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Objectives: The study aimed to describe the prevalence of and risk factors for post-COVID-19 condition (PCC)., Methods: This was a prospective, longitudinal observational cohort study. Hospitalized and nonhospitalized adults were randomly selected to undergo telephone assessment at 1, 3, and 6 months. Participants were assessed using a standardized questionnaire for the evaluation of symptoms and health-related quality of life. We used negative binomial regression models to determine factors associated with the presence of ≥1 symptoms at 6 months., Results: A total of 46.7% of hospitalized and 18.5% of nonhospitalized participants experienced ≥1 symptoms at 6 months (P ≤0.001). Among hospitalized people living with HIV, 40.4% had persistent symptoms compared with 47.1% among participants without HIV (P = 0.108). The risk factors for PCC included older age, female sex, non-Black race, presence of a comorbidity, greater number of acute COVID-19 symptoms, hospitalization/COVID-19 severity, and wave period (lower risk of persistent symptoms for the Omicron compared with the Beta wave). There were no associations between self-reported vaccination status with persistent symptoms., Conclusion: The study revealed a high prevalence of persistent symptoms among South African participants at 6 months but decreased risk for PCC among participants infected during the Omicron BA.1 wave. These findings have serious implications for countries with resource-constrained health care systems., Competing Interests: Declaration of competing interest The authors have no competing interests to declare., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.

Objectives: We aimed to compare the clinical severity of Omicron BA.4/BA.5 infection with BA.1 and earlier variant infections among laboratory-confirmed SARS-CoV-2 cases in the Western Cape, South Africa, using timing of infection to infer the lineage/variant causing infection., Methods: We included public sector patients aged ≥20 years with laboratory-confirmed COVID-19 between May 01-May 21, 2022 (BA.4/BA.5 wave) and equivalent previous wave periods. We compared the risk between waves of (i) death and (ii) severe hospitalization/death (all within 21 days of diagnosis) using Cox regression adjusted for demographics, comorbidities, admission pressure, vaccination, and previous infection., Results: Among 3793 patients from the BA.4/BA.5 wave and 190,836 patients from previous waves, the risk of severe hospitalization/death was similar in the BA.4/BA.5 and BA.1 waves (adjusted hazard ratio [aHR] 1.12; 95% confidence interval [CI] 0.93; 1.34). Both Omicron waves had a lower risk of severe outcomes than previous waves. Previous infection (aHR 0.29, 95% CI 0.24; 0.36) and vaccination (aHR 0.17; 95% CI 0.07; 0.40 for at least three doses vs no vaccine) were protective., Conclusion: Disease severity was similar among diagnosed COVID-19 cases in the BA.4/BA.5 and BA.1 periods in the context of growing immunity against SARS-CoV-2 due to previous infection and vaccination, both of which were strongly protective., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: This study describes the characteristics of admitted HCWs reported to the DATCOV surveillance system, and the factors associated with in-hospital mortality in South African HCWs., Methods: Data from March 5, 2020 to April 30, 2021 were obtained from DATCOV, a national hospital surveillance system monitoring COVID-19 admissions in South Africa. Characteristics of HCWs were compared with those of non-HCWs. Furthermore, a logistic regression model was used to assess factors associated with in-hospital mortality among HCWs., Results: In total, there were 169 678 confirmed COVID-19 admissions, of which 6364 (3.8%) were HCWs. More of these HCW admissions were accounted for in wave 1 (48.6%; n  = 3095) than in wave 2 (32.0%; n  = 2036). Admitted HCWs were less likely to be male (28.2%; n  = 1791) (aOR 0.3; 95% CI 0.3-0.4), in the 50-59 age group (33.1%; n  = 2103) (aOR 1.4; 95% CI 1.1-1.8), or accessing the private health sector (63.3%; n  = 4030) (aOR 1.3; 95% CI 1.1-1.5). Age, comorbidities, race, wave, province, and sector were significant risk factors for COVID-19-related mortality., Conclusion: The trends in cases showed a decline in HCW admissions in wave 2 compared with wave 1. Acquired SARS-COV-2 immunity from prior infection may have been a reason for reduced admissions and mortality of HCWs despite the more transmissible and more severe beta variant in wave 2., (© 2022 The Author(s).)

Objective: Champions are recognised as important to driving organisational change in healthcare quality improvement initiatives in high-income settings. In low-income and middle-income countries with a high disease burden and constrained human resources, their role is highly relevant yet understudied. Within a broader study on policy implementation for decentralised drug-resistant tuberculosis care in South Africa, we characterised the role, strategies and organisational context of emergent policy champions., Design: Interviews with 34 healthcare workers in three South African provinces identified the presence of individuals who had a strong influence on driving policy implementation forward. Additional interviews were conducted with 13 participants who were either identified as champions in phase II or were healthcare workers in facilities in which the champions operated. Thematic analyses using a socio-ecological framework further explored their strategies and the factors enabling or obstructing their agency., Results: All champions occupied senior managerial posts and were accorded legitimacy and authority by their communities. 'Disease-centred' champions had a high level of clinical expertise and placed emphasis on clinical governance and clinical outcomes, while 'patient-centred' champions promoted pathways of care that would optimise patients' recovery while minimising disruption in other spheres of their lives. Both types of champions displayed high levels of resourcefulness and flexibility to adapt strategies to the resource-constrained organisational context., Conclusion: Policymakers can learn from champions' experiences regarding barriers and enablers to implementation to adapt policy. Research is needed to understand what factors can promote the sustainability of champion-led policy implementation, and to explore best management practices to support their initiatives., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Importance: Some individuals experience persistent symptoms after initial symptomatic SARS-CoV-2 infection (often referred to as Long COVID)., Objective: To estimate the proportion of males and females with COVID-19, younger or older than 20 years of age, who had Long COVID symptoms in 2020 and 2021 and their Long COVID symptom duration., Design, Setting, and Participants: Bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. Of the 54 studies, 44 were published and 10 were collaborating cohorts (conducted in Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US). The participant data were derived from the 44 published studies (10 501 hospitalized individuals and 42 891 nonhospitalized individuals), the 10 collaborating cohort studies (10 526 and 1906), and the 2 US electronic medical record databases (250 928 and 846 046). Data collection spanned March 2020 to January 2022., Exposures: Symptomatic SARS-CoV-2 infection., Main Outcomes and Measures: Proportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after SARS-CoV-2 infection in 2020 and 2021, estimated separately for hospitalized and nonhospitalized individuals aged 20 years or older by sex and for both sexes of nonhospitalized individuals younger than 20 years of age., Results: A total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months., Conclusions and Relevance: This study presents modeled estimates of the proportion of individuals with at least 1 of 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings., Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries., Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population., Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome., Funding: Bronner P. Gonçalves, Peter Horby, Gail Carson, Piero L. Olliaro, Valeria Balan, Barbara Wanjiru Citarella, and research costs were supported by the UK Foreign, Commonwealth and Development Office (FCDO) and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z]; and Janice Caoili and Madiha Hashmi were supported by the UK FCDO and Wellcome [222048/Z/20/Z]. Peter Horby, Gail Carson, Piero L. Olliaro, Kalynn Kennon and Joaquin Baruch were supported by the Bill & Melinda Gates Foundation [OPP1209135]; Laura Merson was supported by University of Oxford's COVID-19 Research Response Fund - with thanks to its donors for their philanthropic support. Matthew Hall was supported by a Li Ka Shing Foundation award to Christophe Fraser. Moritz U.G. Kraemer was supported by the Branco Weiss Fellowship, Google.org, the Oxford Martin School, the Rockefeller Foundation, and the European Union Horizon 2020 project MOOD (#874850). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Contributions from Srinivas Murthy, Asgar Rishu, Rob Fowler, James Joshua Douglas, François Martin Carrier were supported by CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and coordinated out of Sunnybrook Research Institute. Contributions from Evert-Jan Wils and David S.Y. Ong were supported by a grant from foundation Bevordering Onderzoek Franciscus; and Andrea Angheben by the Italian Ministry of Health "Fondi Ricerca corrente-L1P6" to IRCCS Ospedale Sacro Cuore-Don Calabria. The data contributions of J.Kenneth Baillie, Malcolm G. Semple, and Ewen M. Harrison were supported by grants from the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. All funders of the ISARIC Clinical Characterisation Group are listed in the appendix., Competing Interests: BG, MH, WJ, VB, CK, AR, JB, LR, AD, JD, BC, MP, US, JB, DA, YA, AR, JC, EH, CF, MM, YC, XW, SB, ST, LS, AR, RR, DO, MH, GC, JD, MK, EW, PH, LM, PO No competing interests declared, SM declares receiving salary support from the Health Research Foundation and Innovative Medicines Canada Chair in Pandemic Preparedness Research, MS reports grants from DHSC National Institute of Health Research UK, from the Medical Research Council UK, and from the Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool, supporting the conduct of the study; other interest in Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work, AM declares consulting fees for Gilead and MSD, presentation fees for GILEAD, Pfizer and MSD, support for attending ECCMID from Gilead, and advisory board fees for MSD and Gilead, AA declares support from Italian Ministry of Health - "Fondi Ricerca Corrente" Line1 Project 5 to IRCCS Sacro Cuore - Don Calabria Hospital, FC declares a grant from the Canadian Institute of Health Research, JG declares support by Pfizer, Angelini and MSD to attend meetings (registration to meetings only), JD declares personal fees from lectures from Sunovion and Merck and consulting fees from Pfizer, AS has been an investigator in COVID-19 clinical trials by Algernon Pharmaceuticals, Atea Pharmaceuticals, Regeneron Pharmaceuticals, Diffusion Pharmaceuticals, Celltrion, Inc and Atriva Therapeutics, outside the scope of the submitted work, RF declares a peer reviewed research grant from the Canadian Institutes of Health Research, See Appendix 1 for competing interests for group author members, (© 2022, Gonçalves et al.)

