Cargando…

Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

BACKGROUND: The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown t...

Descripción completa

Detalles Bibliográficos
Autores principales: Maposa, Innocent, Welch, Richard, Ozougwu, Lovelyn, Arendse, Tracy, Mudara, Caroline, Blumberg, Lucille, Jassat, Waasila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161152/
https://www.ncbi.nlm.nih.gov/pubmed/37147648
http://dx.doi.org/10.1186/s12889-023-15789-3
_version_ 1785037431394795520
author Maposa, Innocent
Welch, Richard
Ozougwu, Lovelyn
Arendse, Tracy
Mudara, Caroline
Blumberg, Lucille
Jassat, Waasila
author_facet Maposa, Innocent
Welch, Richard
Ozougwu, Lovelyn
Arendse, Tracy
Mudara, Caroline
Blumberg, Lucille
Jassat, Waasila
author_sort Maposa, Innocent
collection PubMed
description BACKGROUND: The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. METHODS: COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. RESULTS: The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05–4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46–1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61–3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10–3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. CONCLUSION: The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.
format Online
Article
Text
id pubmed-10161152
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-101611522023-05-06 Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa Maposa, Innocent Welch, Richard Ozougwu, Lovelyn Arendse, Tracy Mudara, Caroline Blumberg, Lucille Jassat, Waasila BMC Public Health Research BACKGROUND: The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. METHODS: COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. RESULTS: The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05–4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46–1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61–3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10–3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. CONCLUSION: The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts. BioMed Central 2023-05-05 /pmc/articles/PMC10161152/ /pubmed/37147648 http://dx.doi.org/10.1186/s12889-023-15789-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Maposa, Innocent
Welch, Richard
Ozougwu, Lovelyn
Arendse, Tracy
Mudara, Caroline
Blumberg, Lucille
Jassat, Waasila
Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa
title Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa
title_full Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa
title_fullStr Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa
title_full_unstemmed Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa
title_short Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa
title_sort using generalized structured additive regression models to determine factors associated with and clusters for covid-19 hospital deaths in south africa
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161152/
https://www.ncbi.nlm.nih.gov/pubmed/37147648
http://dx.doi.org/10.1186/s12889-023-15789-3
work_keys_str_mv AT maposainnocent usinggeneralizedstructuredadditiveregressionmodelstodeterminefactorsassociatedwithandclustersforcovid19hospitaldeathsinsouthafrica
AT welchrichard usinggeneralizedstructuredadditiveregressionmodelstodeterminefactorsassociatedwithandclustersforcovid19hospitaldeathsinsouthafrica
AT ozougwulovelyn usinggeneralizedstructuredadditiveregressionmodelstodeterminefactorsassociatedwithandclustersforcovid19hospitaldeathsinsouthafrica
AT arendsetracy usinggeneralizedstructuredadditiveregressionmodelstodeterminefactorsassociatedwithandclustersforcovid19hospitaldeathsinsouthafrica
AT mudaracaroline usinggeneralizedstructuredadditiveregressionmodelstodeterminefactorsassociatedwithandclustersforcovid19hospitaldeathsinsouthafrica
AT blumberglucille usinggeneralizedstructuredadditiveregressionmodelstodeterminefactorsassociatedwithandclustersforcovid19hospitaldeathsinsouthafrica
AT jassatwaasila usinggeneralizedstructuredadditiveregressionmodelstodeterminefactorsassociatedwithandclustersforcovid19hospitaldeathsinsouthafrica