Undiagnosed comorbidities among individuals hospitalised with COVID-19 in South African public hospitals.

BACKGROUND: Previous studies have reported comorbid disease, including hypertension, diabetes mellitus, chronic cardiac and renal disease, malignancy, HIV, tuberculosis (TB) and obesity, to be associated with COVID­19 mortality. National demographic surveys have reported a high proportion of undiagnosed and untreated comorbid disease in South Africa (SA). OBJECTIVES: To determine the number of individuals with previously undiagnosed HIV, TB and non-communicable diseases (NCDs) among patients hospitalised with COVID­19, and the level of medical control of these chronic diseases. METHODS: We conducted a sentinel surveillance study to collect enhanced data on HIV, TB and NCDs among individuals with COVID­19 admitted to 16 secondary-level public hospitals in six of the nine provinces of SA. Trained surveillance officers approached all patients who met the surveillance case definition for inclusion in the study, and consenting patients were enrolled. The data collection instrument included questions on past medical history to determine the self-reported presence of comorbidities. The results of clinical and laboratory testing introduced as part of routine clinical care for hospitalised COVID­19 patients were collected for the study, to objectively determine the presence of hypertension, diabetes, HIV and TB and the levels of control of diabetes and HIV. RESULTS: On self-reported history, the most prevalent comorbidities were hypertension (n=1 658; 51.5%), diabetes (n=855; 26.6%) and HIV (n=603; 18.7%). The prevalence of self-reported active TB was 3.1%, and that of previous TB 5.5%. There were 1 254 patients admitted with COVID­19 (39.0%) who met the body mass index criteria for obesity. On clinical and laboratory testing, 87 patients were newly diagnosed with HIV, 29 with TB, 215 with diabetes and 40 with hypertension during their COVID­19 admission. There were 151/521 patients living with HIV (29.0%) with a viral load >1 000 copies/mL and 309/570 (54.2%) with a CD4 count <200 cells/µL. Among 901 patients classified as having diabetes, 777 (86.2%) had a glycated haemoglobin (HbA1c) level ≥6.5%. CONCLUSION: The study revealed a high prevalence of comorbid conditions among individuals with COVID­19 admitted to public hospitals in SA. In addition, a significant number of patients had previously undiagnosed hypertension, diabetes, HIV and active TB, and many and poorly controlled chronic disease, as evidenced by high HbA1c levels in patients with diabetes, and high viral loads and low CD4 levels in patients with HIV. The findings highlight the importance of strengthening health systems and care cascades for chronic disease management, which include prevention, screening for and effectively treating comorbidities, and ensuring secure and innovative supplies of medicines in primary healthcare during the COVID­19 pandemic.

Leveraging epidemiology as a decision support tool during the COVID-19 epidemic in South Africa.

By May 2021, South Africa (SA) had experienced two 'waves' of COVID-19 infections, with an initial peak of infections reached in July 2020, followed by a larger peak of infections in January 2021. Public health decisions rely on accurate and timely disease surveillance and epidemiological analyses, and accessibility of data at all levels of government is critical to inform stakeholders to respond effectively. In this paper, we describe the adaptation, development and operation of epidemiological surveillance and modelling systems in SA in response to the COVID-19 epidemic, including data systems for monitoring laboratory-confirmed COVID-19 cases, hospitalisations, mortality and recoveries at a national and provincial level, and how these systems were used to inform modelling projections and public health decisions. Detailed descriptions on the characteristics and completeness of individual datasets are not provided in this paper. Rapid development of robust data systems was necessary to support the response to the SA COVID-19 epidemic. These systems produced data streams that were used in decision-making at all levels of government. While much progress was made in producing epidemiological data, challenges remain to be overcome to address gaps to better prepare for future waves of COVID-19 and other health emergencies.

Guiding equitable prioritisation of COVID-19 vaccine distribution and strategic deployment in South Africa to enhance effectiveness and access to vulnerable communities and prevent waste.

