Platinum mine workers’ exposure to dust particles emitted at mine waste rock crusher plants in Limpopo, South Africa

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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. CONCLUSION : 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.University of Johannesburghttp://www.samj.org.zadm2022Psycholog

The Transformative Impact of Community-Led Monitoring in the South African Health System: A Comprehensive Analysis

Objectives: Community-led monitoring (CLM) is an emerging approach that empowers local communities to actively participate in data collection and decision-making processes within the health system. The research aimed to explore stakeholder perceptions of CLM data and establish a CLM Data Value Chain, covering data collection and its impact.Methods: Qualitative data were collected from stakeholders engaged in health programs in South Africa. Data analysis involved a collaborative workshop that integrated elements of affinity diagramming, thematic analysis, and the systematic coding process outlined in Giorgi’s method. The workshop fostered joint identification, co-creation of knowledge, and collaborative analysis in developing the data value chain.Results: The findings showed that CLM data enabled community-level analysis, fostering program advocacy and local collaboration. It enhanced program redesign, operational efficiency, and rapid response capabilities. Context-specific solutions emerged through the CLM Data Value Chain, promoting sustainable and efficient program implementation.Conclusion: CLM is a powerful tool for improving program implementation, quality, and advocacy in South African healthcare. It strengthens accountability, trust, and transparency by involving local communities in data-driven decision-making. CLM addresses context-specific challenges and tailors interventions to local needs