*Electronic Theses and Dissertations (PhDs)

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    Surveillance of gastrointestinal infections in individuals over the age of 5 years in South Africa
    (2024) Johnstone, Siobhan Lindsay
    Gastrointestinal infections cause significant mortality and morbidity, especially in Africa. While children ≤5 years of age bear the brunt of diarrhoeal disease, there is a significant burden in older age groups. Limited data on aetiology in these older age groups limits appropriate interventions. Diarrhoeal surveillance is important for monitoring disease trends in a population and should inform testing and treatment guidelines, and interventions. This body of work evaluated the epidemiology of diarrhoea at each level of the surveillance pyramid to assist in interpretation of routine health data and identify gaps in surveillance. A household survey was conducted in Soweto to estimate community diarrhoeal prevalence, associated risk factors and healthcare seeking behaviors. An analysis of diagnostic testing practices for diarrhoeal diseases was done, using a doctors’ survey, at three public hospitals in South Africa. Routine diagnostic data and enhanced surveillance data were compared to evaluate patient-related factors associated with requests for diagnostic investigation, type of diagnostic testing offered and the efficiency of available tests. A hospital surveillance study investigated the infectious causes of diarrhoea in hospitalised patients >5 years. Results indicated a high diarrhoeal burden across all age groups in South Africa (5.3% of respondents reported an episode in the preceding 2 weeks). While the majority of infections were mild, 40% required healthcare. Many of those requiring healthcare (34%), specifically adults, were unable to access the required care. Those that did access healthcare were treated empirically and seldom had stool samples collected for diagnostic investigations (approximately 10% of admitted cases). Available diagnostics in public health laboratories detected pathogens in only 13.7% of these submitted stools due to pre-analytical and analytical issues including not testing for all relevant pathogens. Diarrhoeal prevalence was particularly high among HIV-infected patients (67.5% of patients >5 years admitted for diarrhoea were HIV-infected) and these patients presented with a unique aetiology. This research highlights the need for diarrhoeal testing and treatment guidelines based on local epidemiological data with a focus on HIV-infected patients. Current diagnostics require optimisation including specimen collection, standardisation, pathogens included in routine testing panels, turnaround time and methods of detection. This will guide decisions on future public health interventions including vaccines.
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    Estimating and predicting HIV risk using statistical and machine learning methods: a case study using the 2005 to 2015 Zimbabwe demographic health survey data
    (2024) Makota, Rutendo Beauty Birri
    Background: The 90–90–90 targets were launched by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and partners with the aim to diagnose 90% of all HIV-positive persons, provide antiretroviral therapy (ART) for 90% of those diagnosed, and achieve viral suppression for 90% of those treated by 2020. In Zimbabwe, a population-based survey in 2016 reported that 74.2% of people living with HIV (PLHIV) aged 15–64 years knew their HIV status. Among the PLHIV who knew their status, 86.8% self-reported current use of Antiretroviral treatment (ART), with 86.5% of those who self-reported being virally suppressed. For these 90–90–90 targets to be met, prevalence and incidence rate estimates are crucial in understanding the current status of the HIV epidemic and determining whether the trends are improving to achieve the 2030 target. Ultimately, this will contribute to the achievement of Sustainable Development Goals 3 (SDG 3) and the broader goal of promoting sustainable development and eradicating poverty worldwide by 2030. Using data from household surveys, this thesis provides a unique statistical approach for estimating the incidence and prevalence of the Human Immunodeficiency Virus (HIV). To properly assess the efficacy of focused public health interventions and to appropriately forecast the HIV-related burden placed on healthcare systems, a comprehensive assessment of HIV incidence is essential. Targeting certain age groups with a high risk of infection is necessary to increase the effectiveness of public health interventions. To jointly estimate age-and-timedependent HIV incidence and diagnosis rates, the methodological focus of this thesis was on developing a comprehensive statistical framework for age-dependent HIV incidence estimates. Additionally, the risk of HIV infection was also evaluated using interval censoring methods and machine learning. Finally, geospatial modelling techniques were also utilised to determine the spatial patterns of HIV incidence at district levels and identify hot spots for HIV risk to guide policy. The main aim of this thesis was to estimate and predict HIV risk using statistical and machine learning methods. Study objectives: The study objectives of this thesis were: 1. To determine the effect of several drivers/factors of HIV infection on survival time over a decade in Zimbabwe, using current status data. 2. To determine common risk factors of HIV positivity in Zimbabwe and the prediction capability of machine learning models. 3. To estimate HIV incidence using the catalytic and Farrington models and to test the validity of these estimates at the national and sub-national levels. 4. To estimate the age- and time-dependent prevalence and HIV Force-of-infection (FOI) using current status data by comparing parametric, semi-parametric and non-parametric models; and determining which models best fit the data. 5. To investigate the HIV incidence hotspots in Zimbabwe by using geographicallyweighted regression. Methods: We performed secondary data analysis on cross-sectional data collected from the Zimbabwe Demographic Health Survey (ZDHS) from 2005 to 2015. Datasets from three Zimbabwe Demographic Health Survey HIV test results and adult interviews were merged, and records without an HIV test result were excluded from the analysis. The outcome variable was HIV status. Survey and cluster-adjusted logistic regression were used to determine variables for use in survival analysis with HIV status as the outcome variable. Covariates found significant in the logistic regression were used in survival analysis to determine the factors associated with HIV infection over the ten years. The data for the survival analysis was modelled assuming age at survey imputation (Model 1) and interval-censoring (Model 2). To determine the risk of HIV infection using machine learning methods, the prediction model was fit by adopting 80% of the data for learning/training and 20% for testing/prediction. Resampling was done using the stratified 5-fold cross-validation procedure repeatedly. The best algorithm was the one with the highest F1 score, which was then used to identify individuals with a higher likelihood of HIV infection. Considering that the proportion of those HIV negative and positive