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

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...

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Autores principales: Jamieson, Lise, Van Schalkwyk, Cari, Nichols, Brooke E., Meyer-Rath, Gesine, Silal, Sheetal, Pulliam, Juliet, Blumberg, Lucille, Cohen, Cheryl, Moultrie, Harry, Jassat, Waasila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191304/
https://www.ncbi.nlm.nih.gov/pubmed/37195977
http://dx.doi.org/10.1371/journal.pgph.0001073
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author Jamieson, Lise
Van Schalkwyk, Cari
Nichols, Brooke E.
Meyer-Rath, Gesine
Silal, Sheetal
Pulliam, Juliet
Blumberg, Lucille
Cohen, Cheryl
Moultrie, Harry
Jassat, Waasila
author_facet Jamieson, Lise
Van Schalkwyk, Cari
Nichols, Brooke E.
Meyer-Rath, Gesine
Silal, Sheetal
Pulliam, Juliet
Blumberg, Lucille
Cohen, Cheryl
Moultrie, Harry
Jassat, Waasila
author_sort Jamieson, Lise
collection PubMed
description 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.
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spelling pubmed-101913042023-05-18 Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa Jamieson, Lise Van Schalkwyk, Cari Nichols, Brooke E. Meyer-Rath, Gesine Silal, Sheetal Pulliam, Juliet Blumberg, Lucille Cohen, Cheryl Moultrie, Harry Jassat, Waasila PLOS Glob Public Health Research Article 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. Public Library of Science 2023-05-17 /pmc/articles/PMC10191304/ /pubmed/37195977 http://dx.doi.org/10.1371/journal.pgph.0001073 Text en © 2023 Jamieson et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jamieson, Lise
Van Schalkwyk, Cari
Nichols, Brooke E.
Meyer-Rath, Gesine
Silal, Sheetal
Pulliam, Juliet
Blumberg, Lucille
Cohen, Cheryl
Moultrie, Harry
Jassat, Waasila
Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa
title Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa
title_full Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa
title_fullStr Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa
title_full_unstemmed Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa
title_short Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa
title_sort differential in-hospital mortality and intensive care treatment over time: informing hospital pathways for modelling covid-19 in south africa
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191304/
https://www.ncbi.nlm.nih.gov/pubmed/37195977
http://dx.doi.org/10.1371/journal.pgph.0001073
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