Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa

Lise Jamieson; Cari Van Schalkwyk; Brooke E Nichols; Gesine Meyer-Rath; Sheetal Silal; Juliet Pulliam; Lucille Blumberg; Cheryl Cohen; Harry Moultrie; Waasila Jassat

doi:10.1371/journal.pgph.0001073

Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa

PLOS Glob Public Health. 2023 May 17;3(5):e0001073. doi: 10.1371/journal.pgph.0001073. eCollection 2023.

Authors

Lise Jamieson^{1

2

3}, Cari Van Schalkwyk³, Brooke E Nichols^{1

2

4

5}, Gesine Meyer-Rath^{1

3

4}, Sheetal Silal^{6

7}, Juliet Pulliam³, Lucille Blumberg^{8

9}, Cheryl Cohen^{8

10}, Harry Moultrie⁸, Waasila Jassat^{8

9}

Affiliations

¹ Health Economics and Epidemiology Research Office (HE2RO), Department of Internal Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
² Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
³ The South African Department of Science and Innovation/National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, Republic of South Africa.
⁴ Department of Global Health, School of Public Health, Boston University, Boston, Massachusetts, United States of America.
⁵ Foundation for Innovative New Diagnostics, Geneva, Switzerland.
⁶ Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa.
⁷ Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
⁸ National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa.
⁹ Right to Care, Centurion, South Africa.
¹⁰ School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

Abstract

There are limited published data within sub-Saharan Africa describing hospital pathways of COVID-19 patients hospitalized. These data are crucial for the parameterisation of epidemiological and cost models, and for planning purposes for the region. We evaluated COVID-19 hospital admissions from the South African national hospital surveillance system (DATCOV) during the first three COVID-19 waves between May 2020 and August 2021. We describe probabilities and admission into intensive care units (ICU), mechanical ventilation, death, and lengths of stay (LOS) in non-ICU and ICU care in public and private sectors. A log-binomial model was used to quantify mortality risk, ICU treatment and mechanical ventilation between time periods, adjusting for age, sex, comorbidity, health sector and province. There were 342,700 COVID-19-related hospital admissions during the study period. Risk of ICU admission was 16% lower during wave periods (adjusted risk ratio (aRR) 0.84 [0.82-0.86]) compared to between-wave periods. Mechanical ventilation was more likely during a wave overall (aRR 1.18 [1.13-1.23]), but patterns between waves were inconsistent, while mortality risk in non-ICU and ICU were 39% (aRR 1.39 [1.35-1.43]) and 31% (aRR 1.31 [1.27-1.36]) higher during a wave, compared to between-wave periods, respectively. If patients had had the same probability of death during waves vs between-wave periods, we estimated approximately 24% [19%-30%] of deaths (19,600 [15,200-24,000]) would not have occurred over the study period. LOS differed by age (older patients stayed longer), ward type (ICU stays were longer than non-ICU) and death/recovery outcome (time to death was shorter in non-ICU); however, LOS remained similar between time periods. Healthcare capacity constraints as inferred by wave period have a large impact on in-hospital mortality. It is crucial for modelling health systems strain and budgets to consider how input parameters related to hospitalisation change during and between waves, especially in settings with severely constrained resources.

Copyright: © 2023 Jamieson 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.

Grants and funding

The work of GMR and LJ on the SACMC has been made possible by the generous support of the American People and the President’s Emergency Plan for AIDS Relief (PEPFAR) through the United States Agency for International Development (USAID) under the terms of Cooperative Agreement 72067419CA00004 to HE2RO. The contents are the responsibility of the authors and do not necessarily reflect the views of PEPFAR, USAID or the United States Government. https://www.state.gov/pepfar/. SPS is funded by the Wellcome Trust (GN: 2114236/Z/18Z) and the Clinton Health Access Initiative. CVS and JRCP are supported by the Department of Science and Innovation and the National Research Foundation. Any opinion, finding, and conclusion or recommendation expressed in this material is that of the authors, and the NRF does not accept any liability in this regard. The SACMC’s work is also supported by the Bill & Melinda Gates Foundation under Investment INV-035464. The views and opinions expressed in this report do however not necessarily reflect the positions or policies of the Bill & Melinda Gates Foundation.