Trends in COVID-19 admissions and deaths among people living with HIV in South Africa: analysis of national surveillance data.

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 397 082 people with COVID-19 admitted to hospital, 301 407 (75·9%) were discharged alive, 89 565 (22·6%) died, and 6110 (1·5%) had no recorded outcome. 270 737 (68·2%) people with COVID-19 had documented HIV status (22 858 with HIV and 247 879 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.

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 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.

Trends in Cases, Hospitalizations, and Mortality Related to the Omicron BA.4/BA.5 Subvariants in South Africa.

BACKGROUND: In this study, we compared admission incidence risk and the risk of mortality in the Omicron BA.4/BA.5 wave to previous waves. METHODS: Data from South Africa's SARS-CoV-2 case linelist, national COVID-19 hospital surveillance system, and Electronic Vaccine Data System were linked and analyzed. Wave periods were defined when the country passed a weekly incidence of 30 cases/100 000 population. In-hospital case fatality ratios (CFRs) during the Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves were compared using post-imputation random effect multivariable logistic regression models. RESULTS: The CFR was 25.9% (N = 37 538 of 144 778), 10.9% (N = 6123 of 56 384), and 8.2% (N = 1212 of 14 879) in the Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves, respectively. After adjusting for age, sex, race, comorbidities, health sector, and province, compared with the Omicron BA.4/BA.5 wave, patients had higher risk of mortality in the Omicron BA.1/BA.2 wave (adjusted odds ratio [aOR], 1.3; 95% confidence interval [CI]: 1.2-1.4) and Delta wave (aOR, 3.0; 95% CI: 2.8-3.2). Being partially vaccinated (aOR, 0.9; 95% CI: .9-.9), fully vaccinated (aOR, 0.6; 95% CI: .6-.7), and boosted (aOR, 0.4; 95% CI: .4-.5) and having prior laboratory-confirmed infection (aOR, 0.4; 95% CI: .3-.4) were associated with reduced risks of mortality. CONCLUSIONS: Overall, admission incidence risk and in-hospital mortality, which had increased progressively in South Africa's first 3 waves, decreased in the fourth Omicron BA.1/BA.2 wave and declined even further in the fifth Omicron BA.4/BA.5 wave. Mortality risk was lower in those with natural infection and vaccination, declining further as the number of vaccine doses increased.

Effect of HIV status and antiretroviral treatment on treatment outcomes of tuberculosis patients in a rural primary healthcare clinic in South Africa.

BACKGROUND: Tuberculosis (TB) remains the leading cause of death among human immunodeficiency virus (HIV) infected individuals in South Africa. Despite the implementation of HIV/TB integration services at primary healthcare facility level, the effect of HIV on TB treatment outcomes has not been well investigated. To provide evidence base for TB treatment outcome improvement to meet End TB Strategy goal, we assessed the effect of HIV status on treatment outcomes of TB patients at a rural clinic in the Ugu Health District, South Africa. METHODS: We reviewed medical records involving a cohort of 508 TB patients registered for treatment between 1 January 2013 and 31 December 2015 at rural public sector clinic in KwaZulu-Natal province, South Africa. Data were extracted from National TB Programme clinic cards and the TB case registers routinely maintained at study sites. The effect of HIV status on TB treatment outcomes was determined by using multinomial logistic regression. Estimates used were relative risk ratio (RRR) at 95% confidence intervals (95%CI). RESULTS: A total of 506 patients were included in the analysis. Majority of the patients (88%) were new TB cases, 70% had pulmonary TB and 59% were co-infected with HIV. Most of HIV positive patients were on antiretroviral therapy (ART) (90% (n = 268)). About 82% had successful treatment outcome (cured 39.1% (n = 198) and completed treatment (42.9% (n = 217)), 7% (n = 39) died 0.6% (n = 3) failed treatment, 3.9% (n = 20) defaulted treatment and the rest (6.6% (n = 33)) were transferred out of the facility. Furthermore, HIV positive patients had a higher mortality rate (9.67%) than HIV negative patients (2.91%)". Using completed treatment as reference, HIV positive patients not on ART relative to negative patients were more likely to have unsuccessful outcomes [RRR, 5.41; 95%CI, 2.11-13.86]. CONCLUSIONS: When compared between HIV status, HIV positive TB patients were more likely to have unsuccessful treatment outcome in rural primary care. Antiretroviral treatment seems to have had no effect on the likelihood of TB treatment success in rural primary care. The TB mortality rate in HIV positive patients, on the other hand, was higher than in HIV negative patients emphasizing the need for enhanced integrated management of HIV/TB in rural South Africa through active screening of TB among HIV positive individuals and early access to ART among HIV positive TB cases.

