One-third of children in low- and middle-income countries fail to reach developmental milestones

Conducted by Harvard researchers with data on almost 100,000 children, new Canadian Government-funded 'Saving Brains' study is first to directly measure problem; reveals extent of developmental setbacks among 3 and 4 year olds in developing world

In developing countries, one third of children 3 and 4 years old don’t reach basic milestones in cognitive and/or socio-emotional growth, according to a new study from the Harvard T.H. Chan School of Public Health, funded by the Government of Canada through Grand Challenges Canada.

The study authors estimate that 80.8 million of the roughly 240 million preschool-aged children in the world’s 132 low- and middle-income countries fail to develop a core set of age-appropriate skills that allow them to maintain attention, understand and follow simple directions, communicate and get along with others, control aggression, and solve progressively complex problems.

These early abilities are associated with subsequent development, mental and physical health, and ultimately, better learning in school and more productive lives as adults.

Published today by PLoS Medicine, the study draws on data provided by the caregivers of almost 100,000 children living in 35 low- and middle-income countries between 2005 and 2015. The data were collected as part of UNICEF’s Multiple Indicator Cluster Survey program, Demographic and Health Surveys, and global data from the Nutrition Impact Model Study.

This is the first study to directly estimate the global extent of cognitive and/or socio-emotional development deficits; earlier estimates of this unmet potential globally were based on proxy measures of development including poor physical growth and exposure to poverty.

The researchers found that among 3 and 4 year olds in low- and middle-income countries, the problem is most acute in sub-Saharan Africa (29.4 million children not reaching developmental milestones; 44% of all 3 or 4 year olds), followed by South Asia (27.7 million; 38%) and the East Asia and Pacific region (15.1 million; 26%). A significant burden is also notable in Latin America/Caribbean (4.1 million, 19%) and North Africa, Middle East and Central Asia (4.5 million, 18%).

Low development scores were associated with stunting, poverty, male gender, rural residence, and lack of cognitive stimulation.

Says lead author Dana McCoy, Assistant Professor of Education at the Harvard Graduate School of Education: “In addition to the 33% of children overall who did not meet the selected cognitive and socio-emotional milestones, we estimate that 17% were physically stunted, meaning that approximately half of the children in these countries are developing poorly in one way or another.”

The importance of children thriving, not just surviving, is emphasized in the United Nations Sustainable Development Goals and is central to the Every Woman Every Child Global Strategy for Women’s, Children’s and Adolescent’s Health.

“Achieving optimal early child health and development is critical for attaining success in school, and has significant life-long implications for the health and economic wellbeing of individuals, families and communities,” says the project’s principal investigator, Wafaie Fawzi, Professor and Chair of the Department of Global Health and Population at the Harvard T.H. Chan School of Public Health.

He added that quantifying the burden of failing to reach developmental milestones at national and global levels is important to monitoring progress towards the Sustainable Development Goals.

An estimate of the global economic cost of this unrealised human potential is the focus of a companion study conducted at Harvard, also funded by Grand Challenges Canada, with publication planned for later this year.

These studies are part of a larger project to estimate the epidemiologic and economic impacts of risk factors for child development, including a multi-disciplinary team of clinicians, economists, psychologists, epidemiologists, nutritional scientists, disease and risk factor modellers, and statisticians at the Harvard T. H. Chan School of Public Health, Imperial College London, Aga Khan University (Pakistan) and Ifakara Health Institute, Tanzania.

“When one in three children is failing to reach their full potential, we are looking at one of the world’s grandest challenges. This research helps shine an ever brighter light on the value of investing in a child’s earliest years – for the benefit of our children, our world and our future,” said Dr. Peter A. Singer, Chief Executive Officer of Grand Challenges Canada.


