As COVID-19 cases increased during the first weeks of the pandemic in South Africa, the National Institute of Communicable Diseases requested assistance with epidemiologic and surveillance expertise from the US Centers for Disease Control and Prevention South Africa. By leveraging its existing relationship with the National Institute of Communicable Diseases for >2 months, the US Centers for Disease Control and Prevention South Africa supported data capture and file organization, data quality reviews, data analytics, laboratory strengthening, and the development and review of COVID-19 guidance This case study provides an account of the resources and the technical, logistical, and organizational capacity leveraged to support a rapid response to the COVID-19 pandemic in South Africa.

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.

BACKGROUND: Studies have shown that drug-resistant tuberculosis (DR-TB) in South Africa (SA) is clonal and is caused mostly by transmission. Identifying transmission chains is important in controlling DR-TB. This study reports on the sentinel molecular surveillance data of Rifampicin-Resistant (RR) TB in SA, aiming to describe the RR-TB strain population and the estimated transmission of RR-TB cases. METHOD: RR-TB isolates collected between 2014 and 2018 from eight provinces were genotyped using combination of spoligotyping and 24-loci mycobacterial interspersed repetitive-units-variable-number tandem repeats (MIRU-VNTR) typing. RESULTS: Of the 3007 isolates genotyped, 301 clusters were identified. Cluster size ranged between 2 and 270 cases. Most of the clusters (247/301; 82.0%) were small in size (< 5 cases), 12.0% (37/301) were medium sized (5-10 cases), 3.3% (10/301) were large (11-25 cases) and 2.3% (7/301) were very large with 26-270 cases. The Beijing genotype was responsible for majority of RR-TB cases in Western and Eastern Cape, while the East-African-Indian-Somalian (EAI1_SOM) genotype accounted for a third of RR-TB cases in Mpumalanga. The overall proportion of RR-TB cases estimated to be due to transmission was 42%, with the highest transmission-rate in Western Cape (64%) and the lowest in Northern Cape (9%). CONCLUSION: Large clusters contribute to the burden of RR-TB in specific geographic areas such as Western Cape, Eastern Cape and Mpumalanga, highlighting the need for community-wide interventions. Most of the clusters identified in the study were small, suggesting close contact transmission events, emphasizing the importance of contact investigations and infection control as the primary interventions in SA.

OBJECTIVE: This is the first large-scale assessment of the implementation of HIV Rapid Test Quality Improvement Initiative in South Africa. METHODS: We used a quasi-experimental one group post-test only design. The intervention implemented starting April 2014 comprised health-care worker training on quality assurance (QA) of HIV rapid testing and enrolment of the facilities in proficiency testing (PT), targeting 2,077 healthcare facilities in 32 high HIV burden districts. Following the intervention, two consecutive rounds of site assessments were undertaken. The first, conducted after a median of 7.5 months following the training, included 1,915 facilities that participated in the QA training, while the second, conducted after a median of one-year following the first-round assessment included 517 (27.0%) of the 1,915 facilities. In both assessments, the Stepwise-Process-for-Improving-the-quality-of-HIV-Rapid-Testing (SPI-RT) checklist was used to score facilities' performance in 7 domains: training, physical facility, safety, pre-testing, testing, post-testing and external quality assessment. Facilities' level of readiness for national certification was assessed. RESULT: Between 2016 and 2017, there were four PT cycles. PT participation increased from 32.4% (620/1,915) in 2016 to 91.5% (1,753/1,915) in 2017. In each PT cycle, PT results were returned by 76%-87% of facilities and a satisfactory result (>80%) was achieved by >/=95% of facilities. In the SPI-RT assessment, in round-one, 22.3% of facilities were close to or eligible for national certification-this significantly increased to 38.8% in round-two (P-value<0.001). The median SPI-RT score for the domains HIV pre-testing (83.3%) and post-testing (72.2%) remained the same between the two rounds. The median score for the testing domain increased by 5.6% (to 77.8%). CONCLUSION: Facilities performance on the domains that are critical for accuracy of diagnosis (i.e. pre-testing, testing and post-testing) remained largely unchanged. This study provided several recommendations to improve QA implementation in South Africa, including the need to improve routine use of internal quality control for corrective actions.