Omicron lineages BA.4 and BA.5 drove a fifth wave of COVID-19 cases in South Africa. Here, we use the presence/absence of the S-gene target as a proxy for SARS-CoV-2 variant/lineage for infections diagnosed using the TaqPath PCR assay between 1 October 2021 and 26 April 2022. We link national COVID-19 individual-level data including case, laboratory test and hospitalisation data. We assess severity using multivariable logistic regression comparing the risk of hospitalisation and risk of severe disease, once hospitalised, for Delta, BA.1, BA.2 and BA.4/BA.5 infections. After controlling for factors associated with hospitalisation and severe outcome respectively, BA.4/BA.5-infected individuals had a similar odds of hospitalisation (aOR 1.24, 95% CI 0.98-1.55) and severe outcome (aOR 0.72, 95% CI 0.41-1.26) compared to BA.1-infected individuals. Newly emerged Omicron lineages BA.4/BA.5 showed similar severity to the BA.1 lineage and continued to show reduced clinical severity compared to the Delta variant., (© 2022. The Author(s).)

Background: Assessment of disease severity associated with a novel pathogen or variant provides crucial information needed by public health agencies and governments to develop appropriate responses. The SARS-CoV-2 omicron variant of concern (VOC) spread rapidly through populations worldwide before robust epidemiological and laboratory data were available to investigate its relative severity. Here we develop a set of methods that make use of non-linked, aggregate data to promptly estimate the severity of a novel variant, compare its characteristics with those of previous VOCs, and inform data-driven public health responses., Methods: Using daily population-level surveillance data from the National Institute for Communicable Diseases in South Africa (March 2, 2020, to Jan 28, 2022), we determined lag intervals most consistent with time from case ascertainment to hospital admission and within-hospital death through optimisation of the distance correlation coefficient in a time series analysis. We then used these intervals to estimate and compare age-stratified case-hospitalisation and case-fatality ratios across the four epidemic waves that South Africa has faced, each dominated by a different variant., Findings: A total of 3 569 621 cases, 494 186 hospitalisations, and 99 954 deaths attributable to COVID-19 were included in the analyses. We found that lag intervals and disease severity were dependent on age and variant. At an aggregate level, fluctuations in cases were generally followed by a similar trend in hospitalisations within 7 days and deaths within 15 days. We noted a marked reduction in disease severity throughout the omicron period relative to previous waves (age-standardised case-fatality ratios were consistently reduced by >50%), most substantial for age strata with individuals 50 years or older., Interpretation: This population-level time series analysis method, which calculates an optimal lag interval that is then used to inform the numerator of severity metrics including the case-hospitalisation and case-fatality ratio, provides useful and timely estimates of the relative effects of novel SARS-CoV-2 VOCs, especially for application in settings where resources are limited., Funding: National Institute for Communicable Diseases of South Africa, South African National Government., Competing Interests: Declaration of interests WH reports his position as a member of Biobot Analytics’ scientific advisory board and has received stock options in Biobot Analytics, as well as payment for expert witness testimony on the expected course of the COVID-19 pandemic. DS reports previous employment at Pfizer before the initiation of this analysis and stock in Pfizer. DS also reports compensation for occasional 1 h blinded consultancies for several consulting companies over the past 36 months. MS reports receipt of institutional research funds from the Johnson and Johnson Foundation and from Pfizer; neither funder had a role in the design or content of the current manuscript. CC reports grant funding from the Wellcome Trust, South African Medical Research Council, US Centers for Disease Control and Prevention, and Sanofi Pasteur to institute COVID-19 research in the past 36 months, as well as a role on the scientific advisory committee for “BCHW: Burden of COVID-19 among health care workers, assessing infection, risk factors, vaccine effectiveness, working experiences and one-health implications: a mixed methodology, multisite international study”. RW reports shareholding stock in the following health or pharmaceutical companies in South Africa, none related to this work: Adcock Ingram Holdings, Dischem Pharmacies, Discovery, Netcare, and Aspen Pharmacare Holdings. All interests listed are outside the current work. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: The impact of the COVID-19 pandemic on paediatric populations varied between high-income countries (HICs) versus low-income to middle-income countries (LMICs). We sought to investigate differences in paediatric clinical outcomes and identify factors contributing to disparity between countries., Methods: The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) COVID-19 database was queried to include children under 19 years of age admitted to hospital from January 2020 to April 2021 with suspected or confirmed COVID-19 diagnosis. Univariate and multivariable analysis of contributing factors for mortality were assessed by country group (HICs vs LMICs) as defined by the World Bank criteria., Results: A total of 12 860 children (3819 from 21 HICs and 9041 from 15 LMICs) participated in this study. Of these, 8961 were laboratory-confirmed and 3899 suspected COVID-19 cases. About 52% of LMICs children were black, and more than 40% were infants and adolescent. Overall in-hospital mortality rate (95% CI) was 3.3% [=(3.0% to 3.6%), higher in LMICs than HICs (4.0% (3.6% to 4.4%) and 1.7% (1.3% to 2.1%), respectively). There were significant differences between country income groups in intervention profile, with higher use of antibiotics, antivirals, corticosteroids, prone positioning, high flow nasal cannula, non-invasive and invasive mechanical ventilation in HICs. Out of the 439 mechanically ventilated children, mortality occurred in 106 (24.1%) subjects, which was higher in LMICs than HICs (89 (43.6%) vs 17 (7.2%) respectively). Pre-existing infectious comorbidities (tuberculosis and HIV) and some complications (bacterial pneumonia, acute respiratory distress syndrome and myocarditis) were significantly higher in LMICs compared with HICs. On multivariable analysis, LMIC as country income group was associated with increased risk of mortality (adjusted HR 4.73 (3.16 to 7.10))., Conclusion: Mortality and morbidities were higher in LMICs than HICs, and it may be attributable to differences in patient demographics, complications and access to supportive and treatment modalities., Competing Interests: Competing interests: None., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Background: 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: 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: 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: 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: 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: This study showed how an active surveillance system can contribute towards identifying changes in disease trends., Competing Interests: The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article., (© 2022. The Authors.)

