Geographic weighted regression: applicability to epidemiological studies of leprosy

Abstract: INTRODUCTION: Geographic information systems (GIS) enable public health data to be analyzed in terms of geographical variability and the relationship between risk factors and diseases. This study discusses the application of the geographic weighted regression (GWR) model to health data to improve the understanding of spatially varying social and clinical factors that potentially impact leprosy prevalence. METHODS: This ecological study used data from leprosy case records from 1998-2006, aggregated by neighborhood in the Duque de Caxias municipality in the State of Rio de Janeiro, Brazil. In the GWR model, the associations between the log of the leprosy detection rate and social and clinical factors were analyzed. RESULTS: Maps of the estimated coefficients by neighborhood confirmed the heterogeneous spatial relationships between the leprosy detection rates and the predictors. The proportion of households with piped water was associated with higher detection rates, mainly in the northeast of the municipality. Indeterminate forms were strongly associated with higher detections rates in the south, where access to health services was more established. CONCLUSIONS: GWR proved a useful tool for epidemiological analysis of leprosy in a local area, such as Duque de Caxias. Epidemiological analysis using the maps of the GWR model offered the advantage of visualizing the problem in sub-regions and identifying any spatial dependence in the local study area.

Aspectos epidemiológicos da hanseníase: uma abordagem espacial / Epidemiological aspects of leprosy: a spatial approach

O objetivo foi identificar o padrão espacial da ocorrência da hanseníase em Duque de Caxias, município de alta endemicidade da doença no Estado do Rio de Janeiro, Brasil. Foram selecionados todos os casos novos de hanseníase registrados no banco do Sistema de Informação de Agravos de Notificação (SINAN), entre 1998 e 2006. Realizou-se análise por período subdividido a cada três anos, seguido de análise espacial por meio da estimativa bayesiana empírica local e do cálculo da autocorrelação espacial global (Moran) e local (LISA). A análise mostrou melhora acentuada do quadro epidemiológico, com o diagnóstico mais precoce. Houve redução da proporção de casos com grau II de 13,6% para 8,6% (p = 0,04). Verificou-se aumento da detecção de casos com forma indeterminada, de 10,3% para 18% (p = 0,00). A análise espacial identificou cluster na faixa sul-noroeste, não relacionado diretamente às ações de campanhas ou descentralização, mostrando ser uma ferramenta importante para identificação de áreas críticas da endemia e para avaliação do impacto das ações estratégias de combate à doença.

The objective of this study was to identify spatial patterns in the occurrence of leprosy in Duque de Caxias, a municipality (county) with high endemicity for the disease in the State of Rio de Janeiro, Brazil. The authors selected all new leprosy cases reported to the Brazilian National Database on Diseases of Notification (SINAN) from 1998 to 2006. The analysis was performed according to three-year periods, followed by spatial analysis according to the local empirical Bayesian method and calculation of global (Moran) and local (LISA) spatial autocorrelation. The analysis showed marked improvement in the epidemiological situation associated with earlier diagnosis. There was a reduction in the proportion of cases with grade II leprosy, from 13.6% to 8.6% (p = 0.04), and increased detection of cases with the indeterminate form, from 10.3% to 18% (p = 0.00). Spatial analysis identified a cluster in the South-Northwest of the municipality, not directly related to the actions or decentralization campaign, proving to be an important tool for identifying critical areas for the endemic and assessing the impact of strategic measures to combat the disease.