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Item Occupational exposure to chemicals, and health outcomes, among nail technicians in Johannesburg, South Africa(University of the Witwatersrand, Johannesburg, 2023-08) Keretetse, Goitsemang; Brouwer, Derk H.; Nelson, GillIntroduction: Nail technicians are exposed to chemicals emitted from activities performed in nail salons, including simple buffing of nails, basic manicures and pedicures, application of nail polish, and the application and sculpting of artificial nails. The various products used during these processes may contain volatile organic compounds (VOCs), which pose a health risk to both the nail technicians and their clients. Associated health effects include skin, eye, and respiratory irritation, neurologic effects, reproductive effects, and cancer. The aim of this study was to effects within the formal and informal sectors in Johannesburg, South Africa. In this study, informal nail technicians are defined as those working in nail salons that are not licensed or registered with any formal enterprise or establishment, or in their own capacity. The objectives were 1) to estimate the prevalence of self-reported symptoms associated with the use of nail products, 2) to measure exposures to chemicals in nail products used in the formal and informal nail salons, 3) to investigate the feasibility and reliability of self-assessment of exposure as a method of estimating exposure to chemicals, and 4) to investigate the association between respiratory symptoms (chronic and acute) and chemical exposures in both formal and informal nail technicians. Methods: This was a cross-sectional study. A questionnaire, adapted from other studies, was piloted before being administered to the participating nail technicians. Data were collected from 54 formal and 60 informal nail technicians, regarding sociodemographic characteristics, perceptions of working with nail products, and self-reported symptoms of associated health effects. A subset of 20 formal and 20 informal nail technicians was conveniently selected from the 114 participants for the exposure assessment phase. The two groups were further divided into two groups of 10 for the controlled/expert exposure assessment (CAE) and the self-assessment of exposure (SAE). Personal 8-hr exposure measurements were performed using VOC and formaldehyde passive samplers attached to the participant’s breathing zone over three consecutive days. For the SAE approach, participants conducted their own exposure measurements, while the CAE approach was fully conducted by the principal researcher. Task-based measurements were carried out using a photoionization detector (PID) to measure peak concentrations during specific nail application activities. A probabilistic risk assessment was conducted to estimate the carcinogenic and non-carcinogenic life time risks from exposure to VOCs. Chemical analysis was conducted by a SANAS-accredited laboratory. After correcting for their respective evaporation rates, relative to the evaporation rate of d-limonene (the VOC with the lowest evaporation rate), the adjusted total VOC (TVOC) concentrations were calculated using the 13 VOCs that were detected at a frequency of 30% or more. VOC concentration data below the limit of detection (LoD) were imputed, using the regression on order statistic (Robust ROS) approach. The self-reported symptoms were categorised into neurological effects, respiratory effects, eye irritation, and skin irritation. The ACGIH additive effects formula was used to calculate the combined respiratory effect of selected VOCs. Different statistical tools were used to analyse the data for each objective. Results: Formal and informal nail technicians used different nail products, performed different nail applications, serviced different mean numbers of clients, and were exposed to different concentrations of selected VOCs. Acetone concentrations were higher in formal nail salons, due to the soak-off method used for removing existing nail applications, while methyl methacrylate (MMA) concentrations were higher in informal nail salons - related to acrylic methods being used more frequently in the informal than the formal nail salons. All VOC concentrations were below their respective occupational exposure limits, with the exception of formaldehyde (0.21 mg/m3). TVOC levels were higher in formal nail salons, due to the bystander effect from multiple nail technicians performing nail applications simultaneously. Sixty percent of the informal nail technicians reported health-related symptoms, compared to 52% of the formal nail technicians, and informal male nail technicians reported more symptoms than their female counterparts. All nail technicians' median and 95th percentile non-cancer risks exceeded the acceptable risk of 1 for xylene, 2-propanol, and benzene, while the cancer risk estimates (medians and 95th percentiles) for benzene and formaldehyde exceeded the US EPA cancer risk threshold of 1 x 10-6. Conclusion: This is the first study to assess exposures to VOCs in the often-overlooked informal sector and compare these exposures with those in the formal sector of the nail industry. Personal breathing zone concentration data for nail salon workers were generated in this study, including the informal sector, which is always challenging to access for research. Although banned in many countries, MMA is still used in South Africa in the informal nail sector. The SAE study showed that participatory research is feasible and enables a more reliable estimate of the exposure by expanding the amount of data. Using a combination of shift and task-based measurements was particularly effective in creating exposure profiles of employees and identifying activities that require targeted interventions. There is a need for the nail industry, especially the informal salons, to be more closely regulated, concerning the hazardous chemicals frequently encountered in nail products. Nail salons should reduce exposure frequency by regulating working hours, making informed decisions regarding the procurement of nail products, and adopting safe work practices to reduce emissions from harmful chemicals and thus exposure among nail salon workers and their clients.Item Preventing Coal Mine Dust Lung Disease: Application of Bayesian Hierarchical Framework for Occupational Exposure Assessment in The South African Coal Mining Industry(University of the Witwatersrand, Johannesburg, 2023-10) Made, Felix; Brouwer, Derk; Lavoue, Jerome; Kandala, Ngianga-BakwinBackground: The world's largest energy source is coal with nearly 36% of all the fuel used to produce