Risk factors for COVID-19-related in-hospital mortality in a high HIV and tuberculosis prevalence setting in South Africa: a cohort study.

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.

Leveraging gains from African Center for Integrated Laboratory Training to combat HIV epidemic in sub-Saharan Africa.

BACKGROUND: In sub-Saharan Africa, there is dearth of trained laboratorians and strengthened laboratory systems to provide adequate and quality laboratory services for enhanced HIV control. In response to this challenge, in 2007, the African Centre for Integrated Laboratory Training (ACILT) was established in South Africa with a mission to train staffs from countries with high burdens of diseases in skills needed to strengthen sustainable laboratory systems. This study was undertaken to assess the transference of newly gained knowledge and skills to other laboratory staff, and to identify enabling and obstructive factors to their implementation. METHODS: We used Kirkpatrick model to determine training effectiveness by assessing the transference of newly gained knowledge and skills to participant's work environment, along with measuring enabling and obstructive factors. In addition to regular course evaluations at ACILT (pre and post training), in 2015 we sent e-questionnaires to 867 participants in 43 countries for course participation between 2008 and 2014. Diagnostics courses included Viral Load, and systems strengthening included strategic planning and Biosafety and Biosecurity. SAS v9.44 and Excel were used to analyze retrospective de-identified data collected at six months pre and post-training. RESULTS: Of the 867 participants, 203 (23.4%) responded and reported average improvements in accuracy and timeliness in Viral Load programs and to systems strengthening. For Viral Load testing, frequency of corrective action for unsatisfactory proficiency scores improved from 57 to 91%, testing error rates reduced from 12.9% to 4.9%; 88% responders contributed to the first national strategic plan development and 91% developed strategies to mitigate biosafety risks in their institutions. Key enabling factors were team and management support, and key obstructive factors included insufficient resources and staff's resistance to change. CONCLUSIONS: Training at ACILT had a documented positive impact on strengthening the laboratory capacity and laboratory workforce and substantial cost savings. ACILT's investment produced a multiplier effect whereby national laboratory systems, personnel and leadership reaped training benefits. This laboratory training centre with a global clientele contributed to improve existing laboratory services, systems and networks for the HIV epidemic and is now being leveraged for COVID-19 testing that has infected 41,332,899 people globally.