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Parmigiani G. Benefits and Harms of Interception and Early Detection of Cancer. Hematol Oncol Clin North Am 2024; 38:731-741. [PMID: 38789374 DOI: 10.1016/j.hoc.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Strategies for early detection and interception of cancer are based on 2 synergistic elements: proactive search for asymptomatic cancer, precancer, or cancer predisposition and proactive disruption of cancer evolution. Benefits and harms of both these elements will vary widely depending on the screened populations, the types of cancers targeted, the detection modalities, and the health care delivery approaches following diagnosis. This article attempts to identify common elements that can inform the evaluation of alternative strategies across many of these scenarios.
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Affiliation(s)
- Giovanni Parmigiani
- Department of Data Science, Dana Farber Cancer Institute; Department of Biostatistics, Harvard T.H. Chan School of Public Health.
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2
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Hendrick RE, Monticciolo DL. USPSTF Recommendations and Overdiagnosis. JOURNAL OF BREAST IMAGING 2024:wbae028. [PMID: 38865364 DOI: 10.1093/jbi/wbae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Indexed: 06/14/2024]
Abstract
Overdiagnosis is the concept that some cancers detected at screening would never have become clinically apparent during a woman's lifetime in the absence of screening. This could occur if a woman dies of a cause other than breast cancer in the interval between mammographic detection and clinical detection (obligate overdiagnosis) or if a mammographically detected breast cancer fails to progress to clinical presentation. Overdiagnosis cannot be measured directly. Indirect methods of estimating overdiagnosis include use of data from randomized controlled trials (RCTs) designed to evaluate breast cancer mortality, population-based screening studies, or modeling. In each case, estimates of overdiagnosis must consider lead time, breast cancer incidence trends in the absence of screening, and accurate and predictable rates of tumor progression. Failure to do so has led to widely varying estimates of overdiagnosis. The U.S. Preventive Services Task Force (USPSTF) considers overdiagnosis a major harm of mammography screening. Their 2024 report estimated overdiagnosis using summary evaluations of 3 RCTs that did not provide screening to their control groups at the end of the screening period, along with Cancer Intervention and Surveillance Network modeling. However, there are major flaws in their evidence sources and modeling estimates, limiting the USPSTF assessment. The most plausible estimates remain those based on observational studies that suggest overdiagnosis in breast cancer screening is 10% or less and can be attributed primarily to obligate overdiagnosis and nonprogressive ductal carcinoma in situ.
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Affiliation(s)
- R Edward Hendrick
- Department of Radiology, University of Colorado Anschutz School of Medicine, Aurora, CO, USA
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Alagoz O, Zhang Y, Arroyo N, Fernandes-Taylor S, Yang DY, Krebsbach C, Venkatesh M, Hsiao V, Davies L, Francis DO. Modeling Thyroid Cancer Epidemiology in the United States Using Papillary Thyroid Carcinoma Microsimulation Model. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024; 27:367-375. [PMID: 38141816 PMCID: PMC10922958 DOI: 10.1016/j.jval.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 12/25/2023]
Abstract
OBJECTIVES Thyroid cancer incidence increased over 200% from 1992 to 2018, whereas mortality rates had not increased proportionately. The increased incidence has been attributed primarily to the detection of subclinical disease, raising important questions related to thyroid cancer control. We developed the Papillary Thyroid Carcinoma Microsimulation model (PATCAM) to answer them, including the impact of overdiagnosis on thyroid cancer incidence. METHODS PATCAM simulates individuals from age 15 until death in birth cohorts starting from 1975 using 4 inter-related components, including natural history, detection, post-diagnosis, and other-cause mortality. PATCAM was built using high-quality data and calibrated against observed age-, sex-, and stage-specific incidence in the United States as reported by the Surveillance, Epidemiology, and End Results database. PATCAM was validated against US thyroid cancer mortality and 3 active surveillance studies, including the largest and longest running thyroid cancer active surveillance cohort in the world (from Japan) and 2 from the United States. RESULTS PATCAM successfully replicated age- and stage-specific papillary thyroid cancers (PTC) incidence and mean tumor size at diagnosis and PTC mortality in the United States between 1975 and 2015. PATCAM accurately predicted the proportion of tumors that grew more than 3 mm and 5 mm in 5 years and 10 years, aligning with the 95% confidence intervals of the reported rates from active surveillance studies in most cases. CONCLUSIONS PATCAM successfully reproduced observed US thyroid cancer incidence and mortality over time and was externally validated. PATCAM can be used to identify factors that influence the detection of subclinical PTCs.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
| | - Yichi Zhang
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Natalia Arroyo
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Dou-Yan Yang
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Craig Krebsbach
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Manasa Venkatesh
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Vivian Hsiao
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Louise Davies
- Geisel School of Medicine at Dartmouth and The Dartmouth Institute for Health Policy & Clinical Practice, Hanover, NH, USA; Department of Veterans Affairs Medical Center, White River Junction, VT, USA
| | - David O Francis
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