power. South Africa is the world's top exporter and the seventh-largest producer of coal. In the upcoming years, it is expected that South Africa's coal production output rate will rise. Coal mine dust lung disease (CMDLD) is an irreversible lung disease caused by the production of coal, the emission of dust, and prolonged exposure to the dust. When conducting safety evaluation, exposure is typically reported as an eight-hour time-weighted average dust concentration (TWA8h). In occupational exposure contexts, occupational exposure limits (OEL) are often used as a threshold where workers can be exposed repeatedly without adverse health effects. The workers are usually grouped into homogenous exposure groups (HEGs) or similar exposure groups (SEGs). In South Africa, a HEG is a group of coal miners who have had similar levels and patterns of exposure to respirable crystalline silica (RCS) dust in the workplace. Several statistical analysis methods for compliance testing and homogeneity assessment have been put into use internationally as well as in South Africa. The international consensus on occupational exposure analysis is based on guidelines from the American Industrial Hygiene Association (AIHA), the Committee of European Normalisation (CEN), and BOHS British and Dutch Occupational Hygiene Societies' guidelines (BOHS). These statistical approaches are based on Bayesian or frequentist statistics and consider the 90th percentile (P90) and 95th percentile (P95), with- and between-worker variances, and the lognormal distribution of the data. The current existing practices in South Africa could result in poor or incorrect risk and exposure control decision-making. Study Aims: The study aimed to improve the identification of coal dust overexposure by introducing new methods for compliance (reduced dust exposure) and homogeneity (similar dust exposure level) assessment in the South African coal mining industry. Study Objectives: The objectives of this study were: 1. To compare compliance of coal dust exposure by HEGs using DMRE-CoP approach and other global consensus methods. 2. To investigate and compare the within-group exposure variation between HEGs and job titles. 3. To determine the posterior probabilities of locating the exposure level in each of the OEL exposure categories by using the Bayesian framework with previous information from historical data and compare the findings and the DMRE-CoP approach. 4. To investigate the difference in posterior probabilities of the P95 exposure being found in OEL exposure category between previous information acquired from the experts and the current information from the data using Bayesian analysis. Methods: The TWA(8h) respirable coal dust concentrations were obtained in a cross-sectional study with all participants being male underground coal mine workers. The occupational hygiene division of the mining company collected the data between 2009 and 2018. The data were collected according to the South African National Accreditation System (SANAS) standards. From the data, 28 HEGs with a total of 728 participants were included in this study. In objectives 1 and 2, all 728 participants from the 28 HEGs were included in the analysis. For exposure compliance, the DMRE-CoP accepts 10% exceedance of exposure above the OEL (P90 exposure values from HEGs should be below the OEL). The 10% exceedance was compared to the acceptability criterion from international consensus which uses 5% exceedance above the OEL (P95 exposure is below the OEL) of the lognormal exposure data. For exposure data to be regarded as homogenous, the DMRE-CoP requires that the arithmetic mean (AM) and P90 must fall into the same DMRE-CoP OEL exposure category. The DMRE-CoP on assessment of homogeneity was also compared with the international approaches which include the Rappaport ratio (R-ratio) and the global geometric standard deviation (GSD). A GSD greater than 3 and an R-ratio greater than 2 would both indicate non-homogeneity of the exposure data of a HEG. The GSD and DMRE-CoP criteria were used to assess the homogeneity of job titles exposure within a HEG. In objective 3 a total of nine HEGs which have 243 participants, were included in the analysis. To investigate compliance, a Bayesian model was fitted with a Markov chain Monte Carlo (MCMC) simulation. A normal likelihood function with the GM and GSD from lognormal data was defined. The likelihood function was updated using informative prior derived as the GM and GSD with restricted bounds (parameter space) from the HEGs' historical data. The posterior probabilities of the P95 being located in each DMRE exposure band were produced and compared with the non-informative results and the DMRE approach DMRE-CoP using a point estimate inform of the 90 percentiles. In objective 4, a total of 10 job titles were analysed and selected. The selection of the job titles was based on if they have previous year's data so it can be used to develop prior information in the Bayesian model. The same job titles were found across different HEGs, so to ensure the mean is not different across HEGs, the median difference of a job title exposure distribution across HEGs was statistically compared using the Kruskal-Wallis test, a non-parametric alternative to analysis of variance (ANOVA). Job titles with statistically non-significant exposure differences were included in the analysis. Expert judgements about the probability of the P95 located in each of the DMRE exposure bands were elicited. The IDEA (Investigate", "Discuss", "Estimate" and "Aggregate) expert elicitation procedure was used to collect expert judgements. The SHELF tool was then used to produce the lognormal distribution of the expert judgements as GM and GSD to be used as informative prior. A similar Bayesian analysis approach as in objective 3 was used to produce the probability of the P95 falling in each of the DMRE exposure bands. The possible misclassification of exposure arising from the use of bounds in the parameter space was tested in a sensitivity analysis. Results: There were 21 HEGs out of 28 in objectives 1 and 2 that were non-compliant with the OEL across all methods. According to the DMRE-CoP approach, compliance to the OEL, or exposure that is below the OEL, was observed for 7 HEGs. The DMRE-CoP and CEN both had1 HEG with exposures below the OEL. While the DMRE-CoP showed 6 homogeneous HEGs, however, based on the GSDs 11 HEGs were homogeneous. The GSD and the DMRE-CoP agreed on homogeneity in exposures of 4 (14%) HEGs. It was discovered that by grouping according to job titles, most of the job titles within non-homogenous HEGs were homogenous. Five