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A Decision Tree for Tuberculosis Contact Investigation

Lung Health Center; School of Health Related Professions; School of Medicine; Comprehensive Cancer Center, Division of Biostatistics, University of Alabama at Birmingham; Department of Biostatistics, School of Public Health; and Alabama Department of Public Health, Division of Tuberculosis Control, Birmingham, Alabama

Correspondence and requests for reprints should be addressed to Lynn B. Gerald, Ph.D., M.S.P.H., University of Alabama at Birmingham Lung Health Center, NHB 104, 619 19th Street South, Birmingham, AL 35249–7337. E-mail: geraldl{at}uab.edu

ABSTRACT

The University of Alabama at Birmingham and the Alabama Department of Public Health recently developed a logistic regression model showing those variables that are most likely to predict a positive tuberculin skin test in contacts of tuberculosis cases. However, translating such a model into field application requires a stepwise approach. This article describes a decision tree developed to assist public health workers in determining which contacts are most likely to have a positive tuberculin skin test. The Classification and Regression Tree analysis was performed on 292 consecutive cases and their 2,941 contacts seen by the Alabama Department of Public Health from January 1, 1998, to October 15, 1998. Several decision trees were developed and were then tested using prospectively collected data from 366 new tuberculosis cases and their 3,162 contacts from October 15, 1998, to April 30, 2000. Testing showed the trees to have sensitivities of 87–94%, specificities of 22–28%, and false-negative rates between 7 and 10%. The use of the decision trees would decrease the number of contacts investigated by 17–25% while maintaining a false-negative rate that was close to that of the presumed background rate of latent tuberculosis infection in the state of Alabama.

Key Words: tuberculosis • decision tree • contact investigation • Classification and Regression Tree analysis

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