Data retrieval followed the 2010 Global Burden of Diseases study’s comparative risk assessment framework,33 collecting quantitative data on consumption in 16 age- and sex-specific subgroups across 21 world regions (see eTable 1 of data supplement) and two time periods (1990 and 2010). Most published or publically available dietary data were limited or not in the relevant format. For 173 surveys, 99 corresponding members provided original raw data to us or re-analysed their raw data according to our specifications, providing age and sex stratified dietary results in specified metrics and units using a standardised electronic format (appendix 1 of data supplement). Optimal and alternative metrics and units were defined for each dietary factor,19 (link) with optimal units matching those of studies used to evaluate relationships with disease risk as well as major dietary guidelines. Based on these criteria, dietary factors were evaluated as percentage energy (saturated fat, omega 6 polyunsaturated fat, trans fat) or as mg/day standardised using the residual method34 to 2000 kcal/day (dietary cholesterol, seafood omega 3 fat, plant omega 3 fat). The surveys providing data on dietary fats and oils are listed in eTable 2 of data supplement.
For each survey, we extracted data on survey characteristics, dietary metrics, units, and mean and distribution (such as standard deviation) of consumption of each dietary fat and oil, by age and sex (eTable 2). Data were double checked for extraction errors and assessed for plausibility. We assessed survey quality by evaluating evidence for selection bias, sample representativeness, response rate, and validity of diet assessment method.19 (link) Measurement comparability across surveys was maximised by using a standardised data analysis approach that (1) accounted for sampling strategies within the survey by including sampling weights (if available), (2) used the average of all days of dietary assessment to quantify mean intakes, (3) used a corrected population standard deviation to account for within person variation versus between person variation, (4) used standardised dietary metrics and units of measure across surveys, and (5) adjusted for total energy to reduce measurement error and account for differences in body size, metabolic efficiency, and physical activity.34
For each survey, we extracted data on survey characteristics, dietary metrics, units, and mean and distribution (such as standard deviation) of consumption of each dietary fat and oil, by age and sex (eTable 2). Data were double checked for extraction errors and assessed for plausibility. We assessed survey quality by evaluating evidence for selection bias, sample representativeness, response rate, and validity of diet assessment method.19 (link) Measurement comparability across surveys was maximised by using a standardised data analysis approach that (1) accounted for sampling strategies within the survey by including sampling weights (if available), (2) used the average of all days of dietary assessment to quantify mean intakes, (3) used a corrected population standard deviation to account for within person variation versus between person variation, (4) used standardised dietary metrics and units of measure across surveys, and (5) adjusted for total energy to reduce measurement error and account for differences in body size, metabolic efficiency, and physical activity.34