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Kim DD, Wang L, Lauren BN, Liu J, Marklund M, Lee Y, Micha R, Mozaffarian D, Wong JB. Development and Validation of the US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) Model: Health Disparity and Economic Impact Model. Med Decis Making 2023; 43:930-948. [PMID: 37842820 PMCID: PMC10625721 DOI: 10.1177/0272989x231196916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 07/27/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Few simulation models have incorporated the interplay of diabetes, obesity, and cardiovascular disease (CVD); their upstream lifestyle and biological risk factors; and their downstream effects on health disparities and economic consequences. METHODS We developed and validated a US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) model that incorporates demographic, clinical, and lifestyle risk factors to jointly predict overall and racial-ethnic groups-specific obesity, diabetes, CVD, and cause-specific mortality for the US adult population aged 40 to 79 y at baseline. An individualized health care cost prediction model was further developed and integrated. This model incorporates nationally representative data on baseline demographics, lifestyle, health, and cause-specific mortality; dynamic changes in modifiable risk factors over time; and parameter uncertainty using probabilistic distributions. Validation analyses included assessment of 1) population-level risk calibration and 2) individual-level risk discrimination. To illustrate the application of the DOC-M model, we evaluated the long-term cost-effectiveness of a national produce prescription program. RESULTS Comparing the 15-y model-predicted population risk of primary outcomes among the 2001-2002 National Health and Nutrition Examination Survey (NHANES) cohort with the observed prevalence from age-matched cross-sectional 2003-2016 NHANES cohorts, calibration performance was strong based on observed-to-expected ratio and calibration plot analysis. In most cases, Brier scores fell below 0.0004, indicating a low overall prediction error. Using the Multi-Ethnic Study of Atherosclerosis cohorts, the c-statistics for assessing individual-level risk discrimination were 0.85 to 0.88 for diabetes, 0.93 to 0.95 for obesity, 0.74 to 0.76 for CVD history, and 0.78 to 0.81 for all-cause mortality, both overall and in three racial-ethnic groups. Open-source code for the model was posted at https://github.com/food-price/DOC-M-Model-Development-and-Validation. CONCLUSIONS The validated DOC-M model can be used to examine health, equity, and the economic impact of health policies and interventions on behavioral and clinical risk factors for obesity, diabetes, and CVD. HIGHLIGHTS We developed a novel microsimula'tion model for obesity, diabetes, and CVD, which intersect together and - critically for prevention and treatment interventions - share common lifestyle, biologic, and demographic risk factors.Validation analyses, including assessment of (1) population-level risk calibration and (2) individual-level risk discrimination, showed strong performance across the overall population and three major racial-ethnic groups for 6 outcomes (obesity, diabetes, CVD, and all-cause mortality, CVD- and DM-cause mortality)This paper provides a thorough explanation and documentation of the development and validation process of a novel microsimulation model, along with the open-source code (https://github.com/food-price/ DOCM_validation) for public use, to serve as a guide for future simulation model assessments, validation, and implementation.
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Affiliation(s)
- David D. Kim
- Section of Hospital Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Lu Wang
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Brianna N. Lauren
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Junxiu Liu
- Department of Population Health Science and Policy, the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matti Marklund
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Yujin Lee
- Department of Food and Nutrition, Myongji University, Yongin, South Korea
| | - Renata Micha
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - John B. Wong
- Division of Clinical Decision Making, Tufts Medical Center, Boston, MA, USA
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Hiatt RA, Worden L, Rehkopf D, Engmann N, Troester M, Witte JS, Balke K, Jackson C, Barlow J, Fenton SE, Gehlert S, Hammond RA, Kaplan G, Kornak J, Nishioka K, McKone T, Smith MT, Trasande L, Porco TC. A complex systems model of breast cancer etiology: The Paradigm II Model. PLoS One 2023; 18:e0282878. [PMID: 37205649 PMCID: PMC10198497 DOI: 10.1371/journal.pone.0282878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 02/24/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Complex systems models of breast cancer have previously focused on prediction of prognosis and clinical events for individual women. There is a need for understanding breast cancer at the population level for public health decision-making, for identifying gaps in epidemiologic knowledge and for the education of the public as to the complexity of this most common of cancers. METHODS AND FINDINGS We developed an agent-based model of breast cancer for the women of the state of California using data from the U.S. Census, the California Health Interview Survey, the California Cancer Registry, the National Health and Nutrition Examination Survey and the literature. The model was implemented in the Julia programming language and R computing environment. The Paradigm II model development followed a transdisciplinary process with expertise from multiple relevant disciplinary experts from genetics to epidemiology and sociology with the goal of exploring both upstream determinants at the population level and pathophysiologic etiologic factors at the biologic level. The resulting model reproduces in a reasonable manner the overall age-specific incidence curve for the years 2008-2012 and incidence and relative risks due to specific risk factors such as BRCA1, polygenic risk, alcohol consumption, hormone therapy, breastfeeding, oral contraceptive use and scenarios for environmental toxin exposures. CONCLUSIONS The Paradigm II model illustrates the role of multiple etiologic factors in breast cancer from domains of biology, behavior and the environment. The value of the model is in providing a virtual laboratory to evaluate a wide range of potential interventions into the social, environmental and behavioral determinants of breast cancer at the population level.
