[The incidence discriminant model for close contacts of active tuberculosis patients].
OBJECTIVE
To establish a discriminant model and to provide a relatively accurate scientific basis for the early diagnosis of tuberculosis (TB) and detection of the close contacts.


METHODS
Through logistic regression analysis, key factors were selected according to Bayes theory and key factors of TB incidence of the close contacts were screened as well as a discriminant model was established.


RESULTS
The non-TB incidence discriminant function of the close contacts was described as: Y1= -39.831 (constant) + 1.927 X1 (sputum-frequency) + 3.528 X2 (education) + 0.309 X3 (contact time) + 5.893 X4 (evade) +2.140 X5 (ventilation) + 8.706 X6 (cough) + 30.970 X7 (fever). The discriminant function of non-TB incidence of the close contacts was as: Y2 =-57.875 (constant) + 2.343 X1 (sputum-frequency) + 3.965 X2 (education) + 0.361 X3 (contact time) + 6.296 X4 (evade) + 1.348 X5 (ventilation) + 12.984 X6 (cough) + 36.555 X7 (fever).


CONCLUSIONS
The discriminant model night be used to contribute to the early diagnosis, early intervention and timely treatment on those close contacts of tuberculosis cases.
Tao
Hong-Bing
HB
School of Medical and Health Management, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China.
Ye
Jian-Jun
JJ
Miao
Wei-Jun
WJ
Hou
Shuang-Yi
SY
Xiong
Guang-Lian
GL
Yu
Yao
Y
Guo
Shi-Lin
SL
Chen
Pu
P
D001499:Bayes Theorem; D016358:Contact Tracing / Q000379:methods*; D018562:Disease Transmission, Infectious / Q000517:prevention & control; D042241:Early Diagnosis; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D014376:Tuberculosis / Q000175:diagnosis* / Q000517:prevention & control