Mortality rate, infant (per 1,000 live births) - Country Ranking - Europe

Definition: Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.

Source: Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.

See also: Thematic map, Time series comparison

Find indicator:
Rank Country Value Year
1 Moldova 12.50 2020
2 Albania 8.80 2020
3 Turkey 8.10 2020
4 Ukraine 6.90 2020
5 Romania 5.60 2020
5 Malta 5.60 2020
7 North Macedonia 5.20 2020
8 Bulgaria 5.10 2020
9 Bosnia and Herzegovina 4.90 2020
9 Serbia 4.90 2020
11 Slovak Republic 4.70 2020
12 Croatia 3.90 2020
13 Poland 3.70 2020
14 Netherlands 3.60 2020
14 United Kingdom 3.60 2020
14 Greece 3.60 2020
17 Switzerland 3.50 2020
18 Belgium 3.40 2020
18 France 3.40 2020
18 Hungary 3.40 2020
18 Latvia 3.40 2020
22 Germany 3.10 2020
22 Denmark 3.10 2020
24 Austria 3.00 2020
25 Spain 2.70 2020
25 Lithuania 2.70 2020
25 Portugal 2.70 2020
28 Ireland 2.60 2020
29 Monaco 2.50 2020
29 Italy 2.50 2020
31 Andorra 2.40 2020
32 Cyprus 2.30 2020
32 Czech Republic 2.30 2020
32 Luxembourg 2.30 2020
35 Belarus 2.20 2020
36 Sweden 2.10 2020
37 Montenegro 2.00 2020
38 Finland 1.90 2020
39 Norway 1.80 2020
39 Slovenia 1.80 2020
41 Estonia 1.70 2020
42 San Marino 1.60 2020
43 Iceland 1.50 2020

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Development Relevance: Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.

Limitations and Exceptions: Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.

Statistical Concept and Methodology: The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A "complete" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.

Aggregation method: Weighted average

Periodicity: Annual

General Comments: Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development ac