was imbalance with a ratio of 4.2:1, we applied resampling methods to handle the class imbalance. We performed the Synthetic Minority Over-sampling Technique (SMOTE) to balance the classes. We evaluated two alternative methods for predicting HIV incidence in Zimbabwe between 2005 and 2015. We estimated HIV incidence from seroprevalence data using the catalytic and Farrington-2-parameter models. These models were validated at the micro and macro levels using community-based cohort incidence and empirical estimates from UNAIDS EPP/SPECTRUM, respectively. To ascertain the age-time effects of HIV risk, we estimated the age- and time-dependent HIV FOI using current status data. Five generalised additive models were explored, ranging from linear, semi-parametric, non-parametric and nonproportional hazards additive models. The Akaike Information Criteria was used to select the best model. The best model was then used to estimate the age- and time-dependent HIV prevalence and force-of-infection. The OLS model was fitted for each survey year to determine the global relationship between HIV incidence and the significant covariates. The Moran's I spatial autocorrelation method was used to assess the spatial independence of residuals. The Getis-Ord Gi* statistic was used for Hotspot Analysis, which identifies statistically significant hot and cold spots using a set of weighted features. Interpolation maps of HIV incidence were created using Empirical Bayesian Kriging to produce smooth surfaces of HIV incidence for visualisation and data generation at the district level. The Multiscale Geographically Weighted Regression method was used to see if the relationship between HIV incidence and covariates varied by district. The software used in the thesis analysis included R software, STATA, Python, ArcGIS and WinBugs. Results: Model goodness of fit test based on the Cox-Snell residuals against the cumulative hazard indicated that the model with interval censoring was the best. On the contrary, the Akaike Information Criterion (AIC) indicated that the normal survival model was the best. Factors associated with a high risk of HIV infection were being female, the number of sexual partners, and having had an STI in the past year prior to the survey. The machine learning model indicated that the XGBoost model had better performance compared to the other 5 models for both the original data and SMOTE processed data. Identical variablesfor both sexes throughout the three survey years for predicting HIV status were: total lifetime number of sex partners, cohabitation duration (grouped), number of household members, age of household head, times away from home in last 12 months, beating justified and religion. The two most influential variable for both males and females were total lifetime number of sex partners and cohabitation duration (grouped). According to these findings, the catalytic model estimated a higher HIV incidence rate than the Farrington model. Compared to cohort estimates, the estimates were within the observed 95% confidence interval, with 88% and 75% agreement for the catalytic and Farrington models, respectively. The limits of agreement observed in the Bland-Altman plot were narrow for all plots, indicating that our model estimates were comparable to cohort estimates. Compared to UNAIDS estimates, the catalytic model predicted a progressive increase in HIV incidence for males throughout all survey years. Without a doubt, HIV incidence declined with each subsequent survey year for all models. Based on birth year cohort-specific prevalence, the female HIV prevalence peaks at approximately 29 years of age and then declines. Between 15 and 30 years, males have a lower cohort-specific prevalence than females. Male cohort-specific prevalence decreases marginally between ages 33 and 39, then peaks at age 40. In all age categories, the cohort-specific FOI is greater in females than males. Moreover, the cohortspecific HIV FOI peaked at age 22 for females and age 40 for males. A 18-year age gap between the male and female HIV FOI peaks was observed. Throughout the decade covered by this study, the Tsholotsho district remained a 99 % confidence hotspot. The impact of STI, condom use and being married on HIV incidence has been strong in the Eastern parts of Zimbabwe with Mashonaland Central, Mashonaland East and Manicaland provinces. From our findings from the Multiscale Geographically Weighted Regression (MGWR), we observed that Matabeleland North’s HIV incidence rates are driven by wealth index, multiple sex partners, STI and females with older partners. Conclusions: The difference between the results from the Cox-Snell residuals graphical method and the model estimates and AIC value may be due to inadequate methods to test the goodness-of-fit of interval-censored data. We concluded that Model 2 with interval-censoring gave better estimates due to its consistency with the published results from the literature. Even though we consider the interval-censoring model as the superior model with regard to our specific data, the method had its own set of limitations. Programmes targeted at HIV testing could use the machine learning approach to identify high-risk individuals. In addition to other risk reduction techniques, machine learning may aid in identifying those who might require Pre-exposure prophylaxis. Based on our results, older men and younger women resembled patterns of higher HIV prevalence and force-of-infection than younger men and older women. This could be an indication of age-disparate sexual relationships. Therefore, HIV prevention programmes should be targeted more at younger females and older males. Lastly, to improve programmatic and policy decisions in the national HIV response, we recommend the triangulation of multiple methods for incidence estimation and interpretation of results. Multiple estimating approaches should be considered to reduce uncertainty in the estimations from various models. The study spread the message that various factors differ from district to district and over time. The study's findings could be useful to policymakersin terms of resource allocation in the context of public health programs. The findings of this study also highlight the importance of focusing on districts like Tsholotsho, which have consistently had a high HIV burden over time. The main strength of this study is dependent on the quality of the data obtained from the surveys. These data were derived from population-based surveys, which provide more reliable and robust data. Another strength of this study was that we did not restrict our analysis to one method; however, we had the opportunity to determine the risk and incidence of HIV by exploring different methodologies. However, the limited number of variables accessible to us for this study constituted one of its drawbacks. We could not determine the impact of variables including viral load, health care spending, HIV- risk groups, and other HIV-related interventions. Additionally, there were missing values in the data, which required making assumptions about their unpredictability and utilising imputation methods that are inherently flawed. Last but not least, a number of the variables were self-reported and, as a result, were vulnerable to recall bias and social desirability bias.
