Effects of behavioral intervention components to increase COVID-19 testing for African American/Black and Latine frontline essential workers not up-to-date on COVID-19 vaccination: Results of an optimization randomized controlled trial

Racial/ethnic disparities in COVID-19, including incidence, hospitalization, and death rates, are serious and persistent. Among those at highest risk for COVID-19 and its adverse effects are African American/Black and Latine (AABL) frontline essential workers in public-facing occupations (e.g., food services, retail). Testing for COVID-19 in various scenarios (when exposed or symptomatic, regular screening testing) is an essential component of the COVID-19 control strategy in the United States. However, AABL frontline workers have serious barriers to COVID-19 testing at the individual (insufficient knowledge, distrust, cognitive biases), social (norms), and structural levels of influence (access). Thus, testing rates are insufficient and interventions are needed. The present study is grounded in the multiphase optimization strategy (MOST) framework. It tests the main and interaction effects of a set of candidate behavioral intervention components to increase COVID-19 testing rates in this population. The study enrolled adult AABL frontline essential workers who were not up-to-date on COVID-19 vaccination nor recently tested for COVID-19. It used a factorial design to examine the effects of candidate behavioral intervention components, where each component was designed to address a specific barrier to COVID-19 testing. All participants received a core intervention comprised of health education. The candidate components were motivational interviewing counseling (MIC), a behavioral economics intervention (BEI), peer education (PE), and access to testing (either self-test kits [SK] or a navigation meeting [NM]). The primary outcome was COVID-19 testing in the follow-up period. Participants were assessed at baseline, randomly assigned to one of 16 experimental conditions, and assessed six- and 12-weeks later. The study was carried out in English and Spanish. We used a logistic regression model and multiple imputation to examine the main and interaction effects of the four factors (representing components): MIC, BEI, PE, and Access. We also conducted a sensitivity analysis using the complete case analysis. Participants (N = 438) were 35 years old on average (SD = 10). Half identified as men/male (52%), and 48% as women/female/other. Almost half (49%) were African American/Black, and 51% were Latine/Hispanic (12% participated in Spanish). A total of 32% worked in food services. Attendance in components was very high (~ 99%). BEI had positive effect on the outcome (OR = 1.543; 95% CI: [0.977, 2.438]; p-value = 0.063) as did Access, in favor of SK (OR = 1.351; 95% CI: [0.859, 2.125]; p-value = 0.193). We found a three-way interaction among MIC*PE*Access (OR: 0.576; 95% CI: [0.367, 0.903]; p-value = 0.016): when MIC was present, SK tended to increase COVID testing when PE was not present. The study advances intervention science and takes the first step toward creating an efficient and effective multi-component intervention to increase COVID-19 testing rates in AABL frontline workers.