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Über dieses Buch

Operations research tools are ideally suited to providing solutions and insights for the many problems health policy-maker's face. Indeed, a growing body of literature on health policy analysis, based on operations research methods, has emerged to address the problems mentioned above and several others. The research in this field is often multi-disciplinary, being conducted by teams that include not only operations researchers but also clinicians, economists and policy analysts. The research is also often very applied, focusing on a specific question driven by a decision-maker and many times yielding a tool to assist in future decisions.

The goal of this volume was to bring together a group of papers by leading experts that could showcase the current state of the field of operations research applied to health-care policy. There are 18 chapters that illustrate the breadth of this field. The chapters use a variety of techniques, including classical operations research tools, such as optimization, queuing theory, and discrete event simulation, as well as statistics, epidemic models and decision-analytic models. The book spans the field and includes work that ranges from highly conceptual to highly applied. An example of the former is the chapter by Kimmel and Schackman on building policy models, and an example of the latter is the chapter by Coyle and colleagues on developing a Markov model for use by an organization in Ontario that makes recommendations about the funding of new drugs. The book also includes a mix of review chapters, such as the chapter by Hutton on public health response to influenza outbreaks, and original research, such as the paper by Blake and colleagues analyzing a decision by Canadian Blood Services to consolidate services. This volume could provide an excellent introduction to the field of operations research applied to health-care policy, and it could also serve as an introduction to new areas for researchers already familiar with the topic.

The book is divided into six sections. The first section contains two chapters that describe several different applications of operations research in health policy and provide an excellent overview of the field. Sections 2 to 4 present policy models in three focused areas. Section 5 contains two chapters on conceptualizing and building policy models. The book concludes in Section 6 with two chapters describing work that was done with policy-makers and presenting insights gained from working directly with policy-makers.



Applications of Health Policy Modeling


Chapter 1. Public Health Modeling at the Centers for Disease Control and Prevention

At the Centers for Disease Control and Prevention, there is a growing interest in promoting the use of mathematical modeling to support public health policies. This chapter presents three examples of operations research models developed and employed by the Centers for Disease Control and Prevention. First, we discuss the Adult Immunization Scheduler, which uses dynamic programming methods to establish a personalized vaccination schedule for adults aged 19 and older. The second operations research project is a discrete event simulation model used to estimate the throughput and budget for mass vaccination clinics during the 2009–2010 H1N1 pandemic. Lastly, we describe a national HIV resource allocation model that uses nonlinear programming methods to optimize the allocation of funds to HIV prevention programs and populations.
Arielle Lasry, Michael L. Washington, Hannah K. Smalley, Faramroze Engineer, Pinar Keskinocak, Larry Pickering

Chapter 2. OR in Public Health: A Little Help Can Go a Long Way

When deciding which programs to invest in, public health decision makers face a number of challenges including limited resources, competing objectives (e.g., maximize health, achieve equity), and limited information about uncertain events. Despite these difficulties, public health planners must make choices about which programs they will invest in—and the quality of these choices affects the health benefits achieved in the population. To support good decisions, information about the likely costs and health consequences of alternative interventions is needed. This is where OR-based modeling can play a role: by providing a structured framework that uses the best available evidence, imperfect as it may be, and that captures relevant uncertainties, complexities, and interactions, OR-based models can be used to evaluate the potential impact of alternative public health programs. This chapter describes modeling efforts in which OR has played and can play a role in informing public health decision making. We describe work in three areas: hepatitis B control, HIV control, and bioterrorism preparedness and response. We conclude with a discussion of lessons learned.
Margaret L. Brandeau

Health Policy and Operations


Chapter 3. Analytical Long-Term Care Capacity Planning

This chapter discusses the use of analytical approaches for residential long-term care (LTC) capacity planning. The recommended method integrates demographic and survival analysis, discrete event simulation, and optimization. Through a case study based in British Columbia, Canada, it illustrates results of using this approach. Further, it discusses shortcomings of a fixed-ratio approach widely used in practice and the SIPP (stationary, independent, period by period) approach and its modifications developed in the call center literature. It also proposes an easy-to-use and effective planning method, the Average Flow Model. It concludes with a discussion of policy implications and extensions.
Yue Zhang, Martin L. Puterman

Chapter 4. Managing Community-based Care for Chronic Diseases: The Quantitative Approach

