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

The value for Mortality rate, infant, male (per 1,000 live births) in Afghanistan was 48.10 as of 2020. As the graph below shows, over the past 59 years this indicator reached a maximum value of 248.40 in 1961 and a minimum value of 48.10 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
1961 248.40
1962 244.30
1963 240.00
1964 235.90
1965 231.70
1966 227.70
1967 223.60
1968 219.60
1969 215.60
1970 211.50
1971 207.50
1972 203.50
1973 199.50
1974 195.10
1975 191.00
1976 186.90
1977 182.70
1978 178.50
1979 174.10
1980 169.60
1981 165.20
1982 160.70
1983 156.30
1984 152.00
1985 147.80
1986 143.50
1987 139.20
1988 134.80
1989 130.60
1990 126.50
1991 122.70
1992 119.00
1993 115.70
1994 112.50
1995 109.40
1996 106.70
1997 103.80
1998 101.00
1999 98.30
2000 95.60
2001 92.90
2002 90.10
2003 87.30
2004 84.60
2005 81.70
2006 78.90
2007 76.10
2008 73.30
2009 70.60
2010 67.90
2011 65.40
2012 63.00
2013 60.80
2014 58.60
2015 56.50
2016 54.60
2017 52.80
2018 51.10
2019 49.60
2020 48.10

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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