Appendix: Estimated prevalence of children with low Early Childhood Development Index (ECDI) scores in 135 developing countries

Country: Estimated percentage of 3 and 4 year olds with low ECDI scores / Type of estimate / Estimated number of 3 and 4 year olds with low ECDI scores

Afghanistan: 46.9%, Model prediction, 1,024,800
Algeria: 17.4%, Model prediction, 304,100
Angola: 40.4%, Model prediction, 817,300
Antigua and Barbuda: 11.3%, Model prediction, 300
Argentina: 8.3%, Model prediction, 124,100
Armenia: 17.8%, Model prediction, 14,700
Azerbaijan: 15.8%, Model prediction, 60,800
Bahamas: 12.2%, Model prediction, 1,400
Bahrain: 7.4%. Model prediction, 2,800
Bangladesh: 38.3%, MICS/DHS, 2,490,200
Barbados: 18.2% MICS/DHS, 1,300
Belize: 21.6%, MICS/DHS, 3,300
Benin: 44.8%, Model prediction, 322,400
Bhutan: 34.1%, MICS/DHS, 9,800
Bolivia: 26.3%, Model prediction, 133,500
Botswana: 4.4%, MICS/DHS, 4,600
Brazil: 16.1%, Model prediction, 1,006,200
Burkina Faso: 54.3%, Model prediction, 718,500
Burundi: 53.1%, Model prediction, 446,500
Cambodia: 37.5%, Model prediction, 274,200
Cameroon: 53.1%, MICS/DHS, 844,600
Cape Verde: 27.6%, Model prediction, 6,100
Central African Republic: 54.1%, MICS/DHS, 168,600
Chad: 67.0%, MICS/DHS, 755,500
Chile: 8.0%, Model prediction, 38,000
China: 20.2%, Model prediction, 6,667,900
Colombia: 19.5%, Model prediction, 305,100
Comoros: 42.6%, Model prediction, 21,000
Congo: 49.0%, MICS/DHS, 151,000
Costa Rica: 14.8%, Model prediction, 21,200
Cuba: 11.8%, Model prediction, 29,100
Cote d’Ivoire: 47.2%, Model prediction, 725,800
Democratic Republic of the Congo: 47.9%, MICS/DHS, 2,770,300
Djibouti: 46.4%, Model prediction, 20,500
Dominican Republic: 20.0%, Model prediction, 87,600
Ecuador: 18.3%, Model prediction, 119,500
Egypt: 22.1%, Model prediction, 985,100
El Salvador: 25.1%, Model prediction, 55,700
Equatorial Guinea: 31.7%, Model prediction, 16,800
Eritrea: 54.0%, Model prediction, 184,500
Ethiopia: 50.7%, Model prediction, 3,091,200
Fiji: 18.3%, Model prediction, 6,800
Gabon: 24.0%, Model prediction, 23,100
Gambia: 47.6%, Model prediction, 70,100
Georgia: 16.4%, Model prediction, 19,200
Ghana: 32.6%, MICS/DHS, 532,100
Grenada: 16.1%, Model prediction, 700
Guatemala: 29.5%, Model prediction, 249,600
Guinea: 53.3%, Model prediction, 455,600
Guinea-Bissau: 50.6%, Model prediction, 63,800
Guyana: 28.2%, Model prediction, 8,200
Haiti: 44.5%, Model prediction, 236,700
Honduras: 17.0%, MICS/DHS, 59,700
India: 32.2%, Model prediction, 17,147,500
Indonesia: 23.8%, Model prediction, 2,409,000
Iran (Islamic Republic of): 15.5%, Model prediction, 417,600
Iraq: 28.3%, MICS/DHS, 625,200
Jamaica: 17.2%, Model prediction, 17,100
Jordan: 37.8%, MICS/DHS, 138,800
Kazakhstan: 13.6%, MICS/DHS, 99,100
Kenya: 38.3%, Model prediction, 1,134,500
Kiribati: 32.0%, Model prediction, 1,900
Kuwait: 8.5%, Model prediction, 11,400
Kyrgyzstan: 19.1%, MICS/DHS, 53,700
Lao People’s Democratic Republic: 17.7%, MICS/DHS, 62,400
