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Meadows RJ, Figueroa W, Shane‐Carson KP, Padamsee TJ. Predicting breast cancer risk in a racially diverse, community-based sample of potentially high-risk women. Cancer Med 2022; 11:4043-4052. [PMID: 35388639 PMCID: PMC9636513 DOI: 10.1002/cam4.4721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 02/28/2022] [Accepted: 03/11/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Identifying women with high risk of breast cancer is necessary to study high-risk experiences and deliver risk-management care. Risk prediction models estimate individuals' lifetime risk but have rarely been applied in community-based settings among women not yet receiving specialized care. Therefore, we aimed: (1) to apply three breast cancer risk prediction models (i.e., Gail, Claus, and IBIS) to a racially diverse, community-based sample of women, and (2) to assess risk prediction estimates using survey data. METHODS An online survey was administered to women who were determined by a screening instrument to have potentially high risk for breast cancer. Risk prediction models were applied using their self-reported family and medical history information. Inclusion in the high-risk subsample required ≥20% lifetime risk per ≥1 model. Descriptive statistics were used to compare the proportions of women identified as high risk by each model. RESULTS N = 1053 women were initially eligible and completed the survey. All women, except one, self-reported the information necessary to run at least one model; 90% had sufficient information for >1 model. The high-risk subsample included 717 women, of which 75% were identified by one model only; 96% were identified by IBIS, 3% by Claus, <1% by Gail. In the high-risk subsample, 20% were identified by two models and 3% by all three models. CONCLUSIONS Assessing breast cancer risk using self-reported data in a community-based sample was feasible. Different models identify substantially different groups of women who may be at high risk for breast cancer; use of multiple models may be beneficial for research and clinical care.
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Affiliation(s)
- Rachel J. Meadows
- Center for Epidemiology & Healthcare Delivery ResearchJPS Health NetworkFort WorthTexasUSA
| | - Wilson Figueroa
- The Ohio State UniversityCenter for Health Outcomes and Policy Evaluation Studies, College of Public HealthColumbusOhioUSA
- Division of Health Services Management & PolicyCollege of Public Health, The Ohio State UniversityColumbusOhioUSA
| | - Kate P. Shane‐Carson
- Division of Human Genetics, Department of Internal MedicineOhio State University Comprehensive Cancer CenterColumbusOhioUSA
| | - Tasleem J. Padamsee
- Division of Health Services Management & PolicyCollege of Public Health, The Ohio State UniversityColumbusOhioUSA
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Cairns JM, Greenley S, Bamidele O, Weller D. A scoping review of risk-stratified bowel screening: current evidence, future directions. Cancer Causes Control 2022; 33:653-685. [PMID: 35306592 PMCID: PMC8934381 DOI: 10.1007/s10552-022-01568-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 03/02/2022] [Indexed: 12/21/2022]
Abstract
PURPOSE In this scoping review, we examined the international literature on risk-stratified bowel screening to develop recommendations for future research, practice and policy. METHODS Six electronic databases were searched from inception to 18 October 2021: Medline, Embase, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials. Forward and backwards citation searches were also undertaken. All relevant literature were included. RESULTS After de-deduplication, 3,629 records remained. 3,416 were excluded at the title/abstract screening stage. A further 111 were excluded at full-text screening stage. In total, 102 unique studies were included. Results showed that risk-stratified bowel screening programmes can potentially improve diagnostic performance, but there is a lack of information on longer-term outcomes. Risk models do appear to show promise in refining existing risk stratification guidelines but most were not externally validated and less than half achieved good discriminatory power. Risk assessment tools in primary care have the potential for high levels of acceptability and uptake, and therefore, could form an important component of future risk-stratified bowel screening programmes, but sometimes the screening recommendations were not adhered to by the patient or healthcare provider. The review identified important knowledge gaps, most notably in the area of organisation of screening services due to few pilots, and what risk stratification might mean for inequalities. CONCLUSION We recommend that future research focuses on what organisational challenges risk-stratified bowel screening may face and a consideration of inequalities in any changes to organised bowel screening programmes.
