OBJECTIVES:Tuberculosis (TB) disproportionately affects low-income populations because of socioeconomic barriers. Conditional cash transfers (CCTs) are promising strategies to improve treatment adherence, reduce economic hardship, and enhance health outcomes. However, few studies have examined their cost-effectiveness, especially when combined with TB counseling. The objective of this study is to estimate the long-term epidemiological impact and cost-effectiveness of CCT with pre- and posttest TB counseling in South Africa. METHODS:This analysis used results from a trial conducted in South Africa. Trial results on treatment initiation, treatment completion, and on-treatment mortality informed a transmission model of TB, in which a CCT intervention with pre- and posttest TB counseling was offered to all adults diagnosed with drug-susceptible TB between 2025 and 2050. Epidemiological modeling results were combined with cost data to determine the cost-effectiveness of the intervention compared with business as usual from the provider's perspective. RESULTS:Universally applying the CCT intervention between 2025 and 2050 in South Africa would lead to a cumulative reduction of 5.1% (uncertainty interval [UI]: 4.6%-5.6%) incident cases and 9.5% (UI: 9.1%-10.2%) deaths, compared with business as usual. The incremental cost of the intervention was estimated at $80.4 USD per patient, leading to a total increase of 7.1% (UI:6.8-7.6%) in the TB budget and an incremental cost-effectiveness ratio of $144.45 per disability-adjusted life-years averted, making it cost-effective in South Africa, at a threshold of $3314 USD. CONCLUSIONS:CCT interventions with pre- and posttest counseling would be cost-effective in South Africa, leading to a long-term reduction in TB incidence and mortality.

Background Economic and behavioural factors lead to poor outcomes in patients with tuberculosis. We investigated the effects of a package of interventions consisting of pre-test and post-test tuberculosis counselling with conditional cash transfers on patient outcomes in adults undergoing investigation for pulmonary tuberculosis. Methods This pragmatic, open-label, individual randomised controlled trial was done in nine clinics in Johannesburg, South Africa. Participants (aged >= 18 years) undergoing investigation for tuberculosis were randomly assigned (1:1) to the intervention group or control group (standard of care) via permuted block randomisation, stratified by clinic; group assignment was concealed using opaque envelopes. The intervention group received pre-test and post-test tuberculosis counselling, and for participants diagnosed with rifampicin-susceptible tuberculosis, a digital payment (R150; approximately US$10) at treatment initiation and each monthly treatment visit. Payments were contingent on timely attendance: 14 days from initial sputum sample collection and within 7 days on either side of their scheduled monthly appointment. The primary endpoint was successful patient outcome (patients who were cured or completed treatment) or unsuccessful patient outcome (pretreatment loss-to-follow-up, on-treatment loss-to-follow-up, development of rifampicin-resistant tuberculosis while on treatment, treatment failure [ie, smear or culture positive at 5 months or later after commencing treatment], or death). The primary outcome was analysed in the modified intention-to-treat population, defined as all randomly assigned participants with rifampicin-susceptible tuberculosis confirmed before the commencement of tuberculosis treatment. Weighted outcome prevalence, relative risks (RRs), and risk differences were calculated using a multivariable Poisson model with robust standard errors. This trial is registered with the Pan African Clinical Trials Registry (PACTR202410708311054) and is completed. Findings Between Oct 25, 2018, and Dec 9, 2019, 4110 participants were enrolled and randomly assigned, 2059 to the intervention group and 2051 to the control group. 381 (93%) participants had microbiologically confirmed rifampicinsusceptible pulmonary tuberculosis (195 [95%] of 2059 in the intervention group vs 186 [91%] of 2051 in the control group; median age 37 years [IQR 30 to 45], 257 [675%] male, 124 [325%] female). At study closure, primary outcome data were available for 128 (656%) of 195 participants in the intervention group and 139 (747%) of 186 participants in the control group. 105 (820%) of 128 participants in the intervention group and 93 (669%) of 139 participants in the control group had a successful patient outcome; 23 (180%) of 128 participants in the intervention group and 46 (331%) of 139 participants in the control group had an unsuccessful patient outcome. The weighted regression analysis showed a substantial reduction in the risk of unsuccessful patient outcomes in the intervention group compared with the control group (weighted prevalence 159% vs 286%; RR in weighted population 052, 95% CI 033 to 082; risk difference in weighted population-141 percentage points, 95% CI-233 to-48). Pretreatment loss to follow-up was lower in the intervention group than in the control group (unweighted population: five [39%] of 128 participants vs 22 [158%] of 139 participants; risk difference in weighted population-96 percentage points, 95% CI-149 to-42). Interpretation The package of interventions consisting of pre-test and post-test tuberculosis counselling with conditional cash transfers significantly reduced the risk of unsuccessful tuberculosis patient outcomes, bringing one of the 90-90-90 targets within reach (ie, achieving 90% tuberculosis treatment success). Furthermore, reduction in pretreatment loss to follow-up is expected to reduce transmission and lower incidence of the disease over time. Funding South African Medical Research Council, UK Medical Research Council, and Newton Fund. Crown Copyright (c) 2025 Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.

