Behavioral economic methods to inform infectious disease response: Prevention, testing, and vaccination in the COVID-19 pandemic

COVID-19 vaccine refusal is driven by deliberate ignorance and cognitive distortions

Vaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants-particularly those who were anti-vaccination-frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies.

Delay Discounting for HIV/STI Testing

Introduction

Wait time in healthcare is an important barrier to HIV/STI testing. Using a delay discounting approach, the current study examined a systematic reduction in testing likelihood as a function of delay (wait time) until testing.

Methods

In Study 1 (N = 421; data collected in 2019), participants were randomly assigned to either a chlamydia/gonorrhea group or HIV group. A delay discounting task asked them to report how likely they would get tested for the assigned STI if they had to wait for the test (the delay durations varied within persons). In Study 2 (N = 392; data collected in 2020), we added a smaller, sooner outcome (consultation without testing) and tested whether the effect of delay was mediated by perceived severity of the STIs.

Results

In both studies, the subjective value of a delayed STI test was discounted. That is, people were less likely to undergo STI testing as the delay to STI testing increased. The chlamydia/gonorrhea group discounted delayed testing more than the HIV group (i.e., the effect of delay on testing decisions was stronger for the former). This effect was statistically mediated by perceived severity.

Conclusions

We found evidence for delay discounting for HIV/STI testing and that testing decisions were more susceptible to delay when the test was for relatively mild STIs.

Policy Implications

Even mild STIs can cause serious health damage if left untreated. The findings provide strong argument for policies aimed to reduce wait times in healthcare, especially for relatively mild STIs.

Interpretation(s) of essential value in operant demand

The operant demand framework has achieved high levels of adoption as an approach to quantify how various ecological factors influence choice. A central goal of the framework proposed by Hursh and Silberburg (2008) was to isolate the "essential value" of reinforcers-namely, their effects on behavior given various contextual factors. The effect of reinforcers on behavior is a phenomenon that is expected to vary as a function of reinforcer magnitude/dosage (i.e., units of reinforcement), price (i.e., schedule requirements), the intensity of demand (i.e., consumption in free operant conditions), the availability of reinforcers (i.e., supply, presence of alternatives), and the individual's current and historical context. This technical report provides a historical summary of the concept, describes the quantitative basis for essential value in the framework of Hursh and Silberburg (2008), reviews prior attempts to extract a generalizable index of essential value, and presents a newer formulation using exact solution that provides a more succinct and durable index. Proofs and solutions are provided to clarify the bases for novel and existing representations of essential value. Recommendations are provided to improve the precision and accuracy of behavioral economic metrics as well as support consensus regarding their interpretation in the operant demand framework.

Reinforcer pathology, probabilistic choice, and medication adherence in patients with multiple sclerosis

The reinforcer pathology model posits that core behavioral economic mechanisms, including delay discounting and behavioral economic demand, underlie adverse health decisions and related clinical disorders. Extensions beyond substance use disorder and obesity, however, are limited. Using a reinforcer pathology framework, this study evaluates medical adherence decisions in patients with multiple sclerosis. Participants completed behavioral economic measures, including delay discounting, probability discounting, and a medication purchase task. A medical decision-making task was also used to evaluate how sensitivity to mild side effect risk and efficacy contributed to the likelihood of taking a hypothetical disease-modifying therapy. Less steep delay discounting and more intense (greater) medication demand were independently associated with greater adherence to the medication decision-making procedure. More generally, the pattern of interrelations between the medication-specific and general behavioral economic metrics was consistent with and contributes to the reinforcer pathology model. Additional research is warranted to expand these models to different populations and health behaviors, including those of a positive health orientation (i.e., medication adherence).

Hyperbolic modeling and assessment of hypothetical health behaviors during a viral outbreak using crowdsourced samples

The COVID-19 pandemic provided an opportunity to investigate factors related to public response to public health measures, which could help better prepare implementation of similar measures for inevitable future pandemics. To understand individual and environmental factors that influence likelihood in engaging in personal and public health measures, three crowdsourced convenience samples from Amazon Mechanical Turk (MTurk) completed likelihood-discounting tasks of engaging in health behaviors given a variety of hypothetical viral outbreak scenarios. Experiment 1 assessed likelihood of mask wearing for a novel virus. Experiment 2 assessed vaccination likelihood based on efficacy and cost. Experiment 3 assessed likelihood of seeking health care based on number of symptoms and cost of treatment. Volume-based measures and three-dimensional modeling were used to analyze hypothetical decision making. Hypothetical public and personal health participation increased as viral fatality increased and generally followed a hyperbolic function. Public health participation was moderated by political orientation and trust in science, whereas treatment-seeking was only moderated by income. Analytic methods used in this cross-sectional study predicted population-level outcomes that occurred later in the pandemic and can be extended to various health behaviors.

