How COVID Can Help Us Refocus on the How and Why of Value Assessment

Editor’s note: This blog was first published by Health Affairs on October 21, 2021.

Approaching two years since the start of the COVID pandemic, its profound effects on both individuals and societies worldwide continue to unfold.

Widespread illness and death caused by the rapidly evolving virus have brought fear and anxiety while threatening to overwhelm healthcare systems. Societal responses—working from home, remote schooling, masking requirements, business closures, bans on public gatherings (even for worship), and many others—continue to disrupt family life, communities, and local and national economies, fueling politicization of these responses that creates anger, confusion, and distrust of both science and authority. Many of the responses to the COVID pandemic fall out of the realm of “health economics,” but there remain human activities in our “domain” as health economists. What can we learn about these from COVID?

Perhaps most importantly, COVID has caused us to rethink methods for measuring economic value in healthcare and health, policies to grant disadvantaged populations the benefits of scientific innovation, the importance of risk and uncertainty in health-related decisions, and a host of other policy issues that bear on the societal value of medical technology.

What Is Healthcare Value Assessment, and Why Do We Need It?

In market economies, for most goods the interaction of supply and demand lead to appropriate output, investment choices, consumption levels, and prices. Healthcare differs because of the widespread presence of health insurance, either governmental or private, that severs the natural link between value and cost. To restore that lost connection, many societies have turned to formal healthcare technology assessment (HTA) to replace the missing “equilibrium” generated by markets.

HTA, if properly constructed, can replace the missing “market equilibrium” information with comparable information about value and costs. Done well, this can improve decisions that determine coverage for new technologies, reimbursement for treatments, and pricing of drugs, devices, and other technologies. Such choices affect not only access to health care technologies by patients but also incentives for development of new technologies. For these reasons, it is important to “get it right” in the methods we use to assess value in health care.

The most common method to carry out HTA employs the tools of cost-effectiveness analysis (CEA). There, new technologies are compared against existing alternatives in terms of their incremental health benefits (typically measured in Quality-Adjusted Life Years, or QALYs) and incremental costs. A technology’s “Incremental Cost-Effectiveness Ratio” (ICER) can be compared against a predetermined threshold to determine whether the new technology should be adopted or not. The UK’s NICE does this regularly, using a formal threshold of ₤20,000-₤30,000 per QALY, for example, but with numerous notable exceptions for cancer drugs, end-of-life care, rare diseases, and others.

These exceptions illuminate the discontent that often accompanies current HTA methods. In the US, this discontent is similarly apparent in concerns about methods used by organizations like the Institute for Clinical and Economic Review and opposition to efforts to use CEA-based value assessment in coverage decisions (e.g., by CVS Caremark). Somehow, current methods fail to capture widespread societal beliefs that health improvement provides greater value for people who are relatively worse off—dealing with poorer health or greater disability, for example.

To date, none of the standard HTA methods systematically incorporate things we know as economists to be important: the many facets of uncertainty and risk, for example, as well as spillover effects (i.e., “externalities”) affecting individuals and society. Appropriate HTA methods must formally incorporate such issues to improve overall societal welfare.

Lessons From COVID

The strain placed on the US health system during the pandemic highlights the importance of an efficient and well-functioning health care system to the welfare of our entire society. Against this backdrop, the limitations of conventional approaches to value assessment discussed above are particularly clear.

Value Is A Societal Issue

Of the many important aspects of value that standard HTA methods fail to capture, the most obvious are spillover effects of health care investments into other areas and on inequities in access to health care.

Spillovers—positive or negative effects of a health care intervention that extend beyond those receiving the interventions—sit at the center of debates about mandatory vaccination, masking, limitations on public gathering and travel, and others. These uncaptured aspects of value are not limited to the realm of health and health care. While COVID vaccines have clearly saved many lives and reduced hospitalization-related costs, for example, they have also provided the possibility of return to economic “normalcy,” addressing perhaps the greatest hidden cost of the pandemic. Economic theory provides the approach to measure this cost: Willingness to pay (WTP) is a concept intended to capture the tradeoffs members of society would be willing to make, on average, in exchange for a given good, service, or state of the world. WTP is commonly used in evaluation of public goods and policies or programs; many examples focus on environmental benefits of policies, for example, but it is also frequently employed in the health care context.  

How much would we collectively be willing to pay to return to pre-COVID normalcy? It could be trillions of dollars in the US alone—for the US population of 330 million people, if the average willingness to pay for return to normalcy were $10 per day, the aggregate value would be $1.2 trillion per year. Yet even this does not capture the value of relieving indirect effects of the pandemic such as increased depression, domestic violence, educational losses for children, and many others.

Second, decisions that determine allocation of health care resources must align with societal priorities and moral values. For example, addressing inequity in health care and reducing disparities in outcomes are priorities for most Americans. The causes of health inequity in the U.S. are, of course, complex, in no small part because our multi-payer system effectively segregates populations along socioeconomic and racial lines. Allocating resources to therapies or care strategies that increase equity is important to our society, however, and this should be reflected in assessments of value. This extends to other considerations, including, for example, societal preferences for allocating treatment to those with the most severe illnesses and greatest permanent disabilities.

Current CEA methods focus narrowly on incremental health gains and their costs, while ignoring many of the broader societal issues to which COVID has drawn our firm attention. A more complete method for valuing health care interventions—not just drugs and medical treatments, but societal programs that affect public health, reduce mental illness and domestic violence, and reduce inequities in access—must widen the scope of inquiry. Any new methods, we believe, should be grounded in economic theory, thereby increasing their ability to improve overall social welfare.