Given the high global seroprevalence of SARS-CoV-2, understanding the risk of reinfection has become increasingly important. Models developed to track trends in reinfection risk should be robust against possible biases arising from imperfect data observation processes. We performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. The catalytic model assumes the risk of reinfection is proportional to observed infections. Validation involved using simulated primary infections, consistent with the number of observed infections in South Africa. To assess the performance of the catalytic model, we simulated reinfection datasets that incorporated different processes that may bias inference, including imperfect observation and mortality. A Bayesian approach was used to fit the model to simulated data, assuming a negative binomial distribution around the expected number of reinfections, and model projections were compared to the simulated data using different magnitudes of change in reinfection risk. We assessed the model's ability to accurately detect changes in reinfection risk when included in the simulations, as well as the occurrence of false positives when reinfection risk remained constant. The model parameters converged in most scenarios leading to model outputs aligning with anticipated outcomes. The model successfully detected changes in the risk of reinfection when such a change was introduced to the data. Low observation probabilities (10%) of both primary- and reinfections resulted in low numbers of observed cases from the simulated data and poor convergence. The model's performance was assessed on simulated data representative of the South African SARS-CoV-2 epidemic, reflecting its timing of waves and outbreak magnitude. Model performance under similar scenarios may be different in settings with smaller epidemics (and therefore smaller numbers of reinfections). Ensuring model parameter convergence is essential to avoid false-positive detection of shifts in reinfection risk. While the model is robust in most scenarios of imperfect observation and mortality, further simulation-based validation for regions experiencing smaller outbreaks is recommended. Caution must be exercised in directly extrapolating results across different epidemiological contexts without additional validation efforts.

OBJECTIVES:While mobile health (mHealth) interventions are widespread, few studies assess impacts at the population level in low-income and middle-income countries. South Africa's tuberculosis (TB) burden is high, and a substantial share of cases remain undiagnosed. We evaluate the impacts of community activations of TBCheck-a WhatsApp/USSD-based chatbot that allows individuals to evaluate themselves for TB risk. METHODS:We use a quasi-experimental approach comparing treated and control subdistricts nationally before and after community activations using dashboard data from the TBCheck platform and weekly or quarterly subdistrict TB test data from the National Health Laboratory Service. Dependent variables are the number of self-screening tests on the platform, total tests and number of positive tests per subdistrict. We employ dynamic difference-in-difference models accounting for subdistrict unobservables and time trends using weekly data, and synthetic control methods matching on preintervention trends in outcomes using quarterly data. RESULTS:Impact estimates suggest an increase in the number of self-screening tests on the platform (487.53, p-value<0.01) as well as TB tests (107.90, p-value=0.05) in treated relative to control subdistricts due to intervention activities in the week of the intervention. After 2 weeks, impacts on the number of self-screening tests are insignificant (-6.18, p=0.23), and after 1 week, impacts on TB tests are insignificant (36.44, p-value=0.32). DISCUSSION AND CONCLUSION:Activation activities associated with TBCheck led to short-lived and variable impacts on uptake and tests in target subdistricts. Alternative strategies are required for sustained uptake of such mHealth tools.

