Background: We investigated the clinical profile, complications, and outcomes of inpatients with COVID-19 at Parirenyatwa Hospital, Harare, across the first two waves of SARS-CoV-2 infection, and factors associated with mortality.
Methods: We conducted a prospective cohort study on all patients admitted to the COVID-19 unit. Data were extracted from medical records and negative binomial regression with robust standard errors was used to assess the association between sociodemographic and clinical characteristics and mortality. Cox Regression was used for sensitivity analysis.
Results: Of 563 people admitted with COVID-19 between 2 July 2020 and 19 March 2021, 214 (38.0%) died, 340 were discharged and 9 transferred. The median age was 56 (IQR 44-68) years and 53.8% were male. Overall, 38.8% experienced a complication, the most common being acute kidney injury (17.9%) and hyperglycaemia (13.1%). The most common comorbidity was hypertension (41.3%) followed by diabetes (28.6%), HIV (12.1%), cardiovascular disease (10.9%) and chronic kidney disease (7.8%). Among participants who stayed in the ward for more than 1 night, mortality was higher in patients with comorbidity compared to those without any comorbidity (38.7% vs 25.5%, risk ratio (RR) = 1.52 (95% CI 1.11, 2.07), p = 0.008). After adjusting for oxygen saturation, comorbidities, sex and pregnancy, mortality was higher in the second wave than in the first (adjusted RR 1.23, 95% CI 1.00-1.51, p = 0.05). In the second wave 57/161 (35.4%) deaths were attributed to lack of resources, mainly human resources.
Conclusion: The mortality rate was high and clinical COVID-19 care needs to pay careful attention to patient monitoring for complications and management of comorbidities. This will require addressing the critical health workforce shortage issues. Prevention of COVID-19 including vaccination particularly among individuals with comorbidities remains a high priority.
Copyright: © 2024 Fryatt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.