BACKGROUND: In South Africa (SA), >2.4 million cases of COVID­19 and >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 COVID­19 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 COVID­19 epidemic control by reducing the number of COVID­19 transmissions and resultant deaths, while at the same time reducing the risk of vaccine wastage. METHODS: For the 52 districts of SA, 26 COVID­19 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 COVID­19 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.

Decreased severity of disease during the first global omicron variant covid-19 outbreak in a large hospital in tshwane, south africa.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) first reported in Wuhan, China in December 2019 is a global pandemic that is threatening the health and wellbeing of people worldwide. To date there have been more than 274 million reported cases and 5.3 million deaths. The Omicron variant first documented in the City of Tshwane, Gauteng Province, South Africa on 9 November 2021 led to exponential increases in cases and a sharp rise in hospital admissions. The clinical profile of patients admitted at a large hospital in Tshwane is compared with previous waves. METHODS: 466 hospital COVID-19 admissions since 14 November 2021 were compared to 3962 admissions since 4 May 2020, prior to the Omicron outbreak. Ninety-eight patient records at peak bed occupancy during the outbreak were reviewed for primary indication for admission, clinical severity, oxygen supplementation level, vaccination and prior COVID-19 infection. Provincial and city-wide daily cases and reported deaths, hospital admissions and excess deaths data were sourced from the National Institute for Communicable Diseases, the National Department of Health and the South African Medical Research Council. RESULTS: For the Omicron and previous waves, deaths and ICU admissions were 4.5% vs 21.3% (p<0.00001), and 1% vs 4.3% (p<0.00001) respectively; length of stay was 4.0 days vs 8.8 days; and mean age was 39 years vs 49,8 years. Admissions in the Omicron wave peaked and declined rapidly with peak bed occupancy at 51% of the highest previous peak during the Delta wave. Sixty two (63%) patients in COVID-19 wards had incidental COVID-19 following a positive SARS-CoV-2 PCR test . Only one third (36) had COVID-19 pneumonia, of which 72% had mild to moderate disease. The remaining 28% required high care or ICU admission. Fewer than half (45%) of patients in COVID-19 wards required oxygen supplementation compared to 99.5% in the first wave. The death rate in the face of an exponential increase in cases during the Omicron wave at the city and provincial levels shows a decoupling of cases and deaths compared to previous waves, corroborating the clinical findings of decreased severity of disease seen in patients admitted to the Steve Biko Academic Hospital. CONCLUSION: There was decreased severity of COVID-19 disease in the Omicron-driven fourth wave in the City of Tshwane, its first global epicentre.

Guiding equitable prioritisation of COVID-19 vaccine distribution and strategic deployment in South Africa to enhance effectiveness and access to vulnerable communities and prevent waste

Background. In South Africa (SA), >2.4 million cases of COVID­19 and >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 COVID­19 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 COVID­19 epidemic control by reducing the number of COVID­19 transmissions and resultant deaths, while at the same time reducing the risk of vaccine wastage. Methods. For the 52 districts of SA, 26 COVID­19 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 COVID­19 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.

A call to action: Temporal trends of COVID-19 deaths in the South African Muslim community.

Letter by Omar on letter by Jassat et al. (Jassat W, Brey Z, Parker S, et al. A call to action: Temporal trends of COVID-19 deaths in the South African Muslim community. S Afr Med J 2021;111(8):692-694. https://doi.org/10.7196/SAMJ.2021.v111i8.15878); and response by Jassat et al.

Sentinel seroprevalence of SARS-CoV-2 in Gauteng Province, South Africa, August - October 2020.

