Tropical medicine & international health : TM & IH · 2024
Models for predicting the risk of illness in leprosy contacts in Brazil: Leprosy prediction models in Brazilian contacts.
СтатьяНазвание и аннотация: Перевод в очереди
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de Alecrin Edilamar Silva, Martins Maria Auxiliadora Parreiras, de Oliveira Ana Laura Grossi, Lyon Sandra, Lages Ana Thereza Chaves, Reis Ilka Afonso, Pereira Fernando Henrique, Oliveira Dulcinea, Goulart Isabela Maria Bernardes, da Costa Rocha Manoel Otávio
Аннотация
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.
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.