Publications by authors named "R Micha"
Food Compass is a nutrient profiling system used to assess the healthfulness of diverse foods, beverages and meals. Here we present a revised version of Food Compass (Food Compass 2.0) incorporating new data on specific ingredients and the latest diet-health evidence.
View Article and Find Full Text PDFObjective: To quantify global intakes of sugar sweetened beverages (SSBs) and trends over time among children and adolescents.
Design: Population based study.
Setting: Global Dietary Database.
The challenges in the characterization of the nutritional quality of grain foods comprise obstacles to public health actions toward promotion of healthier grain-based foods. The present study investigated how carbohydrate metrics related to glycemic index (GI), glycemic load (GL), and warning labels of grain foods consumed by individuals living in São Paulo, Brazil. Information on intake of grain foods at individual level was obtained using 24 h recalls within a cross-sectional population-based survey conducted in 2015.
View Article and Find Full Text PDFArticle Synopsis
- The Global Dietary Database (GDD) improved its methods to collect and standardize individual-level dietary data from global nutrition surveys, ensuring easier access and analysis of this information.
- Using a detailed food classification system (FoodEx2), the GDD harmonized data from 600 identified surveys, ultimately integrating 52 diverse dietary surveys that spanned various income levels and included participants of all ages and backgrounds.
- The findings revealed that many surveys from lower-income countries reported fewer nutrients, indicating gaps in essential nutrient data, especially for those related to chronic diseases, highlighting the need for improved nutrition tracking.
Background: Few simulation models have incorporated the interplay of diabetes, obesity, and cardiovascular disease (CVD); their upstream lifestyle and biological risk factors; and their downstream effects on health disparities and economic consequences.
Methods: We developed and validated a US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) model that incorporates demographic, clinical, and lifestyle risk factors to jointly predict overall and racial-ethnic groups-specific obesity, diabetes, CVD, and cause-specific mortality for the US adult population aged 40 to 79 y at baseline. An individualized health care cost prediction model was further developed and integrated.