BACKGROUND: Estimates of prevalence of anti-SARS-CoV-2 antibody positivity (seroprevalence) for tracking the COVID-19 epidemic are lacking for most African countries. OBJECTIVES: To determine the prevalence of antibodies against SARS-CoV-2 in a sentinel cohort of patient samples received for routine testing at tertiary laboratories in Johannesburg, South Africa. METHODS: This sentinel study was conducted using remnant serum samples received at three National Health Laboratory Service laboratories in the City of Johannesburg (CoJ) district. Collection was from 1 August to 31 October 2020. We extracted accompanying laboratory results for glycated haemoglobin (HbA1c), creatinine, HIV, viral load and CD4 T-cell count. An anti-SARS-CoV-2 targeting the nucleocapsid (N) protein of the coronavirus with higher affinity for IgM and IgG antibodies was used. We reported crude as well as population-weighted and test-adjusted seroprevalence. Multivariate logistic regression analysis was used to determine whether age, sex, HIV and diabetic status were associated with increased risk for seropositivity. RESULTS: A total of 6 477 samples were analysed, the majority (n=5 290) from the CoJ region. After excluding samples with no age or sex stated, the model population-weighted and test-adjusted seroprevalence for the CoJ (n=4 393) was 27.0% (95% confidence interval (CI) 25.4 - 28.6). Seroprevalence was highest in those aged 45 - 49 years (29.8%; 95% CI 25.5 - 35.0) and in those from the most densely populated areas of the CoJ. Risk for seropositivity was highest in those aged 18 - 49 years (adjusted odds ratio (aOR) 1.52; 95% CI 1.13 - 2.13; p=0.0005) and in samples from diabetics (aOR 1.36; 95% CI 1.13 - 1.63; p=0.001). CONCLUSIONS: Our study conducted between the first and second waves of the pandemic shows high levels of current infection among patients attending public health facilities in Gauteng Province.

Understanding the differential impacts of COVID-19 among hospitalised patients in South Africa for equitable response.

BACKGROUND: There are limited in-depth analyses of COVID-19 differential impacts, especially in resource-limited settings such as South Africa (SA). OBJECTIVES: To explore context-specific sociodemographic heterogeneities in order to understand the differential impacts of COVID-19. METHODS: Descriptive epidemiological COVID-19 hospitalisation and mortality data were drawn from daily hospital surveillance data, National Institute for Communicable Diseases (NICD) update reports (6 March 2020 - 24 January 2021) and the Eastern Cape Daily Epidemiological Report (as of 24 March 2021). We examined hospitalisations and mortality by sociodemographics (age using 10-year age bands, sex and race) using absolute numbers, proportions and ratios. The data are presented using tables received from the NICD, and charts were created to show trends and patterns. Mortality rates (per 100 000 population) were calculated using population estimates as a denominator for standardisation. Associations were determined through relative risks (RRs), 95% confidence intervals (CIs) and p-values <0.001. RESULTS: Black African females had a significantly higher rate of hospitalisation (8.7% (95% CI 8.5 - 8.9)) compared with coloureds, Indians and whites (6.7% (95% CI 6.0 - 7.4), 6.3% (95% CI 5.5 - 7.2) and 4% (95% CI 3.5 - 4.5), respectively). Similarly, black African females had the highest hospitalisation rates at a younger age category of 30 - 39 years (16.1%) compared with other race groups. Whites were hospitalised at older ages than other races, with a median age of 63 years. Black Africans were hospitalised at younger ages than other race groups, with a median age of 52 years. Whites were significantly more likely to die at older ages compared with black Africans (RR 1.07; 95% CI 1.06 - 1.08) or coloureds (RR 1.44; 95% CI 1.33 - 1.54); a similar pattern was found between Indians and whites (RR 1.59; 95% CI 1.47 - 1.73). Women died at older ages than men, although they were admitted to hospital at younger ages. Among black Africans and coloureds, females (50.9 deaths per 100 000 and 37 per 100 000, respectively) had a higher COVID-19 death rate than males (41.2 per 100 000 and 41.5 per 100 000, respectively). However, among Indians and whites, males had higher rates of deaths than females. The ratio of deaths to hospitalisations by race and gender increased with increasing age. In each age group, this ratio was highest among black Africans and lowest among whites. CONCLUSIONS: The study revealed the heterogeneous nature of COVID-19 impacts in SA. Existing socioeconomic inequalities appear to shape COVID-19 impacts, with a disproportionate effect on black Africans and marginalised and low socioeconomic groups. These differential impacts call for considered attention to mitigating the health disparities among black Africans.

