Potentially missed detection with screening mammography: does the quality of radiologist’s interpretation vary by patient socioeconomic advantage/disadvantage?
Open accessAbstract
Purpose We examined whether quality of mammography interpretation as performed by the original reading radiologist varied by patient sociodemographic characteristics.Methods For 149 patients residing in Chicago and diagnosed in 2005-2008, we obtained the original index mammogram that detected the breast cancer and at least one prior mammogram that did not detect the cancer performed within 2 years of the index mammogram. A single breast imaging specialist performed a blinded review of the prior mammogram. Potentially missed detection (PMD) was defined as an actionable lesion seen during a blinded review of the prior mammogram that was in the same quadrant as the cancer on the index mammogram.Results Of 149 prior mammograms originally read as nonmalignant, 46% (N = 68) had a potentially detectable lesion. In unadjusted analyses, PMD was greater among minority patients (54% vs. 39%, P = .07) and for patients with incomes below $30,000 (65% vs. 36%, P < .01), less education (58% vs. 39%, P = .02), and lacking private health insurance (63% vs. 40%, P = .02). Likelihood ratio tests for the inclusion of socioeconomic variables in multivariable logistic regression models were highly significant (P ≤ .02).Conclusions Disadvantaged socioeconomic status appears to be associated with PMD of breast cancer at mammography screening.
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Low SES patients are more likely to be seen at teaching institutions, where clinical reasoning may be systematically different than other clinical settings serving higher SES individuals.
“Low SES patients are more likely to be seen at teaching institutions, where clinical reasoning may be systematically different than other clinical settings serving higher SES individuals [84].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [84]
Implicit cognitive biases of healthcare providers related to patient attributes (gender, race) can lead to variation in diagnoses (misclassification rates) and the quality of care.
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From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [84]
The degree of practitioner misclassification can vary along sociodemographic factors.
“Furthermore, the degree of practitioner misclassification can vary along sociodemographic factors.”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [84]
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- Authors
- Rauscher, G. H., Khan, J. A., Berbaum, M. L., & Conant, E. F.
- Affiliations
- University of Illinois Chicago (US)
- University of Pennsylvania (US)
- Corresponding author
- Rauscher, Garth H.
- Venue
- Annals of Epidemiology
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 23
- Issue
- 4
- Pages
- 210-214
- ISSN
- 1047-2797 (ISSN-L)
- PMC
- PMC3633590
- OpenAlex
- W1973732518
- Cited by
- 39
- References
- 17
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 9 · 2023: 4 · 2022: 1 · 2021: 7 · 2019: 2 · 2018: 5 · 2017: 3 · 2016: 2 · 2015: 1 · 2013: 1
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Annals of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Illinois Division of the American Cancer Society
- Illinois Department of Public Health (86280168)
- National Cancer Institute (2P50CA106743-06)
- National Center for Minority Health Disparities (1 P60MD003424-01)
- Agency for Health Research and Quality (1 R01 HS018366-01A1)
- Topics
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- mammography screening, breast cancer screening, socioeconomic status, racial and ethnic minorities, income, health insurance, educational attainment, missed cancer detection
- MeSH terms
- Adult, Aged, Breast Neoplasms, Chicago, False Positive Reactions, Female, Humans, Interviews as Topic, Mammography, Mass Screening, Middle Aged, Sensitivity and Specificity, Socioeconomic Factors, Health Care Surveys, Healthcare Disparities, Early Detection of Cancer
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