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    The relationship between violence across the life course, protective factors and mental disorders among adult women living in a slum setting in Ibadan, Nigeria
    (2024) Sekoni, Olutoyin Olubunmi
    Research suggests that adult women in Nigeria have experienced traumatic events (TE) across their life course. Violence is a TE that can occur within intimate relationships as well as other spheres of life. TE and adverse life events can increase risk of a mental disorder such as: depression, anxiety and Post Traumatic Stress Disorder (PTSD). Despite experience of TE or adverse life events, some women do not experience the onset of a mental disorder which may be due to protective factors such as resilience and social support. The links between lifecourse TE and the development of common mental disorders have not been well researched on the African continent particularly in slum settings. This thesis aimed to investigate the relationships between adult women’s childhood trauma, experiences of Intimate Partner Violence (IPV) and adverse life events and common mental disorders among adult women living in a slum setting in Ibadan, Nigeria. The thesis also sought to examine the presence of protective factors in these relationships. Methods -A community-based cross-sectional household survey utilizing multistage sampling was carried out among 550 women. Childhood trauma was measured using the short form of the Childhood Trauma Questionnaire. IPV was measured using the WHO Multi-country Study on Women's Health and Domestic Violence Questionnaire. Common mental disorders were measured using the short version of the Depression, Anxiety and Stress Scale (DASS-21) while the Harvard Trauma Questionnaire was used to measure PTSD. Recent stressors were measured using the Life Events Questionnaire. The protective factors of resilience, social support, social connectedness and self- esteem were measured using the Wagnild and Young resilience scale, the Multidimensional Scale of Perceived Social Support, the Social Connectedness Scale (Revised) and the Rosenberg self-esteem scale respectively. Bivariate and multivariate analysis were conducted to identify any associations and net effect of the key independent variables on the primary outcomes of interest while controlling for socio demographic characteristics. Results The prevalence of lifetime and past year experience of IPV were 31.5% and 14.8% respectively. The prevalence of the TE during childhood ranged from 8.9% (sexual abuse), 50.4% physical abuse and 70.4% emotional abuse, while 30.8%, 41.6% and 5.8% had experienced one, two and three forms of childhood trauma respectively. Women who had experienced all three forms of childhood trauma had five times the odds of reporting a lifetime experience of IPV compared to those who had not had any experience of childhood trauma (OR= 5.21; CI= 2.30-11.76). Common mental disorders were reported by 14.0% of the respondents, with PTSD reported by 4.18%. Resilience and social support were found to be protective against reporting symptoms of common mental disorders. Women who reported higher levels of social support and resilience were less likely to report common mental disorders (OR:0.96, 95% CI 0.93, 0.98) and (OR:0.95, 95% CI 0.91, 0.99) respectively. Women who were 65 years and older were also less likely to report the occurrence of common mental disorders (OR:0.38, 95% CI 0.15, 0.98) compared to those aged 18–34 years. Conclusion- The findings from this study show that trauma over the life course is prevalent among the women in these slums as a result of childhood trauma, IPV and recent stressors. The findings also show that even though many of the women were exposed to trauma, most of them did not develop mental disorders. Resilience and social support appeared to play an important role in mitigating the effects of adversity among this population of women even in the light of their extant circumstances within the slum setting. Addressing the use of both child protection programs and IPV reduction as well as fostering resilience and social support among women would be of benefit in reducing the burden of common mental disorders.
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    Exploring healthcare user perspectives on utilisation of prevention of mother to child transmission (PMTCT) services in a high-mobility context in Johannesburg, South Africa
    (2024) Bisnauth, Melanie Ann
    Included in this thesis are four original papers. The first of four papers explored the impact of the Option B+ Prevention of Mother to Child Transmission (PMTCT) of human immunodeficiency virus (HIV) programme on the work of healthcare professionals and, investigated pregnant women living with HIV (WLWH) experiences with antiretroviral therapy (ART) for life, to gain insights in ways to better manage the programme. The first paper (Chapter 6) explored the views of both healthcare providers and user experiences with ART for life at the time the SA’s National Department of Health (NDoH) adopted World Health Organisation (WHO) 2013 guidelines on ARVs for HIV treatment and prevention in 2015. This included changes to PMTCT through Option B+ (now known as lifelong treatment). In 2015, little was known about the impact of these guidelines on the work of healthcare workers (HCWs) and no research at the time had focused on how these changes have affected adherence for the patients. Semistructured interviews were conducted with participants and revealed that work had become difficult to manage for all HCWs because of the need to strengthen indicators for tracking patients to decrease the PMTCT loss to follow-up (LTFU); there was inconsistency in delivery of counselling and support services and a need for communication across clinical departments of the hospital that both offered PMTCT services and had to provide care to the mothers and; a lack of compassion and understanding was existent amongst service providers. The overburdened healthcare environment had affected the overall views and experiences of pregnant WLWH going on ART for life. All patient participants (n=55) responded that they chose the fixed dose combination (FDC) pill for life to protect the health of the baby and felt ART for life could be stopped after giving birth, unaware of the long-term benefits for the mother. Although SA national women were interviewed at the time, RMMCH had provided PMTCT care to many migrants and their experiences needed to be heard. Further research was needed on how to strengthen the programme for long term scalability and sustainability for highly mobile WLWH to better adapt PMTCT programming within the healthcare system. Observations of the population of women accessing PMTCT at RMMCH indicated that many migrant WLWH were utilising the services and called for further investigation and lead into the next two phases of the research study. In addition, Paper 2 (Chapter 7) and Paper 3 (Chapter 8) data collection occurred during the COVID19 pandemic. Paper 2 (Chapter 7) investigated HCWs and their experiences in the provision of PMTCT services to WLWH, specifically migrants that were utilising services during the SARS-CoV-2 (COVID-19) pandemic in SA, to provide further insights on the programme. The COVID-19 pandemic resulted in SA taking preventative and precautionary measures to control the spread of infection, this inevitably proposed challenges to WLWH, especially migrant women by limiting population mobility with border closures and lockdown restrictions. Semi-structured interviews (n=12) conducted with healthcare iii providers across city, provincial, and national levels explored how COVID-19 impacted the healthcare system and affected highly mobile patients’ adherence and utilisation of PMTCT services. Findings revealed; a need for multi-month dispensing (MMD); fear of contracting COVID-19 leading to the disruption in the continuum of care; added stress to the already existent overburdened clinical environment; mistreatment and xenophobic attitudes towards the migrant HIV population and; three key areas for strengthening PMTCT programme sustainability for migrants. Paper 3 (Chapter 8) investigated the insights of migrant WLWH. Migrant typologies were not predetermined a priori. This research allowed for the different mobility typologies of migrant women utilising PMTCT services in a high mobility context of Johannesburg to first surface from the data. By analysing these experiences, it explored further into how belonging to a specific typology may have affected the health care received and their overall experience during the COVID-19 pandemic. Interviews with cross-border migrants (n=22) (individuals who move from one country to another) and internal migrants (n=18) (individuals who transcend borders within a country) revealed that women in cross-border migration patterns compared to interprovincial/intraregional mobility; expressed more fear to utilise services due to xenophobic attitudes from HCWs; were unable to receive ART interrupting adherence due to border closures and; relied on short message service (SMS) reminders to adhere to ART during the pandemic. All 40 women struggled to understand the importance of adherence due to the lack of infrastructure to properly educate them following social distancing protocols. COVID-19 amplified existing challenges for cross-border migrant women to utilise PMTCT services. Future pandemic preparedness should be addressed with differentiated service delivery (DSD) including MMD of ARVs, virtual educational care, and language sensitive information, responsive to the needs of mobile women and to assist in alleviating the burden on the healthcare system. The pandemics’ impact on the study timeline, key lessons learnt and, take away messages when conducting research during this unpredictable time are provided in Chapter 4 (Methods) and Chapter 9 (Discussion). It is important to include these reflections because of the impact it had on all participants and the entire PhD process. Paper 4 will be a future policy piece, drawn from Chapter 9, addressing the need for responsiveness from the SA government and NDoH. Chapter 9 brought together collectively the previous papers 1,2, and 3 and drew overall conclusions, recommendations, and a way forward for both policy and programme implementation. This chapter provided the principal findings of the overall thesis and in relation to other studies in the field, as well as implication for policy practice and research. Chapter 9 concludes with the recommendations for future research on WLWH, mobility typologies, service provision of PMTCT and future pandemic preparedness, and the vision for the South African PMTCT programme.
