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. MethodsIn 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. FindingsAmong 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...

Her research interests include the epidemiology of zoonotic and foodborne diseases and drug-resistant enteric infections. References

Highlights The response to the COVID-19 pandemic in low- and middle-income countries is challenged by fragile health systems and poverty. Expanded access to mobile phones will facilitate their use in the COVID-19 response. Mobile phone technology can improve case-reporting and patient referral pathways. We give examples of initiatives enhancing patient care and public health outcomes. We present a checklist for helpful mobile communication to combat COVID-19.

Background: The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis (TB) are unclear, particularly in low- and middle-income countries (LMIC) in Africa. We investigated this interaction using a nationally representative hospital surveillance system in South Africa. Methods: A national surveillance system for laboratory-confirmed COVID-19 hospital admissions (DATCOV) was established. Using DATCOV data, we describe the demographic characteristics, clinical features, and in-hospital mortality among individuals admitted to public and private hospitals with COVID-19 during 5 March to 11 August 2020. Multivariable logistic regression models were used to compare individuals who were HIV-infected and HIV-uninfected and determine the factors associated with in-hospital mortality. Findings: Hospital admissions peaked at 1,560 admissions per day, in late July. Among the 41,877 individuals admitted with laboratory-confirmed COVID-19, 7,662 (18.3%) died. Comorbidities were documented in 27,555 (65.8%) individuals, most commonly observed were hypertension (36.8%), diabetes (29.6%), obesity (19.7%), and HIV (8.7%); TB was reported in 0.7% of individuals. Increased risk of in-hospital mortality was associated with HIV and TB, as well as other described risk factors for COVID-19, such as increasing age, male sex, non-White race (Black, mixed and Indian race), chronic underlying conditions particularly hypertension, diabetes and obesity. In addition, HIV-infected individuals with immunosuppression had increased risk of mortality (adjusted odds ratio 2.2; 95% confidence interval 1.6-3.1). Among HIV-infected individuals, the prevalence of other comorbidities associated with severe COVID-19 outcomes was 39.9%. The effect of multiple comorbidities on mortality was similar in HIV-infected and -uninfected individuals. Interpretation: These data provide a better understanding of the interaction of non-communicable diseases, chronic infectious diseases like HIV and TB and COVID-19. Increasing age and presence of chronic underlying comorbidities (particularly hypertension and diabetes) are important additional factors associated with COVID-19 mortality in a middle-income African setting and are common among HIV-infected individuals. HIV- and TB-infected individuals, particularly those with additional comorbidities, would benefit from COVID-19 prevention and treatment programmes.

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The National Institute for Communicable Diseases in South Africa participates in national laboratory-based surveillance for human isolates of Salmonella species. Laboratory analysis includes whole-genome sequencing (WGS) of isolates. We report on WGS-based surveillance of Salmonella enterica serovar Typhi ( Salmonella Typhi) in South Africa from 2020 through 2021. We describe how WGS analysis identified clusters of enteric fever in the Western Cape Province of South Africa and describe the epidemiological investigations associated with these clusters. A total of 206 Salmonella Typhi isolates were received for analysis. Genomic DNA was isolated from bacteria and WGS was performed using Illumina NextSeq technology. WGS data were investigated using multiple bioinformatics tools, including those available at the Centre for Genomic Epidemiology, EnteroBase and Pathogenwatch. Core-genome multilocus sequence typing was used to investigate the phylogeny of isolates and identify clusters. Three major clusters of enteric fever were identified in the Western Cape Province; cluster one (n=11 isolates), cluster two (n=13 isolates), and cluster three (n=14 isolates). To date, no likely source has been identified for any of the clusters. All isolates associated with the clusters, showed the same genotype (4.3.1.1.EA1) and resistome (antimicrobial resistance genes: bla TEM-1B, catA1, sul1, sul2, dfrA7). The implementation of genomic surveillance of Salmonella Typhi in South Africa has enabled rapid detection of clusters indicative of possible outbreaks. Cluster identification allows for targeted epidemiological investigations and a timely, coordinated public health response.