Summary Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5·4 million (5·2–5·6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. Interpretation This analysis of age-sex-specific mortality shows that there are remarkably complex patterns in population mortality across countries. The findings of this study highlight global successes, such as the large decline in under-5 mortality, which reflects significant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing. Funding Bill & Melinda Gates Foundation.

Research in context Evidence before this study Several organisations report on aspects of all-cause mortality or life expectancy: the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, and WHO. Additionally, previous iterations of the Global Burden of Disease Study (GBD) have produced these estimates on an annual basis. UNPOP reports age-specific mortality by 5-year age groups for 162 countries and for time periods that cover 5 calendar years; these estimates are updated every 2 years (most recently in June, 2017). The United States Census Bureau produces mortality assessments for 15–25 countries per year, and WHO reports periodically on life expectancy and sometimes on other measures of mortality and bases its estimates on results from the UNPOP. The most recent release of estimates by WHO was in January, 2017, based on UNPOP estimates from 2015. GBD 2016 provided comprehensive assessment of age-sex-specific mortality for 195 countries and territories from 1970 to 2016 that were compliant with the Guidelines on Accurate and Transparent Reporting of Health Estimates. Added value of this study Table Life expectancy at birth and at age 60 years, probability of death between birth and age 5 years, probability of death between ages 15 and 60 years, and total number of deaths, for countries and territories and subnational units in the UK, by sex, 2017 Probability of death between birth and age 5 years Probability of death between ages 15 and 60 years Life expectancy at birth (years) Life expectancy at age 60 years (years) Total deaths (thousands) Male Female Male Female Male Female Male Female Male Female Global 0·04 (0·04 to 0·05) 0·04 (0·03 to 0·04) 0·17 (0·17 to 0·17) 0·10 (0·10 to 0·11) 70·48 (70·12 to 70·82) 75·59 (75·31 to 75·86) 19·39 (19·28 to 19·51) 22·61 (22·5 to 22·73) 30 387 (29 986 to 30 775) 25 558 (25 224 to 25 885) Low SDI 0·07 (0·06 to 0·08) 0·06 (0·06 to 0·07) 0·24 (0·23 to 0·25) 0·18 (0·18 to 0·19) 64·48 (63·8 to 65·13) 67·34 (66·75 to 67·95) 16·77 (16·5 to 17·04) 18·15 (17·84 to 18·45) 4806 (4685 to 4939) 4131 (4023 to 4240) Low-middle SDI 0·06 (0·05 to 0·06) 0·05 (0·05 to 0·05) 0·22 (0·21 to 0·23) 0·16 (0·15 to 0·17) 66·27 (65·67 to 66·86) 70·08 (69·5 to 70·65) 17·28 (17·02 to 17·53) 19·4 (19·11 to 19·69) 6579 (6363 to 6813) 5656 (5465 to 5866) Middle SDI 0·02 (0·02 to 0·02) 0·02 (0·02 to 0·02) 0·17 (0·16 to 0·17) 0·09 (0·09 to 0·09) 71·71 (71·37 to 72·09) 77·42 (77·09 to 77·7) 18·92 (18·69 to 19·18) 22·22 (21·96 to 22·46) 6067 (5911 to 6217) 4536 (4418 to 4662) High-middle SDI 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·15 (0·14 to 0·15) 0·07 (0·07 to 0·07) 73·33 (72·98 to 73·69) 79·42 (79·13 to 79·7) 19·1 (18·85 to 19·37) 22·68 (22·44 to 22·91) 7831 (7607 to 8059) 6259 (6088 to 6439) High SDI 0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·10 (0·10 to 0·10) 0·05 (0·05 to 0·06) 78·47 (78·3 to 78·65) 83·7 (83·53 