job titles had AMs above their parent HEG. For objective 3, the application of the DMRE-CoP (P90) revealed that the exposure of one HEG is below the OEL, indicating compliance. However, no HEG has exposures below the OEL, according to the Bayesian framework. The posterior GSD of the Bayesian analysis from non-informative prior indicated a higher variability of exposure than the informative prior distribution from historical data. Results with a non-informative prior had slightly lower values of the P95 and wider 95% credible intervals (CrI) than those with an informative prior. All the posterior P95 findings from both non-informative and informative prior distribution were classified in exposure control category 4 (i.e., poorly controlled since exceeding the OEL), with posterior probabilities in the informative approach slightly higher than in the non-informative approach. Job titles were selected as an alternative group to assess compliance in objective 4. The posterior GSD indicated lower variability of exposure from expert prior distribution than historical data prior distribution. The posterior P95 exposure was very likely (at least 98% probability) to be found in exposure control category 4 when using prior distribution from expert elicitation compared to the other Bayesian analysis approaches. The probabilities of the P95 from experts' judgements and historical data were similar. The non-informative prior generally showed a higher probability of finding the posterior P95 in lower exposure control categories than both experts and historical data prior distribution. The use of different parameter values to specify the bounds showed comparable results while the use of no parameter space at all put the posterior P95 in exposure category 4 with 100% probability. Conclusions: In comparison to other approaches, the DMRE-CoP tend to show that exposures are compliant more often. Overall, all methods show that the majority of HEGs were non-compliant. The HEGs that suggest non-homogeneity revealed that the constituent job titles were homogenous. Application of the GSD criterion indicated that HEGs are more likely to be considered as homogeneous than when using the DMRE-CoP approach. When using the GSD and the DMRE-CoP guidelines, alternative grouping by specific job titles showed a greater agreement of homogeneity. The use of job titles showed that using HEGs following the DMRE-CoP current guidelines might not show high-exposure job titles and would overestimate compliance. Additionally, since job titles within a HEG may be homogeneous or have a different exposure to the parent HEG, exposure variability is not properly recorded when using HEGs. In compliance assessment, it is important to use the P95 of the lognormal distribution rather than the DMRE-CoP approach that use the empirical P90. Our findings suggest that the subgrouping of exposure according to job titles within a HEG should be used in the retrospective assessment of exposure variability, and compliance with the OEL. Our results imply that the use of a Bayesian framework with informative prior from either historical or expert elicitation may confidently aid concise decision making on coal dust exposure risk. Contrary to informative prior distribution derived from historical data or expert elicitation, Bayesian analysis using the non-informative uniform prior distribution places HEGs in lower exposure categories. Results from noninformative prior distributions typically show high levels of uncertainty and variability, so a decision on dust control would be reached with less confidence. The Bayesian framework should be used in the assessment of coal mining dust exposure along with prior knowledge from historical data or professional judgment, according to this study. For exposure, findings are to be reported with high confidence and for sound decisions to be reached about risk mitigation, an exposure risk assessment should be considered while using historical data to update the current data. The study also promotes the use of experts in situations where it is necessary to combine current data with historical data, but the historical data is unavailable or inapplicable.Item Initial loss to follow up among tuberculosis patients: the role of ward-based outreach teams (wbots) and short message service (sms) technology(University of the Witwatersrand, Johannesburg, 2023-03) Mwansa-Kambafwile, Judith Reegan Mulubwa; Menezes, Colin; Chasela, CharlesIntroduction: 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<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.Item Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics and social contact patterns(University of the Witwatersrand, Johannesburg, 2023-03) Kleynhans, Jacoba Wilhelmina; Cohen, Cheryl; Tempia, StefanoBackground: 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 laboratory-confirmed 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 SARS CoV-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.Item 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(University of the Witwatersrand, Johannesburg, 2023-01) Mukudu, Hillary; Igumbor, Jude; Otwombe, Kennedy; Fusheini, AdamBackground: 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 non-emergency 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 post-intervention 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 non-emergency 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.Item Examining the role of affordability, citizen engagement, and social solidarity in determining health insurance coverage in Kenya(University of the Witwatersrand, Johannesburg, 2023-08) Maritim, Beryl Chelangat; Goudge, Jane; Koon, AdamRationale: Healthcare costs cause severe financial hardship globally and many low-and middle-income countries (LMIC) are turning to social health insurance to provide financial risk protection and increase population coverage. However social health insurance schemes in LMICs experience significant growth challenges owing to difficulties reaching informal workers through contributory health insurance systems. Kenya has undertaken several health sector reforms and efforts to increase health insurance coverage but has had limited success in capturing the large proportion of informal workers. The broad aim of this study was to describe and assess the reasons for low enrolment in the national insurance scheme among the Kenyan informal worker households in Bunyala sub-County, Busia County, Kenya. It focused on the role of affordability of premiums, citizen engagement and social solidarity in NHIF coverage among the informal worker households. Methods: This study employed