Clinical severity of COVID-19 in patients admitted to hospital during the omicron wave in South Africa: a retrospective observational study.

BACKGROUND: Up to the end of January, 2022, South Africa has had four recognisable COVID-19 pandemic waves, each predominantly dominated by one variant of concern: the ancestral strain with an Asp614Gly mutation during the first wave, the beta variant (B.1.351) during the second wave, the delta variant (B.1.617.2) during the third wave, and lastly, the omicron variant (B.1.1.529) during the fourth wave. We aimed to assess the clinical disease severity of patients admitted to hospital with SARS-CoV-2 infection during the omicron wave and compare the findings with those of the preceding three pandemic waves in South Africa. METHODS: We defined the start and end of each pandemic wave as the crossing of the threshold of weekly incidence of 30 laboratory-confirmed SARS-CoV-2 cases per 100 000 population. Hospital admission data were collected through an active national COVID-19-specific surveillance programme. We compared disease severity across waves by post-imputation random effect multivariable logistic regression models. Severe disease was defined as one or more of the following: acute respiratory distress, receipt of supplemental oxygen or mechanical ventilation, admission to intensive care, or death. FINDINGS: We analysed 335 219 laboratory-confirmed SARS-CoV-2 hospital admissions with a known outcome, constituting 10·4% of 3 216 179 cases recorded during the four waves. During the omicron wave, 52 038 (8·3%) of 629 617 cases were admitted to hospital, compared with 71 411 (12·9%) of 553 530 in the Asp614Gly wave, 91 843 (12·6%) of 726 772 in the beta wave, and 131 083 (10·0%) of 1 306 260 in the delta wave (p<0·0001). During the omicron wave, 15 421 (33·6%) of 45 927 patients admitted to hospital had severe disease, compared with 36 837 (52·3%) of 70 424 in the Asp614Gly wave, 57 247 (63·4%) of 90 310 in the beta wave, and 81 040 (63·0%) of 128 558 in the delta wave (p<0·0001). The in-hospital case-fatality ratio during the omicron wave was 10·7%, compared with 21·5% during the Asp614Gly wave, 28·8% during the beta wave, and 26·4% during the delta wave (p<0·0001). Compared with those admitted to hospital during the omicron wave, patients admitted during the other three waves had more severe clinical presentations (adjusted odds ratio 2·07 [95% CI 2·01-2·13] in the Asp614Gly wave, 3·59 [3·49-3·70] in the beta wave, and 3·47 [3·38-3·57] in the delta wave). INTERPRETATION: The trend of increasing cases and admissions across South Africa's first three waves shifted in the omicron wave, with a higher and quicker peak but fewer patients admitted to hospital, less clinically severe illness, and a lower case-fatality ratio compared with the preceding three waves. Omicron marked a change in the SARS-CoV-2 epidemic curve, clinical profile, and deaths in South Africa. Extrapolations to other populations should factor in differing vaccination and previous infection levels. FUNDING: National Institute for Communicable Diseases.

Difference in mortality among individuals admitted to hospital with COVID-19 during the first and second waves in South Africa: a cohort study.

BACKGROUND: The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves. METHODS: In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression. FINDINGS: Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240·4 cases per 100 000 people vs 136·0 cases per 100 000 people; admissions, 27·9 admissions per 100 000 people vs 16·1 admissions per 100 000 people; deaths, 8·3 deaths per 100 000 people vs 3·6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1·19, 95% CI 1·18-1·20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40-64 years (adjusted odds ratio [aOR] 1·22, 95% CI 1·14-1·31), and older than 65 years (aOR 1·38, 1·25-1·52), compared with younger than 40 years; of Mixed race (aOR 1·21, 1·06-1·38) compared with White race; and admitted in the public sector (aOR 1·65, 1·41-1·92); and less likely to be Black (aOR 0·53, 0·47-0·60) and Indian (aOR 0·77, 0·66-0·91), compared with White; and have a comorbid condition (aOR 0·60, 0·55-0·67). For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1·31, 95% CI 1·28-1·35). In-hospital case-fatality risk increased from 17·7% in weeks of low admission (<3500 admissions) to 26·9% in weeks of very high admission (>8000 admissions; aOR 1·24, 1·17-1·32). INTERPRETATION: In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage. FUNDING: DATCOV as a national surveillance system is funded by the National Institute for Communicable Diseases and the South African National Government.

