Completeness of notifications of severe acute respiratory syndrome at the national level and in a regional health care in the state of Minas Gerais, during the COVID-19 pandemic, 2020. / Completude das notificações de síndrome respiratória aguda grave no âmbito nacional e em uma regional de saúde de Minas Gerais, durante a pandemia de COVID-19, 2020.

OBJECTIVE: To analyze the completeness of notifications of severe acute respiratory syndrome cases on the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) during the COVID-19 pandemic, on the national database and on the Regional Health Center database of the state of Minas Gerais, Brazil, in 2020. METHODS: This was a descriptive study of the completeness of sociodemographic variables and those related to etiology, clinical condition, evolution and diagnostic criteria of SIVEP-Gripe. The level of completeness was classified as excellent (> 95%), good (90% to 95%), regular (80% to 90%), poor (50% to 80%) or very poor (< 50%). RESULTS: The percentage of variables with excellent completeness was only 18.1% on the national database, and 27.8% on the regional database. CONCLUSION: Both SIVEP-Gripe databases presented low completeness, making it necessary to improve the work process and routine training of professionals aimed at the correct filling.

Completeness of notifications of severe acute respiratory syndrome nationally and of a regional health in Minas Gerais, Brazil, during the COVID-19 pandemic, 2020 / Completitud de notificaciones de síndrome respiratorio agudo severo a nivel nacional y de salud regional en Minas Gerais, Brasil, durante la pandemia COVID-19, 2020 / Completude das notificações de síndrome respiratória aguda grave no âmbito nacional e em uma regional de saúde de Minas Gerais, durante a pandemia de COVID-19, 2020

Objective: To analyze the completeness of notifications of cases of severe acute respiratory illness from the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) during the COVID-19 pandemic, in the national database and in a regional database in the state of Minas Gerais, Brazil, in 2020. Methods: Descriptive study of the completeness of sociodemographic variables and those related to the etiology, clinical condition, evolution and diagnostic criteria of SIVEP-Influenza. Completeness was classified as excellent (greater than 95%), good (90 to 95%), fair (80 to 90%), poor (50 to 80%), and very poor (less than 50%). Results: The percentage of variables with excellent completeness was only 18.1% in the national database and 27.8% in the regional database. Conclusion: Low completeness of both SIVEP-Gripe databases was evidenced, making it necessary to improve the work process and routine training of professionals for the correct completion.

Objetivo: Analizar la completitud de las notificaciones de casos de síndrome respiratorio agudo severo del Sistema de Información de Vigilancia Epidemiológica de Influenza (SIVEP-Gripe) durante la pandemia de COVID-19, en la base de datos nacional y en una base de datos regional de salud en el estado de Minas Gerais, Brasil, en 2020. Métodos: Estudio descriptivo de la completitud de las variables sociodemográficas y las relacionadas con la etiología, cuadro clínico, evolución y criterios diagnósticos del SIVEP-Influenza. La exhaustividad se clasificó como excelente (más grande que 95%), buena (90 a 95%), regular (80 a 90%), mala (50 a 80%) y muy mala (menos que 50%). Resultados: El porcentaje de variables con excelente completitud fue solo del 18,1% en la base de datos nacional y del 27,8% en la base de datos regional. Conclusión: Se evidenció la baja completitud de ambas bases de datos SIVEP-Gripe, siendo necesario mejorar el proceso de trabajo y la rutina de capacitación de los profesionales para el correcto llenado.

Objetivo: Analisar a completude das notificações de casos de síndrome respiratória aguda grave no Sistema de Informação de Vigilência Epidemiológica da Gripo (SIVEP Gripe) durante a pandemia de COVID-19, na base de dados nacional e na base da Unidade Regional de Saúde do estado de Minas Gerais, Brasil, em 2020. Métodos: Estudo descritivo da completude das variáveis sociodemográficas e das relativas à etiologia, condição clínica, evolução e critérios diagnósticos do SIVEP-Gripe. O nível de completude foi classificado como excelente (>95%), bom (90 a 95%), regular (80 a 90%), ruim (50 a 80%) ou muito ruim (<50%). Resultados: O percentual de variáveis com completudo excelente foi de apenas 18,1% na base de dados nacional, e de 27,8% na base de dados regional. Conclusão: Evidenciou-se baixa completude de ambas bases dados do SIVEP-Gripe, tornando-se necessários aperfeiçoamentos no processo de trabalho e capacitações rotineiras dos profissionais para o correto preenchimento.

