OpenUCT :: Browsing by Author "Silal, Sheetal"

Browsing by Author "Silal, Sheetal"

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Open Access
An agent-based model of the emergency medical services system in Nelson Mandela Bay municipality
(2024) Cope, Sky; Silal, Sheetal
Inefficient EMS systems can lead to delays in accessing urgent medical care and increased mortality for critically ill or injured patients. In the Nelson Mandela Bay district of South Africa's Eastern Cape province, the public EMS system struggles to meet its own response time targets. In addition to long response times, staff and vehicles are not always allocated efficiently, as highly-skilled personnel and specialised vehicles are frequently used for responding to low priority or planned patient transport calls. This decreases the quality of medical care provided to the most critically ill patients. The aim of this research is to improve patient outcomes in Nelson Mandela Bay's under resourced public EMS system, which serves the majority of the local population, including those who are unable to afford private EMS. It therefore has the potential to improve access to EMS for the most underprivileged communities, and enhance healthcare equity in the re gion. To achieve this, the research provides decision-makers in the Eastern Cape Department of Health (ECDoH) with a set of evidence-based recommendations for reducing response times, and improving the efficiency of staff and vehicle allocations. These recommendations are sen sitive to the resource-limited nature of the setting, and prioritises interventions that do not require additional staff or vehicles. The EMS system was modelled using an agent-based simulation model, which enables multiple sources of variation in the system to be explicitly accounted for, and nuanced scenarios to be investigated. The model was built and validated using anonymised EMS call data, a smaller dataset of precise response times, and travel time estimates from Google Maps. A key finding of this research is that the median response time of Priority 1 calls can be reduced to below the 30 minute target by implementing changes to dispatching, rerouting and prioritisation behaviour alone, and without increasing resources. These improvements come at the expense of substantial increases in median response times for lower priority calls, but these increases can be counteracted by moderately scaling up the number of staff employed. Improving the accuracy of dispatchers in triaging calls was identified as a particularly effective method of reducing response times, without considerable increases to response times for other call types. A number of policy recommendations were formulated based on these results. These will be presented to management in the Eastern Cape Department of Health, aiming to guide policy interventions for Nelson Mandela Bay's EMS system.
Open Access
Analysing fuel transactions of government vehicles in the Eastern Cape, South Africa
(2025) Tavares, Jared; Silal, Sheetal
Fuel management and fraud detection in government fleets are critical issues that have far-reaching financial and operational implications. To address these challenges, an investigation of fuel usage patterns and anomalies in the Eastern Cape Province government fleet in South Africa from April 2021 to January 2022 was conducted. Through the application of exploratory data analysis, clustering techniques, and predictive modelling, the research uncovers valuable insights that can be used to optimise fuel consumption and detect fraudulent activities within the fleet. Univariate and bivariate analyses reveal distinct patterns in fleet composition, transaction volumes, and fuel eJiciency across various vehicle makes, model derivatives, and departments. The use of clustering techniques enables the identification of distinct vehicle segments and transaction patterns, emphasising the importance of considering contextual factors when analysing fuel usage. To detect potential fraud, three key indicators are developed: abnormally large transactions, frequent transactions, and fuel price diJerences. Predictive models, including XGBoost, Multi-layer Perceptron, and Random Forest, are employed to automate the classification of transactions based on these fraud indicators. The Multi-layer Perceptron demonstrates the best performance, achieving an accuracy of 87% on the test set. The dissertation acknowledges limitations due to the scope of the data and missing information for certain geographic variables such as district and site. Future research could expand the geographical and temporal range, incorporate qualitative data, explore real-time monitoring systems, and investigate vehicle maintenance and fuel eJiciency. The present research makes a noteworthy contribution to the knowledge of fuel management and fraud detection in government fleets by oJering a data-driven approach to expose ineJiciencies and anomalies. The insights and methodologies presented serve as a foundation for future research and practical applications, ultimately leading to more eJicient, cost-eJective, and transparent fleet operations.
Open Access
Analysis of gender wage gap using mixed effects models
(2025) Chikanya, Magnolia M; Er, Sebnem; Silal, Sheetal
Despite government interventions, the gender wage gap persists in workplaces. While reports on whether the gap is widening or narrowing vary, addressing this issue remains crucial. Traditionally, researchers have employed methods like the Blinder-Oaxaca decomposition and quantile regression to estimate the gender wage gap. However, these approaches often leave a high unexplained variance attributed to discrimination. In existing studies, gender wage gap estimates have typically been aggregated, and attempts to disaggregate the analysis have focused on broader levels such as occupations and salary bands. To delve deeper, human resource data from the National Department of Health in South Africa Eastern Cape province was leveraged. The goal was to analyze the gender wage gap for each job title using a novel approach: linear mixed effects regression. The linear mixed effects model captures both systematic trends and unexplained variability simultaneously to provide a more comprehensive understanding of the gender wage gap. Here are the key findings: 1. The unexplained variance in gender wage gap was remarkably low, accounting for only 3% of total variance. This indicates that the model captures most of the variability in the data as a result there is minimal unexplained variation. 2. Job titles emerged very significant by explaining 83% of the total random variance. This highlights the significance of considering specific roles when analyzing gender wage gap. 3. Over time, interesting patterns were observed. From 2010, the gender wage gap narrowed, but starting around 2015, it gradually widened again. 4. Encouragingly, 42% of the job title groups showed a gender wage gap in favor of women. Additionally, a substantial proportion of females occupied managerial and highly skilled positions. Therefore, incorporating random effects techniques through linear mixed effects regression enriched the analysis of gender wage gap. By examining job titles individually, detailed insights into this complex issue were gained. These findings underscore the importance of considering both fixed and random effects when studying wage disparities.
Open Access
Evidence-based vaccinology: supporting evidence-informed considerations to introduce routine hepatitis A immunization in South Africa
(2023) Patterson, Jenna; Kagina, Benjamin; Cleary Susan; Muloiwa, Rudzani; Hussey, Gregory; Silal, Sheetal
Hepatitis A is a vaccine preventable disease caused by the Hepatitis A Virus (HAV). Currently, South Africa is classified by the World Health Organization (WHO) as a high hepatitis A endemic region where 90% of children are assumed to be “naturally immunised” following HAV exposure before the age of 10 years old. In high hepatitis A endemic settings, routine vaccination against HAV is not necessary due to high rates of “natural immunization”. Recent data suggest a possible shift from high to intermediate HAV endemicity may be occurring in South Africa. Countries with intermediate HAV endemicity and no routine hepatitis A vaccination program have a high risk of experiencing hepatitis A outbreaks and high costs associated with care. Currently, there is no routine vaccination program against HAV in South Africa. The aim of this PhD was to generate evidence for decision making on whether a routine vaccination program against HAV should be considered for introduction into the South African Expanded Program on Immunizations (EPI-SA). The objectives included gathering context-specific evidence on the epidemiologic features of hepatitis A, clinical characteristics of the disease, hepatitis A vaccine characteristics and cost of case management. Using this evidence, the PhD estimated the future epidemiology of hepatitis A and impacts of routine hepatitis A vaccination scenarios in the country. The PhD's overall methods were informed by the principles of Evidence-Based Vaccinology for developing vaccine recommendations. The project included a mixed-methods approach: systematic reviews, a retrospective clinical folder review, mathematical modelling, and economic evaluation. A dynamic transmission model was built to forecast the future epidemiology of hepatitis A and to simulate the impacts of several different childhood hepatitis A vaccination strategies in South Africa. Selected findings have been published in relevant peer-reviewed journals. In addition, a technical dossier was prepared to submit to the South African National Advisory Group on Immunization (NAGI) on behalf of the Hepatitis A Working Group for considerations of introducing hepatitis A vaccination into the South African EPI.
