Low & middle income | Probability of dying among adolescents ages 15-19 years (per 1,000)
Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year. Development relevance: Mortality rates for different age groups (infants, children, adolescents, youth 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 Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.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.
Publisher
The World Bank
Origin
Low & middle income
Records
63
Source
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| 1982 | |
| 1983 | |
| 1984 | |
| 1985 | |
| 1986 | |
| 1987 | |
| 1988 | |
| 1989 | |
| 1990 | 8.58699324 |
| 1991 | 8.4851931 |
| 1992 | 8.34505722 |
| 1993 | 8.34897031 |
| 1994 | 8.8151492 |
| 1995 | 8.13849376 |
| 1996 | 7.97930895 |
| 1997 | 7.73987369 |
| 1998 | 7.53217078 |
| 1999 | 7.38307446 |
| 2000 | 7.14684589 |
| 2001 | 6.97298741 |
| 2002 | 6.82578265 |
| 2003 | 6.66700677 |
| 2004 | 6.60797167 |
| 2005 | 6.39052814 |
| 2006 | 6.19597806 |
| 2007 | 6.1322079 |
| 2008 | 6.13173346 |
| 2009 | 5.98541569 |
| 2010 | 6.00266731 |
| 2011 | 5.86726313 |
| 2012 | 5.81109441 |
| 2013 | 5.74048802 |
| 2014 | 5.6858579 |
| 2015 | 5.61076261 |
| 2016 | 5.52277326 |
| 2017 | 5.43223811 |
| 2018 | 5.2833188 |
| 2019 | 5.14569528 |
| 2020 | 4.96982719 |
| 2021 | 4.83465401 |
| 2022 |
Low & middle income | Probability of dying among adolescents ages 15-19 years (per 1,000)
Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year. Development relevance: Mortality rates for different age groups (infants, children, adolescents, youth 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.
Publisher
The World Bank
Origin
Low & middle income
Records
63
Source