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Affiliation(s)
- J M Cairns
- Hull York Medical School, University of Hull, Cottingham Road, Hull, HU6 7HR, UK.
| | - S Greenley
- Hull York Medical School, University of Hull, Cottingham Road, Hull, HU6 7HR, UK
| | - O Bamidele
- Hull York Medical School, University of Hull, Cottingham Road, Hull, HU6 7HR, UK
| | - D Weller
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
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Li X, Kahn RM, Wing N, Zhou ZN, Lackner AI, Krinsky H, Badiner N, Fogla R, Wolfe I, Bergeron H, Baltich Nelson B, Thomas C, Christos PJ, Sharaf RN, Cantillo E, Holcomb K, Chapman-Davis E, Frey MK. Leveraging Health Information Technology to Collect Family Cancer History: A Systematic Review and Meta-Analysis. JCO Clin Cancer Inform 2021; 5:775-788. [PMID: 34328789 PMCID: PMC8812651 DOI: 10.1200/cci.21.00004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 04/08/2021] [Accepted: 06/09/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Collection of family cancer histories (FCHs) can identify individuals at risk for familial cancer syndromes. The aim of this study is to evaluate the literature on existing strategies whereby providers use information technology to assemble FCH. METHODS A systematic search of online databases (Ovid MEDLINE, Cochrane, and Embase) between 1980 and 2020 was performed. Statistical heterogeneity was assessed through the chi-square test (ie, Cochrane Q test) and the inconsistency statistic (I2). A random-effects analysis was used to calculate the pooled proportions and means. RESULTS The comprehensive search produced 4,005 publications. Twenty-eight studies met inclusion criteria. Twenty-seven information technology tools were evaluated. Eighteen out of 28 studies were electronic surveys administered before visits (18, 64.3%). Five studies administered tablet surveys in offices (5, 17.8%). Four studies collected electronic survey via kiosk before visits (4, 14.3%), and one study used animated virtual counselor during visits (1, 3.6%). Among the studies that use an FCH tool, the pooled estimate of the overall completion rate was 86% (CI, 72% to 96%), 84% (CI, 65% to 97%) for electronic surveys before visits, 89% (CI, 0.74 to 0.98) for tablet surveys, and 85% (CI, 0.66 to 0.98) for surveys via kiosk. Mean time required for completion was 31.0 minutes (CI, 26.1 to 35.9), and the pooled estimate of proportions of participants referred to genetic testing was 12% (CI, 4% to 23%). CONCLUSION Our review found that electronic FCH collection can be completed successfully by patients in a time-efficient manner with high rates of satisfaction.
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Affiliation(s)
- Xuan Li
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Ryan M. Kahn
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Noelani Wing
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Zhen Ni Zhou
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Andreas Ian Lackner
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Hannah Krinsky
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Nora Badiner
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Rhea Fogla
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Isabel Wolfe
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Hannah Bergeron
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Becky Baltich Nelson
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Charlene Thomas
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Paul J. Christos
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Ravi N. Sharaf
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Evelyn Cantillo
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Kevin Holcomb
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Eloise Chapman-Davis
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Melissa K. Frey
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
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Murphy CC, Halm EA, Skinner CS, Balasubramanian BA, Singal AG. Challenges and Approaches to Measuring Repeat Fecal Immunochemical Test for Colorectal Cancer Screening. Cancer Epidemiol Biomarkers Prev 2020; 29:1557-1563. [PMID: 32457184 DOI: 10.1158/1055-9965.epi-20-0230] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/14/2020] [Accepted: 05/11/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Colorectal cancer screening with fecal immunochemical testing (FIT) can reduce colorectal cancer-related mortality. Effectiveness of FIT may be compromised when patients do not adhere to a regular schedule. However, having no standard measure of repeat FIT presents challenges for assessing effectiveness across populations and settings. We compared three measures of repeat FIT in a large, integrated health care system in Dallas, Texas. METHODS We identified 18,257 patients age-eligible (50-60 years) for FIT in January 1-December 31, 2010 and followed over four rounds of screening. Measures included: (i) repeat FIT in prior screeners, or completion of FIT within 9-15 months of the previous; (ii) yes-no patterns, whereby patients were assigned yes or no in 9-15 month windows; and 3) proportion of time covered (PTC), or the amount of time patients were up-to-date with screening relative to time eligible. RESULTS Repeat FIT varied by measure. Using a prior screeners measure, 15.8% of patients with a normal FIT in round 1 completed repeat FIT in round 2. Repeat FIT was notably higher (52.3%) using PTC. The most common yes-no pattern was YNNN or "one-and-done," and only 9.4% of patients completed two consecutive FITs across all rounds (YYNN). CONCLUSIONS Different measures of repeat FIT yielded a range of estimates, making comparison across studies difficult. Researchers should weigh the advantages and disadvantages of each measure and select the most appropriate to their research question. IMPACT Our study highlights the need for future research of repeat FIT measures that best approximate screening effectiveness.
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Affiliation(s)
- Caitlin C Murphy
- Department of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
- Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas
| | - Ethan A Halm
- Department of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
- Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas
| | - Celette Sugg Skinner
- Department of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
- Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas
| | - Bijal A Balasubramanian
- Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas
- Department of Epidemiology, Human Genetics, and Environmental Science, UTHealth School of Public Health in Dallas, Dallas, Texas
| | - Amit G Singal
- Department of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
- Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas
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