A Framework for the Predictive Modelling of Public Health Nutrition Strategies

Within public health nutrition, it is of crucial importance to monitor adequate as well as safe nutritional intakes within a population. Food policy initiatives around dietary intakes include voluntary industry reformulation, portion size reductions, food fortification and consumer behavioral changes. Predictive intake models can be used to assess the likely impact of such policies before their implementation. Creme Nutrition^®, a web based dietary intake software which combines national food consumption and food composition data, includes various models to assess the impact of different strategies, including probabilistic food substitution, portion size modification, and food reformulation. A case study was used to demonstrate the model for sodium reduction using the National Health and Nutrition Examination Survey (NHANES) 2008-2010. In this model, sodium content in bread was reduced by 20%, soups were replaced by low sodium soups containing no more than 120mg/100g and pretzel consumption was substituted by one apple at a replacement probability of 70% to model partial consumer adherence probabilistically. After modelling sodium intakes in the US population, mean total daily sodium intakes in adults decrease from 3671.9±34.1mg/day to 3512.9±33mg/day. For the high sodium consumers (97.5%ile) total daily sodium intakes are reduced from 7337.85±185.6mg/day to 7090.7±170.8mg/day. The proposed approach demonstrates the viability of assessing and combining different scenarios to predict the impact of a change on a population’s or a sub-population’s diet via public health initiatives.

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