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

The value for Mortality rate, infant, male (per 1,000 live births) in Uganda was 35.10 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 140.60 in 1960 and a minimum value of 35.10 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 140.60
1961 138.40
1962 136.30
1963 134.30
1964 132.30
1965 130.40
1966 128.50
1967 126.90
1968 125.20
1969 123.90
1970 123.20
1971 122.90
1972 123.40
1973 124.40
1974 126.10
1975 128.30
1976 130.70
1977 133.20
1978 135.40
1979 136.80
1980 137.90
1981 137.00
1982 134.30
1983 130.80
1984 127.20
1985 124.20
1986 121.90
1987 119.90
1988 118.20
1989 116.50
1990 114.20
1991 111.80
1992 109.30
1993 107.20
1994 105.50
1995 104.50
1996 103.50
1997 102.10
1998 100.20
1999 97.60
2000 94.30
2001 90.30
2002 85.90
2003 81.00
2004 76.10
2005 71.40
2006 67.00
2007 63.10
2008 59.50
2009 56.90
2010 54.10
2011 51.50
2012 48.50
2013 46.60
2014 44.60
2015 42.80
2016 41.00
2017 39.30
2018 37.60
2019 36.40
2020 35.10

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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