Evidence map›Paper›PMID 32894129›Full record

ArticleBMC health services research2020

The influence of patient race on the use of diagnostic imaging in United States emergency departments: data from the National Hospital Ambulatory Medical Care survey.

Andrew B Ross, Vivek Kalia, Brian Y Chan, Geng Li

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
6.8field-weighted citation impact, top 2% 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Observational
  9. A Bayesian zero-inflated spatially varying coefficients model for overdispersed binomial data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors at 3 institutions in 1 country.

Andrew B RossDepartment of Radiology, School of Medicine and Public Health, University of Wisconsin, 600 Highland Ave, Madison, WI, 53792, USA. aross@uwhealth.org.ORCID http://orcid.org/0000-0002-4866-6923
Vivek KaliaDepartment of Radiology, University of Michigan Health System, 1500 E Medical Center Dr, Ann Arbor, MI, 48109, USA.
Brian Y ChanDepartment of Radiology, School of Medicine, University of Utah, 30 N. 1900 E., Salt Lake City, UT, 84132, USA.
Geng LiDepartment of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, 600 Highland Ave, Madison, WI, 53792, USA.
University of Wisconsin–Madison · USUniversity of Michigan · USUniversity of Utah · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn established body of literature has shown evidence of implicit bias in the health care system on the basis of patient race and ethnicity that contributes to well documented disparities in outcomes. However, little is known about the influence of patient race and ethnicity on the decision to order diagnostic radiology exams in the acute care setting. This study examines the role of patient race and ethnicity on the likelihood of diagnostic imaging exams being ordered during United States emergency department encounters.

methodsPublicly available data from the National Hospital Ambulatory Medical Care Survey Emergency Department sample for the years 2006-2016 was compiled. The proportion of patient encounters where diagnostic imaging was ordered was tabulated by race/ethnicity, sub-divided by imaging modality. A multivariable logistic regression model was used to evaluate the influence of patient race/ethnicity on the ordering of diagnostic imaging controlling for other patient and hospital characteristics. Survey weighting variables were used to formulate national-level estimates.

resultsUsing the weighted data, an average of 131,558,553 patient encounters were included each year for the 11-year study period. Imaging was used at 46% of all visits although this varied significantly by patient race and ethnicity with white patients receiving medical imaging at 49% of visits and non-white patients at 41% of visits (p < 0.001). This effect persisted in the controlled regression model and across all imaging modalities with the exception of ultrasound. Other factors with a significant influence on imaging use included patient age, gender, insurance status, number of co-morbidities, hospital setting (urban vs non-urban) and hospital region. There was no evidence to suggest that the disparate use of imaging by patient race and ethnicity changed over the 11-year study time period.

conclusionThe likelihood that a diagnostic imaging exam will be ordered during United States emergency department encounters differs significantly by patient race and ethnicity even when controlling for other patient and hospital characteristics. Further work must be done to understand and mitigate what may represent systematic bias and ensure equitable use of health care resources.

Indexed as

AdultDiagnostic ImagingEmergency Service, HospitalEthnicityFemaleHealthcare DisparitiesHealth Care SurveysHospitalsHumansMaleMiddle AgedPrejudiceRace FactorsUnited StatesBiasDiagnostic imagingEmergency servicesEthnicityHealth servicesRace

Identifiers

PMID32894129
PMCPMC7487740
OpenAlexW3083349471

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.