an explanatory mixed methods study approach with quantitative and qualitative primary data collection. The quantitative phase included a household survey (n=1,773) from which 36 respondents were purposively identified to participate in in-depth household interviews. The study also conducted 6 focus group discussions (FGD) groups with community stakeholders, and 11 key informant interviews with policymakers and implementers at national and sub-national level. Quantitative data was analyzed using R while qualitative data was analyzed thematically using both manual methods and NVIVO software. Results: Only 12% of households reported having health insurance and NHIF was unaffordable for the majority of households, both insured (60%) and uninsured (80%). Rural households spent a significant proportion (an average of 12%) of their household budget on out of pocket (OOP) expenses on health care, with both insured and uninsured households reporting high OOP spending and similar levels of impoverishment due to OOP I found that there was high awareness of NHIF but low levels of knowledge on services, feedback and accountability mechanisms. Barely half (48%) of the insured were satisfied with the NHIF benefit package. Nearly all of the respondents (93%) were unaware of mechanisms to reach NHIF for feedback or complaints. Respondents expressed desire to know the NHIF performance but expressed high levels of mistrust in the fund owing to negative reports on NHIF performance in the media. This study found high willingness to prepay for healthcare among those without insurance (87.1%) with competing priorities, low incomes, poor access and quality of health services, lack of awareness of flexible payment options cited as barriers to enrolment. More than half of respondents expressed willingness to tolerate risk and income cross-subsidization suggesting strong social solidarity, which increased with socio-economic status. Participants expressed concerns about value of health insurance given its cost, availability and quality of services, and financial protection relative to other social and economic household needs. Households resorted to borrowing, fundraising, taking short term loans and selling family assets to meet healthcare costs. Implications: This study provides a nuanced insight into the challenges of increasing coverage among rural informal worker households with considerations for rolling out mandatory NHIF membership. The findings imply that majority of the informal worker households in rural areas need assistance to afford NHIF. These study findings also highlight the importance of fostering and leveraging existing social solidarity to move away from flat rate contributions and apply more progressive contribution that allow for fairer risk and income cross-subsidization. Finally, the government should rapidly scale up the indigent program to cover most rural informal worker households. There is also need to invest in robust strategies to effectively identify subsidy beneficiaries. Significant reforms of NHIF and health system are required to provide adequate health services and financial risk protection for rural informal households in Kenya. NHIF also needs to evaluate their citizen engagement and accountability frameworks to increase awareness, member satisfaction, improve state accountability to citizens and incorporate citizen voice in their processes.Item Occupational Exposure to Chrysotile Asbestos in the Chrysotile Asbestos Cement Manufacturing Industry in Zimbabwe(University of the Witwatersrand, Johannesburg, 2023-08) Mutetwa, Benjamin; Brouwer, Derk; Moyo, DinganiIntroduction: Asbestos is a generic term for a group of naturally occurring silicates that principally include serpentine variety (white chrysotile asbestos) and the amphibole variety, consisting of crocidolite (blue asbestos), amosite (brown asbestos), anthophyllite, actinolite and tremolite. Asbestos exposure has drawn much international, regional and national attention as it presents significant public and occupational health concerns. All asbestos types are known to cause asbestos related disease. Objectives: The objectives of this PhD were: 1. To analyse trends in airborne chrysotile asbestos fibre exposure data obtained by the chrysotile asbestos cement manufacturing factories for the period 1996 to about 2016. 2. To establish a job exposure matrix (JEM) to estimate occupational exposure levels in the Zimbabwe chrysotile asbestos industry using available exposure data. 3. To predict asbestos related diseases (ARDs) namely lung cancer, mesothelioma, gastrointestinal cancer and asbestosis in the chrysotile asbestos cement manufacturing industry through exposure levels obtained in the factories. 4. To assess amphibole contaminants in the chrysotile asbestos fibre being used by the factories in the manufacture of asbestos cement (AC) products. 5. To examine approaches for prevention of exposure to chrysotile asbestos fibre and some perspectives on the debate on asbestos ban. Methodology: A retrospective cross-sectional study using the factories personal chrysotile exposure data was designed to evaluate exposure patterns over time. Analysis involved close to 3000 personal exposure measurements extracted from paper records in the two-asbestos cement (AC) manufacturing factories in Harare and Bulawayo, covering the period 1996-2020. Exposure trends were characterised according to three to four time periods and calendar years to gain insight into exposure trends over time. Operational areas for which personal exposure data were available were saw cutting, fettling table, kollergang, moulded goods, ground hard waste, laundry room, and pipe making operations in the case of the Bulawayo factory. The standard method of the Asbestos International Association (AIA) Recommended Technical Membrane Filter Reference Method (AIA, 1982) was reported to be used to collect personal chrysotile asbestos fibre in various operational areas over the years. Quantitative personal exposure chrysotile fibre concentration data collected by the two factories over the considered period were used to construct the JEM. Analysis of amphiboles in locally produced and imported raw chrysotile fibre samples used in the manufacturing processes was done using Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (SEM). Prediction of asbestos related diseases (ARDs) was done by combining the JEM converted to cumulative exposures, with OSHA’s linear dose effect model in which asbestos related cancers was derived using linear regression equations established for lung cancer, mesothelioma and gastrointestinal cancer by plotting estimates