Continuous health education and care regarding socio-economic issues should be continuously developed and provided to people with CD.

No abstract

Omega-3 long-chain polyunsaturated fatty acids play a central role in neuronal growth and in the development of the human brain, since they are essential elements which depend on intake through diet to ensure an adequate amount. Fish and seafood are the main dietary sources of these fatty acids in Spain and in other countries. In order to assess the effect of the intake of common foods containing high amounts of omega-3 polyunsaturated fatty acids, a food frequency questionnaire was administered to parents of children and adolescents attending a primary school in Valencia (Spain), and the intake of dietary omega-3 such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) was estimated based on their fish/seafood consumption. Low frequencies of intake were significantly (p < 0.05) lower for many types of fish/seafood in children compared to adolescents. 27.5% of children/adolescents did not eat lean fish or other types (19.8% of the sample did not eat fatty fish, and 71.8% did not eat smoked fish) and 20–60% of the sample consumed seafood only once–three times a month, leading to a reduced estimated intake of EPA+DHA below that recommended for both groups by public health agencies. Social aspects, such as the type of work done by mothers and their educational levels are significant factors (p < 0.05 in both cases) affecting children’s/adolescents’ intake of DHA+EPA. Dietary interventions to increase the consumption of fish and seafood are strongly advised, and health promotion strategies should be aimed at the family level and fight against gender disparities.

Abstract

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.