Patients As The Starting Point

Although they often do not face the full billed costs of health care, patients should always be the focal point for assessing the value of medical interventions. Their value structures should focus and define any broader “societal” measures of value.

Value assessments ultimately depend upon the data that support them. This requires a fundamental shift toward value assessment that embraces the heterogeneity needed to provide context-specific insights. This is most obvious in terms of variability of treatment effects. Beyond this, however, the utility of health care to patients is shaped by the disease being treated, cultural norms, patients’ goals and preferences, and socioeconomic factors such as income level.

This begins with the structure of our clinical trial system, which fails to align research with societal priorities and public health needs. The emerging shift to “real world data” may help resolve some of these issues, but we must still improve the overall structure of data-gathering that supports fundamental health care decisions. These include not only approval of new biopharmaceutical products and medical devices, but medical treatment protocols and social programs that affect peoples’ well-being.

Understanding The Role Of Risk

It is impossible to ignore the importance of risk over the course of the pandemic, and our collective pandemic experience demonstrates the value of reducing risk. COVID vaccines provide obvious value by averting death, long-term morbidity, and health care costs for individuals who would have otherwise suffered some or all of these outcomes. However, for many millions of people, the potential value was driven not by these measurable health gains, but rather from the reduced risk of infection, severe illness, and death that they promised.

In most cases, people are risk averse, meaning that reducing the risk of negative outcomes in itself creates utility. This is the primary reason that insurance markets exist. Insurance coverage does not, however, protect against physical risk – but health care goods and services do often reduce physical risk. Not only vaccines, but the availability of and access to ventilators, ICU beds, and other care reduce the anxiety created by the risks of illness and death from COVID.

Applying Pandemic Lessons To Improve HTA Methods

The pandemic experience points to important changes that must be made in the methods we use to assess the value of health care technologies. These come in two parts—methods to incorporate individual patient preferences, and methods to incorporate wider societal issues. We address these in turn.

Individual Patient Values

First, we must improve methods to incorporate a wider array of patient preferences than current methods allow. In one promising example, researchers at the University of Maryland Patient-Driven Values in Healthcare Valuation (PAVE) Center have developed a set of patient-identified value elements across five domains. Using surveys and interviews with patients, a discrete set of ranked elements can be defined from this broader set to reflect the priorities of patients in different contexts; the relative importance of these elements can then be estimated empirically. Previously piloted with chronic obstructive pulmonary disease (COPD) patients, PAVE’s approach is now being tested with major depressive disorder patients through an experimental open-source model being developed by the Innovation and Value Initiative (IVI) as a laboratory for developing and testing innovative approaches to health economic modeling and value assessment. Further research is needed, however, to develop methods for incorporating these insights into aggregate measures of value (e.g., ICERs) in HTA.

On another front, two of us have developed the Generalized Risk Adjusted Cost Effectiveness (GRACE) model that formally incorporates patients’ attitudes towards uncertain treatment outcomes and provides a formal method for incorporating disease severity in the measure of patient value. In GRACE, treatments for more severe illness have greater value than in standard CEA methods, providing an alternative to the usual CEA mantra that “…a QALY is a QALY.” Finally, GRACE demonstrates (in contrast to standard CEA) that treatment for patients with permanent disabilities has greater (not less) value than for comparable people with no disabilities.

Incorporating Wider Societal Values

Methodological approaches focused on value from the perspective of individual actors— including those discussed above—are essential but fail to fully account for broader societal issues such as equity and fairness and the public good aspects of new knowledge. Some emerging methodologies do begin to address these issues, but only in part. For example, GRACE may help to pursue equity in the context of disease severity and disability, but it does not account for equity concerns related to race, ethnicity, or socioeconomic factors. Methods for distributional CEA allow for a more extended examination of equity impacts along multiple dimensions, including race, ethnicity, age, and disease severity. Distributional CEA still falls short of accounting for important issues like race- and income-associated disparities in insurance coverage and access or broader impacts on the economy, however.

Instead, these issues are commonly now considered in the context of “deliberative processes.” These processes are often murky or wholly opaque; lacking formal methods for organizing information about performance of new technologies or health care interventions, they obscure how value is measured for each of these wider societal issues.

Fortunately, methods to formalize such processes do exist and have seen widespread use in other areas of societal decision-making, including environmental policy and regional planning, and even in such areas as national defense and space exploration. These methodologies have seen minimal use in health care decision-making, however, and we urge widespread investigation and expanded use of them on many fronts.

These Multi-Criteria Decision Analysis (MCDA) techniques all provide specific ways to assemble data on actual or potential performance of new technologies, medical interventions, or public health programs, as well as eliciting the preference structures of relevant decision-makers. When combined, these data lead to clear ways of understanding the complex multi-dimensional value created by new and existing programs and interventions. Some of these methods, most notably Multi-Attribute Utility Theory (MAUT) have strong foundations in economic theory, an aspect that we, as economists, value. They require more data to conduct than does CEA and are more complex to undertake and understand—but this added data burden and complexity come not from the methods, but from the problems they are trying to analyze. Ignoring these issues does not solve the problem, but formally dealing with them through wider use of MCDA can improve decision-making at many levels.

Looking To The Future

The COVID pandemic has challenged us to reevaluate the role of health care in our personal lives and our society, with important implications for how we approach value as a guide for allocating limited health care resources. We should not squander these insights as the initial shock of the pandemic fades.

Rather, examining our health care system through the economic lens we’ve described—seeing it as a fundamentally flawed market failing to deliver the societal welfare that it could—can guide ongoing efforts to innovate in value assessment and health care delivery writ large.