of cancer mortality cases versus respective cumulative exposures. The linear regression equations were applied to establish estimates of possible cancer mortality while for asbestosis, the linear in cumulative dose equation, Ra = m(f)(d), where Ra – predicted incidence of asbestosis, m – slope of linear regression taken as 0.055, f – asbestos fibre concentration and d – duration of exposure, was used to estimate possible asbestosis cases over the respective duration of exposure at 1, 10, 20 and 25 years. To examine arguments for approaches used for prevention of exposure to chrysotile asbestos and examine some perspectives on the debate on asbestos ban, a literature search was conducted. Literature materials that advocated for the complete ban of all forms of asbestos including chrysotile as the only means of control of exposure and that, which argues for the controlled use approach, were reviewed. Search words used in literature search were chrysotile asbestos exposure, asbestos-cement, ban asbestos, controlled use, asbestos related disease, mesothelioma, lung cancer and asbestosis. Data analysis was conducted using IBM SPSS version 26. For analysis, monthly averaged personal exposure levels for the factories were used. Mean personal airborne chrysotile fibre concentrations were analysed per operational area per factory and trends in airborne fibre concentrations over the years were displayed graphically. ANOVA was applied with the aim categories and determine whether there was a statistically significant difference in exposure concentrations between four time-periods for various jobs. Additionally, a Tukey Post Hoc Test (Tukey’s Honest Significance Difference test) was run to find out which specific group means of time periods (compared with each other) were different. Results and Discussion: Trends in airborne chrysotile asbestos fibre concentrations in asbestos cement manufacturing factories in Zimbabwe from 1996 to 2016. Mean personal exposure chrysotile asbestos fibre concentrations generally showed a downward trend over the years in both factories. Exposure data showed that over the observed period 57% and 50% of mean personal exposure chrysotile asbestos fibre concentrations in the Harare and Bulawayo factories, respectively, were above the Zimbabwean OEL of 0.1 f/mL, with overexposure generally being exhibited before 2008. Overall, personal exposure asbestos fibre concentrations in the factories dropped from 0.15 f/mL in 1996 to 0.05–0.06 f/mL in 2016, a decrease of 60–67%. Statistically significant relationships were observed over time between exposure levels and calendar year and time periods (p<0.001) for all occupational categories other than fettling table operations in Harare. The general decline in exposure over time from 1996 to 2016 suggests good occupational safety and health (OSH) framework being implemented by the two factories over the years, with the years after 2008 showing much lower exposure levels below the OEL particularly for the Bulawayo factory. However, for the period 2018 to 2020 exposures in the Harare factory were much higher than the proceeding time period of 2009 to 2016 due to movement of trucks within the factory as they come to load concrete tiles and other products making it possible for residual chrysotile fibre left during manufacture of AC products to become airborne. The company reported no clean-up of asbestos in the factory or wetting of the floors to control dust, hence the possible increased levels of chrysotile asbestos fibre for the period 2009 to 2016. The general decreasing trends in exposure to chrysotile asbestos fibre may also be viewed from the fact that industry was responding to anticipated lowering of chrysotile OEL as a result of increased calls to ban all forms asbestos, triggering the scaling up of exposure controls in the factories. Job Exposure Matrix for chrysotile asbestos fibre in the asbestos cement manufacturing (ACM) industry in Zimbabwe. On average, all jobs/occupations in both factories had annual mean personal exposure concentrations exceeding the OEL of 0.1 f/ml, except for the period 2009 to 2016 in the Harare factory and for the time-periods 2009 to 2020 in the Bulawayo factory. Despite Harare factory having no AC manufacturing activity since 2017, personal exposure concentrations showed elevated levels for the period 2018-2020. Amphiboles were detected in almost all presently collected bulk samples of chrysotile asbestos analysed. The established JEM, which was successfully generated from actual local quantitative exposure measurements, can be used in evaluating historical exposure to chrysotile asbestos fibre, to better understand, inform and predict occurrence of ARDs in future. Prediction of Asbestos Related Diseases (ARDs) and chrysotile asbestos exposure concentrations in asbestos-cement (AC) manufacturing factories in Zimbabwe. The results show that more cancer and asbestosis cases were likely to be experienced among those workers exposed before 2008 as exposure levels (0.11-0.19 f/ml) and subsequently cumulative exposures were generally much higher than those experienced after 2008 (0.04-0.10 f/ml). After a possible working exposure period of 25 years, overall cancer cases, i.e., estimates of possible cancer cases in a factory for each respective duration of exposure, predicted in the Harare factory were 325 cases per 100 000 workers while for the Bulawayo factory 347 cancer cases per 100 000 workers exposed may be experienced. Asbestosis cases likely to be detected after 25-years duration of exposure ranged from 50 to 260 cases per 100 000 workers (0.05 to 0.26% incidence of asbestosis) for various jobs. Possible high numbers of ARDs are likely to be associated with specific tasks/job titles, e.g., saw cutting, kollergang, fettling table, ground hard waste and possibly pipe making operations as cumulative exposures though lower than reported in other studies may present higher risk of health impairment. Examining approaches for prevention of exposure to chrysotile asbestos and some perspectives on the debate on ban of asbestos. Different perspectives on approaches to the prevention of exposure to asbestos have been presented. One position argues that there exist major differences in health risk between amphiboles and chrysotile asbestos, that low exposure and risk experienced under today’s workplace conditions are completely different to high-risk exposures experienced in the past where occupational hygiene conditions were very poor and levels of education, awareness and training in the asbestos industry was low compared to the present situation. It is further argued that