Differential in-hospital mortality and intensive care treatment over time
Informing hospital pathways for modelling Covid-19 in South Africa
Bibliographic Data
| ID | 19594431 |
|---|---|
| Authors | Lise Jamieson (0000-0003-2354-4580, University of the Witwatersrand, corresponding author), Cari van Schalkwyk (0000-0001-5154-1390, Stellenbosch University, corresponding author), Brooke E Nichols (0000-0003-4682-4999, Boston University, corresponding author), Gesine Meyer‐Rath (0000-0003-0439-381X, Boston University, corresponding author), Sheetal Silal (0000-0002-3007-5383, University of Cape Town, corresponding author), Juliet R C Pulliam (0000-0003-3314-8223, Stellenbosch University, corresponding author), Lucille Blumberg (0000-0002-2828-7678, National Health Laboratory Service, corresponding author), Cheryl Cohen (0000-0003-0376-2302, National Health Laboratory Service, corresponding author), Harry Moultrie (0000-0001-9722-0699, National Health Laboratory Service, corresponding author), Waasila Jassat (0000-0003-4279-3056, National Health Laboratory Service, corresponding author) |
| Editors | Godfrey Nyangadzayi Musuka |
| Year | 2023 |
| Volume | 3 |
| Issue | 5 |
| Pages | e0001073 |
| Publication date | 2023-05-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | PLOS Global Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2767-3375 • E-ISSN: 2767-3375 |
| Publisher | Public Library of Science (PLoS) (PUBLISHER) |
| DOI | 10.1371/journal.pgph.0001073 |
| PMID | 37195977 |
| OpenAlex | W4376959028 |
| Language | EN |
| Citations received | 3 |
| References cited | 14 |
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
Comorbidity · Confidence interval · Intensive care · Intensive care medicine · Intensive care unit · Mechanical ventilation · Relative risk · COVID-19 and healthcare impacts · COVID-19 Clinical Research Studies · COVID-19 epidemiological studies · Medicine · Emergency Medicine · Epidemiology · Internal Medicine
| Citations per year | 1 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |