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

The value for Mortality rate, under-5, female (per 1,000 live births) in Tanzania was 45.10 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 232.50 in 1960 and a minimum value of 45.10 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 232.50
1961 230.90
1962 229.10
1963 226.90
1964 224.50
1965 221.80
1966 219.10
1967 216.10
1968 212.90
1969 209.80
1970 206.50
1971 203.20
1972 199.50
1973 195.70
1974 191.70
1975 187.60
1976 183.40
1977 179.40
1978 175.60
1979 172.50
1980 170.60
1981 170.10
1982 170.40
1983 171.00
1984 171.30
1985 170.90
1986 169.30
1987 167.10
1988 164.30
1989 161.60
1990 159.20
1991 157.20
1992 155.50
1993 153.60
1994 151.70
1995 149.50
1996 146.60
1997 142.70
1998 137.60
1999 131.40
2000 124.30
2001 116.70
2002 108.80
2003 101.50
2004 95.10
2005 89.20
2006 84.20
2007 79.40
2008 75.20
2009 71.10
2010 67.70
2011 64.30
2012 61.20
2013 58.60
2014 55.80
2015 54.10
2016 52.00
2017 50.10
2018 48.20
2019 46.60
2020 45.10

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