Background. In the absence of more recent national data on underlying causes of death in South Africa (SA), we examined mortality trends from 2010 to 2022 among members of a large private medical scheme. This analysis sheds light on the health profile of this specific demographic. Objective. To investigate trends in Discovery Health Medical Scheme (DHMS) members’ death rates and underlying cause of death patterns between 2010 and 2022. Methods. All-cause deaths were compared across years accounting for demographic changes, by analysing age- and sex-standardised rates using 2019 age and sex population weightings. We used underlying cause-of-death data from death notifications. Results. The 2019 age- and sex-standardised death rate was lower than the 2010 rate by 10%, with a steady decline experienced between 2010 and 2019. We have seen reduced age- and sex-standardised death rates from HIV/AIDS during this period, and despite the high prevalence, reduced age- and sex-standardised death rates from non-communicable diseases. Malignant neoplasms and cardiovascular disease have been and remained the two leading causes of death for Discovery Health Medical Scheme (DHMS) clients between 2012 and 2022. Age- and sex- standardised death rates, however, reached historic high levels during the first 2 years of the COVID-19 pandemic in SA. In 2020, overall age- and sex-standardised death rates for DHMS members increased to 542 deaths per 100 000 life years, which was higher than pre-pandemic levels. Age- and sex-standardised death rates went on to reach their highest level in the history of the scheme in 2021, at 767 deaths per 100 000 life years. Age- and sex-standardised death rates, however, had returned to near 2019 (pre-pandemic) levels by 2022, at 477 deaths per 100 000 life years. Males experienced a higher increase in age-standardised death rates during 2020 and remained at an increased risk of death in 2022 compared with pre-pandemic levels. When COVID-19 -related deaths are excluded, the age-standardised rates for both females and males in 2022 was lower than observed in the pre-pandemic years. While the low mortality experience could be related to competing causes and mortality displacement, further analysis over a longer period is needed to confirm this. Conclusion. DHMS experienced the highest level of age- and sex-standardised death rates during 2020 and 2021, the initial 2 years of the COVID-19 pandemic. Most of this increase was explained by COVID-19 deaths.

Given limited data on safety and effectiveness of heterologous COVID-19 vaccine boosting in lower income, high-HIV prevalence settings, we evaluated a mRNA-1273 boost after Ad26.COV2.S priming in South Africa. SHERPA was a single-arm, open-label, phase 3 study nested in the Sisonke implementation trial of 500000 healthcare workers (HCWs). Sisonke participants were offered mRNA-1273 boosters between May and November 2022, a period of circulating Omicron sub-lineages. Adverse events (AE) were self-reported, and co-primary endpoints (SARS-CoV-2 infections and COVID-19 hospitalizations or deaths) were collected through national databases. We used Cox regression models with booster status as time-varying covariate to determine the relative vaccine effectiveness (rVE) of the mRNA-1273 booster among SHERPA versus unboosted Sisonke participants. Of 11248 SHERPA participants in the rVE analysis cohort (79.3% female, median age 41), 45.4% had received one and 54.6% two Ad26.COV2.S doses. Self-reported comorbidities included HIV (18.7%), hypertension (12.9%) and diabetes (4.6%). In multivariable analysis including 413161 unboosted Sisonke participants, rVE of the booster was 59% (95%CI 29-76%) against SARS-CoV-2 infection: 77% (95%CI 9–94%) in the one-Ad26.COV2.S dose group and 52% (95%CI 13-73%) in the two-dose group. Severe COVID-19 was identified in 148 unboosted participants, and only one SHERPA participant with severe HIV-related immunosuppression. Of 11798 participants in the safety analysis, 271 (2.3%) reported a reactogenicity event or unsolicited AE, more among those with prior SARS-CoV-2 infections (adjusted odds ratio [aOR] 2.03, 95%CI 1.59-2.59) and less among people living with HIV (PLWH) (aOR 0.49, 95%CI 0.34-0.69). No related serious AEs were reported. In an immunogenicity sub-study, mRNA-1273 increased antibody functions and T-cell responses 4 weeks after boosting regardless of the number of prior Ad26.COV2.S doses, or HIV status, and generated Omicron spike-specific cross-reactive responses. mRNA-1273 boosters after one or two Ad26.COV2.S doses were well-tolerated, safe and effective against Omicron SARS-CoV-2 infections among HCWs and PLWH. ### Competing Interest Statement Kate Anteyi, Brett Leav are employees of Moderna, Inc. and may hold stock/stock options in the company. The other authors declare no conflict of interests. ### Clinical Trial PACTR202310615330649 ### Funding Statement The SHERPA study was funded by Moderna, Inc. (Cambridge, Massachusetts, US) and the South African Medical Research Council (SAMRC). Moderna provided mRNA-1273 free-of-charge. The Sisonke trial was funded by: The National Department of Health through baseline funding to the SAMRC the Solidarity Response Fund NPC The Michael & Susan Dell Foundation the ELMA Vaccines and Immunization Foundation (21-V0001) and the Bill & Melinda Gates Foundation (INV-030342). Moderna representatives reviewed the study protocol, participated in safety oversight, and contributed as manuscript co-authors, but were not involved in data collection and analysis. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The SHERPA study received full ethical approval by the following ethics committees in South Africa: Pharma-Ethics, Stellenbosch University Health Research Ethics Committee, University of KwaZulu-Natal Biomedical Research Ethics Committee, University of Cape Town Human Research Ethics Committee, Sefako Makgatho University Research Ethics Committee, The South African Medical Research Council Human Research Ethics Committee and the University of the Witwatersrand Human Research Ethics Committee. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Considering this is a clinical trial, universal access to data may not be possible to protect clinical trial participants. However, SHERPA data will be made available by the authors upon reasonable request. You can access the SHERPA data on the following link: https://medat.samrc.ac.za/index.php/catalog/56