Background: High mortality rates among asymptomatic cryptococcal antigen (CrAg)-positive patients identified through CrAg screening, despite preemptive fluconazole treatment, may be due to undiagnosed cryptococcal meningitis. Methods: Symptoms were reviewed in CrAg-positive patients identified by screening 19233 individuals with human immunodeficiency virus infection and CD4 cell counts <100/microL at 17 clinics and 3 hospitals in Johannesburg from September 2012 until September 2015, and at 2 hospitals until June 2016. Cerebrospinal fluid samples from 90 of 254 asymptomatic patients (35%) and 78 of 173 (45%) with headache only were analyzed for cryptococcal meningitis, considered present if Cryptococcus was identified by means of India ink microscopy, culture, or CrAg test. CrAg titers were determined with stored blood samples from 62 of these patients. The associations between blood CrAg titer, concurrent cryptococcal meningitis, and mortality rate were assessed. Results: Cryptococcal meningitis was confirmed in 34% (95% confidence interval, 25%-43%; 31 of 90) of asymptomatic CrAg-positive patients and 90% (81%-96%; 70 of 78) with headache only. Blood CrAg titer was significantly associated with concurrent cryptococcal meningitis in asymptomatic patients (P < .001) and patients with headache only (P = .003). The optimal titer for predicting cryptococcal meningitis was >160 (sensitivity, 88.2%; specificity, 82.1%); the odds ratio for concurrent cryptococcal meningitis was 34.5 (95% confidence interval, 8.3-143.1; P < .001). Conclusions: About a third of asymptomatic CrAg-positive patients have concurrent cryptococcal meningitis. More effective clinical assessment strategies and antifungal regimens are required for CrAg-positive patients, including investigation for cryptococcal meningitis irrespective of symptoms. Where it is not possible to perform lumbar punctures in all CrAg-positive patients, blood CrAg titers should be used to target those most at risk of cryptococcal meningitis.

BACKGROUND: Globally, per-capita, South Africa reports a disproportionately high number of cases of multidrug-resistant (MDR) tuberculosis and extensively drug-resistant (XDR) tuberculosis. We sought to estimate the prevalence of resistance to tuberculosis drugs in newly diagnosed and retreated patients with tuberculosis provincially and nationally, and compared these with the 2001-02 estimates. METHODS: A cross-sectional survey was done between June 15, 2012-June 14, 2014, using population proportionate randomised cluster sampling in the nine provinces in South Africa. 343 clusters were included, ranging between 31 and 48 per province. A patient was eligible for inclusion in the survey if he or she presented as a presumptive case during the intake period at a drug resistance survey enrolling facility. Consenting participants (>/=18 years old) completed a questionnaire and had a sputum sample tested for resistance to first-line and second-line drugs. Analysis was by logistic regression with robust SEs, inverse probability weighted against routine data, and estimates were derived using a random effects model. FINDINGS: 101 422 participants were tested in 2012-14. Nationally, the prevalence of MDR tuberculosis was 2.1% (95% CI 1.5-2.7) among new tuberculosis cases and 4.6% (3.2-6.0) among retreatment cases. The provincial point prevalence of MDR tuberculosis ranged between 1.6% (95% CI 0.9-2.9) and 5.1% (3.7-7.0). Overall, the prevalence of rifampicin-resistant tuberculosis (4.6%, 95% CI 3.5-5.7) was higher than the prevalence of MDR tuberculosis (2.8%, 2.0-3.6; p=0.01). Comparing the current survey with the previous (2001-02) survey, the overall MDR tuberculosis prevalence was 2.8% versus 2.9% and prevalance of rifampicin-resistant tuberculosis was 3.4% versus 1.8%, respectively. The prevalence of isoniazid mono-resistant tuberculosis was above 5% in all provinces. The prevalence of ethionamide and pyrazinamide resistance among MDR tuberculosis cases was 44.7% (95% CI 25.9-63.6) and 59.1% (49.0-69.1), respectively. The prevalence of XDR tuberculosis was 4.9% (95% CI 1.0-8.8). Nationally, the estimated numbers of cases of rifampicin-resistant tuberculosis, MDR tuberculosis, and isoniazid mono-resistant tuberculosis for 2014 were 13 551, 8249, and 17 970, respectively. INTERPRETATION: The overall prevalence of MDR tuberculosis in South Africa in 2012-14 was similar to that in 2001-02; however, prevalence of rifampicin-resistant tuberculosis almost doubled among new cases. Furthermore, the high prevalence of isoniazid mono-resistant tuberculosis, not routinely screened for, and resistance to second-line drugs has implications for empirical management. FUNDING: President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention under the terms of 1U19GH000571.