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

The value for Mortality rate, infant, female (per 1,000 live births) in Uganda was 28.40 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 122.40 in 1960 and a minimum value of 28.40 in 2020.

Definition: Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female 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 122.40
1961 120.50
1962 118.60
1963 116.70
1964 115.00
1965 113.30
1966 111.40
1967 109.50
1968 107.90
1969 106.60
1970 105.80
1971 105.80
1972 106.30
1973 107.50
1974 109.20
1975 111.50
1976 114.10
1977 116.70
1978 119.00
1979 120.60
1980 121.80
1981 121.10
1982 118.90
1983 115.80
1984 112.70
1985 110.00
1986 107.70
1987 105.60
1988 103.80
1989 101.90
1990 99.60
1991 97.30
1992 95.00
1993 92.80
1994 91.10
1995 89.80
1996 88.50
1997 87.10
1998 85.20
1999 82.60
2000 79.50
2001 76.00
2002 72.20
2003 67.90
2004 63.60
2005 59.50
2006 55.80
2007 52.40
2008 49.30
2009 47.10
2010 44.60
2011 42.30
2012 39.80
2013 38.10
2014 36.40
2015 34.90
2016 33.40
2017 31.90
2018 30.50
2019 29.50
2020 28.40

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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