Current Diets

Overview

The model uses empirical dietary intake data from the Global Dietary Database (GDD) [GDD2024] [Miller2021] to represent current consumption patterns. This baseline data serves multiple purposes:

  • Health impact assessment: Calculating disease burden attributable to current dietary patterns

  • Baseline reference: Comparing optimized diets against current consumption

  • Model constraints: Optionally constrain the optimization to remain near current diets

Data Source

Global Dietary Database (GDD)

  • Provider: Tufts University Friedman School of Nutrition Science and Policy

  • Coverage: 185 countries, individual-level dietary surveys (1990-2018)

  • Variables: 54 dietary factors including foods, beverages, and nutrients

  • Download: Requires free registration at https://globaldietarydatabase.org/data-download

  • Citation: [GDD2024]

The GDD compiles and harmonizes national dietary surveys from around the world using standardized protocols. Data are stratified by age, sex, urban/rural residence, and education level, then aggregated to national-level estimates using population weights.

Weight Conventions

GDD reports all dietary intake values in grams per day using “as consumed” weights [Miller2021]. This means:

  • Fresh vegetables and fruits: Reported in fresh weight (e.g., a raw apple, fresh tomato)

  • Grains: Reported in cooked weight (e.g., cooked rice, prepared bread)

  • Dairy: Reported as total milk equivalents, which includes milk, yogurt, cheese and other dairy products converted to their milk equivalent weight

  • Meats: Reported in cooked/prepared weight

The model preserves these conventions in the processed output files. Units in the output CSV distinguish between general fresh weight (g/day (fresh wt)) and dairy milk equivalents (g/day (milk equiv)).

GDD to Food Group Mapping

The model maps GDD dietary variables to the food groups defined in config/food_groups. This mapping is implemented in workflow/scripts/prepare_gdd_dietary_intake.py.

Food Groups with GDD Data

The following food groups are populated from GDD variables:

Food Group

GDD Code

Description

fruits

v01

Total fruits (whole fruits only, excluding juices)

vegetables

v02

Non-starchy vegetables

starchy_vegetable

v03, v04

Potatoes + other starchy vegetables (aggregated)

legumes

v05

Beans and legumes

nuts_seeds

v06

Nuts and seeds

grain

v07

Refined grains (white flour, white rice)

whole_grains

v08

Whole grains

red_meat

v10

Unprocessed red meats (cattle, pig)

prc_meat

v09

Total processed meats

fish

v11

Total seafoods (fish + shellfish)

eggs

v12

Eggs

dairy

v57

Total Milk (includes milk equivalents from all dairy products)

Notes:

  • Multiple GDD variables can map to a single food group (e.g., starchy_vegetable = v03 potatoes + v04 other starchy veg)

  • When aggregating, values are summed within each food group

  • The dairy food group uses v57 “Total Milk”, which represents milk equivalents from all dairy consumption including liquid milk, cheese, yogurt, and other dairy products

  • The fruits food group uses only v01 (whole fruits), excluding v16 (fruit juices), to align with the GBD fruit risk factor definition used in health impact modeling

Food Groups Without GDD Data

Some food groups in the model do not have direct GDD mappings:

  • oil: Not tracked as a dietary intake in GDD (it’s an ingredient/processed product)

  • poultry: Not tracked separately in GDD (tracked as part of general meat categories)

These food groups rely on model production and trade without baseline dietary constraints.

Data Processing

The GDD data processing pipeline (workflow/scripts/prepare_gdd_dietary_intake.py) performs the following steps:

  1. Load GDD files: Read country-level CSV files (v*_cnty.csv) for each dietary variable

  2. Filter to reference year: Extract data for config.health.reference_year (default: 2018)

  3. Map age groups: Convert GDD age midpoints to GBD-compatible age buckets (0-1, 1-2, 2-5, 6-10, 11-74, 75+ years)

  4. Aggregate strata: Compute national averages by age group across sex/education/urban-rural strata

  5. Map to food groups: Apply the GDD-to-food-group mapping defined in the script

  6. Aggregate variables: Sum multiple GDD variables that map to the same food group (preserving age stratification)

  7. Handle missing countries: Apply proxies for territories without separate GDD data

  8. Validate completeness: Ensure all required countries and food groups are present

  9. Output: Write processing/{name}/gdd_dietary_intake.csv with age-stratified data

Output Format

The processed dietary intake file has the following structure:

unit,item,country,age,year,value
g/day (milk equiv),dairy,USA,0-1 years,2018,252.3
g/day (milk equiv),dairy,USA,1-2 years,2018,258.3
g/day (milk equiv),dairy,USA,11-74 years,2018,174.6
g/day (milk equiv),dairy,USA,All ages,2018,187.1
g/day (fresh wt),fruits,USA,11-74 years,2018,145.2
...

Where:

  • unit: Weight convention specific to the food group

    • g/day (fresh wt): Fresh/cooked “as consumed” weight for most foods

    • g/day (milk equiv): Total milk equivalents for dairy

  • item: Food group name

  • country: ISO 3166-1 alpha-3 country code

  • age: Age group using GBD-compatible naming

    • 0-1 years: Infants under 1 year

    • 1-2 years: Toddlers 1-2 years

    • 2-5 years: Early childhood 2-5 years

    • 6-10 years: Middle childhood 6-10 years

    • 11-74 years: Adults 11-74 years

    • 75+ years: Elderly 75+ years

    • All ages: Population-weighted average across all age groups

  • year: Reference year

  • value: Mean daily intake in grams per person for the specified age group

Country Coverage

The GDD dataset covers 185 countries. For a small number of territories without separate dietary surveys, the model uses proxy data from similar countries:

  • American Samoa (ASM): Uses Samoa (WSM) data

  • French Guiana (GUF): Uses France (FRA) data

  • Puerto Rico (PRI): Uses USA data

  • Somalia (SOM): Uses Ethiopia (ETH) data

These proxies are defined in the COUNTRY_PROXIES dictionary in prepare_gdd_dietary_intake.py.

Workflow Integration

Snakemake rule: prepare_gdd_dietary_intake

Input:

  • data/manually_downloaded/GDD-dietary-intake/Country-level estimates/*.csv

Configuration parameters:

  • config.countries: List of countries to process

  • config.food_groups: Food group definitions (keys used to filter GDD data)

  • config.health.reference_year: Year for dietary intake data

Output:

  • processing/{name}/gdd_dietary_intake.csv

Script: workflow/scripts/prepare_gdd_dietary_intake.py

Baseline diet enforcement in the optimization can be toggled via config.diet.enforce_gdd_baseline. When enabled, the builder reads processing/{name}/gdd_dietary_intake.csv (All ages by default) and adds per-country equality loads for matching food groups, forcing the solution to replicate observed intake. baseline_age and baseline_reference_year override which cohort/year slice the model locks to.

References

[GDD2024] (1,2)

Global Dietary Database. Dietary intake data by country, 2018. Tufts University Friedman School of Nutrition Science and Policy. https://www.globaldietarydatabase.org/ (accessed 2025)

[Miller2021] (1,2)

Miller V, Singh GM, Onopa J, et al. Global Dietary Database 2017: Data Availability and Gaps on 54 Major Foods, Beverages and Nutrients among 5.6 Million Children and Adults from 1220 Surveys Worldwide. BMJ Global Health, 2021;6(2):e003585. https://doi.org/10.1136/bmjgh-2020-003585