@article{100389,
  keywords = {Epidemiology, Contact surveillance, Risk prediction models, Leprosy},
  author = {de Alecrin ES and Martins MAP and de Oliveira ALG and Lyon S and Lages ATC and Reis IA and Pereira FH and Oliveira D and Goulart IMB and da Costa Rocha MO},
  title = {Models for predicting the risk of illness in leprosy contacts in Brazil: Leprosy prediction models in Brazilian contacts},
  abstract = {<p><strong>Objective:</strong> 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.</p>

<p><strong>Methods:</strong> 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.</p>

<p><strong>Results: </strong>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%.</p>

<p><strong>Conclusions: </strong>Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols.</p>
},
  year = {2024},
  journal = {Tropical Medicine &amp; International Health},
  publisher = {Wiley},
  issn = {1360-2276, 1365-3156},
  doi = {10.1111/tmi.14020},
  language = {ENG},
}