Community-based care (C-bC) constitutes an important element of the chronic disease management programs. The design and management of C-bC systems requires the development of new resources and services, the assessment and reorganization of the existing services/facilities as well as the design of interventions. Quantitative decision models can play a major role for helping care providers and policy makers in this context. We present a systematic view of C-bC and provide selective examples of quantitative decision models developed for various chronic diseases. We outline the building blocks of C-bC systems as well as the distinguishing features of these systems that need to be incorporated in quantitative decision models. Then, we present three representative and diverse examples of prevailing quantitative approaches for managing C-bC. Finally, we discuss some avenues for future research.
Beste Kucukyazici, Vedat Verter

Chapter 5. Project Management Approach to Implement Clinical Pathways: An Example for Thyroidectomy

Clinical pathway is a concept that from a managerial point of view promotes variance reduction in the delivery of health care and, therefore, is able to reduce costs. To achieve this, health care providers must improve efficiency in the use of resources while completing delivery of care in time with expected achievements in quality. Implementation of the clinical pathways for a specific disease requires a clear identification of tasks that compose the care delivery process by a multi-professional team including physicians, nurses, various therapists and/or health technologist and so on. From this perspective, implementing clinical pathways for a disease can be, therefore, conceptualized as an integrated project with many tasks. Hence, the management of the care delivery tasks in time nicely fits into project management, an operations research tool. With this conceptualization, we test the potential use of project management to organize the integrated care delivery tasks of the thyroid disease as a project. Probabilistic and deterministic project management models have been implemented and solved for a real case study to demonstrate the estimated duration for the clinical pathway, where critical activities must be carefully monitored by the caregiving team to reduce or eliminate the variation in care delivery.
Yasar A. Ozcan, Elena Tànfani, Angela Testi

Chapter 6. EMS Planning and Management

In this chapter I survey research on planning and management for emergency medical services, emphasizing four topics: forecasting demand, response times, and workload; measuring performance; choosing station locations; and allocating ambulances to stations, based on predictable and unpredictable changes in demand and travel times. I focus on empirical work and the use of analytical stochastic models.
Armann Ingolfsson

Chapter 7. Impact of Inpatient Reimbursement Systems on Hospital Performance: The Austrian Case-Based Payment Strategy

Due to cost-intensive technological advances in high-end medicine and increased life expectancy accompanied by a rising number of multi-morbid elderly people, the health care sector consumes a large part of the gross national product of Austria. As the hospital sector is the main contributor to this increasingly unaffordable cost explosion, reimbursement systems for inpatients worldwide have been undergoing massive restructuring. Case-based systems such as the Austrian performance-oriented LKF-system have been introduced to curb the cost explosion. While macro-perspective studies analyze the efficiency of hospitals based on aggregated input and output data using DEA techniques, micro-perspective studies focus on the main incentives of the LKF-system on several outcome measures using empirical data on inpatients with certain major diseases. This study illustrates its impact on hospitals’ performance as well as on the hospitals’ management subsystem of strategic technology management. Such studies support health regulators in improving their reimbursement schemes by closing loopholes.
Marion S. Rauner, Michaela M. Schaffhauser-Linzatti

HIV Policy Models


Chapter 8. Assessing Prevention for Positives: Cost-Utility Assessment of Behavioral Interventions for Reducing HIV Transmission

Typical studies of HIV behavioral interventions measure relative risk reduction for HIV transmission. Here, we also consider the health benefits of such interventions on secondary transmission. In addition, a sensitivity analysis explores the potential additional benefits that may accrue if partners of those in the intervention group also adopt the risk reducing behavior. To do this, we developed an ordinary differential equation (ODE) model to analyze the cost and utility (measured in quality-adjusted life years, or QALYs) of a published behavioral HIV intervention that aims to reduce the risk of transmission from HIV-infected persons to their sexual partners. The ODE model maps measurements of behavioral risk reduction parameters, estimated from sampling, into costs and QALYs. Monte Carlo sampling was used to perform a probabilistic sensitivity analysis to quantify uncertainty in costs and QALYs due to parameter estimation error for the behavioral HIV intervention. The results suggest that the behavioral intervention is most likely to be cost-saving or, at least, cost-effective. The analysis highlights the step of converting uncertainty about estimates of mean values of parameters that are commonly reported in the literature to uncertainty about the costs and health benefits of an intervention. It also shows the potential importance of considering secondary transmission of HIV and the partial adoption of behavior change by partners of the individuals who undergo the intervention.
Sada Soorapanth, Stephen E. Chick

Chapter 9. Modeling the Impact of New HIV Prevention Technologies in Sub-Saharan Africa

