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

The value for Mortality rate, under-5, female (per 1,000 live births) in Rwanda was 36.80 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 331.50 in 1994 and a minimum value of 36.80 in 2020.

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 210.80
1961 206.10
1962 202.70
1963 200.40
1964 199.40
1965 199.40
1966 200.20
1967 201.90
1968 204.60
1969 207.70
1970 210.80
1971 213.90
1972 217.40
1973 222.60
1974 228.70
1975 234.50
1976 238.30
1977 238.30
1978 232.60
1979 221.90
1980 207.60
1981 191.50
1982 176.70
1983 165.50
1984 157.30
1985 150.90
1986 145.70
1987 141.40
1988 138.20
1989 137.70
1990 141.70
1991 150.90
1992 164.70
1993 181.20
1994 331.50
1995 210.10
1996 214.70
1997 211.90
1998 203.90
1999 192.10
2000 177.00
2001 159.70
2002 142.60
2003 127.00
2004 112.50
2005 99.80
2006 88.60
2007 79.50
2008 71.50
2009 64.90
2010 59.30
2011 54.30
2012 50.80
2013 47.90
2014 45.80
2015 43.80
2016 42.10
2017 40.80
2018 39.60
2019 38.30
2020 36.80

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