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Research and Reporting Methods |

Influence of Study Features and Methods on Overdiagnosis Estimates in Breast and Prostate Cancer Screening

Ruth Etzioni, PhD; Roman Gulati, MS; Leslie Mallinger, MPH; and Jeanne Mandelblatt, MD, MPH
[+] Article, Author, and Disclosure Information

From Fred Hutchinson Cancer Research Center, Seattle, Washington, and Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington, DC.

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

Financial Support: By award numbers U01CA157224 and U01CA088283 and U01CA152958 from the National Cancer Institute and the Centers for Disease Control and Prevention. Additional funding was provided by award numbers KO5CA96940 and P01CA154292.

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M12-3000.

Requests for Single Reprints: Ruth Etzioni, PhD, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024; e-mail, retzioni@fhcrc.org.

Current Author Addresses: Dr. Etzioni, Mr. Gulati, and Ms. Mallinger: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024.

Dr. Mandelblatt: Lombardi Comprehensive Cancer Center, 3300 Whitehaven Street Northwest, Suite 4100, Washington, DC 20007.

Author Contributions: Conception and design: R. Etzioni, R. Gulati, J. Mandelblatt.

Analysis and interpretation of the data: R. Etzioni, R. Gulati, L. Mallinger.

Drafting of the article: R. Etzioni, R. Gulati, J. Mandelblatt.

Critical revision of the article for important intellectual content: R. Etzioni, R. Gulati, L. Mallinger, J. Mandelblatt.

Final approval of the article: R. Etzioni, R. Gulati, L. Mallinger, J. Mandelblatt.

Statistical expertise: R. Etzioni, R. Gulati.

Obtaining of funding: R. Etzioni, R. Gulati.

Administrative, technical, or logistic support: R. Gulati.

Collection and assembly of data: R. Gulati, L. Mallinger.

Ann Intern Med. 2013;158(11):831-838. doi:10.7326/0003-4819-158-11-201306040-00008
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Knowledge of the likelihood that a screening-detected case of cancer has been overdiagnosed is vitally important to make treatment decisions and develop screening policy. An overdiagnosed case is an excess case detected by screening. Estimates of the frequency of overdiagnosis in breast and prostate cancer screening vary greatly across studies. This article identifies features of overdiagnosis studies that influence results and shows their effect by using published research. First, different ways to define and measure overdiagnosis are considered. Second, contextual features and how they affect overdiagnosis estimates are examined. Third, the effect of estimation approach is discussed. Many studies use excess incidence under screening as a proxy for overdiagnosis. Others use statistical models to make inferences about lead time or natural history and then derive the corresponding fraction of cases that are overdiagnosed. This article concludes with questions that readers of overdiagnosis studies can use to evaluate the validity and relevance of published estimates and recommends that authors of studies quantifying overdiagnosis provide information about these features.


Grahic Jump Location
Figure 1.

Link between overdiagnosis and lead time.

A nonoverdiagnosed case (top) is a screening-detected case that would have presented clinically before dying of causes not related to cancer. An overdiagnosed case (bottom) is a screening-detected case that would have died of causes not related to cancer before presenting clinically (i.e., a screening-detected case that would not have been diagnosed without screening). The lead time is the time from screening detection to clinical diagnosis. The longer the lead time, the greater the chance of overdiagnosis. Similarly, the higher the risk for death from another cause, the greater the chance of overdiagnosis.

Grahic Jump Location
Grahic Jump Location
Figure 2.

Incidence trends after the introduction of screening in 3 scenarios with the same baseline incidence (constant, 100 cases per 100 000 persons per year), disease prevalence, test sensitivity, and screening test dissemination.

For each scenario, the number of cases per 100 000 screening-detected cases each year is preceded by a plus sign. The number of deficits in incidence each year due to earlier detection by screening is preceded by a minus sign. Scenario 1. Lead time equals 1 year, no overdiagnosis. Scenario 2. Lead time equals 2 years, no overdiagnosis. Scenario 3. Lead time (among nonoverdiagnosed cases) equals 2 years, with 3 screening-detected cases overdiagnosed per 13 screening-detected cases (23%). Adapted from reference 29.

Grahic Jump Location




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Submit a Comment/Letter
Filtering PSA measurements to improve specificity
Posted on January 4, 2014
David L. Keller, MD
Conflict of Interest: None Declared
The problem of over-diagnosis of prostate cancer must be addressed by careful selection of the population to be screened. However, the problem of poor specificity remains due to the many benign causes of false positive PSA elevations. PSA is a "noisy" signal; elevations of PSA can be caused by conditions such as occult urinary tract infections (including prostatitis), mild trauma due to bicycle riding and other sports, vigorous sexual activity or prolonged abstinence, etc. If a patient's PSA increases enough to trigger a prostate biopsy, the first thing most clinicians do is repeat the test (after ruling out or eliminating possible benign causes of PSA increase). The "noise" obscuring the PSA cancer signal has the helpful characteristic of always being positive (tending to increase the PSA). Thus, a simple and effective "filtering" strategy consists of repeating the PSA several times over an interval of a few weeks, and discarding all but the lowest PSA measured. This last value is the closest to the patient's "true" PSA, contains the best information about the possible presence of malignancy, and is the least likely to trigger an unnecessary biopsy. I have not seen any studies which incorporated this filtering technique, which I propose should be done in order to fairly assess the benefits and harms of PSA screening for prostate cancer.
Submit a Comment/Letter

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