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Kerlikowske K, Zhu W, Su YR, Sprague BL, Stout NK, Onega T, O’Meara ES, Henderson LM, Tosteson ANA, Wernli K, Miglioretti DL. Supplemental magnetic resonance imaging plus mammography compared with magnetic resonance imaging or mammography by extent of breast density. J Natl Cancer Inst 2024; 116:249-257. [PMID: 37897090 PMCID: PMC10852604 DOI: 10.1093/jnci/djad201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Examining screening outcomes by breast density for breast magnetic resonance imaging (MRI) with or without mammography could inform discussions about supplemental MRI in women with dense breasts. METHODS We evaluated 52 237 women aged 40-79 years who underwent 2611 screening MRIs alone and 6518 supplemental MRI plus mammography pairs propensity score-matched to 65 810 screening mammograms. Rates per 1000 examinations of interval, advanced, and screen-detected early stage invasive cancers and false-positive recall and biopsy recommendation were estimated by breast density (nondense = almost entirely fatty or scattered fibroglandular densities; dense = heterogeneously/extremely dense) adjusting for registry, examination year, age, race and ethnicity, family history of breast cancer, and prior breast biopsy. RESULTS Screen-detected early stage cancer rates were statistically higher for MRI plus mammography vs mammography for nondense (9.3 vs 2.9; difference = 6.4, 95% confidence interval [CI] = 2.5 to 10.3) and dense (7.5 vs 3.5; difference = 4.0, 95% CI = 1.4 to 6.7) breasts and for MRI vs MRI plus mammography for dense breasts (19.2 vs 7.5; difference = 11.7, 95% CI = 4.6 to 18.8). Interval rates were not statistically different for MRI plus mammography vs mammography for nondense (0.8 vs 0.5; difference = 0.4, 95% CI = -0.8 to 1.6) or dense breasts (1.5 vs 1.4; difference = 0.0, 95% CI = -1.2 to 1.3), nor were advanced cancer rates. Interval rates were not statistically different for MRI vs MRI plus mammography for nondense (2.6 vs 0.8; difference = 1.8 (95% CI = -2.0 to 5.5) or dense breasts (0.6 vs 1.5; difference = -0.9, 95% CI = -2.5 to 0.7), nor were advanced cancer rates. False-positive recall and biopsy recommendation rates were statistically higher for MRI groups than mammography alone. CONCLUSION MRI screening with or without mammography increased rates of screen-detected early stage cancer and false-positives for women with dense breasts without a concomitant decrease in advanced or interval cancers.
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Affiliation(s)
- Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Weiwei Zhu
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Brian L Sprague
- Departments of Surgery and Radiology, University of Vermont, Burlington, VT, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Tracy Onega
- Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Ellen S O’Meara
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Karen Wernli
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Diana L Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Department of Public Health Sciences, University of California, Davis, CA, USA
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Jayasekera J, Stein S, Wilson OWA, Wojcik KM, Kamil D, Røssell EL, Abraham LA, O'Meara ES, Schoenborn NL, Schechter CB, Mandelblatt JS, Schonberg MA, Stout NK. Benefits and Harms of Mammography Screening in 75 + Women to Inform Shared Decision-making: a Simulation Modeling Study. J Gen Intern Med 2024; 39:428-439. [PMID: 38010458 PMCID: PMC10897118 DOI: 10.1007/s11606-023-08518-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Guidelines recommend shared decision-making (SDM) around mammography screening for women ≥ 75 years old. OBJECTIVE To use microsimulation modeling to estimate the lifetime benefits and harms of screening women aged 75, 80, and 85 years based on their individual risk factors (family history, breast density, prior biopsy) and comorbidity level to support SDM in clinical practice. DESIGN, SETTING, AND PARTICIPANTS We adapted two established Cancer Intervention and Surveillance Modeling Network (CISNET) models to evaluate the remaining lifetime benefits and harms of screening U.S. women born in 1940, at decision ages 75, 80, and 85 years considering their individual risk factors and comorbidity levels. Results were summarized for average- and higher-risk women (defined as having breast cancer family history, heterogeneously dense breasts, and no prior biopsy, 5% of the population). MAIN OUTCOMES AND MEASURES Remaining lifetime breast cancers detected, deaths (breast cancer/other causes), false positives, and overdiagnoses for average- and higher-risk women by age and comorbidity level for screening (one or five screens) vs. no screening per 1000 women. RESULTS Compared to stopping, one additional screen at 75 years old resulted in six and eight more breast cancers detected (10% overdiagnoses), one and two fewer breast cancer deaths, and 52 and 59 false positives per 1000 average- and higher-risk women without comorbidities, respectively. Five additional screens over 10 years led to 23 and 31 additional breast cancer cases (29-31% overdiagnoses), four and 15 breast cancer deaths avoided, and 238 and 268 false positives per 1000 average- and higher-risk screened women without comorbidities, respectively. Screening women at older ages (80 and 85 years old) and high comorbidity levels led to fewer breast cancer deaths and a higher percentage of overdiagnoses. CONCLUSIONS Simulation models show that continuing screening in women ≥ 75 years old results in fewer breast cancer deaths but more false positive tests and overdiagnoses. Together, clinicians and 75 + women may use model output to weigh the benefits and harms of continued screening.
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Affiliation(s)
- Jinani Jayasekera
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Sarah Stein
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Oliver W A Wilson
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kaitlyn M Wojcik
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA
| | - Dalya Kamil
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA
| | | | - Linn A Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ellen S O'Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Nancy Li Schoenborn
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jeanne S Mandelblatt
- Georgetown Lombardi Institute for Cancer and Aging Research and the Cancer Prevention and Control Program at the Georgetown Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington, DC, USA
| | - Mara A Schonberg
- Division of General Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
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3
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Caswell-Jin JL, Sun LP, Munoz D, Lu Y, Li Y, Huang H, Hampton JM, Song J, Jayasekera J, Schechter C, Alagoz O, Stout NK, Trentham-Dietz A, Lee SJ, Huang X, Mandelblatt JS, Berry DA, Kurian AW, Plevritis SK. Analysis of Breast Cancer Mortality in the US-1975 to 2019. JAMA 2024; 331:233-241. [PMID: 38227031 PMCID: PMC10792466 DOI: 10.1001/jama.2023.25881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 11/27/2023] [Indexed: 01/17/2024]
Abstract
Importance Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear. Objective To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality. Design, Setting, and Participants Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated. Exposures Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer. Main Outcomes and Measures Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence. Results The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years). Conclusions and Relevance According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction.
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Affiliation(s)
| | - Liyang P. Sun
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Diego Munoz
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Ying Lu
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Yisheng Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | | | - John M. Hampton
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison School of Medicine and Public Health, Madison
| | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | - Jinani Jayasekera
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Clyde Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School, Boston, Massachusetts
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison School of Medicine and Public Health, Madison
| | - Sandra J. Lee
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Data Sciences, Harvard Medical School, Boston, Massachusetts
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC
- Georgetown-Lombardi Institute for Cancer and Aging, Washington, DC
| | - Donald A. Berry
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | - Allison W. Kurian
- Department of Medicine, Stanford University School of Medicine, Stanford, California
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
| | - Sylvia K. Plevritis
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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4
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Mandelblatt JS, Schechter CB, Stout NK, Huang H, Stein S, Hunter Chapman C, Trentham-Dietz A, Jayasekera J, Gangnon RE, Hampton JM, Abraham L, O’Meara ES, Sheppard VB, Lee SJ. Population simulation modeling of disparities in US breast cancer mortality. J Natl Cancer Inst Monogr 2023; 2023:178-187. [PMID: 37947337 PMCID: PMC10637022 DOI: 10.1093/jncimonographs/lgad023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/13/2023] [Accepted: 07/31/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence. METHODS Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy. RESULTS Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness. CONCLUSION Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities.
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Affiliation(s)
- Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program at Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Clyde B Schechter
- Departments of Family and Social Medicine and of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Natasha K Stout
- Department of Population Sciences, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Hui Huang
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Sarah Stein
- Department of Population Sciences, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Christina Hunter Chapman
- Department of Radiation Oncology, Section of Health Services Research, Baylor College of Medicine and Health Policy, Quality and Informatics Program at the Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Jinani Jayasekera
- Health Equity and Decision Sciences Research Lab, National Institute on Minority Health and Health Disparities, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Ronald E Gangnon
- Departments of Population Health Sciences and of Biostatistics and Medical Informatics and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - John M Hampton
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ellen S O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Vanessa B Sheppard
- Department of Health Behavior and Policy and Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Sandra J Lee
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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5
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Marlow EC, Ducore JM, Kwan ML, Bowles EJA, Greenlee RT, Pole JD, Rahm AK, Stout NK, Weinmann S, Smith-Bindman R, Miglioretti DL. Medical imaging utilization and associated radiation exposure in children with down syndrome. PLoS One 2023; 18:e0289957. [PMID: 37672503 PMCID: PMC10482278 DOI: 10.1371/journal.pone.0289957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 07/28/2023] [Indexed: 09/08/2023] Open
Abstract
OBJECTIVE To evaluate the frequency of medical imaging or estimated associated radiation exposure in children with Down syndrome. METHODS This retrospective cohort study included 4,348,226 children enrolled in six U.S. integrated healthcare systems from 1996-2016, 3,095 of whom were diagnosed with Down syndrome. We calculated imaging rates per 100 person years and associated red bone marrow dose (mGy). Relative rates (RR) of imaging in children with versus without Down syndrome were estimated using overdispersed Poisson regression. RESULTS Compared to other children, children with Down syndrome received imaging using ionizing radiation at 9.5 times (95% confidence interval[CI] = 8.2-10.9) the rate when age <1 year and 2.3 times (95% CI = 2.0-2.5) between ages 1-18 years. Imaging rates by modality in children <1 year with Down syndrome compared with other children were: computed tomography (6.6 vs. 2.0, RR = 3.1[95%CI = 1.8-5.1]), fluoroscopy (37.1 vs. 3.1, RR 11.9[95%CI 9.5-14.8]), angiography (7.6 vs. 0.2, RR = 35.8[95%CI = 20.6-62.2]), nuclear medicine (6.0 vs. 0.6, RR = 8.2[95% CI = 5.3-12.7]), radiography (419.7 vs. 36.9, RR = 11.3[95%CI = 10.0-12.9], magnetic resonance imaging(7.3 vs. 1.5, RR = 4.2[95% CI = 3.1-5.8]), and ultrasound (231.2 vs. 16.4, RR = 12.6[95% CI = 9.9-15.9]). Mean cumulative red bone marrow dose from imaging over a mean of 4.2 years was 2-fold higher in children with Down syndrome compared with other children (4.7 vs. 1.9mGy). CONCLUSIONS Children with Down syndrome experienced more medical imaging and higher radiation exposure than other children, especially at young ages when they are more vulnerable to radiation. Clinicians should consider incorporating strategic management decisions when imaging this high-risk population.
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Affiliation(s)
- Emily C. Marlow
- Department of Surveillance & Health Equity Science, American Cancer Society, Kennesaw, Georgia, United States of America
| | - Jonathan M. Ducore
- Department of Pediatrics, University of California, Davis, California, United States of America
| | - Marilyn L. Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America
| | - Erin J. A. Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
| | - Robert T. Greenlee
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
| | - Jason D. Pole
- Centre for Health Service Research, University of Queensland, Brisbane, Australia
- Dalla Lana School of Public Health University of Toronto, Toronto, Canada
- ICES Toronto, Ontario, Canada
| | - Alanna K. Rahm
- Department of Genomic Health, Geisinger, Danville, PA, United States of America
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America
| | - Sheila Weinmann
- Kaiser Permanente Center for Health Research, Portland, Oregon, United States of America
- Center for Integrated Health Care Research, Kaiser Permanente Hawaii, Honolulu, Hawaii, United States of America
| | - Rebecca Smith-Bindman
- Department of Biostatistics and Epidemiology, University of California, San Francisco, California, United States of America
- Department of Obstetrics, Gynecology, and Reproductive Medicine, University of California, San Francisco, California, United States of America
- Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, California, United States of America
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
- Department of Public Health Sciences, University of California, Davis, California, United States of America
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6
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Sprague BL, Ichikawa L, Eavey J, Lowry KP, Rauscher G, O’Meara ES, Miglioretti DL, Chen S, Lee JM, Stout NK, Mandelblatt JS, Alsheik N, Herschorn SD, Perry H, Weaver DL, Kerlikowske K. Breast cancer risk characteristics of women undergoing whole-breast ultrasound screening versus mammography alone. Cancer 2023; 129:2456-2468. [PMID: 37303202 PMCID: PMC10506533 DOI: 10.1002/cncr.34768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/06/2023] [Accepted: 02/24/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND There are no consensus guidelines for supplemental breast cancer screening with whole-breast ultrasound. However, criteria for women at high risk of mammography screening failures (interval invasive cancer or advanced cancer) have been identified. Mammography screening failure risk was evaluated among women undergoing supplemental ultrasound screening in clinical practice compared with women undergoing mammography alone. METHODS A total of 38,166 screening ultrasounds and 825,360 screening mammograms without supplemental screening were identified during 2014-2020 within three Breast Cancer Surveillance Consortium (BCSC) registries. Risk of interval invasive cancer and advanced cancer were determined using BCSC prediction models. High interval invasive breast cancer risk was defined as heterogeneously dense breasts and BCSC 5-year breast cancer risk ≥2.5% or extremely dense breasts and BCSC 5-year breast cancer risk ≥1.67%. Intermediate/high advanced cancer risk was defined as BCSC 6-year advanced breast cancer risk ≥0.38%. RESULTS A total of 95.3% of 38,166 ultrasounds were among women with heterogeneously or extremely dense breasts, compared with 41.8% of 825,360 screening mammograms without supplemental screening (p < .0001). Among women with dense breasts, high interval invasive breast cancer risk was prevalent in 23.7% of screening ultrasounds compared with 18.5% of screening mammograms without supplemental imaging (adjusted odds ratio, 1.35; 95% CI, 1.30-1.39); intermediate/high advanced cancer risk was prevalent in 32.0% of screening ultrasounds versus 30.5% of screening mammograms without supplemental screening (adjusted odds ratio, 0.91; 95% CI, 0.89-0.94). CONCLUSIONS Ultrasound screening was highly targeted to women with dense breasts, but only a modest proportion were at high mammography screening failure risk. A clinically significant proportion of women undergoing mammography screening alone were at high mammography screening failure risk.
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Affiliation(s)
- Brian L. Sprague
- Office of Health Promotion Research, Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Laura Ichikawa
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Joanna Eavey
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Kathryn P. Lowry
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Garth Rauscher
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
| | - Shuai Chen
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
| | - Janie M. Lee
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC, USA
| | - Nila Alsheik
- Advocate Caldwell Breast Center, Advocate Lutheran General Hospital, 1700 Luther Lane, Park Ridge, IL
| | - Sally D. Herschorn
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Hannah Perry
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Donald L. Weaver
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA
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7
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Lee JM, Ichikawa LE, Wernli KJ, Bowles EJA, Specht JM, Kerlikowske K, Miglioretti DL, Lowry KP, Tosteson ANA, Stout NK, Houssami N, Onega T, Buist DSM. Impact of Surveillance Mammography Intervals Less Than One Year on Performance Measures in Women With a Personal History of Breast Cancer. Korean J Radiol 2023; 24:729-738. [PMID: 37500574 PMCID: PMC10400369 DOI: 10.3348/kjr.2022.1038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 04/29/2023] [Accepted: 05/18/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE When multiple surveillance mammograms are performed within an annual interval, the current guidance for one-year follow-up to determine breast cancer status results in shared follow-up periods in which a single breast cancer diagnosis can be attributed to multiple preceding examinations, posing a challenge for standardized performance assessment. We assessed the impact of using follow-up periods that eliminate the artifactual inflation of second breast cancer diagnoses. MATERIALS AND METHODS We evaluated surveillance mammograms from 2007-2016 in women with treated breast cancer linked with tumor registry and pathology outcomes. Second breast cancers included ductal carcinoma in situ or invasive breast cancer diagnosed during one-year follow-up. The cancer detection rate, interval cancer rate, sensitivity, and specificity were compared using different follow-up periods: standard one-year follow-up per the American College of Radiology versus follow-up that was shortened at the next surveillance mammogram if less than one year (truncated follow-up). Performance measures were calculated overall and by indication (screening, evaluation for breast problem, and short interval follow-up). RESULTS Of 117971 surveillance mammograms, 20% (n = 23533) were followed by another surveillance mammogram within one year. Standard follow-up identified 1597 mammograms that were associated with second breast cancers. With truncated follow-up, the breast cancer status of 179 mammograms (11.2%) was revised, resulting in 1418 mammograms associated with unique second breast cancers. The interval cancer rate decreased with truncated versus standard follow-up (3.6 versus 4.9 per 1000 mammograms, respectively), with a difference (95% confidence interval [CI]) of -1.3 (-1.6, -1.1). The overall sensitivity increased to 70.4% from 63.7%, for the truncated versus standard follow-up, with a difference (95% CI) of 6.6% (5.6%, 7.7%). The specificity remained stable at 98.1%. CONCLUSION Truncated follow-up, if less than one year to the next surveillance mammogram, enabled second breast cancers to be associated with a single preceding mammogram and resulted in more accurate estimates of diagnostic performance for national benchmarks.
