A decision tree for tuberculosis contact investigation.

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.

Predictive model to identify positive tuberculosis skin test results during contact investigations.

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.