Characteristics of hospitalised COVID-19 patients during the first two pandemic waves, Gauteng.
COVID-19
comorbidities
hospitalisation
in-hospital mortality
Journal
Southern African journal of infectious diseases
ISSN: 2313-1810
Titre abrégé: S Afr J Infect Dis
Pays: South Africa
ID NLM: 101646666
Informations de publication
Date de publication:
2022
2022
Historique:
received:
07
04
2022
accepted:
25
07
2022
entrez:
18
10
2022
pubmed:
19
10
2022
medline:
19
10
2022
Statut:
epublish
Résumé
Gauteng province (GP) was one of the most affected provinces in the country during the first two pandemic waves in South Africa. We aimed to describe the characteristics of coronavirus disease 2019 (COVID-19) patients admitted in one of the largest quaternary hospitals in GP during the first two waves. Study objectives were to determine factors associated with hospital admission during the second wave and to describe factors associated with in-hospital COVID-19 mortality. Data from a national hospital-based surveillance system of COVID-19 hospitalisations were used. Multivariable logistic regression models were conducted to compare patients hospitalised during wave 1 and wave 2, and to determine factors associated with in-hospital mortality. The case fatality ratio was the highest (39.95%) during wave 2. Factors associated with hospitalisation included age groups 40-59 years (adjusted odds ratio [aOR]: 2.14, 95% confidence interval [CI]: 1.08-4.27), 60-79 years (aOR: 2.49, 95% CI: 1.23-5.02) and ≥ 80 years (aOR: 3.39, 95% CI: 1.35-8.49). Factors associated with in-hospital mortality included age groups 60-79 years (aOR: 2.55, 95% CI: 1.11-5.84) and ≥ 80 years (aOR: 5.66, 95% CI: 2.12-15.08); male sex (aOR: 1.56, 95% CI: 1.22-1.99); presence of an underlying comorbidity (aOR: 1.76, 95% CI: 1.37-2.26), as well as being admitted during post-wave 2 (aOR: 2.42, 95% CI: 1.33-4.42). Compared to the recent omicron-driven pandemic waves characterised by lower admission rates and less disease severity among younger patients, COVID-19 in-hospital mortality during the earlier waves was associated with older age, being male and having an underlying comorbidity. This study showed how an active surveillance system can contribute towards identifying changes in disease trends.
Sections du résumé
Background
UNASSIGNED
Gauteng province (GP) was one of the most affected provinces in the country during the first two pandemic waves in South Africa. We aimed to describe the characteristics of coronavirus disease 2019 (COVID-19) patients admitted in one of the largest quaternary hospitals in GP during the first two waves.
Objectives
UNASSIGNED
Study objectives were to determine factors associated with hospital admission during the second wave and to describe factors associated with in-hospital COVID-19 mortality.
Method
UNASSIGNED
Data from a national hospital-based surveillance system of COVID-19 hospitalisations were used. Multivariable logistic regression models were conducted to compare patients hospitalised during wave 1 and wave 2, and to determine factors associated with in-hospital mortality.
Results
UNASSIGNED
The case fatality ratio was the highest (39.95%) during wave 2. Factors associated with hospitalisation included age groups 40-59 years (adjusted odds ratio [aOR]: 2.14, 95% confidence interval [CI]: 1.08-4.27), 60-79 years (aOR: 2.49, 95% CI: 1.23-5.02) and ≥ 80 years (aOR: 3.39, 95% CI: 1.35-8.49). Factors associated with in-hospital mortality included age groups 60-79 years (aOR: 2.55, 95% CI: 1.11-5.84) and ≥ 80 years (aOR: 5.66, 95% CI: 2.12-15.08); male sex (aOR: 1.56, 95% CI: 1.22-1.99); presence of an underlying comorbidity (aOR: 1.76, 95% CI: 1.37-2.26), as well as being admitted during post-wave 2 (aOR: 2.42, 95% CI: 1.33-4.42).
Conclusion
UNASSIGNED
Compared to the recent omicron-driven pandemic waves characterised by lower admission rates and less disease severity among younger patients, COVID-19 in-hospital mortality during the earlier waves was associated with older age, being male and having an underlying comorbidity.
Contribution
UNASSIGNED
This study showed how an active surveillance system can contribute towards identifying changes in disease trends.
Identifiants
pubmed: 36254313
doi: 10.4102/sajid.v37i1.434
pii: SAJID-37-434
pmc: PMC9557933
doi:
Types de publication
Journal Article
Langues
eng
Pagination
434Informations de copyright
© 2022. The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
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