to 83·86) 22·46 (22·33 to 22·58) 26·19 (26·05 to 26·32) 4997 (4922 to 5071) 4875 (4797 to 4953) Central Europe, eastern Europe, and central Asia 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·24 (0·24 to 0·25) 0·10 (0·10 to 0·10) 68·5 (68·3 to 68·68) 77·57 (77·41 to 77·74) 17·02 (16·92 to 17·12) 21·99 (21·89 to 22·1) 2427 (2398 to 2457) 2303 (2273 to 2332) Central Asia 0·03 (0·03 to 0·03) 0·02 (0·02 to 0·03) 0·22 (0·21 to 0·23) 0·11 (0·10 to 0·12) 67·37 (66·76 to 67·92) 74·83 (74·26 to 75·4) 15·82 (15·46 to 16·14) 20·30 (19·94 to 20·67) 353 (339 to 369) 277 (266 to 290) Armenia 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·16 (0·16 to 0·17) 0·07 (0·06 to 0·07) 72·38 (71·97 to 72·81) 78·65 (78·23 to 79·06) 17·93 (17·64 to 18·22) 21·51 (21·16 to 21·84) 14 (14 to 15) 14 (13 to 14) Azerbaijan 0·04 (0·03 to 0·05) 0·03 (0·03 to 0·04) 0·19 (0·17 to 0·21) 0·09 (0·08 to 0·10) 67·23 (66·2 to 68·22) 74·66 (73·74 to 75·66) 15·1 (14·51 to 15·71) 20·27 (19·56 to 21) 45 (41 to 48) 31 (28 to 33) Georgia 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·24 (0·23 to 0·25) 0·08 (0·08 to 0·09) 68·39 (67·96 to 68·81) 77·31 (76·89 to 77·73) 16·21 (15·99 to 16·45) 20·83 (20·53 to 21·13) 25 (25 to 26) 25 (24 to 26) Kazakhstan 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·26 (0·24 to 0·27) 0·10 (0·10 to 0·11) 67·46 (66·76 to 68·16) 76·38 (75·75 to 77·06) 16·24 (15·84 to 16·65) 20·93 (20·50 to 21·42) 74 (71 to 78) 61 (57 to 64) Kyrgyzstan 0·02 (0·02 to 0·02) 0·02 (0·02 to 0·02) 0·21 (0·20 to 0·21) 0·10 (0·09 to 0·10) 69·07 (68·7 to 69·44) 76·27 (75·88 to 76·65) 16·83 (16·6 to 17·06) 20·92 (20·64 to 21·21) 19 (18 to 19) 15 (15 to 16) Mongolia 0·03 (0·02 to 0·04) 0·02 (0·02 to 0·03) 0·30 (0·27 to 0·33) 0·14 (0·12 to 0·15) 64·48 (63·18 to 65·94) 73·66 (72·47 to 74·84) 14·9 (14·16 to 15·68) 19·68 (18·85 to 20·52) 13 (12 to 14) 8 (8 to 9) Tajikistan 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·18 (0·16 to 0·20) 0·12 (0·11 to 0·14) 67·67 (66·33 to 68·92) 73·3 (72·06 to 74·54) 17·19 (16·34 to 17·94) 20·75 (19·9 to 21·67) 28 (26 to 30) 20 (18 to 22) Turkmenistan 0·03 (0·03 to 0·04) 0·03 (0·02 to 0·03) 0·25 (0·23 to 0·27) 0·13 (0·12 to 0·14) 66·54 (65·42 to 67·68) 73·87 (72·72 to 74·94) 16·27 (15·66 to 16·93) 20·05 (19·26 to 20·76) 19 (17 to 20) 14 (13 to 16) Uzbekistan 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·21 (0·19 to 0·24) 0·12 (0·11 to 0·14) 67·12 (65·55 to 68·6) 73·75 (72·18 to 75·35) 15 (14·07 to 15·95) 19·38 (18·24 to 20·57) 116 (103 to 130) 89 (78 to 102) Central Europe 0·01 (0·01 to 0·01) 0·01 (<0·01 to 0·01) 0·15 (0·15 to 0·16) 0·07 (0·06 to 0·07) 73·62 (73·34 to 73·92) 80·44 (80·19 to 80·70) 18·69 (18·49 to 18·88) 23·13 (22·94 to 23·34) 678 (663 to 695) 649 (633 to 665) Albania 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·10 (0·08 to 0·13) 0·05 (0·04 to 0·06) 74·93 (72·83 to 77·11) 82·1 (79·9 to 84·32) 19·57 (18·12 to 21·14) 25 (23·18 to 26·9) 13 (11 to 16) 8 (7 to 11) Bosnia and Herzegovina 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·13 (0·12 to 0·14) 0·06 (0·06 to 0·07) 74·34 (73·62 to 75·04) 79·06 (78·39 to 79·74) 18·62 (18·1 to 19·12) 21·57 (21·03 to 22·11) 19 (18 to 20) 18 (17 to 19) Bulgaria 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·19 (0·18 to 0·20) 0·09 (0·08 to 0·09) 71·33 (70·60 to 72·11) 78·58 (77·88 to 79·24) 17·3 (16·83 to 17·82) 22·01 (21·49 to 22·52) 56 (53 to 60) 51 (48 to 