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Affiliation(s)
- Janie M Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Laura E Ichikawa
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine Pasadena, CA, USA
| | - Erin J A Bowles
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jennifer M Specht
- Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Karla Kerlikowske
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Department of Veterans Affairs, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Diana L Miglioretti
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA
| | - Kathryn P Lowry
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Nehmat Houssami
- The Daffodil Centre, University of Sydney and Cancer Council New South Wales, Kings Cross, New South Wales, Australia
| | - Tracy Onega
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Diana S M Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine Pasadena, CA, USA
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8
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Christensen KD, McMahon PM, Galbraith LN, Yeh JM, Stout NK, Lu CY, Stein S, Zhao M, Hylind RJ, Wu AC. Benefits, harms, and costs of newborn genetic screening for hypertrophic cardiomyopathy: Estimates from the PreEMPT model. Genet Med 2023; 25:100797. [PMID: 36727595 PMCID: PMC10168130 DOI: 10.1016/j.gim.2023.100797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 02/02/2023] Open
Abstract
PURPOSE Population newborn genetic screening for hypertrophic cardiomyopathy (HCM) is feasible, however its benefits, harms, and cost-effectiveness are uncertain. METHODS We developed a microsimulation model to simulate a US birth cohort of 3.7 million newborns. Those identified with pathogenic/likely pathogenic variants associated with increased risk of HCM underwent surveillance and recommended treatment, whereas in usual care, individuals with family histories of HCM underwent surveillance. RESULTS In a cohort of 3.7 million newborns, newborn genetic screening would reduce HCM-related deaths through age 20 years by 44 (95% uncertainty interval [UI] = 10-103) however increase the numbers of children undergoing surveillance by 8127 (95% UI = 6308-9664). Compared with usual care, newborn genetic screening costs $267,000 per life year saved (95% UI, $106,000 to $919,000 per life year saved). CONCLUSION Newborn genetic screening for HCM could prevent deaths but at a high cost and would require many healthy children to undergo surveillance. This study shows how modeling can provide insights into the tradeoffs between benefits and costs that will need to be considered as newborn genetic screening is more widely adopted.
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Affiliation(s)
- Kurt D Christensen
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA.
| | - Pamela M McMahon
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | - Lauren N Galbraith
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA; Department of Epidemiology and Biostatistics, School of Public Health, Boston University, Boston, MA
| | - Jennifer M Yeh
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, MA; Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | - Christine Y Lu
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | - Sarah Stein
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | | | - Robyn J Hylind
- Inherited Cardiac Arrhythmia Program, Department of Cardiology, Boston Children's Hospital Boston, MA
| | - Ann Chen Wu
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA; Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, MA
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9
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Shih YCT, Sabik LM, Stout NK, Halpern MT, Lipscomb J, Ramsey S, Ritzwoller DP. Health Economics Research in Cancer Screening: Research Opportunities, Challenges, and Future Directions. J Natl Cancer Inst Monogr 2022; 2022:42-50. [PMID: 35788368 PMCID: PMC9255920 DOI: 10.1093/jncimonographs/lgac008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/03/2022] [Indexed: 01/26/2023] Open
Abstract
Cancer screening has long been considered a worthy public health investment. Health economics offers the theoretical foundation and research methodology to understand the demand- and supply-side factors associated with screening and evaluate screening-related policies and interventions. This article provides an overview of health economic theories and methods related to cancer screening and discusses opportunities for future research. We review 2 academic disciplines most relevant to health economics research in cancer screening: applied microeconomics and decision science. We consider 3 emerging topics: cancer screening policies in national as well as local contexts, "choosing wisely" screening practices, and targeted screening efforts for vulnerable subpopulations. We also discuss the strengths and weaknesses of available data sources and opportunities for methodological research and training. Recommendations to strengthen research infrastructure include developing novel data linkage strategies, increasing access to electronic health records, establishing curriculum and training programs, promoting multidisciplinary collaborations, and enhancing research funding opportunities.
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Affiliation(s)
- Ya-Chen Tina Shih
- Correspondence to: Ya-Chen Tina Shih, PhD, Department of Health Services Research, Unit 1444, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030-4009, USA.
| | - Lindsay M Sabik
- Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Michael T Halpern
- Healthcare Delivery Research Program, National Cancer Institute, Bethesda, MD, USA
| | - Joseph Lipscomb
- Department of Health Policy and Management, Rollins School of Public Health, and the Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Scott Ramsey
- Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Institute, Seattle, WA, USA
| | - Debra P Ritzwoller
- Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO, USA
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10
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Jayasekera J, Lowry KP, Yeh JM, Schwartz MD, Wernli KJ, Isaacs C, Kurian AW, Stout NK. Simulation modeling as a tool to support clinical guidelines and care for breast cancer prevention and early detection in high-risk women. J Clin Oncol 2022. [DOI: 10.1200/jco.2022.40.16_suppl.10525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
10525 Background: To evaluate the incremental short- and long-term benefits and harms of primary prevention with risk reducing medication in high-risk women receiving screening mammography. Methods: We adapted an established, validated Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer discrete event microsimulation model developed to synthesize data the impact of using risk-reducing medication and annual mammography among women with a 3% or higher five-year risk of developing breast cancer. We also examined the effects of supplemental MRI. The model follows a simulated cohort of millions of US women from birth to death. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, breast cancer risk, incidence and stage, screening performance, survival by age, stage, and subtype, treatment efficacy, and other cause mortality. Breast cancer risk was modeled based on family history, breast density, age and history of past breast biopsy. We compared two strategies, annual 3D mammography alone vs. annual 3D mammography and a 5-year course of risk reducing medication at various starting ages, and adding MRI to each approach. Outcomes included the benefits of risk-reducing drugs (avoiding breast cancer) and screening (stage, breast cancer death). Harms included drug side effects and screening false positives and overdiagnosis. Sensitivity analysis tested the impact of uncertainty in model inputs and assumptions on results. Results: Compared to mammography alone, adding risk reducing medication could decrease invasive breast cancer incidence by 30%, and breast cancer deaths by 30% (Table). However, due to reduction in breast cancer incidence, risk reducing medication could result in a 3% increase in false positive results; adding MRI increases benefits but also increases false-positive results. Benefits and harms of risk reducing medication and breast cancer screening strategies for women at high-risk of developing breast cancer. Conclusions: Risk-reducing mediation reduces the risk of hormone-receptor positive breast cancer, and combining this with mammography (and/or MRI) improves earlier detection. Additional work is ongoing to incorporate adverse effects of therapy. Simulation modeling can be used to provide individualized data to facilitate discussions about breast cancer prevention and early detection among high-risk women seen in clinical practice.[Table: see text]
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Affiliation(s)
- Jinani Jayasekera
- Lombardi Cancer Center MedStar Georgetown University Hospital, Washington, DC
| | - Kathryn P. Lowry
- University of Washington, Seattle Cancer Care Alliance, Seattle, WA
| | - Jennifer M Yeh
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Marc D Schwartz
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
| | | | | | | | - Natasha K. Stout
- Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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11
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Caswell-Jin JL, Sun L, Munoz D, Lu Y, Li Y, Huang H, Hampton JM, Song J, Jayasekera J, Schechter C, Alagoz O, Stout NK, Trentham-Dietz A, Mandelblatt JS, Berry DA, Lee SJ, Huang X, Kurian AW, Plevritis S. Contributions of screening, early-stage treatment, and metastatic treatment to breast cancer mortality reduction by molecular subtype in U.S. women, 2000-2017. J Clin Oncol 2022. [DOI: 10.1200/jco.2022.40.16_suppl.1008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
1008 Background: Treatment for metastatic breast cancer has advanced since 2000, but we do not know if those advances have reduced mortality in the general population. Methods: Four Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2017 using national data on mammography use and performance, efficacy and dissemination of estrogen receptor (ER) and HER2-specific treatments of early-stage (stages I-III) and metastatic (stage IV or distant recurrence) disease, and competing mortality. Models compared overall and ER/HER2-specific breast cancer mortality rates from 2000 to 2017 relative to estimated rates with no screening or treatment, and attributed mortality reductions to screening, early-stage or metastatic treatment. Results of an exemplar model are shown. Results: The mortality reduction attributable to early-stage treatment increased from 35.8% in 2000 to 48.2% in 2017, while the proportion attributable to metastatic treatment decreased slightly from 23.9% to 20.6%. The increasing contribution of early-stage treatment reflects the transition of effective metastatic treatments to early-stage disease: accordingly, ten-year distant recurrence-free survival improved (82.5% in 2000, 87.3% in 2017; for ER+HER2+, 78.2% to 90.9%). Survival time after metastatic diagnosis also increased, doubling from 1.48 years in 2000 to 2.80 years in 2017, with the best survival for women with ER+HER2+ cancers (4.08 years) and worst for ER-HER2- (1.22 years). Conclusions: Advances in metastatic breast cancer treatment are reflected in lower population mortality, both through transition to early-stage treatment and gains for women with metastatic disease. These results may inform patient/physician discussions about breast cancer prognosis and expected benefits of treatment. [Table: see text]
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Affiliation(s)
| | - Liyang Sun
- Stanford University School of Medicine, Stanford, CA
| | | | - Ying Lu
- Stanford University and VA Palo Alto Healthcare System, Millbrae, CA
| | - Yisheng Li
- University of Texas MD Anderson Cancer Center, Houston, TX
| | - Hui Huang
- Dana-Farber Cancer Institute, Boston, MA
| | | | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jinani Jayasekera
- Lombardi Cancer Center MedStar Georgetown University Hospital, Washington, DC
| | | | | | - Natasha K. Stout
- Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | | | | | - Donald A. Berry
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sandra J. Lee
- Dana-Farber Cancer Institute/Harvard Medical School, Boston, MA
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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12
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Lowry KP, Bissell MCS, Miglioretti DL, Kerlikowske K, Alsheik N, Macarol T, Bowles EJA, Buist DSM, Tosteson ANA, Henderson L, Herschorn SD, Wernli KJ, Weaver DL, Stout NK, Sprague BL. Breast Biopsy Recommendations and Breast Cancers Diagnosed during the COVID-19 Pandemic. Radiology 2022; 303:287-294. [PMID: 34665032 PMCID: PMC8544262 DOI: 10.1148/radiol.2021211808] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/19/2021] [Accepted: 09/30/2021] [Indexed: 12/23/2022]
Abstract
Background The COVID-19 pandemic reduced mammography use, potentially delaying breast cancer diagnoses. Purpose To examine breast biopsy recommendations and breast cancers diagnosed before and during the COVID-19 pandemic by mode of detection (screen detected vs symptomatic) and women's characteristics. Materials and Methods In this secondary analysis of prospectively collected data, monthly breast biopsy recommendations after mammography, US, or both with subsequent biopsy performed were examined from 66 facilities of the Breast Cancer Surveillance Consortium between January 2019 and September 2020. The number of monthly and cumulative biopsies recommended and performed and the number of subsequent cancers diagnosed during the pandemic period (March 2020 to September 2020) were compared with data from the prepandemic period using Wald χ2 tests. Analyses were stratified by mode of detection and race or ethnicity. Results From January 2019 to September 2020, 17 728 biopsies were recommended and performed, with 6009 cancers diagnosed. From March to September 2020, there were substantially fewer breast biopsy recommendations with cancer diagnoses when compared with the same period in 2019 (1650 recommendations in 2020 vs 2171 recommendations in 2019 [24% fewer], P < .001), predominantly due to fewer screen-detected cancers (722 cancers in 2020 vs 1169 cancers in 2019 [38% fewer], P < .001) versus symptomatic cancers (895 cancers in 2020 vs 965 cancers in 2019 [7% fewer], P = .27). The decrease in cancer diagnoses was largest in Asian (67 diagnoses in 2020 vs 142 diagnoses in 2019 [53% fewer], P = .06) and Hispanic (82 diagnoses in 2020 vs 145 diagnoses in 2019 [43% fewer], P = .13) women, followed by Black women (210 diagnoses in 2020 vs 287 diagnoses in 2019 [27% fewer], P = .21). The decrease was smallest in non-Hispanic White women (1128 diagnoses in 2020 vs 1357 diagnoses in 2019 [17% fewer], P = .09). Conclusion There were substantially fewer breast biopsies with cancer diagnoses during the COVID-19 pandemic from March to September 2020 compared with the same period in 2019, with Asian and Hispanic women experiencing the largest declines, followed by Black women. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Heller in this issue.
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Affiliation(s)
- Kathryn P. Lowry
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Michael C. S. Bissell
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Diana L. Miglioretti
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Karla Kerlikowske
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Nila Alsheik
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Tere Macarol
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Erin J. A. Bowles
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Diana S. M. Buist
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Anna N. A. Tosteson
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Louise Henderson
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Sally D. Herschorn
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Karen J. Wernli
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Donald L. Weaver
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Natasha K. Stout
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
| | - Brian L. Sprague
- From the Department of Radiology, University of Washington, Seattle
Cancer Care Alliance, 1144 Eastlake Ave E, LG-215, Seattle, WA 98109 (K.P.L.);
Division of Biostatistics, Department of Public Health Sciences, University of
California Davis, Davis, Calif (M.C.S.B., D.L.M.); Kaiser Permanente Washington
Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M.,
E.J.A.B., D.S.M.B., K.J.W.); Departments of Medicine and Epidemiology and
Biostatistics, University of California, San Francisco, Calif (K.K.); Advocate
Aurora Health, Downers Grove, Ill (N.A., T.M.); The Dartmouth Institute for
Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel
School of Medicine at Dartmouth, Lebanon, NH (A.N.A.T.); Department of
Radiology, University of North Carolina at Chapel Hill School of Medicine,
Chapel Hill, NC (L.H.); Department of Radiology (S.D.H., B.L.S.), University of
Vermont Cancer Center (S.D.H., D.L.W., B.L.S.), Department of Pathology and
Laboratory Medicine (D.L.W.), and Office of Health Promotion Research,
Department of Surgery (B.L.S.), University of Vermont Larner College of
Medicine, Burlington, Vt; and Department of Population Medicine, Harvard Medical
School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.)
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13
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Lowry KP, Callaway KA, Lee JM, Zhang F, Ross-Degnan D, Wharam JF, Kerlikowske K, Wernli KJ, Kurian AW, Henderson LM, Stout NK. Trends in Annual Surveillance Mammography Participation Among Breast Cancer Survivors From 2004 to 2016. J Natl Compr Canc Netw 2022; 20:379-386.e9. [PMID: 35390766 DOI: 10.6004/jnccn.2021.7081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 07/08/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Annual mammography is recommended for breast cancer survivors; however, population-level temporal trends in surveillance mammography participation have not been described. Our objective was to characterize trends in annual surveillance mammography participation among women with a personal history of breast cancer over a 13-year period. METHODS We examined annual surveillance mammography participation from 2004 to 2016 in a nationwide sample of commercially insured women with prior breast cancer. Rates were stratified by age group (40-49 vs 50-64 years), visit with a surgical/oncology specialist or primary care provider within the prior year, and sociodemographic characteristics. Joinpoint models were used to estimate annual percentage changes (APCs) in participation during the study period. RESULTS Among 141,672 women, mammography rates declined from 74.1% in 2004 to 67.1% in 2016. Rates were stable from 2004 to 2009 (APC, 0.1%; 95% CI, -0.5% to 0.8%) but declined 1.5% annually from 2009 to 2016 (95% CI, -1.9% to -1.1%). For women aged 40 to 49 years, rates declined 2.8% annually (95% CI, -3.4% to -2.1%) after 2009 versus 1.4% annually in women aged 50 to 64 years (95% CI, -1.9% to -1.0%). Similar trends were observed in women who had seen a surgeon/oncologist (APC, -1.7%; 95% CI, -2.1% to -1.4%) or a primary care provider (APC, -1.6%; 95% CI, -2.1% to -1.2%) in the prior year. CONCLUSIONS Surveillance mammography participation among breast cancer survivors declined from 2009 to 2016, most notably among women aged 40 to 49 years. These findings highlight a need for focused efforts to improve adherence to surveillance and prevent delays in detection of breast cancer recurrence and second cancers.