Background: Post COVID-19 condition (PCC), as defined by WHO, refers to a wide range of new, returning, or ongoing health problems in people who have had COVID-19, and it represents a rapidly emerging public health priority. We aimed to establish how this developing condition has affected patients in South Africa and which population groups are at risk., Methods: In this prospective cohort study, we used the DATCOV national hospital surveillance system to identify participants aged 18 years or older who had been hospitalised with laboratory-confirmed SARS-CoV-2 infection in South Africa. Participants underwent telephone follow-up assessment at 1 month and 3 months after hospital discharge. Participants were assessed using a standardised questionnaire for the evaluation of symptoms, functional status, health-related quality of life, and occupational status. We used negative binomial regression models to determine factors associated with PCC., Findings: Of 241 159 COVID-19 admissions reported to DATCOV between Dec 1, 2020, and Aug 23, 2021, 8309 were randomly selected for enrolment. Of the 3094 patients that we were able to contact, 2410 (77·9%) consented to participate in the study at 1 month after discharge. Of these, 1873 (77·7%) were followed up at 3 months after hospital discharge. Participants had a median age of 52 years (IQR 41-62) and 960 (51·3%) were women. At 3 months of follow-up, 1249 (66·7%) of 1873 participants reported new or persistent COVID-19-related symptoms, compared with 1978 (82·1%) of 2410 at 1 month after hospital discharge. The most common symptoms reported at 3 months were fatigue (50·3%), shortness of breath (23·4%), confusion or lack of concentration (17·5%), headaches (13·8%), and problems seeing or blurred vision (10·1%). On multivariable analysis, the factors associated with persistent symptoms after acute COVID-19 were being female (adjusted incident rate ratio 1·20, 95% CI 1·04-1·38) and admission to an intensive care unit (1·17, 1·01-1·37)., Interpretation: Most participants in this cohort of individuals previously hospitalised with COVID-19 reported persistent symptoms 3 months after hospital discharge and a significant impact of PCC on their functional and occupational status. The large burden of PCC symptoms identified in this study emphasises the need for a national health strategy. This should include the development of clinical guidelines and training of health-care workers for identifying, assessing, and caring for patients affected by PCC; establishment of multidisciplinary health services; and provision of information and support to people who have PCC., Funding: Bill & Melinda Gates Foundation, UK Foreign, Commonwealth & Development Office, and Wellcome., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Seroprevalence studies are important for quantifying the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in resource-constrained countries., Methods: We conducted a cross-sectional household survey spanning the second pandemic wave (November 2020 to April 2021) in 3 communities. Blood was collected for SARS-CoV-2 antibody (2 enzyme-linked immunosorbent assays targeting spike and nucleocapsid) and human immunodeficiency virus (HIV) testing. An individual was considered seropositive if testing positive on ≥1 assay. Factors associated with infection, and the age-standardized infection case detection rate, infection hospitalization rate, and infection fatality rate were calculated., Results: Overall, 7959 participants were enrolled, with a median age of 34 years and an HIV prevalence of 22.7%. SARS-CoV-2 seroprevalence was 45.2% (95% confidence interval 43.7%-46.7%) and increased from 26.9% among individuals enrolled in December 2020 to 47.1% among those enrolled in April 2021. On multivariable analysis, seropositivity was associated with age, sex, race, being overweight/obese, having respiratory symptoms, and low socioeconomic status. Persons living with HIV with high viral load were less likely to be seropositive than HIV-uninfected individuals. The site-specific infection case detection rate, infection hospitalization rate, and infection fatality rate ranged across sites from 4.4% to 8.2%, 1.2% to 2.5%, and 0.3% to 0.6%, respectively., Conclusions: South Africa has experienced a large burden of SARS-CoV-2 infections, with <10% of infections diagnosed. Lower seroprevalence among persons living with HIV who are not virally suppressed, likely as a result of inadequate antibody production, highlights the need to prioritize this group for intervention., (© The Author(s) 2022. Published by Oxford University Press for the Infectious Diseases Society of America.)

Background: WHO has established a Global Clinical Platform for the clinical characterisation of COVID-19 among hospitalised individuals. We assessed whether people living with HIV hospitalised with COVID-19 had increased odds of severe presentation and of in-hospital mortality compared with individuals who were HIV-negative and associated risk factors., Methods: Between Jan 1, 2020, and July 1, 2021, anonymised individual-level data from 338 566 patients in 38 countries were reported to WHO. Using the Platform pooled dataset, we performed descriptive statistics and regression analyses to compare outcomes in the two populations and identify risk factors., Findings: Of 197 479 patients reporting HIV status, 16 955 (8·6%) were people living with HIV. 16 283 (96.0%) of the 16 955 people living with HIV were from Africa; 10 603 (62·9%) were female and 6271 (37·1%) were male; the mean age was 45·5 years (SD 13·7); 6339 (38·3%) were admitted to hospital with severe illness; and 3913 (24·3%) died in hospital. Of the 10 166 people living with HIV with known antiretroviral therapy (ART) status, 9302 (91·5%) were on ART. Compared with individuals without HIV, people living with HIV had 15% increased odds of severe presentation with COVID-19 (aOR 1·15, 95% CI 1·10-1·20) and were 38% more likely to die in hospital (aHR 1·38, 1·34-1·41). Among people living with HIV, male sex, age 45-75 years, and having chronic cardiac disease or hypertension increased the odds of severe COVID-19; male sex, age older than 18 years, having diabetes, hypertension, malignancy, tuberculosis, or chronic kidney disease increased the risk of in-hospital mortality. The use of ART or viral load suppression were associated with a reduced risk of poor outcomes; however, HIV infection remained a risk factor for severity and mortality regardless of ART and viral load suppression status., Interpretation: In this sample of hospitalised people contributing data to the WHO Global Clinical Platform for COVID-19, HIV was an independent risk factor for both severe COVID-19 at admission and in-hospital mortality. These findings have informed WHO immunisation policy that prioritises vaccination for people living with HIV. As the results mostly reflect the data contribution from Africa, this analysis will be updated as more data from other regions become available., Funding: None., Translation: For the French translation of the abstract see Supplementary Materials section., Competing Interests: Declaration of interests RH received funding from the Wellcome Trust, Canadian Institute of Health Research, UK Research and Innovation/Medical Research Council, and International COVID-19 Data Alliance–Health Data Research UK. All other authors declare no competing interests., (© 2022 This is an Open Access article published under the CC BY 3.0 IGO license which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any use of this article, there should be no suggestion that WHO endorses any specific organisation, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.)

