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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 rates and lower cure rates. Therefore optimization of treatment policies needs to take imperfect treatment behavior and potential drug resistance into account. Drug-resistant TB prevalence varies by region, and this also contributes to the necessity of geographical specificity when evaluating potential TB control mechanisms.

Latent and active TB can be detected through a variety of different tests of varying sensitivity and specificity, and different tests may be preferred in different regions. For instance, Mantoux tuberculin skin test (TST) or interferon-gamma release assay (IGRA) blood test are used to detect TB infection in many areas with low TB prevalence, whereas sputum smear microscopy tests are commonly used to identify active TB cases in areas of high prevalence (Global Health Education 2014). While sputum smear tests have fast turnaround times and low costs, sputum smear tests have low sensitivity and active TB cases may be overlooked. Bacteriological culture may take up to several weeks but is a more accurate diagnosis method and can be used for drug susceptibility testing (it can be used to identify drug-resistant samples from susceptible TB samples). Initial diagnosis can also be passive (patients self-present at local clinics) or targeted (active case finding, contact tracing, etc.). After entering treatment, patients may undergo different tests sequentially to monitor treatment efficacy and determine if second-line treatment is necessary. The cost and the effectiveness of various screening policies vary by patient behavior, latent and active TB prevalence, and what treatment options are available. Identifying optimal region-specific timing and type of diagnosis is an area of active research (Acuna-Villaorduna et al. 2008; Winetsky et al. 2012).

TB infection and disease may be complicated by comorbidities. TB is often observed along with HIV, which can change the natural history of disease and complicate TB diagnosis and treatment. In 2012, 1.1 million of the 8.6 million new cases of TB were among people living with HIV (World Health Organization 2013). HIV patients have a higher risk of developing TB due to immune system compromise. Diabetes is another comorbidity that can change TB activation rates (World Health Organization 2011). While helping patients with multiple chronic diseases is an increasingly important part of TB control, modeling multiple diseases is challenging since the diseases interact and data to inform joint distributions on risks and rates may be scarce.

1.1.1 TB in India

India is the country with the largest number of TB cases-roughly 23% of the global total-despite large gains in the last few decades in decreasing TB mortality, incidence, and prevalence through TB treatment and diagnosis (World Health Organization 2015). India has a federally funded TB treatment program called the Revised National Tuberculosis Control Program (RNTCP). This program offers the approved antibacterial drug regimens for treating TB, called Directly Observed Treatment, Short Course (DOTS), where health workers help patients administer their drugs to help ensure that they are taken correctly. These regimens require treatment for at least 6 months of treatment and may be longer for those patients who have previously been treated for TB (RNTCP 2010).

Despite this federally funded program, and unlike in many other countries with high TB burdens, many TB patients in India seek care in private sector clinics. Since the symptoms of TB can easily be mistaken for routine respiratory illnesses, many patients tend to first seek care from retail chemists or informal health providers in the private healthcare market. These private clinics may not have health practitioners trained in identifying and treating TB (Tu et al. 2010; Uplekar and Shepard 1991; Vandan et al. 2009), and patients using private clinics may use multiple clinics as they attain temporary relief from symptoms that then recur (Kapoor et al. 2012). This delay to getting appropriate TB care means that patients begin effective treatment at a later stage of their disease, may have infected others with TB, and may have been exposed to anti-TB drugs that can select for drug resistance.

Combating drug-resistant TB is a continuing challenge for India. More than half of the MDR-TB cases notified in 2014 occurred in India, China, and the Russian Federation (World Health Organization 2015). India started the federally funded DOTS-Plus MDR-TB treatment program in 2007, where MDR-TB patients can get access to the necessary 18-24 months of...