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Affiliation(s)
- Kathryn P Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington
| | - Katherine A Callaway
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Janie M Lee
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington
| | - Fang Zhang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Dennis Ross-Degnan
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - J Frank Wharam
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Karla Kerlikowske
- Department of Medicine, and.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Allison W Kurian
- Department of Medicine, Stanford University School of Medicine, Palo Alto, California
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina; and
| | - Natasha K Stout
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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14
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Lowry KP, Geuzinge HA, Stout NK, Alagoz O, Hampton J, Kerlikowske K, de Koning HJ, Miglioretti DL, van Ravesteyn NT, Schechter C, Sprague BL, Tosteson ANA, Trentham-Dietz A, Weaver D, Yaffe MJ, Yeh JM, Couch FJ, Hu C, Kraft P, Polley EC, Mandelblatt JS, Kurian AW, Robson ME. Breast Cancer Screening Strategies for Women With ATM, CHEK2, and PALB2 Pathogenic Variants: A Comparative Modeling Analysis. JAMA Oncol 2022; 8:587-596. [PMID: 35175286 PMCID: PMC8855312 DOI: 10.1001/jamaoncol.2021.6204] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
IMPORTANCE Screening mammography and magnetic resonance imaging (MRI) are recommended for women with ATM, CHEK2, and PALB2 pathogenic variants. However, there are few data to guide screening regimens for these women. OBJECTIVE To estimate the benefits and harms of breast cancer screening strategies using mammography and MRI at various start ages for women with ATM, CHEK2, and PALB2 pathogenic variants. DESIGN, SETTING, AND PARTICIPANTS This comparative modeling analysis used 2 established breast cancer microsimulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) to evaluate different screening strategies. Age-specific breast cancer risks were estimated using aggregated data from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium for 32 247 cases and 32 544 controls in 12 population-based studies. Data on screening performance for mammography and MRI were estimated from published literature. The models simulated US women with ATM, CHEK2, or PALB2 pathogenic variants born in 1985. INTERVENTIONS Screening strategies with combinations of annual mammography alone and with MRI starting at age 25, 30, 35, or 40 years until age 74 years. MAIN OUTCOMES AND MEASURES Estimated lifetime breast cancer mortality reduction, life-years gained, breast cancer deaths averted, total screening examinations, false-positive screenings, and benign biopsies per 1000 women screened. Results are reported as model mean values and ranges. RESULTS The mean model-estimated lifetime breast cancer risk was 20.9% (18.1%-23.7%) for women with ATM pathogenic variants, 27.6% (23.4%-31.7%) for women with CHEK2 pathogenic variants, and 39.5% (35.6%-43.3%) for women with PALB2 pathogenic variants. Across pathogenic variants, annual mammography alone from 40 to 74 years was estimated to reduce breast cancer mortality by 36.4% (34.6%-38.2%) to 38.5% (37.8%-39.2%) compared with no screening. Screening with annual MRI starting at 35 years followed by annual mammography and MRI at 40 years was estimated to reduce breast cancer mortality by 54.4% (54.2%-54.7%) to 57.6% (57.2%-58.0%), with 4661 (4635-4688) to 5001 (4979-5023) false-positive screenings and 1280 (1272-1287) to 1368 (1362-1374) benign biopsies per 1000 women. Annual MRI starting at 30 years followed by mammography and MRI at 40 years was estimated to reduce mortality by 55.4% (55.3%-55.4%) to 59.5% (58.5%-60.4%), with 5075 (5057-5093) to 5415 (5393-5437) false-positive screenings and 1439 (1429-1449) to 1528 (1517-1538) benign biopsies per 1000 women. When starting MRI at 30 years, initiating annual mammography starting at 30 vs 40 years did not meaningfully reduce mean mortality rates (0.1% [0.1%-0.2%] to 0.3% [0.2%-0.3%]) but was estimated to add 649 (602-695) to 650 (603-696) false-positive screenings and 58 (41-76) to 59 (41-76) benign biopsies per 1000 women. CONCLUSIONS AND RELEVANCE This analysis suggests that annual MRI screening starting at 30 to 35 years followed by annual MRI and mammography at 40 years may reduce breast cancer mortality by more than 50% for women with ATM, CHEK2, and PALB2 pathogenic variants. In the setting of MRI screening, mammography prior to 40 years may offer little additional benefit.
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Affiliation(s)
- Kathryn P. Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle
| | - H. Amarens Geuzinge
- Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Healthcare Institute, Boston, Massachusetts
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison
| | - John Hampton
- Carbone Cancer Center, Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin–Madison, Madison
| | - Karla Kerlikowske
- Department of Medicine, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Harry J. de Koning
- Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis
| | | | - Clyde Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington
- Department of Radiology, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Amy Trentham-Dietz
- Carbone Cancer Center, Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin–Madison, Madison
| | - Donald Weaver
- Department of Pathology, University of Vermont Larner College of Medicine, Burlington
| | - Martin J. Yaffe
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer M. Yeh
- Department of Pediatrics, Harvard Medical School, Boston Children’s Hospital, Boston, Massachusetts
| | - Fergus J. Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, New York
| | - Chunling Hu
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, New York
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Eric C. Polley
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Jeanne S. Mandelblatt
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
| | - Allison W. Kurian
- Department of Medicine, Stanford University, Stanford, California
- Department of Epidemiology and Population Health, Stanford University, Stanford, California
| | - Mark E. Robson
- Department of Breast Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
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15
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Kwan ML, Miglioretti DL, Bowles EJA, Weinmann S, Greenlee RT, Stout NK, Rahm AK, Alber SA, Pequeno P, Moy LM, Stewart C, Fong C, Jenkins CL, Kohnhorst D, Luce C, Mor JM, Munneke JR, Prado Y, Buth G, Cheng SY, Deosaransingh KA, Francisco M, Lakoma M, Martinez YT, Theis MK, Marlow EC, Kushi LH, Duncan JR, Bolch WE, Pole JD, Smith-Bindman R. Quantifying cancer risk from exposures to medical imaging in the Risk of Pediatric and Adolescent Cancer Associated with Medical Imaging (RIC) Study: research methods and cohort profile. Cancer Causes Control 2022; 33:711-726. [PMID: 35107724 DOI: 10.1007/s10552-022-01556-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 01/18/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE The Risk of Pediatric and Adolescent Cancer Associated with Medical Imaging (RIC) Study is quantifying the association between cumulative radiation exposure from fetal and/or childhood medical imaging and subsequent cancer risk. This manuscript describes the study cohorts and research methods. METHODS The RIC Study is a longitudinal study of children in two retrospective cohorts from 6 U.S. healthcare systems and from Ontario, Canada over the period 1995-2017. The fetal-exposure cohort includes children whose mothers were enrolled in the healthcare system during their entire pregnancy and followed to age 20. The childhood-exposure cohort includes children born into the system and followed while continuously enrolled. Imaging utilization was determined using administrative data. Computed tomography (CT) parameters were collected to estimate individualized patient organ dosimetry. Organ dose libraries for average exposures were constructed for radiography, fluoroscopy, and angiography, while diagnostic radiopharmaceutical biokinetic models were applied to estimate organ doses received in nuclear medicine procedures. Cancers were ascertained from local and state/provincial cancer registry linkages. RESULTS The fetal-exposure cohort includes 3,474,000 children among whom 6,606 cancers (2394 leukemias) were diagnosed over 37,659,582 person-years; 0.5% had in utero exposure to CT, 4.0% radiography, 0.5% fluoroscopy, 0.04% angiography, 0.2% nuclear medicine. The childhood-exposure cohort includes 3,724,632 children in whom 6,358 cancers (2,372 leukemias) were diagnosed over 36,190,027 person-years; 5.9% were exposed to CT, 61.1% radiography, 6.0% fluoroscopy, 0.4% angiography, 1.5% nuclear medicine. CONCLUSION The RIC Study is poised to be the largest study addressing risk of childhood and adolescent cancer associated with ionizing radiation from medical imaging, estimated with individualized patient organ dosimetry.
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Affiliation(s)
- Marilyn L Kwan
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA, 94612, USA.
| | - Diana L Miglioretti
- Department of Public Health Sciences, University of California, Davis, CA, USA.,Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Erin J A Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA.,Center for Integrated Health Research, Kaiser Permanente Hawaii, Honolulu, HI, USA
| | - Robert T Greenlee
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Alanna Kulchak Rahm
- Center for Health Research, Genomic Medicine Institute, Geisinger, Danville, PA, USA
| | - Susan A Alber
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - Lisa M Moy
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA, 94612, USA
| | - Carly Stewart
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | | | - Charisma L Jenkins
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA
| | - Diane Kohnhorst
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI, USA
| | - Casey Luce
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Joanne M Mor
- Center for Integrated Health Research, Kaiser Permanente Hawaii, Honolulu, HI, USA
| | - Julie R Munneke
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA, 94612, USA
| | - Yolanda Prado
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA
| | - Glen Buth
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI, USA
| | | | - Kamala A Deosaransingh
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA, 94612, USA
| | - Melanie Francisco
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA
| | - Matthew Lakoma
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | | | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Emily C Marlow
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Lawrence H Kushi
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA, 94612, USA
| | - James R Duncan
- Interventional Radiology Section, Washington University in St. Louis, St. Louis, MI, USA
| | - Wesley E Bolch
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Jason D Pole
- ICES, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.,Centre for Health Services Research, The University of Queensland, Brisbane, Australia
| | - Rebecca Smith-Bindman
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.,Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, CA, USA.,Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, CA, USA
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16
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Weinmann S, Francisco MC, Kwan ML, Bowles EJA, Rahm AK, Greenlee RT, Stout NK, Pole JD, Kushi LH, Smith-Bindman R, Miglioretti DL. Positive predictive value and sensitivity of ICD-9-CM codes for identifying pediatric leukemia. Pediatr Blood Cancer 2022; 69:e29383. [PMID: 34773439 PMCID: PMC9933870 DOI: 10.1002/pbc.29383] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/17/2021] [Accepted: 09/08/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND To facilitate community-based epidemiologic studies of pediatric leukemia, we validated use of ICD-9-CM diagnosis codes to identify pediatric leukemia cases in electronic medical records of six U.S. integrated health plans from 1996-2015 and evaluated the additional contributions of procedure codes for diagnosis/treatment. PROCEDURES Subjects (N = 408) were children and adolescents born in the health systems and enrolled for at least 120 days after the date of the first leukemia ICD-9-CM code or tumor registry diagnosis. The gold standard was the health system tumor registry and/or medical record review. We calculated positive predictive value (PPV) and sensitivity by number of ICD-9-CM codes received in the 120-day period following and including the first code. We evaluated whether adding chemotherapy and/or bone marrow biopsy/aspiration procedure codes improved PPV and/or sensitivity. RESULTS Requiring receipt of one or more codes resulted in 99% sensitivity (95% confidence interval [CI]: 98-100%) but poor PPV (70%; 95% CI: 66-75%). Receipt of two or more codes improved PPV to 90% (95% CI: 86-93%) with 96% sensitivity (95% CI: 93-98%). Requiring at least four codes maximized PPV (95%; 95% CI: 92-98%) without sacrificing sensitivity (93%; 95% CI: 89-95%). Across health plans, PPV for four codes ranged from 84-100% and sensitivity ranged from 83-95%. Including at least one code for a bone marrow procedure or chemotherapy treatment had minimal impact on PPV or sensitivity. CONCLUSIONS The use of diagnosis codes from the electronic health record has high PPV and sensitivity for identifying leukemia in children and adolescents if more than one code is required.
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Affiliation(s)
- Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest Portland, OR,Center for Integrated Health Research, Kaiser Permanente Hawaii Honolulu, Hawaii
| | | | - Marilyn L. Kwan
- Division of Research, Kaiser Permanente Northern California Oakland, CA
| | - Erin J. A. Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington Seattle WA
| | - Alanna Kulchak Rahm
- Center for Health Research, Genomic Medicine Institute, Geisinger Health System Danville, PA 17822
| | - Robert T. Greenlee
- Marshfield Clinic Research Institute, Marshfield Clinic Health System Marshfield, WI
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute Boston, MA
| | - Jason D. Pole
- The Hospital for Sick Children, Toronto, Ontario, Canada,ICES, Toronto, Ontario, Canada,Centre for Health Services Research, The University of Queensland Brisbane, Australia
| | - Lawrence H. Kushi
- Division of Research, Kaiser Permanente Northern California Oakland, CA
| | - Rebecca Smith-Bindman
- Department of Radiology and Biomedical Imaging, Epidemiology and Biostatistics, University of California San Francisco,The Philip R. Lee Institute for Health Policy, University of California San Francisco
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17
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Kunst N, Stout NK, O’Brien G, Christensen KD, McMahon PM, Wu AC, Diller LR, Yeh JM. Population-Based Newborn Screening for Germline TP53 Variants: Clinical Benefits, Cost-Effectiveness, and Value of Further Research. J Natl Cancer Inst 2022; 114:722-731. [PMID: 35043946 PMCID: PMC9086756 DOI: 10.1093/jnci/djac013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/01/2021] [Accepted: 01/13/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Identification of children and infants with Li-Fraumeni syndrome prompts tumor surveillance and allows potential early cancer detection. We assessed the clinical benefits and cost-effectiveness of population-wide newborn screening for TP53 variants (TP53-NBS). METHODS We simulated the impact of TP53-NBS using data regarding TP53-associated pediatric cancers and pathogenic or likely pathogenic (P/LP) TP53 variants from Surveillance, Epidemiology, and End Results; ClinVar and gnomAD; and clinical studies. We simulated an annual US birth cohort under usual care and TP53-NBS and estimated clinical benefits, life-years, and costs associated with usual care and TP53-NBS. RESULTS Under usual care, of 4 million newborns, 608 (uncertainty interval [UI] = 581-636) individuals would develop TP53-associated cancers before age 20 years. Under TP53-NBS, 894 individuals would have P/LP TP53 variants detected. These individuals would undergo routine surveillance after detection of P/LP TP53 variants decreasing the number of cancer-related deaths by 7.2% (UI = 4.0%-12.1%) overall via early malignancy detection. Compared with usual care, TP53-NBS had an incremental cost-effectiveness ratio of $106 009 per life-year gained. Probabilistic analysis estimated a 40% probability that TP53-NBS would be cost-effective given a $100 000 per life-year gained willingness-to-pay threshold. Using this threshold, a value of information analysis found that additional research on the prevalence of TP53 variants among rhabdomyosarcoma cases would resolve most of the decision uncertainty, resulting in an expected benefit of 349 life-years gained (or $36.6 million). CONCLUSIONS We found that TP53-NBS could be cost-effective; however, our findings suggest that further research is needed to reduce the uncertainty in the potential health outcomes and costs associated with TP53-NBS.
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Affiliation(s)
- Natalia Kunst
- Correspondence to: Natalia Kunst, PhD, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Dr, Suite 401, Boston, MA 02215, USA (e-mail: )
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Grace O’Brien
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
| | - Kurt D Christensen
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA,Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Pamela M McMahon
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Ann Chen Wu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA,Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
| | - Lisa R Diller
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jennifer M Yeh
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA,Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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18
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Alagoz O, Lowry KP, Kurian AW, Mandelblatt JS, Ergun MA, Huang H, Lee SJ, Schechter CB, Tosteson ANA, Miglioretti DL, Trentham-Dietz A, Nyante SJ, Kerlikowske K, Sprague BL, Stout NK. Impact of the COVID-19 Pandemic on Breast Cancer Mortality in the US: Estimates From Collaborative Simulation Modeling. J Natl Cancer Inst 2021; 113:1484-1494. [PMID: 34258611 PMCID: PMC8344930 DOI: 10.1093/jnci/djab097] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/21/2021] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has disrupted breast cancer control through short-term declines in screening and delays in diagnosis and treatments. We projected the impact of COVID-19 on future breast cancer mortality between 2020 and 2030. METHODS Three established Cancer Intervention and Surveillance Modeling Network breast cancer models modeled reductions in mammography screening use, delays in symptomatic cancer diagnosis, and reduced use of chemotherapy for women with early-stage disease for the first 6 months of the pandemic with return to prepandemic patterns after that time. Sensitivity analyses were performed to determine the effect of key model parameters, including the duration of the pandemic impact. RESULTS By 2030, the models project 950 (model range = 860-1297) cumulative excess breast cancer deaths related to reduced screening, 1314 (model range = 266-1325) associated with delayed diagnosis of symptomatic cases, and 151 (model range = 146-207) associated with reduced chemotherapy use in women with hormone positive, early-stage cancer. Jointly, 2487 (model range = 1713-2575) excess breast cancer deaths were estimated, representing a 0.52% (model range = 0.36%-0.56%) cumulative increase over breast cancer deaths expected by 2030 in the absence of the pandemic's disruptions. Sensitivity analyses indicated that the breast cancer mortality impact would be approximately double if the modeled pandemic effects on screening, symptomatic diagnosis, and chemotherapy extended for 12 months. CONCLUSIONS Initial pandemic-related disruptions in breast cancer care will have a small long-term cumulative impact on breast cancer mortality. Continued efforts to ensure prompt return to screening and minimize delays in evaluation of symptomatic women can largely mitigate the effects of the initial pandemic-associated disruptions.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Kathryn P Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Allison W Kurian
- Departments of Medicine and of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC, USA
| | - Mehmet A Ergun
- Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Hui Huang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sandra J Lee
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Clyde B Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Diana L Miglioretti
- Department of Public Health Sciences, University of California at Davis, Davis, CA, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and the Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Sarah J Nyante
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology/Biostatistics, University of California at San Francisco, San Francisco, CA, USA
| | - Brian L Sprague
- Department of Surgery and the University of Vermont Cancer Center, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
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19
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O'Brien G, Christensen KD, Sullivan HK, Stout NK, Diller L, Yeh JM, Wu AC. Estimated Cost-effectiveness of Genetic Testing in Siblings of Newborns With Cancer Susceptibility Gene Variants. JAMA Netw Open 2021; 4:e2129742. [PMID: 34661666 PMCID: PMC8524309 DOI: 10.1001/jamanetworkopen.2021.29742] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
This economic evaluation examines the costs and benefits of cascade testing of siblings of newborns with cancer susceptibility gene variants.
