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1.
Cien Saude Colet ; 25(7): 2561-2570, 2020 Jul 08.
Article in Portuguese, English | MEDLINE | ID: covidwho-825162

ABSTRACT

The purpose of this paper was to analyze the food intake quality in one year-old children seen by a primary healthcare (PHC) service. This is a cross-sectional studied nested within a child oral health cohort study which collected data regarding children born in 2013 and monitored in Porto Alegre for two years. We applied a questionnaire on maternal variables and frequency of pediatric appointments, weight and height measurements, and children's food intake. To that end, a score was generated based on the points assigned according to SISVAN (meaning 'food and nutrition monitoring system,' run by the Brazilian Ministry of Health). A multivariate model was calculated using Poisson regression with robust variance. The sample comprised 249 children. We found 30.5% (76) of poor/regular dietary quality, which in the multivariate model was associated to the guardian's educational background, considering up to incomplete high school (PR = 2.14, CI95% = 1.03-4.44) and complete high school (PR = 1.70, CI95% = 0.81-3.54), as well as their failure to see a dentist (PR = 2.54, CI95% = 1.33-4.84) or having seen one before the age of four months (PR = 1.94, CI95% = 1.01-3.72). It is our conclusion that failing to see a dentist within the first year of life and lower maternal schooling negatively impact on children's dietary quality.


O objetivo foi analisar a qualidade do consumo alimentar de crianças com um ano de idade acompanhadas por um serviço de Atenção Primária à Saúde (APS). Trata-se de estudo transversal aninhado a uma coorte de saúde bucal infantil em que foram coletados dados de crianças nascidas em 2013 e acompanhadas por 2 anos, em Porto Alegre. Foi aplicado um questionário sobre variáveis maternas e frequência de consultas de puericultura, medidas antropométricas e consumo alimentar da criança. Para tal, foi gerado um escore a partir da pontuação criada conforme o SISVAN (Sistema de Vigilância Alimentar e Nutricional). Foi calculado um modelo multivariado, por meio da Regressão de Poisson com variância robusta. A amostra consistiu de 249 crianças. Encontrou-se 30,5% (76) de qualidade ruim/regular da alimentação, que no modelo multivariado esteve associada com nível educacional do responsável, sendo até ensino médio incompleto (RP = 2,14, IC95% = 1,03-4,44) e ensino médio completo (RP = 1,70, IC95% = 0,81-3,54), assim como não ter consultado com dentista (RP = 2,54, IC95% = 1,33-4,84) ou ter consultado até o quarto mês de idade (RP = 1,94, IC95% = 1,01-3,72). Conclui-se que não consultar com dentista no primeiro ano de vida e menor escolaridade materna repercutem negativamente na qualidade alimentar infantil.


Subject(s)
Eating , Primary Health Care , Brazil , Child , Cohort Studies , Cross-Sectional Studies , Humans , Infant
2.
Epidemiol Serv Saude ; 30(1): e2020680, 2021.
Article in English, Portuguese | MEDLINE | ID: covidwho-1076317

ABSTRACT

OBJECTIVE: To describe the Institute for Health Metrics and Evaluation (IHME) projections for the COVID-19 pandemic in Brazil and the Brazilian states, present their accuracy and discuss their implications. METHODS: The IHME projections from May to August 2020 for Brazil and selected states were compared with the ensuing reported number of cumulative deaths. RESULTS: The pandemic was projected to cause 182,809 deaths by December 1, 2020 in Brazil. An increase in mask use could reduce the projected death toll by ~17,000. The mean error in the cumulative number of deaths at 2, 4 and 6 weeks after the projections were made was 13%, 18% and 22%, respectively. CONCLUSION: Short and medium-term projections provide important and sufficiently accurate data to inform health managers, elected officials, and society at large. After following an arduous course up until August, the pandemic is projected to decline steadily although slowly, with ~400 deaths/day still occurring in early December.


Subject(s)
COVID-19/mortality , Forecasting , Pandemics/statistics & numerical data , SARS-CoV-2 , Brazil/epidemiology , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Humans , Masks/statistics & numerical data , Models, Theoretical , Mortality/trends , Physical Distancing , Time Factors
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