William E Barlow1, Elisabeth F Beaber2, Berta M Geller3, Aruna Kamineni4, Yingye Zheng2, Jennifer S Haas5, Chun R Chao6, Carolyn M Rutter7, Ann G Zauber8, Brian L Sprague9, Ethan A Halm10,11, Donald L Weaver12, Jessica Chubak4, V Paul Doria-Rose6,13, Sarah Kobrin13, Tracy Onega14, Virginia P Quinn, Marilyn M Schapira15, Anna N A Tosteson16, Douglas A Corley17, Celette Sugg Skinner11,18, Mitchell D Schnall19, Katrina Armstrong20, Cosette M Wheeler21,22, Michael J Silverberg17, Bijal A Balasubramanian11,23, Chyke A Doubeni24, Dale McLerran2, Jasmin A Tiro11,18. 1. Cancer Research and Biostatistics, Seattle, WA. 2. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA. 3. Departments of Family Medicine, and the University of Vermont Cancer Center, University of Vermont, Burlington, VT. 4. Kaiser Permanente Washington Health Research Institute, Seattle, WA. 5. Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, Dana Farber, Harvard Cancer Institute, Harvard School of Public Health, Boston, MA. 6. Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA. 7. RAND Corporation, Santa Monica, CA. 8. Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY. 9. Departments of Surgery and Radiology, University of Vermont, Burlington, VT. 10. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX. 11. Simmons Comprehensive Cancer Center, Dallas, TX. 12. Department of Pathology and the UVM Cancer Center, University of Vermont, Burlington, VT. 13. Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD. 14. Departments of Biomedical Data Science, Epidemiology, and the Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH. 15. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, and CMC VA Medical Center, Philadelphia, PA. 16. The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH. 17. Division of Research, Kaiser Permanente Northern California, Oakland, CA. 18. Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX. 19. Department of Radiology, University of Pennsylvania, Perelman School of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA. 20. General Medicine Division, MA General Hospital, Harvard Medical School, Boston, MA. 21. Departments of Pathology and Obstetrics and Gynecology, University of New Mexico Health Science Center, Albuquerque, NM. 22. University of New Mexico Comprehensive Cancer Center, Albuquerque, NM. 23. UTHealth School of Public Health, Dallas, TX. 24. Department of Family Medicine and Community Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Abstract
BACKGROUND: Cancer screening is a complex process encompassing risk assessment, the initial screening examination, diagnostic evaluation, and treatment of cancer precursors or early cancers. Metrics that enable comparisons across different screening targets are needed. We present population-based screening metrics for breast, cervical, and colorectal cancers for nine sites participating in the Population-based Research Optimizing Screening through Personalized Regimens consortium. METHODS: We describe how selected metrics map to a trans-organ conceptual model of the screening process. For each cancer type, we calculated calendar year 2013 metrics for the screen-eligible target population (breast: ages 40-74 years; cervical: ages 21-64 years; colorectal: ages 50-75 years). Metrics for screening participation, timely diagnostic evaluation, and diagnosed cancers in the screened and total populations are presented for the total eligible population and stratified by age group and cancer type. RESULTS: The overall screening-eligible populations in 2013 were 305 568 participants for breast, 3 160 128 for cervical, and 2 363 922 for colorectal cancer screening. Being up-to-date for testing was common for all three cancer types: breast (63.5%), cervical (84.6%), and colorectal (77.5%). The percentage of abnormal screens ranged from 10.7% for breast, 4.4% for cervical, and 4.5% for colorectal cancer screening. Abnormal breast screens were followed up diagnostically in almost all (96.8%) cases, and cervical and colorectal were similar (76.2% and 76.3%, respectively). Cancer rates per 1000 screens were 5.66, 0.17, and 1.46 for breast, cervical, and colorectal cancer, respectively. CONCLUSIONS: Comprehensive assessment of metrics by the Population-based Research Optimizing Screening through Personalized Regimens consortium enabled systematic identification of screening process steps in need of improvement. We encourage widespread use of common metrics to allow interventions to be tested across cancer types and health-care settings.
