Suchergebnisse

Objetivo: desenvolver um modelo capaz de identificar a correlação entre determinantessocioeconômicos e incidência de tuberculose na população indígena do estado do Pará.Método: estudo analítico e quantitativo, no qual se construiu um modelo preditivo por meio deregressão de Poisson para 285 casos novos notificados no Sistema de Informação de Agravo eNotificação, no período de janeiro de 2010 a dezembro de 2015, no Estado do Pará.Resultados: partindo do modelo construído, pode-se confirmar que 86% do total de novos casosde tuberculose pode ser explicada pelas variáveis: recebimento de benefício social do governo,renda, escolaridade e sexo. A primeira variável foi considerada a mais significativa com nível dep<0.10.Conclusão: o modelo preditivo é útil como suporte para tomada de decisão, pois permite direcionarações de controle da tuberculose aos indivíduos sob maior risco: indígenas do sexo masculino, semescolaridade e que não recebem benefícios sociais. ; Objetivo: Desarrollar un modelo capaz de identificar la correlación entre determinantessocioeconómicos e incidencia de tuberculosis en la población indígena del estado de Pará.Método: Estudio analítico, cuantitativo, en el que se construyó un modelo predictivo medianteregresión de Poisson para 285 casos nuevos reportados al Sistema de Informação de Agravoe Notificação entre enero de 2010 y diciembre de 2015 en el estado de Pará.Resultados: Partiendo del modelo construido, puede confirmarse que el 86% del total denuevos casos de tuberculosis encuentra explicación mediante las variables: recepción debeneficio social gubernamental, ingresos, escolarización y sexo. La primera variable fueconsiderada la más significativa, con nivel p<0,10.Conclusión: El modelo predictivo es útil como soporte a la toma de decisiones, permitiendoorientar acciones de control de la tuberculosis hacia los individuos bajo mayores riesgos:indígenas de sexo masculino, sin escolarización, que no reciben beneficios sociales. ; Objective: develop a model that is able to identify the correlation between socioeconomicdeterminants and incidence of tuberculosis in the indigenous population of the state of Pará.Method: quantitative analytical study, which built a predictive model using Poisson regressionfor 285 new cases reported in the Notifiable Diseases Information System, from January 2010to December 2015, in the state of Pará.Results: the model confirmed that 86% of the total number of new cases can be explained bythe variables: receipt of social benefits from the government, income, education and sex. Thefirst variable was considered the most significant with p<0.10.Conclusion: the predictive model is useful as a support for making decisions, since it enablesfocusing tuberculosis control actions on individuals at greater risk: indigenous men, with noeducation, who do not receive social benefits.

Objetivo: desenvolver um modelo capaz de identificar a correlação entre determinantessocioeconômicos e incidência de tuberculose na população indígena do estado do Pará.Método: estudo analítico e quantitativo, no qual se construiu um modelo preditivo por meio deregressão de Poisson para 285 casos novos notificados no Sistema de Informação de Agravo eNotificação, no período de janeiro de 2010 a dezembro de 2015, no Estado do Pará.Resultados: partindo do modelo construído, pode-se confirmar que 86% do total de novos casosde tuberculose pode ser explicada pelas variáveis: recebimento de benefício social do governo,renda, escolaridade e sexo. A primeira variável foi considerada a mais significativa com nível dep<0.10.Conclusão: o modelo preditivo é útil como suporte para tomada de decisão, pois permite direcionarações de controle da tuberculose aos indivíduos sob maior risco: indígenas do sexo masculino, semescolaridade e que não recebem benefícios sociais. ; Objetivo: Desarrollar un modelo capaz de identificar la correlación entre determinantessocioeconómicos e incidencia de tuberculosis en la población indígena del estado de Pará.Método: Estudio analítico, cuantitativo, en el que se construyó un modelo predictivo medianteregresión de Poisson para 285 casos nuevos reportados al Sistema de Informação de Agravoe Notificação entre enero de 2010 y diciembre de 2015 en el estado de Pará.Resultados: Partiendo del modelo construido, puede confirmarse que el 86% del total denuevos casos de tuberculosis encuentra explicación mediante las variables: recepción debeneficio social gubernamental, ingresos, escolarización y sexo. La primera variable fueconsiderada la más significativa, con nivel p<0,10.Conclusión: El modelo predictivo es útil como soporte a la toma de decisiones, permitiendoorientar acciones de control de la tuberculosis hacia los individuos bajo mayores riesgos:indígenas de sexo masculino, sin escolarización, que no reciben beneficios sociales. ; Objective: develop a model that is able to identify the correlation between socioeconomicdeterminants and incidence of tuberculosis in the indigenous population of the state of Pará.Method: quantitative analytical study, which built a predictive model using Poisson regressionfor 285 new cases reported in the Notifiable Diseases Information System, from January 2010to December 2015, in the state of Pará.Results: the model confirmed that 86% of the total number of new cases can be explained bythe variables: receipt of social benefits from the government, income, education and sex. Thefirst variable was considered the most significant with p<0.10.Conclusion: the predictive model is useful as a support for making decisions, since it enablesfocusing tuberculosis control actions on individuals at greater risk: indigenous men, with noeducation, who do not receive social benefits.

