Análise da situação epidemiológica da hanseníase em uma área endêmica no Brasil: distribuição espacial dos períodos 2001 - 2003 e 2010 - 2012

Abstract

RESUMO: Introdução: No Brasil, a distribuição espacial da hanseníase é heterogênea. Áreas com alta transmissão da doença permanecem nas regiões Norte, Centro-Oeste e Nordeste do país. Objetivo: Descrever a distribuição espacial da hanseníase em municípios brasileiros com alto risco de transmissão, nos períodos 2001 - 2003 e 2010 - 2012. Métodos: Trata-se de um estudo ecológico com dados do Sistema de Informação de Agravos de Notificação (SINAN). Foram incluídos todos os municípios localizados nos Estados de Mato Grosso, do Tocantins, de Rondônia, do Pará e do Maranhão. Os seguintes indicadores de hanseníase foram calculados por 100.000 habitantes: taxa de incidência de hanseníase, taxa de incidência em menores de 15 anos e a taxa de casos novos com grau 2 de incapacidade (por 100.000 habitantes). A estatística espacial scan foi usada para detectar clusters significativos (p ≤ 0,05) na área de estudo. Resultados: No período 2001 - 2003, a estatística espacial scan identificou 44 clusters significativos para a taxa de incidência da hanseníase, e 42 clusters significativos no período 2010 - 2012. No período 2001 - 2003, foram identificados 20 clusters significativos para a taxa de incidência em menores de 15 anos, e 14 clusters significativos no período 2010 - 2012. Para a taxa de casos novos com grau 2 de incapacidade, a estatística scan identificou 19 clusters significativos no período 2001 - 2003, e 14 agrupamentos significativos no triênio 2010 - 2012. Conclusão: Apesar da redução na detecção de casos de hanseníase, há uma necessidade de intensificar as ações de controle da doença, especialmente nos clusters identificados.

Spatial Distribution of Newborns With Sickle Cell Trait in Sergipe, Brazil

“…Geographical distribution has been used to study diseases in epidemiological analyses. [24][25][26][27] This tool becomes important in the study of genetic diseases when there is a possibility of intervention with genetic guidance, as in the case of increased incidence of healthy carriers of the gene that causes the condition. Therefore, knowing the regions of Sergipe with higher incidence of heterozygous individuals is useful to guide the planning of health care actions for patients with SCA, as well as informing the situation to asymptomatic carriers and counseling the families, which may change the incidence profile of SCD in this population.…”

Estudo espacial da hanseníase na Bahia, 2001-2012: abordagem a partir do modelo bayesiano empírico local

“…Carlos Dornels Freire de Souza et al Case interpretation parameter categories were defined for each indicator, as per Brazilian Ministry of Health guidelines, 17 as described below. a) Detection rate of new cases of leprosy in the general population: -low, <2.00 per 100,000 inhabitants; -medium, 2.00 to 9.99 per 100,000 inhabitants; -high, 10.00 to 19.99 per 100,000 inhabitants; -very high, 20.00 to 39.99 per 100,000 inhabitants; and -hyperendemic, ≥ 40.00 per 100,000 inhabitants. b) Detection rate of new cases of leprosy in the population aged 0-14 years: -low, <0.50 per 100,000 inhabitants; -medium, 0.50 to 2.49 per 100,000 inhabitants; -high, 2.50 to 4.99 per 100,000 inhabitants; -very high, 5.00 to 9.99 per 100,000 inhabitants; and -hyperendemic, ≥10.00 per 100,000 inhabitants.…”

“…The objective of this model is to identify a posteriori distribution (unobserved quantities of a given phenomenon), based on the application of Bayes' theorem, involving sample data (likelihood function), and the application of a set of observed data (a priori distribution). 9,10 This procedure was performed using the Terra View software, version 4.2.2, provided by the National Institute for Space Research (INPE). Subsequently, vector difference was calculated (observed rate less estimated rate), to enable the construction of a thematic map of the differences, whereby the areas were classified as "neutral", "negative" or "positive".…”

“…7 The spatial study approach to leprosy has contributed to the understanding of its transmission dynamics, identification of factors associated with its occurrence, as well as priority areas for intervention. [8][9][10][11] Standing out among the tools used in these studies is empirical Bayesian modeling, the purpose of which is to reduce the random fluctuation of data by producing corrected and therefore more stable rates. 12,13 The objective of this study was to compare the temporal evolution and spatial distribution of epidemiological indicators of leprosy, both crude and also corrected using the empirical Bayesian model, in Bahia, Brazil, 2001-2012, thus contributing to the understanding of the dynamics of the disease in that state.…”

Prevalence and spatial distribution of Mycobacterium leprae infection in a medium endemicity municipality

“…Leprosy is a disease that persists as a public health problem in Brazil, it has a heterogeneous distribution in the national territory and affects the economically most vulnerable population (1)(2) . Most Brazilian municipalities face challenges in complying with the principles of epidemiological surveillance of leprosy, such as early detection and treatment of new cases, decentralization of prevention and control actions, in order to integrate them into Primary Health Care and surveillance of household and social contacts (1) .…”