1. Preliminary results of: Prediction of TB transmission from Attributes of Infected Cases

2. Review – TB transmission
Reactivation – development of TB disease from latent infection
(R* below)
Primary TB disease – disease after initial exposure
(T below) RT T T RN T

3. Review – TB transmission
How do I know whether a case belongs to Rt, Rn, or T?
Epi investigation – low sensitivity
DNA fingerprinting – long turnaround time or low specificity
Goal – Develop alternative method to classify a TB case into one of 3 classes from infected case’s features

4. Potential features to predict TB transmission from the past retrospective TB epi studies
Cough
Sputum
Chest X-Ray results
HIV
Sputum smear results
Age
Sex
Country of origin
IV Drug-user, homeless, alcoholic
Residence (i.e. apartment or house)
Residential location on the island (CSSS)
Some non-linear (Bivariate interaction) variables:
Residence and X-ray results, Residence and coughing

5. Model Multinomial logit regression with Begg’s & Gray approximation.
3 response classes to be predicted from patient’s attributes
(classes: Rt , Rn, T)
Status of the response class was determined by the DNA fingerprint analysis as well as the date of lab tests
Feature selection:
Bayesian Model Averaging(BMA) – Validated to yield more stable estimates than stepwise with AIC or p-value based methods
Almost all predictor variables have missing data, so multiple imputations were performed with m=15 (ICE method).

6. Estimates of performance and Internal Validation
10 rounds of 10-fold cross-validation on each of 15 imputed datasets
Estimates of predictive performance
AUC for Rt and T using Rn as reference
Brier score For Rt and T using Rn as reference.
1/n*∑(Yi - yi)2 where Yi =observed response {1,0}
and yi = predicted probability
score 0 -> perfect prediction
0.25 -> crap

7. ~ ~ Summary of analysis Original Data Multiple Imputations
15 imputed datasets generated
from Multiple imputations ~ m1 m2 m3
m15
10 rounds of 10-fold cross-validation applied to each m* dataset to obtain predictive performance (i.e. AUC, Brier) of prediction models
Cross-validations ~
est1
est2
est3
est15
Pooling
Pool estimates of prediction errors
From 15 datasets according to
Rubin’s rule
Final Estimates

8. Rt AUC 0.62 (95% CI 0.52, 0.71) T AUC 0.65 (95% CI 0.53, 0.77) Results
Preliminary Estimates from 1-round 10-fold X-validation
Rt AUC (95% CI 0.52, 0.71)
Brier Score 0.07
T AUC (95% CI 0.53, 0.77)
Brier Score 0.12

9. Results
Predicted probabilities too low

10. Results Predictors Sputum Smear 0.01 1.1 0.00 Age -0.01 35.43 1.8
Mean Coef
Mean P.P
Sputum Smear
0.01
1.1
0.00
Age
-0.01
35.43
1.8
Residence
Lung Involvement?
0.02
3.48
0.48
Sex
Previous TB diagnosis?
-0.03
5.50
X-ray result
0.06
0.26
HIV
1.2
99.88
2.48
IV drug use
Alcoholic
0.12
0.07
Coughing
Sputum
Residence * coughing
0.25
Residence * X-ray
96.39
0.05
7.99

11. Results Predictors Mean Coef Mean P.P CSSS - West to Central 0.00
-0.33
54.47
CSSS - East
0.02
5.13
0.12
CSSS –Central
0.45
57.05
WHO – Africa high HIV, Americas
0.42
58.6
WHO - Canada
1.19
100
WHO – E. Medit, E.Eur, S.E. Asia
-0.27
34.61
0.79
WHO - Haiti
0.24
1.32
WHO – Western Pacific
-0.11
15.18
0.03
5.06
CSSS reference: Airport & south of the island
WHO reference: Africa Low HIV, Industrialized nations