Gloribel Bonilla

Background: Diagnostic follow‐up times after abnormal screening mammography vary widely across facilities. However, whether the use of digital breast tomosynthesis (DBT) is associated with follow‐up timeliness, independent of facility context factors, remains unclear. Objective: To evaluate the extent to which facility mammography volume accounts for the association between facility‐level DBT use proportion and recall delay after abnormal screening mammography in Appalachia. Methods: We linked 2016–2018 Medicare fee‐for‐service claims to facility survey data from screening facilities in five Appalachian states (KY, OH, PA, VA, and WV). Recall delay was modeled using linear mixed‐effects models with random intercepts for facility and beneficiary. The models adjusted for DBT use proportion, capacity score, age, race, dual Medicare–Medicaid eligibility, deprivation, rurality, and stand‐alone status. To assess whether facility mammography volume accounted for the association between DBT use proportion and recall delay, we compared two models fitted to the same analytic sample with the same covariates. The models differed only in whether facility mammography volume was included. We estimated 95% confidence intervals for the difference in the DBT coefficient using 5000 bootstrap resamples. Facility‐level models and exploratory mediation analyses were conducted as complementary analyses. Results: DBT use proportion was associated with higher facility mammography volume after adjustment for capacity score and facility covariates ( β = 0.614, 95% CI [0.170, 1.058], p = 0.007). After adjustment for capacity score, a 1‐SD higher facility mammography volume was associated with shorter recall delay ( β = −1.103 days, 95% CI [−2.074, −0.132], p = 0.028). The DBT coefficient changed from −0.970 days before adding facility volume to −0.402 days after volume adjustment, corresponding to a raw coefficient change of 0.568 days (95% bootstrap CI [0.130, 1.088]). The complementary facility‐level model estimated a corresponding raw coefficient change of 0.620 days (95% bootstrap CI [0.149, 1.143]). An exploratory mediation analysis adjusted for capacity score and facility covariates estimated an indirect estimate through facility mammography volume of −0.620 days (95% CI [−1.111, −0.146], p = 0.004). The direct and total estimates were not statistically significant. Conclusions: Facility‐level DBT use proportion was positively associated with facility mammography volume, and higher facility mammography volume was associated with shorter recall delay after adjustment for the capacity score. The association between DBT use proportion and recall delay was not statistically significant either before or after volume adjustment, although the estimated DBT coefficient decreased in magnitude after facility mammography volume was added. These findings suggest that differences in follow‐up timeliness may reflect the facility context in which DBT is implemented and support further study of facility‐level determinants of diagnostic follow‐up.

Purpose: Examine the extent that community and organizational characteristics influence mammogram recall and biopsy rates in Appalachia. Methods: We collected facility survey and Medicare claims (2016‐2018) data on 191 FDA‐registered breast imaging organizations serving 2.65 million women in 5 Appalachian states (KY, OH, PA, VA, and WV). Primary outcomes were recall rates and time to recall and biopsy. Generalized linear mixed‐effects models (GLMM) (patient level with facility random intercepts) and facility‐level structural equation models were used to evaluate multilevel predictors of recall, biopsy, and timeliness. Imaging center service areas (SAs) were constructed using Medicare claims for patient, facility, and community characteristics utilization patterns. Mammogram service capacity was measured using a published tool (BIOPSI). Findings: Rural centers had SAs with higher area deprivation index (ADI) scores, lower capacity despite possessing more machines per capita, and were less likely to offer 3D tomosynthesis (3DT). Recall (8%) and biopsy (∼22%) rates were similar by metropolitan status; however, rural centers were more likely to provide same‐day exams ( P = .021). In GLMM, patient‐level predictors included younger age and nonuse of 3DT for recall; and higher ADI and use of 3DT for biopsy. Days to recall was lengthened with dual Medicaid insurance, 3DT, and screening by a stand‐alone facility; while days to biopsy was lengthened without the use of 3DT. Community‐level pathways were found for days to recall, with rural and smaller facilities achieving significantly fewer days. Conclusions: Characteristics of rural imaging centers influence efficiency through their resources and community contexts and should be considered in breast cancer screening outcomes research.

Annual mammography screening is recommended by the US Preventive Services Task Force (USPSTF) and American Cancer Society (ACS) to reduce mortality through early detection of breast cancer. In rural Appalachia, rates of later-stage breast cancer incidence and mortality are higher than national averages. We explored the ways that providers and staff at breast cancer screening facilities employed novel approaches to overcome patient- and facility-level barriers to access to breast cancer screening in the Appalachian region. We conducted 23 semi-structured interviews with 28 clinical providers and staff of breast health facilities in Appalachia. Themes reflect how limiting features of breast screening facilities influenced access to care; the way patient-level barriers presented challenges to access to breast screening; and that external and regulatory forces presented obstacles to access to care. In addition, the unique geographical and geographical attributes of the Appalachian region shaped access and adherence to mammography screening recommendations. Thematic findings highlight that facilities implemented patient-centered strategies to overcome access-related barriers. Results may inform the ways breast cancer screening facilities address suboptimal access to breast health screening. They may also inform future resource allocations to enable facilities to reach breast cancer screening performance goals.

Purpose: Develop and test a measurement framework of mammogram facility resources, policies, and practices in Appalachia.Methods: Survey items describing 7 domains of imaging facility qualities were developed and tested in the Appalachian regions of Kentucky, Ohio, Pennsylvania, Virginia, and West Virginia. Medicare claims data (2016‐2018) were obtained on catchment area mammogram services. Construct validity was examined from associations with facility affiliation, community characteristics, mammogram screening uptake, and market reach. Analyses were performed with t‐tests and ANOVA.Results: A total of 192 (of 377) sites completed the survey. Five factors were initially selected in exploratory factor analysis (FA) and refined in confirmatory FA: capacity, outreach & marketing, operational support, radiology review (NNFI = .94, GFI = 0.93), and diagnostic services (NNFI = 1.00, GFI = 0.99). Imaging capacity and diagnostic services were associated with screening uptake, with capacity strongly associated with catchment area demographic and economic characteristics. Imaging facilities in economically affluent versus poorer areas belong to larger health systems and have significantly more resources (P < .001). Facilities in economically distressed locations in Appalachia rely more heavily on outreach activities (P < .001). Higher facility capacity was significantly associated (P < .05) with larger catchment area size (median split: 48.5 vs 51.6), mammogram market share (47.4 vs 52.7), and screening uptake (47.6 vs 52.4).Conclusions: A set of 18 items assessing breast imaging services and facility characteristics was obtained, representing policies and practices related to a facility's catchment area size, market share, and mammogram screening uptake.

Prevention is a cornerstone of the guiding mission of the University of Virginia Comprehensive Cancer Center, which is “to reduce the burden of cancer for the patients of today, through skilled, integrated, and compassionate care and to eliminate the threat of cancer for the patients of tomorrow, through research and education in an environment that promotes diversity, equity, and inclusion.” We find it useful to conceptualize different opportunities for cancer prevention using NCI's Health Behaviors Research Branch's multilevel translational framework. The latter considers three intersecting continuums: cancer control—from prevention through survivorship; translation—from basic sciences to dissemination and implementation; and level of influence or impact—from genetics to policy. An advantage of this heuristic is that “prevention” is inherently defined as an inter-programmatic concept cutting across basic, clinical, and population science research rather than solely as a programmatic domain of Population Sciences. Through the UVA community outreach and engagement, we apply this multilevel framework to mitigate the social determinants of cancer risk and outcomes that drive cancer inequities in our catchment area. Below, we provide examples of our prevention research and translation along the model continuums and focus on equity.