Open Access
Exploring inequalities in access to and use of maternal health services in South Africa
(BioMed Central Ltd, 2012) Silal, Sheetal; Penn-Kekana, Loveday; Harris, Bronwyn; Birch, Stephen; McIntyre, Diane
BACKGROUND: South Africa's maternal mortality rate (625 deaths/100,000 live births) is high for a middle-income country, although over 90% of pregnant women utilize maternal health services. Alongside HIV/AIDS, barriers to Comprehensive Emergency Obstetric Care currently impede the country's Millenium Development Goals (MDGs) of reducing child mortality and improving maternal health. While health system barriers to obstetric care have been well documented, "patient-oriented" barriers have been neglected. This article explores affordability, availability and acceptability barriers to obstetric care in South Africa from the perspectives of women who had recently used, or attempted to use, these services. METHODS: A mixed-method study design combined 1,231 quantitative exit interviews with sixteen qualitative in-depth interviews with women (over 18) in two urban and two rural health sub-districts in South Africa. Between June 2008 and September 2009, information was collected on use of, and access to, obstetric services, and socioeconomic and demographic details. Regression analysis was used to test associations between descriptors of the affordability, availability and acceptability of services, and demographic and socioeconomic predictor variables. Qualitative interviews were coded deductively and inductively using ATLAS ti.6. Quantitative and qualitative data were integrated into an analysis of access to obstetric services and related barriers. RESULTS: Access to obstetric services was impeded by affordability, availability and acceptability barriers. These were unequally distributed, with differences between socioeconomic groups and geographic areas being most important. Rural women faced the greatest barriers, including longest travel times, highest costs associated with delivery, and lowest levels of service acceptability, relative to urban residents. Negative provider-patient interactions, including staff inattentiveness, turning away women in early-labour, shouting at patients, and insensitivity towards those who had experienced stillbirths, also inhibited access and compromised quality of care. CONCLUSIONS: To move towards achieving its MDGs, South Africa cannot just focus on increasing levels of obstetric coverage, but must systematically address the access constraints facing women during pregnancy and delivery. More needs to be done to respond to these "patient-oriented" barriers by improving how and where services are provided, particularly in rural areas and for poor women, as well as altering the attitudes and actions of health care providers.
Open Access
Factors associated with patterns of plural healthcare utilization among patients taking antiretroviral therapy in rural and urban South Africa: a cross-sectional study
(BioMed Central Ltd, 2012) Moshabela, Mosa; Schneider, Helen; Silal, Sheetal; Cleary, Susan
BACKGROUND: In low-resource settings, patients' use of multiple healthcare sources may complicate chronic care and clinical outcomes as antiretroviral therapy (ART) continues to expand. However, little is known regarding patterns, drivers and consequences of using multiple healthcare sources. We therefore investigated factors associated with patterns of plural healthcare usage among patients taking ART in diverse South African settings. METHODS: A cross-sectional study of patients taking ART was conducted in two rural and two urban sub-districts, involving 13 accredited facilities and 1266 participants selected through systematic random sampling. Structured questionnaires were used in interviews, and participant's clinic records were reviewed. Data collected included household assets, healthcare access dimensions (availability, affordability and acceptability), healthcare utilization and pluralism, and laboratory-based outcomes. Multiple logistic regression models were fitted to identify predictors of healthcare pluralism and associations with treatment outcomes. Prior ethical approval and informed consent were obtained. RESULTS: Nineteen percent of respondents reported use of additional healthcare providers over and above their regular ART visits in the prior month. A further 15% of respondents reported additional expenditure on self-care (e.g. special foods). Access to health insurance (Adjusted odds ratio [aOR] 6.15) and disability grants (aOR 1.35) increased plural healthcare use. However, plural healthcare users were more likely to borrow money to finance healthcare (aOR 2.68), and incur catastrophic levels of healthcare expenditure (27%) than non-plural users (7%). Quality of care factors, such as perceived disrespect by staff (aOR 2.07) and lack of privacy (aOR 1.50) increased plural healthcare utilization. Plural healthcare utilization was associated with rural residence (aOR 1.97). Healthcare pluralism was not associated with missed visits or biological outcomes. CONCLUSION: Increased plural healthcare utilization, inequitably distributed between rural and urban areas, is largely a function of higher socioeconomic status, better ability to finance healthcare and factors related to poor quality of care in ART clinics. Plural healthcare utilization may be an indication of patients' dissatisfaction with perceived quality of ART care provided. Healthcare expenditure of a catastrophic nature remained a persistent complication. Plural healthcare utilization did not appear to influence clinical outcomes. However, there were potential negative impacts on the livelihoods of patients and their households.
