COVID-19 Health Preference Research: 4 Lessons Learned
Benjamin M. Craig, PhD, University of South Florida, Tampa, FL, USA; Juan Marcos González Sepúlveda, PhD, Duke University, Durham, USA; F. Reed Johnson, PhD, Duke University, Durham, USA; Roselinde Kessels, PhD, Maastricht University, Maastricht, The Netherlands; Shelby D. Reed, PhD, Duke University, Durham, USA; Erlend Sandorf, PhD, University of Stirling, Stirling, Scotland, UK; Koonal Shah, PhD, PHMR, London, England, UK; Tommi Tervonen, PhD, Evidera, London, England, UK, and University of Groningen, The Netherlands; Verity Watson, PhD, University of Aberdeen, Aberdeen, Scotland, UK
Like the rest of the world, the pandemic has reshaped the lives and livelihoods of scientists in the health economics and outcomes research community. Many investigators in health preference research reacted by conducting empirical studies to better understand the value of health and health-related alternatives affected by COVID-19. The group identified 18 health preference studies currently under review or ongoing. On 17 June 2020, the first COVID-19 health preference research roundtable was held as an open meeting of the minds, allowing a healthy exchange of ideas between these study teams and other attendees interested in the area. From that discussion, this summary characterizes the state of science for the broader scientific community and for regulators and other decision makers looking for preference evidence regarding COVID-19.
As defined by the International Academy of Health Preference Research, a health preference study uses observational and experimental methods to collect empirical evidence on health-related choices. The 18 studies discussed here focused on stated preferences, specifically testing the causal relationships between attributes and preferential choice behaviors related to COVID-19. Due to their preliminary nature, the studies are not cited directly here; instead, this summary will attempt to describe initial experiences of the health preference research community, not the findings of individual studies.
"COVID-19 has affected nearly all aspects of our daily lives and COVID-19 health preference research will likely expand immensely over the coming months with little risk of duplication."
Given that COVID-19 is a new topic in health preference research, it was remarkable to hear about so many independent studies from Australia, Canada, China, France, Germany, New Zealand, The Netherlands, the United Kingdom, and the United States. Most sought to study preferences of the general population on alternative policies and the tradeoffs involved, either in a single country or in multiple countries. Others focused on individual health interventions, attempting to predict uptake of COVID-19 vaccines and of contact-tracing apps. Many topics remain untouched, such as clinical trial participation, labor decisions, testing, and long-term care. COVID-19 has affected nearly all aspects of our daily lives and COVID-19 health preference research will likely expand immensely over the coming months with little risk of duplication. This article emphasizes the lessons learned that are particularly relevant when conducting a COVID-19 health preference research study.
First Lesson Learned
The first lesson is that public perspectives regarding COVID-19 are evolving. In practical terms, starting and finishing a study quickly (ie, within 30 days) may be exciting and expeditious, but in retrospect, early evidence may age more quickly. A once perfectly valid instrument may be out of date 2 weeks later when preparing the results for dissemination. Although these rapid-cycle studies could provide near real-time information for policy decisions, traditional peer review timelines limit their potential. Nevertheless, publication of these studies can provide important snapshots of the public’s perspectives and can inform the design of sequential studies. Study teams may be wise to consider pairing their study objectives with dissemination plans to avoid the potential obsolescence of preference evidence due to changes in the context or as respondents gain greater firsthand experience with COVID-19.
Second Lesson Learned
The second lesson is that nearly all epidemiologic interventions and their social impacts are legitimately complex, which makes them challenging to convey to the general population in a health preference study. Furthermore, trading off among attributes often presents a moral dilemma, calling for the interpretation of a philosopher as much as an economist. For example, choosing to wait for a more effective vaccine may imply time preferences, but also has distributional consequences within the population. In most countries, the burden of COVID-19 in terms of health and employment has been far from uniform.
Third Lesson Learned
The third lesson concerns the inelastic demand for goods and services, such as masks, contact tracing apps, and vaccines. Some people are nontraders who are implacably in favor of or against a good or service (eg, anti-vaxxers), regardless of the attributes. Such persons absolutely will or will not comply with public health recommendations. Knowing the proportion of nontraders is required to accurately predict uptake; however, surveying their preferences on alternatives is uninformative. Some researchers have argued that stated preference surveys are bad at predicting uptake but good at quantifying rates of substitution. The ideal approach may be to pool stated and revealed preference evidence to identify both rates of substitution and predicted uptake. In any case, it is important to acknowledge the limits of preference evidence when capturing the factors that drive real-world behaviors (eg, working to support a household).
Some characteristics of nontraders make health preference researchers feel uneasy. Nontraders can have nuanced reasoning or be willfully ignorant. Others have distinct political views that are well outside the mainstream. Each country has its own aberrant subpopulations. Such eccentric views on the burden of disease or the risks may be scientifically valid or unfounded, but, if a preference study attempts to change them, the study will fail to predict real-world behavior. For example, completing forced choice tasks on vaccinations (without an opt-out) may train persons to choose vaccination when given an opt-out. By altering respondents’ perspectives, study teams switch from positive economics (ie, describing what is) to normative economics (what should be).
Fourth and Final Lesson
The last lesson is to recognize that the world is in the midst of a pandemic like no other in terms of infectious spread and media attention. Understanding what drives uptake and other health-related behaviors is critically important for nearly every country. Capturing preference evidence now on tradeoffs and priorities can provide an evidentiary basis for health system reforms in the near and long-term. Some researchers are using this moment to develop innovative prioritization tools (eg, ventilator allocation) that may have applications across multiple future areas. Even when this challenge is overcome, there will likely be another “someday” with similar traits.The attendees of the first roundtable expressed unanimous support for COVID-19 health preference research and hoped to see these and many more studies published in the coming months to inform regulatory decisions, economic evaluations, clinical practice, and health policy.