Objetivo: desenvolver um modelo capaz de identificar a correlação entre determinantessocioeconômicos e incidência de tuberculose na população indígena do estado do Pará.Método: estudo analítico e quantitativo, no qual se construiu um modelo preditivo por meio deregressão de Poisson para 285 casos novos notificados no Sistema de Informação de Agravo eNotificação, no período de janeiro de 2010 a dezembro de 2015, no Estado do Pará.Resultados: partindo do modelo construído, pode-se confirmar que 86% do total de novos casosde tuberculose pode ser explicada pelas variáveis: recebimento de benefício social do governo,renda, escolaridade e sexo. A primeira variável foi considerada a mais significativa com nível dep<0.10.Conclusão: o modelo preditivo é útil como suporte para tomada de decisão, pois permite direcionarações de controle da tuberculose aos indivíduos sob maior risco: indígenas do sexo masculino, semescolaridade e que não recebem benefícios sociais. ; Objetivo: Desarrollar un modelo capaz de identificar la correlación entre determinantessocioeconómicos e incidencia de tuberculosis en la población indígena del estado de Pará.Método: Estudio analítico, cuantitativo, en el que se construyó un modelo predictivo medianteregresión de Poisson para 285 casos nuevos reportados al Sistema de Informação de Agravoe Notificação entre enero de 2010 y diciembre de 2015 en el estado de Pará.Resultados: Partiendo del modelo construido, puede confirmarse que el 86% del total denuevos casos de tuberculosis encuentra explicación mediante las variables: recepción debeneficio social gubernamental, ingresos, escolarización y sexo. La primera variable fueconsiderada la más significativa, con nivel p<0,10.Conclusión: El modelo predictivo es útil como soporte a la toma de decisiones, permitiendoorientar acciones de control de la tuberculosis hacia los individuos bajo mayores riesgos:indígenas de sexo masculino, sin escolarización, que no reciben beneficios sociales. ; Objective: develop a model that is able to identify the correlation between socioeconomicdeterminants and incidence of tuberculosis in the indigenous population of the state of Pará.Method: quantitative analytical study, which built a predictive model using Poisson regressionfor 285 new cases reported in the Notifiable Diseases Information System, from January 2010to December 2015, in the state of Pará.Results: the model confirmed that 86% of the total number of new cases can be explained bythe variables: receipt of social benefits from the government, income, education and sex. Thefirst variable was considered the most significant with p<0.10.Conclusion: the predictive model is useful as a support for making decisions, since it enablesfocusing tuberculosis control actions on individuals at greater risk: indigenous men, with noeducation, who do not receive social benefits.