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    Modelling space and time patterns of HIV interventions on HIV burden in a high priority district in South Africa
    (2024) Otwombe, Lucy Chimoyi
    Background: Ekurhuleni Metropolitan Municipality (EMM) collects monthly data from primary healthcare facilities on the HIV programmes to inform its HIV response. To study patterns of HIV burden and uptake of HIV services at a population level, the application of small area analysis offered a powerful epidemiological approach while investigating on a geographical scale, the risk, and confounding factors of certain health outcomes. This PhD thesis was aimed at highlighting and understanding the heterogeneity of HIV prevalence and selected HIV outcomes at a ward-level between 2012 and 2016. Materials and Methods: Materials and Methods: A mixed-methods approach using the HIV result chain logical framework was applied to several sources of data. Firstly, data from a National HIV Survey, the South African National Census analysed using Bayesian techniques in WINBUGS to provide an epidemiological profile of the risk factors for HIV prevalence, sub-optimal condom use and non-ART use. Secondly, a model of time and space using R-INLA applied to routinely collected HIV program data (clinical and laboratory) assessed the predictors of viral load suppression (VLS) [<1000 copies/mL (WHO) and <400 copies/mL (SA)]. Forecasting of VLS (five years post-2016) was conducted using ARIMA models. Lastly, a thematic analysis using the social cognitive theory framework on in-depth interviews with patients and healthcare staff was conducted to understand factors influencing uptake of selected HIV services in different geographical settings Results and findings: There were several clusters of high HIV infection, sub-optimal condom, non-ART use and VLS in EMM driven by different risk factors discussed in this PhD thesis. The proportion of VLS increased from 2012-2015 and decreased in 2016, and heterogeneity was observed at ward-level. As the female population and ART initiation rates increased at ward-level, VLS increased. However, this observed relationship was strong in some areas and weak in others. Lastly negative sequalae including stigma from healthcare workers and communities prevented optimum uptake of HIV services, particularly in women. Social support, availability of services and differentiated care encourage utilisation of HIV services. Conclusions: Findings highlighted the heterogenous nature of health events in EMM and are likely to inform targeted interventions to improve HIV programmes at ward-level towards achieving the 95-95-95 targets.
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    Examining the bidirectional relationship between comorbid depression and Type 2 diabetes: a managed healthcare perspective
    (2024) Naidoo, Lovina Asha Corrien
    Introduction-Type 2 diabetes mellitus (T2DM) is common and has devastating outcomes for patients diagnosed with this disease. In Africa, the prevalence of T2DM is reaching epidemic proportions, especially in developing countries like Ghana, Nigeria and South Africa (SA). The financial burden of T2DM is seen in the public and private healthcare sectors in Africa. Major depressive disorder (MDD) frequently co-occurs as a discordant comorbidity with T2DM. MDD is an important component in the holistic management of T2DM care as the outcomes of both conditions are exacerbated by the presence of the other. T2DM patients are at high risk for cardiovascular (CV) morbidity and mortality. The comorbidity of MDD among these individuals is associated with poor diabetes-related cardiovascular disease (CVD) outcomes such as myocardial infarction, stroke and cardiac failure, because MDD is a highly prevalent risk factor for CVD and T2DM alike. Little is known of the prevalence of MDD as a comorbidity of T2DM in SA or if MDD is a risk factor for the onset of T2DM. It is also unclear whether the treatment of depressive disorders in T2DM would improve glycaemic control. While the association between depression and T2DM in America and Europe is established, understanding the relationship between these two non-communicable diseases (NCDs) is lacking in SA. The relationship between T2DM and associated co-morbidities, particularly MDD, is poorly acknowledged in chronic disease management practices in SA. The management of co-morbid conditions may influence managed healthcare costs and hospitalisation rates. Aim and objectives -This thesis investigated the bidirectional relationship between T2DM and comorbid MDD within a South African privately managed healthcare organisation. The objectives of the study were to estimate the comorbidity incidence, resource utilisation (medicine, services and hospital), assess the cost between two T2DM management funding models, the influence of MDD on glycaemia, blood pressure and lipid control (ABC guidelines) and finally identify the depressive symptom and CV risk profiles of patients with T2DM with or without MDD and those with MDD alone. Method -The thesis comprised four quantitative studies that analysed claims data from a privately funded healthcare insurer and electronic health records (EHR) from 2012 to 2019, and a cross-sectional survey from 2016 to 2019. The methodology in the first study was a retrospective descriptive analysis of 902 adult patients with T2DM in 2014. Patients were identified with T2DM and their comorbidities and categorised as those with concordant comorbidities (CC), and those with discordant comorbidities (DC). Hospital admissions of patients with T2DM, with MDD (T2DM+MDD) versus those without MDD (T2DM-MDD), were further analysed. The second study analysed the claims data of patients with T2DM and T2DM+MDD from 2012 to 2016. Annual healthcare costs were assessed between two funding models and categorised as in-hospital and out-of-hospital medicines and out-of-hospital services. Diabetes-related and other medicine-plus-services and hospitalisation costs between T2DM and T2DM+MDD were estimated In the third study, the cardiometabolic indices control of 1211 patients with T2DM+MDD, T2DMMDD and MDD only were measured using their EHR for the year 2019. Claims for lipid-lowering therapy, hypoglycaemic agents, antihypertensives and antidepressant