Available GDD 2018 Estimates and Datafiles
Final estimates for a set of dietary factors in GDD 2018 are available for publication as of June 2, 2021. Proper citation language can be found in our Terms and Conditions of Use. All data are property of Tufts University. Estimates for additional dietary factors will be made available on a rolling basis. These data and estimates, though considered final, may include minor refinements through the end of 2021. Please sign up for our mailing list to be notified of all future GDD 2018 updates and releases.
We welcome opinions from all of our data users. If you have any questions or comments upon data download, we invite you to contact us here.
Currently Available Dietary Factors
- Non-starchy vegetables
- Other starchy vegetables
- Beans and legumes
- Nuts and seeds
- Refined grains
- Whole grains
- Total processed meats
- Total seafood
- Yogurt including fermented milk
- Sugar-sweetened beverages
- Fruit juices
- Total milk
- Total protein
- Dietary fiber
- Vitamin A with supplements
- Vitamin B1
- Vitamin B2
- Vitamin B3
- Vitamin B6
- Vitamin B9
- Vitamin C
- Vitamin D
- Vitamin E
Estimates for the remaining dietary factors will be finalized and made publicly available on a rolling basis.
CSV files for each diet factor are available in three geographical levels (by country, by region, overall global).
The GDD prediction model generates estimates of dietary intake jointly stratified by age (20 groups), sex (male vs. female), education level (low, medium, high), and residence (urban vs. rural). However, it is also important that GDD predictions be flexible in this stratification to support analyses that are not simultaneously concerned with all levels of demographic detail. As such, we have adapted our model to generate estimates at less granular demographic levels (i.e. not stratified by certain characteristics). These less granular estimates are denoted in GDD data files with the coding “999,” representing an “overall” estimate for a given demographic characteristic.
All GDD data downloads are accompanied by a corresponding codebook. If you still have questions about datafile coding or format after reviewing the codebook, please contact us for further assistance.
Truncation of implausible estimates
The GDD team has established age- and region-specific thresholds of maximum plausible intakes for each diet factor to eliminate outliers due to model error. In the case of implausible estimates, any value more than 30% above the maximum threshold is replaced with the exact cutoff value. The exact values of these truncation cutoffs can be found in the accompanying codebook for all data downloads. This truncation typically affects less than 1% of strata across all estimated country-years of intake.
Available Population Characteristics
GDD 2018 estimates dietary intake for the global population by the following characteristics.
1990, 1995, 2000, 2005, 2010, 2015, 2018.
185 countries, listed here
0-11mo., 12-23mo., 2-5, 6-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+
Male and female
Urban and rural (as defined by each survey's characteristics)
Low (0-6 years formal education), medium (6.01-12.0 years), and high (12.01+ years)
GDD 2018 Improvements
Compared to GDD 2010, GDD 2018 now:
- Contains close to 1,500 survey-years of individual-level dietary intake data, considerably increasing our catalog of inputs
- Contains data for all ages, including infants, children, and youth
- Estimates intake stratified by education (in 3 levels) and urban versus rural residence
- Contains data for 54 dietary factors
- Utilizes dietary intake data through the year 2018
- Estimates intake for close to 98% of the world's population, compared to 82% in the previous iteration
How We Develop These Estimates
There are multiple steps involved in developing our dietary intake estimates:
- Systematic searches of literature to identify public and private data sources
- Collection of individual-level dietary data
- Harmonization and standardization of data
- Incorporating covariate data
- Modeling individual-level dietary intake
Detailed methodology about the above steps can be found here.