Objective: To estimate the incidence, mortality and lethality rates of COVID-19 among Indigenous Peoples in the Brazilian Amazon. Additionally, to analyze how external threats can contribute to spread the disease in Indigenous Lands (IL). Methods: The Brazilian Amazon is home to nearly half a million Indigenous persons, representing more than 170 ethnic groups. As a pioneer in heading Indigenous community-based surveillance (I-CBS) in Brazil, the Coordination of the Indigenous Organizations of the Brazilian Amazon (COIAB) started to monitor Indigenous COVID-19 cases in March of 2020. Brazil's Ministry of Health (MOH) was the main source of data regarding non-Indigenous cases and deaths; to contrast the government's tally, we used the information collected by I-CBS covering 25 Special Indigenous Sanitary Districts (DSEI) in the Brazilian Amazon. The incidence and mortality rates of COVID-19 were calculated using the total number of new cases and deaths accumulated between the 9th and 40th epidemiological weeks. We studied (a) the availability of health care facilities to attend to Indigenous Peoples; (b) illegal mines, land grabbing, and deforestation to perform a geospatial analysis to assess how external threats affect Indigenous incidence and mortality rates. We used the Generalized Linear Model (GLM) with Poisson regression to show the results. Results: MOH registered 22,127 cases and 330 deaths, while COIAB's survey recorded 25,356 confirmed cases and 670 deaths, indicating an under-reporting of 14 and 103%, respectively. Likewise, the incidence and mortality rates were 136 and 110% higher among Indigenous when compared with the national average. In terms of mortality, the most critical DSEIs were Alto Rio Solimões, Cuiabá, Xavante, Vilhena and Kaiapó do Pará. The GLM model reveals a direct correlation between deforestation, land grabbing and mining, and the incidence of cases among the Indigenous. Conclusion: Through this investigation it was possible to verify that not only the incidence and mortality rates due to COVID-19 among Indigenous Peoples are higher than those observed in the general population, but also that the data presented by the federal government are underreported. Additionally, it was evident that the presence of illegal economic activities increased the risk of spreading COVID-19 in ILs.

Objective: To estimate the incidence, mortality and lethality rates of COVID-19 among Indigenous Peoples in the Brazilian Amazon. Additionally, to analyze how external threats can contribute to spread the disease in Indigenous Lands (IL). Methods: The Brazilian Amazon is home to nearly half a million Indigenous persons, representing more than 170 ethnic groups. As a pioneer in heading Indigenous community-based surveillance (I-CBS) in Brazil, the Coordination of the Indigenous Organizations of the Brazilian Amazon (COIAB) started to monitor Indigenous COVID-19 cases in March of 2020. Brazil's Ministry of Health (MOH) was the main source of data regarding non-Indigenous cases and deaths; to contrast the government's tally, we used the information collected by I-CBS covering 25 Special Indigenous Sanitary Districts (DSEI) in the Brazilian Amazon. The incidence and mortality rates of COVID-19 were calculated using the total number of new cases and deaths accumulated between the 9th and 40th epidemiological weeks. We studied (a) the availability of health care facilities to attend to Indigenous Peoples; (b) illegal mines, land grabbing, and deforestation to perform a geospatial analysis to assess how external threats affect Indigenous incidence and mortality rates. We used the Generalized Linear Model (GLM) with Poisson regression to show the results. Results: MOH registered 22,127 cases and 330 deaths, while COIAB's survey recorded 25,356 confirmed cases and 670 deaths, indicating an under-reporting of 14 and 103%, respectively. Likewise, the incidence and mortality rates were 136 and 110% higher among Indigenous when compared with the national average. In terms of mortality, the most critical DSEIs were Alto Rio Solimões, Cuiabá, Xavante, Vilhena and Kaiapó do Pará. The GLM model reveals a direct correlation between deforestation, land grabbing and mining, and the incidence of cases among the Indigenous. Conclusion: Through this investigation it was possible to verify that not only the incidence and mortality ...