there are low levels of exposure below which risk of health impairment becomes insignificant, hence controlled use approach as a measure of exposure control can be successfully applied. However, the other position holds that all forms of asbestos including chrysotile are equipotent, that there is no safe level of exposure, that controlled use is not practical and that there is no merit in continuing use of chrysotile asbestos in light of safer alternatives available today. Both positions appear plausible. Banning as a form of control measure occupies a high level in the hierarchy of controls with potential to eliminate the hazard and risk; nonetheless, the banning of chrysotile may imply substitution with materials that have been reported to carry health risk of cancer and other health impairments. On balance, banning may possibly not be the panacea of elimination of ARDs, in view of the fact that some other forms of mining such as diamond and gold mining have been associated with exposure to amphibole asbestos. The controlled use approach may provide real possibilities of prevention of exposure to levels that presents minimal risk to health if effectively implemented as applied to a range of occupational hazards with success. Conclusion: Not much is known about exposure to airborne chrysotile asbestos fibre exposure in Zimbabwe chrysotile asbestos cement (AC) manufacturing industry. This study may constitute the single largest characterisation of personal exposure chrysotile asbestos fibre concentrations data set in Zimbabwe in which about 3000 airborne personal exposure measurements collected from company records spanning a period of about 25 years, were used in assessing exposure trends over time, building a job exposure matrix, and predicting possible ARDs namely lung cancer, mesothelioma, gastrointestinal cancer and asbestosis in Zimbabwe AC manufacturing industry. The study adds considerably to future epidemiological studies, gives insights into possible magnitude of ARDs that may be observed in AC factories and possibly analysis of exposure response relationships that may be linked to exposure episodes in the distant past. The study also gives some insights into possible amphibole contaminants that may be associated with local and imported chrysotile asbestos that is used in the AC manufacturing processes and thus providing support for a more comprehensive investigation into the presence of amphiboles in chrysotile asbestos in Zimbabwe. The study also provides some perspectives on approaches to prevention of exposure to asbestos and some aspects on the call to ban all forms of asbestos including chrysotile. Personal exposure chrysotile fibre concentration data in the two AC manufacturing factories showed a downward trend over the years, and that overexposure as evaluated against the OEL of 0.1 f/ml were being exhibited largely before 2008. The job categories with high exposure levels were saw cutting, fettling, ground hard waste, laundry room and multi-cutter operator and such jobs are likely to be associated with high risk of ARDs particularly for exposures happening before 2008. Moulded goods operators were associated with low exposures as process is generally a wet process. Despite exposure concentrations being high in the earlier time periods of 1996 to 2008, declines over time particularly for Bulawayo factory which has continued to use chrysotile to date, suggests that controlled use approach may yield exposures that may present minimal risk to health of those exposed to chrysotile asbestos. While banning can still be considered as a way to eliminating ARDs, it may not necessarily be the panacea for prevention of ARDs, as controlled use approach may perhaps still present real possibilities of prevention of exposure to levels that may present minimal risk to health impairment if effectively implemented as applied to a range of hazards with some success. Banning would possibly imply substitution by materials reported to be hazardous to health. These results can be used in future epidemiological studies, and in predicting the occurrence of asbestos-related diseases in Zimbabwe.Item Engaging the public in priority setting for health in rural South Africa(University of the Witwatersrand, Johannesburg, 2023-10) Tugendhaft, Aviva Chana; Hofman, Karen; Kahn, Kathleen; Christofides, NicolaIntroduction: The importance of public engagement in health priority setting is widely recognised as a means to promote more inclusive, fair, and legitimate decision-making processes. This is particularly critical in the context of Universal Health Coverage, where there is often an imbalance between the demands for and the available health resources. In South Africa, public engagement is protected in the Constitution and entrenched in policy documents; yet context specific tools and applications to enable this are lacking. Where public engagement initiatives do occur, marginalised voices are frequently excluded, and the process and outcomes of these initiatives are not fully evaluated. This hampers our understanding of public engagement approaches and how to meaningfully include important voices in the priority setting agenda. The aim of this doctoral (PhD) research was to investigate the feasibility and practicality of including the public in resource allocation and priority setting for health in a rural setting in South Africa using an adapted deliberative engagement tool called CHAT (Choosing All Together). Methods: The PhD involved the modification and implementation of the CHAT tool with seven groups in a rural community in South Africa to determine priorities for a health services package. For the modification of CHAT, desktop review of published literature and policy documents was conducted, as well as three focus group discussions, with policy makers and implementers at national and local levels of the health system and the community, and modified Delphi method to identify health topics/issues and related interventions appropriate for a rural setting in South Africa. Cost information was drawn from various national sources and an existing actuarial model used in previous CHAT exercises was employed to create the board. The iterative participatory modification process was documented in detail. The implementation process was analysed in terms of the negotiations that took place within the groups and what types of deliberations and engagement with trade-offs the participants faced when resources were constrained. In terms of the outcomes, the study focused on what priorities were most important to the rural community within a constrained budget and the values