Evidence map›Paper›PMID 38150046›Full record

ArticleGeospatial health2023

The impact of COVID-19 on healthcare coverage and access in racial and ethnic minority populations in the United States.

Lauren Freelander, David S Rickless, Corey Anderson, Frank Curriero, Sarah Rockhill, Amir Mirsajedin, Caleb J Colón, Jasmine Lusane, Alexander Vigo-Valentín, David Wong

Open access · goldAbstract read
In one paragraph

Article in Geospatial health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Lauren FreelanderCenters for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry, Office of Innovation and Analytics, Geospatial Research, Analysis, and Services, Atlanta, GA. syu1@cdc.gov.
David S RicklessCenters for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry, Office of Innovation and Analytics, Geospatial Research, Analysis, and Services, Atlanta, GA. opv1@cdc.gov.
Corey AndersonCenters for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry, Office of Innovation and Analytics, Geospatial Research, Analysis, and Services, Atlanta, GA. teq2@cdc.gov.
Frank CurrieroCenters for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry, Office of Innovation and Analytics, Geospatial Research, Analysis, and Services, Atlanta, GA; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD. fgc0@cdc.gov.
Sarah RockhillCenters for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry, Office of Innovation and Analytics, Geospatial Research, Analysis, and Services, Atlanta, GA. ppk8@cdc.gov.
Amir MirsajedinCenters for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry, Office of Innovation and Analytics, Geospatial Research, Analysis, and Services, Atlanta, GA. pje5@cdc.gov.
Caleb J ColónUS Department of Health and Human Services, Office of Minority Health, Rockville, MD. caleb.colon@hhs.gov.
Jasmine LusaneUS Department of Health and Human Services, Office of Minority Health, Rockville, MD. jasmine.lusane@hhs.gov.
Alexander Vigo-ValentínUS Department of Health and Human Services, Office of Minority Health, Rockville, MD. ukx1@cdc.gov.
David WongUS Department of Health and Human Services, Office of Minority Health, Rockville, MD. david.wong@hhs.gov.
Centers for Disease Control and Prevention · USOffice of Minority Health · US

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

This study described spatiotemporal changes in health insurance coverage, healthcare access, and reasons for non-insurance among racial/ethnic minority populations in the United States during the COVID-19 pandemic using four national survey datasets. Getis-Ord Gi* statistic and scan statistics were used to analyze geospatial clusters of health insurance coverage by race/ethnicity. Logistic regression was used to estimate odds of reporting inability to access healthcare across two pandemic time periods by race/ethnicity. Racial/ethnic differences in insurance were observed from 2010 through 2019, with the lowest rates being among Hispanic/Latino, African American, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander populations. Pre-pandemic insurance coverage rates were geographically clustered. The percentage of adults citing change in employment status as the reason for non-insurance increased by about 7% after the start of the pandemic, with a small decrease observed among African American adults. Almost half of adults reported reduced healthcare access in June 2020, with 38.7% attributing reduced access to the pandemic; however, by May 2021, the percent of respondents reporting reduced access for any reason and due to the pandemic fell to 26.9% and 12.7%, respectively. In general, racial/ethnic disparities in health insurance coverage and healthcare access worsened during the pandemic. Although coverage and access improved over time, pre-COVID disparities persisted with African American and Hispanic/Latino populations being the most affected by insurance loss and reduced healthcare access. Cost, unemployment, and eligibility drove non-insurance before and during the pandemic.

Indexed as

COVID-19EthnicityHealth Services AccessibilityInsurance, HealthRacial GroupsAdultHumansMinority GroupsPandemicsUnited States

Identifiers

PMID38150046
PMCPMC10790404
OpenAlexW4390280109

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.