selective-serotoninreuptake inhibitors (SSRI) were assessed between the study groups. Frequencies of patients achieving target glycated haemoglobin (HbA1c), systolic blood pressure (SBP) and low-densitylipoprotein (LDL-C) were compared between groups. A stepwise multivariate logistic regression analysis was performed to identify predictors of HbA1c and LDL-C control of the study groups. The fourth study conducted a cross-sectional survey of a random sample of members with T2DM+MDD, T2DM-MDD, MDD only, and a healthy control group between the years 2016 to 2019. The survey comprised a Patient Health Questionnaire-9 (PHQ-9) to assess possible depressive symptoms, and anthropometric measures (body mass index (BMI), family history of diabetes and/or heart disease, and smoking status as CV risk profiles). Findings- The first study revealed a high incidence of CV concordant comorbidities (hypertension )and hyperlipidaemia) in patients with T2DM+MDD, with MDD being the most prevalent discordant comorbidity of T2DM (17%). A higher percentage of patients with T2DM+MDD were admitted to 3 hospital (42%, p=0.004) compared with those with T2DM-MDD (30%). The number of overnight admissions was higher among the T2DM+MDD (76%, p=0.016) compared with T2DM-MDD (66%). The second study focused on health care costs and the funding models associated with managed care. The direct medical costs of patients with T2DM and T2DM+MDD registered with a medical scheme over a 5-year period between two funding models were estimated and compared: a capitation risk-sharing model (CM) versus a traditional fee-for-service (FFS) model. Of the identified T2DM patients, 64% were enrolled in CM in 2012 and this rose to 81% by 2016. The implementation of CM resulted in a significantly higher cost to the scheme ($1,095) compared to FFS ($296) in 2016 (p<0.0001). Forty-six T2DM patients in this study incurred hospitalisation costs of ≥ $24,243 for T2DM-related or other hospital admissions (non T2DM-related). The healthcare expenditure consumed by patients with T2DM and T2DM+MDD on a capitation model of care for diabetes was high compared to patients on FFS. While the diabetes-related treatment and management were similar between patients with T2DM+MDD and T2DM-MDD, other medicine and services, expenditure was significantly higher in the T2DM+MDD group, for example T2DM+MDD patients had a median expenditure of $1,414 in 2016 compared to a median of $614 in T2DM-MDD patients (p<0.0001). The third study assessed the HbA1c, SBP and LDL-C control target attainment (as per South African ABC guidelines) in patients with T2DM+MDD and T2DM-MDD and those with MDD alone. Only 13% of the patients in T2DM+MDD group and 7.1% in the T2DM-MDD group achieved ABC (HbA1c<7%, LDL-C<1.8mmol/l and SBP<140/90 mmHg) targets, despite hypoglycaemic, lipidlowering therapy and antihypertensive claims, indicating a possible risk for CVD in T2DM+MDD and T2DM-MDD patients. A higher proportion of patients with T2DM+MDD (56%) achieved an HbA1c target of <7% compared to the T2DM-MDD group (45%, p<0.05). Multiple regression analysis showed that HbA1c control was independently associated (p<0.001) with older age, claims for statins and having a history of MDD, after adjusting for claims for antihypertensive therapy, metformin, newer hypoglycaemic agents, sex, and interaction factor of newer hypoglycaemic agents and metformin. Only 24% of patients in both the T2DM+MDD and T2DMMDD groups reached the LDL-C target <1.8mmol/l. The predictors of LDL-C control between the T2DM+MDD and T2DM-MDD groups were older age (p<0.0001) and claiming statin therapy (p=0.001), after adjusting for antihypertensive therapy and metformin claims and sex The fourth study identified the depressive symptoms and CV risk factors (such as obesity, smoker status and family history of diabetes and heart disease) in individuals with T2DM+MDD, T2DMMDD or MDD alone compared to a healthy control. The PHQ-9 scores revealed that patients in all four groups were within a range of mild to moderate-severe depressive symptoms. The T2DM+MDD group had moderate-severe (PHQ-9≥10) depressive symptoms (58.8%) similar to the MDD group (54.2%, p=1.0) suggesting a poor response to antidepressants. Patients with T2DM-MDD had underlying unrecognized depressive symptoms: 20.5% had moderate-severe (PHQ-9≥10) depressive symptoms and 23.1% had mild (PHQ-9=5-9) depressive symptoms. Of concern was that 25% of the control (healthy) group recorded having moderate-severe (PHQ9≥10) depressive symptoms and 21.4% of having mild depressive (PHQ-9=5-9) symptoms. The majority of the T2DM+MDD group obese (76.5%) whereas 46.2% of the T2DM-MDD group were overweight. However, the control group, with no stated disease, were overweight (37.5%) or obese (30.4%). This study highlights the undetected MDD and high CV risk prevalent in this setting. Conclusion- Within this South African private managed healthcare setting, comorbidities associated in patients with T2DM, i.e. MDD and CVD, are managed discretely. High-risk individuals with T2DM increase costs and resource utilisation within the private managed healthcare setting. In summary, the relevance of the research was to increase awareness of the consequences of comorbidity of T2DM and MDD and encourage routine screening for depression in T2DM patients, and glycaemic screening among patients with MDD. Managed care programmes should consider a patient-centric approach to assist patients in engaging with their T2DM and comorbidities more effectively by listening to their difficulties in terms of medication compliance, offering regular glycaemic and lipid blood tests and encouraging healthier diet through visits to dieticians or nurse educators. Targeting primary healthcare as an intervention has the potential to reduce the hospitalisation burden by initially stabilizing patients with T2DM+MDD, providing cost-effective and appropriate medicine management (i.e. statins), improving attainment of ABC control targets and early screening for depression and non-invasive CV risk factors. Resource allocation for a coordinated care team that includes health professionals such as dieticians, endocrinologists, drug review utilisation (DUR) pharmacists, psychologists and nurse educators to treat patients with T2DM+MDD is indicated.
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    Initial loss to follow up among tuberculosis patients: the role of Ward-Based Outreach Teams and short message service (SMS) technology
    (2024) Mwansa-Kambafwile, Judith Reegan Mulubwa
    Introduction: In South Africa, tuberculosis (TB) is still a serious public health problem with rates of initial loss to follow up (initial LTFU) varying between 14.9% and 22.5%. Poor clinician-patient communication resulting in lack of clarity on next steps, patients not prioritizing their healthcare and patients not knowing that their results are ready at the clinic are some reasons for initial LTFU. This PhD aimed to assess the effectiveness of Ward-based Outreach Teams (WBOTs) or Short Message Service (SMS) technology in reducing TB initial LTFU in Johannesburg, South Africa between 2018 and 2020. Methods: A mixed methods approach comprising two phases (formative and intervention) was employed. In the formative phase, secondary data were analyzed for frequency distributions to determine the rates of initial LTFU in the study area. In addition, in-depth interviews with WBOT Managers and with TB Program Managers were conducted to determine their perceived reasons for TB initial LTFU. In the intervention phase, two interventions (WBOTs/SMS technology) were tested using a 3 arm randomized controlled trial (RCT) comparing each of the interventions to standard of care (SOC). The WBOTs delivered paper slip reminders while SMS intervention entailed sending reminder SMS messages to patients as soon as TB results were available. Chi square statistics, Poisson regression and Kaplan-Meier estimates were used to analyze the data. The RCT was followed by in-depth interviews with WBOT members and with some of the trial participants who had tested TB positive and had received reminder messages. To identify themes in the qualitative studies, both inductive and deductive coding were used in the hybrid analytic approach. Results: From the formative phase, the TB initial LTFU among the 271 patients was found to be 22.5% and the overall time to treatment initiation was 9 days. Interviews with managers revealed that relocation and “shopping around” were the main patient related factors found as the reasons for initial LTFU. Health system related factors for initial LTFU were communication and staff rotations. In terms of TB related work, WBOTs screened household members for TB and referred them for TB testing. The services of the WBOT/TB programs which were found to be integrated were: referral of symptomatic patients for TB testing and adherence monitoring in patients already on TB treatment. There was minimal involvement of the WBOTs in the treatment initiation of patients diagnosed with TB. Findings from the trial were that 11% (314/2850) of the participants tested positive for TB. The 314 TB patients were assigned to one of the 3 arms (SOC=104, WBOTs=105, and SMS=105). Overall, 255 patients (81.2%) were initiated treatment across all study arms. More patients in the SMS arm were initiated TB treatment than in the SOC arm (92/105; 88% and 81/104; 78% respectively; P=0.062). Patients in the SMS arm also had a shorter time to treatment initiation than those in the SOC arm (4 days versus 8 days; P 8 days; P<0.001). A comparison of the WBOTs arm and the SOC arm showed similar proportions initiated on treatment (45/62; 73% and 44/61; 72% respectively) as well as similar times to treatment initiation. Findings from the post-trial interviews showed that delivery of the reminder paper slips by the WBOTs during the trial was something new, but possible to incorporate into their daily schedule. The patient interviews revealed that various emotions (happiness, fear, worry etc.) were experienced upon receipt of the reminder messages. Participants also reported that receiving the reminder message did influence their decision to go back to collect the results. Conclusion: Reminder messages to patients are beneficial in TB treatment initiation. National TB programs can use SMS messaging because it is an affordable and feasible method. Although implementation of the WBOTs intervention was suboptimal, findings show that with proper integration of TB and WBOT programs, WBOTs have the potential to contribute to improved treatment initiation.
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    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics and social contact patterns
    (2024) Kleynhans, Jacoba Wilhelmina (Jackie)
    Background- Understanding the community burden and transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can assist to make informed decisions for prevention policies. Methods-From August through October 2018, before the SARS-CoV-2 pandemic, we performed a cross-sectional contact survey nested in a prospective household cohort in an urban (Jouberton, North West Province) and a rural community (Agincourt, Mpumalanga Province) in South Africa to measure contact rates in 535 study participants. Participants were interviewed to collect details on all contact events (within and outside of the household). During the SARS-CoV-2 pandemic we enrolled 1211 individuals from 232 randomly selected households in the same urban and rural community, and followed the cohort prospectively for 16 months (July 2020 through November 2021), collecting blood every two months to test for SARS-CoV-2 antibodies. Using these longitudinal SARS-CoV-2 seroprevalence estimates and comparing these with reported laboratory-confirmed cases, hospitalizations and deaths, we investigated the community burden and severity of SARS-CoV-2. We also performed a case-ascertained household transmission study of symptomatic SARS-CoV-2 index cases living with HIV (LWH) and not LWH (NLWH) in two urban communities (Jouberton, North West Province and Soweto, Gauteng Province) from October 2020 through September 2021. We enrolled 131 SARS-CoV-2 index cases at primary healthcare clinics. The index cases and their 457 household contacts were followed up for six weeks with thrice weekly visits to collect nasal swabs for SARS-CoV-2 testing on reverse transcription real-time polymerase chain reaction (rRT-PCR), irrespective of symptoms. We assessed household cumulative infection risk (HCIR), duration of virus detection and the interval between index and contact symptom onset (serial interval). By collecting high-resolution household contact patterns in these households using wearable sensors, we assessed the association between contact patterns and SARS-CoV-2 household transmission. Results -During the contact survey, we observed an overall contact rate of 14 (95% confidence interval (CI), 13- 15) contacts per day, with higher contact rates in children aged 14-18 years (22, 95%CI 8-35) compared to children <7 years (15, 95%CI 12-17). We found higher contact rates in the rural site (21, 95%CI 14- 28) compared to the urban site (12, 95%CI 11-13). When comparing the household cohort seroprevalence estimates to district SARS-CoV-2 laboratoryconfirmed infections, we saw that only 5% of SARS-CoV-2 infections were reported to surveillance. Three percent of infections resulted in hospitalization and 0.7% in death. People LWH were not more likely to be seropositive for SARS-CoV-2 (odds ratio [OR] 1.0, 95%CI 0.7–1.5), although the sample size for people LWH was small (159/1131 LWH). During the case-ascertained household transmission study for SARS-CoV-2, we estimated a HCIR of 59% (220/373) in susceptible household members, with similar rates in households with an index LWH and NLWH (60% LWH vs 58% NLWH). We observed a higher risk of transmission from index cases aged 35–59 years (adjusted OR [aOR] 3.4, 95%CI 1.5–7.8) and ≥60 years (aOR 3.1, 95% CI 1.0–10.1) compared with those aged 18–34 years, and index cases with a high SARS-CoV-2 viral load (using cycle threshold values (Ct) <25 as a proxy, aOR 5.3, 95%CI 1.6–17.6). HCIR was also higher in contacts aged 13–17 years (aOR 7.1, 95%CI 1.5–33.9) and 18–34 years (aOR 4.4, 95% CI 1.0–18.4) compared with <5 years. Through the deployment of wearable sensors, we were able to measure high-resolution within household contact patterns in the same households. We did not find an association between duration (aOR 1.0 95%CI 1.0-1.0) and frequency (aOR 1.0 95%CI 1.0-1.0) of close-proximity contact with SARSCoV-2 index cases and household members and transmission. Conclusion- We found high contact rates in school-going children, and higher contact rates in the rural community compared to the urban community. These contact rates add to the limited literature on measured contact patterns in South Africa. The burden of SARS-CoV-2 is underestimated in national surveillance, highlighting the importance of serological surveys to determine the true burden. Under-ascertainment of cases can hinder containment efforts through isolation and contact tracing. Based on seroprevalence estimates in our study, people LWH did not have higher SARS-CoV-2 community attack rates. In the household transmission study, we observed a high HCIR in households with symptomatic index cases, and that index cases LWH did not infect more household members compared to people NLWH. We found a correlation between age and SARS-CoV-2 transmission and acquisition, as well as between age and contact rates. Although we did not observe an association between household contact patterns and SARS-CoV-2 transmission, we generated SARS-CoV-2 transmission parameters and community and household contact data that can be used to parametrize infectious disease models for both SARS-CoV-2 and other pathogens to assist with forecasting and intervention assessments. The availability of robust data is important in the face of a pandemic where intervention strategies have to be adapted continuously.
