Reza Yaesoubi

Dynamic Health Policies for Controlling the Spread of Emerging Infections: Influenza as an Example

The recent appearance and spread of novel infectious pathogens provide motivation for using models as tools to guide public health decision-making. Here we describe a modeling approach for developing dynamic health policies that allow for adaptive decision-making as new data become available during an epidemic. In contrast to static health policies which have generally been selected by comparing the performance of a limited number of pre-determined sequences of interventions within simulation or mathematical models, dynamic health policies produce “real-time” recommendations for the choice of the best current intervention based on the observable state of the epidemic. Using cumulative real-time data for disease spread coupled with current information about resource availability, these policies provide recommendations for interventions that optimally utilize available resources to preserve the overall health of the population. We illustrate the design and implementation of a dynamic health policy for the control of a novel strain of influenza, where we assume that two types of intervention may be available during the epidemic: (1) vaccines and antiviral drugs, and (2) transmission reducing measures, such as social distancing or mask use, that may be turned “on” or “off” repeatedly during the course of epidemic. In this example, the optimal dynamic health policy maximizes the overall populations health during the epidemic by specifying at any point of time, based on observable conditions, (1) the number of individuals to vaccinate if vaccines are available, and (2) whether the transmission-reducing intervention should be either employed or removed.

Identifying dynamic tuberculosis case-finding policies for HIV/TB coepidemics

The global tuberculosis (TB) control plan has historically emphasized passive case finding (PCF) as the most practical approach for identifying TB suspects in high burden settings. The success of this approach in controlling TB depends on infectious individuals recognizing their symptoms and voluntarily seeking diagnosis rapidly enough to reduce onward transmission. It now appears, at least in some settings, that more intensified case-finding (ICF) approaches may be needed to control TB transmission; these more aggressive approaches for detecting as-yet undiagnosed cases obviously require additional resources to implement. Given that TB control programs are resource constrained and that the incremental yield of ICF is expected to wane over time as the pool of undiagnosed cases is depleted, a tool that can help policymakers to identify when to implement or suspend an ICF intervention would be valuable. In this article, we propose dynamic case-finding policies that allow policymakers to use existing observations about the epidemic and resource availability to determine when to switch between PCF and ICF to efficiently use resources to optimize population health. Using mathematical models of TB/HIV coepidemics, we show that dynamic policies strictly dominate static policies that prespecify a frequency and duration of rounds of ICF. We also find that the use of a diagnostic tool with better sensitivity for detecting smear-negative cases (e.g., Xpert MTB/RIF) further improves the incremental benefit of these dynamic case-finding policies.

Payment contracts in a preventive health care system: A perspective from Operations Management

We consider a health care system consisting of two noncooperative parties: a health purchaser (payer) and a health provider, where the interaction between the two parties is governed by a payment contract. We determine the contracts that coordinate the health purchaser-health provider relationship; i.e. the contracts that maximize the populations welfare while allowing each entity to optimize its own objective function. We show that under certain conditions (1) when the number of customers for a preventive medical intervention is verifiable, there exists a gate-keeping contract and a set of concave piecewise linear contracts that coordinate the system, and (2) when the number of customers is not verifiable, there exists a contract of bounded linear form and a set of incentive-feasible concave piecewise linear contracts that coordinate the system.

A game-theoretic framework for estimating a health purchaser’s willingness-to-pay for health and for expansion

A health purchaser’s willingness-to-pay (WTP) for health is defined as the amount of money the health purchaser (e.g. a health maximizing public agency or a profit maximizing health insurer) is willing to spend for an additional unit of health. In this paper, we propose a game-theoretic framework for estimating a health purchaser’s WTP for health in markets where the health purchaser offers a menu of medical interventions, and each individual in the population selects the intervention that maximizes her prospect. We discuss how the WTP for health can be employed to determine medical guidelines, and to price new medical technologies, such that the health purchaser is willing to implement them. The framework further introduces a measure for WTP for expansion, defined as the amount of money the health purchaser is willing to pay per person in the population served by the health provider to increase the consumption level of the intervention by one percent without changing the intervention price. This measure can be employed to find how much to invest in expanding a medical program through opening new facilities, advertising, etc. Applying the proposed framework to colorectal cancer screening tests, we estimate the WTP for health and the WTP for expansion of colorectal cancer screening tests for the 2005 US population.

A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models

Stochastic transmission dynamic models are especially useful for studying the early emergence of novel pathogens given the importance of chance events when the number of infectious individuals is small. However, methods for parameter estimation and prediction for these types of stochastic models remain limited. In this manuscript, we describe a calibration and prediction framework for stochastic compartmental transmission models of epidemics. The proposed method, Multiple Shooting for Stochastic systems (MSS), applies a linear noise approximation to describe the size of the fluctuations, and uses each new surveillance observation to update the belief about the true epidemic state. Using simulated outbreaks of a novel viral pathogen, we evaluate the accuracy of MSS for real-time parameter estimation and prediction during epidemics. We assume that weekly counts for the number of new diagnosed cases are available and serve as an imperfect proxy of incidence. We show that MSS produces accurate estimates of key epidemic parameters (i.e. mean duration of infectiousness, R0, and Reff) and can provide an accurate estimate of the unobserved number of infectious individuals during the course of an epidemic. MSS also allows for accurate prediction of the number and timing of future hospitalizations and the overall attack rate. We compare the performance of MSS to three state-of-the-art benchmark methods: 1) a likelihood approximation with an assumption of independent Poisson observations; 2) a particle filtering method; and 3) an ensemble Kalman filter method. We find that MSS significantly outperforms each of these three benchmark methods in the majority of epidemic scenarios tested. In summary, MSS is a promising method that may improve on current approaches for calibration and prediction using stochastic models of epidemics.

Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study

Summary Background In high-incidence settings, recurrent disease among previously treated individuals contributes substantially to the burden of incident and prevalent tuberculosis. The extent to which interventions targeted to this high-risk group can improve tuberculosis control has not been established. We aimed to project the population-level effect of control interventions targeted to individuals with a history of previous tuberculosis treatment in a high-incidence setting. Methods We developed a transmission-dynamic model of tuberculosis and HIV in a high-incidence setting with a population of roughly 40 000 people in suburban Cape Town, South Africa. The model was calibrated to data describing local demography, TB and HIV prevalence, TB case notifications and treatment outcomes using a Bayesian calibration approach. We projected the effect of annual targeted active case finding in all individuals who had previously completed tuberculosis treatment and targeted active case finding combined with lifelong secondary isoniazid preventive therapy. We estimated the effect of these targeted interventions on local tuberculosis incidence, prevalence, and mortality over a 10 year period (2016–25). Findings We projected that, under current control efforts in this setting, the tuberculosis epidemic will remain in slow decline for at least the next decade. Additional interventions targeted to previously treated people could greatly accelerate these declines. We projected that annual targeted active case finding combined with secondary isoniazid preventive therapy in those who previously completed tuberculosis treatment would avert 40% (95% uncertainty interval [UI] 21–56) of incident tuberculosis cases and 41% (16–55) of tuberculosis deaths occurring between 2016 and 2025. Interpretation In this high-incidence setting, the use of targeted active case finding in combination with secondary isoniazid preventive therapy in previously treated individuals could accelerate decreases in tuberculosis morbidity and mortality. Studies to measure cost and resource implications are needed to establish the feasibility of this type of targeted approach for improving tuberculosis control in settings with high tuberculosis and HIV prevalence. Funding National Institutes of Health, German Research Foundation.

Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions

Mathematical modelling is commonly used to evaluate infectious disease control policy and is influential in shaping policy and budgets. Mathematical models necessarily make assumptions about disease natural history and, if these assumptions are not valid, the results of these studies can be biased. We did a systematic review of published tuberculosis transmission models to assess the validity of assumptions about progression to active disease after initial infection (PROSPERO ID CRD42016030009). We searched PubMed, Web of Science, Embase, Biosis, and Cochrane Library, and included studies from the earliest available date (Jan 1, 1962) to Aug 31, 2017. We identified 312 studies that met inclusion criteria. Predicted tuberculosis incidence varied widely across studies for each risk factor investigated. For population groups with no individual risk factors, annual incidence varied by several orders of magnitude, and 20-year cumulative incidence ranged from close to 0% to 100%. A substantial proportion of modelled results were inconsistent with empirical evidence: for 10-year cumulative incidence, 40% of modelled results were more than double or less than half the empirical estimates. These results demonstrate substantial disagreement between modelling studies on a central feature of tuberculosis natural history. Greater attention to reproducing known features of epidemiology would strengthen future tuberculosis modelling studies, and readers of modelling studies are recommended to assess how well those studies demonstrate their validity.

A modification of Cheng'S method: An alternative Factor Screening method for stochastic simulation models

Factor Screening experiments identify those factors with significant effect on a selected output. We propose a modification of Chengs method as a new factor screening alternative for simulation models whose output has homogeneous variance and can be described by a second-order polynomial function. The performance of the proposed model is compared with several other factor screening alternatives through an empirical evaluation. The results show that the proposed method sustains its efficiency and accuracy as the number of factors or the homogeneous variance increases. However, its accuracy degrades as variance heterogeneity increases.

How much is a health insurer willing to pay for colorectal cancer screening tests

Colorectal Cancer (CRC) screening tests have proven to be cost-effective in preventing cancer incidence. Yet, as recent studies have shown, CRC screening tests are noticeably underutilized. Among the factors influencing CRC screening test utilization, the role of health insurers has gained considerable attention in recent studies. In this paper, we propose an analytical model for the market of CRC screening tests and show how the insurer can benefit from a computer simulation model to cope with the problem of incomplete and asymmetric information inherent in this market. Our estimates reveal that promoting CRC screening tests is not necessarily economically attractive to the insurer, unless the insurer¿s valuation of life is greater than a certain limit. We use the proposed model to estimate such a threshold - the insurer¿s willingness-to-pay to acquire one additional life year by covering the CRC screening tests.

A Cost-Effectiveness Analysis of a Pediatric Operating Room in Uganda

Abstract This study examines the cost‐effectiveness of constructing a dedicated pediatric operating room (OR) in Uganda, a country where access to surgical care is limited to 4 pediatric surgeons serving a population of over 20 million children under 15 years of age. Methods A simulation model using a decision tree template was developed to project the cost and disability‐adjusted life‐years saved by a pediatric OR in a low‐income setting. Parameters are informed by patient outcomes of the surgical procedures performed. Costs of the OR equipment and a literature review were used to calculate the incremental cost‐effectiveness ratio of a pediatric OR. One‐way and probabilistic sensitivity analysis were performed to assess parameter uncertainty. Economic monetary benefit was calculated using the value of a statistical life approach. Results A pediatric OR averted a total of 6,447 disability‐adjusted life‐years /year (95% uncertainty interval 6,288–6,606) and cost