When to Initiate, When to Switch, and How to Sequence HIV Therapies: A Markov Decision Process Approach

HIV and AIDS are major health care problems throughout the world,with 40 million people living with HIV by the end of 2005. Inthat year alone, 5 million people acquired HIV, and 3 millionpeople died of AIDS. For many patients, advances in therapies overthe past ten years have changed HIV from a fatal disease to achronic, yet manageable condition. The purpose of thisdissertation is to address the challenge of effectively managingHIV therapies, with a goal of maximizing a patient's totalexpected lifetime or quality-adjusted lifetime.Perhaps the most important issue in HIV care is when a patientshould initiate therapy. Benefits of delaying therapy includeavoiding the negative side effects and toxicities associated withthe drugs, delaying selective pressures that induce thedevelopment of resistant strains of the virus, and preserving alimited number of treatment options. On the other hand, the risksof delayed therapy include the possibility of irreversible damageto the immune system, development of AIDS-related complications,and death. We develop a Markov decision process (MDP) model thatexamines this question, and we solve it using clinical data.Because of the development of resistance to administered therapiesover time, an extension to the initiation question arises: whenshould a patient switch therapies? Also, inherent in both theinitiation and switching questions is the question of whichtherapy to use each time. We develop MDP models that consider theswitching and sequencing problems, and we discuss the challengesinvolved in solving these models

MATHEMATICAL MODELING FOR DENTAL DECAY PREVENTION IN CHILDREN AND ADOLESCENTS

The high prevalence of dental caries among children and adolescents, especially those from lower socio-economic backgrounds, is a significant nationwide health concern. Early prevention, such as dental sealants and fluoride varnish (FV), is essential, but access to this care remains limited and disparate. In this research, a national dataset is utilized to assess sealants\u27 reach and effectiveness in preventing tooth decay, particularly focusing on 2nd molars that emerge during early adolescence, a current gap in the knowledge base. FV is recommended to be delivered during medical well-child visits to children who are not seeing a dentist. Challenges and facilitators in implementing this recommendation are explored using the Consolidated Framework for Implementation Research (CFIR). Finally, ordinary differential equations are used to evaluate FV’s potential impact and cost-effectiveness in preventing and treating early-stage tooth decay, which is a new use of an effective mathematical modeling technique. This comprehensive approach combines data analysis, implementation science, and mathematical modeling to effectively address urgent oral health challenges. This research offers valuable insights into dental healthcare strategies and decision-making processes

Scaling up ART in Rwanda: the financial and economic costs

Rwanda has been rolling-out free antiretroviral treatment (ART) since 2004. This scale up could only be realised through significantly increased funding to the HIV/AIDS sub-account. Funding grew from US$9 million in 2003 to US$43 million in 2004 (UNAIDS, 2006b) and has continued to grow since this time given increased grants from GFATM and PEPFAR. Although international funding has been pivotal in the initiation of ART roll-out in resource poor settings, national programmes must look inwards for long term sustainability. This raises the question of whether the country will be able to sustain this level of funding once these grants cease ot are significantly reduced. This question could be answered to a large extent if one knew the lifetime costs of providing ART in Rwanda and the capacity of the country to raise domestic revenue. Unfortunately the body of evidence on unit and lifetime costs for providing ART in Rwanda is nonexistent. The study aimed to determine the economic costs of scaling up ART in Rwanda. Costing from the provider's perspective was undertaken based on data from 3,310 patients in 3 ART sites. The health care utilisation and cost data obtained, supplemented by appropriate secondary data, were used to estimate the cost perpatient period and lifetime costs. These were then used to model the costs of scaling up and to explore the financial sustainability of ART in Rwanda

2013 IMSAloquium, Student Investigation Showcase

This year, we are proudly celebrating the twenty-fifth anniversary of IMSA’s Student Inquiry and Research (SIR) Program. Our first IMSAloquium, then called Presentation Day, was held in 1989 with only ten presentations; this year we are nearing two hundred.https://digitalcommons.imsa.edu/archives_sir/1005/thumbnail.jp

The Value of Direct-Acting Antivirals for the Treatment of Chronic Hepatitis C In An Integrated Healthcare System