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Affiliation(s)
| | - Kurt D Christensen
- Harvard Pilgrim Health Care Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Haley K Sullivan
- Harvard Pilgrim Health Care Institute, Boston, Massachusetts
- Harvard University, Cambridge, Massachusetts
| | - Natasha K Stout
- Harvard Pilgrim Health Care Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Lisa Diller
- Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jennifer M Yeh
- Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Ann Chen Wu
- Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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20
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Sprague BL, Lowry KP, Miglioretti DL, Alsheik N, Bowles EJA, Tosteson ANA, Rauscher G, Herschorn SD, Lee JM, Trentham-Dietz A, Weaver DL, Stout NK, Kerlikowske K. Changes in Mammography Use by Women's Characteristics During the First 5 Months of the COVID-19 Pandemic. J Natl Cancer Inst 2021; 113:1161-1167. [PMID: 33778894 PMCID: PMC8083761 DOI: 10.1093/jnci/djab045] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/08/2021] [Accepted: 03/18/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic led to a near-total cessation of mammography services in the United States in mid-March 2020. It is unclear if screening and diagnostic mammography volumes have recovered to prepandemic levels and whether use has varied by women's characteristics. METHODS We collected data on 461 083 screening mammograms and 112 207 diagnostic mammograms conducted during January 2019 through July 2020 at 62 radiology facilities in the Breast Cancer Surveillance Consortium. We compared monthly screening and diagnostic mammography volumes before and during the pandemic stratified by age, race and ethnicity, breast density, and family history of breast cancer. RESULTS Screening and diagnostic mammography volumes in April 2020 were 1.1% (95% confidence interval [CI] = 0.5% to 2.4%) and 21.4% (95% CI = 18.7% to 24.4%) of the April 2019 prepandemic volumes, respectively, but by July 2020 had rebounded to 89.7% (95% CI = 79.6% to 101.1%) and 101.6% (95% CI = 93.8% to 110.1%) of the July 2019 prepandemic volumes, respectively. The year-to-date cumulative volume of screening and diagnostic mammograms performed through July 2020 was 66.2% (95% CI = 60.3% to 72.6%) and 79.9% (95% CI = 75.4% to 84.6%), respectively, of year-to-date volume through July 2019. Screening mammography rebound was similar across age groups and by family history of breast cancer. Monthly screening mammography volume in July 2020 for Black, White, Hispanic, and Asian women reached 96.7% (95% CI = 88.1% to 106.1%), 92.9% (95% CI = 82.9% to 104.0%), 72.7% (95% CI = 56.5% to 93.6%), and 51.3% (95% CI = 39.7% to 66.2%) of the July 2019 prepandemic volume, respectively. CONCLUSIONS Despite a strong overall rebound in mammography volume by July 2020, the rebound lagged among Asian and Hispanic women, and a substantial cumulative deficit in missed mammograms accumulated, which may have important health consequences.
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Affiliation(s)
- Brian L Sprague
- Office of Health Promotion Research, Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Kathryn P Lowry
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Nila Alsheik
- Advocate Caldwell Breast Center, Advocate Lutheran General Hospital, 1700 Luther Lane, Park Ridge, IL
| | - Erin J A Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | - Garth Rauscher
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL
| | - Sally D Herschorn
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Janie M Lee
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
| | - Donald L Weaver
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA
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21
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Lee JM, Ichikawa LE, Wernli KJ, Bowles E, Specht JM, Kerlikowske K, Miglioretti DL, Lowry KP, Tosteson ANA, Stout NK, Houssami N, Onega T, Buist DSM. Digital Mammography and Breast Tomosynthesis Performance in Women with a Personal History of Breast Cancer, 2007-2016. Radiology 2021; 300:290-300. [PMID: 34003059 PMCID: PMC8328154 DOI: 10.1148/radiol.2021204581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/01/2021] [Accepted: 03/12/2021] [Indexed: 01/13/2023]
Abstract
Background Since 2007, digital mammography and digital breast tomosynthesis (DBT) replaced screen-film mammography. Whether these technologic advances have improved diagnostic performance has, to the knowledge of the authors, not yet been established. Purpose To evaluate the performance and outcomes of surveillance mammography (digital mammography and DBT) performed from 2007 to 2016 in women with a personal history of breast cancer and compare with data from 1996 to 2007 and the performance of digital mammography screening benchmarks. Materials and Methods In this observational cohort study, five Breast Cancer Surveillance Consortium registries provided prospectively collected mammography data linked with tumor registry and pathologic outcomes. This study identified asymptomatic women with American Joint Committee on Cancer anatomic stages 0-III primary breast cancer who underwent surveillance mammography from 2007 to 2016. The primary outcome was a second breast cancer diagnosis within 1 year of mammography. Performance measures included the recall rate, cancer detection rate, interval cancer rate, positive predictive value of biopsy recommendation, sensitivity, and specificity. Results Among 32 331 women who underwent 117 971 surveillance mammographic examinations (112 269 digital mammographic examinations and 5702 DBT examinations), the mean age at initial diagnosis was 59 years ± 12 (standard deviation). Of 1418 second breast cancers diagnosed, 998 were surveillance-detected cancers and 420 were interval cancers. The recall rate was 8.8% (10 365 of 117 971; 95% CI: 8.6%, 9.0%), the cancer detection rate was 8.5 per 1000 examinations (998 of 117 971; 95% CI: 8.0, 9.0), the interval cancer rate was 3.6 per 1000 examinations (420 of 117 971; 95% CI: 3.2, 3.9), the positive predictive value of biopsy recommendation was 31.0% (998 of 3220; 95% CI: 29.4%, 32.7%), the sensitivity was 70.4% (998 of 1418; 95% CI: 67.9%, 72.7%), and the specificity was 98.1% (114 331 of 116 553; 95% CI: 98.0%, 98.2%). Compared with previously published studies, interval cancer rate was comparable with rates from 1996 to 2007 in women with a personal history of breast cancer and was higher than the published digital mammography screening benchmarks. Conclusion In transitioning from screen-film to digital mammography and digital breast tomosynthesis, surveillance mammography performance demonstrated minimal improvement over time and remained inferior to the performance of screening mammography benchmarks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moy and Gao in this issue.
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Affiliation(s)
- Janie M. Lee
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Laura E. Ichikawa
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Karen J. Wernli
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Erin Bowles
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Jennifer M. Specht
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Karla Kerlikowske
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Diana L. Miglioretti
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Kathryn P. Lowry
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Anna N. A. Tosteson
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Natasha K. Stout
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Nehmat Houssami
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Tracy Onega
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
| | - Diana S. M. Buist
- From the Departments of Radiology (J.M.L., K.P.L.) and Medicine
(J.M.S.), University of Washington School of Medicine, Seattle, Wash; Seattle
Cancer Care Alliance, 1144 Eastlake Ave East, LG2-200, Seattle, WA 98109
(J.M.L., J.M.S., K.P.L.); Kaiser Permanente Washington Health Research
Institute, Seattle, Wash (L.E.I., K.J.W., E.B., D.L.M., D.S.M.B.); Department of
Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine,
Pasadena, Calif (K.J.W., D.S.M.B.); Department of Medicine, Division of General
Internal Medicine, Department of Veterans Affairs, and Department of
Epidemiology and Biostatistics, University of California, San Francisco, San
Francisco, Calif (K.K.); Division of Biostatistics, Department of Public Health
Sciences, University of California Davis School of Medicine, Davis, Calif
(D.L.M.); Dartmouth Institute for Health Policy and Clinical Practice (A.N.A.T.,
T.O.) and Norris Cotton Cancer Center (A.N.A.T.), Geisel School of Medicine,
Dartmouth College, Lebanon, NH; Department of Population Medicine, Harvard
Medical School and Harvard Pilgrim Health Care Institute, Harvard University,
Boston, Mass (N.K.S.); Faculty of Medicine and Health, Sydney School of Public
Health, University of Sydney, New South Wales, Australia (N.H.); and Huntsman
Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.)
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22
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Yeh JM, Lowry KP, Schechter CB, Diller LR, O'Brien G, Alagoz O, Armstrong GT, Hampton JM, Hudson MM, Leisenring W, Liu Q, Mandelblatt JS, Miglioretti DL, Moskowitz CS, Nathan PC, Neglia JP, Oeffinger KC, Trentham-Dietz A, Stout NK. Breast Cancer Screening Among Childhood Cancer Survivors Treated Without Chest Radiation: Clinical Benefits and Cost-Effectiveness. J Natl Cancer Inst 2021; 114:235-244. [PMID: 34324686 DOI: 10.1093/jnci/djab149] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/22/2021] [Accepted: 07/22/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Early initiation of breast cancer screening is recommended for high-risk women, including survivors of childhood cancer treated with chest radiation. Recent studies suggest that female survivors of childhood leukemia or sarcoma treated without chest radiation are also at elevated early onset breast cancer risk. However, the potential clinical benefits and cost-effectiveness of early breast cancer screening among these women are uncertain. METHODS Using data from the Childhood Cancer Survivor Study, we adapted two Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer simulation models to reflect the elevated risks of breast cancer and competing mortality among leukemia and sarcoma survivors. Costs and utility weights were based on published studies and databases. Outcomes included breast cancer deaths averted, false-positive-screening results, benign biopsies, and incremental cost-effectiveness ratios (ICERs). RESULTS In the absence of screening, the lifetime risk of dying from breast cancer among survivors was 6.8% to 7.0% across models. Early initiation of annual mammography with MRI screening between ages 25 and 40 would avert 52.6% to 64.3% of breast cancer deaths. When costs and quality of life impacts were considered, screening starting at age 40 was the only strategy with an ICER below the $100,000 per quality-adjusted life-year (QALY) gained cost-effectiveness threshold ($27,680 to $44,380 per QALY gained across models). CONCLUSIONS Among survivors of childhood leukemia or sarcoma, early initiation of breast cancer screening at age 40 may reduce breast cancer deaths by half and is cost-effective. These findings could help inform screening guidelines for survivors treated without chest radiation.
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Affiliation(s)
- Jennifer M Yeh
- Division of General Pediatrics, Boston Children's Hospital, Boston, MA.,Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Kathryn P Lowry
- University of Washington, Seattle Cancer Care Alliance, Seattle, WA
| | - Clyde B Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY
| | - Lisa R Diller
- Department of Pediatrics, Harvard Medical School, Boston, MA.,Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA
| | - Grace O'Brien
- Division of General Pediatrics, Boston Children's Hospital, Boston, MA
| | | | - Gregory T Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN
| | - John M Hampton
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN.,Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN
| | | | - Qi Liu
- University of Alberta, Edmonton, Alberta, Canada
| | | | - Diana L Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NY, NY
| | | | - Joseph P Neglia
- Department of Pediatrics, University of Minnesota Medical School
| | | | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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23
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Marlow EC, Ducore J, Kwan ML, Cheng SY, Bowles EJA, Greenlee RT, Pole JD, Rahm AK, Stout NK, Weinmann S, Smith-Bindman R, Miglioretti DL. Leukemia Risk in a Cohort of 3.9 Million Children with and without Down Syndrome. J Pediatr 2021; 234:172-180.e3. [PMID: 33684394 PMCID: PMC8238875 DOI: 10.1016/j.jpeds.2021.03.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/18/2021] [Accepted: 03/01/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To assess leukemia risks among children with Down syndrome in a large, contemporary cohort. STUDY DESIGN Retrospective cohort study including 3 905 399 children born 1996-2016 in 7 US healthcare systems or Ontario, Canada, and followed from birth to cancer diagnosis, death, age 15 years, disenrollment, or December 30, 2016. Down syndrome was identified using International Classification of Diseases, Ninth and Tenth Revisions, diagnosis codes. Cancer diagnoses were identified through linkages to tumor registries. Incidence and hazard ratios (HRs) of leukemia were estimated for children with Down syndrome and other children adjusting for health system, child's age at diagnosis, birth year, and sex. RESULTS Leukemia was diagnosed in 124 of 4401 children with Down syndrome and 1941 of 3 900 998 other children. In children with Down syndrome, the cumulative incidence of acute myeloid leukemia (AML) was 1405/100 000 (95% CI 1076-1806) at age 4 years and unchanged at age 14 years. The cumulative incidence of acute lymphoid leukemia in children with Down syndrome was 1059/100 000 (95% CI 755-1451) at age 4 and 1714/100 000 (95% CI 1264-2276) at age 14 years. Children with Down syndrome had a greater risk of AML before age 5 years than other children (HR 399, 95% CI 281-566). Largest HRs were for megakaryoblastic leukemia before age 5 years (HR 1500, 95% CI 555-4070). Children with Down syndrome had a greater risk of acute lymphoid leukemia than other children regardless of age (<5 years: HR 28, 95% CI 20-40, ≥5 years HR 21, 95% CI 12-38). CONCLUSIONS Down syndrome remains a strong risk factor for childhood leukemia, and associations with AML are stronger than previously reported.
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Affiliation(s)
- Emily C Marlow
- Graduate Group in Epidemiology, University of California, Davis, Davis, CA; Department of Public Health Sciences, University of California, Davis, Davis, CA
| | - Jonathan Ducore
- Department of Pediatrics, University of California, Davis, Davis, CA
| | - Marilyn L Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | | | - Erin J A Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Salt Lake City, UT
| | - Robert T Greenlee
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI
| | - Jason D Pole
- ICES, Toronto, Ontario, Canada; Centre for Health Service Research, University of Queensland, Brisbane, Australia; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | | | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR; Center for Integrated Health Care Research, Kaiser Permanente Hawaii, Honolulu, HI
| | - Rebecca Smith-Bindman
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA; Department of Obstetrics, Gynecology and Reproductive Medicine, University of California, San Francisco, San Francisco, CA; Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, San Francisco, CA
| | - Diana L Miglioretti
- Department of Public Health Sciences, University of California, Davis, Davis, CA; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Salt Lake City, UT.
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24
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Yeh JM, Stout NK, Chaudhry A, Christensen KD, Gooch M, McMahon PM, O'Brien G, Rehman N, Blout Zawatsky CL, Green RC, Lu CY, Rehm HL, Williams MS, Diller L, Wu AC. Universal newborn genetic screening for pediatric cancer predisposition syndromes: model-based insights. Genet Med 2021; 23:1366-1371. [PMID: 33767345 PMCID: PMC8263476 DOI: 10.1038/s41436-021-01124-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 02/05/2021] [Accepted: 02/08/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Genetic testing for pediatric cancer predisposition syndromes (CPS) could augment newborn screening programs, but with uncertain benefits and costs. METHODS We developed a simulation model to evaluate universal screening for a CPS panel. Cohorts of US newborns were simulated under universal screening versus usual care. Using data from clinical studies, ClinVar, and gnomAD, the presence of pathogenic/likely pathogenic (P/LP) variants in RET, RB1, TP53, DICER1, SUFU, PTCH1, SMARCB1, WT1, APC, ALK, and PHOX2B were assigned at birth. Newborns with identified variants underwent guideline surveillance. Survival benefit was modeled via reductions in advanced disease, cancer deaths, and treatment-related late mortality, assuming 100% adherence. RESULTS Among 3.7 million newborns, under usual care, 1,803 developed a CPS malignancy before age 20. With universal screening, 13.3% were identified at birth as at-risk due to P/LP variant detection and underwent surveillance, resulting in a 53.5% decrease in cancer deaths in P/LP heterozygotes and a 7.8% decrease among the entire cohort before age 20. Given a test cost of $55, universal screening cost $244,860 per life-year gained; with a $20 test, the cost fell to $99,430 per life-year gained. CONCLUSION Population-based genetic testing of newborns may reduce mortality associated with pediatric cancers and could be cost-effective as sequencing costs decline.
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Affiliation(s)
- Jennifer M Yeh
- Harvard Medical School, Boston, MA, USA.