AbstractBackgroundThere are few data on the real-world effectiveness of COVID-19 vaccines and boosting in Africa, which experienced high levels of SARS-CoV-2 infection in a mostly vaccine-naïve population, and has limited vaccine coverage and competing health service priorities. We assessed the association between vaccination and severe COVID-19 in the Western Cape, South Africa.MethodsWe performed an observational cohort study of >2 million adults during 2020-2022. We described SARS-CoV-2 testing, COVID-19 outcomes, and vaccine uptake over time. We used multivariable cox models to estimate the association of BNT162b2 and Ad26.COV2.S vaccination with COVID-19-related hospitalisation and death, adjusting for demographic characteristics, underlying health conditions, socioeconomic status proxies and healthcare utilisation.ResultsBy end 2022, only 41% of surviving adults had completed vaccination and 8% a booster dose, despite several waves of severe COVID-19. Recent vaccination was associated with notable reductions in severe COVID-19 during distinct analysis periods dominated by Delta, Omicron BA.1/2 and BA.4/5 (sub)lineages: within 6 months of completing vaccination or boosting, vaccine effectiveness was 46-92% for death (range across periods), 45-92% for admission with severe disease or death, and 25-90% for any admission or death. During the Omicron BA.4/5 wave, within 3 months of vaccination or boosting, BNT162b2 and Ad26.COV2.S were each 84% effective against death (95% CIs: 57-94 and 49-95, respectively). However, there were distinct reductions of VE at larger times post completing or boosting vaccination.ConclusionsContinued emphasis on regular COVID-19 vaccination including boosting is important for those at high risk of severe COVID-19 even in settings with widespread infection-induced immunity.

There are few data on the real-world effectiveness of COVID-19 vaccines and boosting in Africa, which experienced widespread SARS-CoV-2 infection before vaccine availability. We assessed the association between vaccination and severe COVID-19 in the Western Cape, South Africa, in an observational cohort study of >2 million adults during 2020–2022. We described SARS-CoV-2 testing, COVID-19 outcomes, and vaccine uptake over time. We used multivariable cox models to estimate the association of BNT162b2 and Ad26.COV2.S vaccination with COVID-19-related hospitalization and death, adjusting for demographic characteristics, underlying health conditions, socioeconomic status proxies, and healthcare utilization. We found that by the end of 2022, 41% of surviving adults had completed vaccination and 8% had received a booster dose. Recent vaccination was associated with notable reductions in severe COVID-19 during periods dominated by Delta, and Omicron BA.1/2 and BA.4/5 (sub)lineages. During the latest Omicron BA.4/5 wave, within 3 months of vaccination or boosting, BNT162b2 and Ad26.COV2.S were each 84% effective against death (95% CIs: 57–94 and 49–95, respectively). However, distinct reductions of effectiveness occurred at longer times post completing or boosting vaccination. Results highlight the importance of continued emphasis on COVID-19 vaccination and boosting for those at high risk of severe COVID-19, even in settings with widespread infection-induced immunity.

