Tracy Onega1, Christoph I Lee2, David Benkeser3, Jennifer Alford-Teaster4, Jennifer S Haas5, Anna N A Tosteson6, Deirdre Hill7, Xun Shi8, Louise M Henderson9, Rebecca A Hubbard10. 1. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. Electronic address: tracy.l.onega@dartmouth.edu. 2. Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Department of Health Services, University of Washington School of Public Health, Seattle, Washington; Fred Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington. 3. Group Health Research Institute, Seattle, Washington; Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington. 4. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. 5. Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts. 6. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. 7. Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico. 8. The Geography Department, Dartmouth College, Hanover, New Hampshire. 9. Department of Radiology, The University of North Carolina, Chapel Hill, North Carolina. 10. Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Abstract
PURPOSE: Mammography, unlike MRI, is relatively geographically accessible. Additional travel time is often required to access breast MRI. However, the amount of additional travel time and whether it varies on the basis of sociodemographic or breast cancer risk factors is unknown. METHODS: The investigators examined screening mammography and MRI between 2005 and 2012 in the Breast Cancer Surveillance Consortium by (1) travel time to the closest and actual mammography facility used and the difference between the two, (2) women's breast cancer risk factors, and (3) sociodemographic characteristics. Logistic regression was used to examine the odds of traveling farther than the closest facility in relation to women's characteristics. RESULTS: Among 821,683 screening mammographic examinations, 76.6% occurred at the closest facility, compared with 51.9% of screening MRI studies (n = 3,687). The median differential travel time among women not using the closest facility for mammography was 14 min (interquartile range, 8-25 min) versus 20 min (interquartile range, 11-40 min) for breast MRI. Differential travel time for both imaging modalities did not vary notably by breast cancer risk factors but was significantly longer for nonurban residents. For non-Hispanic black compared with non-Hispanic white women, the adjusted odds of traveling farther than the closest facility were 9% lower for mammography (odds ratio, 0.91; 95% confidence interval, 0.87-0.95) but more than two times higher for MRI (odds ratio, 2.64; 95% confidence interval, 1.36-5.13). CONCLUSIONS: Breast cancer risk factors were not related to excess travel time for screening MRI, but sociodemographic factors were, suggesting the possibility that geographic distribution of advanced imaging may exacerbated disparities for some vulnerable populations.
PURPOSE: Mammography, unlike MRI, is relatively geographically accessible. Additional travel time is often required to access breast MRI. However, the amount of additional travel time and whether it varies on the basis of sociodemographic or breast cancer risk factors is unknown. METHODS: The investigators examined screening mammography and MRI between 2005 and 2012 in the Breast Cancer Surveillance Consortium by (1) travel time to the closest and actual mammography facility used and the difference between the two, (2) women's breast cancer risk factors, and (3) sociodemographic characteristics. Logistic regression was used to examine the odds of traveling farther than the closest facility in relation to women's characteristics. RESULTS: Among 821,683 screening mammographic examinations, 76.6% occurred at the closest facility, compared with 51.9% of screening MRI studies (n = 3,687). The median differential travel time among women not using the closest facility for mammography was 14 min (interquartile range, 8-25 min) versus 20 min (interquartile range, 11-40 min) for breast MRI. Differential travel time for both imaging modalities did not vary notably by breast cancer risk factors but was significantly longer for nonurban residents. For non-Hispanic black compared with non-Hispanic white women, the adjusted odds of traveling farther than the closest facility were 9% lower for mammography (odds ratio, 0.91; 95% confidence interval, 0.87-0.95) but more than two times higher for MRI (odds ratio, 2.64; 95% confidence interval, 1.36-5.13). CONCLUSIONS:Breast cancer risk factors were not related to excess travel time for screening MRI, but sociodemographic factors were, suggesting the possibility that geographic distribution of advanced imaging may exacerbated disparities for some vulnerable populations.
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