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    Implementation of universal health coverage in South Africa: formative effects, perceived quality of healthcare and modelling of health service utilisation indicators in a national health insurance pilot district
    (2024) Mukudu, Hillary
    Background- According to the World Health Organisation, member countries should attain universal health coverage by 2030. To achieve this goal, South Africa introduced the National Health Insurance programme in 2012. Since then, the first phase of the pilot programme has been implemented in Tshwane and ten other country districts. Historically, no other health system reform in South Africa has generated more interest than the National Health Insurance. This 15-year preliminary plan and pilot received optimism and criticism depending on several factors. The pilot programme focusing on primary health care was implemented along with several other interventions. The components of the intervention included setting up: ward-based primary healthcare outreach teams, integrated school health programmes, district clinical specialist teams, centralised chronic medicine dispensing and distribution programmes, health patient registration systems, stock visibility systems, and contracting of private non-specialised (general) medical practitioners to provide services in public primary health care facilities. These interventions were envisaged to improve healthcare quality at the primary healthcare level and offset the burden of non-emergency (secondary) care at the hospital outpatient level. However, studies have yet to be done to determine population-level formative effects on primary and non-emergency secondary healthcare indicators, their relationships, and interdependencies. These data are needed to forecast and develop measures to meet the possible increase in health service utilisation. In addition, this information is essential to guide the possible scale-up of South Africa's National Health Insurance mechanism. Such guidance may be in setting benchmarks to monitor policy implementation, determine facility staffing, the package of health services, training needs, budget for medicines and consumables, and other resource allocation. Aim- Therefore, this study first aimed to determine the formative effects of implementing the Medical Practitioners' contracting of the National Health Insurance pilot program on primary healthcare utilisation indicators measured at both primary and non-emergency secondary levels of care. A comparison was made between Tshwane national health insurance pilot district and Ekurhuleni district, which is not a pilot district. Furthermore, the study aimed to determine the relationships between healthcare utilisation indicators and their interdependencies and then provide a forecast for 2025. Methods- This quasi-experimental and ecological study used selected primary health care and outpatient department indicators in the District Health Information System monthly reports between January 2010 and December 2019 for the Tshwane district and Ekurhuleni district. Thus, to determine the formative effects on primary healthcare utilisation indicators, the selected period was from June 2010 to May 2014. A total of 48-time periods (months), with 24 before (June 2010 to May 2012) and 24 after (June 2012 to May 2014) implementation of Medical Practitioners contracting of the National Health Insurance pilot programme. Similarly, June 2012 to May 2014 was the selected period to determine the effects on the perceived quality of care. A total of 24 months, with 12 before (June 2012 to May 2013) and 12 after (June 2013 to May 2014) implementation of the Medical Practitioners' contracting of the National Health Insurance pilot programme. To determine the relationship and interdependence between Primary Health Care and Outpatient Department indicators and forecasts for 2025, 113 time periods (quarters) were selected. There were 28 quarters before and 84 quarters after implementing the National Health Insurance pilot programme. Similar methodological approaches were used to determine the effects of Medical Practitioners contracting in the National Health Insurance pilot programme on Primary Healthcare utilisation indicators and perceived healthcare quality. All study data types used in the thesis were continuous; thus, they were initially evaluated descriptively using means (standard deviations) and medians (interquartile ranges). The range was evaluated using minimum and maximum values. An Independent t-test assuming unequal variances was used to compare the means of Outpatient Department indicators in determining the effect of Medical Practitioners contracting in the National Health Insurance pilot programme on the perceived quality of healthcare. Single- and multiple-group (controlled) interrupted time series analysis was used to determine the effect of the National Health Insurance pilot project implementation on the utilisation of selected primary and non-emergency outpatient department indicators and perceived healthcare quality. A different methodological approach was used to determine the interdependencies and relationships between selected primary healthcare and non-emergency outpatient department indicators and their forecasts for 2025. Initially, data were evaluated descriptively using means (standard deviations) and medians (interquartile ranges) and the range was evaluated using minimum and maximum values. Prior to the development of the vector error correction model, several steps were taken. Firstly, a natural log transformation of all time series data was done to enhance additivity, linearity, and validity. Additionally, the level of lags at which variables were interconnected or endogenously obtained was determined due to the sensitivity of causality. Furthermore, the stationarity of time series data was determined using both graphical means and the Augmented Dick Fuller test to confirm the stability of each time series. Finally, cointegration was determined using the Johansen cointegration test to check for the correlation between two or more nonstationary series. After developing the Vector Error Correction Model, the Granger causality test was done to determine whether one series is helpful for forecasting another. Then the Vector Error Correction Model relationships between variables of selected primary healthcare and non-emergency outpatient department indicators were used to forecast the utilisation of both levels of services by 2025. Results- The findings showed changes in primary healthcare indicators measured at primary and nonemergency secondary levels before and after contracting private medical practitioners of the National Health Insurance pilot programme. The study also confirmed the influence of selected primary health care and outpatient department headcounts on each other by finding four cointegration relationships between the variables. There were differences between single-group and controlled interrupted time series analysis findings for Tshwane district and Ekurhuleni district considered independently and collectively on the utilisation of primary health care services. Thus, the positive impact observed in primary healthcare utilisation post-June 2012 is not attributable to the implementation of the Medical Practitioners' contracting of the National Health Insurance pilot programme. Conversely, there were similarities between single-group and controlled interrupted time series analysis findings for Tshwane district and Ekurhuleni district considered independently and collectively on the perceived quality of primary healthcare. In the interpretation of this finding, the similarities indicated that implementing the Medical Practitioners' contracting of the National Health Insurance pilot programme positively influenced the perception of a better quality