ERASE-TB: prediction models for Mtb infection dataset
Calderwood, C
, Marambire, E and Kranzer, K
(2025).
ERASE-TB: prediction models for Mtb infection dataset.
[Dataset].
London School of Hygiene & Tropical Medicine, London, United Kingdom.
10.17037/DATA.00004571.
Data from the ERASE-TB study, used to develop prediction models for Mtb infection among adolescent and adult tuberculosis household contacts and evaluate the risk of Mtb infection by HIV status. This is a cross-sectional dataset, with one row per person, indicating the participant interferon gamma release assay status, demographics and clinical history, and data on the index person with tuberculosis in the household (index case) and household-level variables. All candidate predictors for prediction model development are included.
Keywords
Tuberculosis, Latent TB, Prediction modelling, Transmission of disease, HIVItem Type | Dataset |
---|---|
Capture method | Questionnaire, Measurements and tests |
Collection Period |
From To 1 March 2021 31 March 2021 |
Date | 3 March 2025 |
Language(s) of written materials | English |
Creator(s) |
Calderwood, C |
Associated roles | Kranzer, K (Principal Investigator), Marambire, E (Work Package Leader) and Larsson, L (Data Manager) |
LSHTM Faculty/Department | Faculty of Infectious and Tropical Diseases > Dept of Clinical Research |
Research Centre | TB Centre |
Research Group | The Health Research Unit Zimbabwe |
Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom, LMU University of Munich, Munich, Germany, Biomedical Research and Training Institute, National Institute for Medical Research (MMRC - Mbeya), Mbeya, Tanzania, Instituto Nacional de Saúde |
Funders |
Project Funder Grant Number Funder URI ERASE-TB RIA2018D-2508 http://dx.doi.org/10.13039/501100001713 |
Date Deposited | 03 Mar 2025 10:53 |
Last Modified | 03 Mar 2025 14:37 |
Publisher | London School of Hygiene & Tropical Medicine |
ERASE_predictionmodel_data.csv
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ORCID: https://orcid.org/0000-0002-1926-7464
ORCID: https://orcid.org/0000-0001-5691-7270