Limited studies have been conducted on the safety and effectiveness of heterologous COVID-19 vaccine boosting in lower income settings, especially those with high-HIV prevalence., The Sisonke Heterologous mRNA-1273 boost after prime with Ad26.COV2.S (SHERPA) trial evaluated a mRNA-1273 boost after Ad26.COV2.S priming in South Africa. SHERPA was a single-arm, open-label, phase 3 study nested in the Sisonke implementation trial of 500000 healthcare workers (HCWs). Sisonke participants were offered mRNA-1273 boosters between May and November 2022, when Omicron sub-lineages were circulating. Adverse events (AE) were self-reported, and co-primary endpoints (SARS-CoV-2 infections and COVID-19 hospitalizations or deaths) were collected through national databases. We used Cox regression models with booster status as a time-varying covariate to determine the relative vaccine effectiveness (rVE) of the mRNA-1273 booster among SHERPA versus unboosted Sisonke participants. Of 11248 SHERPA participants in the rVE analysis cohort (79.3% female, median age 41), 45.4% had received one and 54.6% two Ad26.COV2.S doses. Self-reported comorbidities included HIV (18.7%), hypertension (12.9%) and diabetes (4.6%). In multivariable analysis including 413161 unboosted Sisonke participants, rVE of the booster was 59% (95%CI 29-76%) against SARS-CoV-2 infection: 77% (95%CI 9-94%) in the one-Ad26.COV2.S dose group and 52% (95%CI 13-73%) in the two-dose group. Severe COVID-19 was identified in 148 unboosted Sisonke participants, and only one SHERPA participant with severe HIV-related immunosuppression. Of 11798 participants in the safety analysis, 228 (1.9%) participants reported 575 reactogenicity events within 7 days of the booster (most commonly injection site pain, malaise, myalgia, swelling, induration and fever). More reactogenicity events were reported among those with prior SARS-CoV-2 infections (adjusted odds ratio [aOR] 2.03, 95%CI 1.59-2.59) and less among people living with HIV (PLWH) (aOR 0.49, 95%CI 0.34-0.69). There were 115 unsolicited adverse events (AEs) within 28 days of vaccination. No related serious AEs were reported. In an immunogenicity sub-study, mRNA-1273 increased binding and neutralizing antibody titres and spike-specific T-cell responses 4 weeks after boosting regardless of the number of prior Ad26.COV2.S doses, or HIV status, and generated Omicron spike-specific cross-reactive responses. mRNA-1273 boosters after one or two Ad26.COV2.S doses were well-tolerated, safe and effective against Omicron SARS-CoV-2 infections among HCWs and PLWH. Trial registration: The SHERPA study is registered in the Pan African Clinical Trials Registry (PACTR): PACTR202310615330649 and the South African National Clinical Trial Registry (SANCTR): DOH-27-052022-5778.

INTRODUCTION:In South Africa, Xpert® MTB/RIF Ultra (Ultra) is the recommended diagnostic assay for TB with line-probe assays for first- (LPAfl) and second-line drugs (LPAsl) providing additional drug susceptibility testing (DST) for samples that were rifampicin-resistant (RR-TB). To guide implementation of the recently launched Xpert® MTB/XDR (MTB/XDR) assay, a cost-outcomes analysis was conducted comparing total costs for genotypic DST (gDST) for persons diagnosed with RR-TB considering three strategies: replacing LPAfl/LPAsl (centralised level) with MTB/XDR vs. Ultra reflex testing (decentralised level). Further, DST was performed using residual specimen following RR-TB diagnosis. METHODS:The total cost of gDST was determined for three strategies, considering loss to follow-up (LTFU), unsuccessful test rates, and specimen volume. RESULTS:For 2019, 9,415 persons were diagnosed with RR-TB. A 35% LTFU rate between RR-TB diagnosis and LPAfl/LPAsl-DST was estimated. Unsuccessful test rates of 37% and 23.3% were reported for LPAfl and LPAsl, respectively. The estimated total costs were $191,472 for the conventional strategy, $122,352 for the centralised strategy, and $126,838 for the decentralised strategy. However, it was found that sufficient residual volume for reflex MTB/XDR testing is a limiting factor at the decentralised level. CONCLUSION:Centralising the implementation of XDR testing, as compared to LPAfl/LPAsl, leads to significant cost savings.

Understanding factors associated with increased risk for tuberculosis (TB) recurrence is essential in lowering the TB burden. We aimed to quantify the burden, risk factors, and timing of TB presumptive recurrence.We analyzed test results from 2013 to 2017 in the South African National Health Laboratory Service's database. We defined a person's TB episode to start with their first positive TB test. In the absence of treatment outcome data, we assumed the episode concluded 6 months later for rifampicin-susceptible TB (RS-TB) and 18 months later for rifampicin-resistant TB (RR-TB), provided that at least one negative smear or culture test was recorded within this period. We defined a presumptive recurrent TB episode to start with a positive TB test after the completion of a prior episode. We calculated recurrence measures stratified by various demographics and RR-TB status.Of 574,316 people with RS-TB, 4.7% experienced at least one presumptive recurrent TB episode. Higher local TB notification rates, HIV coinfection, and males experienced higher recurrence rates. Most (89.4%) of the first RS-TB recurrences occurred within a year of the initial episode.Our findings of when and among whom recurrent TB is more likely to occur can be used to assist early interventions and inform impact on patient care..

