Celivane Cavalcanti Barbosa1,2, Cristine Vieira do Bonfim3, Cintia Michele Gondim de Brito2,4, Andrea Torres Ferreira2, Vera Rejane do Nascimento Gregório4, André Luiz Sá de Oliveira5, José Luiz Portugal6, Zulma Maria de Medeiros4,7. 1. Department of Collective Health, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil. 2. Department of Epidemiological Surveillance, First Health District of the State Health Department of Pernambuco, Recife, Brazil. 3. Postgraduate Program in Public Health, Federal University of Pernambuco, Recife, Brazil. 4. Postgraduate Health Sciences, University of Pernambuco, Recife, Brazil. 5. Nucleus of Statistics and Geoprocessing, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil. 6. Department of Cartographic Engineering, Federal University of Pernambuco, Recife, Brazil. 7. Department of Parasitology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil.
Abstract
OBJECTIVE: To analyse the spatial distribution of the incidence of leprosy and identify areas at risk for occurrences of hyper-endemic disease in Northeastern Brazil. METHODS: Ecological study using municipalities as the analysis unit. Data on new cases of leprosy came from the Health Hazard Notification System (SINAN). This study focused on Pernambuco and covered the years 2005 to 2014. Indicators for monitoring were calculated per 100 000 inhabitants. The local empirical Bayes method was used to minimise rate variance, and spatial autocorrelation maps were used for spatial pattern analysis (box maps and Moran maps). RESULTS: A total of 28 895 new cases were registered in the study period. The average incidence was 21.88/100 000; the global Moran's I index was 0.36 (P < 0.01), thus indicating the existence of spatial dependence; and the Moran map identified 20 municipalities with high priority for attention. The average incidence rate among individuals under 15 years of age was 8.78/100 000; the global Moran's I index showed the presence of positive spatial autocorrelation (0.43; P < 0.01), and the Moran map showed a main cluster of 15 hyper-endemic municipalities. The average rate of grade 2 physical disability at the time of diagnosis was 1.12/100 000; the global Moran index presented a positive spatial association (0.17; P < 0.01); and the Moran map located clusters of municipalities (high-high) in three mesoregions. CONCLUSION: Application of different spatial analysis methods made it possible to locate areas that would not have been identified by epidemiological indicators alone.
OBJECTIVE: To analyse the spatial distribution of the incidence of leprosy and identify areas at risk for occurrences of hyper-endemic disease in Northeastern Brazil. METHODS: Ecological study using municipalities as the analysis unit. Data on new cases of leprosy came from the Health Hazard Notification System (SINAN). This study focused on Pernambuco and covered the years 2005 to 2014. Indicators for monitoring were calculated per 100 000 inhabitants. The local empirical Bayes method was used to minimise rate variance, and spatial autocorrelation maps were used for spatial pattern analysis (box maps and Moran maps). RESULTS: A total of 28 895 new cases were registered in the study period. The average incidence was 21.88/100 000; the global Moran's I index was 0.36 (P < 0.01), thus indicating the existence of spatial dependence; and the Moran map identified 20 municipalities with high priority for attention. The average incidence rate among individuals under 15 years of age was 8.78/100 000; the global Moran's I index showed the presence of positive spatial autocorrelation (0.43; P < 0.01), and the Moran map showed a main cluster of 15 hyper-endemic municipalities. The average rate of grade 2 physical disability at the time of diagnosis was 1.12/100 000; the global Moran index presented a positive spatial association (0.17; P < 0.01); and the Moran map located clusters of municipalities (high-high) in three mesoregions. CONCLUSION: Application of different spatial analysis methods made it possible to locate areas that would not have been identified by epidemiological indicators alone.
Authors: Celivane Cavalcanti Barbosa; Cristine Vieira do Bonfim; Cintia Michele Gondim de Brito; Wayner Vieira de Souza; Marcella Fernandes de Oliveira Melo; Zulma Maria de Medeiros Journal: Rev Inst Med Trop Sao Paulo Date: 2020-11-27 Impact factor: 1.846
Authors: Dorcas O Ogunsumi; Vivek Lal; Karl Philipp Puchner; Wim van Brakel; Eva-Maria Schwienhorst-Stich; Christa Kasang; Joseph Chukwu; Saskia Kreibich; Sandra Parisi; Jan Hendrik Richardus; David J Blok Journal: PLoS Negl Trop Dis Date: 2021-09-20