Abstract

A prediction model of prevalent pulmonary tuberculosis (TB) in HIV negative/unknown individuals was developed to assist systematic screening. Data from a large TB screening trial were used. A multivariable logistic regression model was developed in the South African (SA) training dataset, using TB symptoms and risk factors as predictors. The model was converted into a scoring system for risk stratification and was evaluated in separate SA and Zambian validation datasets. The number of TB cases were 355, 176, and 107 in the SA training, SA validation, and Zambian validation datasets respectively. The area under curve (AUC) of the scoring system was 0·68 (95% CI 0·64-0·72) in the SA validation set, compared to prolonged cough (0·58, 95% CI 0·54-0·62) and any TB symptoms (0·6, 95% CI 0·56–0·64). In the Zambian dataset the AUC of the scoring system was 0·66 (95% CI 0·60–0·72). In the cost-effectiveness analysis, the scoring system dominated the conventional strategies. The cost per TB case detected ranged from 429 to 1,848 USD in the SA validation set and from 171 to 10,518 USD in the Zambian dataset. The scoring system may help targeted TB case finding under budget constraints.

Details

Title
Development and validation of a prediction model for active tuberculosis case finding among HIV-negative/unknown populations
Author
Yun-Ju, Shih 1 ; Ayles, Helen 2 ; Lönnroth Knut 3 ; Claassens Mareli 4 ; Hsien-Ho, Lin 1 

 National Taiwan University, Institute of Epidemiology and Preventive Medicine, Taipei, Taiwan (GRID:grid.19188.39) (ISNI:0000 0004 0546 0241) 
 London School of Hygiene and Tropical Medicine, Department of Clinical Research, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X) 
 Karolinska Institutet, Department of Public Health Sciences, Stockholm, Sweden (GRID:grid.4714.6) (ISNI:0000 0004 1937 0626) 
 Stellenbosch University, Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Cape Town, South Africa (GRID:grid.11956.3a) (ISNI:0000 0001 2214 904X) 
Publication year
2019
Publication date
2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2210430087
Copyright
© The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.