Papua New Guinea - Mortality rate, infant, female (per 1,000 live births)

The value for Mortality rate, infant, female (per 1,000 live births) in Papua New Guinea was 32.10 as of 2020. As the graph below shows, over the past 60 years this indicator reached a maximum value of 125.60 in 1960 and a minimum value of 32.10 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 125.60
1961 121.90
1962 118.40
1963 114.90
1964 111.50
1965 108.30
1966 105.10
1967 101.90
1968 98.90
1969 95.90
1970 93.00
1971 90.10
1972 87.50
1973 84.90
1974 82.40
1975 80.10
1976 77.90
1977 75.90
1978 74.00
1979 72.20
1980 70.50
1981 68.90
1982 67.30
1983 65.90
1984 64.60
1985 63.40
1986 62.30
1987 61.20
1988 60.10
1989 59.10
1990 58.00
1991 57.10
1992 56.10
1993 55.20
1994 54.30
1995 53.50
1996 52.70
1997 52.00
1998 51.20
1999 50.50
2000 49.70
2001 49.00
2002 48.30
2003 47.50
2004 46.70
2005 45.90
2006 45.00
2007 44.10
2008 43.20
2009 42.30
2010 41.40
2011 40.40
2012 39.50
2013 38.60
2014 37.70
2015 36.80
2016 35.90
2017 35.00
2018 34.00
2019 33.10
2020 32.10

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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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