BACKGROUND:The South African COVID-19 Modelling Consortium (SACMC) was established in late March 2020 to support planning and budgeting for COVID-19 related healthcare in South Africa. We developed several tools in response to the needs of decision makers in the different stages of the epidemic, allowing the South African government to plan several months ahead.METHODS:Our tools included epidemic projection models, several cost and budget impact models, and online dashboards to help government and the public visualise our projections, track case development and forecast hospital admissions. Information on new variants, including Delta and Omicron, were incorporated in real time to allow the shifting of scarce resources when necessary.RESULTS:Given the rapidly changing nature of the outbreak globally and in South Africa, the model projections were updated regularly. The updates reflected 1) the changing policy priorities over the course of the epidemic; 2) the availability of new data from South African data systems; and 3) the evolving response to COVID-19 in South Africa, such as changes in lockdown levels and ensuing mobility and contact rates, testing and contact tracing strategies and hospitalisation criteria. Insights into population behaviour required updates by incorporating notions of behavioural heterogeneity and behavioural responses to observed changes in mortality. We incorporated these aspects into developing scenarios for the third wave and developed additional methodology that allowed us to forecast required inpatient capacity. Finally, real-time analyses of the most important characteristics of the Omicron variant first identified in South Africa in November 2021 allowed us to advise policymakers early in the fourth wave that a relatively lower admission rate was likely.CONCLUSION:The SACMC's models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead, expand hospital capacity when needed, allocate budgets and procure additional resources where possible. Across four waves of COVID-19 cases, the SACMC continued to serve the planning needs of the government, tracking waves and supporting the national vaccine rollout.

There are limited published data within sub-Saharan Africa describing hospital pathways of COVID-19 patients hospitalized. These data are crucial for the parameterisation of epidemiological and cost models, and for planning purposes for the region. We evaluated COVID-19 hospital admissions from the South African national hospital surveillance system (DATCOV) during the first three COVID-19 waves between May 2020 and August 2021. We describe probabilities and admission into intensive care units (ICU), mechanical ventilation, death, and lengths of stay (LOS) in non-ICU and ICU care in public and private sectors. A log-binomial model was used to quantify mortality risk, ICU treatment and mechanical ventilation between time periods, adjusting for age, sex, comorbidity, health sector and province. There were 342,700 COVID-19-related hospital admissions during the study period. Risk of ICU admission was 16% lower during wave periods (adjusted risk ratio (aRR) 0.84 [0.82-0.86]) compared to between-wave periods. Mechanical ventilation was more likely during a wave overall (aRR 1.18 [1.13-1.23]), but patterns between waves were inconsistent, while mortality risk in non-ICU and ICU were 39% (aRR 1.39 [1.35-1.43]) and 31% (aRR 1.31 [1.27-1.36]) higher during a wave, compared to between-wave periods, respectively. If patients had had the same probability of death during waves vs between-wave periods, we estimated approximately 24% [19%-30%] of deaths (19,600 [15,200-24,000]) would not have occurred over the study period. LOS differed by age (older patients stayed longer), ward type (ICU stays were longer than non-ICU) and death/recovery outcome (time to death was shorter in non-ICU); however, LOS remained similar between time periods. Healthcare capacity constraints as inferred by wave period have a large impact on in-hospital mortality. It is crucial for modelling health systems strain and budgets to consider how input parameters related to hospitalisation change during and between waves, especially in settings with severely constrained resources.

In March 2020 the South African COVID-19 Modelling Consortium was formed to support government planning for COVID-19 cases and related healthcare. Models were developed jointly by local disease modelling groups to estimate cases, resource needs and deaths due to COVID-19. The National COVID-19 Epi Model (NCEM) while initially developed as a deterministic compartmental model of SARS-Cov-2 transmission in the nine provinces of South Africa, was adapted several times over the course of the first wave of infection in response to emerging local data and changing needs of government. By the end of the first wave, the NCEM had developed into a stochastic, spatially-explicit compartmental transmission model to estimate the total and reported incidence of COVID-19 across the 52 districts of South Africa. The model adopted a generalised Susceptible-Exposed-Infectious-Removed structure that accounted for the clinical profile of SARS-COV-2 (asymptomatic, mild, severe and critical cases) and avenues of treatment access (outpatient, and hospitalisation in non-ICU and ICU wards). Between end-March and early September 2020, the model was updated 11 times with four key releases to generate new sets of projections and scenario analyses to be shared with planners in the national and provincial Departments of Health, the National Treasury and other partners. Updates to model structure included finer spatial granularity, limited access to treatment, and the inclusion of behavioural heterogeneity in relation to the adoption of Public Health and Social Measures. These updates were made in response to local data and knowledge and the changing needs of the planners. The NCEM attempted to incorporate a high level of local data to contextualise the model appropriately to address South Africa’s population and health system characteristics that played a vital role in producing and updating estimates of resource needs, demonstrating the importance of harnessing and developing local modelling capacity.

