01987nas a2200265   4500000000100000008004100001260001000042653001700052653002500069653002700094653001200121100001800133700001600151700002000167700001100187700001400198700001200212700001500224700001500239700001600254700002200270245012500292520127900417022002501696       2024                            d  bWiley10aEpidemiology10aContact surveillance10aRisk prediction models10aLeprosy1 ade Alecrin ES1 aMartins MAP1 ade Oliveira ALG1 aLyon S1 aLages ATC1 aReis IA1 aPereira FH1 aOliveira D1 aGoulart IMB1 ada Costa Rocha MO00aModels for predicting the risk of illness in leprosy contacts in Brazil: Leprosy prediction models in Brazilian contacts3 a<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>
  a1360-2276, 1365-3156