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

The value for Mortality rate, under-5, male (per 1,000 live births) in Rwanda was 43.90 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 350.40 in 1994 and a minimum value of 43.90 in 2020.

Definition: Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male 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 230.60
1961 225.60
1962 222.30
1963 220.00
1964 219.10
1965 219.30
1966 220.20
1967 222.20
1968 225.20
1969 228.80
1970 232.10
1971 235.20
1972 239.20
1973 244.80
1974 251.60
1975 258.00
1976 262.30
1977 262.40
1978 256.60
1979 245.00
1980 229.50
1981 212.00
1982 196.10
1983 184.00
1984 175.30
1985 168.70
1986 163.20
1987 158.90
1988 155.40
1989 154.70
1990 158.50
1991 167.80
1992 181.80
1993 199.30
1994 350.40
1995 229.40
1996 233.90
1997 230.70
1998 222.10
1999 209.00
2000 193.00
2001 174.60
2002 156.30
2003 139.70
2004 124.40
2005 110.70
2006 99.00
2007 89.20
2008 80.90
2009 74.00
2010 68.10
2011 62.70
2012 58.90
2013 55.90
2014 53.50
2015 51.40
2016 49.70
2017 48.20
2018 46.90
2019 45.50
2020 43.90

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