Integrated Spreadsheets for Nutritional Analysis of Population Diet Surveys.

It can be challenging to calculate nutrient intakes in population diet surveys because existing nutritional analysis software is generally oriented toward analyzing intakes of individuals and may not allow users to input or easily modify the food composition data used in the analysis. These are drawbacks that are more problematic in low- and middle-income country settings. While there are numerous software-assisted dietary assessment platforms that conduct onboard nutritional analysis and are appropriate for use in large surveys, they are often similarly limited, and further restrict users to specific assessment modalities. This paper presents a multifunctional system of integrated spreadsheets for nutritional analysis of population diet surveys (ISNAPDS) that provides a solution for situations in which data have been collected but cannot be adequately analyzed with existing software. The protocol involves supplying the system with fully customizable data on food composition, food group classifications, and food intake (food intake in g/day may be entered directly or calculated based on user-supplied intake frequencies and either standard or variable serving sizes). Following data entry, the user modifies a set of simple pre-populated formulas to match them to the structure of the input data and the system applies these formulas to calculate nutrient and food group intakes, and the contributions of food groups to nutrient intakes for all members of the survey population. The flexibility of the ISNAPDS system allows it to accommodate the global diversity of foods consumed and analyze quantitative, semiquantitative, and nonquantitative food consumption data collected using prospective and retrospective assessment methods employing different reference periods and portion size estimation methods. To date, the system has been applied in published and ongoing analyses of 24 h recall, diet record, food frequency, and disaggregated household consumption data from population surveys in China, Ethiopia, India, Mongolia, Thailand, and a multi-country analysis of 10 sub-Saharan African countries.