ABSTRACT
Background: Since there are currently no specific SARS-CoV-2 prognostic viral biomarkers for predicting disease severity, there has been interest in using SARS-CoV-2 polymerase chain reaction (PCR) cycle-threshold (Ct) values to predict disease progression. Objective: This study assessed the association between in-hospital mortality of hospitalized COVID-19 cases and Ct-values of gene targets specific to SARS-CoV-2. Methods: Clinical data of hospitalized COVID-19 cases from Gauteng Province from April 2020-July 2022 were obtained from a national surveillance system and linked to laboratory data. The study period was divided into pandemic waves: Asp614Gly/wave1 (7 June-22 Aug 2020); beta/wave2 (15 Nov 2020-6 Feb 2021); delta/wave3 (9 May-18 Sept 2021) and omicron/wave4 (21 Nov 2021-22 Jan 2022). Ct-value data of genes specific to SARS-CoV-2 according to testing platforms (Roche-ORF gene; GeneXpert-N2 gene; Abbott-RdRp gene) were categorized as low (Ct < 20), mid (Ct20-30) or high (Ct > 30). Results: There were 1205 recorded cases: 836(69.4%; wave1), 122(10.1%;wave2) 21(1.7%; wave3) and 11(0.9%;in wave4). The cases' mean age(±SD) was 49 years(±18), and 662(54.9%) were female. There were 296(24.6%) deaths recorded: 241(81.4%;wave1), 27 (9.1%;wave2), 6 (2%;wave3), and 2 (0.7%;wave4) (p < 0.001). Sample distribution by testing platforms was: Roche 1,033 (85.7%), GeneXpert 169 (14%) and Abbott 3 (0.3%). The median (IQR) Ct-values according to testing platform were: Roche 26 (22-30), GeneXpert 38 (36-40) and Abbott 21 (16-24). After adjusting for sex, age and presence of a comorbidity, the odds of COVID-19 associated death were high amongst patients with Ct values 20-30[adjusted Odds Ratio (aOR) 2.25; 95% CI: 1.60-3.18] and highest amongst cases with Ct-values <20 (aOR 3.18; 95% CI: 1.92-5.27), compared to cases with Ct-values >30. Conclusion: Although odds of COVID19-related death were high amongst cases with Ct-values <30, Ct values were not comparable across different testing platforms, thus precluding the comparison of SARS-CoV-2 Ct-value results.
ABSTRACT
Abattoir workers are liable to zoonotic infections from animals and animal products, primarily to diseases with asymptomatic and chronic clinical manifestations in animals, such as brucellosis. No published reports exist on the seroprevalence of brucellosis in abattoir workers in South Africa. Therefore, this cross-sectional study was conducted to estimate the occurrence and risk factors for Brucella exposure in abattoir workers in Gauteng Province. A total of 103 abattoir workers and managers from 6 abattoirs, where brucellosis-positive slaughtered cattle and sheep were previously detected, were interviewed and tested with serological assays using the Rose Bengal test (RBT), BrucellaCapt, and IgG-ELISA. A pre-tested questionnaire was administered to consenting respondents to obtain information on risk factors for brucellosis. Of the 103 respondents tested, the distribution of female and male workers was 16 (15.5%) and 87 (84.5%), respectively. The seroprevalence for exposure to brucellosis was 21/103 (20.4%, 95%CI: 13.1-29.5) using a combination of RBT, BrucellaCapt, or IgG-ELISA. For test-specific results, seroprevalences by RBT, BrucellaCapt, and IgG-ELISA were 13/103 (12.6%, 95%CI: 6.9-20.6), 9/103 (8.74%, 95%CI: 4.1-15.9), and 18/103 (17.5%, 95%CI: 10.7-26.2), respectively. Low-throughput abattoirs were identified as associated risks, as 29.3% of workers were seropositive compared with 12.7% of workers in high-throughput abattoirs, which highlights that direct contact at abattoirs poses higher risk to workers than indirect and direct contact outside abattoirs. This study confirms the occurrence of Brucella spp. antibodies among abattoir workers in South Africa, possibly due to occupational exposure to Brucella spp., and highlights the occupational hazard to workers. Furthermore, findings underscore that abattoir facilities can serve as points for active and passive surveillance for indicators of diseases of public health importance. We recommend periodic implementation of brucellosis testing of abattoir workers country-wide to establish baseline data for informing appropriate preventive practices and reducing the potential burden of infection rates among these high-risk workers.
ABSTRACT
BACKGROUND: In 2021, the HIV prevalence among South African adults was 18% and more than 2 million people had uncontrolled HIV and, therefore, had increased risk of poor outcomes with SARS-CoV-2 infection. We investigated trends in COVID-19 admissions and factors associated with in-hospital COVID-19 mortality among people living with HIV and people without HIV. METHODS: In this analysis of national surveillance data, we linked and analysed data collected between March 5, 2020, and May 28, 2022, from the DATCOV South African national COVID-19 hospital surveillance system, the SARS-CoV-2 case line list, and the Electronic Vaccination Data System. All analyses included patients hospitalised with SARS-CoV-2 with known in-hospital outcomes (ie, who were discharged alive or had died) at the time of data extraction. We used descriptive statistics for admissions and mortality trends. Using post-imputation random-effect multivariable logistic regression models, we compared characteristics and the case fatality ratio of people with HIV and people without HIV. Using modified Poisson regression models, we compared factors associated with mortality among all people with COVID-19 admitted to hospital and factors associated with mortality among people with HIV. FINDINGS: Among 397â082 people with COVID-19 admitted to hospital, 301â407 (75·9%) were discharged alive, 89â565 (22·6%) died, and 6110 (1·5%) had no recorded outcome. 270â737 (68·2%) people with COVID-19 had documented HIV status (22â858 with HIV and 247â879 without). Comparing characteristics of people without HIV and people with HIV in each COVID-19 wave, people with HIV had increased odds of mortality in the D614G (adjusted odds ratio 1·19, 95% CI 1·09-1·29), beta (1·08, 1·01-1·16), delta (1·10, 1·03-1·18), omicron BA.1 and BA.2 (1·71, 1·54-1·90), and omicron BA.4 and BA.5 (1·81, 1·41-2·33) waves. Among all COVID-19 admissions, mortality was lower among people with previous SARS-CoV-2 infection (adjusted incident rate ratio 0·32, 95% CI 0·29-0·34) and with partial (0·93, 0·90-0·96), full (0·70, 0·67-0·73), or boosted (0·50, 0·41-0·62) COVID-19 vaccination. Compared with people without HIV who were unvaccinated, people without HIV who were vaccinated had lower risk of mortality (0·68, 0·65-0·71) but people with HIV who were vaccinated did not have any difference in mortality risk (1·08, 0·96-1·23). In-hospital mortality was higher for people with HIV with CD4 counts less than 200 cells per µL, irrespective of viral load and vaccination status. INTERPRETATION: HIV and immunosuppression might be important risk factors for mortality as COVID-19 becomes endemic. FUNDING: South African National Institute for Communicable Diseases, the South African National Government, and the United States Agency for International Development.
Subject(s)
COVID-19 , HIV Infections , Adult , Humans , South Africa/epidemiology , SARS-CoV-2 , COVID-19 Vaccines , HIV Infections/complications , HIV Infections/epidemiologyABSTRACT
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.
Subject(s)
COVID-19 , Humans , Bayes Theorem , Hospitalization , Hospitals , SARS-CoV-2 , South Africa/epidemiologyABSTRACT
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.
ABSTRACT
Tanapox is a rarely diagnosed zoonosis known to be endemic to equatorial Africa. All previously reported human cases were acquired within 10° north or south of the Equator, most recently 19 years ago. We describe a human case of tanapox in South Africa (24° south of the Equator). Expanded surveillance for this pathogen is warranted.
