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

The value for Mortality rate, under-5 (per 1,000 live births) in Micronesia was 24.70 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 125.40 in 1960 and a minimum value of 24.70 in 2020.

Definition: Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to 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 125.40
1961 120.30
1962 115.30
1963 110.60
1964 106.00
1965 101.60
1966 97.40
1967 93.30
1968 89.60
1969 85.90
1970 82.60
1971 79.50
1972 76.60
1973 74.00
1974 71.50
1975 69.20
1976 67.10
1977 65.30
1978 63.70
1979 62.30
1980 61.20
1981 60.30
1982 59.50
1983 58.80
1984 57.80
1985 56.70
1986 55.40
1987 53.80
1988 52.10
1989 50.50
1990 49.10
1991 47.80
1992 46.80
1993 45.80
1994 44.90
1995 43.90
1996 42.80
1997 41.60
1998 40.30
1999 39.00
2000 37.80
2001 36.70
2002 35.90
2003 35.20
2004 34.80
2005 34.70
2006 34.60
2007 34.50
2008 34.20
2009 33.70
2010 33.10
2011 32.30
2012 31.60
2013 30.70
2014 29.90
2015 28.90
2016 28.00
2017 27.00
2018 26.20
2019 25.40
2020 24.70

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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