Current Diets

Overview

The model uses a hybrid approach to represent current consumption patterns, combining empirical dietary intake data from the Global Dietary Database (GDD) [GDD2024] [Miller2021] with food supply data from FAOSTAT Food Balance Sheets (FBS). 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 Sources

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]

FAOSTAT Food Balance Sheets (FBS)

  • Provider: FAO Statistics Division

  • Coverage: Global, annual estimates of food supply

  • Variables: Food supply quantity (kg/capita/year)

  • Usage: Supplements GDD for food groups where intake survey data is sparse or inconsistent (Dairy, Poultry, Vegetable Oils)

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

sugar

v15, v35

Sugar-sweetened beverages and added sugars

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 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 Sourced from FAOSTAT

The following food groups are populated from FAOSTAT Food Balance Sheets (FBS) because intake survey data (GDD) is often sparse, inconsistent, or structurally missing for these commodities:

Food Group

Description & Source Items

dairy

Total Milk Equivalent. Aggregated from FAOSTAT items: Milk - Excluding Butter (2848), Butter/Ghee (2740), and Cream (2743). Butter and cream are converted to milk equivalents using FAO dairy commodity tree extraction rates (≈21.3× for butter/ghee, ≈6.7× for cream); milk-excl.-butter is taken as-is.

poultry

Poultry Meat (2734).

oil

Vegetable Oils (2586).

Methodology for FAOSTAT Data: FAOSTAT reports “Food Supply” (retail weight), which typically includes household waste. The model converts this to “Dietary Intake” (consumed weight) by applying country-specific waste fractions derived from the UNSD Food Waste Index (see Food Processing & Trade).

Data Processing

The dietary data processing pipeline involves three stages:

  1. Prepare GDD Data (workflow/scripts/prepare_gdd_dietary_intake.py): Processes GDD survey data for most food groups.

  2. Prepare FAOSTAT Data (workflow/scripts/prepare_faostat_dietary_intake.py): Fetches FAOSTAT supply data for dairy, poultry, and oil; converts supply to intake by subtracting waste; fills missing countries using proxies.

  3. Merge Sources (workflow/scripts/merge_dietary_sources.py): Combines the datasets into a unified dietary_intake.csv.

The GDD processing step (Step 1) performs the following:

  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 rules:

  • prepare_gdd_dietary_intake

  • prepare_faostat_dietary_intake

  • merge_dietary_sources

Input:

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

  • FAOSTAT API (live fetch)

Configuration parameters:

  • config.countries: List of countries to process

  • config.food_groups.included: Food groups to filter and aggregate in GDD data

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

Output:

  • processing/{name}/dietary_intake.csv (Final merged file)

Scripts:

  • workflow/scripts/prepare_gdd_dietary_intake.py

  • workflow/scripts/prepare_faostat_dietary_intake.py

  • workflow/scripts/merge_dietary_sources.py

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