Understanding the differential impacts of COVID-19 among hospitalised patients in South Africa for equitable response

Background. There are limited in-depth analyses of COVID-19 differential impacts, especially in resource-limited settings such as South Africa (SA).Objectives. To explore context-specific sociodemographic heterogeneities in order to understand the differential impacts of COVID-19. Methods. Descriptive epidemiological COVID-19 hospitalisation and mortality data were drawn from daily hospital surveillance data, National Institute for Communicable Diseases (NICD) update reports (6 March 2020 - 24 January 2021) and the Eastern Cape Daily Epidemiological Report (as of 24 March 2021). We examined hospitalisations and mortality by sociodemographics (age using 10-year age bands, sex and race) using absolute numbers, proportions and ratios. The data are presented using tables received from the NICD, and charts were created to show trends and patterns. Mortality rates (per 100 000 population) were calculated using population estimates as a denominator for standardisation. Associations were determined through relative risks (RRs), 95% confidence intervals (CIs) and p-values <0.001. Results. Black African females had a significantly higher rate of hospitalisation (8.7% (95% CI 8.5 - 8.9)) compared with coloureds, Indians and whites (6.7% (95% CI 6.0 - 7.4), 6.3% (95% CI 5.5 - 7.2) and 4% (95% CI 3.5 - 4.5), respectively). Similarly, black African females had the highest hospitalisation rates at a younger age category of 30 - 39 years (16.1%) compared with other race groups. Whites were hospitalised at older ages than other races, with a median age of 63 years. Black Africans were hospitalised at younger ages than other race groups, with a median age of 52 years. Whites were significantly more likely to die at older ages compared with black Africans (RR 1.07; 95% CI 1.06 - 1.08) or coloureds (RR 1.44; 95% CI 1.33 - 1.54); a similar pattern was found between Indians and whites (RR 1.59; 95% CI 1.47 - 1.73). Women died at older ages than men, although they were admitted to hospital at younger ages. Among black Africans and coloureds, females (50.9 deaths per 100 000 and 37 per 100 000, respectively) had a higher COVID-19 death rate than males (41.2 per 100 000 and 41.5 per 100 000, respectively). However, among Indians and whites, males had higher rates of deaths than females. The ratio of deaths to hospitalisations by race and gender increased with increasing age. In each age group, this ratio was highest among black Africans and lowest among whites.Conclusions. The study revealed the heterogeneous nature of COVID-19 impacts in SA. Existing socioeconomic inequalities appear to shape COVID-19 impacts, with a disproportionate effect on black Africans and marginalised and low socioeconomic groups. These differential impacts call for considered attention to mitigating the health disparities among black Africans.

Drug-resistant tuberculosis patient care journeys in South Africa: a pilot study using routine laboratory data.

SETTING: Thirteen districts in Eastern Cape (EC), KwaZulu-Natal (KZN) and Western Cape (WC) Provinces, South Africa.OBJECTIVE: To pilot a methodology for describing and visualising healthcare journeys among drug-resistant tuberculosis (DR-TB) patients using routine laboratory records.DESIGN: Laboratory records were obtained for 195 patients with laboratory-detected rifampicin-resistant TB (RR-TB) during July-September 2016. Health facility visits identified from these data were plotted to visualise patient healthcare journeys. Data were verified by facility visits.RESULTS: In the 9 months after the index RR-TB sample was collected, patients visited a mean of 2.3 health facilities (95% CI 2.1-2.6), with 9% visiting ≥4 facilities. The median distance travelled by patients from rural areas (116 km, interquartile range [IQR] 50-290) was greater than for urban patients (51 km, IQR 9-140). A median of 21% of patient's time was spent under the care of primary healthcare facilities: this was respectively 6%, 37% and 39% in KZN, EC and WC. Journey patterns were generally similar within districts. Some reflected a semi-centralised model of care where patients were referred to regional hospitals; other journeys showed greater involvement of primary care.CONCLUSION: Routine laboratory data can be used to explore DR-TB patient healthcare journeys and show how the use of healthcare services for DR-TB varies in different settings.