Completude das notificações de síndrome respiratória aguda grave no âmbito nacional e em uma regional de saúde de Minas Gerais, durante a pandemia de COVID-19, 2020 / Completitud de notificaciones de síndrome respiratorio agudo severo a nivel nacional y en una regional de salud en Minas Gerais, Brasil, durante la pandemia de COVID-19, 2020 / Completeness of notifications of severe acute respiratory syndrome at the national level and in a regional health care in the state of Minas Gerais, during the COVID-19 pandemic, 2020

Objetivo: Analisar a completude das notificações de casos de síndrome respiratória aguda grave no Sistema de Informação de Vigilância Epidemiológica da Gripe (SIVEP-Gripe) durante a pandemia de COVID-19, na base de dados nacional e na base da Unidade Regional de Saúde do estado de Minas Gerais, Brasil, em 2020. Métodos: Estudo descritivo da completude das variáveis sociodemográficas e das relativas à etiologia, condição clínica, evolução e critérios diagnósticos do SIVEP-Gripe. O nível de completude foi classificado como excelente (> 95%), bom (90% a 95%), regular (80% a 90%), ruim (50% a 80%) ou muito ruim (< 50%). Resultados: O percentual de variáveis com completude excelente foi de apenas 18,1% na base de dados nacional, e de 27,8% na base de dados regional. Conclusão: Evidenciou-se baixa completude de ambas as bases de dados do SIVEP-Gripe, tornando-se necessários aperfeiçoamentos no processo de trabalho e capacitações rotineiras dos profissionais para o correto preenchimento.

Resumen Objetivo: Analizar la completitud de las notificaciones de casos de síndrome respiratorio agudo severo del Sistema de Información de Vigilancia Epidemiológica de la Gripe (SIVEP-Gripe) durante la pandemia de COVID-19, en la base de datos nacional y en una base de datos regional de salud en el estado de Minas Gerais, Brasil, en 2020. Métodos: Estudio descriptivo de la completitud de las variables sociodemográficas y las relacionadas con la etiología, cuadro clínico, evolución y criterios diagnósticos del SIVEP-Gripe. El nivel de completitud se clasificó como excelente (> 95%), bueno (90% a 95%), regular (80% a 90%), malo a (50% a 80%) y muy malo (< 50%). Resultados: El porcentaje de variables con excelente completitud fue solo del 18,1% en la base de datos nacional y del 27,8% en la base de datos regional. Conclusión: Se evidenció la baja completitud de ambas bases de datos SIVEP-Gripe, lo que hace necesario mejorar el proceso de trabajo y la rutina de capacitación de los profesionales.

Objective: To analyze the completeness of notifications of severe acute respiratory syndrome cases on the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) during the COVID-19 pandemic, on the national database and on the Regional Health Center database of the state of Minas Gerais, Brazil, in 2020. Methods: This was a descriptive study of the completeness of sociodemographic variables and those related to etiology, clinical condition, evolution and diagnostic criteria of SIVEP-Gripe. The level of completeness was classified as excellent (> 95%), good (90% to 95%), regular (80% to 90%), poor (50% to 80%) or very poor (< 50%). Results: The percentage of variables with excellent completeness was only 18.1% on the national database, and 27.8% on the regional database. Conclusion: Both SIVEP-Gripe databases presented low completeness, making it necessary to improve the work process and routine training of professionals aimed at the correct filling.

Diet-induced obesity in animal models: points to consider and influence on metabolic markers.

Overweight and obesity are a worldwide public health problem. Obesity prevalence has increased considerably, which indicates the need for more studies to better understand these diseases and related complications. Diet induced-obesity (DIO) animal models can reproduce human overweight and obesity, and there are many protocols used to lead to excess fat deposition. So, the purpose of this review was to identify the key points for the induction of obesity through diet, as well as identifying which are the necessary endpoints to be achieved when inducing fat gain. For this, we reviewed the literature in the last 6 years, looking for original articles that aimed to induce obesity through the diet. All articles evaluated should have a control group, in order to verify the results found, and had worked with Sprague-Dawley and Wistar rats, or with C57BL-/-6 mice strain. Articles that induced obesity by other methods, such as genetic manipulation, surgery, or drugs were excluded, since our main objective was to identify key points for the induction of obesity through diet. Articles in humans, in cell culture, in non-rodent animals, as well as review articles, articles that did not have obesity induction and book chapters were also excluded. Body weight and fat gain, as well as determinants related to inflammation, hormonal concentration, blood glycemia, lipid profile, and liver health, must be evaluated together to better determination of the development of obesity. In addition, to select the best model in each circumstance, it should be considered that each breed and sex respond differently to diet-induced obesity. The composition of the diet and calorie overconsumption are also relevant to the development of obesity. Finally, it is important that a non-obese control group is included in the experimental design.