BackgroundLike many low- and middle-income countries, South Africa established a dedicated HIV monitoring and evaluation (M&E) system to track the national response to HIV/AIDS. Its implementation in the public health sector has however not been assessed. Since responsibility for health services management lies at the district (sub-national) level, this study aimed to assess the extent to which the HIV M&E system is integrated with the overall health system M&E function at district level. This study describes implementation of the HIV M&E system, determines the extent to which it is integrated with the district health information system (DHIS), and evaluates factors influencing HIV M&E integration.MethodsThe study was conducted in one health district in South Africa. Data were collected through key informant interviews with programme and health facility managers and review of M&E records at health facilities providing HIV services. Data analysis assessed the extent to which processes for HIV data collection, collation, analysis and reporting were integrated with the DHIS.ResultsThe HIV M&E system is top-down, over-sized, and captures a significant amount of energy and resources to primarily generate antiretroviral treatment (ART) indicators. Processes for producing HIV prevention indicators are integrated with the DHIS. However processes for the production of HIV treatment indicators by-pass the DHIS and ART indicators are not disseminated to district health managers. Specific reporting requirements linked to ear-marked funding, politically-driven imperatives, and mistrust of DHIS capacity are key drivers of this silo approach.ConclusionsParallel systems that bypass the DHIS represent a missed opportunity to strengthen system-wide M&E capacity. Integrating HIV M&E (staff, systems and process) into the health system M&E function would mobilise ear-marked HIV funding towards improving DHIS capacity to produce quality and timely HIV indicators that would benefit both programme and health system M&E functions. This offers a practical way of maximising programme-system synergies and translating the health system strengthening intents of existing HIV policies into tangible action.
South Africa's cervical screening policy recommends three free Pap smears at ten-year intervals for all women over 30 years of age, aiming to achieve 70% coverage by 2010 by targeting the age group most at risk of developing pre-cancerous cervical lesions. Attaining wide coverage requires an adequate supply of motivated and supported public sector health workers with appropriate training and skills, working in a functional health system. Given the dearth of doctors in South Africa, professional nurses were tasked with performing the bulk of Pap smears at primary care level. Coverage remains sub-optimal and a significant proportion of women with precursor lesions do not receive treatment. Further, health system strengthening - essential for cytology-based screening - has not happened. Research to evaluate alternative screening technologies has proliferated in recent years, but regrettably, strengthening of the health system required to make the new technology work has not received similar attention. Using the South African experience, this article argues that technological interventions and innovations alone are not sufficient to improve cervical screening programmes. Task-shifting is limited unless other human resource concerns (e.g. training, increasing demands on personnel, attrition, and skills mix) are concurrently addressed within a comprehensive workforce development strategy, alongside work to make the health care delivery system functional.
Background Limitations in laboratory testing capacity undermine the ability to quantify the overall burden of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Methods We undertook a population-based serosurvey for SARS-CoV-2 infection in 26 subdistricts, Gauteng Province (population 15.9 million), South Africa, to estimate SARS-CoV-2 infection, infection fatality rate (IFR) triangulating seroprevalence, recorded COVID-19 deaths and excess-mortality data. We employed three-stage random household sampling with a selection probability proportional to the subdistrict size, stratifying the subdistrict census-sampling frame by housing type and then selecting households from selected clusters. The survey started on 4 November 2020, 8 weeks after the end of the first wave (SARS-CoV-2 nucleic acid amplification test positivity had declined to <10% for the first wave) and coincided with the peak of the second wave. The last sampling was performed on 22 January 2021, which was 9 weeks after the SARS-CoV-2 resurgence. Serum SARS-CoV-2 receptor-binding domain (RBD) immunoglobulin-G (IgG) was measured using a quantitative assay on the Luminex platform. Results From 6332 individuals in 3453 households, the overall RBD IgG seroprevalence was 19.1% [95% confidence interval (CI): 18.1–20.1%] and similar in children and adults. The seroprevalence varied from 5.5% to 43.2% across subdistricts. Conservatively, there were 2 897 120 (95% CI: 2 743 907–3 056 866) SARS-CoV-2 infections, yielding an infection rate of 19 090 per 100 000 until 9 January 2021, when 330 336 COVID-19 cases were recorded. The estimated IFR using recorded COVID-19 deaths (n = 8198) was 0.28% (95% CI: 0.27–0.30) and 0.67% (95% CI: 0.64–0.71) assuming 90% of modelled natural excess deaths were due to COVID-19 (n = 21 582). Notably, 53.8% (65/122) of individuals with previous self-reported confirmed SARS-CoV-2 infection were RBD IgG seronegative. Conclusions The calculated number of SARS-CoV-2 infections was 7.8-fold greater than the recorded COVID-19 cases. The calculated SARS-CoV-2 IFR varied 2.39-fold when calculated using reported COVID-19 deaths (0.28%) compared with excess-mortality-derived COVID-19-attributable deaths (0.67%). Waning RBD IgG may have inadvertently underestimated the number of SARS-CoV-2 infections and conversely overestimated the mortality risk. Epidemic preparedness and response planning for future COVID-19 waves will need to consider the true magnitude of infections, paying close attention to excess-mortality trends rather than absolute reported COVID-19 deaths.
BackgroundThe Integrated Chronic Disease Management (ICDM) model has been implemented in South Africa to enhance quality of clinical services in Primary Healthcare (PHC) clinics in a context of a high prevalence of chronic conditions and multi-morbidity. This study aimed to assess the implementation fidelity (adherence to guidelines) of the ICDM model.MethodsA cross-sectional study in 16 PHC clinics in two health districts in South Africa: Dr. Kenneth Kaunda (DKK) and West Rand (WR). A fidelity assessment tool with 89 activities and maximum score of 158 was developed from the four interrelated ICDM model components: facility re-organization, clinical supportive management, assisted self-management and strengthening of support systems. Value stream mapping of patient flow was conducted to analyse waiting time and identify operational inefficiencies. ICDM items were scored based on structured observations, facility document reviews and structured questionnaires completed by healthcare workers. Fidelity scores were summarized using medians and proportions and compared by facilities and districts using Chi-Square and Kruskal Wallis test.ResultsThe monthly patient headcount over a six-month period in these 16 PHC clinics was a median of 2430 (IQR: 1685–2942) individuals over 20 years. The DKK district had more newly diagnosed TB patients per month [median 5.5 (IQR: 4.00–9.33) vs 2.0 (IQR: 1.67–2.92)], and fewer medical officers per clinic [median 1 (IQR: 1–1) vs 3.5 (IQR:2–4.5)] compared to WR district. The median fidelity scores in both districts for facility re-organization, clinical supportive management, assisted self-management and strengthening of support systems were 78% [29/37, IQR: 27–31)]; 77% [30/39 (IQR: 27–34)]; 77% [30/39 (IQR: 28–34)]; and 80% [35/44 (IQR: 30–37)], respectively. The overall median implementation fidelity of the ICDM model was 79% (125/158, IQR, 117–132); WR was 80% (126/158, IQR, 123–132) while DKK was 74% (117/158, IQR, 106–130), p = 0.1409. The lowest clinic fidelity score was 66% (104/158), while the highest was 86% (136/158). A patient flow analysis showed long (2–5 h) waiting times and one stream of care for acute and chronic services.ConclusionThere was some variability of scores on components of the ICDM model by PHC clinics. More research is needed on contextual adaptations of the model.
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