Probabilistic Mathematical Model for Dietary Consumption and Exposure Assessment in the Absence of Data from National Nutrition Surveys: A Case Study on Saudi Arabia

Abstract

<b><i>Introduction:</i></b> Noncommunicable diseases, which account for 71% of annual global deaths, necessitate the implementation of targeted legislative measures to reduce exposure to risk factors, particularly those related to nutrition. Accurately assessing these risk factors is challenging owing to infrequent national surveys, high costs, and uncertainties from human behavior. Thus, addressing these issues is crucial. This study aimed to develop a model for estimating dietary exposure. <b><i>Methods:</i></b> Our study utilizes the National Nutrient Consumption (NNC) model, which employs a probabilistic and cost-effective approach. This model leverages open data and focuses on supply chain dynamics and uncertainties, integrating import, production, and stock levels. It estimates commodity supply, consumption, and nutrient intake using predictive coefficients from historical data. Model accuracy was assessed using mean absolute error, correlation coefficient, and one-sample <i>t</i> test. The findings were evaluated against the recommended intake outlined in the Technical Regulation of Food Labeling (SFDA.FD 2233). <b><i>Results:</i></b> The model demonstrated high accuracy in estimating dietary intake, with minimal error margins. For both genders, the 50th percentile nutrient exposure exceeded the reference values – with males consuming 3,310 kcal (+65%), 144 g of total fat (+105%), 33.8 g of saturated fat (+69%), 86 g of free sugar (+72%), and 2,545 mg of sodium daily (+0.06%); females consumed 3,306 kcal (+65%), 143 g of total fat (+104%), 33.5 g of saturated fat (+67%), 86 g of free sugar (72%), and 2,500 mg of sodium (+4%). <b><i>Conclusion:</i></b> The model, both cost-effective and accurate, identifies elevated nutritional risk factors in Saudi Arabia, informing targeted health policies.