Build Your Own Chart
Create custom charts from global diet and health data for 184 countries. Choose your variables, see the results, and embed the chart on your website.
Visualise Diet and Health Relationships
The relationship between what a country eats and how its population's health fares is one of the most important questions in public health. This chart builder gives you direct access to the underlying data: FAO Food Balance Sheet supply variables on one axis, and IHME Global Burden of Disease health outcomes on the other. You can create scatter plots to explore correlations or bar charts to rank countries by any variable.
The dietary variables include per-capita supply of major food groups — cereals, fruits, vegetables, meat, dairy, sugar, and oils — measured in kilograms per person per year from the FAO's latest data. The health outcome variables come from the GBD study and include deaths and disability-adjusted life years (DALYs) attributed to cardiovascular disease, diabetes, cancer, and other diet-related conditions across 184 countries.
A critical caveat: these are ecological (country-level) correlations, not evidence of causation. A scatter plot showing that countries with higher fruit supply have lower heart disease rates does not prove that eating fruit prevents heart disease at the individual level — wealthier countries tend to have both more fruit supply and better healthcare. Still, these visualisations are valuable for generating hypotheses, contextualising clinical evidence, and communicating the global scale of dietary health challenges. Every chart you create can be embedded on your own website using the code provided below.
About the Chart Builder
The Chart Builder lets you create custom visualizations from two of the world's most comprehensive public health datasets: FAO Food Balance Sheets (food supply data for 184 countries from 1961 to the present) and the Global Burden of Disease Study (GBD 2023) from the Institute for Health Metrics and Evaluation.
Choose any combination of dietary supply variables (per capita consumption of fruits, vegetables, meat, fish, sugar, oil, and more) and health outcome metrics (cardiovascular disease mortality, diabetes prevalence, cancer rates, life expectancy, and others) to explore how national dietary patterns correlate with population health across the globe.
Charts can be downloaded as high-resolution PNG images or embedded on your website or blog using the auto-generated embed code. All charts include proper source attribution and are licensed under CC BY 4.0.
Available Variables
The builder offers dozens of variables organized into two categories:
Dietary Supply (FAO)
- Total calorie supply per capita
- Fruit & vegetable supply (kg/yr)
- Meat supply (total, red, poultry)
- Fish & seafood supply
- Sugar & sweetener supply
- Vegetable oil supply
- Dairy, eggs, pulses, cereals
- Alcohol supply
Health Outcomes (GBD)
- Cardiovascular disease mortality
- Type 2 diabetes prevalence
- Colorectal cancer rates
- Obesity & overweight prevalence
- Life expectancy at birth
- All-cause mortality rates
- Diet-attributable DALYs
- Child stunting & wasting
Scatter plots display each country as a data point and automatically compute the Pearson correlation coefficient and regression line. Bar charts rank countries from highest to lowest for the selected Y-axis variable.
Understanding Ecological Data
The charts in this tool use ecological (country-level) data, which is fundamentally different from individual-level dietary studies. It is important to understand what this data can and cannot tell us:
- Food supply ≠ food intake. FAO Food Balance Sheets measure the food available at the national level (production + imports - exports - waste). Actual individual consumption is lower due to household and plate waste, and varies enormously within a country.
- Correlation ≠ causation. A strong correlation between a dietary variable and a health outcome at the country level does not prove a causal relationship. Wealthy countries tend to eat more meat AND have better healthcare — both independently affect mortality. This is known as confounding.
- Ecological fallacy. Country-level averages obscure individual variation. A country with high average fruit supply may still have large populations with very low fruit intake.
These charts are most useful for hypothesis generation, educational presentations, and exploring broad global patterns. For causal evidence, refer to randomized controlled trials, prospective cohort studies, and systematic reviews.
Frequently Asked Questions
Can I embed these charts on my website?
Yes. Each chart generates an embed code (an iframe snippet) that you can copy and paste into any website, blog, or CMS. The embedded chart is interactive and loads data directly from our servers. The charts are free to use under CC BY 4.0, which requires attribution to the Human Nutrition Explorer and the original data sources (FAO and IHME GBD).
How recent is the data?
FAO Food Balance Sheet data typically has a 2-3 year lag. The current dataset includes food supply data up to the most recent year available from FAOSTAT. The GBD health data comes from the 2023 release of the Global Burden of Disease Study, which includes modeled estimates up to the most recent year. Both datasets are updated periodically as new releases become available.
What does the R-value mean in scatter plots?
The R-value (Pearson correlation coefficient) measures the strength and direction of the linear relationship between two variables. Values range from -1 (perfect inverse correlation) to +1 (perfect positive correlation). Values near 0 indicate no linear relationship. In ecological data like this, R-values above 0.5 or below -0.5 are generally considered strong. The p-value shown alongside indicates statistical significance — whether the correlation is likely real rather than due to chance.
Why are some countries missing from the charts?
Countries may be absent if the FAO or GBD does not publish data for them for the selected variable. Small island nations, territories, and countries in conflict zones often have incomplete data coverage. The number of countries shown is noted on each chart.