- Boston Children's Hospital, Boston, MA, USA.
| | - Natasha K Stout
- Harvard Medical School, Boston, MA, USA
- Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | | | - Kurt D Christensen
- Harvard Medical School, Boston, MA, USA
- Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Michael Gooch
- Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | | | | | | | | | - Robert C Green
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital and Broad Institute, Boston, MA, USA
| | - Christine Y Lu
- Harvard Medical School, Boston, MA, USA
- Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Heidi L Rehm
- Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Lisa Diller
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ann Chen Wu
- Harvard Medical School, Boston, MA, USA
- Harvard Pilgrim Health Care Institute, Boston, MA, USA
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25
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Lowry KP, Geuzinge HA, Stout NK, Alagoz O, Hampton JM, Kerlikowske K, Miglioretti DL, Schecter C, Sprague BL, Trentham-Dietz A, Tosteson AN, Van Ravesteyn N, Yaffe M, Yeh J, Couch F, Kraft P, Polley E, Mandelblatt JS, Kurian AW, Robson ME. Breast cancer screening for carriers of ATM, CHEK2, and PALB2 pathogenic variants: A comparative modeling analysis. J Clin Oncol 2021. [DOI: 10.1200/jco.2021.39.15_suppl.10500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
10500 Background: Inherited pathogenic variants in ATM, CHEK2, and PALB2 confer moderate to high risks of breast cancer. The optimal approach to screening in these women has not been established. Methods: We used two simulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) and data from the Cancer Risk Estimates Related to Susceptibility consortium (CARRIERS) to project lifetime breast cancer incidence and mortality in ATM, CHEK2, and PALB2 carriers. We simulated screening with annual mammography from ages 40-74 alone and with annual magnetic resonance imaging (MRI) starting at ages 40, 35, 30, and 25. Joint and separate mammography and MRI screening performance was based on published literature. Lifetime outcomes per 1,000 women were reported as means and ranges across both models. Results: Estimated risk of breast cancer by age 80 was 22% (21-23%) for ATM, 28% (26-30%) for CHEK2, and 40% (38-42%) for PALB2. Screening with MRI and mammography reduced breast cancer mortality by 52-60% across variants (Table). Compared to no screening, starting MRI at age 30 increased life years (LY)/1000 women by 501 (478-523) in ATM, 620 (587-652) in CHEK2, and 1,025 (998-1,051) in PALB2. Starting MRI at age 25 versus 30 gained 9-12 LY/1000 women with 517-518 additional false positive screens and 197-198 benign biopsies. Conclusions: For women with ATM, CHEK2, and PALB2 pathogenic variants, breast cancer screening with MRI and mammography halves breast cancer mortality. These mortality benefits are similar to those for MRI screening for BRCA1/2 mutation carriers and should inform practice guidelines.[Table: see text]
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Affiliation(s)
- Kathryn P. Lowry
- University of Washington, Seattle Cancer Care Alliance, Seattle, WA
| | | | - Natasha K. Stout
- Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | | | | | | | | | - Clyde Schecter
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | | | | | | | | | - Martin Yaffe
- Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Jennifer Yeh
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Fergus Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
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26
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Alagoz O, Lowry KP, Kurian AW, Mandelblatt JS, Ergun MA, Huang H, Lee SJ, Schecter C, Tosteson AN, Miglioretti DL, Trentham-Dietz A, Nyante S, Kerlikowske K, Sprague BL, Stout NK. Impact of disruptions in breast cancer control due to the COVID-19 pandemic on breast cancer mortality in the United States: Estimates from collaborative simulation modeling. J Clin Oncol 2021. [DOI: 10.1200/jco.2021.39.15_suppl.6562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
6562 Background: The COVID-19 pandemic has disrupted breast cancer control through short-term declines in screening, delays in diagnosis and reduced/delayed treatments. We projected the impact of COVID-19 on future breast cancer mortality.Methods: Three established Cancer Intervention and Surveillance Modeling Network (CISNET) models projected the impact of pandemic-related care disruptions on breast cancer mortality between 2020 and 2030 vs. pre-pandemic care patterns. Based on Breast Cancer Surveillance Consortium data, we modeled reductions in mammography screening utilization, delays in symptomatic cancer diagnosis, and reduced use of chemotherapy for women with early-stage disease for the first six months of the pandemic with return to pre-pandemic patterns after that time. Sensitivity analyses were performed to determine the effect of key model parameters, including the duration of the pandemic impact. Results: By 2030, the models project 1,297 (model range: 1,054-1,900) cumulative excess deaths related to reduced screening; 1,325 (range: 266-2,628) deaths from delayed diagnosis of symptomatic women, and 207 (range: 146-301) deaths from reduced chemotherapy use for early-stage cancer. Overall, the models predict 2,487 (range 1,713-4,875) excess deaths, representing a 0.56% (range: 0.36%-0.99%) cumulative increase over deaths that would be expected by 2030 in the absence of the pandemic’s disruptions. Sensitivity analyses indicated that the impact on mortality would approximately double if the disruptions lasted for a 12-month period. Conclusions: The impact of the initial pandemic-related disruptions in breast cancer care will have a small long-term cumulative impact on breast cancer mortality. The impact of the initial pandemic-related disruptions on breast cancer mortality will largely be mitigated by the rapid return to usual care. As the pandemic continues it will be important to monitor trends in care and reassess the mortality impact.[Table: see text]
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Affiliation(s)
| | | | | | | | | | - Hui Huang
- Dana-Farber Cancer Institute, Boston, MA
| | - Sandra J. Lee
- Dana-Farber Cancer Institute/Harvard Medical School, Boston, MA
| | - Clyde Schecter
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | | | | | | | - Sarah Nyante
- University of North Carolina-Chapel Hill, Chapel Hill, NC
| | | | | | - Natasha K. Stout
- Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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27
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Bowles EJA, Miglioretti DL, Kwan ML, Bartels U, Furst A, Cheng SY, Lau C, Greenlee RT, Weinmann S, Marlow EC, Rahm AK, Stout NK, Bolch WE, Theis MK, Smith-Bindman R, Pole JD. Long-term medical imaging use in children with central nervous system tumors. PLoS One 2021; 16:e0248643. [PMID: 33882069 PMCID: PMC8059842 DOI: 10.1371/journal.pone.0248643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 02/15/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Children with central nervous system (CNS) tumors undergo frequent imaging for diagnosis and follow-up, but few studies have characterized longitudinal imaging patterns. We described medical imaging in children before and after malignant CNS tumor diagnosis. PROCEDURE We conducted a retrospective cohort study of children aged 0-20 years diagnosed with CNS tumors between 1996-2016 at six U.S. integrated healthcare systems and Ontario, Canada. We collected computed topography (CT), magnetic resonance imaging (MRI), radiography, ultrasound, nuclear medicine examinations from 12 months before through 10 years after CNS diagnosis censoring six months before death or a subsequent cancer diagnosis, disenrollment from the health system, age 21 years, or December 31, 2016. We calculated imaging rates per child per month stratified by modality, country, diagnosis age, calendar year, time since diagnosis, and tumor grade. RESULTS We observed 1,879 children with median four years follow-up post-diagnosis in the U.S. and seven years in Ontario, Canada. During the diagnosis period (±15 days of diagnosis), children averaged 1.10 CTs (95% confidence interval [CI] 1.09-1.13) and 2.14 MRIs (95%CI 2.12-2.16) in the U.S., and 1.67 CTs (95%CI 1.65-1.68) and 1.86 MRIs (95%CI 1.85-1.88) in Ontario. Within one year after diagnosis, 19% of children had ≥5 CTs and 45% had ≥5 MRIs. By nine years after diagnosis, children averaged one MRI and one radiograph per year with little use of other imaging modalities. CONCLUSIONS MRI and CT are commonly used for CNS tumor diagnosis, whereas MRI is the primary modality used during surveillance of children with CNS tumors.
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Affiliation(s)
- Erin J. A. Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
- * E-mail:
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
- Department of Public Health Sciences, University of California, Davis, Davis, California, United States of America
- University of California Davis Comprehensive Cancer Center, Davis, California, United States of America
| | - Marilyn L. Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America
| | - Ute Bartels
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Adam Furst
- Department of Public Health Sciences, University of California, Davis, Davis, California, United States of America
| | | | | | - Robert T. Greenlee
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
| | - Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon, United States of America
- Center for Integrated Health Research, Kaiser Permanente Hawaii, Honolulu, Hawaii, United States of America
| | - Emily C. Marlow
- Department of Public Health Sciences, University of California, Davis, Davis, California, United States of America
| | - Alanna K. Rahm
- Center for Health Research, Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, United States of America
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America
| | - Wes E. Bolch
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
| | - Rebecca Smith-Bindman
- Department of Radiology and Biomedical Imaging, Epidemiology and Biostatistics and The Philip R. Lee Institute for Health Policy, University of California, San Francisco, San Francisco, California, United States of America
| | - Jason D. Pole
- The Hospital for Sick Children, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
- Centre for Health Services Research, The University of Queensland, Brisbane, Australia
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van Ravesteyn NT, Schechter CB, Hampton JM, Alagoz O, van den Broek JJ, Kerlikowske K, Mandelblatt JS, Miglioretti DL, Sprague BL, Stout NK, de Koning HJ, Trentham-Dietz A, Tosteson ANA. Trade-Offs Between Harms and Benefits of Different Breast Cancer Screening Intervals Among Low-Risk Women. J Natl Cancer Inst 2021; 113:1017-1026. [PMID: 33515225 PMCID: PMC8502479 DOI: 10.1093/jnci/djaa218] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 10/30/2020] [Accepted: 12/21/2020] [Indexed: 01/25/2023] Open
Abstract
Background A paucity of research addresses breast cancer screening strategies for women at lower-than-average breast cancer risk. The aim of this study was to examine screening harms and benefits among women aged 50-74 years at lower-than-average breast cancer risk by breast density. Methods Three well-established, validated Cancer Intervention and Surveillance Network models were used to estimate the lifetime benefits and harms of different screening scenarios, varying by screening interval (biennial, triennial). Breast cancer deaths averted, life-years and quality-adjusted life-years gained, false-positives, benign biopsies, and overdiagnosis were assessed by relative risk (RR) level (0.6, 0.7, 0.85, 1 [average risk]) and breast density category, for US women born in 1970. Results Screening benefits decreased proportionally with decreasing risk and with lower breast density. False-positives, unnecessary biopsies, and the percentage overdiagnosis also varied substantially by breast density category; false-positives and unnecessary biopsies were highest in the heterogeneously dense category. For women with fatty or scattered fibroglandular breast density and a relative risk of no more than 0.85, the additional deaths averted and life-years gained were small with biennial vs triennial screening. For these groups, undergoing 4 additional screens (screening biennially [13 screens] vs triennially [9 screens]) averted no more than 1 additional breast cancer death and gained no more than 16 life-years and no more than 10 quality-adjusted life-years per 1000 women but resulted in up to 232 more false-positives per 1000 women. Conclusion Triennial screening from age 50 to 74 years may be a reasonable screening strategy for women with lower-than-average breast cancer risk and fatty or scattered fibroglandular breast density.
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Affiliation(s)
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - John M Hampton
- Carbone Cancer Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Oguzhan Alagoz
- Carbone Cancer Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.,Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Jeroen J van den Broek
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Karla Kerlikowske
- Department of Medicine and Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, WA, USA
| | - Diana L Miglioretti
- Department of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA.,Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Brian L Sprague
- Department of Surgery and University of Vermont Cancer Center, College of Medicine, University of Vermont, Burlington, VT, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Harry J de Koning
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Amy Trentham-Dietz
- Carbone Cancer Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.,Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Anna N A Tosteson
- Norris Cotton Cancer Center and the Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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29
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Lowry KP, Trentham-Dietz A, Schechter CB, Alagoz O, Barlow WE, Burnside ES, Conant EF, Hampton JM, Huang H, Kerlikowske K, Lee SJ, Miglioretti DL, Sprague BL, Tosteson ANA, Yaffe MJ, Stout NK. Long-Term Outcomes and Cost-Effectiveness of Breast Cancer Screening With Digital Breast Tomosynthesis in the United States. J Natl Cancer Inst 2021; 112:582-589. [PMID: 31503283 DOI: 10.1093/jnci/djz184] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 08/01/2019] [Accepted: 09/05/2019] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Digital breast tomosynthesis (DBT) is increasingly being used for routine breast cancer screening. We projected the long-term impact and cost-effectiveness of DBT compared to conventional digital mammography (DM) for breast cancer screening in the United States. METHODS Three Cancer Intervention and Surveillance Modeling Network breast cancer models simulated US women ages 40 years and older undergoing breast cancer screening with either DBT or DM starting in 2011 and continuing for the lifetime of the cohort. Screening performance estimates were based on observational data; in an alternative scenario, we assumed 4% higher sensitivity for DBT. Analyses used federal payer perspective; costs and utilities were discounted at 3% annually. Outcomes included breast cancer deaths, quality-adjusted life-years (QALYs), false-positive examinations, costs, and incremental cost-effectiveness ratios (ICERs). RESULTS Compared to DM, DBT screening resulted in a slight reduction in breast cancer deaths (range across models 0-0.21 per 1000 women), small increase in QALYs (1.97-3.27 per 1000 women), and a 24-28% reduction in false-positive exams (237-268 per 1000 women) relative to DM. ICERs ranged from $195 026 to $270 135 per QALY for DBT relative to DM. When assuming 4% higher DBT sensitivity, ICERs decreased to $130 533-$156 624 per QALY. ICERs were sensitive to DBT costs, decreasing to $78 731 to $168 883 and $52 918 to $118 048 when the additional cost of DBT was reduced to $36 and $26 (from baseline of $56), respectively. CONCLUSION DBT reduces false-positive exams while achieving similar or slightly improved health benefits. At current reimbursement rates, the additional costs of DBT screening are likely high relative to the benefits gained; however, DBT could be cost-effective at lower screening costs.
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Affiliation(s)
- Kathryn P Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, WA
| | | | - Clyde B Schechter
- University of Wisconsin-Madison, Madison, WI; Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY
| | - Oguzhan Alagoz
- Carbone Cancer Center and Department of Population Health Sciences.,School of Medicine and Public Health, and Department of Industrial and Systems Engineering
| | - William E Barlow
- Cancer Research and Biostatistics, University of Washington, Seattle, WA
| | | | - Emily F Conant
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - John M Hampton
- Carbone Cancer Center and Department of Population Health Sciences
| | - Hui Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA
| | - Sandra J Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA.,Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Brian L Sprague
- Departments of Surgery and Radiology, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | - Martin J Yaffe
- Departments of Medical Biophysics and Medical Imaging, University of Toronto, Toronto, Canada
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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30
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Wang RC, Miglioretti DL, Marlow EC, Kwan ML, Theis MK, Bowles EJA, Greenlee RT, Rahm AK, Stout NK, Weinmann S, Smith-Bindman R. Trends in Imaging for Suspected Pulmonary Embolism Across US Health Care Systems, 2004 to 2016. JAMA Netw Open 2020; 3:e2026930. [PMID: 33216141 PMCID: PMC7679949 DOI: 10.1001/jamanetworkopen.2020.26930] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE In response to calls to reduce unnecessary diagnostic testing with computed tomographic pulmonary angiography (CTPA) for suspected pulmonary embolism (PE), there have been growing efforts to create and implement decision rules for PE testing. It is unclear if the use of advanced imaging tests for PE has diminished over time. OBJECTIVE To assess the use of advanced imaging tests, including chest computed tomography (CT) (ie, all chest CT except for CTPA), CTPA, and ventilation-perfusion (V/Q) scan, for PE from 2004 to 2016. DESIGN, SETTING, AND PARTICIPANTS Cohort study of adults by age group (18-64 years and ≥65 years) enrolled in 7 US integrated and mixed-model health care systems. Joinpoint regression analysis was used to identify years with statistically significant changes in imaging rates and to calculate average annual percentage change (growth) from 2004 to 2007, 2008 to 2011, and 2012 to 2016. Analyses were conducted between June 11, 2019, and March 18, 2020. MAIN OUTCOMES AND MEASURES Rates of chest CT, CTPA, and V/Q scan by year and age, as well as annual change in rates over time. RESULTS Overall, 3.6 to 4.8 million enrollees were included each year of the study, for a total of 52 343 517 person-years of follow-up data. Adults aged 18 to 64 years accounted for 42 223 712 person-years (80.7%) and those 65 years or older accounted for 10 119 805 person-years (19.3%). Female enrollees accounted for 27 712 571 person-years (52.9%). From 2004 and 2016, chest CT use increased by 66.3% (average annual growth, 4.4% per year), CTPA use increased by 450.0% (average annual growth, 16.3% per year), and V/Q scan use decreased by 47.1% (decreasing by 4.9% per year). The use of CTPA increased most rapidly from 2004 to 2006 (44.6% in those aged 18-64 years and 43.9% in those ≥65 years), with ongoing rapid growth from 2006 to 2010 (annual growth, 19.8% in those aged 18-64 years and 18.3% in those ≥65 years) and persistent but slower growth in the most recent years (annual growth, 4.3% in those aged 18-64 years and 3.0% in those ≥65 years from 2010 to 2016). The use of V/Q scanning decreased steadily since 2004. CONCLUSIONS AND RELEVANCE From 2004 to 2016, rates of chest CT and CTPA for suspected PE continued to increase among adults but at a slower pace in more contemporary years. Efforts to combat overuse have not been completely successful as reflected by ongoing growth, rather than decline, of chest CT use. Whether the observed imaging use was appropriate or was associated with improved patient outcomes is unknown.