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., Competing Interests: Declaration of interests CC reports grants from Sanofi Pasteur, the US Centers for Disease Control and Prevention (CDC), PATH, South African Medical Research Council (SAMRC), the Bill & Melinda Gates Foundation, and the Wellcome Trust, outside the submitted work. MJG reports grants from SAMRC and the Bill & Melinda Gates Foundation, outside the submitted work. AvG reports grants from CDC (Atlanta), African Society for Laboratory Medicine–Africa CDC (from the Bill & Melinda Gates Foundation), SAMRC, AFRO, the Fleming Fund, and the Wellcome Trust; and other support from WHO, during the conduct of the study. NW reports grants from the Bill & Melinda Gates Foundation and Sanofi, outside the submitted work. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objectives: The objective was to compare COVID-19 outcomes in the Omicron-driven fourth wave with prior waves in the Western Cape, assess the contribution of undiagnosed prior infection to differences in outcomes in a context of high seroprevalence due to prior infection and determine whether protection against severe disease conferred by prior infection and/or vaccination was maintained., Methods: In this cohort study, we included public sector patients aged ≥20 years with a laboratory-confirmed COVID-19 diagnosis between 14 November and 11 December 2021 (wave four) and equivalent prior wave periods. We compared the risk between waves of the following outcomes using Cox regression: death, severe hospitalisation or death and any hospitalisation or death (all ≤14 days after diagnosis) adjusted for age, sex, comorbidities, geography, vaccination and prior infection., Results: We included 5144 patients from wave four and 11,609 from prior waves. The risk of all outcomes was lower in wave four compared to the Delta-driven wave three (adjusted hazard ratio (aHR) [95% confidence interval (CI)] for death 0.27 [0.19; 0.38]. Risk reduction was lower when adjusting for vaccination and prior diagnosed infection (aHR: 0.41, 95% CI: 0.29; 0.59) and reduced further when accounting for unascertained prior infections (aHR: 0.72). Vaccine protection was maintained in wave four (aHR for outcome of death: 0.24; 95% CI: 0.10; 0.58)., Conclusions: In the Omicron-driven wave, severe COVID-19 outcomes were reduced mostly due to protection conferred by prior infection and/or vaccination, but intrinsically reduced virulence may account for a modest reduction in risk of severe hospitalisation or death compared to the Delta-driven wave., (© 2022 The Authors Tropical Medicine & International Health Published by John Wiley & Sons Ltd.)

The global tuberculosis burden remains substantial, with more than 10 million people newly ill per year. Nevertheless, tuberculosis incidence has slowly declined over the past decade, and mortality has decreased by almost a third in tandem. This positive trend was abruptly reversed by the COVID-19 pandemic, which in many parts of the world has resulted in a substantial reduction in tuberculosis testing and case notifications, with an associated increase in mortality, taking global tuberculosis control back by roughly 10 years. Here, we consider points of intersection between the tuberculosis and COVID-19 pandemics, identifying wide-ranging approaches that could be taken to reverse the devastating effects of COVID-19 on tuberculosis control. We review the impact of COVID-19 at the population level on tuberculosis case detection, morbidity and mortality, and the patient-level impact, including susceptibility to disease, clinical presentation, diagnosis, management, and prognosis. We propose strategies to reverse or mitigate the deleterious effects of COVID-19 and restore tuberculosis services. Finally, we highlight research priorities and major challenges and controversies that need to be addressed to restore and advance the global response to tuberculosis., Competing Interests: Declaration of interests NM reports grants from Pfizer and Roche to his institution, outside the submitted work. CC reports research funding from Sanofi Pasteur, awarded to her institution; she is a member of the scientific advisory committee for the BCHW: Burden of COVID-19 Among Health Care Workers project. The other authors declare no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Globally, long-term care facilities (LTCFs) experienced a large burden of deaths during the COVID-19 pandemic. The study aimed to describe the temporal trends as well as the characteristics and risk factors for mortality among residents and staff who tested positive for SARS-CoV-2 in selected LTCFs across South Africa., Method: We analysed data reported to the DATCOV sentinel surveillance system by 45 LTCFs. Outbreaks in LTCFs were defined as large if more than one-third of residents and staff had been infected or there were more than 20 epidemiologically linked cases. Multivariable logistic regression was used to assess risk factors for mortality amongst LTCF residents., Results: A total of 2324 SARS-CoV-2 cases were reported from 5 March 2020 through 31 July 2021; 1504 (65%) were residents and 820 (35%) staff. Among LTCFs, 6 reported sporadic cases and 39 experienced outbreaks. Of those reporting outbreaks, 10 (26%) reported one and 29 (74%) reported more than one outbreak. There were 48 (66.7%) small outbreaks and 24 (33.3%) large outbreaks reported. There were 30 outbreaks reported in the first wave, 21 in the second wave and 15 in the third wave, with 6 outbreaks reporting between waves. There were 1259 cases during the first COVID-19 wave, 362 during the second wave, and 299 during the current third wave. The case fatality ratio was 9% (138/1504) among residents and 0.5% (4/820) among staff. On multivariable analysis, factors associated with SARS-CoV-2 mortality among LTCF residents were age 40-59 years, 60-79 years and ≥ 80 years compared to < 40 years and being a resident in a LTCF in Free State or Northern Cape compared to Western Cape. Compared to pre-wave 1, there was a decreased risk of mortality in wave 1, post-wave 1, wave 2, post-wave 2 and wave 3., Conclusion: The analysis of SARS-CoV-2 cases in sentinel LTCFs in South Africa points to an encouraging trend of decreasing numbers of outbreaks, cases and risk for mortality since the first wave. LTCFs are likely to have learnt from international experience and adopted national protocols, which include improved measures to limit transmission and administer early and appropriate clinical care., (© 2022. The Author(s).)

Healthcare workers (HCWs) are among the most vulnerable in regard to contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Comorbidities are reported to increase the risk for more severe COVID-19 outcomes, often requiring hospitalization. However, the evidence on disease severity and comorbidities among South African HCWs is lacking. This retrospective study analyzed the prevalence of comorbidities among HCW hospitalized with COVID-19 and its association with the severity of outcomes. Data from public and private hospitals in nine provinces of South Africa were extracted from the national hospital surveillance database for COVID-19 admissions. A total of 10,149 COVID-19 HCWs admissions were reported from 5 March 2020 to 31 December 2021. The risk of disease severity among HCWs increased with age, with those older (≥60 years) having seven times the odds of disease severity (aOR 7.0; 95% CI 4.2-11.8) compared to HCWs in the younger age (20-29 years) group. The most commonly reported comorbidity was hypertension (36.3%), followed by diabetes (23.3%) and obesity (16.7%). Hypertension (aOR 1.3; 95% CI 1.0-1.6), diabetes (aOR 1.6; 95% CI 1.3-2.0), and HIV (aOR 1.6; 95% CI 1.2-2.1) were significantly associated with disease severity. In conclusion, age, gender, and existing comorbidities were strong predictors of the prognosis of severe COVID-19 among HCWs in South Africa. The information is important in the development of occupational health policies and vulnerability risk assessments for HCWs in light of future COVID-19 waves or similar outbreaks.

By November 2021, after the third wave of severe acute respiratory syndrome coronavirus 2 infections in South Africa, seroprevalence was 60% in a rural community and 70% in an urban community. High seroprevalence before the Omicron variant emerged may have contributed to reduced illness severity observed in the fourth wave.