Acute mental health care according to recent mental health legislation Part II. Activity-based costing.

OBJECTIVE: This is the second of three reports on the follow-up review of mental health care at Helen Joseph Hospital (HJH). Objectives for the review were to provide realistic estimates of cost for unit activities and to establish a quality assurance cycle that may facilitate cost centre management. METHOD: The study described and used activity-based costing (ABC) as an approach to analyse the recurrent cost of acute in-patient care for the financial year 2007-08. Fixed (e.g. goods and services, staff salaries) and variable recurrent costs (including laboratory' 'pharmacy') were calculated. Cost per day, per user and per diagnostic group was calculated. RESULTS: While the unit accounted for 4.6% of the hospital's total clinical activity (patient days), the cost of R8.12 million incurred represented only 2.4% of the total hospital expenditure (R341.36 million). Fixed costs constituted 90% of the total cost. For the total number of 520 users that stayed on average 15.4 days, the average cost was R1,023.00 per day and R15748.00 per user. Users with schizophrenia accounted for the most (35%) of the cost, while the care of users with dementia was the most expensive (R23,360.68 per user). Costing of the application of World Health Organization norms for acute care staffing for the unit, projected an average increase of 103% in recurrent costs (R5.1 million), with the bulk (a 267% increase) for nursing. CONCLUSION: In the absence of other guidelines, aligning clinical activity with the proportion of the hospital's total budget may be an approach to determine what amount should be afforded to acute mental health in-patient care activities in a general regional hospital such as HJH. Despite the potential benefits of ABC, its continued application will require time, infrastructure and staff investment to establish the capacity to maintain routine annual cost analyses for different cost centres.

Acute mental health care according to recent mental health legislation Part II. Activity-based costing

Objective: This is the second of three reports on the follow-up review of mental health care at Helen Joseph Hospital (HJH). Objectives for the review were to provide realistic estimates of cost for unit activities and to establish a quality assurance cycle that may facilitate cost centre management. Method: The study described and used activity-based costing (ABC) as an approach to analyse the recurrent cost of acute in-patient care for the financial year 2007-08. Fixed (e.g. goods and services; staff salaries) and variable recurrent costs (including laboratory'; 'pharmacy') were calculated. Cost per day; per user and per diagnostic group was calculated. Results: While the unit accounted for 4.6of the hospital's total clinical activity (patient days); the cost of R8.12 million incurred represented only 2.4of the total hospital expenditure (R341.36 million). Fixed costs constituted 90of the total cost. For the total number of 520 users that stayed on average 15.4 days; the average cost was R1;023.00 per day and R15748.00 per user. Users with schizophrenia accounted for the most (35) of the cost; while the care of users with dementia was the most expensive (R23;360.68 per user). Costing of the application of World Health Organization norms for acute care staffing for the unit; projected an average increase of 103in recurrent costs (R5.1 million); with the bulk (a 267increase) for nursing. Conclusion: In the absence of other guidelines; aligning clinical activity with the proportion of the hospital's total budget may be an approach to determine what amount should be afforded to acute mental health in-patient care activities in a general regional hospital such as HJH. Despite the potential benefits of ABC; its continued application will require time; infrastructure and staff investment to establish the capacity to maintain routine annual cost analyses for different cost centres