Every Right Counts: The diet divide
What data on dietary diversity reveals about early inequality
In the critical first two years of a child’s life, their diet plays a vital role in shaping their growth, health and future potential. In this first article in our new series, Every Right Counts, we explore data for the Europe and Central Asia (ECA) region on Minimum Dietary Diversity (MDD), a key indicator for assessing whether young children are getting the variety of foods they need.
How do we track diversity in children’s diets?
That’s where the MDD indicator comes in. Now part of the Sustainable Development Goals (as the newly introduced indicator 2.2.4) and included under Nutrition in the ECA Child Rights Monitoring Framework, MDD is collected through household surveys such as UNICEF’s Multiple Indicator Cluster Surveys (MICS). It provides a snapshot of the quality of a child’s diet by measuring the variety of foods consumed by children aged 6–23 months and helps to identify where more support is needed to improve early childhood nutrition.
While there are 55 countries across the whole of the Europe and Central Asia region, recent data on MDD are available for only 14 — revealing a significant data gap. In the 14 countries with available data, just 55 per cent of young children consume a diverse diet. The data expose sharp disparities between countries, with national rates ranging from 22 per cent to 86 per cent. Within countries, there are also disparities by factors such as household wealth, age and location.
Younger children, less variety
Across all countries, children under one year of age are far less likely to consume a diverse diet than older children. In some countries, such as Tajikistan, the gap is particularly wide — with older children almost four times more likely to meet the MDD threshold than infants aged 6–11 months.
This disparity confirms the challenges of introducing diverse and complementary foods early enough. Research suggests that many caregivers lack access to a range of nutritious foods, as well as the guidance and support needed to introduce them appropriately.
Do richer households provide more diverse diets?
In general, the data shows that children from the richest households are more likely to meet the MDD threshold than those from the poorest. However, this pattern is not universal. In Turkmenistan, children from the poorest households have a higher MDD rate than those from the wealthiest — a sign that income alone does not determine the quality of a child’s diet.
This suggests that a family's ability to afford diverse foods is only part of the picture. Cultural practices, caregiver knowledge, and local food availability also shape children’s diets. These findings underscore the need for context-specific strategies that address both economic and non-economic barriers to achieving MDD.
Inequalities across regions and communities
In addition to household-level factors, such as ethnicity and the language spoken by the household head, subnational data reveal striking differences in children’s diets within countries. In Azerbaijan, for example, the national average stands at 57 per cent, but only 34 per cent of children aged 6–23 months in the Daghlig Shirvan region consume a sufficiently diverse diet, which shows that access to varied foods can differ greatly by region.
These regional gaps in diet diversity reinforce the urgent need to act at both national and local levels to close the gap and ensure no child is overlooked.
So, what needs to be done to give every child the chance to eat well, no matter their age, location or family's income?
To explore MDD rates across the region and view country-level trends, visit the TransMonEE Dashboard. To dive deeper into the data, access the ECA Child Inequity Dashboard, which offers more detailed disaggregations of this indicator and many others.
Because every child has the right to good nutrition — and Every Right Counts when tracking progress.
Annexes
UNICEF, Child Food Poverty. Nutrition Deprivation in Early Childhood. Child Nutrition Report, 2024. UNICEF, New York, 2024.
UNICEF, Child Food Poverty: A Nutrition Crisis in Early Childhood. UNICEF, New York, 2022.
UNICEF, Fed to Fail? The crisis of children’s diets in early life. UNICEF, New York, 2021.
WHO, Indicators for assessing infant and young child feeding practices: Definitions and measurement methods. World Health Organization, Geneva, 2021.
| Country | Year | Primary Source |
|---|---|---|
| Albania | 2017 | Albania Demographic and Health Survey 2017-18. Tirana Albania Institute of Statistics Institute of Public Health and ICF |
| Armenia | 2016 | Armenia Demographic and Health Survey 2015-16. Rockville Maryland USA National Statistical Service Ministry of Health and ICF. |
| Azerbaijan | 2023 | Azerbaijan Multiple Indicator Cluster Survey (2023) dataset. |
| Belarus | 2019 | Belarus Multiple Indicator Cluster Survey (2019) dataset. |
| Georgia | 2018 | Georgia Multiple Indicator Cluster Survey (2018) dataset. |
| Kazakhstan | 2015 | Kazakhstan Multiple Indicator Cluster Survey (2015) dataset. Reanalysed by UNICEF DAPM. |
| Kosovo (UNSCR 1244) | 2020 | Kosovo (UNSCR 1244) Multiple Indicator Cluster Survey (2020) |
| Kyrgyzstan | 2023 | Kyrgyzstan Multiple Indicator Cluster Survey (2023) dataset. |
| Montenegro | 2018 | Montenegro Multiple Indicator Cluster Survey (2018) dataset. |
| North Macedonia | 2019 | North Macedonia Multiple Indicator Cluster Survey (2018-19) dataset. |
| Serbia | 2019 | Serbia Multiple Indicator Cluster Survey (2019) dataset. |
| Tajikistan | 2017 | Tajikistan Demographic and Health Survey 2017. Dushanbe Republic of Tajikistan and Rockville Maryland USA Statistical Agency under the President of the Republic of Tajikistan (SA). |
| Turkmenistan | 2019 | Turkmenistan Multiple Indicator Cluster Survey (2019) dataset. |
| Uzbekistan | 2022 | Uzbekistan Multiple Indicator Cluster Survey (2021-22) dataset. |
- MICS data has been sourced from the ECA Child Inequity Database, except for Kazakhstan, where reanalysed data were accessed from the UNICEF Indicator Data Warehouse.
- DHS data has been sourced from the TransMonEE Database (originally extracted from UNICEF Indicator Data Warehouse).