Evidence map›Paper›PMID 37735647›Full record

Observational studyBMC public health2023

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 and 15 more

Open access · goldAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 47% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors at 6 institutions in 1 country.

Carly HerbertProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
Qiming ShiProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
Jonggyu BaekDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Biqi WangProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
Vik KheterpalCareEvolution LLC, Ann Arbor, MI, USA.
Christopher NowakCareEvolution LLC, Ann Arbor, MI, USA.
Thejas SuvarnaCareEvolution LLC, Ann Arbor, MI, USA.
Aditi SinghProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
Paul HartinProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
Basyl DurnamCareEvolution LLC, Ann Arbor, MI, USA.
Summer SchraderCareEvolution LLC, Ann Arbor, MI, USA.
Emma HarmanCareEvolution LLC, Ann Arbor, MI, USA.
Ben GerberDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Bruce BartonDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Adrian ZaiDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Michael Cohen-WolkowiezDepartment of Pediatrics, Duke University School of Medicine, Durham, NC, USA.
Giselle Corbie-SmithDepartment of Social Medicine, Department of Medicine, Center for Health Equity Research, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
Warren KibbeDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Juan MarquezWashtenaw County Health Department, Washtenaw, MI, USA.
Nathaniel HaferCenter for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA.
John BroachDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Honghuang LinProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
William HeetderksNational Institute of Biomedical Imaging and Bioengineering, NIH, Via Contract With Kelly Services, Bethesda, MD, USA.
David D McManusProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
Apurv SoniProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA. apurv.soni@umassmed.edu.
University of Massachusetts Chan Medical School · USCareEvolution (United States) · USDuke University · USNational Institute of Biomedical Imaging and Bioengineering · USUniversity of North Carolina at Chapel Hill · USWashtenaw Community College · US

Funding

The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Nathaniel Scott Hafer · 2018 to 2026
$141.0M
University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
University of Massachusetts Center for Clinical and Translational ScienceTL1TR001454 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LEWIS, BRIAN C · 2015 to 2024
$3.6M
NCATS NIH HHS TL1 TR001454NCATS NIH HHS UL1 TR001453NHLBI NIH HHS U54 HL143541
6 · The paper itself

Abstract

backgroundMany 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.

methodsThis 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.

resultsIn 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.

conclusionsOur 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.

Indexed as

COVID-19CensusesCluster AnalysisEducational StatusHumansSociodemographic FactorsCOVID-19Direct-to-consumerGeospatial analysisRapid antigen tests

Identifiers

PMID37735647
PMCPMC10515232
OpenAlexW4386917629

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.