Objective: This study aims to develop and validate predictive models that assess the risk of leprosy development among contacts, contributing to an enhanced understanding of disease occurrence in this population.
Methods: A cohort of 600 contacts of people with leprosy treated at the National Reference Center for Leprosy and Health Dermatology at the Federal University of Uberlândia (CREDESH/HC‐UFU) was followed up between 2002 and 2022. The database was divided into two parts: two‐third to construct the disease risk score and one‐third to validate this score. Multivariate logistic regression models were used to construct the disease score.
Results: Of the four models constructed, model 3, which included the variables anti‐phenolic glycolipid I immunoglobulin M positive, absence of Bacillus Calmette‐Guérin vaccine scar and age ≥60 years, was considered the best for identifying a higher risk of illness, with a specificity of 89.2%, a positive predictive value of 60% and an accuracy of 78%.
Conclusions: Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols.
BT - Tropical Medicine & International Health DO - 10.1111/tmi.14020 LA - ENG M3 - Article N2 -Objective: This study aims to develop and validate predictive models that assess the risk of leprosy development among contacts, contributing to an enhanced understanding of disease occurrence in this population.
Methods: A cohort of 600 contacts of people with leprosy treated at the National Reference Center for Leprosy and Health Dermatology at the Federal University of Uberlândia (CREDESH/HC‐UFU) was followed up between 2002 and 2022. The database was divided into two parts: two‐third to construct the disease risk score and one‐third to validate this score. Multivariate logistic regression models were used to construct the disease score.
Results: Of the four models constructed, model 3, which included the variables anti‐phenolic glycolipid I immunoglobulin M positive, absence of Bacillus Calmette‐Guérin vaccine scar and age ≥60 years, was considered the best for identifying a higher risk of illness, with a specificity of 89.2%, a positive predictive value of 60% and an accuracy of 78%.
Conclusions: Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols.
PB - Wiley PY - 2024 T2 - Tropical Medicine & International Health TI - Models for predicting the risk of illness in leprosy contacts in Brazil: Leprosy prediction models in Brazilian contacts SN - 1360-2276, 1365-3156 ER -