Background: At present, it is unclear whether the extent of reduced risk of severe disease seen with SARS-Cov-2 Omicron variant infection is caused by a decrease in variant virulence or by higher levels of population immunity., Methods: RdRp target delay (RTD) in the Seegene Allplex TM 2019-nCoV PCR assay is a proxy marker for the Delta variant. The absence of this proxy marker in the transition period was used to identify suspected Omicron infections. Cox regression was performed for the outcome of hospital admission in those who tested positive for SARS-CoV-2 on the Seegene Allplex TM assay from November 1 to December 14, 2021 in the Western Cape Province, South Africa, in the public sector. Adjustments were made for vaccination status and prior diagnosis of infection., Results: A total of 150 cases with RTD and 1486 cases without RTD were included. Cases without RTD had a lower hazard of admission (adjusted hazard ratio [aHR], 0.56; 95% confidence interval [CI], 0.34-0.91). Complete vaccination was protective against admission, with an aHR of 0.45 (95% CI, 0.26-0.77)., Conclusion: Omicron has resulted in a lower risk of hospital admission compared with contemporaneous Delta infection, when using the proxy marker of RTD. Under-ascertainment of reinfections with an immune escape variant remains a challenge to accurately assessing variant virulence., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: South Africa reported a notable increase in COVID-19 cases from mid-November, 2021, onwards, starting in Tshwane District, which coincided with the rapid community spread of the SARS-CoV-2 omicron (B.1.1.529) variant. This increased infection rate coincided with a rapid increase in paediatric COVID-19-associated admissions to hospital (hereafter referred to as hospitalisations)., Methods: The Tshwane Maternal-Child COVID-19 study is a multicentre observational study in which we investigated the clinical manifestations and outcomes of paediatric patients (aged ≤19 years) who had tested positive for SARS-CoV-2 and were admitted to hospital for any reason in Tshwane District during a 6-week period at the beginning of the fourth wave of the COVID-19 epidemic in South Africa. We used five data sources, which were: (1) COVID-19 line lists; (2) collated SARS-CoV-2 testing data; (3) SARS-CoV-2 genomic sequencing data; (4) COVID-19 hospitalisation surveillance; and (5) clinical data of public sector COVID-19-associated hospitalisations among children aged 13 years and younger., Findings: Between Oct 31 and Dec 11, 2021, 6287 children and adolescents in Tshwane District were recorded as having COVID-19. During this period, 2550 people with COVID-19 were hospitalised, of whom 462 (18%) were aged 19 years or younger. The number of paediatric cases was higher than in the three previous SARS-CoV-2 waves, uncharacteristically increasing ahead of adult hospitalisations. 75 viral samples from adults and children in the district were sequenced, of which 74 (99%) were of the omicron variant. Detailed clinical notes were available for 138 (75%) of 183 children aged ≤13 years with COVID-19 who were hospitalised. 87 (63%) of 138 children were aged 0-4 years. In 61 (44%) of 138 cases COVID-19 was the primary diagnosis, among whom symptoms included fever (37 [61%] of 61), cough (35 [57%]), shortness of breath (19 [31%]), seizures (19 [31%]), vomiting (16 [26%]), and diarrhoea (15 [25%]). Median length of hospital stay was 2 days [IQR 1-3]). 122 (88%) of 138 children with available data needed standard ward care and 27 (20%) needed oxygen therapy. Seven (5%) of 138 children were ventilated and four (3%) died during the study period, all related to complex underlying copathologies. All children and 77 (92%) of 84 parents or guardians with available data were unvaccinated to COVID-19., Interpretation: Rapid increases in paediatric COVID-19 cases and hospitalisations mirror high community transmission of the SARS-CoV-2 omicron variant in Tshwane District, South Africa. Continued monitoring is needed to understand the long-term effect of the omicron variant on children and adolescents., Funding: South African Medical Research Council, South African Department of Science & Innovation, G7 Global Health Fund., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: The B.1.1.529 (omicron) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified on November 25, 2021, in Gauteng province, South Africa. Data regarding the seroprevalence of SARS-CoV-2 IgG in Gauteng before the fourth wave of coronavirus disease 2019 (Covid-19), in which the omicron variant was dominant, are needed., Methods: We conducted a seroepidemiologic survey from October 22 to December 9, 2021, in Gauteng to determine the seroprevalence of SARS-CoV-2 IgG. Households included in a previous seroepidemiologic survey (conducted from November 2020 to January 2021) were contacted; to account for changes in the survey population, there was a 10% increase in the households contacted, with the use of the same sampling framework. Dried-blood-spot samples were tested for IgG against SARS-CoV-2 spike protein and nucleocapsid protein with the use of quantitative assays. We also evaluated Covid-19 epidemiologic trends in Gauteng, including cases, hospitalizations, recorded deaths, and excess deaths from the start of the pandemic through January 12, 2022., Results: Samples were obtained from 7010 participants, of whom 1319 (18.8%) had received a Covid-19 vaccine. The seroprevalence of SARS-CoV-2 IgG ranged from 56.2% (95% confidence interval [CI], 52.6 to 59.7) among children younger than 12 years of age to 79.7% (95% CI, 77.6 to 81.5) among adults older than 50 years of age. Vaccinated participants were more likely to be seropositive for SARS-CoV-2 than unvaccinated participants (93.1% vs. 68.4%). Epidemiologic data showed that the incidence of SARS-CoV-2 infection increased and subsequently declined more rapidly during the fourth wave than it had during the three previous waves. The incidence of infection was decoupled from the incidences of hospitalization, recorded death, and excess death during the fourth wave, as compared with the proportions seen during previous waves., Conclusions: Widespread underlying SARS-CoV-2 seropositivity was observed in Gauteng before the omicron-dominant wave of Covid-19. Epidemiologic data showed a decoupling of hospitalizations and deaths from infections while omicron was circulating. (Funded by the Bill and Melinda Gates Foundation.)., (Copyright © 2022 Massachusetts Medical Society.)

Background: We aimed to assess the effectiveness of a single dose of the Ad26.COV2.S vaccine (Johnson & Johnson) in health-care workers in South Africa during two waves of the South African COVID-19 epidemic., Methods: In the single-arm, open-label, phase 3B implementation Sisonke study, health-care workers aged 18 years and older were invited for vaccination at one of 122 vaccination sites nationally. Participants received a single dose of 5 × 10 10 viral particles of the Ad26.COV2.S vaccine. Vaccinated participants were linked with their person-level data from one of two national medical insurance schemes (scheme A and scheme B) and matched for COVID-19 risk with an unvaccinated member of the general population. The primary outcome was vaccine effectiveness against severe COVID-19, defined as COVID-19-related admission to hospital, hospitalisation requiring critical or intensive care, or death, in health-care workers compared with the general population, ascertained 28 days or more after vaccination or matching, up to data cutoff. This study is registered with the South African National Clinical Trial Registry, DOH-27-022021-6844, ClinicalTrials.gov, NCT04838795, and the Pan African Clinical Trials Registry, PACTR202102855526180, and is closed to accrual., Findings: Between Feb 17 and May 17, 2021, 477 102 health-care workers were enrolled and vaccinated, of whom 357 401 (74·9%) were female and 119 701 (25·1%) were male, with a median age of 42·0 years (33·0-51·0). 215 813 vaccinated individuals were matched with 215 813 unvaccinated individuals. As of data cutoff (July 17, 2021), vaccine effectiveness derived from the total matched cohort was 83% (95% CI 75-89) to prevent COVID-19-related deaths, 75% (69-82) to prevent COVID-19-related hospital admissions requiring critical or intensive care, and 67% (62-71) to prevent COVID-19-related hospitalisations. The vaccine effectiveness for all three outcomes were consistent across scheme A and scheme B. The vaccine effectiveness was maintained in older health-care workers and those with comorbidities including HIV infection. During the course of the study, the beta (B.1.351) and then the delta (B.1.617.2) SARS-CoV-2 variants of concerns were dominant, and vaccine effectiveness remained consistent (for scheme A plus B vaccine effectiveness against COVID-19-related hospital admission during beta wave was 62% [95% CI 42-76] and during delta wave was 67% [62-71], and vaccine effectiveness against COVID-19-related death during beta wave was 86% [57-100] and during delta wave was 82% [74-89])., Interpretation: The single-dose Ad26.COV2.S vaccine shows effectiveness against severe COVID-19 disease and COVID-19-related death after vaccination, and against both beta and delta variants, providing real-world evidence for its use globally., Funding: National Treasury of South Africa, the National Department of Health, Solidarity Response Fund NPC, The Michael & Susan Dell Foundation, The Elma Vaccines and Immunization Foundation, and the Bill & Melinda Gates Foundation., Competing Interests: Declaration of interests L-GB declares honoraria for advisory roles from MSD, ViiV Health Care, and Gilead. RJL declares Department of Science and Innovation and South African Medical Research Council (SAMRC) funding to the KwaZulu-Natal Research Innovation and Sequencing Platform at the University of KwaZulu-Natal for the Network for Genomic Surveillance South Africa, which supported the genomic sequencing for this study; and committee membership of the Ministerial Advisory Committee on COVID-19 Vaccines (a committee that makes recommendations to the Minister of South Africa on the national COVID-19 vaccine programme. CC declares grants or contracts from CDC, PATH, the Bill & Melinda Gates Foundation, SAMRC, Wellcome Trust, and Sanofi Pasteur in the past 36 months. MG reports grants from SAMRC during the conduct of the study, and grants from the Bill & Melinda Gates Foundation outside of the submitted work. DBa reports grants from US National Institutes of Health (NIH) and Janssen during the conduct of the study; grants from Defense Advanced Research projects Agency, Massachusetts Consortium on Pathogen Readiness, Ragon Institute, the Bill & Melinda Gates Foundation, SAMRC, Henry Jackson Foundation, Musk Foundation, Gilead, Legend Bio, CureVac, Sanofi, Intima Bio, Alkermes, and Zentalis; and personal fees from SZQ Bio, Pfizer, Celsion, Avidea, Laronde, Meissa, and Vector Sciences outside of the submitted work DBr has three patents (63/121,482; 63/133,969; 63/135,182) licensed to Janssen. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: In South Africa (SA), &gt;2.4 million cases of COVID‑19 and &gt;72 000 deaths were recorded between March 2020 and 1 August 2021, affecting the country's 52 districts to various extents. SA has committed to a COVID‑19 vaccine roll-out in three phases, prioritising frontline workers, the elderly, people with comorbidities and essential workers. However, additional actions will be necessary to support efficient allocation and equitable access for vulnerable, access-constrained communities., Objectives: To explore various determinants of disease severity, resurgence risk and accessibility in order to aid an equitable, effective vaccine roll-out for SA that would maximise COVID‑19 epidemic control by reducing the number of COVID‑19 transmissions and resultant deaths, while at the same time reducing the risk of vaccine wastage., Methods: For the 52 districts of SA, 26 COVID‑19 indicators such as hospital admissions, deaths in hospital and mobility were ranked and hierarchically clustered with cases to identify which indicators can be used as indicators for severity or resurgence risk. Districts were then ranked using the estimated COVID‑19 severity and resurgence risk to assist with prioritisation of vaccine roll-out. Urban and rural accessibility were also explored as factors that could limit vaccine roll-out in hard-to-reach communities., Results: Highly populated urban districts showed the most cases. Districts such as Buffalo City, City of Cape Town and Nelson Mandela Bay experienced very severe first and second waves of the pandemic. Districts with high mobility, population size and density were found to be at highest risk of resurgence. In terms of accessibility, we found that 47.2% of the population are within 5 km of a hospital with ≥50 beds, and this percentage ranged from 87.0% in City of Cape Town to 0% in Namakwa district., Conclusions: The end goal is to provide equal distribution of vaccines proportional to district populations, which will provide fair protection. Districts with a high risk of resurgence and severity should be prioritised for vaccine roll-out, particularly the major metropolitan areas. We provide recommendations for allocations of different vaccine types for each district that consider levels of access, numbers of doses and cold-chain storage capability.