Lebanon: 22.9%, MICS/DHS, 29,600
Lesotho: 44.4%, Model prediction, 51,400
Liberia: 51.5%, Model prediction, 149,700
Libyan Arab Jamahiriya: 14.1%, Model prediction, 38,700
Madagascar: 40.9%, Model prediction, 615,100
Malawi: 40.0%, MICS/DHS, 486,700
Malaysia: 12.7%, Model prediction, 121,000
Maldives: 21.9%, Model prediction, 3,100
Mali: 51.0%, Model prediction, 707,900
Mauritania: 42.7%, Model prediction, 107,300
Mauritius: 14.2%, Model prediction, 4,300
Mexico: 15.2%, Model prediction, 723,800
Micronesia (Federated States of): 26.7%, Model prediction, 1,300
Mongolia: 20.6%, Model prediction, 26,300
Morocco: 29.6%, Model prediction, 401,000
Mozambique: 51.9%, Model prediction, 1,037,300
Myanmar: 39.2%, Model prediction, 799,800
Namibia: 29.6%, Model prediction, 39,200
Nepal: 42.0%, MICS/DHS, 522,800
Nicaragua: 28.7%, Model prediction, 72,900
Niger: 59.9%, Model prediction, 992,500
Nigeria: 45.7%, MICS/DHS, 5,999,500
Occupied Palestinian Territory: 23.3%, Model prediction, 64,000
Oman: 10.0%, Model prediction, 13,600
Pakistan: 48.1%, MICS/DHS, 4,928,800
Panama: 13.6%, Model prediction, 20,100
Papua New Guinea: 42.1%, Model prediction, 174,000
Paraguay: 23.5%, Model prediction, 65,500
Peru: 18.2%, Model prediction, 223,600
Philippines: 24.9%, Model prediction, 1,150,500
Qatar: 4.8%, Model prediction, 2,000
Rwanda: 46.3%, Model prediction, 335,100
Saint Lucia: 11.0%, MICS/DHS, 600
Saint Vincent and the Grenadines: 18.9%, Model prediction, 700
Samoa: 20.5%, Model prediction, 2,100
Sao Tome and Principe: 36.7%, Model prediction, 4,500
Saudi Arabia: 9.0%, Model prediction, 109,100
Senegal: 46.0%, Model prediction, 468,700
Seychelles: 15.5%, Model prediction, 500
Sierra Leone: 54.3%, MICS/DHS, 244,300
Solomon Islands: 42.0%, Model prediction, 14,300
South Africa: 26.1%, Model prediction, 579,900
Sri Lanka: 16.0%, Model prediction, 112,300
Sudan: 45.0%, Model prediction, 1,133,300
Suriname: 32.0%, MICS/DHS, 6,400
Swaziland: 42.5%, MICS/DHS, 31,300
Syrian Arab Republic: 26.6%, Model prediction, 261,400
Tajikistan: 29.9%, Model prediction, 137,900
Thailand: 18.4%, Model prediction, 288,000
Timor-Leste: 30.7%, Model prediction, 26,000
Togo: 47.3%, MICS/DHS, 226,900
Tonga: 18.7%, Model prediction, 1,000
Trinidad and Tobago: 12.5%, Model prediction, 5,000
Tunisia: 27.9%, MICS/DHS, 105,500
Turkey: 16.1%, Model prediction, 420,100
Turkmenistan: 23.7%, Model prediction, 52,100
Uganda: 44.2%, Model prediction, 1,321,100
United Arab Emirates: 6.4%, Model prediction, 11,500
United Republic of Tanzania: 41.4%, Model prediction, 1,549,400
Uruguay: 11.6%, Model prediction, 11,500
Uzbekistan: 24.9%, Model prediction, 317,900
Vanuatu: 31.8%, Model prediction, 4,200
Venezuela (Bolivarian Republic of): 14.0%, Model prediction, 168,100
Viet Nam: 16.8%, MICS/DHS, 516,800
Yemen: 41.7%, Model prediction, 678,600
Zambia: 35.5%, Model prediction, 416,100
Zimbabwe: 37.5%, MICS/DHS, 380,200

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