Dataset Information

Total and Added Sugar Intake: Assessment in Eight Latin American Countries.

ABSTRACT: Non-communicable diseases are growing at an alarming rate in Latin America. We assessed total and added sugar intake in Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela, to verify the adequacy of the World Health Organization's recommendations, considering gender, socioeconomic level (SEL) and age. A total of 9218 non-institutionalized individuals living in urban areas (age range 15-65 years) were assessed in the Latin American Study of Nutrition and Health (ELANS), a multicenter household population-based cross-sectional survey. Socio-demographic data were collected. Total and added sugar intakes were measured using two non-consecutive 24-h dietary recalls. The prevalence of excessive sugar intake was estimated. A large proportion of individuals showed high consumption of total and added sugar intake, which reflected in the high prevalence of excessive sugar intake. With minimal differences across countries, in general, women, individuals with high SEL, and younger people had higher percentages of total energy intake from total and added sugar intake, and of contribution of carbohydrates from total and added sugars. Thus, there is high consumption of total and added sugar intake in the Latin American countries with some peculiarities considering socio-demographic variables, which should be considered in each country's health intervention proposals.

SUBMITTER: Fisberg M

PROVIDER: S-EPMC5946174 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Total and Added Sugar Intake: Assessment in Eight Latin American Countries.

Fisberg Mauro M Kovalskys Irina I Gómez Georgina G Rigotti Attilio A Sanabria Lilia Yadira Cortés LYC García Martha Cecilia Yépez MCY Torres Rossina Gabriella Pareja RGP Herrera-Cuenca Marianella M Zimberg Ioná Zalcman IZ Koletzko Berthold B Pratt Michael M Aznar Luis A Moreno LAM Guajardo Viviana V Fisberg Regina Mara RM Sales Cristiane Hermes CH Previdelli Ágatha Nogueira ÁN

Nutrients 20180322 4

Non-communicable diseases are growing at an alarming rate in Latin America. We assessed total and added sugar intake in Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela, to verify the adequacy of the World Health Organization's recommendations, considering gender, socioeconomic level (SEL) and age. A total of 9218 non-institutionalized individuals living in urban areas (age range 15-65 years) were assessed in the Latin American Study of Nutrition and Health (ELANS), a ...[more]

PMID: 29565308

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Project description:BackgroundNovel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates.ObjectiveThe aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America.MethodsA machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information.ResultsOur results show that ARGO-like models' predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available.ConclusionsWe demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates.

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Dataset Information

Total and Added Sugar Intake: Assessment in Eight Latin American Countries.

Publications

Total and Added Sugar Intake: Assessment in Eight Latin American Countries.

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