Proactive Measures to Combat a SARS-CoV-2 Transmission Among High Risk Patients and Health Care Workers in an Outpatient Dialysis Facility.

Background: End-stage-renal-failure (ESRF) patients attending clustered out-patient dialysis are susceptible to SARS-CoV-2 infection. Comorbidities render them vulnerable to severe COVID-19. Although preventative and mitigation strategies are recommended, the effect of these are unknown. A period of "potential-high-infectivity" results if a health-care-worker (HCWs) or a patient becomes infected. Aim: We describe and analyze early, universal SARS-CoV-2 real time reverse transcription polymerase chain reaction (RT-PCR) tests, biomarker monitoring and SARS-CoV-2 preventative strategies, in a single dialysis center, after a positive patient was identified. Methodology: The setting was a single outpatient dialysis center in Johannesburg, South Africa which had already implemented preventative strategies. We describe the management of 57 patients and 11 HCWs, after one of the patients tested positive for SARS-CoV-2. All individuals were subjected to RT-PCR tests and biomarkers (Neutrophil-Lymphocyte Ratio, C-reactive protein, and D-Dimer) within 72 h (initial-tests). Individuals with initial negative RT-PCR and abnormal biomarkers (one or more) were subjected to repeat RT-PCR and biomarkers (retest subgroup) during the second week. Additional stringent measures (awareness of viral transmission, dialysis distancing and screening) were implemented during the period of "potential high infectivity." The patient retest subgroup also underwent clustered dialysis until retest results became available. Results: A second positive-patient was identified as a result of early universal RT-PCR tests. In the two positive-patients, biomarker improvement coincided with RT-PCR negative tests. We identified 13 individuals for retesting. None of these retested individuals tested positive for SARS-CoV-2 and there was no deterioration in median biomarker values between initial and retests. Collectively, none of the negative individuals developed COVID-19 symptoms during the period "potential high infectivity." Conclusion: A SARS-CoV-2 outbreak may necessitate additional proactive steps to counteract spread of infection. This includes early universal RT-PCR testing and creating further awareness of the risk of transmission and modifying preventative strategies. Abnormal biomarkers may be poorly predictive of SARS-CoV-2 infection in ESRF patients due to underlying illnesses. Observing dynamic changes in biomarkers in RT-PCR positive and negative-patients may provide insights into general state of health.

Determinants of adherence to clinic appointments among tuberculosis and HIV co-infected individuals attending care at Helen Joseph Hospital, Johannesburg, South Africa.

INTRODUCTION: tuberculosis (TB) is one of the leading causes of morbidity and mortality among people living with HIV/AIDS. The growing burden of TB/HIV co-infection continues to strain the healthcare system due to association with long duration of treatment. This is a catalyst for poor adherence to clinic appointments, which results in poor treatment adherence and patient outcome. This study evaluated the factors associated with adherence to clinic appointments among TB/HIV co-infected patients in Johannesburg, South Africa. METHODS: this was a cross-sectional study that involved 10427 patients ≥18 years of age with HIV infection and co-infected with TB. We used a proxy measure "md clinic appointments" to assess adherence, then multivariable logistic regression to evaluate factors associated with adherence. RESULTS: one thousand, five hundred and twenty-eight patients were co-infected with TB, of these, 17.4% attained good adherence. Patients with TB/HIV co-infection who were on treatment for a longer period were less likely to adhere to clinic appointments (AOR: 0.98 95% CI: 0.97, 0.99). CONCLUSION: duration on treatment among TB/HIV co-infected patients is associated with adherence to clinic appointments. It is therefore vital to reinforce public health interventions that would enhance sustained adherence to clinic appointments and mitigate its impact on treatment adherence and patient outcome.