Methods for the cost-effectiveness modelling of screening interventions in an uncertain landscape:Application to screening for prostate cancer

Decisions need to be made about who and how to screen for diseases to optimise health inthe population. Cost-effectiveness analyses of screening interventions can be associatedwith many areas of uncertainty due to a constantly changing landscape in screeningmethods, diagnostic tests, treatments and understanding of natural history. A failure toaccount for such uncertainty may result in incorrect or poorly informed decisions. Prostatecancer is an example of a disease where recent developments in the understanding of whoand how to screen have provided challenges to the analyst trying to makerecommendations on the most cost-effective screening strategy.Using prostate cancer screening as a case study, this dissertation explores methods tohandle uncertainty when modelling the cost-effectiveness of screening interventions in anuncertain landscape. The dissertation shows how a systematic review of previous modelscan identify areas of parameter and structural uncertainty, how to gain expert consensuswith respect to relevant screening strategies, and how to appropriately adapt and calibratean existing natural history model to a new setting.  It will demonstrate how the 22 studies identified in the systematic review informed thestructure and data parameters of the natural history model and how a modified-Delphiprocess identified prostate cancer screening strategies that were deemed relevant byexperts, including risk-stratified and adaptive approaches. It will also show how a decisionmodel was adapted and calibrated to UK data to find that, of the strategies identified in theDelphi, a once-off screening at age 50 years was most cost-effective.  Many methods are available for dealing with uncertainty in cost-effectiveness modelling ofscreening interventions. The dissertation will conclude with a discussion on the merits andlimitations of the methods used, with recommendations given for practice. The aim is toprovide a guide to identifying and addressing the uncertainty inherent in cost-effectivenessanalyses of screening strategies.

Neighbourhood effects: spatial inequalities in tooth decay

Objectives: Little theoretical work has been conducted on the topic of neighbourhood effects on health outcomes, let alone within dentistry. Previous work has often quantified and described outcomes without proper investigation of potential causal mechanisms and pathways. Therefore, the aim of this exploratory research was to investigate features of neighbourhood environments that may influence tooth decay in adults. Methods: Relevant literature was mapped onto a neighbourhood based theoretical framework to create numerous pathways by which neighbourhoods influence decay. Spatial microsimulation was used to combine data from the Adult Dental Health Survey (2009) with Census data to create a synthetic dataset of individuals at the small area level for the city of Sheffield (UK), including associated socio-economic, demographic and dental characteristics. This data formed the basis of the agent-based models which were used to test the theoretical pathways in two contrasting study areas in Sheffield, as well as a hypothetical scenario involving an extra shop being added to each location. Results: The trends of the agent-based models indicated that the same pathway (the interaction between shops, diet and sugar intake) had the largest impact in both study areas, leading to statistically significant increases in decay in both cases (p < 0.05). The results of the hypothetical simulation involving an extra shop revealed a statistically significant decrease in decay in the more affluent study area (p < 0.05), while decay scores remained similar in the less affluent study area. Conclusions: The findings suggest the interactions between shops, diet and sugar intake may be the most important neighbourhood based mechanisms for tooth decay, regardless of socio-economic status. However, additional simulations pointed to more opportunities to reduce decay in the more affluent study area through the local food environment. The implications of these findings are discussed in light of previous research and future work

Estimating the effectiveness and cost-effectiveness of body weight interventions for the prevention of non-communicable disease in local authority areas of England

Background Non-Communicable Diseases related to Body Mass Index (BMI) account for approximately 10% of all disease burden in England. Addressing this burden and its associated health inequalities are major challenges for local and national policymakers, with increasing need to also understand how BMI interventions might affect local areas differently. The aim was set to develop a new local authority-level health model to estimate the disease burden and healthcare cost implications of BMI interventions. Methods Diseases included in the new model were asthma, low back pain, osteoarthritis of the hip, osteoarthritis of the knee, Ischaemic Heart Disease, stroke, hypertensive heart disease, type-2 diabetes mellitus, atrial fibrillation/ flutter, colorectal cancer, breast cancer and oesophageal cancer. Local-level data were estimated via different approaches: adult BMI distributions were produced via individual-level synthetic estimation, child BMI distributions were interpolated from measured BMI and healthcare costs estimated using new high-quality cost collection data. Disease epidemiology was estimated at the Index of Multiple Deprivation quintile level using a Bayesian modelling tool. A scenario on restricting television advertising of unhealthy foods in each of 315 local authorities in England was then used to explore the new model’s capabilities. Findings Patterns to adult and child BMI supported conventional ideas that raised BMI is associated with age and deprivation. Healthcare costs formed very positively skewed distributions without an obvious geographic pattern. Disease epidemiology showed burden was broadly higher in more deprived quintiles. Modelling showed that restricting the advertising of unhealthy foods is likely to benefit more deprived areas more overall, but with overlaps between quintiles of deprivation. It is estimated that 0.0222-0.0521 Quality-Adjusted Life-Years per person could be saved over the lifetime of the 2018 population of children and £11.7-£42.8 per person in healthcare costs. Conclusions This model offers the most local-specific health modelling for BMI interventions in England. Some areas may be harder to reach with a given intervention, creating mismatch between need and ability to intervene

Modelling the impact of prevention strategies on cervical cancer incidence in South Africa

