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

The value for Mortality rate, infant, male (per 1,000 live births) in Rwanda was 33.20 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 197.80 in 1994 and a minimum value of 33.20 in 2020.

Definition: Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male 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:

Year Value
1960 139.30
1961 136.60
1962 134.60
1963 133.30
1964 132.80
1965 132.70
1966 133.30
1967 134.20
1968 136.00
1969 138.00
1970 139.90
1971 141.70
1972 144.10
1973 147.50
1974 151.50
1975 155.40
1976 158.00
1977 158.10
1978 154.40
1979 147.40
1980 138.20
1981 128.30
1982 119.50
1983 112.90
1984 108.10
1985 104.60
1986 101.50
1987 99.00
1988 97.10
1989 96.60
1990 98.60
1991 103.70
1992 111.40
1993 120.70
1994 197.80
1995 136.80
1996 139.00
1997 136.90
1998 131.80
1999 124.80
2000 116.20
2001 106.40
2002 96.40
2003 87.00
2004 78.10
2005 70.50
2006 64.00
2007 58.80
2008 54.40
2009 51.00
2010 47.80
2011 44.30
2012 42.30
2013 40.50
2014 39.00
2015 37.70
2016 36.80
2017 36.00
2018 35.00
2019 34.10
2020 33.20

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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