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Affiliation(s)
- Ralph C. Wang
- Department of Emergency Medicine, University of California, San Francisco
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California, Davis
- Comprehensive Cancer Center, University of California, Davis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Emily C. Marlow
- Department of Public Health Sciences, University of California, Davis
| | - Marilyn L. Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - May K. Theis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Erin J. A. Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Robert T. Greenlee
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin
| | - Alanna K. Rahm
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | - Natasha K. Stout
- Massachusetts Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston
| | - Sheila Weinmann
- now with Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon
- Center for Integrated Health Research, Kaiser Permanente Hawaii, Honolulu
| | - Rebecca Smith-Bindman
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
- Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco
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31
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Wernli KJ, Callaway KA, Henderson LM, Kerlikowske K, Lee JM, Ross‐Degnan D, Wallace JK, Wharam JF, Zhang F, Stout NK. Trends in screening breast magnetic resonance imaging use among US women, 2006 to 2016. Cancer 2020; 126:5293-5302. [DOI: 10.1002/cncr.33140] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 06/05/2020] [Accepted: 06/27/2020] [Indexed: 12/31/2022]
Affiliation(s)
- Karen J. Wernli
- Kaiser Permanente Washington Health Research Institute Seattle Washington
| | - Katherine A. Callaway
- Department of Population Medicine Harvard Medical SchoolHarvard Pilgrim Health Care Institute Boston Massachusetts
| | - Louise M. Henderson
- Department of Radiology University of North Carolina at Chapel Hill Chapel Hill North Carolina
| | - Karla Kerlikowske
- Department of Medicine University of California at San Francisco San Francisco California
- Department of Epidemiology and Biostatistics University of California at San Francisco San Francisco California
- General Internal Medicine Section Department of Veterans Affairs University of California at San Francisco San Francisco California
| | - Janie M. Lee
- Department of Radiology University of Washington Seattle Washington
| | - Dennis Ross‐Degnan
- Department of Population Medicine Harvard Medical SchoolHarvard Pilgrim Health Care Institute Boston Massachusetts
| | - Jamie K. Wallace
- Department of Population Medicine Harvard Medical SchoolHarvard Pilgrim Health Care Institute Boston Massachusetts
| | - J. Frank Wharam
- Department of Population Medicine Harvard Medical SchoolHarvard Pilgrim Health Care Institute Boston Massachusetts
| | - Fang Zhang
- Department of Population Medicine Harvard Medical SchoolHarvard Pilgrim Health Care Institute Boston Massachusetts
| | - Natasha K. Stout
- Department of Population Medicine Harvard Medical SchoolHarvard Pilgrim Health Care Institute Boston Massachusetts
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32
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Yeh JM, Lowry KP, Schechter CB, Diller LR, Alagoz O, Armstrong GT, Hampton JM, Leisenring W, Liu Q, Mandelblatt JS, Miglioretti DL, Moskowitz CS, Oeffinger KC, Trentham-Dietz A, Stout NK. Clinical Benefits, Harms, and Cost-Effectiveness of Breast Cancer Screening for Survivors of Childhood Cancer Treated With Chest Radiation : A Comparative Modeling Study. Ann Intern Med 2020; 173:331-341. [PMID: 32628531 PMCID: PMC7510774 DOI: 10.7326/m19-3481] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Surveillance with annual mammography and breast magnetic resonance imaging (MRI) is recommended for female survivors of childhood cancer treated with chest radiation, yet benefits, harms, and costs are uncertain. OBJECTIVE To compare the benefits, harms, and cost-effectiveness of breast cancer screening strategies in childhood cancer survivors. DESIGN Collaborative simulation modeling using 2 Cancer Intervention and Surveillance Modeling Network breast cancer models. DATA SOURCES Childhood Cancer Survivor Study and published data. TARGET POPULATION Women aged 20 years with a history of chest radiotherapy. TIME HORIZON Lifetime. PERSPECTIVE Payer. INTERVENTION Annual MRI with or without mammography, starting at age 25, 30, or 35 years. OUTCOME MEASURES Breast cancer deaths averted, false-positive screening results, benign biopsy results, and incremental cost-effectiveness ratios (ICERs). RESULTS OF BASE-CASE ANALYSIS Lifetime breast cancer mortality risk without screening was 10% to 11% across models. Compared with no screening, starting at age 25 years, annual mammography with MRI averted the most deaths (56% to 71%) and annual MRI (without mammography) averted 56% to 62%. Both strategies had the most screening tests, false-positive screening results, and benign biopsy results. For an ICER threshold of less than $100 000 per quality-adjusted life-year gained, screening beginning at age 30 years was preferred. RESULTS OF SENSITIVITY ANALYSIS Assuming lower screening performance, the benefit of adding mammography to MRI increased in both models, although the conclusions about preferred starting age remained unchanged. LIMITATION Elevated breast cancer risk was based on survivors diagnosed with childhood cancer between 1970 and 1986. CONCLUSION Early initiation (at ages 25 to 30 years) of annual breast cancer screening with MRI, with or without mammography, might reduce breast cancer mortality by half or more in survivors of childhood cancer. PRIMARY FUNDING SOURCE American Cancer Society and National Institutes of Health.
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Affiliation(s)
- Jennifer M. Yeh
- Department of Pediatrics, Harvard Medical School and Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
| | - Kathryn P. Lowry
- University of Washington, Seattle Cancer Care Alliance, 825 Eastlake Ave. E., Seattle, WA 98109
| | - Clyde B. Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Block Building 406, Bronx, NY 10461
| | - Lisa R. Diller
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, 450 Brookline Avenue, Boston, MA 02115
| | - Oguzhan Alagoz
- University of Wisconsin–Madison, 1513 University Avenue, Madison, WI 53706
| | - Gregory T. Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, 262 Danny Thomas Pl, Memphis, TN 38105
| | - John M. Hampton
- University of Wisconsin Carbone Cancer Center, 610 Walnut Street, WARF Room 307, Madison, WI 53726
| | - Wendy Leisenring
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, WA, 98109
| | - Qi Liu
- University of Alberta, 11405 87th Avenue, Edmonton, Alberta, Canada T6G 1C9
| | - Jeanne S. Mandelblatt
- Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street Northwest, Suite 4100, Washington, DC 20007
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, One Shields Avenue, Med-Sci 1C, Room 145, Davis, CA 95616
| | - Chaya S. Moskowitz
- Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd floor, NY, NY 10017
| | | | - Amy Trentham-Dietz
- University of Wisconsin Carbone Cancer Center, 610 Walnut Street, WARF Room 307, Madison, WI 53726
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Landmark Center, 401 Park Drive, Suite 401, Boston, MA 02215
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33
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Conant EF, Barlow WE, Herschorn SD, Weaver DL, Beaber EF, Tosteson ANA, Haas JS, Lowry KP, Stout NK, Trentham-Dietz A, diFlorio-Alexander RM, Li CI, Schnall MD, Onega T, Sprague BL. Association of Digital Breast Tomosynthesis vs Digital Mammography With Cancer Detection and Recall Rates by Age and Breast Density. JAMA Oncol 2020; 5:635-642. [PMID: 30816931 DOI: 10.1001/jamaoncol.2018.7078] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Importance Breast cancer screening examinations using digital breast tomosynthesis (DBT) has been shown to be associated with decreased false-positive test results and increased breast cancer detection compared with digital mammography (DM). Little is known regarding the size and stage of breast cancer types detected and their association with age and breast density. Objective To determine whether screening examinations using DBT detect breast cancers that are associated with an improved prognosis and to compare the detection rates by patient age and breast density. Design, Setting, and Participants This retrospective analysis of prospective cohort data from 3 research centers in the Population-based Research Optimizing Screening Through Personalized Regimens (PROSPR) consortium included data of women aged 40 to 74 years who underwent screening examinations using DM and DBT from January 1, 2011, through September 30, 2014. Statistical analysis was performed from November 8, 2017, to August 14, 2018. Exposures Use of DBT as a supplement to DM at breast cancer screening examination. Main Outcomes and Measures Recall rate, cancer detection rate, positive predictive value, biopsy rate, and distribution of invasive cancer subtypes. Results Among 96 269 women (mean [SD] patient age for all examinations, 55.9 [9.0] years), patient age was 56.4 (9.0) years for DM and 54.6 (8.9) years for DBT. Of 180 340 breast cancer screening examinations, 129 369 examinations (71.7%) used DM and 50 971 examinations (28.3%) used DBT. Screening examination with DBT (73 of 99 women [73.7%]) was associated with the detection of smaller, more often node-negative, HER2-negative, invasive cancers compared with DM (276 of 422 women [65.4%]). Screening examination with DBT was also associated with lower recall (odds ratio, 0.64; 95% CI, 0.57-0.72; P < .001) and higher cancer detection (odds ratio, 1.41; 95% CI, 1.05-1.89; P = .02) compared with DM for all age groups even when stratified by breast density. The largest increase in cancer detection rate and the greatest shift toward smaller, node-negative invasive cancers detected with DBT was for women aged 40 to 49 years. For women aged 40 to 49 years with nondense breasts, the cancer detection rate for examinations using DBT was 1.70 per 1000 women higher compared with the rate using DM; for women with dense breasts, the cancer detection rate was 2.27 per 1000 women higher for DBT. For these younger women, screening with DBT was associated with only 7 of 28 breast cancers (25.0%) categorized as poor prognosis compared with 19 of 47 breast cancers (40.4%) when screening with DM. Conclusions and Relevance The findings suggest that screening with DBT is associated with increased specificity and an increased proportion of breast cancers detected with better prognosis compared with DM. In the subgroup of women aged 40 to 49 years, routine DBT screening may have a favorable risk-benefit ratio.
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Affiliation(s)
- Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - Sally D Herschorn
- Department of Radiology, University of Vermont, Burlington.,University of Vermont Cancer Center, University of Vermont, Burlington
| | - Donald L Weaver
- University of Vermont Cancer Center, University of Vermont, Burlington.,Department of Pathology, University of Vermont, Burlington
| | - Elisabeth F Beaber
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Anna N A Tosteson
- Department of Community & Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.,The Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Jennifer S Haas
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts
| | | | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin, Madison
| | | | - Christopher I Li
- Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mitchell D Schnall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Tracy Onega
- Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.,Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.,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
| | - Brian L Sprague
- Department of Radiology, University of Vermont, Burlington.,University of Vermont Cancer Center, University of Vermont, Burlington.,Department of Surgery, University of Vermont, Burlington
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Ozanne EM, Soeteman DI, Frank ES, Clarke J, Hassett MJ, Stout NK, Punglia RS. Commentary: Creating a patient-centered decision aid for ductal carcinoma in situ. Breast J 2020; 26:1498-1499. [PMID: 32034829 DOI: 10.1111/tbj.13779] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/22/2020] [Accepted: 01/23/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Elissa M Ozanne
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah
| | - Djøra I Soeteman
- Harvard T.H. Chan School of Public Health, Center for Health Decision Science, Boston, Massachusetts
| | - Elizabeth S Frank
- Dana-Farber/Harvard Center Breast Cancer Advocacy Group, DFCI, Boston, Massachusetts
| | - John Clarke
- Cornerstone Systems Northwest Inc, Boston, Massachusetts
| | | | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
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Smith-Bindman R, Kwan ML, Marlow EC, Theis MK, Bolch W, Cheng SY, Bowles EJA, Duncan JR, Greenlee RT, Kushi LH, Pole JD, Rahm AK, Stout NK, Weinmann S, Miglioretti DL. Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016. JAMA 2019; 322:843-856. [PMID: 31479136 PMCID: PMC6724186 DOI: 10.1001/jama.2019.11456] [Citation(s) in RCA: 306] [Impact Index Per Article: 61.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 07/24/2019] [Indexed: 12/17/2022]
Abstract
Importance Medical imaging increased rapidly from 2000 to 2006, but trends in recent years have not been analyzed. Objective To evaluate recent trends in medical imaging. Design, Setting, and Participants Retrospective cohort study of patterns of medical imaging between 2000 and 2016 among 16 million to 21 million patients enrolled annually in 7 US integrated and mixed-model insurance health care systems and for individuals receiving care in Ontario, Canada. Exposures Calendar year and country (United States vs Canada). Main Outcomes and Measures Use of computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine imaging. Annual and relative imaging rates by imaging modality, country, and age (children [<18 years], adults [18-64 years], and older adults [≥65 years]). Results Overall, 135 774 532 imaging examinations were included; 5 439 874 (4%) in children, 89 635 312 (66%) in adults, and 40 699 346 (30%) in older adults. Among adults and older adults, imaging rates were significantly higher in 2016 vs 2000 for all imaging modalities other than nuclear medicine. For example, among older adults, CT imaging rates were 428 per 1000 person-years in 2016 vs 204 per 1000 in 2000 in US health care systems and 409 per 1000 vs 161 per 1000 in Ontario; for MRI, 139 per 1000 vs 62 per 1000 in the United States and 89 per 1000 vs 13 per 1000 in Ontario; and for ultrasound, 495 per 1000 vs 324 per 1000 in the United States and 580 per 1000 vs 332 per 1000 in Ontario. Annual growth in imaging rates among US adults and older adults slowed over time for CT (from an 11.6% annual percentage increase among adults and 9.5% among older adults in 2000-2006 to 3.7% among adults in 2013-2016 and 5.2% among older adults in 2014-2016) and for MRI (from 11.4% in 2000-2004 in adults and 11.3% in 2000-2005 in older adults to 1.3% in 2007-2016 in adults and 2.2% in 2005-2016 in older adults). Patterns in Ontario were similar. Among children, annual growth for CT stabilized or declined (United States: from 10.1% in 2000-2005 to 0.8% in 2013-2016; Ontario: from 3.3% in 2000-2006 to -5.3% in 2006-2016), but patterns for MRI were similar to adults. Changes in annual growth in ultrasound were smaller among adults and children in the United States and Ontario compared with CT and MRI. Nuclear medicine imaging declined in adults and children after 2006. Conclusions and Relevance From 2000 to 2016 in 7 US integrated and mixed-model health care systems and in Ontario, rates of CT and MRI use continued to increase among adults, but at a slower pace in more recent years. In children, imaging rates continued to increase except for CT, which stabilized or declined in more recent periods. Whether the observed imaging utilization was appropriate or was associated with improved patient outcomes is unknown.
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Affiliation(s)
- Rebecca Smith-Bindman
- Department of Radiology and Biomedical Imaging, Epidemiology and Biostatistics, and Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco
| | - Marilyn L. Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Emily C. Marlow
- Department of Public Health Sciences, University of California, Davis
- Graduate Group in Epidemiology, University of California, Davis
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Wesley Bolch
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville
| | | | | | - James R. Duncan
- Interventional Radiology Section, Washington University in St Louis, St Louis, Missouri
| | | | - Lawrence H. Kushi
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Jason D. Pole
- ICES, Toronto, Ontario, Canada
- Pediatric Oncology Group of Ontario and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Alanna K. Rahm
- Center for Health Research, Genomic Medical Institute, Geisinger, Danville, Pennsylvania
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California, Davis
- Kaiser Permanente Washington Health Research Institute, Seattle
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Abstract
OBJECTIVE Breast cancer simulation models must take changing mortality rates into account to evaluate the potential impact of cancer control interventions. We estimated mortality rates due to breast cancer and all other causes combined to determine their impact on overall mortality by year, age, and birth cohort. METHODS Based on mortality rates from publicly available datasets, an age-period-cohort model was used to estimate the proportion of deaths due to breast cancer for US women aged 0 to 119 years, with birth years 1900 to 2000. Breast cancer mortality was calculated as all-cause mortality multiplied by the proportion of deaths due to breast cancer; other-cause mortality was the difference between all-cause and breast cancer mortality. RESULTS Breast cancer and other-cause mortality rates were higher for older ages and birth cohorts. The percent of deaths due to breast cancer increased across birth cohorts from 1900 to 1940 then decreased. Among 50-year-old women, in the 1920 birth cohort, 52 (9.9%) of 100,000 deaths (95% CI, 9.8% to 10.1%) were attributed to breast cancer whereas 476 of 100,000 were due to other causes; in the 1960 birth cohort, 22 (8.5%) of 100,000 deaths (95% CI, 8.3% to 8.7%) were attributed to breast cancer with 242 of 100,000 deaths due to other causes. The percentage of all deaths due to breast cancer was highest (4.1% to 12.9%) for women in their 40s and 50s for all birth cohorts. CONCLUSIONS This study offers evidence that advances in breast cancer screening and treatment have reduced breast cancer mortality for women across the age spectrum, and provides estimates of age-, year- and birth cohort-specific competing mortality rates for simulation models. Other-cause mortality estimates are important in these models because most women die from causes other than breast cancer.
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Affiliation(s)
- Ronald E Gangnon
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.,Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.,Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Oguzhan Alagoz
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.,Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI.,Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - John M Hampton
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.,Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
| | - Brian L Sprague
- Department of Surgery and University of Vermont Cancer Center, Burlington, VT, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.,Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
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Alagoz O, Berry DA, de Koning HJ, Feuer EJ, Lee SJ, Plevritis SK, Schechter CB, Stout NK, Trentham-Dietz A, Mandelblatt JS. Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models. Med Decis Making 2019; 38:3S-8S. [PMID: 29554472 DOI: 10.1177/0272989x17737507] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Donald A Berry
- Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Harry J de Koning
- Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eric J Feuer
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
| | - Sandra J Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sylvia K Plevritis
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
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Kwan ML, Miglioretti DL, Marlow EC, Aiello Bowles EJ, Weinmann S, Cheng SY, Deosaransingh KA, Chavan P, Moy LM, Bolch WE, Duncan JR, Greenlee RT, Kushi LH, Pole JD, Rahm AK, Stout NK, Smith-Bindman R. Trends in Medical Imaging During Pregnancy in the United States and Ontario, Canada, 1996 to 2016. JAMA Netw Open 2019; 2:e197249. [PMID: 31339541 PMCID: PMC6659354 DOI: 10.1001/jamanetworkopen.2019.7249] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
IMPORTANCE The use of medical imaging has sharply increased over the last 2 decades. Imaging rates during pregnancy have not been quantified in a large, multisite study setting. OBJECTIVE To evaluate patterns of medical imaging during pregnancy. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort study was performed at 6 US integrated health care systems and in Ontario, Canada. Participants included pregnant women who gave birth to a live neonate of at least 24 weeks' gestation between January 1, 1996, and December 31, 2016, and who were enrolled in the health care system for the entire pregnancy. EXPOSURES Computed tomography (CT), magnetic resonance imaging, conventional radiography, angiography and fluoroscopy, and nuclear medicine. MAIN OUTCOMES AND MEASURES Imaging rates per pregnancy stratified by country and year of child's birth. RESULTS A total of 3 497 603 pregnancies in 2 211 789 women were included. Overall, 26% of pregnancies were from US sites. Most (92%) were in women aged 20 to 39 years, and 85% resulted in full-term births. Computed tomography imaging rates in the United States increased from 2.0 examinations/1000 pregnancies in 1996 to 11.4/1000 pregnancies in 2007, remained stable through 2010, and decreased to 9.3/1000 pregnancies by 2016, for an overall increase of 3.7-fold. Computed tomography rates in Ontario, Canada, increased more gradually by 2.0-fold, from 2.0/1000 pregnancies in 1996 to 6.2/1000 pregnancies in 2016, which was 33% lower than in the United States. Overall, 5.3% of pregnant women in US sites and 3.6% in Ontario underwent imaging with ionizing radiation, and 0.8% of women at US sites and 0.4% in Ontario underwent CT. Magnetic resonance imaging rates increased steadily from 1.0/1000 pregnancies in 1996 to 11.9/1000 pregnancies in 2016 in the United States and from 0.5/1000 pregnancies in 1996 to 9.8/1000 pregnancies in 2016 in Ontario, surpassing CT rates in 2013 in the United States and in 2007 in Ontario. In the United States, radiography rates doubled from 34.5/1000 pregnancies in 1996 to 72.6/1000 pregnancies in 1999 and then decreased to 47.6/1000 pregnancies in 2016; rates in Ontario slowly increased from 36.2/1000 pregnancies in 1996 to 44.7/1000 pregnancies in 2016. Angiography and fluoroscopy and nuclear medicine use rates were low (5.2/1000 pregnancies), but in most years, higher in Ontario than the United States. Imaging rates were highest for women who were younger than 20 years or aged 40 years or older, gave birth preterm, or were black, Native American, or Hispanic (US data only). Considering advanced imaging only, chest imaging of pregnant women was more likely to use CT in the United States and nuclear medicine imaging in Ontario. CONCLUSIONS AND RELEVANCE The use of CT during pregnancy substantially increased in the United States and Ontario over the past 2 decades. Imaging rates during pregnancy should be monitored to avoid unnecessary exposure of women and fetuses to ionizing radiation.
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Affiliation(s)
- Marilyn L. Kwan
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California, Davis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Emily C. Marlow
- Department of Public Health Sciences, University of California, Davis
| | - E. J. Aiello Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon
- Center for Health Research, Kaiser Permanente Hawaii, Honolulu
| | | | | | - Prachi Chavan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Lisa M. Moy
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Wesley E. Bolch
- Department of Biomedical Engineering, University of Florida, Gainesville
| | - James R. Duncan
- Interventional Radiology Section, Washington University in St Louis, St Louis, Missouri
| | - Robert T. Greenlee
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin
| | - Lawrence H. Kushi
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Jason D. Pole
- ICES, Toronto, Ontario, Canada
- Pediatric Oncology Group of Ontario, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Alanna K. Rahm
- Center for Health Research, Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - R. Smith-Bindman
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco
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Yeh J, Lowry KP, Schechter CB, Diller L, Alagoz O, Armstrong GT, Hampton JM, Leisenring W, Liu Q, Mandelblatt JS, Miglioretti DL, Moskowitz CS, Oeffinger KC, Trentham-Dietz A, Stout NK. Clinical outcomes and cost-effectiveness of breast cancer screening for childhood cancer survivors treated with chest radiation: A comparative modeling study. J Clin Oncol 2019. [DOI: 10.1200/jco.2019.37.15_suppl.6525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
6525 Background: Survivors of childhood cancer previously treated with chest radiation face elevated breast cancer risk similar to BRCA1 carriers. Children’s Oncology Group (COG) guidelines recommend annual mammography with breast MRI, yet the benefits and costs of various screening strategies are uncertain. Methods: We used two breast cancer simulation models (Model 1 and 2) from the Cancer Intervention and Surveillance Modeling Network (CISNET) and data from the Childhood Cancer Survivor Study to reflect high breast cancer and competing mortality risks among survivors. We simulated 3 screening strategies: annual mammography with MRI starting at age 25 (COG25), annual MRI starting at 25 (MRI25), and biennial mammography starting at 50 (Mammo50). Performance of mammography+/-MRI was based on published studies in BRCA1/2 carriers who have similar cancer risk. Costs and quality of life weights were based on US averages and published studies. Results: Among a simulated cohort of 25-year-old survivors treated with chest radiation, the lifetime breast cancer mortality risk in the absence of screening was 10-11% across models. Compared to no screening, Mammo50, MRI25, and COG25 screening avert approximately 23-25%, 56-62% and 56-71% of deaths, respectively; averted deaths for COG25 compared to MRI25 were higher in Model 1 than Model 2 (9% vs. <1%). In Model 1, both MRI25 and COG25 were cost-effective; in Model 2, MRI25 was preferable (more effective, less costly than COG25). Conclusions: Compared to no screening, initiating annual screening at younger ages for at-risk survivors averts >50% of breast cancer deaths and is cost-effective. Additional data on test performance are needed to inform recommendations on screening modality. [Table: see text]
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Affiliation(s)
- Jennifer Yeh
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | | | - Clyde B. Schechter
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY
| | - Lisa Diller
- Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | | | | | | | | | - Qi Liu
- University of Alberta, Edmonton, AB, Canada
| | | | | | | | | | | | - Natasha K. Stout
- Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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Yeh J, Stout NK, Chaudhry A, Gooch M, McMahon P, Christensen KD, Diller L, Wu AC. Population-based cancer predisposition testing as a component of newborn screening: A cost-effectiveness analysis. J Clin Oncol 2019. [DOI: 10.1200/jco.2019.37.15_suppl.10021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
10021 Background: The role of population-based newborn genetic testing to identify infants at high risk of childhood-onset cancers has not been studied, despite the availability of cancer surveillance guidelines for early detection in high-risk infants and children. Methods: We developed the Precision Medicine Prevention and Treatment (PreEMPT) Model to estimate the value of targeted population-based newborn genomic sequencing (tNBS) for a select panel of genes associated with early onset pediatric malignancy. Cohorts of US newborns were simulated under tNBS screening vs. usual care, from birth to death. Six pediatric cancer predisposition syndromes were included in the model with mutations in RET, RB1, TP53, DICER1, SUFU or SMARCB1 assigned at birth, using mutation prevalence and disease risks drawn from the published literature, as well as SEER, ClinVar and gnoMAD databases. Newborns with mutations underwent cancer surveillance based on established guidelines for each gene-related pediatric malignancy. Survival benefit was modeled as a reduction in proportion of advanced disease, cancer deaths, and treatment-related late mortality risks. Costs were based on published literature and national databases. Results: In a typical US birth cohort of 4 million newborns, we estimated 1280 cancer cases in the malignancies associated with this gene panel would be detected before age 20 under usual care, resulting in 451 cancer deaths and 490 living with radiation exposure risks. tNBS would prevent 8 cancers (in RET mutation carriers), avert 34 deaths through surveillance, result in 3190 life-years (LY) gained and a 13% relative reduction in proportion of adult survivors at risk for radiation-associated late mortality. Given a sequencing cost of $30 (e.g., $5/gene), the incremental cost-effectiveness ratio (ICER) for tNBS was $230,500 per LY saved; if no additional cost was incurred beyond standard newborn screening, the ICER decreased to $101,100/LY. Conclusions: Population-based genetic testing of newborns can reduce mortality associated with pediatric cancers and could potentially be cost-effective as sequencing costs decline. Further work will include modeling a broader panel of predisposition genes.
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Affiliation(s)
- Jennifer Yeh
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Natasha K. Stout
- Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | | | | | | | | | - Lisa Diller
- Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Ann C. Wu
- Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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Wernli KJ, Callaway KA, Henderson LM, Kerlikowske K, Lee JM, Ross-Degnan D, Wallace JK, Wharam JF, Zhang F, Stout NK. Trends in Breast MRI Use Among Women with BRCA Mutations: A National Claims Analysis 2006–2016. Cancer Epidemiol Biomarkers Prev 2019. [DOI: 10.1158/1055-9965.epi-19-0079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Abstract
Women with BRCA mutations are recommended to receive breast MRI as an adjunct to annual mammography for breast cancer screening however adoption of these guidelines is unclear. We estimated breast MRI use from 2006–2016 among insured US women to understand use over time. Methods: Using medical care claims, we conducted a cohort study of breast MRI use in commercially-insured women aged 20–64 years continuously enrolled for ≥1 year in a large national insurer between 2006–2016. Women were identified as BCRA mutation carriers without a personal history of breast cancer using ICD9/10 diagnosis codes. We used CPT codes to identify breast MRIs and developed claims-based algorithms to categorized MRI indication as: screening, diagnostic or other. We calculated annual age-specific and age-adjusted rates of use overall and by indication among BCRA mutation carrier women. We used autoregressive time series models to estimate the yearly trend. Results: We identified 12,457 women with BRCA mutations during the study period. Breast MRI use overall among BRCA+ women aged 20–64 was 47/1000 women in 2006 and increased on average by 11 MRIs per year to a rate of 174/1000 in 2016 (P < 0.001). Across this time period, use for screening accounted for over 80% of breast MRIs and rates mirrored the overall trend with a 4.8-fold increase from 31/1000 in 2006 to 146/1000 women by 2016. Over the same time period, use of breast MRI for diagnostic workup or other indications remained stable. Use of screening breast MRI was highest among older women aged 50–64 compared with women <40 and 40–49 years (in 2016, 189, 95, and 177/1000, respectively) Discussion: Breast MRI screening increased dramatically over the past decade in women with BRCA mutations concordant with clinical guidelines. Additional research is needed to understand use of breast imaging relative to health outcomes for this high-risk population.
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Lansdorp-Vogelaar I, Jagsi R, Jayasekera J, Stout NK, Mitchell SA, Feuer EJ. Evidence-based sizing of non-inferiority trials using decision models. BMC Med Res Methodol 2019; 19:3. [PMID: 30612554 PMCID: PMC6322228 DOI: 10.1186/s12874-018-0643-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 12/13/2018] [Indexed: 12/26/2022] Open
Abstract
Background There are significant challenges to the successful conduct of non-inferiority trials because they require large numbers to demonstrate that an alternative intervention is “not too much worse” than the standard. In this paper, we present a novel strategy for designing non-inferiority trials using an approach for determining the appropriate non-inferiority margin (δ), which explicitly balances the benefits of interventions in the two arms of the study (e.g. lower recurrence rate or better survival) with the burden of interventions (e.g. toxicity, pain), and early and late-term morbidity. Methods We use a decision analytic approach to simulate a trial using a fixed value for the trial outcome of interest (e.g. cancer incidence or recurrence) under the standard intervention (pS) and systematically varying the incidence of the outcome in the alternative intervention (pA). The non-inferiority margin, pA – pS = δ, is reached when the lower event rate of the standard therapy counterbalances the higher event rate but improved morbidity burden of the alternative. We consider the appropriate non-inferiority margin as the tipping point at which the quality-adjusted life-years saved in the two arms are equal. Results Using the European Polyp Surveillance non-inferiority trial as an example, our decision analytic approach suggests an appropriate non-inferiority margin, defined here as the difference between the two study arms in the 10-year risk of being diagnosed with colorectal cancer, of 0.42% rather than the 0.50% used to design the trial. The size of the non-inferiority margin was smaller for higher assumed burden of colonoscopies. Conclusions The example demonstrates that applying our proposed method appears feasible in real-world settings and offers the benefits of more explicit and rigorous quantification of the various considerations relevant for determining a non-inferiority margin and associated trial sample size.
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Affiliation(s)
- Iris Lansdorp-Vogelaar
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | | | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Sandra A Mitchell
- Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
| | - Eric J Feuer
- Statistical Research and Applications Branch, Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, Room 4E534, Bethesda, MD, 20892-9765, USA.
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Caswell-Jin JL, Plevritis SK, Tian L, Cadham CJ, Xu C, Stout NK, Sledge GW, Mandelblatt JS, Kurian AW. Change in Survival in Metastatic Breast Cancer with Treatment Advances: Meta-Analysis and Systematic Review. JNCI Cancer Spectr 2018; 2:pky062. [PMID: 30627694 PMCID: PMC6305243 DOI: 10.1093/jncics/pky062] [Citation(s) in RCA: 165] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/22/2018] [Accepted: 10/04/2018] [Indexed: 12/17/2022] Open
Abstract
Background Metastatic breast cancer (MBC) treatment has changed substantially over time, but we do not know whether survival post-metastasis has improved at the population level. Methods We searched for studies of MBC patients that reported survival after metastasis in at least two time periods between 1970 and the present. We used meta-regression models to test for survival improvement over time in four disease groups: recurrent, recurrent estrogen (ER)-positive, recurrent ER-negative, and de novo stage IV. We performed sensitivity analyses based on bias in some studies that could lead earlier cohorts to include more aggressive cancers. Results There were 15 studies of recurrent MBC (N = 18 678 patients; 3073 ER-positive and 1239 ER-negative); meta-regression showed no survival improvement among patients recurring between 1980 and 1990, but median survival increased from 21 (95% confidence interval [CI] = 18 to 25) months to 38 (95% CI = 31 to 47) months from 1990 to 2010. For ER-positive MBC patients, median survival increased during 1990–2010 from 32 (95% CI = 23 to 43) to 57 (95% CI = 37 to 87) months, and for ER-negative MBC patients from 14 (95% CI = 11 to 19) to 33 (95% CI = 21 to 51) months. Among eight studies (N = 35 831) of de novo stage IV MBC, median survival increased during 1990–2010 from 20 (95% CI = 16 to 24) to 31 (95% CI = 24 to 39) months. Results did not change in sensitivity analyses. Conclusion By bridging studies over time, we demonstrated improvements in survival for recurrent and de novo stage IV MBC overall and across ER-defined subtypes since 1990. These results can inform patient-doctor discussions about MBC prognosis and therapy.
