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Original Research |

Personalizing Age of Cancer Screening Cessation Based on Comorbid Conditions: Model Estimates of Harms and BenefitsPersonalizing Age of Screening Cessation by Comorbid Conditions

Iris Lansdorp-Vogelaar, PhD; Roman Gulati, MS; Angela B. Mariotto, PhD; Clyde B. Schechter, MD, MA; Tiago M. de Carvalho, MSc; Amy B. Knudsen, PhD; Nicolien T. van Ravesteyn, PhD; Eveline A.M. Heijnsdijk, PhD; Chester Pabiniak, MSc; Marjolein van Ballegooijen, MD, PhD; Carolyn M. Rutter, PhD; Karen M. Kuntz, ScD; Eric J. Feuer, PhD; Ruth Etzioni, PhD; Harry J. de Koning, MD, PhD; Ann G. Zauber, PhD*; and Jeanne S. Mandelblatt, MD, MPH*
[+] Article and Author Information

* Drs. Zauber and Mandelblatt contributed equally to this work as senior authors.


From Erasmus University Medical Center, Rotterdam, the Netherlands; Fred Hutchinson Cancer Research Center and Group Health Research Institute, Seattle, Washington; National Cancer Institute, Bethesda, Maryland; Albert Einstein College of Medicine, Yeshiva University, Bronx, New York; Massachusetts General Hospital, Boston, Massachusetts; University of Minnesota, Minneapolis, Minnesota; Memorial Sloan Kettering Cancer Center, New York, New York; and Georgetown University, Washington, DC.

Disclaimer: The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health, the National Cancer Institute, or the Centers for Disease Control and Prevention.

Acknowledgment: The authors thank Drs. Hyunsoon Cho, Carrie Klabunde, and Robin Yabroff from the National Cancer Institute for their permission to use the comorbid condition–specific life tables.

Grant Support: By the National Cancer Institute at the National Institutes of Health and the Centers for Disease Control and Prevention (grants U01CA097426, U01CA115953, U01CA152959, U01CA157224, U01CA088283, and U01CA152958) and in part by the National Cancer Institute at the National Institutes of Health (grants KO5CA96940 to Dr. Mandelblatt and P01CA154292 to Drs. Mandelblatt, Schechter, van Ravesteyn, Heijnsdijk, and de Koning), the Dutch Cancer Society (grants 94-869, 98-1657, 2002-277, and 2006-3518 to Drs. Heijnsdijk and de Koning), and the Netherlands Organisation for Health Research and Development (grants 002822820, 22000106, and 50-50110-98-311 to Drs. Heijnsdijk and de Koning). Breast Cancer Surveillance Consortium data collection and sharing used in some variables for modeling breast cancer screening was supported by the National Cancer Institute at the National Institutes of Health (grants U01CA63740, U01CA86076, U01CA86082, U01CA63736, U01CA70013, U01CA69976, U01CA63731, U01CA70040, and HHSN261201100031C).

Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M13-2867.

Reproducible Research Statement:Study protocol and data set: Available from Dr. Lansdorp-Vogelaar (e-mail, i.vogelaar@erasmusmc.nl). Statistical code: Model profiles are available at http://cisnet.cancer.gov/profiles, and additional information is available from Dr. Lansdorp-Vogelaar (e-mail, i.vogelaar@erasmusmc.nl).

Requests for Single Reprints: Iris Lansdorp-Vogelaar, PhD, Department of Public Health, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands; e-mail, i.vogelaar@erasmusmc.nl.

Current Author Addresses: Drs. Lansdorp-Vogelaar, van Ravesteyn, Heijnsdijk, van Ballegooijen, and de Koning and Mr. de Carvalho: Department of Public Health, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands.

Mr. Gulati and Dr. Etzioni: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B230, Seattle, WA, 98109-1024.

Dr. Mariotto: Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892.

Dr. Schechter: Department of Family and Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461.

Dr. Knudsen: Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac Street, 10th Floor, Boston, MA 02114.

Mr. Pabiniak and Dr. Rutter: Group Health Research Institute, 1630 Minor Avenue, Seattle, WA 98101.

Dr. Kuntz: Division of Health Policy and Management, School of Public Health, University of Minnesota, 420 Delaware Street Southeast, Minneapolis, MN 55455.

Dr. Feuer: Statistical Methodology and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892.

Dr. Zauber: Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 East 63rd Street, Room 357, New York, NY 10065.

Dr. Mandelblatt: Georgetown Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Boulevard, Washington, DC 20007.

Author Contributions: Conception and design: I. Lansdorp-Vogelaar, C.B. Schechter, N.T. van Ravesteyn, M. van Ballegooijen, E.J. Feuer, A.G. Zauber, J.S. Mandelblatt.

Analysis and interpretation of the data: I. Lansdorp-Vogelaar, R. Gulati, A.B. Mariotto, C.B. Schechter, T.M. de Carvalho, A.B. Knudsen, N.T. van Ravesteyn, E.A.M. Heijnsdijk, C. Pabiniak, C.M. Rutter, K.M. Kuntz, H.J. de Koning, J.S. Mandelblatt.

Drafting of the article: I. Lansdorp-Vogelaar, C.B. Schechter, A.B. Knudsen, A.G. Zauber, J.S. Mandelblatt.

Critical revision of the article for important intellectual content: I. Lansdorp-Vogelaar, R. Gulati, A.B. Mariotto, C.B. Schechter, A.B. Knudsen, N.T. van Ravesteyn, E.A.M. Heijnsdijk, M. van Ballegooijen, K.M. Kuntz, H.J. de Koning, A.G. Zauber, J.S. Mandelblatt.

Final approval of the article: I. Lansdorp-Vogelaar, R. Gulati, A.B. Mariotto, C.B. Schechter, A.B. Knudsen, N.T. van Ravesteyn, E.A.M. Heijnsdijk, C.M. Rutter, M. van Ballegooijen, K.M. Kuntz, E.J. Feuer, R. Etzioni, H.J. de Koning, A.G. Zauber, J.S. Mandelblatt.

Statistical expertise: I. Lansdorp-Vogelaar, R. Gulati, C.B. Schechter, K.M. Kuntz, E.J. Feuer, R. Etzioni, A.G. Zauber.

Obtaining of funding: I. Lansdorp-Vogelaar, M. van Ballegooijen, A.G. Zauber, J.S. Mandelblatt.

Administrative, technical, or logistic support: E.J. Feuer, A.G. Zauber.

Collection and assembly of data: I. Lansdorp-Vogelaar, R. Gulati, A.B. Mariotto, A.B. Knudsen, C. Pabiniak, C.M. Rutter, J.S. Mandelblatt.


Ann Intern Med. 2014;161(2):104-112. doi:10.7326/M13-2867
Text Size: A A A

Background: Harms and benefits of cancer screening depend on age and comorbid conditions, but reliable estimates are lacking.

Objective: To estimate the harms and benefits of cancer screening by age and comorbid conditions to inform decisions about screening cessation.

Design: Collaborative modeling with 7 cancer simulation models and common data on average and comorbid condition level–specific life expectancy.

Setting: U.S. population.

Patients: U.S. cohorts aged 66 to 90 years in 2010 with average health or 1 of 4 comorbid condition levels: none, mild, moderate, or severe.

Intervention: Mammography, prostate-specific antigen testing, or fecal immunochemical testing.

Measurements: Lifetime cancer deaths prevented and life-years gained (benefits); false-positive test results and overdiagnosed cancer cases (harms). For each comorbid condition level, the age at which harms and benefits of screening were similar to that for persons with average health having screening at age 74 years.

Results: Screening 1000 women with average life expectancy at age 74 years for breast cancer resulted in 79 to 96 (range across models) false-positive results, 0.5 to 0.8 overdiagnosed cancer cases, and 0.7 to 0.9 prevented cancer deaths. Although absolute numbers of harms and benefits differed across cancer sites, the ages at which to cease screening were consistent across models and cancer sites. For persons with no, mild, moderate, and severe comorbid conditions, screening until ages 76, 74, 72, and 66 years, respectively, resulted in harms and benefits similar to average-health persons.

Limitation: Comorbid conditions influenced only life expectancy.

Conclusion: Comorbid conditions are an important determinant of harms and benefits of screening. Estimates of screening benefits and harms by comorbid condition can inform discussions between providers and patients about personalizing screening cessation decisions.

Primary Funding Source: National Cancer Institute and Centers for Disease Control and Prevention.

Figures

Grahic Jump Location
Appendix Figure.

Observed and simulated cancer incidence rates, by calendar year of diagnosis (breast and prostate cancer) or age at diagnosis (colorectal cancer).

Panels show age-standardized breast cancer incidence rates per 100 000 women aged 30 to 79 y for MISCAN-Fadia and G-E models, age-standardized prostate cancer incidence rates per 100 000 men aged 50 to 84 y for MISCAN-Prostate and FHCRC models, and colorectal incidence rates per 100 000 persons for years 1975 to 1979 for MISCAN-Colon, CRC-SPIN, and SimCRC models. CRC-SPIN = Colorectal Cancer Simulated Population Model for Incidence and Natural History; Fadia = fatal diameter; FHCRC = Fred Hutchinson Cancer Research Center; G-E = Georgetown–Einstein; MISCAN = Microsimulation Screening Analysis; SEER = Surveillance, Epidemiology, and End Results; SimCRC = Simulating Colorectal Cancer.

Grahic Jump Location
Grahic Jump Location
Figure 1.

NNS/LYG for screening at the plotted age, by comorbid condition level.

The horizontal dashed line represents the NNS/LYG for screening the average-health population until age 76 y (75 y for colorectal cancer). The vertical dashed lines indicate the age for each comorbid condition group at which screening provided harms and benefits similar to screening at age 74 y in the average-health population (oldest age for which the NNS/LYG falls under the vertical dotted line). Panels show NNS/LYG projected by different models. CRC-SPIN = Colorectal Cancer Simulated Population Model for Incidence and Natural History; Fadia = fatal diameter; FHCRC = Fred Hutchinson Cancer Research Center; G-E = Georgetown–Einstein; MISCAN = Microsimulation Screening Analysis; NNS/LYG = number needed to screen per life-year gained; SimCRC = Simulating Colorectal Cancer.

Grahic Jump Location
Grahic Jump Location
Figure 2.

Age at which harms and benefits of screening were similar to those for persons with average health having screening at age 74 y.

The green bars represent the median age, and the uncertainty bars represent the range across all models and cancer sites. For no comorbid conditions, the lowest cessation age across models and cancer sites coincides with the median age (see Tables 2 to 8 of the Supplement and Figure 1).

Grahic Jump Location

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Summary for Patients

Personalizing Age of Cancer Screening Cessation

The full report is titled “Personalizing Age of Cancer Screening Cessation Based on Comorbid Conditions: Model Estimates of Harms and Benefits.” It is in the 15 July 2014 issue of Annals of Internal Medicine (volume 161, pages 104-112). The authors are I. Lansdorp-Vogelaar, R. Gulati, A.B. Mariotto, C.B. Schechter, T.M. de Carvalho, A.B. Knudsen, N.T. van Ravesteyn, E.A.M. Heijnsdijk, C. Pabiniak, M. van Ballegooijen, C.M. Rutter, K.M. Kuntz, E.J. Feuer, R. Etzioni, H.J. de Koning, A.G. Zauber, and J.S. Mandelblatt.

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