Objectives: The adverse effects of the COVID-19 pandemic on tuberculosis (TB) detection have been well documented. Despite shared symptoms, guidance for integrated screening for TBand COVID-19 are limited, and opportunities for health systems strengthening curtailed by lockdowns. We partnered with a high TB burden district in KwaZulu-Natal, South Africa, to co-develop an integrated approach to assessing COVID-19 and TB, delivered using online learning and quality improvement, and evaluated its performance on TB testing and detection.Methods: We conducted a mixed methods study incorporating a quasi-experimental design and process evaluation in 10 intervention and 18 control clinics. Nurses in all 28 clinics were all provided access to a four-session online course to integrate TB and COVID-19 screening and testing, which was augmented with some webinar and in-person support at the 10 intervention clinics. We estimated the effects of exposure to this additional support using interrupted time series Poisson regression mixed models. Process evaluation data comprised interviews before and after the intervention. Thematic coding was employed to provide explanations for effects of the intervention.Results: Clinic-level support at intervention clinics was associated with a markedly higher uptake (177 nurses from 10 intervention clinics vs. 19 from 18 control clinics). Lack of familiarity with online learning, and a preference for group learning hindered the transition from face-to-face to online learning. Even so, any exposure to training was initially associated with higher rates of GeneXpert testing (adjusted incidence ratio [IRR] 1.11, 95% confidence interval 1.07-1.15) and higher positive TB diagnosis (IRR 1.38, 1.11-1.71).Conclusions: These results add to the knowledge base regarding the effectiveness of interventions to strengthen TB case detection during the COVID-19 pandemic. The findings support the feasibility of a shift to online learning approaches in low-resource settings with appropriate support and suggest that even low-intensity interventions are capable of activating nurses to integrate existing disease control priorities during pandemic conditions.

The National Health Laboratory Service (NHLS) collects all public health laboratory test results in South Africa, providing a cohort from which to identify groups, by age, sex, HIV, and viral suppression status, that would benefit from increased tuberculosis (TB) testing. Using NHLS data (2012–2016), we assessed levels and trends over time in TB diagnostic tests performed (count and per capita) and TB test positivity. Estimates were stratified by HIV status, viral suppression, age, sex, and province. We used logistic regression to estimate the odds of testing positive for TB by viral suppression status. Nineteen million TB diagnostic tests were conducted during period 2012–2016. Testing per capita was lower among PLHIV with viral suppression than those with unsuppressed HIV (0.08 vs 0.32) but lowest among people without HIV (0.03). Test positivity was highest among young adults (aged 15–35 years), males of all age groups, and people with unsuppressed HIV. Test positivity was higher for males without laboratory evidence of HIV than those with HIV viral suppression, despite similar individual odds of TB. Our results are an important national baseline characterizing who received TB testing in South Africa. People without evidence of HIV, young adults, and males would benefit from increased TB screening given their lower testing rates and higher test positivity. These high-test positivity groups can be used to guide future expansions of TB screening.

An investigation was carried out to examine the use of national Xpert MTB/RIF data (2013–2017) and GIS technology for MTB/RIF surveillance in South Africa. The aim was to exhibit the potential of using molecular diagnostics for TB surveillance across the country. The variables analysed include Mycobacterium tuberculosis (Mtb) positivity, the mycobacterial proportion of rifampicin-resistant Mtb (RIF), and probe frequency. The summary statistics of these variables were generated and aggregated at the facility and municipal level. The spatial distribution patterns of the indicators across municipalities were determined using the Moran’s I and Getis Ord (Gi) statistics. A case-control study was conducted to investigate factors associated with a high mycobacterial load. Logistic regression was used to analyse this study’s results. There was striking spatial heterogeneity in the distribution of Mtb and RIF across South Africa. The median patient age, urban setting classification, and number of health care workers were found to be associated with the mycobacterial load. This study illustrates the potential of using data generated from molecular diagnostics in combination with GIS technology for Mtb surveillance in South Africa. Spatially targeted interventions can be implemented in areas where high-burden Mtb persists.

