Clusters of risk for the occurrence of leprosy and disabilities in children under 15 years of age in Cuiabá: a geospatial study

• Published in: Revista brasileira de epidemiologia Vol. 26; p. e230006
• Main Authors: Martoreli Júnior, José Francisco, Ramos, Antônio Carlos Vieira, Berra, Thaís Zamboni, Nascimento, Murilo César do, Tavares, Reginaldo Bazon Vaz, Moura, Heriederson Sávio Dias, Mello, Débora Falleiros de, Alves, Josilene Dália, Arcêncio, Ricardo Alexandre
• Format: Journal Article
• Language: English
• Published: Brazil 2023
• Subjects: Adolescent; Brazil - epidemiology; Child; Cities; Disabled Persons; Humans; Leprosy - epidemiology; Brazil
• ISSN: 1415-790X; 1980-5497
• DOI: 10.1590/1980-549720230006

This study aimed to analyze the spatial distribution of leprosy and disabilities in children under 15 years of age in Cuiabá. Ecological study carried out in the city of Cuiabá, Mato Grosso, Brazil. The study population consisted of leprosy cases in children under 15 years old notified in the Notifiable Diseases Information System, between 2008 and 2018. Based on residential addresses, cases were georeferenced. In the analysis of the spatial distribution of the cases, the estimation of the Kernel density was used and, later, the statistics of spatial, spatio-temporal and Spatial Variation in Temporal Trends were applied. 514 cases of leprosy were reported in children under 15 years of age in Cuiabá, with a percentage of 10.1% of cases with degree of physical disability 1 and 2.3% with degree of physical disability 2 at the time of diagnosis. With the techniques of spatial and spatio-temporal scanning, clusters of risk for leprosy were identified in the North, West, East and South regions of Cuiabá, and with the technique of Spatial Variation in Temporal Trends, a cluster was identified in the West region of Cuiabá. In Cuiabá, cases of leprosy in children under 15 years of age with disabilities were distributed throughout the urban area of the city, with the highest density of cases in the North and West regions, followed by the East region. The clusters with the highest Relative Risk were identified in the East and West regions, characterized by having low and medium income levels.