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

The value for Mortality rate, infant, female (per 1,000 live births) in Nepal was 21.40 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 209.90 in 1960 and a minimum value of 21.40 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 209.90
1961 207.30
1962 204.20
1963 200.40
1964 196.50
1965 192.20
1966 187.60
1967 182.90
1968 178.40
1969 174.20
1970 170.10
1971 166.20
1972 162.60
1973 159.10
1974 155.70
1975 152.00
1976 148.30
1977 144.40
1978 140.40
1979 136.60
1980 132.70
1981 128.80
1982 124.70
1983 120.50
1984 116.40
1985 112.40
1986 108.30
1987 104.00
1988 99.60
1989 95.10
1990 90.60
1991 86.30
1992 82.20
1993 78.30
1994 74.40
1995 70.70
1996 67.20
1997 63.80
1998 60.40
1999 57.30
2000 54.40
2001 51.70
2002 49.10
2003 46.80
2004 44.40
2005 42.30
2006 40.30
2007 38.40
2008 36.60
2009 35.00
2010 33.50
2011 32.00
2012 30.60
2013 29.20
2014 27.80
2015 26.50
2016 25.30
2017 24.10
2018 23.10
2019 22.20
2020 21.40

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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