Human migration facilitates the spread of infectious disease. However, little is known about the contribution of migration to the spread of tuberculosis in South Africa. We analyzed longitudinal data on all tuberculosis test results recorded by South Africa’s National Health Laboratory Service (NHLS), January 2011–July 2017, alongside municipality-level migration flows estimated from the 2016 South African Community Survey. We first assessed migration patterns in people with laboratory-diagnosed tuberculosis and analyzed demographic predictors. We then quantified the impact of cross-municipality migration on tuberculosis incidence in municipality-level regression models. The NHLS database included 921,888 patients with multiple clinic visits with TB tests. Of these, 147,513 (16%) had tests in different municipalities. The median (IQR) distance travelled was 304 (163 to 536) km. Migration was most common at ages 20–39 years and rates were similar for men and women. In municipality-level regression models, each 1% increase in migration-adjusted tuberculosis prevalence was associated with a 0.47% (95% CI: 0.03% to 0.90%) increase in the incidence of drug-susceptible tuberculosis two years later, even after controlling for baseline prevalence. Similar results were found for rifampicin-resistant tuberculosis. Accounting for migration improved our ability to predict future incidence of tuberculosis.

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 × 1010 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.

Background Bedaquiline improves outcomes of patients with rifampicin-resistant and multidrug-resistant (MDR) tuberculosis; however, emerging resistance threatens this success. We did a cross-sectional and longitudinal analysis evaluating the epidemiology, genetic basis, and treatment outcomes associated with bedaquiline resistance, using data from South Africa (2015-19). Methods Patients with drug-resistant tuberculosis starting bedaquiline-based treatment had surveillance samples submitted at baseline, month 2, and month 6, along with demographic information. Culture-positive baseline and post-baseline isolates had phenotypic resistance determined. Eligible patients were aged 12 years or older with a positive culture sample at baseline or, if the sample was invalid or negative, a sample within 30 days of the baseline sample submitted for bedaquiline drug susceptibility testing. For the longitudinal study, the first surveillance sample had to be phenotypically susceptible to bedaquiline for inclusion. Whole-genome sequencing was done on bedaquilineresistant isolates and a subset of bedaquiline-susceptible isolates. The National Institute for Communicable Diseases tuberculosis reference laboratory, and national tuberculosis surveillance databases were matched to the Electronic Drug-Resistant Tuberculosis Register. We assessed baseline resistance prevalence, mutations, transmission, cumulative resistance incidence, and odds ratios (ORs) associating risk factors for resistance with patient outcomes. Findings Between Jan 1, 2015, and July 31, 2019, 8041 patients had surveillance samples submitted, of whom 2023 were included in the cross-sectional analysis and 695 in the longitudinal analysis. Baseline bedaquiline resistance prevalence was 3.8% (76 of 2023 patients; 95% CI 2.9-4.6), and it was associated with previous exposure to bedaquiline or clofazimine (OR 7.1, 95% CI 2.3-21.9) and with rifampicin-resistant or MDR tuberculosis with additional resistance to either fluoroquinolones or injectable drugs (pre-extensively-drug resistant [XDR] tuberculosis: 4.2, 1.7-10.5) or to both (XDR tuberculosis: 4.8, 2.0-11.7). Rv0678 mutations were the sole genetic basis of phenotypic resistance. Baseline resistance could be attributed to previous bedaquiline or clofazimine exposure in four (5.3%) of 76 patients and to primary transmission in six (7.9%). Odds of successful treatment outcomes were lower in patients with baseline bedaquiline resistance (0.5, 0.3-1). Resistance during treatment developed in 16 (2.3%) of 695 patients, at a median of 90 days (IQR 62-195), with 12 of these 16 having pre-XDR or XDR. Interpretation Bedaquiline resistance was associated with poorer treatment outcomes. Rapid assessment of bedaquiline resistance, especially when patients were previously exposed to bedaquiline or clofazimine, should be prioritised at baseline or if patients remain culture-positive after 2 months of treatment. Preventing resistance by use of novel combination therapies, current treatment optimisation, and patient support is essential. Copyright (C) 2021 Elsevier Ltd. All rights reserved.