Matching, behavioral economics, and teleological behaviorism: Final cause analysis of substance use and health behavior

Howard Rachlin and his contemporaries pioneered basic behavioral science innovations that have been usefully applied to advance understanding of human substance use disorder and related health behaviors. We briefly summarize the innovations of molar behaviorism (the matching law), behavioral economics, and teleological behaviorism. Behavioral economics and teleological behaviorism's focus on final causes are especially illuminating for these applied fields. Translational and applied research are summarized for laboratory studies of temporal discounting and economic demand, cohort studies of alcohol and other drug use in the natural environment, and experimental behavioral economic modeling of health behavior-related public health policies. We argue that the teleological behavioral perspective on health behavior is conducive to and merges seamlessly with the contemporary socioecological model of health behavior, which broadens the contextual influences (e.g., community, economic, infrastructure, health care access and policy) of individuals' substance use and other health risk behaviors. Basic-to-applied translations to date have been successful and bode well for continued applications of basic science areas pioneered by Howard Rachlin and his contemporaries.

Hyperbolic discounting and exponentiated demand: Modeling demand for cigarettes in three dimensions

Behavioral economics has been a fruitful area of research in substance use. Mathematical descriptions of how individuals temporally discount the value of a commodity have been correlated with substance use and mathematical descriptions of drug consumption decreasing as a function of price (i.e., demand) predict maladaptive substance use. While there is a logical assumption that temporal factors affect demand for a drug, little has been done to merge these models. Thus, the purpose of this study was to combine models of discounting and demand, extending Howard Rachlin's work and contributions to novel areas of study. Data from 85 participants from Amazon Mechanical Turk (MTurk) who completed a hypothetical cigarette purchase task that included price of and delay to cigarettes were analyzed. Multilevel modeling was used to determine descriptive accuracy of combined additive and multiplicative models of discounting and demand. Of the discounting models used in conjunction with the exponentiated demand equation, the Rachlin hyperboloid best described the delay dimension of consumption. The multiplicative version of the Rachlin equation applied to both delay and price outperformed other models tested. Therefore, existing models of discounting and demand can be extended to modeling consumption data from complex multidimensional experimental arrangements.

Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes

Visualization research often focuses on perceptual accuracy or helping readers interpret key messages. However, we know very little about how chart designs might influence readers' perceptions of the people behind the data. Specifically, could designs interact with readers' social cognitive biases in ways that perpetuate harmful stereotypes? For example, when analyzing social inequality, bar charts are a popular choice to present outcome disparities between race, gender, or other groups. But bar charts may encourage deficit thinking, the perception that outcome disparities are caused by groups' personal strengths or deficiencies, rather than external factors. These faulty personal attributions can then reinforce stereotypes about the groups being visualized. We conducted four experiments examining design choices that influence attribution biases (and therefore deficit thinking). Crowdworkers viewed visualizations depicting social outcomes that either mask variability in data, such as bar charts or dot plots, or emphasize variability in data, such as jitter plots or prediction intervals. They reported their agreement with both personal and external explanations for the visualized disparities. Overall, when participants saw visualizations that hide within-group variability, they agreed more with personal explanations. When they saw visualizations that emphasize within-group variability, they agreed less with personal explanations. These results demonstrate that data visualizations about social inequity can be misinterpreted in harmful ways and lead to stereotyping. Design choices can influence these biases: Hiding variability tends to increase stereotyping while emphasizing variability reduces it.

Using Health Behavior Theory to Address COVID-19 Vaccine Hesitancy: A Scoping Review of Communication and Messaging Interventions

Vaccine hesitancy has been among the most vexing challenges during the COVID-19 pandemic, ultimately leading to maladaptive health behaviors such as vaccine delay and refusal. A variety of approaches have been employed to address this problem, including communication and messaging interventions targeting the underlying determinants of vaccine hesitancy. However, there exists no published evidence synthesis examining how such interventions are using health behavior theory to address COVID-19 vaccine hesitancy. The purpose of this study was to conduct a scoping review of health communication and messaging interventions aimed at addressing COVID-19 vaccine hesitancy, and to systematically evaluate the use of health behavior theory in the design of these interventions. The review followed a five-step iterative framework proposed by Levac and colleagues. Comprehensive searches using an exhaustive list of keyword combinations were used across three online databases to identify articles to screen for inclusion. A structured, validated coding scheme was then applied to assess the use of health behavior theory. Additional study data were extracted using a separate structured form. A total of 36 articles published between January 2020 and February 2022 met inclusion criteria and were included in the review. Ten studies (27.7%) did not mention or use health behavior theory at all. Most studies (n = 26) at least mentioned theory or theory-relevant constructs, with 26 different theories and 52 different theoretical constructs represented in the sample. Although theory and theoretical determinants of vaccination behavior were often mentioned, few studies used theory to specify and target causal pathways of behavior change, and only one study targeted misinformation as a determinant of vaccine hesitancy. The findings from this review provide critical insight into the state of theory-based intervention design and point to significant gaps in the literature to prioritize in future research.