Background: The SARS-CoV-2 omicron variant of concern was identified in South Africa in November, 2021, and was associated with an increase in COVID-19 cases. We aimed to assess the clinical severity of infections with the omicron variant using S gene target failure (SGTF) on the Thermo Fisher Scientific TaqPath COVID-19 PCR test as a proxy., Methods: We did data linkages for national, South African COVID-19 case data, SARS-CoV-2 laboratory test data, SARS-CoV-2 genome data, and COVID-19 hospital admissions data. For individuals diagnosed with COVID-19 via TaqPath PCR tests, infections were designated as either SGTF or non-SGTF. The delta variant was identified by genome sequencing. Using multivariable logistic regression models, we assessed disease severity and hospitalisations by comparing individuals with SGTF versus non-SGTF infections diagnosed between Oct 1 and Nov 30, 2021, and we further assessed disease severity by comparing SGTF-infected individuals diagnosed between Oct 1 and Nov 30, 2021, with delta variant-infected individuals diagnosed between April 1 and Nov 9, 2021., Findings: From Oct 1 (week 39), 2021, to Dec 6 (week 49), 2021, 161 328 cases of COVID-19 were reported in South Africa. 38 282 people were diagnosed via TaqPath PCR tests and 29 721 SGTF infections and 1412 non-SGTF infections were identified. The proportion of SGTF infections increased from two (3·2%) of 63 in week 39 to 21 978 (97·9%) of 22 455 in week 48. After controlling for factors associated with hospitalisation, individuals with SGTF infections had significantly lower odds of admission than did those with non-SGTF infections (256 [2·4%] of 10 547 vs 121 [12·8%] of 948; adjusted odds ratio [aOR] 0·2, 95% CI 0·1-0·3). After controlling for factors associated with disease severity, the odds of severe disease were similar between hospitalised individuals with SGTF versus non-SGTF infections (42 [21%] of 204 vs 45 [40%] of 113; aOR 0·7, 95% CI 0·3-1·4). Compared with individuals with earlier delta variant infections, SGTF-infected individuals had a significantly lower odds of severe disease (496 [62·5%] of 793 vs 57 [23·4%] of 244; aOR 0·3, 95% CI 0·2-0·5), after controlling for factors associated with disease severity., Interpretation: Our early analyses suggest a significantly reduced odds of hospitalisation among individuals with SGTF versus non-SGTF infections diagnosed during the same time period. SGTF-infected individuals had a significantly reduced odds of severe disease compared with individuals infected earlier with the delta variant. Some of this reduced severity is probably a result of previous immunity., Funding: The South African Medical Research Council, the South African National Department of Health, US Centers for Disease Control and Prevention, the African Society of Laboratory Medicine, Africa Centers for Disease Control and Prevention, the Bill & Melinda Gates Foundation, the Wellcome Trust, and the Fleming Fund., Competing Interests: Declaration of interests CC has received grant support from the South African Medical Research Council, the UK Foreign, Commonwealth and Development Office, the Wellcome Trust, the US Centers for Disease Control and Prevention, and Sanofi Pasteur. NW and MdP have received grant support from Sanofi Pasteur and the Bill & Melinda Gates Foundation. AvG has received grant support from Sanofi Pasteur, the US Centers for Disease Control and Prevention, the South African Medical Research Council, the Bill & Melinda Gates Foundation, WHO, the Fleming Fund, and the Wellcome Trust. RW declares personal shareholding in Adcock Ingram Holdings, Dischem Pharmacies, Discovery, Netcare, and Aspen Pharmacare Holdings. WS has received grant support from the South African Medical Research Council, with funds received from the Department of Science and Innovation, and the Bill & Melinda Gates Foundation. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Introduction: We describe epidemiology and outcomes of confirmed SARS-CoV-2 infection and positive admissions among children <18 years in South Africa, an upper-middle income setting with high inequality., Methods: Laboratory and hospital COVID-19 surveillance data, 28 January - 19 September 2020 was used. Testing rates were calculated as number of tested for SARS-CoV-2 divided by population at risk; test positivity rates were calculated as positive tests divided by total number of tests. In-hospital case fatality ratio (CFR) was calculated based on hospitalized positive admissions with outcome data who died in-hospital and whose death was judged SARS-CoV-2 related by attending physician., Findings: 315 570 children aged <18 years were tested for SARS-CoV-2; representing 8.9% of all 3 548 738 tests and 1.6% of all children in the country. Of children tested, 46 137 (14.6%) were positive. Children made up 2.9% (n = 2007) of all SARS-CoV-2 positive admissions to sentinel hospitals. Among children, 47 died (2.6% case-fatality). In-hospital deaths were associated with male sex [adjusted odds ratio (aOR) 2.18 (95% confidence intervals [CI] 1.08-4.40)] vs female; age <1 year [aOR 4.11 (95% CI 1.08-15.54)], age 10-14 years [aOR 4.20 (95% CI1.07-16.44)], age 15-17 years [aOR 4.86 (95% 1.28-18.51)] vs age 1-4 years; admission to a public hospital [aOR 5.07(95% 2.01-12.76)] vs private hospital and ≥1 underlying conditions [aOR 12.09 (95% CI 4.19-34.89)] vs none., Conclusions: Children with underlying conditions were at greater risk of severe SARS-CoV-2 outcomes. Children > 10 years, those in certain provinces and those with underlying conditions should be considered for increased testing and vaccination., (© 2021 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections may be underestimated because of limited access to testing. We measured SARS-CoV-2 seroprevalence in South Africa every 2 months during July 2020-March 2021 in randomly selected household cohorts in 2 communities. We compared seroprevalence to reported laboratory-confirmed infections, hospitalizations, and deaths to calculate infection-case, infection-hospitalization, and infection-fatality ratios in 2 waves of infection. Post-second wave seroprevalence ranged from 18% in the rural community children <5 years of age, to 59% in urban community adults 35-59 years of age. The second wave saw a shift in age distribution of case-patients in the urban community (from persons 35-59 years of age to persons at the extremes of age), higher attack rates in the rural community, and a higher infection-fatality ratio in the urban community. Approximately 95% of SARS-CoV-2 infections were not reported to national surveillance.