Background: In 2019, the World Health Organisation called for the elimination of cervical cancer as a public health concern. In South Africa, despite having a national screening policy in place since 2000, diagnosed cervical cancer incidence has shown no signs of decline. Since 2014, girls aged 9 have been vaccinated against HPV infection using the bivalent vaccine, with high coverage. However, due to the long delay between HPV infection and progression to cancer, the impact that vaccination will have on cervical cancer incidence will be unobservable in the near future. This thesis sets out to quantify this impact using a mathematical model, and will estimate the impact of scaling up current cancer prevention strategies, as well as proposed alternative strategies. Methods: This research extends a previously developed individual-based model for HIV to include infection with 13 high-risk HPV types and progression to cervical cancer. HPV infection and cervical disease parameters were calibrated to a wide range of South African data sources using a likelihood based approach. In the process of developing an appropriate model for cervical cancer incidence in South Africa, important aspects related to HIV/HPV co-infection dynamics, the natural history of HPV and the current and historic levels of cervical cancer prevention in the Western Cape were investigated. The calibrated and validated model was used to estimate the impact of current and proposed alternative prevention strategies on cervical cancer incidence in the next century. Findings: Using a model structure that does not include a biological transmission co-factor, we show that simulated associations between HIV and HPV transmission are similar to corresponding empirical estimates and therefore these associations may result from residual confounding by sexual behavioural factors and network-level effects. Using simulated vaccine trials, we show that viral latency and reactivation of latent infections is necessary in the natural history of HPV to match results from empirical trials. The model's screening algorithm reflects findings from the Western Cape's public health sector – low levels of screening coverage and linkage to treatment facilities, and poor adherence to screening schedules. The model matches stable trends in diagnosed cervical cancer incidence in South Africa, but it estimates increases in cervical cancer incidence over the last number of years (due to increased life expectancy of women on ART), which will result in sharp increases in diagnoses. While decreasing HIV prevalence and HPV vaccination will substantially reduce cervical cancer incidence in the long term, improvements in South Africa's current screening strategy, as well as switching to new screening technologies, will have significant impact in the short term. Conclusions: This thesis presents an epidemiological model of cervical cancer in South Africa – the first to dynamically simulate infection with both HIV and HPV at national level. It allows for estimation of the impact of both HIV and cervical cancer prevention on cancer incidence, and provides the opportunity to identify the vaccination and screening strategies with the greatest public health significance

Computer simulations in health policy: methodology and applications in the management of chronic diseases

Among the main challenges of public-health policy makers is reducing gaps in the delivery of care, given limited human and monetary resources. In a public health setting, decision-analysis tools such as simulation models can be used to inform decision-makers in answering what-if policy questions in order to improve public health and clinical practice, optimize resource allocation, or guide funding and reimbursement decisions. Of the main public-health challenges in the United States is the burden of chronic infectious diseases. The prevalence and associated cost of chronic infectious diseases, such as hepatitis C virus (HCV) and sexually transmitted diseases (STDs) has increased in the United States due to rising life expectancy and social changes. Many of these diseases have effective therapies, but there are gaps in research on effective mitigation strategies. The public health significance of this dissertation was to apply rigorous decision-sciences methods using computer simulations in health services research and to expand the application of existing methods to answer real-world questions in health policy of chronic infectious diseases. In the first section of this dissertation, I quantified the the effects of new HCV therapies and updated screening guidelines on the burden of HCV and associated disease outcomes in the United States using an individual-level state-transition microsimulation model. The second section of this dissertation, estimated the status of HCV disease burden and the potential budget impact of various treatment strategies in the Pennsylvania Medicaid population using the HCV microsimulation model that was calibrated to Pennsylvania Medicaid according to the claims data from 2007–2012. The last section of this dissertation, included the development and maintenance of sexual partnership networks using an agent-based simulation modeling approach, according to serial cross-sectional data obtained from the 2007–2014 National Health and Nutrition Examination Survey. This study provides a tool for understanding the dynamics of sexual partnership networks which is critical to improve the impacts of STD mitigation strategies that focus on the sexual behaviors of individuals. In conclusion, this dissertation provided the details of two computer-simulation applications in health-related multi-disciplinary policy research, and delivers insights on how to use computer simulation in medical decision-sciences and policy problems

A Data-Driven Approach for Modeling Agents

Agents are commonly created on a set of simple rules driven by theories, hypotheses, and assumptions. Such modeling premise has limited use of real-world data and is challenged when modeling real-world systems due to the lack of empirical grounding. Simultaneously, the last decade has witnessed the production and availability of large-scale data from various sensors that carry behavioral signals. These data sources have the potential to change the way we create agent-based models; from simple rules to driven by data. Despite this opportunity, the literature has neglected to offer a modeling approach to generate granular agent behaviors from data, creating a gap in the literature. This dissertation proposes a novel data-driven approach for modeling agents to bridge the research gap. The approach is composed of four detailed steps including data preparation, attribute model creation, behavior model creation, and integration. The connection between and within each step is established using data flow diagrams. The practicality of the approach is demonstrated with a human mobility model that uses millions of location footprints collected from social media. In this model, the generation of movement behavior is tested with five machine learning/statistical modeling techniques covering a large number of model/data configurations. Results show that Random Forest-based learning is the most effective for the mobility use case. Furthermore, agent attribute values are obtained/generated with machine learning and translational assignment techniques. The proposed approach is evaluated in two ways. First, the use case model is compared to another model which is developed using a state-of-the-art data-driven approach. The model’s prediction performance is comparable to the state-of-the-art model. The plausibility of behaviors and model structure in the use case model is found to be closer to real-world than the state-of-the-art model. This outcome indicates that the proposed approach produces realistic results. Second, a standard mobility dataset is used for driving the mobility model in place of social media data. Despite its small size, the data and model resembled the results gathered from the primary use case indicating the possibility of using different datasets with the proposed approach

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly