Why the integration of behavioral economic analysis in epidemiology is crucial to improving vaccine acceptance among the hesitant.
Contributing Writer: Rohan Snah | May, 2022
“The purely economic man is indeed close to being a social moron. Economic theory has been much preoccupied with this rational fool.”
-Richard H. Thaler
This rational fool, says one of the founding fathers of behavioral economics, has provided the basis of classical economic models for decades. At the core of many economic models is the idea of rationality. This idea posits that consumers will correctly evaluate risks, rewards, costs, benefits and use this information to make decisions that, everywhere and always, maximize utility. While this simplifying assumption can be extremely helpful in modeling consumer behavior generally, the burgeoning field of behavioral economics seeks to qualify this assumption. Titans in the field of behavioral economics such as Richard Thaler, Daniel Kahneman, and Cass Sunstein argue that consumers often do not act rationally in utility-maximizing manners. Consumers may often act irrationally, ignoring sound statistical and economic thinking, and end up worse off.
Two central ideas, heuristics and biases, lay the foundation for the wide applications of behavioral economics. Heuristics can be thought of as quick substitutes for decision making and together, these two guiding concepts help to back out consumer irrationality; heuristics and biases go hand-in-hand, with the former leading to the latter (shortcuts in decision making, ex-ante, lead to biases, ex-post). In a seminal publication, Nobel prize winners in Economics Amos Tversky and Daniel Kahneman detail the three most important heuristics and biases: representativeness, availability, and adjustment and anchoring. These biases can be summarized.
Representativeness bias: arises when probabilities are evaluated by the “degree to which A is representative of B” (Kahneman and Tversky).
Availability bias: arises from judging the probability of scenarios based on how readily similar occurrences can be brought to mind. For example, overestimating the odds that someone may have a heart attack given that multiple acquaintances have suffered heart attacks in the past.
Adjustment and anchoring bias: arises when estimating an outcome given a starting value. We tend to decide on outcomes that are biased towards the initial value (ex: estimating 1x2x3x4x5x6x7x8 lower than 8x7x6x5x4x3x2x1)
While there are numerous other biases, these three provide the setting for behavioral economics and serve as the guide when applying behavioral economic analysis. Due to the wide implementation of behavioral economics, this analysis can be set against the backdrop of finance, marketing, public policy, and in any instance where consumers are required to make a decision inside of a given choice architecture (the design in which choices are presented).
In particular however, the field of public health is a notable standout when now more than ever, public health is firmly planted in the forefront of society due to the availability of information on social media. Just as the notoriety of figures such as Anthony Fauci spreads, so does the SARS-CoV-2 virus in America. With vaccination efforts ramping up, the U.S. still lags behind most other developed nations in regards to the proportion of fully vaccinated individuals. Public health professionals seem to still be running into the age-old issue of vaccine hesitancy but now under Operation Warp Speed (the multi-organizational accelerated development of COVID-19 vaccines) and though it is not a catch-all, behavioral economic insights may hold the key to the gap in vaccine acceptance today.
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“So much of what public health has been able to accomplish, uses the fact that it’s a trusted institution… that trust is getting degraded, people are pushing back. How do you maintain the trust of the public in this environment where trust is being undermined…that’s the challenge.”
-Dr. Ryan Westergaard, Chief Medical Officer and State Epidemiologist for Wisconsin
In the annals of pandemic mitigation and public health intervention, a variety of mechanisms have been employed, with public health officials continually adapting to the specific epidemiological markers that differentiate different outbreaks. However it is in the strategies surrounding risk communication and reducing pandemic spread where the field of public health has met its largest challenges as of late.
Risk communication involves the dissemination of critical protective information to the public en masse. Historically this has involved the spread of basic information on pathogen activity including how the pathogen is spread, high-risk practices, and protective behavioral measures (Madhav et al., 2017). In conjunction, strategies in reducing spread have included limiting the interaction of infected and uninfected, reducing infectiousness of infected patients, and reducing the susceptibility of uninfected individuals (i.e. vaccination). These historical strategies are time-honored and proven with significant amounts of public health research; they are still mainstays in the arsenal of public health officials. However, with the notable public outcry against COVID-19 vaccine mandates, public health leaders have been forced to reassess the efficacy of these strategies.