Background: We describe the epidemiology of COVID-19 in South Africa following importation and during implementation of stringent lockdown measures., Methods: Using national surveillance data including demographics, laboratory test data, clinical presentation, risk exposures (travel history, contacts and occupation) and outcomes of persons undergoing COVID-19 testing or hospitalised with COVID-19 at sentinel surveillance sites, we generated and interpreted descriptive statistics, epidemic curves, and initial reproductive numbers (Rt)., Findings: From 4 March to 30 April 2020, 271,670 SARS-CoV-2 PCR tests were performed (462 tests/100,000 persons). Of these, 7,892 (2.9%) persons tested positive (median age 37 years (interquartile range 28-49 years), 4,568 (58%) male, cumulative incidence of 13.4 cases/100,000 persons). Hospitalization records were found for 1,271 patients (692 females (54%)) of whom 186 (14.6%) died. Amongst 2,819 cases with data, 489/2819 (17.3%) travelled internationally within 14 days prior to diagnosis, mostly during March 2020 (466 (95%)). Cases diagnosed in April compared with March were younger (median age, 37 vs. 40 years), less likely female (38% vs. 53%) and resident in a more populous province (98% vs. 91%). The national initial R t was 2.08 (95% confidence interval (CI): 1.71-2.51)., Interpretation: The first eight weeks following COVID-19 importation were characterised by early predominance of imported cases and relatively low mortality and transmission rates. Despite stringent lockdown measures, the second month following importation was characterised by community transmission and increasing disease burden in more populous provinces., Competing Interests: KMM, PNN, JE, GN, YP, LE, DM, AMS, KS, MM, KM, NW, TM, JK, TK, WJ, LHB, SM, ST, SW declare no competing interests. AvG reports receipt of funding and equipment to the NICD to support surveillance for SARS-CoV-2 from the South African Medical Research Committee (funding), Wellcome Trust (funding), European Union, Germany (equipment), the Jack Ma Foundation (Allibaba) (testing equipment and reagents), the Centres for Disease Control, Atlanta (funding), the EDCTP (funding), the Africa CDC (funding) and the Fleming Fund (funding). CC reports grants from US CDC, grants from Wellcome Trust, grants from South Africa MRC during the conduct of the study and grants from Sanofi Pasteur outside the submitted work; NPG reports grants from NIH, grants from US CDC, grants from CDC Foundation, grants from Bill and Melinda Gates Foundation, grants from NHLS Research Trust, grants from UK MRC (JGHT scheme) all outside the submitted work. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of the authors’ affiliated institutions or funding agencies., (© 2021 The Authors.)

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., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15-49 years and a tuberculosis prevalence of 0·7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19., Methods: In this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality., Findings: Among the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23·3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37·4%) of 163 350, diabetes in 43 885 (27·4%) of 159 932, and HIV in 13 793 (9·1%) of 151 779. Tuberculosis was reported in 5282 (3·6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1·34, 95% CI 1·27-1·43), past tuberculosis (1·26, 1·15-1·38), current tuberculosis (1·42, 1·22-1·64), and both past and current tuberculosis (1·48, 1·32-1·67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1·45, 95% CI 1·22-1·72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29·2% compared with 30·8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with increased COVID-19 in-hospital mortality risk in both people with HIV and HIV-uninfected individuals., Interpretation: Individuals identified as being at high risk of COVID-19 in-hospital mortality (older individuals and those with chronic comorbidities and people with HIV, particularly those not on ART) would benefit from COVID-19 prevention programmes such as vaccine prioritisation as well as early referral and treatment., Funding: South African National Government., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

In 2011, the South African National TB Programme launched a policy of decentralized management of drug-resistant tuberculosis (DR-TB) in order to expand the capacity of facilities to treat patients with DR-TB, minimize delays to access care and improve patient outcomes. This policy directive was implemented to varying degrees within a rapidly evolving diagnostic and treatment landscape for DR-TB, placing new demands on already-stressed health systems. The variable readiness of district-level systems to implement the policy prompted questions not only about differences in health systems resources but also front-line actors' capacity to implement change in resource-constrained facilities. Using a grounded theory approach, we analysed data from in-depth interviews and small group discussions conducted between 2016 and 2018 with managers (n = 9), co-ordinators (n = 15), doctors (n = 7) and nurses (n = 18) providing DR-TB care. Data were collected over two phases in district-level decentralized sites of three South African provinces. While health systems readiness assessments conventionally map the availability of 'hardware', i.e. resources and skills to deliver an intervention, a notable absence of systems 'hardware' meant that systems 'software', i.e. health care workers (HCWs) agency, behaviours and interactions provided the basis of locally relevant strategies for decentralized DR-TB care. 'Software readiness' was manifest in four areas of DR-TB care: re-organization of service delivery, redressal of resource shortages, creation of treatment adherence support systems and extension of care parameters for vulnerable patients. These strategies demonstrate adaptive capacity and everyday resilience among HCW to withstand the demands of policy change and innovation in stressed systems. Our work suggests that a useful extension of health systems 'readiness' assessments would include definition and evaluation of HCW 'software' and adaptive capacities in the face of systems hardware gaps., (© The Author(s) 2021. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.)

Objective: To evaluate the safety of combination antiretroviral therapy (ART) in conception and pregnancy in different health systems., Design: A pilot ART registry to measure the prevalence of birth defects and adverse pregnancy outcomes in South Africa and Zambia., Methods: HIV-infected pregnant women on ART prior to conception were enrolled until delivery, and their infants were followed until 1 year old., Results: Between October 2010 and April 2011, 600 women were enrolled. The median CD4 cell count at study enrollment was lower in South Africa than Zambia (320 vs. 430 cells/μl; P < 0.01). The most common antiretroviral drugs at the time of conception included stavudine, lamivudine, and nevirapine. There were 16 abortions (2.7%), one ectopic pregnancy (0.2%), 12 (2.0%) stillbirths, and 571 (95.2%) live infants. Deliveries were more often preterm (29.7 vs. 18.4%; P = 0.01) and the infants had lower birth weights (2900 vs. 2995 g; P = 0.11) in Zambia compared to South Africa. Thirty-six infants had birth defects: 13 major and 23 minor. There were more major anomalies detected in South Africa and more minor ones in Zambia. No neonatal deaths were attributed to congenital birth defects., Conclusions: An Africa-specific, multi-site antiretroviral drug safety registry for pregnant women is feasible. Different prevalence for preterm delivery, delivery mode, and birth defect types between women on preconception ART in South Africa and Zambia highlight the potential impact of health systems on pregnancy outcomes. As countries establish ART drug safety registries, documenting health facility limitations may be as essential as the specific ART details.

