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

Abstract

Objective: to compare the temporal evolution and spatial distribution of epidemiological indicators of leprosy, both crude and also corrected using the empirical Bayesian model, Bahia, Brazil, 2001. Methods: this was an ecological study using data from the Notifiable Diseases Information System; all 417 municipalities in Bahia were included and the following indicators per 100,000 inhabitants were analyzed -detection rate of new cases in the general population, in those <15 years old, and in those with grade 2 physical disability -; the local empirical Bayesian model was used to smoothen the indicators, and Student's t-test was used to compare means. Results: indicators estimated by the model were higher than crude indicators; estimated detection rates in the general population and in those <15 years old were higher than crude rates in 253 (60.7%) and 209 (50.1%) municipalities, respectively; areas of greatest risk were concentrated in the northwestern and southern regions of the state. Conclusion: spatial distribution of the disease was heterogeneous and there was possible underreporting of cases.

Comportamento epidemiológico da hanseníase em Roraima após as estratégias de eliminação da Organização Mundial de Saúde

“…Não obstante, somente a partir de 2003 que houve queda importante e expressiva desse indicador, seguindo a tendência nacional da taxa de detecção geral que era de 29,37/100.000 habitantes no mesmo ano e passou a ser 12,94 em 2017. Tais resultados corroboram com Souza et al (2018) que encontraram redução nas taxas de detecção na Bahia com tendência semelhante, desde o ano de 2004, com 28,7 casos por 100 mil habitantes, ao ano de 2014 com 17,4. Dessa maneira, encontraram mudança na classificação da magnitude de muito alta para alta.…”

“…Outros estudos também mostraram aumento na taxa de casos novos com grau 2 de incapacidade física na Bahia para o período das três estratégias iniciais(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) e na região Norte entre os anos de 2005 e 2015, corroborando com os dados levantados neste estudo. Isso mostra que os serviços responsáveis pelo controle da doença não estão sendo eficientes em captar todos os casos existentes de modo oportuno, o que sugere prevalência oculta da doença(SOUZA et al, 2018;OLIVEIRA, 2018).A proporção de cura de hanseníase também não tem sido satisfatória no estado de Roraima. Observa-se que há oscilação importante entre os valores sem ao menos atingir 90% -valor de corte que define o serviço como bom.…”

Fatores epidemiológicos e incidência de hanseníase no estado da Bahia

“…Thus, it is noticeable that precarious living conditions favor the longevity of the epidemiological chain of bacillus transmission 35 . It is necessary to develop prevention strategies for the population with low education, focusing on access to knowledge of the entire infectious process of the pathogen through socio-educational activities, public policies, and lectures, among others 36 .…”

Spatial distribution of leprosy in municipalities of the state of Goiás in the year 2020

“…The use of spatial distribution maps, including the local empirical Bayesian model to smooth the impact of instability between bordering municipalities population and identify the likelihood between them (Souza et al, 2018), represents an important and accessible tool for identifying needs. That spans access and organization of health services, qualification of professionals involved in surveillance and assistance, fighting poverty, educational improvements, among other factors related to the disease (Araújo et al, 2020;GOIÁS, 2017).…”

“…It is understood that the hyperendemic picture in some macro regions -pointed out by this study and already identified in previous investigations (GOIÁS, 2017) -may be influenced by the immigration of cases from endemic regions, such as Tocantins, Mato Grosso and Bahia. This idea is supported by the fact that smoothed indicators make it feasible to understand the areas with the greatest risk for leprosy (even in places with small populations), while the long incubation period of the disease may influence the moment of its diagnosis (Souza et al, 2018), and taking into consideration the mobility between States and regions.…”