Research has shown that several new technologies can be effective in reducing the transmission of HIV infection. Male circumcision was shown to reduce susceptibility to new infection by about 60% in trials in 2005 and 2007. In 2009, a large scale trial of an HIV vaccine showed some protective benefits. Research results released in 2010 showed effectiveness of oral pre-exposure prophylaxis and topical pre-exposure prophylaxis at levels around 40%. When new technologies become available national policy makers are faced with questions about whether to implement them, how much they will cost and how they should be implemented. Funders face similar questions. We have developed computer models to aid in policy development and planning. These models are intended to investigate questions such as “What will the impact be in terms of infections averted?”, “How much would a new program cost?”, and “Would the new program be cost-effective?” This chapter discusses models for male circumcision, pre-exposure prophylaxis, and HIV vaccines and their applications to inform policy makers.
John Stover, Carel Pretorius, Kyeen Andersson

Chapter 10. REACH: A Practical HIV Resource Allocation Tool for Decision Makers

With more than 34 million people currently living with HIV and 1.8 million dying from HIV annually, there is a great need for continued HIV control efforts. However, funds for HIV prevention and treatment continue to fall short of estimated need and are further jeopardized by the current global economic downturn. Thus, efficient allocation of resources among interventions for preventing and treating HIV is crucial. Decision makers, who face budget constraints and other practical considerations, need tools to help them identify sets of interventions that will yield optimal results for their specific settings in terms of their demographic, epidemic, cultural, and economic contexts and resources available to them. Existing theoretical models are often too complex for practical use by decision makers, whereas the practical tools that have been developed are often too simple. As a result, decisions are often made based on historical patterns, political interests, and decision maker heuristics, and may not make the most effective use of limited HIV control resources. To address this gap between theory and practice, we developed a planning tool for use by regional and country-level decision makers in evaluating potential resource allocations. The Resource Allocation for Control of HIV (REACH) model, implemented in Microsoft Excel, has a user-friendly design and allows users to customize key parameters to their own setting, such as demographics, epidemic characteristics and transmission modes, and economic setting. In addition, the model incorporates epidemic dynamics; accounts for how intervention effectiveness depends on the target population and the level of scale up; captures benefit and cost differentials for combinations of interventions versus single interventions, including both treatment and prevention interventions; incorporates key constraints on potential funding allocations; identifies optimal or near-optimal solutions based on epidemic characteristics, local realities, and available level of investment; and estimates the impact of HIV interventions on the health care system and resulting resource needs. In this chapter we describe the model and then present example analyses for three different settings, Uganda, Ukraine, and Saint Petersburg, Russia. We conclude with a discussion of insights gained from application of the model thus far, and we describe our ongoing work in further developing and applying the model.
Sabina S. Alistar, Margaret L. Brandeau, Eduard J. Beck

Chapter 11. Review of Operations Research Tools and Techniques Used for Influenza Pandemic Planning

Many public health officials are concerned about a possible influenza pandemic. The three pandemics in the twentieth century killed between 50 and 100 million people and the 2009 H1N1 “Swine Flu” exposed vulnerabilities that we still have to influenza epidemics. Operations research tools and techniques can analyze public health interventions to mitigate the impact of pandemic influenza. In this chapter we review an array of examples of how operations research tools can be used to improve pandemic influenza prevention and response. From this, we derive insights into the appropriateness of certain techniques for answering specific questions and we propose preliminary policy recommendations. We then discuss opportunities for future research.
David W. Hutton

Pharmaceutical Policy


Chapter 12. Active Vaccine and Drug Surveillance

Towards a 100 Million Member System
After the withdrawal of rofecoxib (known by the trade name Vioxx) from the US pharmaceutical market in 2004, post-approval drug safety and surveillance came under serious scrutiny. In 2008 the FDA announced the Sentinel Initiative, which includes an active surveillance system based on 100 million people’s health-care data. In this chapter we describe a number of challenges involved in active drug and vaccine surveillance and provide an overview of state-of-the-art surveillance methodologies. We also address the statistical tradeo-ffs involved in surveillance, highlight some areas for future research, and frame the policy issues that designers of surveillance systems will have to address.
Margrét V. Bjarnadóttir, David Czerwinski

Chapter 13. Application of Operations Research to Funding Decisions for Treatments with Rare Disease