Open Access
Investigating the Effectiveness of Supermarket Transmission Control Measures on the Spread of COVID-19 in the Presence of Super-Spreaders through Agent-Based Modelling
(2022) Mountford, Timothy; Silal, Sheetal
An examination of the effectiveness of transmission control measures for COVID-19 in a supermarket setting, factoring for the inclusion of Super-Spreaders, must extend beyond the direct effects the control measure has on transmission in order to account for the indirect effects changes in human movement dynamics have on the spread of disease. The analysis makes use of Agent-Based Modelling simulation techniques to model changes in customer movement and disease transmission dynamics resulting from the isolated and combined implementation of COVID-19 transmission control measures. The bottom-up approach of agent-based modelling allows for the inclusion of heterogeneous, individual-level chances of infectiousness, compliance, and consumer behaviours, allowing for a more realistic representation of real-world behaviours. The model used for analysis is built entirely in the NetLogo environment, designed to be interactive, adaptable to user-varied inputs, and visually engaging. This allows for the model to adapt to changes in disease parameters and easily communicate model effects in a manner accessible to users in and out of the field. Control measures considered include: Vaccinations, Capacity Limiting, Social Distancing, Staff COVID-19 Testing, and the use of Sanitizers. Results indicate high levels of effectiveness for the use of Vaccinations at reducing transmission with minimal impact on customer dynamics. The results also highlight the negative effects changes in customer dynamics can have on transmission, indicated by increased shop-queue transmissions resulting from the use of Capacity Limiting or other measures slowing customer entrance to the shop. The positive effects of interactions between control measures are highlighted by the additional implementation of Social Distancing in reducing these increases. The implications of these findings involve the need to factor for changes in human movement dynamics when assessing the effectiveness of transmission control measures implemented in any environment. The findings further reinforce the benefits of implementing social distancing practises in conjunction with mechanisms that reduce the flow of movement, as well as the benefits of increased vaccination coverage in the population. Lastly, the findings provide an effective comparison of the control measures considered, allowing for the direct assessment of their implementation and the resulting effects on transmission and customer dynamics.
Open Access
Modeling the relationship between precipitation and malaria incidence in Mpumalanga, South Africa
(BioMed Central Ltd, 2012) Silal, Sheetal
Climatic or weather-driven factors such as rainfall have considerable impact on vector abundance and the extrinsic cycles that parasites undergo in mosquitoes. Climate models therefore allow for a better understanding of the dynamics of malaria transmission. While malaria seasons occur regularly between October and May in Mpumalanga, there is considerable variation in the starting point, peak and magnitude of the season. The relationship between rainfall and malaria incidence may be used to better model the variation in the malaria season. As a first step, this study seeks to explore the complex association between rainfall and malaria incidence through time series methods.
