Spatial analysis of epidemiological and quality indicators of health services for leprosy in hyperendemic areas in Northeastern Brazil

• Autor(a) principal: Barbosa, Celivane Cavalcanti
• Data de Publicação: 2020
• Outros Autores: Bonfim, Cristine Vieira do, Brito, Cintia Michele Gondim de, Souza, Wayner Vieira de, Melo, Marcella Fernandes de Oliveira, Medeiros, Zulma Maria de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista do Instituto de Medicina Tropical de São Paulo
• DOI: 10.1590/s1678-9946202062093
• Texto Completo: https://www.revistas.usp.br/rimtsp/article/view/183550
• topic: Leprosy; Epidemiology; Health information systems; Spatial analysis

Leprosy is a public health problem due to the physical disabilities and deformities it causes. This study aimed to describe new leprosy cases using an operational classification and analyzing spatial patterns by means of epidemiological and quality indicators of health services in Pernambuco State, Brazil, between 2005 and 2014. This was an ecological study performed in 184 municipalities grouped into 12 health regions units for analysis. To analyze spatial patterns, the Bayesian local empirical method and Moran’s spatial autocorrelation indicator were applied and box and Moran maps were used. Individuals aged ≥15 years old, grade zero physical disability and complete remission as the treatment outcome were predominant in both paucibacillary and multibacillary cases, the only difference was the predominance of females (n=9,286; 63.00%) and males (n=8,564; 60.70%), respectively. These variables were correlated (p<0.05) with the operational classification. The overall detection rate showed three high-priority areas; the indicator rate of grade 2 physical disability revealed clusters in regions IV, V, and VI; and the indicator rate of cases with some degree of disability showed precarious municipalities in seven health regions. Pernambuco maintains an active chain of transmission and ongoing endemicity of leprosy. Therefore, spatial analysis methods allow the identification of priority areas for intervention, thereby supporting the disease elimination strategy.