachega, Department of Epidemiology, Infectious Diseases and Microbiology, Center for Global Health, University of Pittsburgh Graduate School of Public Health, 130 DeSoto St., Crabtree Hall A531, Pittsburgh, PA 15261. E-mail: jbn16@pitt.edu

Financial support: J. B. N. is an infectious diseases internist and epidemiologist supported by the U.S. NIH/National Institutes of Allergy and Infectious Diseases grant number 5U01AI069521 (Stellenbosch University Clinical Trial Unit of the AIDS Clinical Trial Group) as well as NIH/Fogarty International Center grant numbers 1R25TW011217-01 (African Association for Health Professions Education and Research) and 1D43TW010937-01A1 (University of Pittsburgh HIV Comorbidities Research Training Program in South Africa) and is coprincipal investigator of TOGETHER, an adaptive randomized clinical trial of novel agents for treatment of high-risk outpatient COVID-19 patients in South Africa supported by the Bill & Melinda Gates Foundation. DKI, CB-PN, PM-K, ENK, GMM, JMS, MTP, SA-M, J-JMT, DJML, and J-MK are members of the DRC Ministry of Health′s COVID-19 Multi-Sectoral Response Committee. A. Z. is a coprincipal investigator of the Pan-African Network on Emerging and Re-Emerging Infections (PANDORA-ID-NET; https://www.pandora-id.net/) funded by the EU Horizon 2020 Framework Program for Research and Innovation and is in receipt of an U.K. NIH Research Senior Investigator award. N. A. S. A. is a clinician-scientist with pediatric infectious disease expertise and is funded by the NIH/National Institute of Child Health and Human Development grant R01HD089866, and by an NIH/Fogarty International Center award under the Adolescent HIV Prevention and Treatment Implementation Science Alliance (AHISA), for the Central and West Africa Implementation Science Alliance (CAWISA).

Authors’ addresses: Jean B. Nachega, Department of Medicine, Centre for Infectious Diseases, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa, Department of Epidemiology and International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, and Department of Epidemiology, Infectious Diseases and Microbiology, and Center for Global Health, University of Pittsburgh, Pittsburgh, PA, E-mail: jbn16@pitt.edu. Daniel Katuashi Ishoso, Community Health Department, Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, E-mail: dishosok@gmail.com. John Otshudiema Otokoye, Joule Ntwan Madinga, and Marie Claire Kolié, Epidemiological Surveillance Team, COVID-19 Response, Health Emergencies Program, World Health Organization, Kinshasa, Democratic Republic of the Congo, E-mails: johnotokoye@gmail.com, jmadinga@yahoo.fr, and marieclairekolie@gmail.com. Michel P. Hermans, Department of Endocrinology and Nutrition, Cliniques Universitaires St-Luc, Brussels, Belgium, E-mail: michel.hermans@uclouvain.be. Rhoderick Neri Machekano, African Center of Biostatistics Excellence (ACBE), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa, E-mail: rhoderick@sun.ac.za. Nadia A. Sam-Agudu, Institute of Human Virology Nigeria, International Research Center of Excellence, Abuja, Nigeria, and Division of Epidemiology and Prevention, Institute of Human Virology, Baltimore, MD, E-mail: nsamagudu@ihvnigeria.org. Christian Bongo-Pasi Nswe and Don Jethro Mavungu Landu, Department of Public Health, Centre Interdisciplinaire de Recherche en Ethnopharmacologie, Faculty of Medicine, Université Notre-Dame du Kasayi, Kananga, Democratic Republic of the Congo, and Faculty of Public Health, Université Moderne de Kinkole, Kinshasa, Democratic Republic of the Congo, E-mails: bongopanoudji@gmail.com and jethromavungu@gmail.com. Placide Mbala-Kingebeni, Department of Microbiology, Institut National de Recherche Biomedicale, Kinshasa, Democratic Republic of the Congo, E-mail: mbalaplacide@gmail.com. Stéphane Mukendi, Edith N. Nkwembe, and Jean-Jacques Muyembe-Tamfum, Department of Virology, Faculty of Medicine, National Institute of Biomedical Research (INRB), University of Kinshasa, Kinshasa, Democratic Republic of the Congo, E-mails: mukendisteph1@gmail.com, edithnkwembe1@gmail.com, and jjmuyembet@gmail.com. Steve Ahuka-Mundeke, Technical Secretariat, Ebola Response, Goma, Democratic Republic of the Congo, E-mail: amstev04@yahoo.fr. Gisele M. Mbuyi and Justus M. Nsio, Direction Surveillance Épidémiologique (DSE), Direction Générale de Lutte contre la Maladie (DGLM), Ministère de la Santé Publique et Riposte COVID-19, Kinshasa, Democratic Republic of the Congo, E-mails: drgmbuyi@gmail.com and justsusnsio@gmail.com. Didier Mukeba Tshialala, Department of Medicine, Faculty of Medicine, University of Mbuji-Mayi, Mbuji-Mayi, Democratic Republic of the Congo, E-mail: dimutshia@yahoo.fr. Michel Tshiasuma Pipo, Department of Public Health, Faculty of Medicine, Centre Interdisciplinaire de Recherche en Ethnopharmacologie, Université Notre-Dame du Kasayi, Kananga, Democratic Republic of the Congo, E-mail: mycky1974@gmail.com. Lynne Mofenson, Elizabeth Glaser Pediatric AIDS Foundation, Washington, DC, E-mail: mofensol@gmail.com. Gerald Smith, Department of Real World & Advanced Analytics, Cytel, Vancouver, Canada, E-mail: gerald.smith@cytel.com. Edward J. Mills, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada, E-mail: emills@mteksciences.com. John W. Mellors, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, School of Medicine, Pittsburgh, PA, E-mail: jwm1@pitt.edu. Alimuddin Zumla, Division of Infection and Immunity, Department of Infection, University College London, London, United Kingdom, E-mail: a.zumla@ucl.ac.uk. Jean-Marie Kayembe, Department of Medical Microbiology and Virology, Faculty of Medicine, National Institute of Biomedical Research (INRB), University of Kinshasa, Kinshasa, Democratic Republic of the Congo, E-mail: jm.kayembe@unikin.ac.cd.

These authors contributed equally to this work.

These authors contributed equally to this work.

Save