Cost-Effectiveness Analysis in Health

A Practical Approach

Peter Muennig, Mark Bounthavong

• Publisher: Jossey-Bass
• Year: 2016
• ISBN:
  9781119011286
• Edition: 3
• Topic: Medicina
• Subtopic: Salud pública, administración y atención

The field's bestselling reference, updated with the latest tools, data, techniques, and the latest recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine

Cost-Effectiveness Analysis in Health is a practical introduction to the tools, methods, and procedures used worldwide to perform cost-effective research. Covering every aspect of a complete cost-effectiveness analysis, this book shows you how to find which data you need, where to find it, how to analyze it, and how to prepare a high-quality report for publication. Designed for the classroom or the individual learner, the material is presented in simple and accessible language for those who lack a biostatistics or epidemiology background, and each chapter includes real-world examples and "tips and tricks" that highlight key information. Exercises throughout allow you to test your understanding with practical application, and the companion website features downloadable data sets for students, as well as lecture slides and a test bank for instructors. This new third edition contains new discussion on meta-analysis and advanced modeling techniques, a long worked example using visual modeling software TreeAge Pro, and updated recommendations from the U.S. Public Health Service's Panel on Cost-Effectiveness in Health and Medicine. This is the second printing of the 3rd Edition, which has been corrected and revised for 2018 to reflect the latest standards and methods.

Cost-effectiveness analysis is used to evaluate medical interventions worldwide, in both developed and developing countries. This book provides process-specific instruction in a concise, structured format to give you a robust working knowledge of common methods and techniques.

• Develop a thoroughly fleshed-out research project
• Work accurately with costs, probabilities, and models
• Calculate life expectancy and quality-adjusted life years
• Prepare your study and your data for publication

Comprehensive analysis skills are essential for students seeking careers in public health, medicine, biomedical research, health economics, health policy, and more. Cost-Effectiveness Analysis in Health walks you through the process from a real-world perspective to help you build a skillset that's immediately applicable in the field.

Chapter 1
Introduction to Cost-Effectiveness

Learning Objectives

• Explain why cost-effectiveness is useful.
• List the elements of a cost-effectiveness analysis.
• Distinguish between an average cost-effectiveness ratio and an incremental cost-effectiveness ratio.
• Explain the difference between efficacy and effectiveness.
• Describe how cost-effectiveness analysis influences policy.

Overview

Imagine that you are the director of a large cancer society. Your day-to-day duties require you to conduct some research and oversee employees whose job is to compile data and make health recommendations. One morning you sit down with a cup of coffee and toast, and when you open the morning paper you find that one of your society's recommendations—that women between the ages of 40 and 60 receive screening mammography for breast cancer—has made the headline news. An elderly-rights group is suing your society. This group argues that your recommendation unfairly discriminates against the elderly because you have implied that women over the age of 60 should not be screened for breast cancer.

You rush to the office and find that the teams who made the recommendation are already in a heated meeting. They have split into two factions, and each group is now accusing the other of making bad decisions. But did they? You manage to calm everyone down and review the process they used to arrive at their recommendation. You learn that both groups were concerned that recommending mammograms for women over a wider age range might become very costly, thereby jeopardizing screening for women who might benefit from screening mammography the most.

One group argued that it made sense to screen older rather than younger women. Mammography works better in older women, who have less dense breast tissue. Older women, they reasoned, were less likely to have a falsely positive mammogram and therefore would be less likely to suffer unnecessary procedures or surgery. Unnecessary interventions, they noted, place women at risk for surgical complications, are psychologically traumatic, are costly, and may do more harm than good.

The other group argued that it was unwise to actively screen all elderly women with mammography, because women who had breast cancer would die from other natural causes before the cancer had a chance to spread. After all, breast cancer can take more than a decade to kill, and the life expectancy of older people is limited. Therefore, they reasoned, elderly women would be subjected to an uncomfortable and expensive screening test that would have little impact on the length of their lives. Besides, who would want to undergo chemotherapy in the precious remaining years of their lives?

Both factions made arguments based on sound scientific, economic, and social research, but which group's approach would be best for patients? You and your employees decide to conduct a more extensive analysis of the costs and benefits of breast cancer screening and plan to send out a press release to this effect. But where do you start?

You might start by having a team estimate the likelihood that older women will die of breast cancer if they are not screened and have another team estimate the number of women who are likely to have false-positive mammograms at different ages. You might also wish to obtain information on the number of years of life that mammography will save, the quality of life for women who have different stages of breast cancer, and the psychological impact of a positive test result among women who do not in fact have breast cancer (false-positive test results). Because both teams were concerned about the costs of mammography, you may also wish to calculate the cost of screening mammography and the cost of all of the medical care that might be averted by detecting breast cancer at an early stage. Finally, because each team is interested in knowing whether women in both age groups might benefit from mammography, you decide that the costs and health benefits of screening each group should be compared to not screening women at all. If all of these factors were put together in a systematic manner, you would have conducted a cost-effectiveness analysis.

Why Cost-Effectiveness Is Useful

Now let's take a step back and consider why all of this is important in the first place. Certainly you want to know whether mammography is going to lead to net improvements or net declines in health relative to some standard of care. If it's only going to hurt people, we certainly don't want to do it. But if we know it helps, we also want to know whether it is affordable.

