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Prioritizing breast imaging services during the COVID pandemic: A survey of breast imaging facilities within the Breast Cancer Surveillance Consortium.

ABSTRACT: The COVID-19 pandemic disrupted breast cancer screening and diagnostic imaging in the United States. We sought to evaluate how medical facilities prioritized breast imaging services during periods of reduced capacity or upon re-opening after closures. In fall 2020, we surveyed 77 breast imaging facilities within the Breast Cancer Surveillance Consortium in the United States. The survey ascertained the pandemic's impact on clinical practices during March-September 2020. Nearly all facilities (97%) reported closing or operating at reduced capacity at some point during this period. All facilities were open by August 2020, though 14% were still operating at reduced capacity in September 2020. During periods of re-opening or reduced capacity, 93% of facilities reported prioritizing diagnostic breast imaging over breast cancer screening. For diagnostic imaging, facilities prioritized based on rescheduling canceled appointments (89%), specific indication for diagnostic imaging (89%), patient demand (84%), individual characteristics and risk factors (77%), and time since last imaging examination (72%). For screening mammography, facilities prioritized based on rescheduled cancelations (96%), patient demand (83%), individual characteristics and risk factors (73%), and time since last mammogram (71%). For biopsy services, more than 90% of facilities reported prioritization based on rescheduling of canceled exams, patient demand, patient characteristics and risk factors and level of suspicion on imaging. The observed patterns from this large and geographically diverse sample of facilities in the United States indicate that multiple factors were commonly used to prioritize breast imaging services during periods of reduced capacity.

SUBMITTER: Sprague BL

PROVIDER: S-EPMC8241650 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Prioritizing breast imaging services during the COVID pandemic: A survey of breast imaging facilities within the Breast Cancer Surveillance Consortium.

Sprague Brian L BL O'Meara Ellen S ES Lee Christoph I CI Lee Janie M JM Henderson Louise M LM Buist Diana S M DSM Alsheik Nila N Macarol Teresita T Perry Hannah H Tosteson Anna N A ANA Onega Tracy T Kerlikowske Karla K Miglioretti Diana L DL

Preventive medicine 20210630

The COVID-19 pandemic disrupted breast cancer screening and diagnostic imaging in the United States. We sought to evaluate how medical facilities prioritized breast imaging services during periods of reduced capacity or upon re-opening after closures. In fall 2020, we surveyed 77 breast imaging facilities within the Breast Cancer Surveillance Consortium in the United States. The survey ascertained the pandemic's impact on clinical practices during March-September 2020. Nearly all facilities (97% ...[more]

PMID: 34217424

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Project description:BackgroundAdvanced breast cancer is an outcome used to evaluate screening effectiveness. The advanced cancer definition resulting in the best discrimination of breast cancer death has not been studied in a breast imaging population.MethodsA total of 52 496 women aged 40-79 years participating in the Breast Cancer Surveillance Consortium diagnosed with invasive cancer were staged using the 8th edition of American Joint Committee on Cancer (AJCC) anatomic and prognostic pathologic systems and Tomosynthesis Mammographic Imaging Screening Trial (TMIST) tumor categories. We calculated the area under the receiver operating characteristic curve for predicting 5-year breast cancer death and the sensitivity and specificity for predicting 5-year breast cancer death for 3 advanced cancer classifications: anatomic stage IIB or higher, prognostic pathologic stage IIA or higher, and TMIST advanced cancer.ResultsThe area under the receiver operating characteristic curves for predicting 5-year breast cancer death for AJCC anatomic stage, AJCC prognostic pathologic stage, and TMIST tumor categories were 0.826 (95% confidence interval [CI] = 0.817 to 0.835), 0.856 (95% CI = 0.846 to 0.866), and 0.789 (95% CI = 0.780 to 0.797), respectively. AJCC prognostic pathologic stage had statistically significantly better discrimination than AJCC anatomic stage (difference = 0.030, bootstrap 95% CI = 0.024 to 0.037) and TMIST tumor categories (difference = 0.067, bootstrap 95% CI = 0.059 to 0.075). The sensitivity and specificity for predicting 5-year breast cancer death for AJCC anatomic stage IIB or higher, AJCC prognostic pathologic stage IIA or higher, and TMIST advanced cancer were 72.6%, 76.7%, and 96.1%; and 78.9%, 81.6%, and 41.1%, respectively.ConclusionsDefining advanced cancer as AJCC prognostic pathologic stage IIA or higher most accurately predicts breast cancer death. Use of this definition by investigators will facilitate comparing breast cancer screening effectiveness studies.