Subject(s)
Poxviridae Infections , Yatapoxvirus , Animals , Humans , South Africa/epidemiology , Zoonoses , Poxviridae Infections/diagnosisABSTRACT
In South Africa, rabies cycles are sustained by both domestic and wildlife host species. Despite the fact that the majority of human rabies cases are associated with dog bite exposures, wildlife species can potentially transmit rabies virus (RABV) infection to humans. In July 2021, a honey badger (Mellivora capensis) from the Kromdraai area (Gauteng Province) bit a dog on a small farm. The following day the same honey badger attacked three adults in the area, with one of the victims requiring hospitalization for management of her injuries. The honey badger was subsequently shot and the carcass submitted to the Agricultural Research Council-Onderstepoort Veterinary Research (ARC-OVR) for RABV diagnosis. A positive rabies diagnosis was confirmed and phylogenetic analysis of the amplified glycoprotein gene of the rabies virus demonstrated the virus to be of dog origin.
ABSTRACT
We conducted an epidemiologic survey to determine the seroprevalence of SARS-CoV-2 anti-nucleocapsid (anti-N) and anti-spike (anti-S) protein IgG from 1 March to 11 April 2022 after the BA.1-dominant wave had subsided in South Africa and prior to another wave dominated by the BA.4 and BA.5 (BA.4/BA.5) sub-lineages. We also analysed epidemiologic trends in Gauteng Province for cases, hospitalizations, recorded deaths, and excess deaths were evaluated from the inception of the pandemic through 17 November 2022. Despite only 26.7% (1995/7470) of individuals having received a COVID-19 vaccine, the overall seropositivity for SARS-CoV-2 was 90.9% (95% confidence interval (CI), 90.2 to 91.5) at the end of the BA.1 wave, and 64% (95% CI, 61.8 to 65.9) of individuals were infected during the BA.1-dominant wave. The SARS-CoV-2 infection fatality risk was 16.5-22.3 times lower in the BA.1-dominant wave compared with the pre-BA.1 waves for recorded deaths (0.02% vs. 0.33%) and estimated excess mortality (0.03% vs. 0.67%). Although there are ongoing cases of COVID-19 infections, hospitalization and death, there has not been any meaningful resurgence of COVID-19 since the BA.1-dominant wave despite only 37.8% coverage by at least a single dose of COVID-19 vaccine in Gauteng, South Africa.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , COVID-19 Vaccines , South Africa/epidemiology , Incidence , Seroepidemiologic Studies , SARS-CoV-2ABSTRACT
This second Special Issue in a series of Special Issues in Tropical Medicine and Infectious Disease looks at recent global research on the current Coronavirus (COVID-19) Pandemic [...].
ABSTRACT
The Sudan virus disease outbreak in Uganda in 2022 showed our vulnerability to viral haemorrhagic fevers (VHFs). Although there are regular outbreaks of VHFs with high morbidity and mortality, which disproportionally affect low-income settings, our understanding of how to treat them remains inadequate. In this systematic review, we aim to explore the availability, scope, standardisation, and quality of clinical management guidelines for VHFs. We identified 32 guidelines, 25 (78%) of which were low quality and did not have supporting evidence and eight (25%) of which had been produced or updated in the past 3 years. Guidance on supportive care and therapeutics had little detail and was sometimes contradictory. Guidelines based on uncertain evidence are a risk to patients, an ethical challenge for clinicians, and a challenge to implementing trials due to heterogeneous standards of care. We recommend a standard living guideline framework to improve the quality, scope, and applicability of guidelines. Furthermore, investments into trials should aim to identify optimal treatment strategies for VHFs and prioritise affordable and scalable interventions to improve outcomes globally.
Subject(s)
Hemorrhagic Fevers, Viral , Standard of Care , Humans , Hemorrhagic Fevers, Viral/epidemiology , Disease Outbreaks , Uganda/epidemiologyABSTRACT
BACKGROUND: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. METHODS: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). RESULTS: Data were available for 689â572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. CONCLUSIONS: Age was the strongest determinant of risk of death, with a â¼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.