Probability of dying at age 5-14 years (per 1,000 children age 5) - Country Ranking

Definition: Probability of dying between age 5-14 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified 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: Thematic map, Time series comparison

Find indicator:
Rank Country Value Year
1 Niger 40.30 2016
2 Cameroon 29.60 2016
3 Dem. Rep. Congo 28.20 2016
4 Côte d'Ivoire 27.90 2016
5 Chad 26.40 2016
6 Burkina Faso 25.50 2016
7 Somalia 24.10 2016
8 Mali 23.50 2016
9 Togo 22.50 2016
10 Benin 22.40 2016
11 Guinea 22.30 2016
12 Central African Republic 22.20 2016
13 Sierra Leone 20.80 2016
14 Nigeria 20.60 2016
15 Burundi 20.20 2016
16 Guinea-Bissau 17.60 2016
17 Ethiopia 17.40 2016
18 Liberia 17.30 2016
19 Equatorial Guinea 16.80 2016
20 Senegal 16.20 2016
21 Uganda 16.10 2016
22 Gabon 15.70 2016
23 Mozambique 15.40 2016
23 Angola 15.40 2016
25 Zimbabwe 15.30 2016
26 Haiti 14.50 2016
27 Zambia 13.90 2016
28 Lesotho 13.80 2016
29 Malawi 13.60 2016
30 Madagascar 13.40 2016
31 Ghana 12.60 2016
32 The Gambia 12.50 2016
32 Eritrea 12.50 2016
34 Tanzania 12.20 2016
35 Rwanda 12.00 2016
36 Swaziland 11.40 2016
37 Pakistan 11.30 2016
38 Congo 11.20 2016
39 Namibia 10.60 2016
39 Kenya 10.60 2016
41 Lao PDR 9.90 2016
42 Afghanistan 9.50 2016
43 São Tomé and Principe 9.40 2016
44 Mauritania 9.30 2016
45 Sudan 9.10 2016
46 Kiribati 8.60 2016
46 Papua New Guinea 8.60 2016
48 Myanmar 8.20 2016
49 Timor-Leste 8.00 2016
50 Botswana 7.80 2016
51 Bhutan 7.10 2016
52 Philippines 6.50 2016
53 Bolivia 6.40 2016
54 India 6.30 2016
55 Comoros 6.20 2016
56 Yemen 6.10 2016
56 Guyana 6.10 2016
58 Nauru 5.90 2016
59 Iraq 5.60 2016
60 Indonesia 5.30 2016
60 Cambodia 5.30 2016
62 South Africa 5.20 2016
63 Nepal 5.00 2016
63 Vanuatu 5.00 2016
65 Egypt 4.90 2016
65 Bangladesh 4.90 2016
65 Libya 4.90 2016
68 Solomon Islands 4.70 2016
68 Tuvalu 4.70 2016
70 Jordan 4.40 2016
71 Algeria 4.10 2016
71 Fiji 4.10 2016
73 Honduras 3.90 2016
73 Dem. People's Rep. Korea 3.90 2016
75 Seychelles 3.80 2016
75 Nicaragua 3.80 2016
77 Guatemala 3.70 2016
77 Turkmenistan 3.70 2016
77 St. Vincent and the Grenadines 3.70 2016
77 El Salvador 3.70 2016
81 Mongolia 3.60 2016
81 Paraguay 3.60 2016
83 Peru 3.50 2016
83 Samoa 3.50 2016
85 Ecuador 3.40 2016
85 Morocco 3.40 2016
85 Tonga 3.40 2016
85 Tajikistan 3.40 2016
85 Uzbekistan 3.40 2016
90 Palau 3.30 2016
91 Syrian Arab Republic 3.20 2016
91 Suriname 3.20 2016
91 Thailand 3.20 2016
94 Tunisia 3.10 2016
94 Dominican Republic 3.10 2016
96 Kazakhstan 3.00 2016
96 Panama 3.00 2016
96 Colombia 3.00 2016
99 Azerbaijan 2.90 2016
99 Belize 2.90 2016
101 Grenada 2.80 2016
101 The Bahamas 2.80 2016
101 Georgia 2.80 2016
101 Vietnam 2.80 2016
101 Trinidad and Tobago 2.80 2016
101 China 2.80 2016
107 Kyrgyz Republic 2.70 2016
107 Jamaica 2.70 2016
107 Iran 2.70 2016
110 Venezuela 2.60 2016
110 Malaysia 2.60 2016
112 Mexico 2.50 2016
112 Brazil 2.50 2016
114 Russia 2.40 2016
114 Cabo Verde 2.40 2016
114 Dominica 2.40 2016
114 St. Lucia 2.40 2016
114 Saudi Arabia 2.40 2016
119 Sri Lanka 2.30 2016
119 Brunei 2.30 2016
119 Turkey 2.30 2016
122 Moldova 2.20 2016
122 Armenia 2.20 2016
124 Oman 2.10 2016
124 Cuba 2.10 2016
124 Albania 2.10 2016
124 Argentina 2.10 2016
128 Djibouti 2.00 2016
128 Barbados 2.00 2016
130 St. Kitts and Nevis 1.90 2016
131 Antigua and Barbuda 1.80 2016
131 Mauritius 1.80 2016
131 Bahrain 1.80 2016
131 Romania 1.80 2016
131 Qatar 1.80 2016
131 Costa Rica 1.80 2016
131 Kuwait 1.80 2016
131 Ukraine 1.80 2016
139 Uruguay 1.70 2016
139 Bulgaria 1.70 2016
139 Lithuania 1.70 2016
142 Latvia 1.50 2016
142 Chile 1.50 2016
142 Belarus 1.50 2016
145 Slovak Republic 1.30 2016
145 United States 1.30 2016
145 Serbia 1.30 2016
145 Lebanon 1.30 2016
145 United Arab Emirates 1.30 2016
150 Montenegro 1.20 2016
150 Bosnia and Herzegovina 1.20 2016
150 Estonia 1.20 2016
153 Croatia 1.10 2016
153 Macedonia 1.10 2016
155 New Zealand 1.00 2016
155 Poland 1.00 2016
155 Hungary 1.00 2016
158 San Marino 0.90 2016
158 Czech Republic 0.90 2016
158 Iceland 0.90 2016
158 Greece 0.90 2016
158 Australia 0.90 2016
158 Austria 0.90 2016
158 Cyprus 0.90 2016
158 Belgium 0.90 2016
158 Malta 0.90 2016
158 Monaco 0.90 2016
158 Israel 0.90 2016
169 Korea 0.80 2016
169 Germany 0.80 2016
169 Netherlands 0.80 2016
169 Portugal 0.80 2016
169 Canada 0.80 2016
169 Finland 0.80 2016
169 United Kingdom 0.80 2016
169 Spain 0.80 2016
169 Japan 0.80 2016
169 Andorra 0.80 2016
169 Sweden 0.80 2016
169 Ireland 0.80 2016
169 Slovenia 0.80 2016
182 Switzerland 0.70 2016
182 France 0.70 2016
182 Singapore 0.70 2016
182 Italy 0.70 2016
186 Norway 0.60 2016
187 Luxembourg 0.50 2016
187 Denmark 0.50 2016

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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