Open Access
Modelling attrition in the Eastern Cape public health system using multilevel survival analysis and machine learning methods
(2023) Perrie, Cailin; Silal, Sheetal; Er Sebnem
The size of South Africa's public health workforce is influenced by many factors including, but not limited to, inter-facility transfers, emigration, voluntary exits, illness, death and retirement. Understanding the rate at which public health workers exit or move within the public health system (i.e. the attrition rate), is essential for adequately formulating effective workforce policies and strategies. South Africa's public health system budget currently accounts for an annual 5% attrition rate for health facilities in general. This rate does not consider fluctuations in attrition rates between cadres, across facilities, or across districts. Presently, there are no guidelines or models for predicting attrition within the Eastern Cape (EC) public health care system from an individual, cadre, facility, or district level. As a result, staffing levels are determined entirely by the discretion of facility or departmental managers. The purpose of this investigation was, therefore, to explore and utilize human resource (HR) data within South Africa's public healthcare system, with specific focus on the EC province, to predict attrition rates within and across cadres, health facilities, and districts. The study places a large focus on using the findings of the study to improve budgeting and health care staffing levels. The study thus aims to develop predictive models that are capable of handling data that is hierarchical in nature, use these models to identify level specific factors that both negatively and positively impact annual attrition rates, and compare predictive models to determine the most effective model for predicting attrition rates in the EC public health sector. The study further aims to perform a historical data analysis on the HR data to identify areas of high concern regarding attrition. Based on a preliminary and historical exploratory data analysis (EDA) of the EC province's public heath HR data, the annual attrition rates between 2010 and 2020 have consistently exceeded this budgeted 5%, with the annual attrition rate in some years reaching as high as 15.65%. The preliminary analysis further indicated that attrition rates are subject to high variation when computed at different levels (i.e. cadre and facility level groupings) as well as across different years. Consequently, the Eastern Cape Department of Health (EC DOH) have been historically and holistically under budgeting for attrition. Additionally, by catering for attrition at a provincial level only, the department has been neglecting the effects that within and between-group variation in attrition has on budget formulation. The historical EDA further identified several cadres that consistently experienced high levels of attrition namely, the Medical Services, Nursing, and Primary Health Care cadres. The job titles that fall within these cadres (i.e. Medical Specialists, Clinic Specialists, and Nurses) are considered i critical to the functioning of any health facility as they are responsible for providing medical care to patients. The historically high attrition levels obtained in these cadres are, therefore, alarming as they suggest that the EC province can expect to consistently see the same or a degrading level of patient care in the years to come. The findings from the historical EDA, and the potential risks associated with over or under-budgeting for attrition, suggest that there is a financial incentive for the EC DOH to develop models capable of accurately predicting future attrition rates within and between multiple levels within the EC province. The application of both statistical and machine learning (ML) modelling techniques were thus explored in this investigation, however, only one statistical modelling method (multilevel discrete-time event models) and three ML modelling methods (multi-layer perceptron neural networks, generalized linear mixed-model trees, and tree-based mixed effect models) were explored. This was due to their potential ability to handle and, effectively model, the complex multilevel and longitudinal HR data available for use in this study. Unfortunately, all multilevel machine learning models explored failed to converge, resulted in excessive computational time forcing an abort, or simply resulted in poor model performance when evaluated on unseen data. Based on these findings, and within the limitations of the study scope, it is accepted that these three modelling methods are unable to outperform traditional multilevel statistical methods at this time. The multilevel discrete-time event models, however, are able to handle the complex data used in this investigation. Based on model performance metrics, the best multilevel discrete-time event model developed in this investigation is considered feasible for use in attrition prediction for the EC DOH. The model is further capable of being used to determine time-indicator and healthcare worker level variables influencing attrition. Overall, the insights gained from this investigation can be used to help guide intervention planning, optimize HR capacity planning processes and, in turn, improve overall budgeting for the EC health system. The findings and limitations of this investigation, however, open up opportunities for future work both as improvements to, or extensions of, the data preparation processes as well as model formulations and optimizations. Such follow-up work may include the exploration of different attrition definitions and the impact that has on the investigations findings, exploring methods for reducing HR healthcare data integrity issues, and provisioning or implementing re-sampling techniques, different cadre grouping strategies, or virtual machines to improve the performance of the machine learning models proposed.
