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Predictive Model to Identify Positive Tuberculosis Skin Test Results During Contact Investigations
William C. Bailey, MD;
Lynn B. Gerald, PhD,MSPH;
Michael E. Kimerling, MD,MPH;
David Redden, PhD;
Nancy Brook, MPH;
Frank Bruce, BS;
Shenghui Tang, PhD;
Steve Duncan, BS;
C. Michael Brooks, EdD;
Nancy E. Dunlap, MD,PhD
JAMA. 2002;287:996-1002.
Context Budgetary constraints in tuberculosis (TB) control programs require streamlining contact investigations without sacrificing disease control.
Objective To develop more efficient methods of TB contact investigation by creating a model of TB transmission using variables that best predict a positive tuberculin skin test among contacts of an active TB case.
Design, Setting, and Subjects After standardizing the interview and documentation process, data were collected on 292 consecutive TB cases and their 2941 contacts identified by the Alabama Department of Public Health between January and October 1998. Generalized estimating equations were used to create a model for predicting positive skin test results in contacts of active TB cases. The model was then validated using data from a prospective cohort of 366 new TB cases and their 3162 contacts identified between October 1998 and April 2000.
Main Outcome Measure Tuberculin skin test result.
Results Using generalized estimating equations to build a predictive model, 7 variables were found to significantly predict a positive tuberculin skin test result among contacts of an active TB case. Further testing showed this model to have a sensitivity, specificity, and positive predictive value of approximately 89%, 36%, and 26%, respectively. The false-negative rate was less than 10%, and about 40% of the contact workload could be eliminated using this model.
Conclusions Certain characteristics can be used to predict contacts most likely to have a positive tuberculin skin test result. Use of such models can significantly reduce the number of contacts that public health officials need to investigate while still maintaining excellent disease control.
Author Affiliations: Divisions of Pulmonary and Critical Care Medicine (Drs Bailey, Gerald, Kimerling, Brooks, and Dunlap, and Messrs Bruce and Duncan), General Internal Medicine (Dr Kimerling), and Biostatistics (Dr Tang), Schools of Medicine (Drs Bailey, Gerald, Kimerling, Tang, Brooks, and Dunlap, and Messrs Bruce and Duncan) and Health-Related Professions (Drs Gerald and Brooks), and Department of Biostatistics, School of Public Health (Dr Redden), University of Alabama at Birmingham; and Alabama Department of Public Health, Division of Tuberculosis Control, Birmingham (Ms Brook).
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