Problem: Hepatitis C (HCV) affects over 3 million people in the United States. The disease is now curable with new all-oral direct-acting antiviral (DAA) therapies with clinical trial efficacy rates between 90%-100%. However, because the list prices of these drugs are prohibitively high, treatment has not been universally prescribed to all patients with chronic HCV for reasons that vary across payer and healthcare system. This dissertation explores the utilization and value of the new DAAs in the Kaiser Permanente Mid-Atlantic States (KPMAS) health care system by determining predictors of treatment initiation, effects of treatment on resource utilization and cost-effectiveness of different triaging treatment policies. Methods: The association between patient and provider characteristics and treatment initiation was evaluated with a cox-proportional hazards model. Due to the non-randomized treatment assignment and variations in treatment timing, we created a propensity score matched sample and conducted a time series analysis to assess the effect of treatment of subsequent resource utilization. Cost-effectiveness of triaging treatment approaches was evaluated using a Markov model using probabilistic sensitivity and value of information analyses. Results: Fibrosis score was not associated with the likelihood of being treated with a DAA. Older patients were more likely to be treated, while those with a history of a substance use disorder were less likely to be treated in our study sample. We did not find any differences in likelihood of treatment across race or insurance type. While we found a downward effect on the rate of post-treatment resource utilization, these effects were not statistically significant. Universal access to treatment, for patients across all fibrosis scores, was the optimal treatment strategy at the $150,000/QALY threshold. Sensitivity analyses showed these results were robust to parameter variations. Conclusions: KPMAS is providing equitable access to care across characteristics that typically induce disparities, but is uniquely positioned to enhance their linkage to care for some vulnerable patient subgroups. Longer follow-up may demonstrate more significant spillover effects as more advanced disease develops over many years. Expanding access to treatment seems to be the most efficient treatment strategy for chronic HCV from both perspectives

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

Economic Evaluations of Companion Cancer Biomarkers for Targeted Therapies

Background: Companion biomarkers for targeted therapies have increased the expectation that biomarkers can improve health outcomes or potentially save health resources without compromising patient outcomes. However, few countries provide health economic assessment methods guidance (e.g. health technology assessment guide) specifically for co-dependent technologies such as companion diagnostics. Aim: This thesis aims to explore good practices for evaluating companion biomarker tests as part of health economic assessments of their co-dependent targeted therapies in cancer. Scope of the study: Cancer biomarkers for targeted therapies investigated in this thesis are restricted to companion biomarkers, classifying patients into responders and non-responders for a specific targeted therapeutic agent. Methods: Four research activities were designed: two systematic literature reviews (SLR) and two health economic models. The first SLR (Chapter 2) was conducted to demonstrate the impact of companion biomarker tests on the cost-effectiveness of targeted therapies, focusing on metastatic colorectal cancer (mCRC). The second SLR (Chapter 3) considered all cancer areas. It investigated current and best practice for modelling and incorporating companion biomarker tests when assessing the cost-effectiveness of targeted cancer therapies. The findings from these two SLRs were then applied to the cost-effectiveness modelling of a novel candidate companion biomarker test, Heat Shock Protein 27 (HSP27) expression (Chapter 4). The final work (Chapter 5) developed a practical guide to modelling companion biomarker tests as part of economic evaluations of corresponding targeted therapies; a global model was constructed and provided as a worked example coupled with step-by-step guide for readers to follow. Results: The first SLR study showed that the use of companion biomarker tests saved some costs however, the saving was not high enough to change materially the cost-effectiveness of co-dependent therapeutic agents. The second SLR found that there was inconsistency in the methods for evaluating companion biomarker tests in the appraisal of co-dependent agents. The cost-effectiveness analysis of HSP27 expression showed conflicting results depending on the structure of the comparative analysis. Finally, the modelling guide coupled with a worked example of a global model demonstrated how to model characteristics of companion biomarker tests in economic evaluations of test-guided therapies. Conclusion: This thesis highlights the need to reach a consensus on the methods of evaluating companion testing technologies as part of economic evaluations of their corresponding test-guided therapies. Built upon the consensus, a methods guide for co-dependent technologies needs to be developed and introduced, providing a coherent and unified guidance on good practices, reference case, evidentiary standards and data requirements for economic evaluations