253003 Misclassification of sex: Assessing an automated sex edit and the example of male breast cancer

Monday, October 31, 2011

Recinda Sherman, MPH, CTR , Florida Cancer Data System, University of Miami, Miller School of Medicine, Miami, FL
Jackie Button, MS , Florida Cancer Data System (FCDS), Miami, FL
Laura Soloway, PhD , New York State Cancer Registry, New York Department of Health, Menands, NY
Francis Boscoe, PhD , New York State Cancer Registry, New York Department of Health, Menands, NY
David J. Lee, PhD , Epidemiology and Public Health, University of Miami, Miller School of Medicine, Miami, FL
Background: Breast cancer (BC) incidence is increasing at a statistically significant faster rate among Florida males compared to US males. Epidemiological studies of male BC in Florida could advance etiologic knowledge, but these data must first be evaluated for validity. Large central cancer registries routinely utilize sex-specific automated edits and other computerized processes to ensure data integrity. However, <15% of cancers are sex-specific. Therefore, the effectiveness of a newly-developed algorithm for identifying miscoded sex, based on name and decade of birth, was investigated. Methods: The algorithm was tested against four cancer sites plus a dataset of BC patients reported as male but identified female after follow-up. The impact of misclassification of sex on cancer rates in Florida was evaluated. Results: For tested sites, the algorithm agreed with reported sex on 68% of cases, 31% could not be assessed, and 0.5% were flagged as incorrect. Results differed by subpopulation. For example, the percents of unassessable cases were higher for Hispanics and nonwhites compared to whites. And a dramatic 21% of male BC cases were identified as incorrect sex. When testing against the dataset of “false male” BC cases, the algorithm correctly identified 81% as female, 19% were unassessed, and none were incorrectly identified as male. Correcting the data resulted in a significant decline in male BC rates. Implications: Clinically significant differences in Florida male BC rates compared to US are unlikely. Although less sensitive for non-whites/ Hispanics, applying the algorithm as an edit improves quality of cancer registry sex data.

Learning Areas:
Communication and informatics

Learning Objectives:
1. Describe the problem, both extent and root, of sex misclassification in central cancer registries. 2. Assess the impact of a newly developed, automated sex edit on the Florida Cancer Registry data. 3. Compare male breast cancer rates in Florida to SEER rates pre and post application of automated sex edit.

Keywords: Cancer, Data/Surveillance

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I fit the requirements for authorship by envisioning, designing and implementing the research, obtaining the data and conducting analysis, and writing up the results. I am fully responsible for the content. I am qualified to present because, although I am a current PhD Candidate and have not yet attended APHA, I have worked in the cancer surveillance field for over a decade and am involved in both state and national cancer surveillance research. I have presented cancer surveillance research at local and national levels since 2000.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.