In this chapter, the focus is on the application of decision analytic tools to assist in reimbursement decisions related to drugs for rare diseases. Focus is on the evaluative framework developed by the Ontario Ministry of Health's Drugs for Rare Diseases Working Group. The chapter describes the framework and illustrates the role of decision analytic methods through the application of the framework to idursulfase treatment of Hunter disease, an enzyme deficiency syndrome. The chapter highlights the development of a Markov model designed to mirror the natural disease history and to simulate the possible benefits of treatment. This process led to the Ministry of Health developing funding recommendations for the treatment of Hunter disease.
Doug Coyle, Chaim M. Bell, Joe T. R. Clarke, Gerald Evans, Anita Gadhok, Janet Martin, Mona Sabharwal, Eric Winquist

Chapter 14. Modeling Risk Sharing Agreements and Patient Access Schemes

Risk sharing agreements are becoming an increasingly common type of contract between drug manufacturers and third party payers such as private insurance companies and public sector health plans. In a risk sharing agreement a payer will agree to include a drug on its formulary in the presence of a contract that reduces some of the payer’s risk. Payer risk may be caused by high uncertainty in sales volume, cost, effectiveness, or cost-effectiveness of a new drug. In this chapter we review the literature on risk sharing agreements, identify some opportunities for future research in the area, and highlight some policy implications associated with their use.
Gregory S. Zaric, Hui Zhang, Reza Mahjoub

Building Health Policy Models


Chapter 15. Considerations for Developing Applied Health Policy Models: The Example of HIV Treatment Expansion in Resource-Limited Settings

This chapter describes steps for developing health policy models. The discussion begins with considerations for identifying a research question and developing a model conceptual framework. It next provides guidance on how to build and implement the model, as well as how to populate or parameterize a model. We end by examining the techniques for verifying model performance. Special emphasis is placed on developing applied health policy models, particularly those used to inform policy decisions in resource-limited settings.
April D. Kimmel, Bruce R. Schackman

Chapter 16. Cost-Effectiveness Analysis Using Registry and Administrative Data

Health administrative databases and disease registries can serve as valuable data sources for decision modeling and cost-effectiveness analyses. In this chapter, we give an overview of administrative databases in Canada and discuss how data from multiple registries and administrative databases can be linked, analyzed, and combined with experimental data to fit a decision analytic model. We illustrate with two examples of cost-effectiveness analyses of genetic tests used in cancer diagnosis and treatment decisions.
Malek B. Hannouf, Gregory S. Zaric

Working with Policy Makers


Chapter 17. Evaluating Health Care Policy Decisions: Canadian Blood Services in Atlantic Canada

In 2009, Canadian Blood Services, one of two nonprofit agencies that manage the supply of blood and blood products in Canada, announced plans to consolidate a number of its production facilities in an effort to standardize processes and workflows. One of the elements of the plan involved moving existing production facilities in Saint John, New Brunswick and Halifax, Nova Scotia, into a single facility to be located in Dartmouth, Nova Scotia. The plan drew criticism from some stakeholder groups. In this chapter, we describe how operations research techniques were used to analyze this difficult policy issue. We provide a discussion of the motivation for the study, an overview of the methodology, and the results of the studies conducted to evaluate the proposed change. The analysis involved a statistical comparison of transport modes as well as a series of simulation models to evaluate the impact of consolidation on product availability. The results of this analysis suggested that, on the balance of metrics considered, customer service would not be adversely affected by the consolidation of facilities.
John Blake, Michelle Rogerson, Dorothy Harris

Chapter 18. Improving the Efficiency of Cost-effectiveness Analysis to Inform Policy Decisions in the Real World: Lessons from the Pharmacoeconomics Research Unit at Cancer Care Ontario

There are important challenges in the application of using operations research (OR) and cost-effectiveness analysis (CEA) in the real world that highlight the great divide between academic research and practical application. The difficulty is magnified in cancer. Nevertheless, the potential for CEA to inform policy decisions is also great. The best estimate of a new drug’s cost-effectiveness is not knowledge for knowledge’s sake; this type of information is the foundation of accountability for the hundreds of millions of dollars being spent. In 2007, Cancer Care Ontario (CCO) established Canada’s first in-house Pharmacoeconomics Research Unit comprised of independent researchers. This chapter reviews the initial years of the Unit at CCO after briefly describing Canada’s cancer drug funding landscape. The chapter concludes by sharing lessons from the Pharmacoeconomics Research Unit’s experience and pointing out directions for future research aimed at reaching decision makers in the real world.
Jeffrey S. Hoch


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