Evidence map›Paper›PMID 35479582›Full record

ArticleSSM - population health2022

Discrimination, trust, and withholding information from providers: Implications for missing data and inequity.

Paige Nong, Alicia Williamson, Denise Anthony, Jodyn Platt, Sharon Kardia

Open access · goldAbstract read
In one paragraph

Article in SSM - population health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
12.5field-weighted citation impact, top 1% 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

26 citing papers in PubMed, 48 citations in OpenAlex.

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  7. Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.IEEE transactions on knowledge and data engineering · 2025
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  10. Fear of Childbirth Among Pregnant People Facing Anti-Fat Bias.International journal of environmental research and public health · 2024
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  12. Public comfort with the use of ChatGPT and expectations for healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024
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  16. Public perspectives on the use of different data types for prediction in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024
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  17. Article
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  20. Applying anti-racist approaches to informatics: a new lens on traditional frames.Journal of the American Medical Informatics Association : JAMIA · 2023
    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

5 authors at 1 institution in 1 country.

Paige NongUniversity of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
Alicia WilliamsonUniversity of Michigan School of Information, 105 S. State St, Ann Arbor, MI 48109, USA.
Denise AnthonyUniversity of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
Jodyn PlattUniversity of Michigan Department of Learning Health Sciences, 300 N Ingalls St, Ann Arbor, MI, 48109, USA.
Sharon KardiaUniversity of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
University of Michigan · US

Funding

The Lifecycle of Health Data: Polices and PracticesR01CA214829 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KARDIA, SHARON L, PLATT, JODYN ELIZABETH · 2018 to 2021
$2.4M
NCI NIH HHS R01 CA214829
6 · The paper itself

Abstract

Quality care requires collaborative communication, information exchange, and decision-making between patients and providers. Complete and accurate data about patients and from patients are especially important as high volumes of data are used to build clinical decision support tools and inform precision medicine initiatives. However, systematically missing data can bias these tools and threaten their effectiveness. Data completeness relies in many ways on patients being comfortable disclosing information to their providers without prohibitive concerns about security or privacy. Patients are likely to withhold information in the context of low trust relationships with providers, but it is unknown how experiences of discrimination in the healthcare system also relate to non-disclosure. In this study, we assess the relationship between withholding information from providers, experiences of discrimination, and multiple types of patient trust. Using a nationally representative sample of US adults (n = 2,029), weighted logistic regression modeling indicated a statistically significant relationship between experiences of discrimination and withholding information from providers (OR 3.7; CI [2.6-5.2], p < .001). Low trust in provider disclosure of conflicts of interest and low trust in providers' responsible use of health information were also positively associated with non-disclosure. We further analyzed the relationship between non-disclosure and the five most common types of discrimination (e.g., discrimination based on race, education/income, weight, gender, and age). We observed that all five types were statistically significantly associated with non-disclosure (p < .05). These results suggest that experiences of discrimination and specific types of low trust have a meaningful association with a patient's willingness to share information with their provider, with important implications for the quality of data available for medical decision-making and care. Because incomplete information can contribute to lower quality care, especially in the context of data-driven decision-making, patients experiencing discrimination may be further disadvantaged and harmed by systematic data missingness in their records.

Identifiers

PMID35479582
PMCPMC9035429
OpenAlexW4224295195

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.