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Affiliation(s)
- Robert A. Hiatt
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
| | - Lee Worden
- Francis I. Proctor Foundation for Research in Ophthalmology, University of California San Francisco, San Francisco, California, United States of America
| | - David Rehkopf
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America
| | - Natalie Engmann
- Genentech, Inc. South San Francisco, San Francisco, California, United States of America
| | - Melissa Troester
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - John S. Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America
| | - Kaya Balke
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
| | - Christian Jackson
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America
| | - Janice Barlow
- Zero Breast Cancer (retired), San Rafael, California, United States of America
| | - Suzanne E. Fenton
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, North Carolina, United States of America
| | - Sarah Gehlert
- Suzanne Dworak-Peck School, University of Southern California, Los Angeles, United States of America
| | - Ross A. Hammond
- Brown School, Washington University, St Louis, Missouri, United States of America
| | - George Kaplan
- University of Michigan (retired), Ann Arbor, Michigan, United States of America
| | - John Kornak
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Krisida Nishioka
- School of Law, University of California, Berkeley, Berkeley, California, United States of America
| | - Thomas McKone
- School of Public Health, University of California, Berkeley, (Emeritus), Berkeley, California, United States of America
| | - Martyn T. Smith
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Leonardo Trasande
- Department of Pediatrics, NYU Grossman School of Medicine, New York City, New York, United States of America
| | - Travis C. Porco
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Francis I. Proctor Foundation for Research in Ophthalmology, University of California San Francisco, San Francisco, California, United States of America
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Strandberg R, Abrahamsson L, Isheden G, Humphreys K. Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study. Cancers (Basel) 2023; 15:cancers15030912. [PMID: 36765870 PMCID: PMC9913080 DOI: 10.3390/cancers15030912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/26/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.
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Affiliation(s)
- Rickard Strandberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
- Correspondence: (R.S.); (K.H.)
| | - Linda Abrahamsson
- Center for Primary Health Care Research, Lund University, 205 02 Malmö, Sweden
| | | | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
- Correspondence: (R.S.); (K.H.)
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Heggland T, Vatten LJ, Opdahl S, Weedon-Fekjær H. Interpreting Breast Cancer Mortality Trends Related to Introduction of Mammography Screening: A Simulation Study. MDM Policy Pract 2022; 7:23814683221131321. [PMID: 36225967 PMCID: PMC9549205 DOI: 10.1177/23814683221131321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 09/10/2022] [Indexed: 11/24/2022] Open
Abstract
UNLABELLED Background. Several studies have evaluated the effect of mammography screening on breast cancer mortality based on overall breast cancer mortality trends, with varied conclusions. The statistical power of such trend analyses is, however, not carefully studied. Methods. We estimated how the effect of screening on overall breast cancer mortality is likely to unfold. Because a screening effect is based on earlier treatment, screening can affect only new incident cases after screening introduction. To evaluate the likelihood of detecting screening effects on overall breast cancer mortality time trends, we calculated the statistical power of joinpoint regression analysis on breast cancer mortality trends around screening introduction using simulations. Results. We found that a very gradual increase in population-level screening effect is expected due to prescreening incident cases. Assuming 25% effectiveness of a biennial screening program in reducing breast cancer mortality among women 50 to 69 y of age, the expected reduction in overall breast cancer mortality was 3% after 2 y and reached a long-term effect of 18% after 20 y. In common settings, the statistical power to detect any screening effects using joinpoint regression analysis is very low (<50%), even in an artificial setting of constant risk of baseline breast cancer mortality over time. Conclusions. Population effects of screening on breast cancer mortality emerge very gradually and are expected to be considerably lower than the effects reported in trials excluding women diagnosed before screening. Studies of overall breast cancer mortality time trends have too low statistical power to reliably detect screening effects in most populations. Implications. Researchers and policy makers evaluating mammography screening should avoid using breast cancer mortality trend analysis that does not separate pre- and postscreening incident cases. HIGHLIGHTS Population-level mammography screening effects on breast cancer mortality emerge gradually following screening introduction, resulting in very low statistical power of trend analysis.Researchers and policy makers evaluating mammography screening should avoid relying on population-wide breast cancer mortality trends.Expected mammography screening effects at population level are lower than those from screening trials, as many cases of breast cancer fall outside the screening age range.
