Diagnostic Testing Preferences in Rural and Vulnerable Populations During a Pandemic: Discrete Choice Experiment.

Background: A particular challenge during the COVID-19 pandemic was to provide testing and treatment for already disadvantaged and vulnerable populations. Many states implemented testing in a sporadic and disorganized way, and it is unclear to what extent this disproportionally affected population experienced barriers to accessing care. It is also unclear whether potential barriers to testing were caused by systemic challenges, such as rurality, or by individuals' motivations for not getting tested. Objective: The objective of this study was to understand the trade-offs individuals in rural and vulnerable populations make between attributes of COVID-19 testing and how these vary across individuals. The study was part of RADx-UP, a consortium of more than 125 projects studying COVID-19 testing patterns in communities across the United States. Methods: First, we conducted 7 focus groups to identify barriers to COVID-19 testing and optimal strategies to increase testing. These barriers and strategies were then used to develop hypothetical choice scenarios in a discrete choice experiment. Data regarding preferences for testing were collected from an online panel (n=780) and oversampled in rural populations. We used quota sampling for age, gender, household income, and race: 50% of household incomes were above and below the median rural income of $52k per year 2023, and the maximum number of White, non-Hispanic respondents was 615. The data were analyzed using a conditional logit model (CL) and latent class analysis (LCA). Results: We found that the attributes for testing locations were almost all significant and had the expected signs. As hypothesized, respondents were less likely to choose a test location that had a higher wait time (coefficient -0.183, SE 0.006); more travel time to get tested (coefficient -1.129, SE0.054); that was higher cost (coefficient -0.020, SE 0.000); where someone else would collect the sample (coefficient -0.230, SE 0.036); where it would take more time to receive results (coefficient -0.032, SE 0.006); and where the tests would cause more discomfort (coefficient -0.125, SE 0.007). They were more likely to choose a mail-order option (coefficient 0.494, SE 0.075) and options that had higher test accuracy (coefficient 0.026, SE 0.001). While respondents cared about these structural factors, these were not the primary drivers of choice for testing. Some important covariates were driving preferences, including age, gender, medical vulnerability, insurance status, trust in government organizations, and previous flu vaccination, which may be a proxy for compliance. These covariates helped explain the observed preference heterogeneity. Conclusions: The results suggest that important social, behavioral, and policy factors affect choice for testing. Contrary to our hypotheses, rurality did not significantly impact preferences for testing; however, attitudes toward government and other beliefs did. Health care interventions intended to reduce rural health disparities that do not reflect the underlying values of individuals in those subpopulations are unlikely to be successful.