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

The value for Mortality rate, infant, male (per 1,000 live births) in Tanzania was 37.70 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 150.50 in 1960 and a minimum value of 37.70 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 150.50
1961 149.40
1962 148.10
1963 146.80
1964 145.30
1965 143.70
1966 141.80
1967 139.90
1968 138.00
1969 136.10
1970 134.20
1971 132.30
1972 130.20
1973 128.00
1974 125.70
1975 123.30
1976 120.80
1977 118.40
1978 116.10
1979 114.40
1980 113.30
1981 112.70
1982 112.50
1983 112.50
1984 112.40
1985 111.90
1986 110.90
1987 109.50
1988 108.00
1989 106.30
1990 104.80
1991 103.40
1992 102.20
1993 101.10
1994 99.90
1995 98.40
1996 96.50
1997 94.00
1998 91.00
1999 87.30
2000 83.00
2001 78.40
2002 73.90
2003 69.60
2004 65.80
2005 62.30
2006 59.60
2007 56.70
2008 54.70
2009 52.30
2010 50.60
2011 49.00
2012 47.40
2013 46.30
2014 45.20
2015 44.20
2016 42.90
2017 41.30
2018 39.80
2019 38.80
2020 37.70

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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