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Affiliation(s)
- Torunn Heggland
- Torunn Heggland, Oslo Centre for
Biostatistics and Epidemiology (OCBE), Research Support Services, Oslo
University Hospital, Postboks 4950 Nydalen, Oslo, 0424, Norway;
()
| | - Lars Johan Vatten
- Department of Public Health and Nursing,
Faculty of Medicine and Health Science, Norwegian University of Science and
Technology, Trondheim, Norway
| | - Signe Opdahl
- Department of Public Health and Nursing,
Faculty of Medicine and Health Science, Norwegian University of Science and
Technology, Trondheim, Norway
| | - Harald Weedon-Fekjær
- Oslo Centre for Biostatistics and Epidemiology,
Research Support Services, Oslo University Hospital, Oslo, Norway
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8
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Ergun MA, Hajjar A, Alagoz O, Rampurwala M. Optimal breast cancer risk reduction policies tailored to personal risk level. Health Care Manag Sci 2022; 25:363-388. [PMID: 35687269 PMCID: PMC10445480 DOI: 10.1007/s10729-022-09596-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/17/2022] [Indexed: 11/04/2022]
Abstract
Depending on personal and hereditary factors, each woman has a different risk of developing breast cancer, one of the leading causes of death for women. For women with a high-risk of breast cancer, their risk can be reduced by two main therapeutic approaches: 1) preventive treatments such as hormonal therapies (i.e., tamoxifen, raloxifene, exemestane); or 2) a risk reduction surgery (i.e., mastectomy). Existing national clinical guidelines either fail to incorporate or have limited use of the personal risk of developing breast cancer in their proposed risk reduction strategies. As a result, they do not provide enough resolution on the benefit-risk trade-off of an intervention policy as personal risk changes. In addressing this problem, we develop a discrete-time, finite-horizon Markov decision process (MDP) model with the objective of maximizing the patient's total expected quality-adjusted life years. We find several useful insights some of which contradict the existing national breast cancer risk reduction recommendations. For example, we find that mastectomy is the optimal choice for the border-line high-risk women who are between ages 22 and 38. Additionally, in contrast to the National Comprehensive Cancer Network recommendations, we find that exemestane is a plausible, in fact, the best, option for high-risk postmenopausal women.
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Affiliation(s)
- Mehmet A Ergun
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 3242 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, USA
- Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ali Hajjar
- Harvard Medical School, Boston, Massachusetts, Boston, USA
- Massachusetts General Hospital Institute for Technology Assessment, Boston, USA
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 3242 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, USA.
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9
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The Early Detection of Breast Cancer Using Liquid Biopsies: Model Estimates of the Benefits, Harms, and Costs. Cancers (Basel) 2022; 14:cancers14122951. [PMID: 35740615 PMCID: PMC9220983 DOI: 10.3390/cancers14122951] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/24/2022] [Accepted: 06/07/2022] [Indexed: 01/20/2023] Open
Abstract
Simple Summary Breast cancer screening is associated with benefits, such as mortality reduction and improved quality of life, and harms, such as false-positive results, overdiagnoses, and costs. Novel screen tests could be considered to reduce the harms and increase the benefits of screening. Liquid biopsies have been proposed as a novel method for the early detection of breast cancer. However, studies show that liquid biopsies based on cell-free DNA have a low sensitivity for early-stage breast cancer. Using the microsimulation model MISCAN-Fadia, we model the benefits, harms, and costs of the early detection of breast cancer using liquid biopsies for varying levels of liquid biopsy sensitivity and specificity. We found that liquid biopsies are unlikely to be an alternative to digital mammography, given the test performance based on a CCGA substudy. When liquid biopsies are unable to detect the precursor lesion of breast cancer—ductal carcinoma in situ (DCIS)—they need to be able to detect small, early-stage tumors, with high specificity, at low costs in order to be an alternative to digital mammography. We estimated a maximum liquid biopsy price of USD 187, which is substantially lower than currently listed prices. Abstract Breast cancer screening is associated with harms, such as false-positives and overdiagnoses, and, thus, novel screen tests can be considered. Liquid biopsies have been proposed as a novel method for the early detection of cancer, but low cell-free DNA tumor fraction might pose a problem for the use in population screening. Using breast cancer microsimulation model MISCAN-Fadia, we estimated the outcomes of using liquid biopsies in breast cancer screening in women aged 50 to 74 in the United States. For varying combinations of test sensitivity and specificity, we quantify the impact of the use of liquid biopsies on the harms and benefits of screening, and we estimate the maximum liquid biopsy price for cost-effective implementation in breast cancer screening at a cost-effectiveness threshold of USD 50,000. We investigate under what conditions liquid biopsies could be a suitable alternative to digital mammography and compare these conditions to a CCGA substudy. Outcomes were compared to digital mammography screening, and include mortality reduction, overdiagnoses, quality-adjusted life-years (QALYs), and the maximum price of a liquid biopsy for cost-effective implementation. When liquid biopsies are unable to detect DCIS, a large proportion of overdiagnosed cases is prevented but overall breast cancer mortality reduction and quality of life are lower, and costs are higher compared to digital mammography screening. Liquid biopsies prices should be restricted to USD 187 per liquid biopsy depending on test performance. Overall, liquid biopsies that are unable to detect ductal carcinoma in situ (DCIS) need to be able to detect small, early-stage tumors, with high specificity, at low costs in order to be an alternative to digital mammography. Liquid biopsies might be more suitable as an addition to digital mammography than as an alternative.
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10
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Mabry PL, Pronk NP, Amos CI, Witte JS, Wedlock PT, Bartsch SM, Lee BY. Cancer systems epidemiology: Overcoming misconceptions and integrating systems approaches into cancer research. PLoS Med 2022; 19:e1004027. [PMID: 35714096 PMCID: PMC9205504 DOI: 10.1371/journal.pmed.1004027] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Patricia Mabry and coauthors discuss application of systems approaches in cancer research.