driving these priorities, but also how priorities might differ amongst individuals within the same community and the characteristics associated with these choices. Qualitative data were analysed from the seven group deliberations using the engagement tool. Content analysis was conducted, and inductive and deductive coding was used. Descriptive statistics was used to describe the study participants using the data from a demographic questionnaire and to show the group choices from the stickers allocated on the boards from the groups rounds. The investment level (sticker allocation) of all study participants was recorded at each stage of the study. From these the number of stickers allocated to each topic by the participants was calculated by adding up the number of stickers across interventions selected by the participant by topic. The median and interquartile range across study participants was calculated for the topic totals. To examine differences in sticker allocations, Wilcoxon rank sum tests were performed for differences across participant categories and sticker allocations in the final round of CHAT. Findings: Based on the outcomes, seven areas of health need and related interventions specific for a rural community context were identified and costed for inclusion in the CHAT board. These include maternal, new-born and reproductive health; child health; woman and child abuse; HIV/AIDS and TB; lifestyle diseases; quality/access; and malaria. The CHAT SA board reflects both priority options of policymakers/ experts and of community members and demonstrates some of the context specific coverage decisions that will need to be made under NHI. The CHAT implementation shows that the rural communities mostly prioritised curative services over primary prevention due to perceived inefficacy of existing health education and prevention programmes. The exercise fostered strong debates and deliberations. Specifically, the groups engaged deeply with trade-offs between costly treatment for HIV/AIDS and those for non-communicable disease. Barriers to healthcare access were of particular concern and some priorities included investing in more mobile clinic. The individual level priorities were mostly aligned with societal ones, and there were no statistically significant differences between the individual and group choices. However, there were some statistically significant differences between individual priorities based on demographic characteristics such as age. The study demonstrates that giving individuals greater control and agency in designing health services packages can increase their participation in the priority setting process, align individual and community priorities, and enhance the legitimacy and acceptability of the decision-making process. In terms of reconciling plurality in priority setting for health, group deliberative approaches help to identify social values and reconcile some of the differences, but additional individual voices may also need to be considered alongside group processes, especially among the most vulnerable. Conclusion: This research marks the first instance of modifying and implementing a deliberative tool for priority setting in a South African rural context. The findings shed light on the process and some of the outcomes of this approach within a vulnerable community, offering insights into public engagement in priority setting more broadly. The study demonstrates that participatory methods are feasible in modifying public engagement tools such as CHAT and can be adapted to different country contexts, potentially enhancing the priority setting process. Regarding the implementation of CHAT, the study provides an example of how a rural community grappled with resource allocation decisions, considered different perspectives and societal implications, and set priorities together. The research also highlights the priorities of this rural community, the social values driving their choices, and individual characteristics that are important to consider when setting priorities. The work demonstrates that meaningful public engagement includes various factors that interrelate and impact one another and that could inform a dynamic and cyclical approach going forward, as well as the importance of transparency during all stages of the process.Item The relationship between antenatal food insecurity, maternal depression and birthweight and stunting: results from the National Income Dynamics Study (NIDS)(University of the Witwatersrand, Johannesburg, 2023-07) Harper, Abigail Joan; Mall, Sumaya; Rothberg, Alan; Chirwa, EsnatBackground: Maternal food insecurity is an important social determinant of health and has been associated with adverse birth and pregnancy outcomes as well as depressive symptoms. Pregnant women and new mothers are vulnerable to both food insecurity and depression. This thesis investigated the relationships between maternal food insecurity, depressive symptoms and low birthweight and stunting using nationally representative longitudinal data from the National Income Dynamics Study (NIDS). In addition, the thesis also examined the association between various food security indicators and adult and child anthropometry. Methods: The NIDS data included three experiential indicators of food security (adult and child hunger in the household in the past twelve months and household food sufficiency in the past 12 months) as well as household dietary diversity in the past thirty days and household food expenditure in the past thirty days. Three of the included studies utilised NIDS data. a) Chapter 4 was a scoping review that examined dietary diversity and maternal depression. b) Chapter 5 gives a broad overview by using cross-sectional data from wave 1 to examine food security indicators in relation to adult and child anthropometry. c) Chapter 6 used maternal data from Wave 1 of NIDS and child data from wave 3 of NIDS to longitudinally examine maternal depression and food insecurity during the periconceptional and antenatal period in relation to a continuous measure of birthweight and children’s height-for-age scores. In this vein, Chapter 6 employs different statistical measures to achieve longitudinal perspectives. d) Chapter 7 used the same dataset as Chapter 6 to examine various maternal exposures in more depth including food security indicators, alcohol use and other maternal characteristics in relation to binary measures of low birthweight and stunting among children born during the reference period. e) The final article used mobile survey data from the MomConnect database, a government database of pregnant and postnatal women. Results: