Inteligência geoespacial e análises da saúde : sua aplicação e utilidade em uma cidade com alta incidência de tuberculose

Introdução: Inteligência geoespacial e análises de saúde têm sido utilizadas para identificação de hotspots de tuberculose (TB) e para melhor entendimento da sua relação com fatores sociais e econômicos. O objetivo desse estudo foi utilizar a inteligência geoespacial para avaliar a distribuição da TB e suas correlações com o índice de desenvolvimento humano (IDH) em uma cidade com alta incidência de TB no Brasil. Métodos: Foi realizado um estudo ecológico, utilizando dados do Sistema de Informação Nacional de Doenças de Notificação (SINAN) para identificar os casos de TB em Canoas, cidade da região metropolitana da capital do Rio Grande do Sul. O georreferenciamento foi realizado utilizando o software QGIS 2.0 e o Google Maps API 3.0. Foi aplicada inteligência geoespacial para detectar onde na cidade existiam os clusters para os casos de TB, e avaliar a associação do IDH de uma área (longevidade, educação e renda) com a distribuição espacial da TB. Resultados: Durante o período do estudo (2011 – 2013), houve 737 casos de TB. Os casos de TB apresentaram heterogeneidade ao longo dos 29 bairros da cidade. Os bairros com IDH-renda menor que a média tiveram maior incidência de TB (p= 0.036). Conclusão: Em conclusão, encontramos vários hotspots de TB nos 29 bairros, e uma associação inversa entre IDH-renda e a incidência de TB. Estes achados fornecem informações úteis e podem ajudar a guiar os programas de controle de TB.Background: Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors.The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. Methods: We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases in Canoas, city of the metropolitan region of the capital of Rio Grande do Sul. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area’s HDI (each one of the components – longevity, education, and income) with TB spatial distribution. Results: During the study period (2011-2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p=0.036). Conclusions: In conclusion, we found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs

Macrophage migration inhibitory factor -173 G>C single nucleotide polymorphism and its association with active pulmonary tuberculosis

Purpose The establishment of candidate genes associated with susceptibility to TB is a challenge especially due to divergent frequencies among different populations. The objective of this study was to evaluate the association between macrophage migration inhibitory factor (MIF) -173 G>C single nucleotide polymorphism (SNP) and susceptibility to pulmonary TB in a population of southern Brazil. Methods Case-control study. Patients > 18 years old, diagnosed with pulmonary TB were included. The control group consisted of blood donors and household contacts, not relatives, healthy and > 18 years old. MIF -173 G>C SNPs were genotyped using real-time PCR using a TaqMan SNP Genotyping assay. Results 174 patients and 166 controls were included. There were no statistically significant differences between cases and controls regarding genotype prevalence (p>0.05). Comparing patients with normal genotype (GG) with those with at least one C allele, there was also no statistically significant difference (p = 0.135). Also, there was no statistically significant difference comparing the homozygous for the mutation (CC) with the other patients (GG and CG) (p = 0.864). Conclusions We did not find association between MIF -173 G>C polymorphism and susceptibility to pulmonary TB

LEISHMANIOSE VISCERAL CANINA: DETECÇÃO DE DNA EM SORO POR PCR EM TEMPO REAL

O diagnóstico de leishmaniose visceral canina (LVC) pelas técnicas imunológicas ainda não é satisfatório, uma vez que a detecção de anticorpos nem sempre corresponde a presença do parasita no animal. O objetivo deste trabalho foi detectar DNA de Leishmania em soro de caninos por PCR em tempo real e comparar os resultados com os obtidos pelos métodos imunológicos (DPP® e ELISA). O DNA foi extraído de soro de 60 caninos e analisado por PCR em tempo real utilizando como alvo a região do cinetoplasto. Na comparação dos resultados, o teste molecular mostrou uma sensibilidade e especificidade de 86,2% e 93,5%, respectivamente. É possível concluir que este método poderia auxiliar em um diagnóstico de LVC mais preciso, uma vez que confirma a presença do DNA de Leishmania nos cães infectados.

Inteligência geoespacial e análises da saúde : sua aplicação e utilidade em uma cidade com alta incidência de tuberculose

Introdução: Inteligência geoespacial e análises de saúde têm sido utilizadas para identificação de hotspots de tuberculose (TB) e para melhor entendimento da sua relação com fatores sociais e econômicos. O objetivo desse estudo foi utilizar a inteligência geoespacial para avaliar a distribuição da TB e suas correlações com o índice de desenvolvimento humano (IDH) em uma cidade com alta incidência de TB no Brasil. Métodos: Foi realizado um estudo ecológico, utilizando dados do Sistema de Informação Nacional de Doenças de Notificação (SINAN) para identificar os casos de TB em Canoas, cidade da região metropolitana da capital do Rio Grande do Sul. O georreferenciamento foi realizado utilizando o software QGIS 2.0 e o Google Maps API 3.0. Foi aplicada inteligência geoespacial para detectar onde na cidade existiam os clusters para os casos de TB, e avaliar a associação do IDH de uma área (longevidade, educação e renda) com a distribuição espacial da TB. Resultados: Durante o período do estudo (2011 – 2013), houve 737 casos de TB. Os casos de TB apresentaram heterogeneidade ao longo dos 29 bairros da cidade. Os bairros com IDH-renda menor que a média tiveram maior incidência de TB (p= 0.036). Conclusão: Em conclusão, encontramos vários hotspots de TB nos 29 bairros, e uma associação inversa entre IDH-renda e a incidência de TB. Estes achados fornecem informações úteis e podem ajudar a guiar os programas de controle de TB.Background: Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors.The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. Methods: We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases in Canoas, city of the metropolitan region of the capital of Rio Grande do Sul. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area’s HDI (each one of the components – longevity, education, and income) with TB spatial distribution. Results: During the study period (2011-2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p=0.036). Conclusions: In conclusion, we found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs

Macrophage migration inhibitory factor -173 G>C single nucleotide polymorphism and its association with active pulmonary tuberculosis.

PurposeThe establishment of candidate genes associated with susceptibility to TB is a challenge especially due to divergent frequencies among different populations. The objective of this study was to evaluate the association between macrophage migration inhibitory factor (MIF) -173 G>C single nucleotide polymorphism (SNP) and susceptibility to pulmonary TB in a population of southern Brazil.MethodsCase-control study. Patients > 18 years old, diagnosed with pulmonary TB were included. The control group consisted of blood donors and household contacts, not relatives, healthy and > 18 years old. MIF -173 G>C SNPs were genotyped using real-time PCR using a TaqMan SNP Genotyping assay.Results174 patients and 166 controls were included. There were no statistically significant differences between cases and controls regarding genotype prevalence (p>0.05). Comparing patients with normal genotype (GG) with those with at least one C allele, there was also no statistically significant difference (p = 0.135). Also, there was no statistically significant difference comparing the homozygous for the mutation (CC) with the other patients (GG and CG) (p = 0.864).ConclusionsWe did not find association between MIF -173 G>C polymorphism and susceptibility to pulmonary TB

Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil

Background: Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. Methods: We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area’s HDI (each one of the components — longevity, education, and income) with TB spatial distribution. Results: During the study period (2011–2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036). Conclusions: We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs