RESUMO
INTRODUÇÃO: A dengue é considerada uma das principais arboviroses mundiais, caracterizada no Brasil pelo aumento de casos graves e óbitos. OBJETIVO: realizar análise espacial dos casos prováveis de dengue em São Luís - MA. MÉTODOS: Estudo ecológico de base populacional dos casos prováveis de dengue, notificados no Sistema de Informação de Agravos de Notificação (SINAN) em 2015 e 2016, ocorridos no município de São Luís MA. Foram georreferenciados 4.681 casos prováveis de dengue por setores censitários, calculadas as taxas de incidência e ajustadas através do estimador bayesiano empírico local. Foi utilizado o estimador de densidade de Kernel e Moran Global e Local para a análise espacial. RESULTADOS: Evidenciou-se através do estimador de densidade de Kernel, áreas quentes (alta-densidade) nos setores censitários da região noroeste do município. As taxas de incidência foram ajustadas pela aplicação do método bayesiano empírico local, identificando-se maior quantidade de setores com média e alta incidência. A partir do índice de Moran global foi evidenciada autocorrelação espacial positiva estatisticamente significativa para as taxas de incidência de dengue (I=0,69; p<0,001) e para as taxas de incidência ajustadas pelo método bayesiano (I=0,80; p<0,001). De acordo com o índice de Moran local, identificou-se clusters de setores de alta incidência de dengue em áreas com alta densidade populacional na região nordeste e noroeste do município. CONCLUSÃO: A pesquisa demonstrou que os estimadores bayesianos ajudaram a minimizar os problemas de subnotificação e da influência do tamanho populacional nos setores censitários.
INTRODUCTION: Dengue is considered one of the main arboviruses in the world, characterized in Brazil by the increase in severe cases and deaths. OBJECTIVE: to perform spatial analysis of probable dengue cases in São Luís - MA. METHODS: Population-based ecological study of probable dengue cases, reported in the Notifiable Diseases Information System (SINAN) in 2015 and 2016, which took place in the city of São Luís - MA. 4,681 probable dengue cases were georeferenced by census sectors, incidence rates were calculated and adjusted using the local empirical Bayesian estimator. The Kernel and Moran Global and Local density estimator was used for spatial analysis. RESULTS: Hot areas (high-density) in the census sectors of the northwest region of the municipality were evidenced through the Kernel density estimator. Incidence rates were adjusted by applying the local empirical Bayesian method, identifying a greater number of sectors with medium and high incidence. From the global Moran index, statistically significant positive spatial autocorrelation was evidenced for the dengue incidence rates (I = 0.69; p <0.001) and for the incidence rates adjusted by the Bayesian method (I = 0.80; p <0.001). According to the local Moran index, clusters of sectors with a high incidence of dengue were identified in areas with high population density in the northeast and northwest regions of the municipality. CONCLUSION: The research demonstrated that Bayesian estimators helped to minimize the problems of underreporting and the influence of population size on census tracts.
INTRODUCCIÓN: El dengue es considerado una de las principales arbovirosis a nivel mundial, caracterizada en Brasil por el aumento de casos graves y muertes. OBJETIVO: Realizar un análisis espacial de los casos probables de dengue en São Luís - MA. MÉTODOS: Estudio ecológico de base poblacional de los casos probables de dengue, notificados en el Sistema de Informação de Agravos de Notificação (SINAN) en 2015 y 2016, ocurridos en el municipio de São Luís - MA. Se georreferenciaron 4.681 casos probables de dengue por sectores censales, se calcularon las tasas de incidencia y se ajustaron mediante el estimador empírico bayesiano local. Para el análisis espacial se utilizó el estimador de densidad Kernel y Moran global y local. RESULTADOS: Se evidenció a través del estimador de densidad Kernel, áreas calientes (de alta densidad) en los sectores censales de la región noroeste del municipio. Las tasas de incidencia se ajustaron mediante la aplicación del método bayesiano empírico local, identificándose una mayor cantidad de setores con incidencia media y alta. A partir del índice global de Moran se evidenció una autocorrelación espacial positiva estadísticamente significativa para las tasas de incidencia de dengue (I=0,69; p<0,001) y para las tasas de incidencia ajustadas por el método bayesiano (I=0,80; p<0,001). Según el índice local de Moran, se identificaron clusters de sectores de alta incidencia de dengue en áreas con alta densidad de población en las regiones noreste y noroeste del municipio. CONCLUSIÓN: La investigación demostró que los estimadores bayesianos ayudaron a minimizar los problemas de infradeclaración y la influencia del tamaño de la población en los sectores censales.
Assuntos
Humanos , Masculino , Feminino , Incidência , Dengue/prevenção & controle , Vigilância em Saúde Pública/métodos , Análise Espacial , Saúde Pública/estatística & dados numéricos , Densidade Demográfica , Monitoramento Epidemiológico , Sistemas de Informação em Saúde/instrumentação , Setor CensitárioRESUMO
BACKGROUND: Brazilian Primary Care Facilities (PCF) provide primary care and must offer dental services for diagnosis, prevention, and treatment of diseases. According to a logic of promoting equity, PCF should be better structured in less developed places and with higher need for oral health services. OBJECTIVE: To analyze the structure of dental caries services in the capitals of the Brazilian Federative Units and identify whether socioeconomic factors and caries (need) are predictors of the oral health services structure. METHODS: This is an ecological study with variables retrieved from different secondary databases, clustered for the level of the federative capitals. Descriptive thematic maps were prepared, and structural equations were analyzed to identify oral health service structure's predictors (Alpha = 5%). Four models with different outcomes related to dental caries treatment were tested: 1) % of PCF with a fully equipped office; 2) % of PCF with sufficient instruments, and 3) % of PCF with sufficient supplies; 4) % of PCF with total structure. RESULTS: 21.6% of the PCF of the Brazilian capitals had a fully equipped office; 46.9% had sufficient instruments, and 30.0% had sufficient supplies for caries prevention and treatment. The four models evidenced proper fit indexes. A correlation between socioeconomic factors and the structure of oral health services was only noted in model 3. The worse the socioeconomic conditions, the lower the availability of dental supplies (standard factor loading: 0.92, P = 0.012). Estimates of total, direct and indirect effects showed that dental caries experience observed in the Brazilian population by SB-Brasil in 2010 did not affect the outcomes investigated. CONCLUSION: Material resources are not equitably distributed according to the socioeconomic conditions and oral health needs of the population of the Brazilian capitals, thus contributing to persistent oral health inequities in the country.