BACKGROUND:Cancer screening is a complex process encompassing risk assessment, the initial screening examination, diagnostic evaluation, and treatment of cancer precursors or early cancers. Metrics that enable comparisons across different screening targets are needed. We present population-based screening metrics for breast, cervical, and colorectal cancers for nine sites participating in the Population-based Research Optimizing Screening through Personalized Regimens consortium. METHODS: We describe how selected metrics map to a trans-organ conceptual model of the screening process. For each cancer type, we calculated calendar year 2013 metrics for the screen-eligible target population (breast: ages 40-74 years; cervical: ages 21-64 years; colorectal: ages 50-75 years). Metrics for screening participation, timely diagnostic evaluation, and diagnosed cancers in the screened and total populations are presented for the total eligible population and stratified by age group and cancer type. RESULTS: The overall screening-eligible populations in 2013 were 305 568 participants for breast, 3 160 128 for cervical, and 2 363 922 for colorectal cancer screening. Being up-to-date for testing was common for all three cancer types: breast (63.5%), cervical (84.6%), and colorectal (77.5%). The percentage of abnormal screens ranged from 10.7% for breast, 4.4% for cervical, and 4.5% for colorectal cancer screening. Abnormal breast screens were followed up diagnostically in almost all (96.8%) cases, and cervical and colorectal were similar (76.2% and 76.3%, respectively). Cancer rates per 1000 screens were 5.66, 0.17, and 1.46 for breast, cervical, and colorectal cancer, respectively. CONCLUSIONS: Comprehensive assessment of metrics by the Population-based Research Optimizing Screening through Personalized Regimens consortium enabled systematic identification of screening process steps in need of improvement. We encourage widespread use of common metrics to allow interventions to be tested across cancer types and health-care settings.
Authors: Andrea N Burnett-Hartman; Shivan J Mehta; Yingye Zheng; Nirupa R Ghai; Dale F McLerran; Jessica Chubak; Virginia P Quinn; Celette Sugg Skinner; Douglas A Corley; John M Inadomi; Chyke A Doubeni Journal: Am J Prev Med Date: 2016-04-01 Impact factor: 5.043
Authors: Marilyn M Schapira; William E Barlow; Emily F Conant; Brian L Sprague; Anna N A Tosteson; Jennifer S Haas; Tracy Onega; Elisabeth F Beaber; Martha Goodrich; Anne Marie McCarthy; Sally D Herschorn; Celette Sugg Skinner; Tory O Harrington; Berta Geller Journal: Acad Radiol Date: 2018-02-09 Impact factor: 3.173
Authors: Anne Marie McCarthy; William E Barlow; Emily F Conant; Jennifer S Haas; Christopher I Li; Brian L Sprague; Katrina Armstrong Journal: JAMA Oncol Date: 2018-07-01 Impact factor: 31.777
Authors: Jennifer S Haas; Brian L Sprague; Carrie N Klabunde; Anna N A Tosteson; Jane S Chen; Asaf Bitton; Elisabeth F Beaber; Tracy Onega; Jane J Kim; Charles D MacLean; Kimberly Harris; Phillip Yamartino; Kathleen Howe; Loretta Pearson; Sarah Feldman; Phyllis Brawarsky; Marilyn M Schapira Journal: J Gen Intern Med Date: 2016-01 Impact factor: 5.128
Authors: Michael K Gould; Lori C Sakoda; Debra P Ritzwoller; Michael J Simoff; Christine M Neslund-Dudas; Lawrence H Kushi; Lisa Carter-Harris; Heather Spencer Feigelson; George Minowada; V Paul Doria-Rose Journal: Ann Am Thorac Soc Date: 2017-12
Authors: Elisabeth F Beaber; Jane J Kim; Marilyn M Schapira; Anna N A Tosteson; Ann G Zauber; Ann M Geiger; Aruna Kamineni; Donald L Weaver; Jasmin A Tiro Journal: J Natl Cancer Inst Date: 2015-05-07 Impact factor: 13.506
Authors: Elisabeth F Beaber; Anna N A Tosteson; Jennifer S Haas; Tracy Onega; Brian L Sprague; Donald L Weaver; Anne Marie McCarthy; Chyke A Doubeni; Virginia P Quinn; Celette Sugg Skinner; Ann G Zauber; William E Barlow Journal: Breast Cancer Res Treat Date: 2016-09-24 Impact factor: 4.872
Authors: Carrie N Klabunde; Yingye Zheng; Virginia P Quinn; Elisabeth F Beaber; Carolyn M Rutter; Ethan A Halm; Jessica Chubak; Chyke A Doubeni; Jennifer S Haas; Aruna Kamineni; Marilyn M Schapira; Pamela M Vacek; Michael P Garcia; Douglas A Corley Journal: Am J Prev Med Date: 2016-06-22 Impact factor: 5.043
Authors: Pranesh P Chowdhury; Tebitha Mawokomatanda; Fang Xu; Sonya Gamble; David Flegel; Carol Pierannunzi; William Garvin; Machell Town Journal: MMWR Surveill Summ Date: 2016-04-29
Authors: Nirupa R Ghai; Christopher D Jensen; Sophie A Merchant; Joanne E Schottinger; Jeffrey K Lee; Jessica Chubak; Aruna Kamineni; Ethan A Halm; Celette Sugg Skinner; Jennifer S Haas; Beverly B Green; Nancy T Cannizzaro; Jennifer L Schneider; Douglas A Corley Journal: Cancer Prev Res (Phila) Date: 2020-07-15
Authors: Caitlin C Murphy; Ethan A Halm; Celette Sugg Skinner; Bijal A Balasubramanian; Amit G Singal Journal: Cancer Epidemiol Biomarkers Prev Date: 2020-05-26 Impact factor: 4.254
Authors: A Shaukat; T L Marsh; S D Crockett; S Syngal; R S Bresalier; D E Brenner Journal: Clin Gastroenterol Hepatol Date: 2021-09-20 Impact factor: 11.382
Authors: Elisabeth F Beaber; Aruna Kamineni; Andrea N Burnett-Hartman; Brian Hixon; Sarah C Kobrin; Christopher I Li; Malia Oliver; Katharine A Rendle; Celette Sugg Skinner; Kaitlin Todd; Yingye Zheng; Rebecca A Ziebell; Erica S Breslau; Jessica Chubak; Douglas A Corley; Robert T Greenlee; Jennifer S Haas; Ethan A Halm; Stacey Honda; Christine Neslund-Dudas; Debra P Ritzwoller; Joanne E Schottinger; Jasmin A Tiro; Anil Vachani; V Paul Doria-Rose Journal: Cancer Epidemiol Biomarkers Prev Date: 2022-08-02 Impact factor: 4.090
Authors: Claudia Santucci; Heidy N Medina; Greta Carioli; Eva Negri; Carlo La Vecchia; Paulo S Pinheiro Journal: Eur J Cancer Prev Date: 2021-08-26 Impact factor: 2.164
Authors: Glen B Taksler; Elisabeth F P Peterse; Isarah Willems; Kevin Ten Haaf; Erik E L Jansen; Inge M C M de Kok; Nicolien T van Ravesteyn; Harry J de Koning; Iris Lansdorp-Vogelaar Journal: JAMA Oncol Date: 2021-06-01 Impact factor: 31.777
Authors: Amit G Singal; Anna S Lok; Ziding Feng; Fasiha Kanwal; Neehar D Parikh Journal: Clin Gastroenterol Hepatol Date: 2020-09-19 Impact factor: 11.382
Authors: Vui Heng Chong; Lydiana Kadir; Zakaria Kamis; Norhayati Kassim; Muhammad Abdul Mabood Khalil; Jackson Tan; Elvynna Leong; Sok King Ong; Chee Fui Chong Journal: Asian Pac J Cancer Prev Date: 2020-08-01