ArticleThe Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale2019
Development of a Predictive Model of Tuberculosis Transmission among Household Contacts.
Article in The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning approaches to predict the risk of tuberculosis among household contacts of index TB patients in Central Ethiopia.Scientific reports · 2026Article
- Prediction of latent tuberculosis infection in Venezuelan immigrants: construction and validation of a surveillance model.Revista da Sociedade Brasileira de Medicina Tropical · 2025Article
- Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India.Journal of public health (Oxford, England) · 2023Observational
- Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters.American journal of epidemiology · 2022Article
- Sensitivity and specificity of tuberculosis signs and symptoms screening and adjunct role of social pathology characteristics in predicting bacteriologically confirmed tuberculosis in Myanmar.Tropical medicine and health · 2021Article
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Authors and funding
1 author.
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Abstract
backgroundHousehold contacts of patients with tuberculosis (TB) are at great risk of TB infection. The aim of this study was to develop a predictive model of TB transmission among household contacts.
methodThis was a secondary analysis of data from a prospective cohort study, in which a total of 700 TB patients and 3417 household contacts were enrolled between 2010 and 2013 at two study sites in Peru. The incidence of secondary TB cases among household contacts of index cases was recorded. The LASSO regression method was used to reduce the data dimension and to filter variables. Multivariate logistic regression analysis was applied to develop the predictive model, and internal validation was performed. A nomogram was constructed to display the model, and the AUC was calculated. The calibration curve and decision curve analysis (DCA) were also evaluated.
resultsThe incidence of TB disease among the contacts of index cases was 4.4% (149/3417). Ten variables (gender, age, TB history, diabetes, HIV, index patient's drug resistance, socioeconomic status, spoligotypes, and the index-contact share sleeping room status) filtered through the LASSO regression technique were finally included in the predictive model. The model showed good discriminatory ability, with an AUC value of 0.761 (95% CI, 0.723-0.800) for the derivation and 0.759 (95% CI, 0.717-0.796) for the internal validation. The predictive model showed good calibration, and the DCA demonstrated that the model was clinically useful.
conclusionA predictive model was developed that incorporates characteristics of both the index patients and the contacts, which may be of great value for the individualized prediction of TB transmission among household contacts.
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Registered trials
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