BMC Public Health (Sep 2023)

Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities

  • Carly Herbert,
  • Qiming Shi,
  • Jonggyu Baek,
  • Biqi Wang,
  • Vik Kheterpal,
  • Christopher Nowak,
  • Thejas Suvarna,
  • Aditi Singh,
  • Paul Hartin,
  • Basyl Durnam,
  • Summer Schrader,
  • Emma Harman,
  • Ben Gerber,
  • Bruce Barton,
  • Adrian Zai,
  • Michael Cohen-Wolkowiez,
  • Giselle Corbie-Smith,
  • Warren Kibbe,
  • Juan Marquez,
  • Nathaniel Hafer,
  • John Broach,
  • Honghuang Lin,
  • William Heetderks,
  • David D McManus,
  • Apurv Soni

DOI
https://doi.org/10.1186/s12889-023-16642-3
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 10

Abstract

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Abstract Background Many interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively. Methods This observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O’ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence. Results In total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran’s I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana. Conclusions Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.

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