Trends in Cases, Hospitalisation and Mortality Related to the Omicron BA.4/BA.5 Sub-Variants in South Africa

Jassat, Waasila; Karim, Salim Safurdeen. Abdool; Ozougwu, Lovelyn; Welch, Richard; Mudara, Caroline; Masha, Maureen; Rousseau, Petro; Wolmarans, Milani; Selikow, Anthony; Govender, Nevashan; Walaza, Sibongile; Gottberg, Anne von; Wolter, Nicole; Pisa, Pedro T.; Sanne, Ian; Govender, Sharlene; Blumberg, Lucille; Cohen, Cheryl; Groome, Michelle

doi:10.1101/2022.08.24.22279197

Cited by 12 publications

(17 citation statements)

References 34 publications

(43 reference statements)

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“…The adjusted odds ratios were highest for the 80+ age group (29.77; 95% CI 21.63–40.98), males (1.44; 95% CI 1.30–1.60), unvaccinated (16.1; 95% CI 13.8–18.8), and patients with three or more comorbidities (13.1; 95% CI 10.9–15.8). In a similar report, Jassat et al [ 61 ] took nationwide data from the South Africa’s National Hospital Surveillance System to gauge the risk factors associated with mortality among SARS-CoV-2 hospitalized patients during the combined period of Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves. The odds ratio scores indicate the 60+ age (17.9; 95% CI 16.2–19.8), males (1.3; 95% CI 1.3–1.3), and presence of comorbidities (1.5; 95% CI 1.4–1.5) as potential risk factors.…”

Section: Discussionmentioning

confidence: 99%

Risk Factors Associated with Mortality in Hospitalized Patients with COVID-19 during the Omicron Wave in Brazil

et al. 2022

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Considering the imminence of new SARS-CoV-2 variants and COVID-19 vaccine availability, it is essential to understand the impact of the disease on the most vulnerable groups and those at risk of death from the disease. To this end, the odds ratio (OR) for mortality and hospitalization was calculated for different groups of patients by applying an adjusted logistic regression model based on the following variables of interest: gender, booster vaccination, age group, and comorbidity occurrence. A massive number of data were extracted and compiled from official Brazilian government resources, which include all reported cases of hospitalizations and deaths associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Brazil during the “wave” of the Omicron variant (BA.1 substrain). Males (1.242; 95% CI 1.196–1.290) aged 60–79 (3.348; 95% CI 3.050–3.674) and 80 years or older (5.453; 95% CI 4.966–5.989), and hospitalized patients with comorbidities (1.418; 95% CI 1.355–1.483), were more likely to die. There was a reduction in the risk of death (0.907; 95% CI 0.866–0.951) among patients who had received the third dose of the anti-SARS-CoV-2 vaccine (booster). Additionally, this big data investigation has found statistical evidence that vaccination can support mitigation plans concerning the current scenario of COVID-19 in Brazil since the Omicron variant and its substrains are now prevalent across the entire country.

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Section: Discussionmentioning

confidence: 99%

Risk Factors Associated with Mortality in Hospitalized Patients with COVID-19 during the Omicron Wave in Brazil

et al. 2022

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“…Jassat et al similarly reveal in their study that in-hospital case-fatality ratio during the Omicron wave was 10•7%, compared with 21•5% during the rst wave, 28•8% during the second wave, and 26•4% during the third wave (7,8,33). The Beta and Delta VOCs drove the second and third COVID-19 waves in South Africa.…”

Section: Discussionmentioning

confidence: 82%

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

Maposa

Welch

Ozougwu

et al. 2022

Preprint

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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.

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“…Besides showing spatial structure in the COVID-19 in-hospital mortality in South Africa, our study using the non-linear effects of months as a proxy for temporal evolution of the epidemic highlight higher risk of deaths during the second and third waves. Jassat et al similarly reveal in their study that in-hospital case-fatality ratio during the Omicron wave was 10•7%, compared with 21•5% during the first wave, 28•8% during the second wave, and 26•4% during the third wave (7,8,33). The Beta and Delta VOCs drove the second and third COVID-19 waves in South Africa.…”

Section: Discussionmentioning

confidence: 83%

“…Over the past two years, the world has grappled with COVID-19, a disease caused by a novel coronavirus, SARS-CoV-2. This was first noted in December 2019 in Wuhan, China, where a cluster of patients with pneumonia of unknown cause was identified (1)(2)(3) South Africa recorded its first confirmed COVID-19 case in March 2020 and the government took decisive action to mitigate the spread of the disease through implementing a state of disaster and then adjusted mitigation levels (4) The first wave (peak) of COVID-19 infections occurred in July 2020 in South Africa and four more waves followed (5)(6)(7)(8). The most recent waves dominated by Omicron sub-variants have been less severe due to high levels of immunity from vaccination and prior infection in South Africa (5,9,10).…”

Section: Introductionmentioning

confidence: 99%

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

Maposa

Welch²,

Ozougwu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundThe 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.MethodsCOVID-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.ResultsThe 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.ConclusionThe 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.

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Trends in Cases, Hospitalisation and Mortality Related to the Omicron BA.4/BA.5 Sub-Variants in South Africa

Cited by 12 publications

References 34 publications

Risk Factors Associated with Mortality in Hospitalized Patients with COVID-19 during the Omicron Wave in Brazil

Risk Factors Associated with Mortality in Hospitalized Patients with COVID-19 during the Omicron Wave in Brazil

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

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