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

William C. Bailey, Lynn B Gerald, Michael E. Kimerling, David Redden, Nancy Brook, Frank Bruce, Shenghui Tang, Steve Duncan, C. Michael Brooks, Nancy E. Dunlap

Research output: Contribution to journalArticle

75 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)996-1002
Number of pages7
JournalJournal of the American Medical Association
Volume287
Issue number8
StatePublished - Feb 27 2002
Externally publishedYes

Fingerprint

Skin Tests
Tuberculosis
Tuberculin Test
Public Health
Workload
Documentation
Outcome Assessment (Health Care)
Interviews
Sensitivity and Specificity

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Bailey, W. C., Gerald, L. B., Kimerling, M. E., Redden, D., Brook, N., Bruce, F., ... Dunlap, N. E. (2002). Predictive model to identify positive tuberculosis skin test results during contact investigations. Journal of the American Medical Association, 287(8), 996-1002.

Predictive model to identify positive tuberculosis skin test results during contact investigations. / Bailey, William C.; Gerald, Lynn B; Kimerling, Michael E.; Redden, David; Brook, Nancy; Bruce, Frank; Tang, Shenghui; Duncan, Steve; Michael Brooks, C.; Dunlap, Nancy E.

In: Journal of the American Medical Association, Vol. 287, No. 8, 27.02.2002, p. 996-1002.

Research output: Contribution to journalArticle

Bailey, WC, Gerald, LB, Kimerling, ME, Redden, D, Brook, N, Bruce, F, Tang, S, Duncan, S, Michael Brooks, C & Dunlap, NE 2002, 'Predictive model to identify positive tuberculosis skin test results during contact investigations', Journal of the American Medical Association, vol. 287, no. 8, pp. 996-1002.
Bailey, William C. ; Gerald, Lynn B ; Kimerling, Michael E. ; Redden, David ; Brook, Nancy ; Bruce, Frank ; Tang, Shenghui ; Duncan, Steve ; Michael Brooks, C. ; Dunlap, Nancy E. / Predictive model to identify positive tuberculosis skin test results during contact investigations. In: Journal of the American Medical Association. 2002 ; Vol. 287, No. 8. pp. 996-1002.
@article{5c75a909963e46d5aabc7432d7632436,
title = "Predictive model to identify positive tuberculosis skin test results during contact investigations",
abstract = "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 = "Bailey, {William C.} and Gerald, {Lynn B} and Kimerling, {Michael E.} and David Redden and Nancy Brook and Frank Bruce and Shenghui Tang and Steve Duncan and {Michael Brooks}, C. and Dunlap, {Nancy E.}",
year = "2002",
month = "2",
day = "27",
language = "English (US)",
volume = "287",
pages = "996--1002",
journal = "JAMA - Journal of the American Medical Association",
issn = "0002-9955",
publisher = "American Medical Association",
number = "8",

}

TY - JOUR

T1 - Predictive model to identify positive tuberculosis skin test results during contact investigations

AU - Bailey, William C.

AU - Gerald, Lynn B

AU - Kimerling, Michael E.

AU - Redden, David

AU - Brook, Nancy

AU - Bruce, Frank

AU - Tang, Shenghui

AU - Duncan, Steve

AU - Michael Brooks, C.

AU - Dunlap, Nancy E.

PY - 2002/2/27

Y1 - 2002/2/27

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0037181182&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037181182&partnerID=8YFLogxK

M3 - Article

C2 - 11866647

AN - SCOPUS:0037181182

VL - 287

SP - 996

EP - 1002

JO - JAMA - Journal of the American Medical Association

JF - JAMA - Journal of the American Medical Association

SN - 0002-9955

IS - 8

ER -