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

The value for Mortality rate, under-5, male (per 1,000 live births) in Tanzania was 52.40 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 250.30 in 1960 and a minimum value of 52.40 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 250.30
1961 248.40
1962 246.50
1963 244.20
1964 241.80
1965 239.20
1966 236.20
1967 233.00
1968 229.70
1969 226.40
1970 223.00
1971 219.40
1972 215.70
1973 211.70
1974 207.60
1975 203.30
1976 198.90
1977 194.50
1978 190.50
1979 187.20
1980 185.30
1981 184.30
1982 184.40
1983 184.60
1984 184.70
1985 183.80
1986 182.00
1987 179.70
1988 177.00
1989 174.20
1990 171.70
1991 169.60
1992 167.90
1993 166.20
1994 164.40
1995 161.90
1996 158.60
1997 154.30
1998 149.00
1999 142.50
2000 135.00
2001 126.90
2002 118.90
2003 111.20
2004 104.50
2005 98.40
2006 93.10
2007 88.10
2008 83.80
2009 79.40
2010 76.00
2011 72.30
2012 69.20
2013 66.50
2014 63.80
2015 62.00
2016 59.80
2017 57.80
2018 55.80
2019 54.10
2020 52.40

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