In the U.S., as opposed to less developed countries, the healthcare apparatus benefits from the domestic powerhouse pharmaceutical industry. The supply of COVID-19 vaccines is immense with both Pfizer and Moderna operating out of the U.S. In the early stages of the pandemic, vaccines were being produced at a record rate. They were brought to market with unprecedented velocity aided by the breakthrough technological advancement of mRNA vaccines, but without much foresight into how many vaccines would be demanded. All the while, public health officials raised their concerns over what would happen if supply far outpaced demand. Now, it seems demand is in fact the limiting factor in vaccine uptake for Americans. For example in Wisconsin, efforts such as the conversion of the Alliant Energy Center into a COVID-19 testing and vaccination site were massive undertakings that demonstrated the strengths of traditional public health strategy, but “those were all people that came to us” Dr. Westergaard says (Shah, 2021). Now, implies Dr. Westergaard those who wanted to be vaccinated have been, and those who did not, have not. As Dr. Westergaard explains, while there are those who are very high-risk and still vaccine-hesitant, they are in the minority; “I think it’s in the middle ground where there is more variability.” The middle ground that Dr. Westergaard is referring to consists of those with lower levels of risk of serious health outcomes but to whom the public health messaging that asserts “the life you save may not be your own”, is largely ineffective. The drawbacks to traditional public health strategies are now strikingly apparent.
Dr. Westergaard attributes this truly seismic shift in the impact of public health messaging, this recent ineffectiveness of historically impactful risk communication, to the growing influence that social media exerts. And indeed, in a 2020 study, researchers Steven Lloyd Wilson and Charles Wiysonge found that a “1-point shift upwards in the 5-point disinformation scale is associated with a 2-percentage point drop in mean vaccination coverage year over year,” using a cross-country regression framework that included social media data and polls from the World Health Organization (Wilson and Wiysonge). The positive feedback loops and virtual echo chambers that algorithms like those used by Facebook perpetuate can directly counteract positive and medically accurate public health messaging. Those who are not at the same risk as the elderly or who do not have underlying health conditions are more easily influenced to be vaccine hesitant and less likely to opt into vaccination. This middle ground is the modern battlefield for public health. No longer do public health professionals have unfettered credibility, but instead, have to combat the misinformation that has stymied the COVID-19 vaccination efforts to date. It is precisely in this battle where behavioral economic analysis may be able to turn the tide.
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“We’ll have to wait until it is over and we look back… It’s almost like the fog of war. After the war is over, you then look back and say, ‘Wow, this plan, as great as it was, didn’t quite work once they started throwing hand grenades at us.’”
-Dr. Anthony Fauci in an interview with Science
In behavioral economic analysis, researchers analyze purchase tasks, “validated behavioral economic demand procedures,” that allow for researchers to evaluate demand over a range of hypothetical circumstances (Hursh et al., 2020). This is where behavioral economic analysis thrives, in the forecasting of intentions regarding “consumption of novel commodities.” These novel commodities are often not yet available but can be described in simulated markets. In the case of Steven Hursh and his colleagues at the Johns Hopkins School of Medicine and the University of Kansas, who published a study in December of 2020, vaccines are the novel commodity and vaccine demand is the purchase task in question. Using data collected from an online survey from June 2020, the researchers fit exponential demand curves to participant acceptance of a vaccine at varying efficacies. However, they collected this data under two varying premises that sought to root out the bias and irrationality in vaccine uptake: “Standard” vaccine development and a “Warp Speed” condition. Standard conditions reflect historical vaccine development before the COVID-19 pandemic while Warp Speed conditions reflect the accelerated pace of COVID-19 mRNA vaccine development.
Standard condition: “Suppose a COVID-19 vaccine was developed in a total of 18 months, with delivery to the general population by July 2021. Imagine the vaccine has been approved by the Food and Drug Administration (FDA) and the vaccine has undergone a standard and rigorous vaccine evaluation. This evaluation included all three phases of human clinical trials to determine the vaccine’s safety and effectiveness. You can get the vaccine through your doctor, at no cost to you” (Hursh et al., 2020).
Warp Speed condition: “Suppose a COVID-19 vaccine was developed in a total of 6 months, with delivery to the general population by November 2020. Imagine the vaccine has been approved by the Food and Drug Administration (FDA) as part of an accelerated partnership between the FDA, Centers for Disease Control (CDC), and pharmaceutical companies (this effort is called Operation Warp Speed)… The FDA has relaxed some of its strict evaluation criteria to get the vaccine to the public quickly, but this vaccine will still be approved by the FDA” (Hursh et al., 2020).