Behavioral economic methods predict future COVID-19 vaccination

Increasing vaccine utilization is critical for numerous diseases, including COVID-19, necessitating novel methods to forecast uptake. Behavioral economic methods have been developed as rapid, scalable means of identifying mechanisms of health behavior engagement. However, most research using these procedures is cross-sectional and evaluates prediction of behaviors with already well-established repertories. Evaluation of the validity of hypothetical tasks that measure behaviors not yet experienced is important for the use of these procedures in behavioral health. We use vaccination during the COVID-19 pandemic to test whether responses regarding a novel, hypothetical behavior (COVID-19 vaccination) are predictive of later real-world response. Participants (N = 333) completed a behavioral economic hypothetical purchase task to evaluate willingness to receive a hypothetical COVID-19 vaccine based on efficacy. This was completed in August 2020, before clinical trial data on COVID-19 vaccines. Participants completed follow-up assessments approximately 1 year later when the COVID-19 vaccines were widely available in June 2021 and November 2021 with vaccination status measured. Prediction of vaccination was made based on data collected in August 2020. Vaccine demand was a significant predictor of vaccination after controlling for other significant predictors including political orientation, delay discounting, history of flu vaccination, and a single-item intent to vaccinate. These findings show predictive validity of a behavioral economic procedure explicitly designed to measure a behavior for which a participant has limited-to-no direct prior experience or exposure. Positive correspondence supports the validity of these hypothetical arrangements for predicting vaccination utilization and advances behavioral economic methods.

Probability Discounting in College Students' Willingness to Isolate During COVID-19: Implications for Behavior Analysis and Public Health

The present study was a preliminary analysis of college students' willingness to self-isolate and socially isolate during the COVID-19 pandemic analyzed through a probability discounting framework. Researchers developed a pandemic likelihood discounting task where willingness to isolate from others was measured in days as a function of the perceived probability of the escalation of a virus to pandemic levels. Experiment 1 was conducted immediately prior to the World Health Organization (WHO) declaring COVID-19 a pandemic and results showed that participants were more willing to self-isolate when the perceived probability of reaching pandemic levels was high and when there was a guarantee that others in the community would do the same. Experiment 2 was conducted with a subset of participants from Experiment 1 with the same discounting task, and results showed that participants were more willing to self-isolate 2 months following the onset of the pandemic, supporting the view that willingness to isolate from others is a dynamic process. Finally, Experiment 3 evaluated willingness to socially distance and introduced a hypothetical timescale to evaluate common trends with the real-world temporal dynamics observed in Experiments 1 and 2. Results showed similar trends in the data, supporting the use of hypothetical scenarios within probability discounting tasks in future behavior analytic research related to public health.

Application of behavioral economics for understanding health behaviors among adolescents and young adults

PURPOSE OF REVIEW

Behavioral economics (BE) concepts have become well studied tools in addressing patient issues, such as weight loss, smoking cessation, and medication adherence. Although predominantly studied in adult populations, emerging literature has shown BE's utility for adolescent/young adult (AYA) populations, offering a practical framework to safeguard AYA health and influence healthy decision making.

RECENT FINDINGS

We identified substantive areas in which BE concepts have been applied in AYA populations (e.g., substance use) and outline how these concepts have been used as a tool to identify individuals at risk for poor outcomes and to leverage behavioral insights to improve health behaviors.

SUMMARY

BE research holds significant promise as a tool for clinicians and researchers to encourage healthy decision making in AYA populations. Yet, there are opportunities for BE research to expand further into current trends impacting adolescent health, such as electronic nicotine delivery systems, social media apps, and coronavirus disease 2019 vaccinations. Furthermore, the full degree of BE utility remains to be explored, as few studies demonstrate the translation of associative findings into direct interventions. Additional work is needed to formalize BE techniques into best practices that clinicians can implement in their daily practice.

Short-sighted decision-making by those not vaccinated against COVID-19

Widespread vaccination is necessary to minimize or halt the effects of many infectious diseases, including COVID-19. Stagnating vaccine uptake can prolong pandemics, raising the question of how we might predict, prevent, and correct vaccine hesitancy and unwillingness. In a multinational sample (N = 4,452) recruited from 13 countries that varied in pandemic severity and vaccine uptake (July 2021), we examined whether short-sighted decision-making as exemplified by steep delay discounting-choosing smaller immediate rewards over larger delayed rewards-predicts COVID-19 vaccination status. Delay discounting was steeper in unvaccinated individuals and predicted vaccination status over and above demographics or mental health. The results suggest that delay discounting, a personal characteristic known to be modifiable through cognitive interventions, is a contributing cause of differences in vaccine compliance.

Time Cost and Demand: Implications for Public Policy

The success of policy involves not only good design but a good understanding of how the public will respond behaviorally to the benefits or detriments of that policy. Behavioral science has greatly contributed to how we understand the impact of monetary costs on behavior and has therefore contributed to policy design. Consumption taxes are a direct result of this; for example, cigarette taxes that aim to reduce cigarette consumption. In addition to monetary costs, time may also be conceptualized as a constraint on consumption. Time costs may therefore have policy implications, for example, long waiting times could deter people from accessing certain benefits. Recent data show that behavioral economic demand curve methods used to understand monetary cost may also be used to understand time costs. In this article we discuss how the impact of time cost can be conceptualized as a constraint on demand for public benefits utilization and public health when there are delays to receiving the benefits. Policy examples in which time costs may be relevant and demand curve methods may be useful are discussed in the areas of government benefits, public health, and transportation design.