of primary healthcare in the Tshwane district. Regarding primary healthcare indicators, there were differences between single-group and controlled interrupted time series analysis. Single-group interrupted time series analysis showed a 65% and 32% increase in the number of adults remaining on anti-retroviral therapy in Tshwane and Ekurhuleni districts, respectively (relative risk [RR]: 1.65; 95% confidence interval [CI]: 1.64–1.66; p < 0.0001 and RR: 1.32; 95% CI: 1.32–1.33; p < 0.0001, respectively). However, controlled interrupted time series analysis did not reveal any differences in any of the post-intervention parameters. Furthermore, single-group interrupted time series analysis showed a 2% and 6% increase in the number of clients seen by a professional nurse in the Tshwane and Ekurhuleni districts, respectively (RR: 1.02; 95% CI: 1.01–1.02; p < 0.0001 and RR: 1.06; 95% CI: 1.05–1.07; p < 0.0001, respectively). However, controlled interrupted time series analysis did not show any differences in any of the postintervention parameters. In addition, single-group interrupted time series analysis revealed that there was a 2% decrease and 1% increase in the primary healthcare headcounts for clients aged ≥5 years in Tshwane and Ekurhuleni district (RR: 0.98; 95% CI: 0.97–0.98; p < 0.0001 and RR: 1.01; 95% CI: 1.01–1.02; p < 0.0001, respectively). Similarly, there was a 2% decrease and a 5% increase in the total primary healthcare headcounts in the Tshwane district and Ekurhuleni districts, respectively (RR: 0.98; 95% CI: 0.97–0.98; p < 0.001 and RR: 1.05; 95% CI: 1.04–1.06, p < 0.0001, respectively). However, controlled interrupted time-series analysis revealed no difference in all parameters before and after intervention in terms of total primary healthcare headcounts and primary healthcare headcounts for clients aged ≥5 years. Regarding secondary non-emergency outpatient department headcounts, single-group and controlled interrupted time series analyses revealed similar findings. Despite these similarities, single-group interrupted time series analysis showed a disparate increase in the outpatient department not referred headcounts, which were lower in the Tshwane district (3 387 [95%CI 901, 5 873] [p = 0.010]) than in Ekurhuleni district (5 399 [95% CI: 1 889, 8 909] [p = 0.004]). Conversely, while there was no change in outpatient department referred headcounts in the Tshwane district, there was an increase in headcounts in the Ekurhuleni district (21 010 [95% CI: 5 407, 36 611] [p = 0.011]). Regarding the outpatient department not referred rate, there was a decrease in the Tshwane district (-1.7 [95% CI: -2.1 to -1.2] [p < 0.0001]), but not in the Ekurhuleni district. Controlled interrupted time series analysis showed differences in headcounts for outpatient department follow-up (24 382 [95% CI: 14 643, 34 121] [p < 0.0001]), the outpatient department not referred (529 [95% CI: 29, 1 029 [p = 0.038]), and outpatient department not referred rate (-1.8 [95% CI: -2.2 to -1.1] [p < 0.0001]) between Tshwane the reference district and Ekurhuleni district. Four common long-run trends were found in the relationships and dependencies between primary healthcare indicators measured at the primary healthcare level and the non-emergency secondary level of care needed to forecast future utilisation. First, a 10% increase in outpatient departments not referred headcounts resulted in a 42% (95% CI: 28-56, p < 0.0001) increase in new primary healthcare diabetes mellitus clients, 231% (95% CI: 156-307, p < 0.0001) increase in primary healthcare clients seen by a public medical practitioner, 37% (95% CI: 28-46, p < 0.0001) increase in primary healthcare clients on ART, and 615% (95% CI: 486- 742, p < 0.0001) increase in primary healthcare clients seen by a professional nurse. Second, a 10% increase in outpatient department referrals resulted in an 8% (95% CI: 3-12, p < 0.0001) increase in new primary healthcare diabetes mellitus clients, a 73% (95% CI: 51-95, p < 0.0001) increase in primary healthcare headcounts for clients seen by a medical professional, a 25% (95% CI: 23-28, p < 0.0001) increase in primary healthcare headcounts for clients on ART, and a 44% (95% CI: 4-71, p = 0.026) increase in primary healthcare headcounts for clients seen by a professional nurse. Third, a 10% increase in outpatient department follow-up headcounts resulted in a 12% (95% CI: 8-16, p < 0.0001) increase in primary healthcare headcounts for new diabetes mellitus, 67% (95% CI: 45-89, p < 0.0001) increase in primary healthcare headcounts for clients seen by public medical practitioners, 22% (95% CI: 19-24, p < 0.0001) increase in primary healthcare headcounts for clients on ART, and 155% (95% CI: 118-192, p < 0.0001) increase in primary healthcare headcounts for clients seen by a professional nurse. Fourth, a 10% increase in headcounts for total primary healthcare clients resulted in a 0.4% (95% CI: 0.1-0.8, p < 0.0001) decrease in primary healthcare headcounts for new diabetes clients. Based on these relationships and dependencies, the outpatient department follow-up headcounts would increase from 337 945 in the fourth quarter of 2019 to 534 412 (95% CI: 327 682–741 142) in the fourth quarter of 2025, while the total primary healthcare headcounts would only marginally decrease from 1 345 360 in the fourth quarter of 2019 to 1 166 619 (95% CI: 633 650–1 699 588) in the fourth quarter of 2025. Conclusion -The study findings suggested that improvements in primary health care indicators in National Health Insurance pilot districts could not be attributed to the implementation of contracting private medical practitioners but were likely a result of other co-interventions and transitions in the district. However, it might have resulted in an improved perception of quality of care at primary health care facilities, evidenced by a reduction in the self-referral rate for nonemergency hospital outpatient departments. The study also confirmed the influence of selected primary healthcare and non-emergency outpatient department headcounts on each other by finding four common long-run trends of relationships. Based on these relationships and trends, outpatient department follow-up headcounts are forecasted to increase by two-thirds. Conversely, the total headcount for primary healthcare clients seen by a professional nurse will marginally decrease. Recommendations- Based on the study findings, the bidirectional referral between primary and non-emergency secondary levels of care in the Tshwane district should be strengthened to offset the burden of care at outpatient departments of district hospitals. Thus, the district health information system should include a down-referral indicator to monitor this activity. With the implementation of National Health Insurance, there is a need to improve the perception of quality of care at the primary healthcare level through appropriate training, recruitment, and placement of medical practitioners. Similarly, professional nurses, the core providers of primary healthcare services, should be supported and capacitated in line with the epidemiological transition.
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    Transformation of human resources for health in South Africa: contributions to knowledge and policy
    (2022) Rispel, Laetitia Charmaine
    A health system is defined as “all organisations, people, and actions whose primary intent is to promote, restore, or maintain health. This includes the organisation of people, institutions, and resources (also known as the building blocks) that deliver health care services, as well as intersectoral action to address the determinants of health” (WHO, 2007, p. 2). The core goals of health systems are to improve population health outcomes, ensure responsiveness to communities, and make efficient use of available resources (WHO, 2000).