The results of this experiment showed that as expected vaccine efficacy decreased, so did vaccine demand. Under Warp Speed conditions, the evaluation of the demand curves “indicated greater reductions in vaccine demand by efficacy.” At a 50% level of vaccine efficacy, vaccine acceptance decreases by 10% from standard conditions to warp speed conditions (Hursh et al., 2020). Further, on the individual level, “rapid vaccine development increased minimum required efficacy for vaccination by over 9% points.” It is clear that Standard conditions are preferred to those of the Warp Speed conditions, but therein lies the irrationality that behavioral economic analysis can help explain. After all, in ‘relaxing some strict criteria’, Operation Warp Speed merely allowed for an expedited timeline (with some phases of clinical trials overlapping, tests in animals being approved concurrently, and large-scale manufacturing beginning during trials) with no safety measures being sacrificed (USGA).
Under the two conditions, the vaccine is subject to the same levels of FDA approval, indicating both vaccines meet the same safety standards. The only differences are the choice architectures in both conditions. Framing bias is plain to see. Similar to representative bias, framing bias states that given two equally effective options, people tend to choose the option with positive framing as opposed to the one with negative framing (for example, a hand sanitizer that kills 95% of germs opposed to one that leaves 5% of germs alive). In the experiment carried out by Hursh and his colleagues, “Standard” conditions are viewed as safer than “Warp Speed” due to the negative framing associated with haste and therefore carelessness. The participants, who varied across gender and political alignment, have acted irrationally. Their decisions to vaccinate at varying efficacies reflect the impact of perception of the vaccine that is directly driven by the messaging of public health officials. Here is the crux of the argument for behavioral economic integration into epidemiology and public health. Without the use of behavioral economics analysis, public health officials who are stumped by lower vaccine uptake would fail to recognize the direct impact that their messaging would have on the public; that the simple choice of wording in their messaging would lead to a stark gap in uptake with vaccine options of identical FDA approval. Moreover, public health officials would have unclear direction on how to proceed in minimizing the gap amongst the hesitant without recognizing how cognitive biases play a role. Merely following traditional public health strategy would not have the desired effect as opposed to targeted behavioral approaches that “effectively address false beliefs and skepticism about the rigor of vaccine development.”
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“The confidence that individuals have in their beliefs depends mostly on the quality of the story they can tell about what they see, even if they see little.”
-Daniel Kahneman
The findings from Hursh and his colleagues provide evidence for the claim that public health can address vaccine hesitancy from a new, and potentially very fruitful, angle through the integration of behavioral economics. The ability to quantify the irrationality of those who are vaccine hesitant is a large leap forward for the public health agenda. In the effort to make us healthier, public health officials can take into account our cognitive biases, our irrational behavior, and focus their efforts accordingly. This exciting opportunity for a new wave of public health informed by novel behavioral economic analysis is the kind of interdisciplinary integration that can internalize the lack of trust towards the public health institution, making us all healthier… even if we see little.
REFERENCE
Cifuentes-Faura, J. (2020, June). The Importance of Behavioral Economics during COVID-19. Journal of Economics and Behavioral Studies, 12. https://ojs.amhinternational.com/index.php/jebs/article/view/3038/1940
Hursh, S. R., Strickland, J. C., Schwartz, L. P., & Reed, D. D. (2020, December 3). Quantifying the Impact of Public Perceptions on Vaccine Acceptance Using Behavioral Economics. Frontiers in Public Health. https://www.frontiersin.org/articles/10.3389/fpubh.2020.608852/full
Madhav, N., Oppenheim, B., Gallivan, M., Mulembakani, P., Rubin, E., & Wolfe, N. (2017). Pandemics: Risks, Impacts, and Mitigation. In Disease Control Priorities: Improving Health and Reducing Poverty. https://europepmc.org/article/nbk/nbk525302
Office, U. S. G. A. (2021, February 11). Operation Warp Speed: Accelerated COVID-19 Vaccine Development Status and Efforts to Address Manufacturing Challenges | U.S. GAO. https://www.gao.gov/products/gao-21-319
Shah, R. (2021). Interview with Dr. Ryan Westergaard [An interview with Chief Medical Officer and State Epidemiologist for Wisconsin]. Zoom, Madison, WI.
Soofi, M., Najafi, F., & Karami-Matin, B. (2020, May 21). Using Insights from Behavioral Economics to Mitigate the Spread of COVID-19. Applied Health Economics and Health Policy, 18. https://link.springer.com/article/10.1007/s40258-020-00595-4
Tversky, A., & Kahneman, D. (1974, September 27). Judgement Under Uncertainty: Heuristics and Biases. Science, 185. https://www2.psych.ubc.ca/~schaller/Psyc590Readings/TverskyKahneman1974.pdf
Wilson, S. L., & Wiysonge, C. (2020, October 1). Social Media and vaccine hesitancy. BMJ Global Health. https://gh.bmj.com/content/5/10/e004206
Read the full article at: https://issuu.com/uwequilibrium.com/docs/eq_final_2022/31