a) For the scoping review, a total of 813 records were screened and 11 articles from 13 different studies met the inclusion criteria. The findings on maternal depression and maternal dietary diversity were mixed; The findings on maternal depression and children’s dietary diversity were also mixed. In the studies that examined maternal depression and dietary diversity as predictor variables for child outcomes, the findings on depression were mixed but dietary diversity was consistently associated with both cognitive and linear growth outcomes among children. b) Among children, the prevalence of stunting was 18.4% and the prevalence of wasting and overweight was 6.8% and 10.4% respectively. Children <5 and adolescents with medium dietary diversity were significantly more likely to be stunted than children with high dietary diversity. None of the indicators were associated with stunting in children aged 5-9. Among stunted children, 70.2% lived with an overweight or obese adult, the double burden of malnutrition. Among adults, increased dietary diversity increased the risk of adult overweight and obesity. c) Maternal food insecurity significantly increased the risk of depression among periconceptional and pregnant women but there was no association between maternal depression, food insecurity and mean birthweight or height-for age scores among children. d) Women who reported a child going hungry in the household in the past 12 months were significantly more likely to give birth to a low birthweight infant during the reference period. Low dietary diversity among periconceptional and pregnant women was associated with stunting among children five years later. Low birthweight significantly increased the risk of stunting among children. e) The prevalence of depression in the sample was 16% and pregnant women and new mothers who reported hunger in the household were significantly more likely to be depressed. The qualitative component of the study revealed that women’s main worries could be broadly divided into three categories; worries about hunger and food insecurity, fears that they or their children would be infected with Covid 19 and concerns about unemployment during the lockdown. Conclusion: The studies included in this PhD study demonstrate that food insecurity is an important social determinant of both physical and mental health and a potentially modifiable risk factor for low birthweight and stunting. In both studies that examined maternal depression, food insecurity significantly increased the risk of depression among periconceptional women as well as pregnant women and new mothers. In addition, food insecurity is associated with adverse child health outcomes (low birthweight, stunting and wasting). However, experiential measures of food insecurity are not associated with stunting among young children or adolescents while dietary diversity is. Dietary diversity consistently emerged as an important indicator for children’s linear growth as well as cognitive development in the scoping review. Holistic interventions that focus on the social determinants of health such as food security may improve maternal depressive symptoms among women in resource poor settings. Dietary diversity tools could be refined to also include a category for processed foods given the nutrition transition occurring in many LMICS. More longitudinal research with repeated measurements is required to elucidate the relationship between maternal depression and child health outcomes.Item Comparing health inequalities in maternal health: An analysis of the South African Demographic and Health Surveys (SADHS) 1998 and 2016(University of the Witwatersrand, Johannesburg, 2023-09) Holden, Celeste Claire; Blaauw, DuaneBackground: Inadequate access to maternal health services (MHS) is directly linked to maternal and neonatal mortality and morbidity. South Africa (SA) is known to be an unequal society. Researching and documenting the utilisation and access to MHS can assist in the appropriate redirection of services to ensure equitable service delivery. The study identifies differences in MHS access between ethnicity groups, residence, province, maternal education level and household wealth quintile. The study quantifies the inequalities in access to MHS in SA in 1998 and 2016, and then evaluates the change in inequalities between the two periods. Methods: Data was analysed from the 1998 and 2016 South African Demographic and Health Surveys. First. the study identifies differences in MHS access between ethnic groups, residence, province, maternal education level and household wealth quintile using regression analyses. Then, the inequalities related to access of MHS in 1998 and 2016 are calculated using the relative (RII) and slope (SII) index of inequality and the concentration index (CI). Lastly, the inequalities between 1998 and 2016 were compared using generalised linear models, indicating whether inequalities increased, decreased, or remained the same. All analyses were done in Stata and adjusted for the multistage-stratified sampling of the surveys. Results: Utilisation of MHS in SA varies between different groups based on ethnicity, residence, province, mothers’ education level, and wealth quintile. In 1998 and 2016, Black/African women have the least utilisation of all MHS. A clear pattern is seen where women with higher education and high wealth quintile, have increased MHS utilisation. In most cases, the inequalities narrowed between 1998 and 2016 for all MHS. However, inequalities are still present in 2016 for many MHS. For example, using simple inequality measures, the largest inequalities in 2016 are seen between women of different ethnicities accessing four or more antenatal visits (ANC4), where there is a 11.1 percentage point difference between the highest group (White & Indian/Asian) and the lowest group (Black/African). For complex inequality measures, there are still significant relative and absolute inequalities in antenatal visits in 2016 for maternal education (RII: 1.25; SII: 1.14) and household wealth quintile (RII: 1.23; SII: 1.11). Conclusions: Between 1998 and 2016, population-level utilisation to MHS increased in all MHS and the majority of within group inequalities narrowed over time. However, inequalities still exist in all maternal health outcomes. SA has implemented multiple programmes and policies to address inequalities in MHS and decrease maternal mortality and morbidity. However, these need to be continuously monitored and evaluated based on the latest data to ensure that efforts are going towards addressing the specific groups where inequalities are still present.