What does “affordable” mean when you are talking about human life? Take a moment to imagine what we could do with an infinite amount of money. We could build a huge public transportation system that eliminates car accidents, pollution, and noise. We could use only solar power and switch to 100 percent recycling, eliminating the major remaining sources of pollution; this would greatly reduce environmental carcinogens and oxidizing agents that cause cancer, heart disease, and premature aging. We could completely mechanize industry, eliminating occupational accidents. Finally, we could create a highly advanced health system that provides full MRI body scans and comprehensive laboratory screening tests for everyone in the population to ensure that cancers and other disorders are detected at the earliest possible stage.

As it is, there are very few nations that can even provide safe drinking water to all their citizens. The challenge, then, is to figure out how best to spend the money we have so that the quantity and quality of life can be maximized.

Thus, even if mammography screening for breast cancer is on the whole effective, it is conceivable that the money spent on it could save more lives if it went toward something else. Cost-effectiveness analysis (CEA) helps determine how to maximize the quality and quantity of life in a particular society that is constrained by a particular budget.

We'll get deeper into this later in the book, but let's examine the specifics of the example to illustrate how resource allocation might work. Assume that the U.S. Congress decided to allocate $1 trillion to the competing health projects we mentioned. It could choose public transportation, greatly reducing pollution (a cause of pneumonia, cancer, and heart disease) and motor vehicle accidents (the fifth leading cause of death). It could invest in clean energy, reducing dependence on oil while reducing air pollution. Or it could choose the universal MRI strategy, detecting more tumor-producing cancers, some of which can be cured if detected early. If Congress knew the cost per year of life saved, it would know how to maximize the number of lives saved with the $1 trillion investment.

One thing that might strike some readers as a bit strange about this hypothetical situation is that we are essentially deciding who lives and who dies. If we save the mothers and fathers with cancerous tumors by opting for universal MRI examinations, many sons and daughters will die in car accidents as a result. Behind these numbers are real people affected by whatever decision is ultimately made. The more tangible these lives are made to the decision makers, the more difficult the decision becomes.

As one physician, Paul Farmer, points out, you cannot let a person die in front of you when you know that an effective treatment exists (Farmer, 2004). Is the solution, therefore, to start a medical clinic, even if it comes at the expense of a more effective vaccination campaign? We might know that one intervention saves more lives than the other. However, when the most cost-effective intervention saves lives we will never see—lives that lie abstracted in numbers—it is more difficult to rationalize the choice.

Nevertheless, policymakers must often make abstracted decisions based on data from cost-effectiveness analysis, and these sometimes involve decisions that improve survival for one group at the cost of survival for another. (We'll see an actual example of this later in the book.) These decisions become more abstract when quality-of-life issues are added to the mix of life-and-death issues.

The sad reality is that making decisions based on “gut feelings” leads to more suffering and more death than making decisions based on science. While the US tends to operate on “gut policy,” other rich nations use science to allocate scarce resources. This may partly explain why health and longevity are declining in the US but are increasing in other rich nations.

Elements of Cost-Effectiveness Analysis

Just as a driver really only needs to know about the accelerator, brake, and gearshift before driving a car for the first time, this section provides the basic parts of a cost-effectiveness analysis that you need to have in your head before you can start getting down to business. As we get further into the book, you'll be introduced to more advanced and complex methods that will build on the foundations of earlier chapters.

Health Interventions

A health intervention is a treatment, screening test, or primary prevention technique (for example, vaccinating children to prevent measles). Health interventions typically reduce the incidence rate of disease or its complications, improve the quality of life lived with disease, or improve life expectancy. Most produce some combination of these benefits. The benefits of a health intervention are referred to as outcomes. Health outcomes can assume any form, but the most common health outcomes are big-picture items, such as hospitalizations prevented, illnesses avoided, or deaths averted ...

Table of contents

• Cover
• Title Page
• Copyright
• Table of Contents
• List of Tables, Figures, and Exhibits
• Preface
• Acknowledgments
• About the Authors
• Chapter 1: Introduction to Cost-Effectiveness
• Chapter 2: Principles of Cost-Effectiveness Analysis
• Chapter 3: Developing a Research Project
• Chapter 4: Working with Costs
• Chapter 5: Probabilities and Decision Analysis Models
• Chapter 6: Calculating Life Expectancy
• Chapter 7: Working with Health-Related Quality-of-Life Measures
• Chapter 8: Calculating QALYs
• Chapter 9: Conducting a Sensitivity Analysis
• Chapter 10: Preparing Your Study for Publication
• Chapter 11: Basic Concepts in Epidemiology and Application to Cost-Effectiveness Analysis
• Chapter 12: Finding the Data You Need
• Chapter 13: A Worked Example
• Appendix A: Answer Key to Exercises
• Appendix B: Life Expectancy and Quality-Adjusted Life Expectancy Tables
• Appendix C: EQ-5D-5L Health Questionnaire (English version for the United States)
• Appendix D: Diagnosis, Charges, Medicare Reimbursement, Average Length of Stay, and Cost-to-Charge Ratio by Diagnosis-Related Groups, 2011
• Glossary
• Index
• End User License Agreement