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Affiliation(s)
- Patricia L. Mabry
- HealthPartners Institute, Bloomington, Minnesota, United States of America
| | - Nicolaas P. Pronk
- HealthPartners Institute, Bloomington, Minnesota, United States of America
- University of Minnesota, School of Public Health, Minneapolis, Minnesota, United States of America
| | - Christopher I. Amos
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America
- Baylor College of Medicine, Institute for Clinical and Translational Research, Houston, Texas, United States of America
| | - John S. Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, California, United States of America
| | - Patrick T. Wedlock
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Sarah M. Bartsch
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
| | - Bruce Y. Lee
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York, United States of America
- * E-mail:
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11
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Tunç S, Alagoz O, Burnside ES. A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis. PRODUCTION AND OPERATIONS MANAGEMENT 2022; 31:2361-2378. [PMID: 35915601 PMCID: PMC9313854 DOI: 10.1111/poms.13691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 01/19/2022] [Indexed: 06/15/2023]
Abstract
Overdiagnosis of breast cancer, defined as diagnosing a cancer that would otherwise not cause symptoms or death in a patient's lifetime, costs U.S. health care system over $1.2 billion annually. Overdiagnosis rates, estimated to be around 10%-40%, may be reduced if indolent breast findings can be identified and followed with noninvasive imaging rather than biopsy. However, there are no validated guidelines for radiologists to decide when to choose imaging options recognizing cancer grades and types. The aim of this study is to optimize breast cancer diagnostic decisions based on cancer types using a large-scale finite-horizon Markov decision process (MDP) model with 4.6 million states to help reduce overdiagnosis. We prove the optimality of a divide-and-search algorithm that relies on tight upper bounds on the optimal decision thresholds to find an exact optimal solution. We project the high-dimensional MDP onto two lower dimensional MDPs and obtain feasible upper bounds on the optimal decision thresholds. We use real data from two private mammography databases and demonstrate our model performance through a previously validated simulation model that has been used by the policy makers to set the national screening guidelines in the United States. We find that a decision-analytical framework optimizing diagnostic decisions while accounting for breast cancer types has a strong potential to improve the quality of life and alleviate the immense costs of overdiagnosis. Our model leads to a20 % reduction in overdiagnosis on the screening population, which translates into an annual savings of approximately $300 million for the U.S. health care system.
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Affiliation(s)
- Sait Tunç
- Grado Department of Industrial and Systems EngineeringVirginia TechBlacksburgVirginiaUSA
| | - Oguzhan Alagoz
- Department of Industrial and Systems EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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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] [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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Yong JHE, Nadeau C, Flanagan WM, Coldman AJ, Asakawa K, Garner R, Fitzgerald N, Yaffe MJ, Miller AB. The OncoSim-Breast Cancer Microsimulation Model. Curr Oncol 2022; 29:1619-1633. [PMID: 35323336 PMCID: PMC8947518 DOI: 10.3390/curroncol29030136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/22/2022] [Accepted: 02/28/2022] [Indexed: 01/02/2023] Open
Abstract
Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breast model simulates the onset, growth, and spread of invasive and ductal carcinoma in situ tumours. It combines Canadian cancer incidence, mortality, screening program, and cost data to project population-level outcomes. Users can change the model input to answer specific questions. Here, we compared its projections with observed data. First, we compared the model’s projected breast cancer trends with the observed data in the Canadian Cancer Registry and from Vital Statistics. Next, we replicated a screening trial to compare the model’s projections with the trial’s observed screening effects. Results: OncoSim-Breast’s projected incidence, mortality, and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and from Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. Conclusions: OncoSim-Breast’s ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer.
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Affiliation(s)
- Jean H. E. Yong
- Canadian Partnership Against Cancer, Toronto, ON M5H 1J8, Canada;
- Correspondence:
| | - Claude Nadeau
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | - William M. Flanagan
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | - Andrew J. Coldman
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada;
| | - Keiko Asakawa
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | - Rochelle Garner
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | | | | | - Anthony B. Miller
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada;
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14
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van Duuren LA, Ozik J, Spliet R, Collier NT, Lansdorp-Vogelaar I, Meester RGS. An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening. Front Physiol 2022; 12:718276. [PMID: 35153804 PMCID: PMC8826712 DOI: 10.3389/fphys.2021.718276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 12/21/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Fecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history. METHODS We present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments. RESULTS In Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations. DISCUSSION The method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.
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Affiliation(s)
- Luuk A. van Duuren
- Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jonathan Ozik
- Decision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, United States
| | - Remy Spliet
- Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Nicholson T. Collier
- Decision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, United States
| | | | - Reinier G. S. Meester
- Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
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Barajas R, Hair B, Lai G, Rotunno M, Shams-White MM, Gillanders EM, Mechanic LE. Facilitating cancer systems epidemiology research. PLoS One 2022; 16:e0255328. [PMID: 34972102 PMCID: PMC8719747 DOI: 10.1371/journal.pone.0255328] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Systems epidemiology offers a more comprehensive and holistic approach to studies of cancer in populations by considering high dimensionality measures from multiple domains, assessing the inter-relationships among risk factors, and considering changes over time. These approaches offer a framework to account for the complexity of cancer and contribute to a broader understanding of the disease. Therefore, NCI sponsored a workshop in February 2019 to facilitate discussion about the opportunities and challenges of the application of systems epidemiology approaches for cancer research. Eight key themes emerged from the discussion: transdisciplinary collaboration and a problem-based approach; methods and modeling considerations; interpretation, validation, and evaluation of models; data needs and opportunities; sharing of data and models; enhanced training practices; dissemination of systems models; and building a systems epidemiology community. This manuscript summarizes these themes, highlights opportunities for cancer systems epidemiology research, outlines ways to foster this research area, and introduces a collection of papers, "Cancer System Epidemiology Insights and Future Opportunities" that highlight findings based on systems epidemiology approaches.
