Evidence map›Paper›PMID 39844174›Full record

ArticleInternational journal for equity in health2025

Demographic disparities in access to COVID-19 clinical trial sites across the United States: a geospatial analysis.

Raphael Cuomo, Tiana McMann, Qing Xu, Zhuoran Li, Joshua Yang, Julie Hsieh, Christine Lee, Milena Lolic, Richardae Araojo, Tim Mackey

Abstract read
In one paragraph

Article in International journal for equity in health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Raphael CuomoSchool of Medicine, University of California, 8950 Villa La Jolla Drive, A203, San Diego, CA, 92037, USA. racuomo@ucsd.edu.
Tiana McMannGlobal Health Policy and Data Institute, San Diego, CA, USA.
Qing XuS-3 Research, San Diego, CA, USA.
Zhuoran LiDepartment of Anthropology, University of California, San Diego, CA, USA.
Joshua YangDepartment of Public Health, California State University, Fullerton, Fullerton, CA, USA.
Julie HsiehOffice of Minority Health and Health Equity, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Christine LeeOffice of Minority Health and Health Equity, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Milena LolicOffice of Minority Health and Health Equity, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Richardae AraojoOffice of Minority Health and Health Equity, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Tim MackeyGlobal Health Policy and Data Institute, San Diego, CA, USA.

Funding

FDA HHS U01 FD007558
6 · The paper itself

Abstract

Throughout the COVID-19 pandemic, underserved populations, such as racial and ethnic minority communities, were disproportionately impacted by illness and death. Ensuring people from diverse backgrounds have the ability to participate in clinical trials is key to advancing health equity. We sought to analyze the spatial variability in locations of COVID-19 trials sites and to test associations with demographic correlates. All available and searchable COVID-19 studies listed on ClinicalTrials.gov until 04/04/2022 and conducted in the United States were extracted at the trial-level, and locations were geocoded using the Microsoft Bing API. Publicly available demographic data were available at the county level for national analysis and the census tract level for local analysis. Independent variables included eight racial and ethnic covariates, both sexes, and twelve age categories, all of which were population-normalized. The county-level, population-normalized count of study site locations, by type, was used as the outcome for national analysis, thereby enabling the determination of demographic associations with geospatial availability to enroll as a participant in a COVID-19 study. Z-scores of the Getis-Ord Gi statistic were used as the outcome for local analysis in order to account for areas close to those with clinical study sites. For both national (p < 0.001) and local analysis (p = 0.006 for Los Angeles, p = 0.030 for New York), areas with greater proportions of men had significantly fewer studies. Sites were more likely to be found in counties with higher proportions of Asian (p < 0.001) and American Indian or Alaska Native residents (p < 0.001). Areas with greater concentrations of Black or African American residents had significantly lower concentrations of observational (p < 0.001) and government-sponsored COVID-19 studies (p = 0.003) in national analysis and significantly fewer concentrations of study sites in both Los Angeles (p < 0.001) and New York (p = 0.007). Though there appear to be a large number of COVID-19 studies that commenced in the US, they are distributed unevenly, both nationally and locally.

Indexed as

Clinical Trials as TopicCOVID-19Healthcare DisparitiesHealth Services AccessibilityAdultEthnicityFemaleHumansMaleMiddle AgedMinority GroupsSARS-CoV-2Spatial AnalysisUnited StatesClinical trialsCOVID-19DemographicsGeospatial analysisHealth equity

Identifiers

PMID39844174
PMCPMC11756076

What OpenQuestion holds

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Registered trials

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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.