BACKGROUND: African American/Black and Latino (AABL) persons suffer higher COVID-19 infection rates and adverse consequences (hospitalization, death) compared to White persons, especially among frontline essential workers who are not up-to-date on COVID-19 vaccination. Regular COVID-19 testing plays an essential role in mitigating such disparities.
PURPOSE: The New York Community Action Project (NCAP), conducted in New York City between July 2022 and February 2024, was motivated by the goal of developing an effective and implementable behavioral intervention to promote COVID-19 testing among AABL frontline essential workers. In the present study, we selected an optimized intervention, based on data from the NCAP optimization randomized controlled trial (“optimization RCT”), which was guided by the multiphase optimization strategy (MOST) framework.
METHODS: The NCAP optimization RCT (N = 438) used a full 24 factorial design, testing the following candidate intervention components: motivational interviewing counseling (MIC; off vs. on); text messaging grounded in behavioral economics (behavioral economics intervention [BEI; off vs. on]); peer education (PE; off vs. on); and access to COVID-19 testing via either a navigation meeting [NM] or self-test kits [SK]). We applied Decision Analysis for Intervention Value Efficiency (DAIVE) to identify the optimized intervention (ie, the optimized combination of the four intervention components) that is effective at promoting the COVID-19 testing rate while remaining implementable. To estimate expected outcomes on the primary outcome (binary; self-reported COVID-19 testing at two follow-up assessments), we applied three alternative strategies for handling missing data and investigated decision-making robustness.
RESULTS: The three missing data strategies did not differ in ways that would shape optimization decision-making, suggesting robustness across strategies. The optimized intervention that strategically balances effectiveness and implementability contained motivational interviewing counseling (MIC), the behavioral economics intervention (BEI), and self-test kits (SK).
CONCLUSIONS: The optimized intervention we identified based on empirical data from the NCAP optimization RCT shows potential to effectively increase the COVID-19 testing rate among AABL frontline essential workers who are not up to date on COVID-19 vaccination. Our demonstration of missing data strategies under the DAIVE framework offers meaningful practical guidance for behavioral medicine researchers.
Selecting optimized behavioral interventions from an optimization randomized controlled trial on increasing COVID-19 testing for African American/Black and Latino frontline essential workers not up-to-date on COVID-19 vaccination
Annals of Behavioral Medicine, 60 (1). doi: 10.1093/abm/kaag005.