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Affiliation(s)
| | - Sylvia K Plevritis
- Department of Biomedical Data Science, Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Lu Tian
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA
| | - Christopher J Cadham
- Department of Oncology, Georgetown University Medical Center, Lombardi Comprehensive Cancer Center, Cancer Prevention and Control Program, Washington, DC
| | - Cong Xu
- Department of Biomedical Data Science, Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Natasha K Stout
- Department of Population Health, Harvard Pilgrim Health Care, Boston, MA
| | - George W Sledge
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Lombardi Comprehensive Cancer Center, Cancer Prevention and Control Program, Washington, DC
| | - Allison W Kurian
- Department of Medicine, Stanford University School of Medicine, Stanford, CA.,Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA
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Yaffe MJ, Mittmann N, Alagoz O, Trentham-Dietz A, Tosteson AN, Stout NK. The effect of mammography screening regimen on incidence-based breast cancer mortality. J Med Screen 2018; 25:197-204. [PMID: 30049249 DOI: 10.1177/0969141318780152] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVES Incidence-based mortality quantifies the distribution of cancer deaths and life-years lost, according to age at detection. We investigated the temporal distribution of the disease burden, and the effect of starting and stopping ages and interval between screening mammography examinations, on incidence-based mortality. METHODS Incidence-based mortality was estimated using an established breast cancer simulation model, adapted and validated to simulate breast cancer incidence, screening performance, and delivery of therapies in Canada. Ten strategies were examined, with varying starting age (40 or 50), stopping age (69 or 74), and interval (1, 2, 3 years), and "No Screening." Life-years lost were computed as the difference between model predicted time of breast cancer death and that estimated from life tables. RESULTS Without screening, 70% of the burden in terms of breast cancer deaths extends between ages 45 and 75. The mean of the distribution of ages of detection of breast cancers that will be fatal in an unscreened population is 61.8 years, while the mean age of detection weighted by the number of life-years lost is 55, a downward shift of 6.8 years. Similarly, the mean age of detection for the distribution of life-years gained through screening is lower than that for breast cancer deaths averted. CONCLUSION Incidence-based mortality predictions from modeling elucidate the age dependence of the breast cancer burden and can provide guidance for optimizing the timing of screening regimens to achieve maximal impact. Of the regimens studied, the greatest lifesaving effect was achieved with annual screening beginning at age 40.
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Affiliation(s)
- Martin J Yaffe
- 1 Physical Sciences Program, Sunnybrook Research Institute, Toronto, Canada.,2 Departments of Medical Biophysics and Medical Imaging, University of Toronto, Toronto, Canada.,3 Ontario Institute for Cancer Research, Toronto, Canada
| | - Nicole Mittmann
- 4 Health Outcomes and PharmacoEconomic (HOPE) Research Centre, Sunnybrook Research Institute, Toronto, Canada.,5 Applied Research in Cancer Control, Department of Pharmacology, University of Toronto, Toronto, Canada.,7 Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, USA
| | - Oguzhan Alagoz
- 7 Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, USA.,8 Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, USA
| | - Amy Trentham-Dietz
- 7 Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, USA
| | - Anna Na Tosteson
- 9 The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, USA
| | - Natasha K Stout
- 10 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA
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45
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Alagoz O, Ergun MA, Cevik M, Sprague BL, Fryback DG, Gangnon RE, Hampton JM, Stout NK, Trentham-Dietz A. The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update. Med Decis Making 2018; 38:99S-111S. [PMID: 29554470 PMCID: PMC5862066 DOI: 10.1177/0272989x17711927] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also referred to as Model W, is a discrete-event microsimulation model that uses a systems engineering approach to replicate breast cancer epidemiology in the US over time. This population-based model simulates the lifetimes of individual women through 4 main model components: breast cancer natural history, detection, treatment, and mortality. A key feature of the UWBCS is that, in addition to specifying a population distribution in tumor growth rates, the model allows for heterogeneity in tumor behavior, with some tumors having limited malignant potential (i.e., would never become fatal in a woman's lifetime if left untreated) and some tumors being very aggressive based on metastatic spread early in their onset. The model is calibrated to Surveillance, Epidemiology, and End Results (SEER) breast cancer incidence and mortality data from 1975 to 2010, and cross-validated against data from the Wisconsin cancer reporting system. The UWBCS model generates detailed outputs including underlying disease states and observed clinical outcomes by age and calendar year, as well as costs, resource usage, and quality of life associated with screening and treatment. The UWBCS has been recently updated to account for differences in breast cancer detection, treatment, and survival by molecular subtypes (defined by ER/HER2 status), to reflect the recent advances in screening and treatment, and to consider a range of breast cancer risk factors, including breast density, race, body-mass-index, and the use of postmenopausal hormone therapy. Therefore, the model can evaluate novel screening strategies, such as risk-based screening, and can assess breast cancer outcomes by breast cancer molecular subtype. In this article, we describe the most up-to-date version of the UWBCS.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI
| | - Mehmet Ali Ergun
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI
| | | | - Brian L Sprague
- Department of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington, VT
| | - Dennis G Fryback
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI
| | - Ronald E Gangnon
- Department of Population Health Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI
| | - John M Hampton
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
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46
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van den Broek JJ, van Ravesteyn NT, Mandelblatt JS, Huang H, Ergun MA, Burnside ES, Xu C, Li Y, Alagoz O, Lee SJ, Stout NK, Song J, Trentham-Dietz A, Plevritis SK, Moss SM, de Koning HJ. Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49. Med Decis Making 2018; 38:140S-150S. [PMID: 29554468 PMCID: PMC5862071 DOI: 10.1177/0272989x17718168] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results. METHODS Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial. RESULTS The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial. CONCLUSIONS The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.
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Affiliation(s)
| | | | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown-Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington DC, USA
| | - Hui Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
| | - Mehmet Ali Ergun
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Elizabeth S Burnside
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Cong Xu
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Yisheng Li
- Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Sandra J Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Juhee Song
- Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Amy Trentham-Dietz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Sylvia K Plevritis
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sue M Moss
- Department of cancer prevention, Wolfson Institute, Queen Mary University of London, London, UK
| | - Harry J de Koning
- Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
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47
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van den Broek JJ, van Ravesteyn NT, Mandelblatt JS, Cevik M, Schechter CB, Lee SJ, Huang H, Li Y, Munoz DF, Plevritis SK, de Koning HJ, Stout NK, van Ballegooijen M. Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology. Med Decis Making 2018; 38:112S-125S. [PMID: 29554471 PMCID: PMC5862068 DOI: 10.1177/0272989x17743244] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Collaborative modeling has been used to estimate the impact of potential cancer screening strategies worldwide. A necessary step in the interpretation of collaborative cancer screening model results is to understand how model structure and model assumptions influence cancer incidence and mortality predictions. In this study, we examined the relative contributions of the pre-clinical duration of breast cancer, the sensitivity of screening, and the improvement in prognosis associated with treatment of screen-detected cases to the breast cancer incidence and mortality predictions of 5 Cancer Intervention and Surveillance Modeling Network (CISNET) models. METHODS To tease out the impact of model structure and assumptions on model predictions, the Maximum Clinical Incidence Reduction (MCLIR) method compares changes in the number of breast cancers diagnosed due to clinical symptoms and cancer mortality between 4 simplified scenarios: 1) no-screening; 2) one-time perfect screening exam, which detects all existing cancers and perfect treatment (i.e., cure) of all screen-detected cancers; 3) one-time digital mammogram and perfect treatment of all screen-detected cancers; and 4) one-time digital mammogram and current guideline-concordant treatment of all screen-detected cancers. RESULTS The 5 models predicted a large range in maximum clinical incidence (19% to 71%) and in breast cancer mortality reduction (33% to 67%) from a one-time perfect screening test and perfect treatment. In this perfect scenario, the models with assumptions of tumor inception before it is first detectable by mammography predicted substantially higher incidence and mortality reductions than models with assumptions of tumor onset at the start of a cancer's screen-detectable phase. The range across models in breast cancer clinical incidence (11% to 24%) and mortality reduction (8% to 18%) from a one-time digital mammogram at age 62 y with observed sensitivity and current guideline-concordant treatment was considerably smaller than achievable under perfect conditions. CONCLUSIONS The timing of tumor inception and its effect on the length of the pre-clinical phase of breast cancer had a substantial impact on the grouping of models based on their predictions for clinical incidence and breast cancer mortality reduction. This key finding about the timing of tumor inception will be included in future CISNET breast analyses to enhance model transparency. The MCLIR approach should aid in the interpretation of variations in model results and could be adopted in other disease screening settings to enhance model transparency.
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Affiliation(s)
| | | | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown-Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington, DC, USA
| | - Mucahit Cevik
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, WI, USA
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Sandra J Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
| | - Hui Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
| | - Yisheng Li
- Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Diego F Munoz
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sylvia K Plevritis
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Harry J de Koning
- Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
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48
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Buist DSM, Abraham L, Lee CI, Lee JM, Lehman C, O'Meara ES, Stout NK, Henderson LM, Hill D, Wernli KJ, Haas JS, Tosteson ANA, Kerlikowske K, Onega T. Breast Biopsy Intensity and Findings Following Breast Cancer Screening in Women With and Without a Personal History of Breast Cancer. JAMA Intern Med 2018; 178:458-468. [PMID: 29435556 PMCID: PMC5876894 DOI: 10.1001/jamainternmed.2017.8549] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
IMPORTANCE There is little evidence on population-based harms and benefits of screening breast magnetic resonance imaging (MRI) in women with and without a personal history of breast cancer (PHBC). OBJECTIVE To evaluate biopsy rates and yield in the 90 days following screening (mammography vs magnetic resonance imaging with or without mammography) among women with and without a PHBC. DESIGN, SETTING, AND PARTICIPANTS Observational cohort study of 6 Breast Cancer Surveillance Consortium (BCSC) registries. Population-based sample of 812 164 women undergoing screening, 2003 through 2013. EXPOSURES A total of 2 048 994 digital mammography and/or breast MRI screening episodes (mammogram alone vs MRI with or without screening mammogram within 30 days). MAIN OUTCOMES AND MEASURES Biopsy intensity (surgical greater than core greater than fine-needle aspiration) and yield (invasive cancer greater than ductal carcinoma in situ greater than high-risk benign greater than benign) within 90 days of a screening episode. We computed age-adjusted rates of biopsy intensity (per 1000 screening episodes) and biopsy yield (per 1000 screening episodes with biopsies). Outcomes were stratified by PHBC and by BCSC 5-year breast cancer risk among women without PHBC. RESULTS We included 101 103 and 1 939 455 mammogram screening episodes in women with and without PHBC, respectively; MRI screening episodes included 3763 with PHBC and 4673 without PHBC. Age-adjusted core and surgical biopsy rates (per 1000 episodes) doubled (57.1; 95% CI, 50.3-65.1) following MRI compared with mammography (23.6; 95% CI, 22.4-24.8) in women with PHBC. Differences (per 1000 episodes) were even larger in women without PHBC: 84.7 (95% CI, 75.9-94.9) following MRI and 14.9 (95% CI, 14.7-15.0) following mammography episodes. Ductal carcinoma in situ and invasive biopsy yield (per 1000 episodes) was significantly higher following mammography compared with MRI episodes in women with PHBC (mammography, 404.6; 95% CI, 381.2-428.8; MRI, 267.6; 95% CI, 208.0-337.8) and nonsignificantly higher, but in the same direction, in women without PHBC (mammography, 279.3; 95% CI, 274.2-284.4; MRI, 214.6; 95% CI, 158.7-280.8). High-risk benign lesions were more commonly identified following MRI regardless of PHBC. Higher biopsy rates and lower cancer yield following MRI were not explained by increasing age or higher 5-year breast cancer risk. CONCLUSIONS AND RELEVANCE Women with and without PHBC who undergo screening MRI experience higher biopsy rates coupled with significantly lower cancer yield findings following biopsy compared with screening mammography alone. Further work is needed to identify women who will benefit from screening MRI to ensure an acceptable benefit-to-harm ratio.
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Affiliation(s)
- Diana S M Buist
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
| | - Janie M Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
| | | | - Ellen S O'Meara
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | | | - Deirdre Hill
- Department of Internal Medicine, University of New Mexico, Albuquerque
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Jennifer S Haas
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Anna N A Tosteson
- Dartmouth Institute for Health Policy and Clinical Practice, Department of Medicine, and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
| | - Tracy Onega
- Department of Biomedical Data Science, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
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49
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Christensen KD, Vassy JL, Phillips KA, Blout CL, Azzariti DR, Lu CY, Robinson JO, Lee K, Douglas MP, Yeh JM, Machini K, Stout NK, Rehm HL, McGuire AL, Green RC, Dukhovny D. Short-term costs of integrating whole-genome sequencing into primary care and cardiology settings: a pilot randomized trial. Genet Med 2018; 20:1544-1553. [PMID: 29565423 PMCID: PMC6151171 DOI: 10.1038/gim.2018.35] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 01/30/2018] [Indexed: 12/26/2022] Open
Abstract
Purpose Great uncertainty exists about the costs associated with whole genome sequencing (WGS). Methods One hundred cardiology patients with cardiomyopathy diagnoses, and 100 ostensibly healthy primary care patients were randomized to receive a family history report alone or with a WGS report. Cardiology patients also reviewed prior genetic test results. WGS costs were estimated by tracking resource use and staff time. Downstream costs were estimated by identifying services in administrative data, medical records, and patient surveys for 6 months. Results The incremental cost per patient of WGS testing was $5,098 in cardiology settings and $5,073 in primary care settings compared to family history alone. Mean six month downstream costs did not differ statistically between the control and WGS arms in either setting (cardiology: difference = −$1,560, 95%CI −$7,558 to $3,866, p=0.36; primary care: difference = $681, 95%CI −$884 to $2,171, p=0.70). Scenario analyses showed the cost reduction of omitting or limiting the types of secondary findings was less than $69 and $182 per patient in cardiology and primary care, respectively. Conclusion Short-term costs of WGS were driven by the costs of sequencing and interpretation rather than downstream healthcare. Disclosing additional types of secondary findings has a limited cost impact following disclosure.
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Affiliation(s)
- Kurt D Christensen
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA. .,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
| | - Jason L Vassy
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Section of General Internal Medicine, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Kathryn A Phillips
- Department of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, California, USA.,Philip R. Lee Institute for Health Policy and Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Carrie L Blout
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Danielle R Azzariti
- Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts, USA
| | - Christine Y Lu
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.,Department of Population Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Jill O Robinson
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA
| | - Kaitlyn Lee
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA
| | - Michael P Douglas
- Department of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, California, USA
| | - Jennifer M Yeh
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Division of General Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Kalotina Machini
- Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts, USA.,Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.,Department of Population Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Heidi L Rehm
- Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts, USA.,Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Amy L McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.,Partners HealthCare Personalized Medicine, Boston, Massachusetts, USA
| | - Dmitry Dukhovny
- Department of Pediatrics, Oregon Health & Science University, Portland, Oregon, USA
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50
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Hill DA, Haas JS, Wellman R, Hubbard RA, Lee CI, Alford-Teaster J, Wernli KJ, Henderson LM, Stout NK, Tosteson ANA, Kerlikowske K, Onega T. Utilization of breast cancer screening with magnetic resonance imaging in community practice. J Gen Intern Med 2018; 33:275-283. [PMID: 29214373 PMCID: PMC5834962 DOI: 10.1007/s11606-017-4224-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 09/29/2017] [Accepted: 10/31/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND Breast cancer screening with magnetic resonance imaging (MRI) may be a useful adjunct to screening mammography in high-risk women, but MRI uptake may be increasing rapidly among low- and average-risk women for whom benefits are unestablished. Comparatively little is known about use of screening MRI in community practice. OBJECTIVE To assess relative utilization of MRI among women who do and do not meet professional society guidelines for supplemental screening, and describe utilization according to breast cancer risk indications. DESIGN Prospective cohort study conducted between 2007 and 2014. PARTICIPANTS In five regional imaging registries participating in the Breast Cancer Surveillance Consortium (BCSC), 348,955 women received a screening mammogram, of whom 1499 underwent screening MRI. MAIN MEASURES Lifetime breast cancer risk (< 20% or ≥ 20%) estimated by family history of two or more first-degree relatives, and Gail model risk estimates. Breast Imaging Reporting and Data System breast density and benign breast diseases also were assessed. Relative risks (RR) for undergoing screening MRI were estimated using Poisson regression. KEY RESULTS Among women with < 20% lifetime risk, which does not meet professional guidelines for supplementary MRI screening, and no first-degree breast cancer family history, screening MRI utilization was elevated among those with extremely dense breasts [RR 2.2; 95% confidence interval (CI) 1.7-2.8] relative to those with scattered fibroglandular densities and among women with atypia (RR 7.4; 95% CI 3.9-14.3.) or lobular carcinoma in situ (RR 33.1; 95% CI 18.0-60.9) relative to women with non-proliferative disease. Approximately 82.9% (95% CI 80.8%-84.7%) of screening MRIs occurred among women who did not meet professional guidelines and 35.5% (95% CI 33.1-37.9%) among women considered at low-to-average breast cancer risk. CONCLUSION Utilization of screening MRI in community settings is not consistent with current professional guidelines and the goal of delivery of high-value care.
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Affiliation(s)
- Deirdre A Hill
- Department of Internal Medicine and Comprehensive Cancer Center, University of New Mexico School of Medicine, Albuquerque, NM, USA. .,Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, MSC 10-5550, 87131-0001, Albuquerque, NM, USA.
| | - Jennifer S Haas
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Rebecca A Hubbard
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.,Department of Health Services, University of Washington School of Public Health, Seattle, WA, USA
| | - Jennifer Alford-Teaster
- Departments of Biomedical Data Science and Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | | | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Anna N A Tosteson
- Department of Medicine, The Dartmouth Institute for Health Policy and Clinical Management and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology/Biostatistics, University of California, San Francisco, CA, USA
| | - Tracy Onega
- Departments of Biomedical Data Science and Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Department of Medicine, The Dartmouth Institute for Health Policy and Clinical Management and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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