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

The value for Mortality rate, infant, female (per 1,000 live births) in Tanzania was 31.60 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 134.90 in 1960 and a minimum value of 31.60 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 134.90
1961 133.80
1962 132.80
1963 131.60
1964 130.30
1965 128.70
1966 127.00
1967 125.30
1968 123.60
1969 121.80
1970 119.90
1971 118.10
1972 116.20
1973 114.10
1974 112.00
1975 109.70
1976 107.60
1977 105.50
1978 103.60
1979 101.90
1980 101.00
1981 100.80
1982 101.10
1983 101.60
1984 102.00
1985 101.90
1986 101.20
1987 99.90
1988 98.30
1989 96.80
1990 95.30
1991 94.10
1992 92.90
1993 91.80
1994 90.40
1995 89.10
1996 87.30
1997 85.10
1998 82.10
1999 78.60
2000 74.50
2001 70.10
2002 65.60
2003 61.50
2004 57.80
2005 54.50
2006 51.80
2007 49.00
2008 47.20
2009 45.00
2010 43.40
2011 41.90
2012 40.40
2013 39.40
2014 38.40
2015 37.50
2016 36.30
2017 34.80
2018 33.50
2019 32.60
2020 31.60

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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