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

Abstract

The role of human behavior to thwart transmission of infectious diseases like COVID-19 is evident. Psychological and behavioral science are key areas to understand decision-making processes underlying engagement in preventive health behaviors. Here we adapt well validated methods from behavioral economic discounting and demand frameworks to evaluate variables (e.g., delay, cost, probability) known to impact health behavior engagement. We examine the contribution of these mechanisms within a broader response class of behaviors reflecting adherence to public health recommendations made during the COVID-19 pandemic. Four crowdsourced samples (total N = 1,366) completed individual experiments probing a response class including social (physical) distancing, facemask wearing, COVID-19 testing, and COVID-19 vaccination. We also measure the extent to which choice architecture manipulations (e.g., framing, opt-in/opt-out) may promote (or discourage) behavior engagement. We find that people are more likely to socially distance when specified activities are framed as high risk, that facemask use during social interaction decreases systematically with greater social relationship, that describing delay until testing (rather than delay until results) increases testing likelihood, and that framing vaccine safety in a positive valence improves vaccine acceptance. These findings collectively emphasize the flexibility of methods from diverse areas of behavioral science for informing public health crisis management.

Time Cost and Demand: Implications for Public Policy

“…In order to accurately measure the utilization of free testing, policymakers might want to understand how such delays affect the likelihood of accessing tests. Strickland et al ( 2022 ) asked participants the likelihood of accessing COVID-19 diagnostic tests when there was a delay to receiving the test or a delay to receiving the results. The price of the test ($125 or free) was also manipulated.…”

“…If there is an additional delay, then there may be a risk that acceptance wanes. The COVID-19 pandemic stimulated useful behavioral economic studies of vaccine acceptance that can serve as a model for effective public health response to any future health crisis, be it an infectious disease or other environmental threat (Hursh et al, 2020 ; Strickland et al, 2022 ).…”

“…For example, waiting to get diagnostic test and then waiting for the results of the test are two different parts of a sequence. Strickland et al ( 2022 ) showed that participants were more tolerate of delays to a test than delays to the results, indicating time costs may not be additive. There are also methodological considerations regarding the HPTs.…”

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

“…The current results are in line with previous findings showing that steeper delay discounting is a key predictor of numerous health-related issues, including obesity, addictive behaviors, and anxiety and mood disorders 23 , many of which are exacerbated by pandemic conditions 25 , 26 . The extent to which delay discounting is a predictor of compliance with pandemic-mitigating behaviors other than vaccination (e.g., physical distancing, mask-wearing) is less clear 34 – 36 . Seemingly weak or contradictory findings may reflect the influence of confounding factors, such as psychological distress, which tends to be positively related to delay discounting 37 .…”

Commentary on Motschman et al.: Moving behavioral economic demand into the real world means moving beyond single schedules of reinforcement

“…Analysis of value using a demand curve perspective is hypothesized to separate distinct behavioral mechanisms-demand intensity and demand elasticity-characterizing allocation of behavior under differing conditions of constraint. Research using demand procedures has demonstrated their use in diverse areas of public health and policy by showing the concurrent and prognostic validity of demand indices in predicting substance use risk [6,7] and the sensitivity of demand procedures to experimental manipulations evaluating novel interventions [8] and simulating policy changes [9][10][11].…”