Hungary - Mortality rate, under-5, female (per 1,000 live births)

The value for Mortality rate, under-5, female (per 1,000 live births) in Hungary was 3.70 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 48.30 in 1960 and a minimum value of 3.70 in 2019.

Definition: Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified 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:

Year Value
1960 48.30
1961 45.70
1962 43.90
1963 42.60
1964 41.30
1965 39.90
1966 38.70
1967 37.50
1968 36.60
1969 35.60
1970 34.40
1971 33.80
1972 34.00
1973 34.30
1974 33.70
1975 31.40
1976 28.50
1977 25.90
1978 23.70
1979 22.00
1980 20.70
1981 19.80
1982 19.40
1983 19.30
1984 19.30
1985 19.00
1986 18.40
1987 17.40
1988 16.40
1989 15.60
1990 15.30
1991 14.90
1992 14.00
1993 12.90
1994 11.90
1995 11.20
1996 10.70
1997 10.30
1998 10.00
1999 9.60
2000 9.20
2001 8.70
2002 8.10
2003 7.60
2004 7.20
2005 6.90
2006 6.60
2007 6.20
2008 5.90
2009 5.70
2010 5.50
2011 5.40
2012 5.30
2013 5.10
2014 4.90
2015 4.60
2016 4.30
2017 4.10
2018 3.90
2019 3.70
2020 3.70

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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

Classification

Topic: Health Indicators

Sub-Topic: Mortality