Open Access
Predicting district level HIV prevalence in South Africa using medicine ordering data
(2025) Liebenberg, Juandre; Silal, Sheetal; Er , Sebnem
The Human Immunodeficiency Virus has been at the forefront of South Africa's public health challenges, placing the healthcare system under immense pressure. As a result of HIV planning by policymakers, more than 5.5 million People Living with HIV have access to antiretroviral treatment at present day. Dynamic, mechanistic models such as the Thembisa and Naomi Bayesian models have been used to generate provincial and district-level estimates such as HIV prevalence, People Living with HIV, and the number of residents on antiretroviral treatment. An alternative methodology for estimating drug utilisation and predicting HIV estimates was explored by using medicine ordering data as the primary input for analysis from 2020 to 2022. Two objectives were set out, the first being a drug utilisation analysis aimed at approximating the number of individuals per 1000 inhabitants per day taking antiretroviral drugs to determine if the adequate stock was ordered at district and provincial levels. The second was to predict HIV prevalence by fitting panel data and spatial linear models to predict district prevalence and People Living with HIV; the estimations for People Living with HIV were converted to prevalence to compare the direct estimation of prevalence to the calculated. Results from the drug utilisation analysis suggested that district municipalities hold insufficient stock to meet the demands of those inflicted with the disease. In contrast, larger metropolitan municipalities hold excess medication, implying that people travel across district boundaries to receive treatment. The fitted spatial models generated better prevalence estimates than fixed-effect panel data models for the predicted and calculated prevalence with root mean square error metrics of 0.009 (0.87%) and 0.012(1.24%) compared to that of 0.012(1.21%) and 0.015(1.53%) from the fixed-effect panel data models. The impact of high quantities of antiretroviral drugs ordered by metropolitan municipalities resulted in an underestimation of prevalence in those regions due to the negative relationship between the dependent variable Prevalence and the independent Quantity variable. From the spatial models fitted, the best performing spatial model accurately estimated the prevalence rates for 51 out of 52 districts, which fell within the acceptable range defined by the Naomi Model. The results of the study have shown that the use of ordering data to predict disease prevalence has the potential to serve as an alternative methodology in the absence of established models.
Open Access
Predicting the impact of border control on malaria transmission: a simulated focal screen and treat campaign
(BioMed Central Ltd, 2015) Silal, Sheetal; Little, Francesca; Barnes, Karen; White, Lisa
BACKGROUND: South Africa is one of many countries committed to malaria elimination with a target of 2018 and all malaria-endemic provinces, including Mpumalanga, are increasing efforts towards this ambitious goal. The reduction of imported infections is a vital element of an elimination strategy, particularly if a country is already experiencing high levels of imported infections. Border control of malaria is one tool that may be considered. METHODS: A metapopulation, non-linear stochastic ordinary differential equation model is used to simulate malaria transmission in Mpumalanga and Maputo province, Mozambique (the source of the majority of imported infections) to predict the impact of a focal screen and treat campaign at the Mpumalanga-Maputo border. This campaign is simulated by nesting an individual-based model for the focal screen and treat campaign within the metapopulation transmission model. RESULTS: The model predicts that such a campaign, simulated for different levels of resources, coverage and take-up rates with a variety of screening tools, will not eliminate malaria on its own, but will reduce transmission substantially. Making the campaign mandatory decreases transmission further though sub-patent infections are likely to remain undetected if the diagnostic tool is not adequately sensitive. Replacing screening and treating with mass drug administration results in substantially larger decreases as all (including sub-patent) infections are treated before movement into Mpumalanga. CONCLUSIONS: The reduction of imported cases will be vital to any future malaria control or elimination strategy. This simulation predicts that FSAT at the Mpumalanga-Maputo border will be unable to eliminate local malaria on its own, but may still play a key role in detecting and treating imported infections before they enter the country. Thus FSAT may form part of an integrated elimination strategy where a variety of interventions are employed together to achieve malaria elimination.
Open Access
Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa
(2021) Mayet, Saadiyah; Silal, Sheetal; Durbach, Ian
The COVID-19 pandemic has had devastating consequences across the globe, and has led many governments into completely new decision making territory. Developing models which are capable of producing realistic projections of disease spread under extreme uncertainty has been paramount for supporting decision making by many levels of government. In South Africa, this role has been fulfilled by the South African COVID-19 Modelling Consortium's generalised Susceptible-ExposedInfectious-Removed compartmental model, known as the National COVID-19 Epi Model. This thesis adapted and contributed to the Model; its primary contribution has been to incorporate the feature that resources available to the health system are limited. Building capacity constraints into the Model allowed it to be used in the resource-scarce context of a pandemic. This thesis further designed and implemented a goal programming framework to shift ICU beds between districts intra-provincially in a way that aimed to minimise deaths caused by the non-availability of ICU beds. The results showed a 15% to 99% decrease in lives lost when ICU beds were shifted, depending on the scenario considered. Although there are limitations to the scope and assumptions of this thesis, it demonstrates that it is possible to combine mathematical modelling with optimisation in a way that may save lives through optimal resource allocation.

Browsing by Author "Silal, Sheetal"

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