Decision Analytics and Optimization in Disease Prevention and Treatment

Nan Kong, Shengfan Zhang, Nan Kong, Shengfan Zhang

• Publisher: Wiley
• Year: 2018
• Print ISBN: 9781118960127
• eBook ISBN: 9781118960141
• Edition: 1
• Topic: Negocios y empresa
• Subtopic: Toma de decisiones

A systematic review of the most current decision models and techniques for disease prevention and treatment

Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment.With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making.

This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment:

• Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research
• Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology
• Includes contributions bywell-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators
• Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area

Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.

PART 1
INFECTIOUS DISEASE CONTROL AND MANAGEMENT

1
OPTIMIZATION IN INFECTIOUS DISEASE CONTROL AND PREVENTION: TUBERCULOSIS MODELING USING MICROSIMULATION

Sze‐chuan Suen

Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA

Compared with many other optimization problems, optimization of treatments for national infectious disease control often involves a relatively small set of feasible interventions. The challenge is in accurately forecasting the costs and benefits of an intervention; once that can be evaluated for the limited set of interventions, the best one can be easily identified. Predicting the outcome of an intervention can be difficult due to the complexity of the disease natural history, the interactions between individuals that influence transmission, and the lack of data. It is therefore important to understand how a particular disease affects patients, spreads, and is treated in order to design effective control policies against it.

One such complex disease is tuberculosis (TB), which kills millions of people every year. It is transmitted through respiratory contacts, has a latent stage, and is difficult to diagnose and cure in resource‐constrained settings, and treatment success varies by demographic factors like age and sex. Moreover, the mechanisms of disease transmission are not fully known, making modeling of transmission difficult, and it is particularly prevalent in areas of the world where reliable disease statistics are hard to find.

All of these characteristics make TB a difficult disease to model in the settings where choosing an optimal control policy is most important. Traditional compartmental disease models may become intractable if all relevant demographic and treatment stratifications are specified (state space explosion), so a microsimulation may be a good alternative for modeling TB dynamics. In a microsimulation, individual health and treatment states are probabilistically simulated over time and averaged together to form population statistics. This allows for greater modeling flexibility and a more tractable model but may also result in problems of model stochasticity.

In this chapter, we first discuss the epidemiology of the disease, illustrating why TB modeling is necessary and highlighting challenging aspects of this disease. In the second section, we provide a brief overview of simulation and then discuss in depth a microsimulation model of TB to illustrate subtleties of using microsimulation to evaluate policies in infectious disease control.

1.1 TUBERCULOSIS EPIDEMIOLOGY AND BACKGROUND

In order to understand how to pick a model framework and implement a useful model, it is important first to understand the epidemiological characteristics and background of the disease. TB is caused by the bacteria Mycobacterium tuberculosis, which can attack the lungs (pulmonary TB) or other parts of the body (extrapulmonary TB). TB is a respiratory disease and transmitted through the air by coughing or sneezing. It has been declared a global public health emergency, killing 1.3 million people in 2012, while 8.6 million people developed the disease. The majority of cases were in Southeast Asia, African, or Western Pacific regions (Zumla et al. 2013). However, the disease varies by region and cannot be treated identically in all areas—for example, many African cases are concurrent with HIV, while in other regions, like India, HIV prevalence is low although TB prevalence is high (World Health Organization 2013). This means that models for one country may not be easily adapted to another, since comorbidities and the driving factors of the epidemic may be quite different.

Once contracted, TB may stay latent for many years and only activates in about 10% of cases. Latent TB is asymptomatic and cannot be transmitted. Activation rates depend on immunological health and have been observed to vary by demographic factors, like age (Horsburgh 2004; Vynnycky and Fine 1997), and behavioral factors, like smoking (Lin et al. 2007). Transmission of TB, which occurs through respiratory contact, may vary by age (Horby et al. 2011; Mossong et al. 2008), demographic patterns, and cultural trends but is poorly documented or understood.

Nondrug‐resistant strains of TB, whether latent or active, are treatable using antibiotics, but misuse of first‐line antibiotic regimens may lead to drug‐resistant or multidrug‐resistant (MDR) TB, defined as strains that are resistant to at least isoniazid and rifampin, two first‐line TB drugs. Premature treatment default or failure can result in the development of drug resistance, and drug‐resistant strains may then be transmitted to other individuals. Drug‐resistant TB can be treatable, depending on the level of drug resistance (pan‐resistant TB strains have emerged), but require more expensive second‐line antibiotic regimens of longer duration (drugs need to be taken many times a week for up to 2 years) with higher toxicity rat...

Table of contents

• COVER
• TITLE PAGE
• TABLE OF CONTENTS
• CONTRIBUTORS
• PREFACE
• PART 1: INFECTIOUS DISEASE CONTROL AND MANAGEMENT
• PART 2: NONCOMMUNICABLE DISEASE PREVENTION
• PART 3: TREATMENT TECHNOLOGY AND SYSTEM
• INDEX
• END USER LICENSE AGREEMENT