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Affiliation(s)
- Rolando Barajas
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Brionna Hair
- DCCPS, NCI, NIH, Bethesda, Maryland, United States of America
| | - Gabriel Lai
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Melissa Rotunno
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Marissa M. Shams-White
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Elizabeth M. Gillanders
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Leah E. Mechanic
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
- * E-mail:
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16
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Chapman CH, Schechter CB, Cadham CJ, Trentham-Dietz A, Gangnon RE, Jagsi R, Mandelblatt JS. Identifying Equitable Screening Mammography Strategies for Black Women in the United States Using Simulation Modeling. Ann Intern Med 2021; 174:1637-1646. [PMID: 34662151 PMCID: PMC9997651 DOI: 10.7326/m20-6506] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Screening mammography guidelines do not explicitly consider racial differences in breast cancer epidemiology, treatment, and survival. OBJECTIVE To compare tradeoffs of screening strategies in Black women versus White women under current guidelines. DESIGN An established model from the Cancer Intervention and Surveillance Modeling Network simulated screening outcomes using race-specific inputs for subtype distribution; breast density; mammography performance; age-, stage-, and subtype-specific treatment effects; and non-breast cancer mortality. SETTING United States. PARTICIPANTS A 1980 U.S. birth cohort of Black and White women. INTERVENTION Screening strategies until age 74 years with varying initiation ages and intervals. MEASUREMENTS Outcomes included benefits (life-years gained [LYG], breast cancer deaths averted, and mortality reduction), harms (mammographies, false positives, and overdiagnoses), and benefit-harm ratios (tradeoffs) by race. Efficiency (benefits per unit resource), mortality disparity reduction, and equity in tradeoffs were evaluated. Equitable strategies for Black women were defined as those with tradeoffs closest to benchmark values for screening White women biennially from ages 50 to 74 years. RESULTS Biennial screening from ages 45 to 74 years was most efficient for Black women, whereas biennial screening from ages 40 to 74 years was most equitable. Initiating screening 10 years earlier in Black versus White women reduced Black-White mortality disparities by 57% with similar LYG per mammogram for both populations. Selection of the most equitable strategy was sensitive to assumptions about disparities in real-world treatment effectiveness: The less effective treatment was for Black women, the more intensively Black women could be screened before tradeoffs fell short of those experienced by White women. LIMITATION Single model. CONCLUSION Initiating biennial screening in Black women at age 40 years reduces breast cancer mortality disparities and yields benefit-harm ratios that are similar to tradeoffs of White women screened biennially from ages 50 to 74 years. PRIMARY FUNDING SOURCE National Cancer Institute at the National Institutes of Health.
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Affiliation(s)
- Christina Hunter Chapman
- Center for Clinical Management Research, VA Ann Arbor Healthcare System, and University of Michigan Medical School, Ann Arbor, Michigan (C.H.C.)
| | | | - Christopher J Cadham
- Georgetown University Medical Center and Georgetown Lombardi Comprehensive Cancer Center, Washington, DC (C.J.C., J.S.M.)
| | - Amy Trentham-Dietz
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin (A.T., R.E.G.)
| | - Ronald E Gangnon
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin (A.T., R.E.G.)
| | - Reshma Jagsi
- Center for Bioethics and Social Sciences in Medicine, University of Michigan Medical School, Ann Arbor, Michigan (R.J.)
| | - Jeanne S Mandelblatt
- Georgetown University Medical Center and Georgetown Lombardi Comprehensive Cancer Center, Washington, DC (C.J.C., J.S.M.)
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17
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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: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [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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18
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Trentham-Dietz A, Alagoz O, Chapman C, Huang X, Jayasekera J, van Ravesteyn NT, Lee SJ, Schechter CB, Yeh JM, Plevritis SK, Mandelblatt JS. Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts. PLoS Comput Biol 2021; 17:e1009020. [PMID: 34138842 PMCID: PMC8211268 DOI: 10.1371/journal.pcbi.1009020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts. Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.