Background: In an attempt to address a complex disease burden, including improving progress towards MDGs 4 and 5, South Africa recently introduced a re-engineered Primary Health Care (PHC) strategy, which has led to the development of a national community health worker (CHW) programme. The present study explored the development of a cell phone-based and paper-based monitoring and evaluation (M&E) system to support the work of the CHWs., Methods: One sub-district in the North West province was identified for the evaluation. One outreach team comprising ten CHWs maintained both the paper forms and mHealth system to record household data on community-based services. A comparative analysis was done to calculate the correspondence between the paper and phone records. A focus group discussion was conducted with the CHWs. Clinical referrals, data accuracy and supervised visits were compared and analysed for the paper and phone systems., Results: Compared to the mHealth system where data accuracy was assured, 40% of the CHWs showed a consistently high level (>90% correspondence) of data transfer accuracy on paper. Overall, there was an improvement over time, and by the fifth month, all CHWs achieved a correspondence of 90% or above between phone and paper data. The most common error that occurred was summing the total number of visits and/or activities across the five household activity indicators. Few supervised home visits were recorded in either system and there was no evidence of the team leader following up on the automatic notifications received on their cell phones., Conclusions: The evaluation emphasizes the need for regular supervision for both systems and rigorous and ongoing assessments of data quality for the paper system. Formalization of a mHealth M&E system for PHC outreach teams delivering community based services could offer greater accuracy of M&E and enhance supervision systems for CHWs.

Introduction: Rodents are troublesome urban pests, with potentially serious health implications. Preventive efforts require greater understanding of social contexts in which they are prevalent. This study aimed to determine rodent prevalence and identify factors associated with rodent infestations in urban residential settings., Methods: The Health, Environment and Development study is a longitudinal panel study conducted in five settlements across Johannesburg. Data on socio-economic status, domestic behaviour and housing quality are collected annually. Logistic regression revealed risk factors for rodent prevalence at household level., Results: Rodents are a major household problem in all study areas (prevalence 54%). Factors associated with increased prevalence of rats included lower income, living in informal areas, overcrowding, cracks in dwelling walls and internal damp., Conclusion: Socio-economic status, housing quality, domestic behaviour and environmental health services are associated with exposure to rodents in urban Johannesburg communities. This information served as a platform to launch rodent awareness campaigns at study sites.

Background: Supplemental immunisation activity (SIA) campaigns provide children with an additional dose of measles vaccine and deliver other child health interventions including vitamin A supplements, deworming medications and oral polio vaccines. They also require the mobilisation of a large health workforce. We assess the impact of the implementation of SIA campaigns on selected routine child and maternal health services in South Africa (SA)., Methods: We use district-level monthly headcount data for 52 South African districts for the period 2001-2010, sourced from the District Health Information System, SA. The data include 12 child and maternal health headcount indicators including routine immunisation, and maternal and reproductive health indicators. We analyse the association between the implementation of the 2010 SIA campaign and the change (decrease/increase) in headcounts, using a linear regression model., Results: We find a significant decrease for eight indicators. The total number of fully immunised children before age 1 decreased by 29% (95% CI 23% to 35%, p<0.001) during the month of SIA implementation; contraceptive use and antenatal visits decreased by 7-17% (p ≤ 0.02) and about 10% (p<0.001), respectively., Conclusions: SIA campaigns may negatively impact health systems during the period of implementation by disrupting regular functioning and diverting resources from other activities, including routine child and maternal health services. SIA campaigns present multidimensional costs that need to be explicitly considered in benefit-cost assessments.

Background: Supplementary immunization activity (SIA) campaigns provide children with an additional dose of measles vaccine and deliver other interventions, including vitamin A supplements, deworming medications, and oral polio vaccines., Objective: To assess the cost-effectiveness of the full SIA delivery platform in South Africa (SA)., Design: We used an epidemiologic cost model to estimate the cost-effectiveness of the 2010 SIA campaign. We used province-level campaign data sourced from the District Health Information System, SA, and from planning records of provincial coordinators of the Expanded Programme on Immunization. The data included the number of children immunized with measles and polio vaccines, the number of children given vitamin A supplements and Albendazole tablets, and costs., Results: The campaign cost $37 million and averted a total of 1,150 deaths (95% uncertainty range: 990-1,360). This ranged from 380 deaths averted in KwaZulu-Natal to 20 deaths averted in the Northern Cape. Vitamin A supplementation alone averted 820 deaths (95% UR: 670-1,040); measles vaccination alone averted 330 deaths (95% UR: 280-370). Incremental cost-effectiveness was $27,100 (95% UR: $18,500-34,400) per death averted nationally, ranging from $11,300 per death averted in the Free State to $91,300 per death averted in the Eastern Cape., Conclusions: Cost-effectiveness of the SIA child health delivery platform varies substantially across SA provinces, and it is substantially more cost-effective when vitamin A supplementation is included in the interventions administered. Cost-effectiveness assessments should consider health system delivery platforms that integrate multiple interventions, and they should be conducted at the sub-national level.

Background: Due to intensified measles immunization efforts, measles mortality has decreased substantially worldwide, particularly in Sub-Saharan Africa (SSA). The World Health Organization (WHO) estimated a 92% decrease in measles-related deaths in the WHO AFRO region for the period 2000-2008. Recently, the AFRO region established a measles pre-elimination goal and experts have suggested engaging in a measles eradication campaign at the global level. However, recent large-scale outbreaks in many Sub-Saharan African countries present a challenge to measles control efforts. This paper examines measles immunization and the impact of measles supplemental immunization activities (SIAs) on routine immunization coverage in South Africa (SA)., Methods: We reported on immunization coverage trends in SA for the period 2001-2010 at the province and district levels. The data included routine immunization for 1st and 2nd doses of measles vaccine (MCV1, MCV2), SIAs, 1st dose of Bacille Calmette-Guérin vaccine, 1st and 3rd doses of oral polio vaccine (OPV1, OPV3), 3rd dose of Diphtheria-Tetanus-Pertussis-Haemophilus-influenzae-B vaccine (DTP-Hib3), and the number of under-one-year-olds having completed a primary course of immunization (Imm1). A regression model looked at the SIA impact on routine coverage., Results: Over the past decade, MCV1 and MCV2 coverage have increased nationally from 68% and 57% in 2001 to 95% and 83% in 2010, respectively. SIA coverage has remained at high levels, around 90%, over the same period. Substantial heterogeneity in MCV1 and MCV2 coverage is present across SA districts, with differences in coverage of 56% (MCV1) and 51% (MCV2) in 2010. In any given year, occurrence of SIAs was associated with a decrease in routine immunization coverage of MCV1, MCV2, OPV1, OPV3, DTP-Hib3, and Imm1, at the district level., Conclusions: The heterogeneity in measles vaccination coverage across SA districts challenges the goal of measles elimination in SA and SSA. The reduction in routine immunization coverage associated with the occurrence of SIAs raises the legitimate concern that SIAs may negatively impact health systems' functioning., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The long-term immunogenicity and vaccine efficacy (VE) of a 9-valent conjugate pneumococcal vaccine was studied in HIV infected and HIV non-infected children. VE against vaccine-serotype invasive pneumococcal disease following 6.16 years of follow-up persisted in HIV non-infected children (77.8%; 95% CI 34.4-92.5 compared to 83% after 2.3 years of follow-up), and declined from 65% to 38.8% (95% CI -7.8 to 65.2) in HIV infected children. HIV non-infected vaccinees had equal (serotypes 4, 6B, 14, 19F) or greater (serotypes 9V, 18C, 23F) proportions of serotype-specific antibody concentrations of > or =0.2microg/ml to vaccine-serotypes analyzed compared to HIV infected vaccinees at 5.3 years of age.