Project description:Importance:First-degree family history is a strong risk factor for breast cancer, but controversy exists about the magnitude of the association among older women. Objective:To determine whether first-degree family history is associated with increased risk of breast cancer among older women, and identify whether the association varies by breast density. Design, Setting, and Participants:Prospective cohort study between 1996 and 2012 from 7 Breast Cancer Surveillance Consortium (BCSC) registries located in New Hampshire, North Carolina, San Francisco Bay area, western Washington state, New Mexico, Colorado, and Vermont. During a mean (SD) follow-up of 6.3 (3.2) years, 10?929 invasive breast cancers were diagnosed in a cohort of 403?268 women 65 years and older with data from 472?220 mammography examinations. We estimated the 5-year cumulative incidence of invasive breast cancer by first-degree family history, breast density, and age groups. Cox proportional hazards models were fit to estimate the association of first-degree family history with risk of invasive breast cancer (after adjustment for breast density, BCSC registry, race/ethnicity, body mass index, postmenopausal hormone therapy use, and benign breast disease for age groups 65 to 74 years and 75 years and older, separately). Data analyses were performed between June 2016 and June 2017. Exposure:First-degree family history of breast cancer. Main Outcomes and Measures:Incident breast cancer. Results:In 403 268 women 65 years and older, first-degree family history was associated with an increased risk of breast cancer among women ages 65 to 74 years (hazard ratio [HR],?1.48; 95% CI, 1.35-1.61) and 75 years and older (HR, 1.44; 95% CI, 1.28-1.62). Estimates were similar for women 65 to 74 years with first-degree relative's diagnosis age younger than 50 years (HR, 1.47; 95% CI, 1.25-1.73) vs 50 years and older (HR, 1.33; 95% CI, 1.17-1.51) and for women ages 75 years and older with the relative's diagnosis age younger than 50 years (HR, 1.31; 95% CI, 1.05-1.63) vs 50 years and older (HR, 1.55; 95% CI, 1.33-1.81). Among women ages 65 to 74 years, the risk associated with first-degree family history was highest among those with fatty breasts (HR, 1.67; 95% CI, 1.27-2.21), whereas in women 75 years and older the risk associated with family history was highest among those with dense breasts (HR, 1.55; 95% CI, 1.29-1.87). Conclusions and Relevance:First-degree family history was associated with increased risk of invasive breast cancer in all subgroups of older women irrespective of a relative's age at diagnosis.

Project description:The 2018 outbreak of Nipah virus in Kerala, India, highlights the need for global surveillance of henipaviruses in bats, which are the reservoir hosts for this and other viruses. Nipah virus, an emerging paramyxovirus in the genus Henipavirus, causes severe disease and stuttering chains of transmission in humans and is considered a potential pandemic threat. In May 2018, an outbreak of Nipah virus began in Kerala, > 1800 km from the sites of previous outbreaks in eastern India in 2001 and 2007. Twenty-three people were infected and 21 people died (16 deaths and 18 cases were laboratory confirmed). Initial surveillance focused on insectivorous bats (Megaderma spasma), whereas follow-up surveys within Kerala found evidence of Nipah virus in fruit bats (Pteropus medius). P. medius is the confirmed host in Bangladesh and is now a confirmed host in India. However, other bat species may also serve as reservoir hosts of henipaviruses. To inform surveillance of Nipah virus in bats, we reviewed and analyzed the published records of Nipah virus surveillance globally. We applied a trait-based machine learning approach to a subset of species that occur in Asia, Australia, and Oceana. In addition to seven species in Kerala that were previously identified as Nipah virus seropositive, we identified at least four bat species that, on the basis of trait similarity with known Nipah virus-seropositive species, have a relatively high likelihood of exposure to Nipah or Nipah-like viruses in India. These machine-learning approaches provide the first step in the sequence of studies required to assess the risk of Nipah virus spillover in India. Nipah virus surveillance not only within Kerala but also elsewhere in India would benefit from a research pipeline that included surveys of known and predicted reservoirs for serological evidence of past infection with Nipah virus (or cross reacting henipaviruses). Serosurveys should then be followed by longitudinal spatial and temporal studies to detect shedding and isolate virus from species with evidence of infection. Ecological studies will then be required to understand the dynamics governing prevalence and shedding in bats and the contacts that could pose a risk to public health.

Project description:BackgroundNon-communicable diseases (NCDs) account for an estimated 71% of all global deaths annually and nearly 80% of these deaths occur in low- and middle-income countries. This study aimed to assess the readiness of existing healthcare systems at different levels of health care in delivering NCDs management and prevention services in Kenya.MethodsA cross-sectional survey of 258 facilities was conducted between June 2019 and December 2020 using multistage sampling, examining facility readiness based on the availability of indicators such as equipment, diagnostic capacity, medicines and commodities, trained staff and guidelines for NCDs management. Readiness scores were calculated as the mean availability of tracer items expressed as a percentage and a cut-off threshold of ≥ 70% was used to classify facilities as "ready" to manage NCDs. Descriptive and bivariate analyses were performed to assess the readiness of facilities by type, level, and location settings. Logistic regressions were used to identify factors associated with the readiness of facilities to provide disease-specific services.ResultsOf the surveyed facilities, 93.8% offered chronic respiratory disease (CRD) diagnosis and/or management services, 82.2% diabetes mellitus, 65.1% cardiovascular disease (CVD), and only 24.4% cervical cancer screening services. The mean readiness scores for diabetes mellitus (71%; 95% CI: 67-74) and CVD (69%; 95% CI: 66-72) were relatively high. Although CRD services were reportedly the most widely available, its mean readiness score was low (48%; 95% CI: 45-50). The majority of facilities offering cervical cancer services had all the necessary tracer items available to provide these services. Modeling results revealed that private facilities were more likely to be "ready" to offer NCDs services than public facilities. Similarly, hospitals were more likely "ready" to provide NCDs services than primary health facilities. These disparities in service readiness extended to the regional and urban/rural divide.ConclusionsImportant gaps in the current readiness of facilities to manage NCDs in Kenya at different levels of health care were revealed, showing variations by disease and healthcare facility type. A collective approach is therefore needed to bridge the gap between resource availability and population healthcare needs.

