Evidence map›Paper›PMID 41052417›Full record

ArticleInteractive journal of medical research2025

Community Comfort With Automatic Sharing of Race, Ethnicity, and Language Data Between Health Care Settings: Cross-Sectional Study.

Noah Brazer, Baylah Tessier-Sherman, Deron Galusha, Sakinah C Suttiratana, Corrine Liu, Katherine K Kim, Mark E Abraham, Marcella Nunez-Smith, Karen H Wang

Abstract read
In one paragraph

Article in Interactive journal of medical research, 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

9 authors.

Noah BrazerDepartment of Internal Medicine, Yale School of Medicine, 100 Church Street South, Suite A200, New Haven, CT, 06519, United States, +1 (203) 785-5233.ORCID http://orcid.org/0000-0003-4270-894X
Baylah Tessier-ShermanDepartment of Internal Medicine, Yale School of Medicine, 100 Church Street South, Suite A200, New Haven, CT, 06519, United States, +1 (203) 785-5233.ORCID http://orcid.org/0000-0002-8697-672X
Deron GalushaDepartment of Internal Medicine, Yale School of Medicine, 100 Church Street South, Suite A200, New Haven, CT, 06519, United States, +1 (203) 785-5233.ORCID http://orcid.org/0000-0003-1136-6422
Sakinah C SuttiratanaDepartment of Internal Medicine, Yale School of Medicine, 100 Church Street South, Suite A200, New Haven, CT, 06519, United States, +1 (203) 785-5233.ORCID http://orcid.org/0000-0001-9775-7736
Corrine LiuDepartment of Internal Medicine, Yale School of Medicine, 100 Church Street South, Suite A200, New Haven, CT, 06519, United States, +1 (203) 785-5233.ORCID http://orcid.org/0009-0004-0879-1326
Katherine K KimDepartment of Public Health Sciences, School of Medicine, University of California Davis, Sacramento, CA, United States.ORCID http://orcid.org/0000-0001-5766-3938
Mark E AbrahamDataHaven, New Haven, CT, United States.ORCID http://orcid.org/0009-0006-0662-7180
Marcella Nunez-SmithDepartment of Internal Medicine, Yale School of Medicine, 100 Church Street South, Suite A200, New Haven, CT, 06519, United States, +1 (203) 785-5233.ORCID http://orcid.org/0000-0003-2797-4756
Karen H WangDepartment of Internal Medicine, Yale School of Medicine, 100 Church Street South, Suite A200, New Haven, CT, 06519, United States, +1 (203) 785-5233.ORCID http://orcid.org/0000-0002-7866-9498

Funding

Accelerating Health Information Resource Equity for Multiracial PopulationsG08LM013801 · NLM · YALE UNIVERSITY · PI WANG, KAREN H · 2021 to 2023
$420k
NLM NIH HHS G08 LM013801
6 · The paper itself

Abstract

Background: Little is known regarding patient attitudes toward automatic sharing of race, ethnicity, and language (REL) data in health care settings despite the universal practice of data sharing across health care institutions and providers. Objective: This study aims to assess public comfort with disclosing and automatically sharing REL data in health care settings and understand the social factors associated with these attitudes. Methods: Using the 2022 DataHaven Community Wellbeing Survey from 1196 adult Connecticut residents, we examined factors associated with public comfort with disclosing and automatically sharing REL data across health care settings. We generated unadjusted and adjusted logistic models to examine associations between factors and responses to the data-sharing questions. Results: Most residents surveyed were White (n=873, 73%), followed by African American or Black (n=167, 14%), Asian or Native Hawaiian or other Pacific Islander (n=31, 2.6%), multiracial (n=31, 2.6%), and American Indian or Alaska Native (n=12, 1%). The majority of respondents were not Hispanic or Latino (n=1051, 87.9%). More than half of respondents reported excellent or very good self-rated health (SRH; n=635, 53.1%), and most participants reported almost always trusting their health care provider (n=939, 78.5%). Most participants reported being willing to share race and ethnicity data at a hospital or clinic (n=1008, 84.3%) and REL data automatically (n=947, 79.2%) in health care settings. Hispanic or Latino (adjusted odds ratio [AOR] 0.049, 95% CI 0.25-0.94) and multiracial (AOR 0.32, 95% CI 0.14-0.76) respondents were less likely to be willing to disclose race and ethnicity data compared to those who were not Hispanic or Latino and who were White, respectively. Individuals who sometimes trust health care providers (AOR 0.57, 95% CI 0.35-0.94) or rarely/never (AOR 0.35, 95% CI 0.15-0.85) were less likely to be willing to disclose race and ethnicity data than those who almost always trust health care providers. African American or Black (AOR 0.46, 95% CI 0.29-0.72) and American Indian or Alaska Native (AOR 0.18, 95% CI 0.04-0.75) individuals were less likely to be willing to share REL data automatically than White individuals. Those who sometimes trust health care providers (AOR 0.48, 95% CI 0.31-0.74) or rarely/never trust health care providers (AOR 0.25, 95% CI 0.11-0.56) were less likely to be willing to share REL data automatically than those who almost always trust health care providers. Those with poor/fair SRH versus very good/excellent SRH were less likely to be willing to share REL data automatically (AOR 0.54, 95% CI 0.34-0.85). Conclusions: Racial and ethnic identity, SRH, and trust in health care providers affect willingness to share REL information with providers and other health systems.

Indexed as

data autonomydata sharingdisparityethnic identityethnicityhealth care disparitieshealth care justiceracerace, ethnicity, and languageracial identityREL

Identifiers

PMID41052417
PMCPMC12500222

What OpenQuestion holds

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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.