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Affiliation(s)
- Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- * E-mail:
| | - Oguzhan Alagoz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Christina Chapman
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Xuelin Huang
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Jinani Jayasekera
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, United States of America
| | | | - Sandra J. Lee
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Clyde B. Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jennifer M. Yeh
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sylvia K. Plevritis
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, United States of America
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, United States of America
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19
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Phillips CJ, Schoen RE. Screening For Colorectal Cancer in the Age of Simulation Models: A Historical Lens. Gastroenterology 2020; 159:1201-1204. [PMID: 32682768 PMCID: PMC7365068 DOI: 10.1053/j.gastro.2020.07.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/30/2020] [Accepted: 07/02/2020] [Indexed: 12/28/2022]
Affiliation(s)
| | - Robert E Schoen
- Division of Gastroenterology, Hepatology and Nutrition, Departments of Medicine and Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
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20
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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] [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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How simulation modeling can support the public health response to the opioid crisis in North America: Setting priorities and assessing value. THE INTERNATIONAL JOURNAL OF DRUG POLICY 2020; 88:102726. [PMID: 32359858 DOI: 10.1016/j.drugpo.2020.102726] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 02/13/2020] [Accepted: 03/04/2020] [Indexed: 12/31/2022]
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Krahn M, Bryan S, Lee K, Neumann PJ. Embracing the science of value in health. CMAJ 2020; 191:E733-E736. [PMID: 31266787 DOI: 10.1503/cmaj.181606] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Murray Krahn
- Toronto Health Economics and Technology Assessment Collaborative (Krahn); Toronto General Hospital Research Institute (Krahn), Toronto, Toronto, Ont.; BC SUPPORT Unit (Bryan), BC Academic Health Science Network, Vancouver, BC; School of Population and Public Health (Bryan), University of British Columbia; Centre for Clinical Epidemiology & Evaluation (Bryan), Vancouver Coastal Health Research Institute, Vancouver, BC; Canadian Agency for Drugs and Technologies in Health (Lee); School of Epidemiology and Public Health (Lee), University of Ottawa, Ottawa, Ont.; Center for the Evaluation of Value and Risk in Health ( Neumann), Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Mass.
| | - Stirling Bryan
- Toronto Health Economics and Technology Assessment Collaborative (Krahn); Toronto General Hospital Research Institute (Krahn), Toronto, Toronto, Ont.; BC SUPPORT Unit (Bryan), BC Academic Health Science Network, Vancouver, BC; School of Population and Public Health (Bryan), University of British Columbia; Centre for Clinical Epidemiology & Evaluation (Bryan), Vancouver Coastal Health Research Institute, Vancouver, BC; Canadian Agency for Drugs and Technologies in Health (Lee); School of Epidemiology and Public Health (Lee), University of Ottawa, Ottawa, Ont.; Center for the Evaluation of Value and Risk in Health ( Neumann), Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Mass
| | - Karen Lee
- Toronto Health Economics and Technology Assessment Collaborative (Krahn); Toronto General Hospital Research Institute (Krahn), Toronto, Toronto, Ont.; BC SUPPORT Unit (Bryan), BC Academic Health Science Network, Vancouver, BC; School of Population and Public Health (Bryan), University of British Columbia; Centre for Clinical Epidemiology & Evaluation (Bryan), Vancouver Coastal Health Research Institute, Vancouver, BC; Canadian Agency for Drugs and Technologies in Health (Lee); School of Epidemiology and Public Health (Lee), University of Ottawa, Ottawa, Ont.; Center for the Evaluation of Value and Risk in Health ( Neumann), Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Mass
| | - Peter J Neumann
- Toronto Health Economics and Technology Assessment Collaborative (Krahn); Toronto General Hospital Research Institute (Krahn), Toronto, Toronto, Ont.; BC SUPPORT Unit (Bryan), BC Academic Health Science Network, Vancouver, BC; School of Population and Public Health (Bryan), University of British Columbia; Centre for Clinical Epidemiology & Evaluation (Bryan), Vancouver Coastal Health Research Institute, Vancouver, BC; Canadian Agency for Drugs and Technologies in Health (Lee); School of Epidemiology and Public Health (Lee), University of Ottawa, Ottawa, Ont.; Center for the Evaluation of Value and Risk in Health ( Neumann), Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Mass
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Benefits and Harms of Mammography Screening for Women With Down Syndrome: a Collaborative Modeling Study. J Gen Intern Med 2019; 34:2374-2381. [PMID: 31385214 PMCID: PMC6848489 DOI: 10.1007/s11606-019-05182-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 03/20/2019] [Accepted: 06/07/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Women with Down syndrome have a lower breast cancer risk and significantly lower life expectancies than women without Down syndrome. Therefore, it is not clear whether mammography screening strategies used for women without Down syndrome would benefit women with Down syndrome in the same way. OBJECTIVE To determine the benefits and harms of various mammography screening strategies for women with Down syndrome using collaborative simulation modeling. DESIGN Two established Cancer Intervention and Surveillance Modeling Network (CISNET) simulation models estimated the benefits and harms of various screening strategies for women with Down syndrome over a lifetime horizon. PARTICIPANTS We modeled a hypothetical cohort of US women with Down syndrome who were born in 1970. INTERVENTIONS Annual, biennial, triennial, and one-time digital mammography screenings during the ages 40-74. MAIN MEASURES The models estimated numbers of mammograms, false-positives, benign biopsies, breast cancer deaths prevented, and life-years gained per 1000 screened women when compared with no screening. KEY RESULTS In average-risk women 50-74, biennial screening incurred 122 mammograms, 10 false-positive mammograms, and 1.4 benign biopsies per one life-year gained compared with no screening. In women with Down syndrome, the same screening strategy incurred 2752 mammograms, 242 false-positive mammograms, and 34 benign biopsies per one life-year gained compared with no screening. The harm/benefit ratio varied for other screening strategies, and was most favorable for one-time screening at age 50, which incurred 1629 mammograms, 144 false-positive mammograms, and 20 benign biopsies per one life-year gained compared with no screening. CONCLUSIONS The harm/benefit ratios for various mammography screening strategies in women with Down syndrome are not as favorable as those for average-risk women. The benefit of screening mammography for women with Down syndrome is less pronounced due to lower breast cancer risk and shorter life expectancy.