Project description:Purpose To establish performance benchmarks for modern screening digital mammography and assess performance trends over time in U.S. community practice. Materials and Methods This HIPAA-compliant, institutional review board-approved study measured the performance of digital screening mammography interpreted by 359 radiologists across 95 facilities in six Breast Cancer Surveillance Consortium (BCSC) registries. The study included 1 682 504 digital screening mammograms performed between 2007 and 2013 in 792 808 women. Performance measures were calculated according to the American College of Radiology Breast Imaging Reporting and Data System, 5th edition, and were compared with published benchmarks by the BCSC, the National Mammography Database, and performance recommendations by expert opinion. Benchmarks were derived from the distribution of performance metrics across radiologists and were presented as 50th (median), 10th, 25th, 75th, and 90th percentiles, with graphic presentations using smoothed curves. Results Mean screening performance measures were as follows: abnormal interpretation rate (AIR), 11.6 (95% confidence interval [CI]: 11.5, 11.6); cancers detected per 1000 screens, or cancer detection rate (CDR), 5.1 (95% CI: 5.0, 5.2); sensitivity, 86.9% (95% CI: 86.3%, 87.6%); specificity, 88.9% (95% CI: 88.8%, 88.9%); false-negative rate per 1000 screens, 0.8 (95% CI: 0.7, 0.8); positive predictive value (PPV) 1, 4.4% (95% CI: 4.3%, 4.5%); PPV2, 25.6% (95% CI: 25.1%, 26.1%); PPV3, 28.6% (95% CI: 28.0%, 29.3%); cancers stage 0 or 1, 76.9%; minimal cancers, 57.7%; and node-negative invasive cancers, 79.4%. Recommended CDRs were achieved by 92.1% of radiologists in community practice, and 97.1% achieved recommended ranges for sensitivity. Only 59.0% of radiologists achieved recommended AIRs, and only 63.0% achieved recommended levels of specificity. Conclusion The majority of radiologists in the BCSC surpass cancer detection recommendations for screening mammography; however, AIRs continue to be higher than the recommended rate for almost half of radiologists interpreting screening mammograms. © RSNA, 2016 Online supplemental material is available for this article.

Project description:Purpose To establish contemporary performance benchmarks for diagnostic digital mammography with use of recent data from the Breast Cancer Surveillance Consortium (BCSC). Materials and Methods Institutional review board approval was obtained for active or passive consenting processes or to obtain a waiver of consent to enroll participants, link data, and perform analyses. Data were obtained from six BCSC registries (418 radiologists, 92 radiology facilities). Mammogram indication and assessments were prospectively collected for women undergoing diagnostic digital mammography and linked with cancer diagnoses from state cancer registries. The study included 401 548 examinations conducted from 2007 to 2013 in 265 360 women. Results Overall diagnostic performance measures were as follows: cancer detection rate, 34.7 per 1000 (95% confidence interval [CI]: 34.1, 35.2); abnormal interpretation rate, 12.6% (95% CI: 12.5%, 12.7%); positive predictive value (PPV) of a biopsy recommendation (PPV2), 27.5% (95% CI: 27.1%, 27.9%); PPV of biopsies performed (PPV3), 30.4% (95% CI: 29.9%, 30.9%); false-negative rate, 4.8 per 1000 (95% CI: 4.6, 5.0); sensitivity, 87.8% (95% CI: 87.3%, 88.4%); and specificity, 90.5% (95% CI: 90.4%, 90.6%). Among cancers detected, 63.4% were stage 0 or 1 cancers, 45.6% were minimal cancers, the mean size of invasive cancers was 21.2 mm, and 69.6% of invasive cancers were node negative. Performance metrics varied widely across diagnostic indications, with cancer detection rate (64.5 per 1000) and abnormal interpretation rate (18.7%) highest for diagnostic mammograms obtained to evaluate a breast problem with a lump. Compared with performance during the screen-film mammography era, diagnostic digital performance showed increased abnormal interpretation and cancer detection rates and decreasing PPVs, with less than 70% of radiologists within acceptable ranges for PPV2 and PPV3. Conclusion These performance measures can serve as national benchmarks that may help transform the marked variation in radiologists' diagnostic performance into targeted quality improvement efforts. © RSNA, 2017 Online supplemental material is available for this article.

Dataset Information

Prioritizing breast imaging services during the COVID pandemic: A survey of breast imaging facilities within the Breast Cancer Surveillance Consortium.

Publications

Prioritizing breast imaging services during the COVID pandemic: A survey of breast imaging facilities within the Breast Cancer Surveillance Consortium.

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