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Munoz DF, Xu C, Plevritis SK. A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010. Med Decis Making 2019; 38:89S-98S. [PMID: 29554473 DOI: 10.1177/0272989x17737508] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
We present a Monte Carlo simulation model that reproduces US invasive breast cancer incidence and mortality trends from 1975 to 2010 as a function of screening and adjuvant treatment. This model was developed for multiple purposes, including to quantify the impact of screening and adjuvant therapy on past and current trends, predict future trends, and evaluate potential outcomes under hypothetical screening and treatment interventions. The model first generates the life histories of individual breast cancer patients by determining the patient's age, tumor size, estrogen receptor (ER) status, human epidermal growth factor 2 (HER2) status, SEER (Surveillance, Epidemiology, and End Results) historic stage, detection mode at time of detection, preclinical tumor course, and death age and cause of death (breast cancer v. other causes). The model incorporates common inputs used by the Cancer Intervention and Surveillance Modeling Network (CISNET), including the dissemination patterns for screening mammography, breast cancer survival in the absence of adjuvant therapy, dissemination and efficacy of treatment by ER and HER2 status, and death from causes other than breast cancer. In this article, predicted mortality outcomes are compared assuming proportional v. nonproportional hazards effects of treatment on breast cancer survival. We found that the proportional hazards treatment effects are sufficient for ER-negative disease. However, for ER-positive disease, the treatment effects appear to be higher during the early years following diagnosis and then diminish over time. Using nonproportional hazards effects for ER-positive cases, the predicted breast cancer mortality rates closely match the SEER mortality trends from 1975 to 2010, particularly after 1995. Our work indicates that population-level simulation modeling may have a broader role in assessing the time dependence of treatment effects.
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Affiliation(s)
- Diego F Munoz
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Cong Xu
- 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
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Gangnon RE, Stout NK, Alagoz O, Hampton JM, Sprague BL, Trentham-Dietz A. Contribution of Breast Cancer to Overall Mortality for US Women. Med Decis Making 2019; 38:24S-31S. [PMID: 29554467 DOI: 10.1177/0272989x17717981] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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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Calin A, Martin M, Lopez-Tarruella S. Simulation modeling approaches to answer clinically relevant questions in breast cancer low-risk populations. ANNALS OF TRANSLATIONAL MEDICINE 2019; 6:S80. [PMID: 30613655 DOI: 10.21037/atm.2018.10.68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Ana Calin
- Radiation Oncology Service, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Miguel Martin
- Medical Oncology Service, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Universidad Complutense, CiberOnc, GEICAM, Madrid, Spain
| | - Sara Lopez-Tarruella
- Medical Oncology Service, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Universidad Complutense, CiberOnc, GEICAM, Madrid, Spain
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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: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [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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Huang X, Li Y, Song J, Berry DA. A Bayesian Simulation Model for Breast Cancer Screening, Incidence, Treatment, and Mortality. Med Decis Making 2018; 38:78S-88S. [PMID: 28627297 PMCID: PMC5711634 DOI: 10.1177/0272989x17714473] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The important but complicated research questions regarding the optimization of mammography screening for the detection of breast cancer are unable to be answered through any single trial or a simple meta-analysis of related trials. The Cancer Intervention and Surveillance Network (CISNET) breast groups provide answers using complex statistical models to simulate population dynamics. Among them, the MD Anderson Cancer Center (Model M) takes a unique approach by not making any assumptions on the natural history of breast cancer, such as the distribution of the indolent time before detection, but simulating only the observable part of a woman's disease and life. METHODS The simulations start with 4 million women in the age distribution found in the year 1975, and follow them over several years. Input parameters are used to describe their breast cancer incidence rates, treatment efficacy, and survival. With these parameters, each woman's history of breast cancer diagnosis, treatment, and survival are generated and recorded each year. Research questions can then be answered by comparing the outcomes of interest, such as mortality rates, quality-adjusted life years, number of false positives, differences between hypothetical scenarios, such as different combinations of screening and treatment strategies. We use our model to estimate the relative contributions of screening and treatments on the mortality reduction in the United States, for both overall and different molecular (ER, HER2) subtypes of breast cancer. RESULTS We estimate and compare the benefits (life-years gained) and harm (false-positives, over-diagnoses) of mammography screening strategies with different frequencies (annual, biennial, triennial, mixed) and different starting (40 and 50 years) and end ages (70 and 80 years). CONCLUSIONS We will extend our model in future studies to account for local, regional, and distant disease recurrences.
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Affiliation(s)
- Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yisheng Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Donald A Berry
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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