55) Croatia <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·12 (0·11 to 0·13) 0·05 (0·04 to 0·05) 75·39 (74·71 to 76·08) 81·61 (80·95 to 82·28) 19·28 (18·81 to 19·8) 23·57 (23·03 to 24·15) 25 (24 to 27) 26 (24 to 28) Czech Republic <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·11 (0·10 to 0·12) 0·05 (0·05 to 0·06) 76·31 (75·6 to 77) 81·96 (81·29 to 82·6) 19·95 (19·42 to 20·46) 24·06 (23·52 to 24·57) 56 (52 to 59) 55 (51 to 59) Hungary 0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·17 (0·15 to 0·18) 0·08 (0·07 to 0·08) 73·19 (72·42 to 73·89) 80·20 (79·5 to 80·86) 18·13 (17·59 to 18·63) 23·02 (22·47 to 23·55) 60 (57 to 64) 62 (58 to 66) Macedonia 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·13 (0·12 to 0·14) 0·07 (0·06 to 0·08) 73·88 (73·19 to 74·58) 79·68 (79·15 to 80·26) 18·34 (17·86 to 18·84) 22·91 (22·56 to 23·34) 12 (11 to 12) 8 (7 to 8) Montenegro <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·13 (0·12 to 0·15) 0·07 (0·06 to 0·08) 74·06 (72·92 to 75·15) 78·93 (78·13 to 79·72) 18·2 (17·38 to 19) 21·55 (20·93 to 22·18) 3 (3 to 4) 3 (3 to 3) Poland <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·16 (0·15 to 0·17) 0·06 (0·06 to 0·07) 74·07 (73·35 to 74·8) 81·85 (81·2 to 82·44) 19·3 (18·81 to 19·8) 24·32 (23·81 to 24·8) 204 (192 to 216) 191 (180 to 203) Romania 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·19 (0·18 to 0·21) 0·08 (0·07 to 0·08) 71·55 (70·82 to 72·26) 78·95 (78·35 to 79·61) 17·8 (17·33 to 18·27) 22·27 (21·8 to 22·77) 136 (129 to 144) 125 (117 to 132) Serbia 0·01 (0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·14 (0·13 to 0·15) 0·08 (0·07 to 0·08) 73·59 (72·93 to 74·24) 77·86 (77·2 to 78·54) 18·02 (17·58 to 18·48) 20·55 (20·04 to 21·09) 57 (54 to 60) 67 (62 to 71) Slovakia 0·01 (0·01 to 0·01) 0·01 (<0·01 to 0·01) 0·14 (0·13 to 0·15) 0·06 (0·06 to 0·07) 74·09 (73·4 to 74·77) 80·57 (79·86 to 81·27) 18·73 (18·25 to 19·22) 23·18 (22·63 to 23·74) 27 (25 to 28) 26 (24 to 28) Slovenia <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·10 (0·09 to 0·11) 0·04 (0·04 to 0·05) 77·92 (77·17 to 78·71) 84·22 (83·45 to 84·99) 21·31 (20·75 to 21·89) 26·01 (25·36 to 26·64) 10 (9 to 11) 10 (9 to 11) Eastern Europe 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·30 (0·29 to 0·30) 0·11 (0·11 to 0·11) 66·49 (66·28 to 66·71) 77·24 (77·06 to 77·43) 16·14 (16·02 to 16·27) 21·66 (21·52 to 21·79) 1395 (1375 to 1415) 1376 (1355 to 1398) Belarus 0·01 (0·01 to 0·01) 0·01 (<0·01 to 0·01) 0·23 (0·22 to 0·25) 0·08 (0·08 to 0·09) 68·96 (68·2 to 69·68) 78·78 (78·14 to 79·45) 16·01 (15·56 to 16·45) 22·06 (21·58 to 22·57) 60 (56 to 63) 61 (58 to 65) Estonia <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·17 (0·14 to 0·19) 0·06 (0·05 to 0·07) 73·64 (71·97 to 75·29) 82·08 (80·69 to 83·49) 19·19 (18·11 to 20·29) 24·6 (23·52 to 25·72) 7 (6 to 8) 8 (7 to 9) Latvia 0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·23 (0·20 to 0·25) 0·08 (0·07 to 0·10) 70·13 (68·55 to 71·75) 79·85 (78·38 to 81·3) 17·22 (16·26 to 18·22) 23·07 (21·99 to 24·17) 13 (12 to 15) 15 (13 to 17) Lithuania 0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·24 (0·22 to 0·26) 0·08 (0·07 to 0·09) 69·63 (68·72 to 70·51) 80·20 (79·43 to 80·97) 17·16 (16·63 to 17·7) 23·39 (22·84 to 23·97) 20 (19 to 21) 21 (19 to 22) Moldova 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·25 (0·24 to 0·26) 0·10 (0·09 to 0·10) 68·2 (67·78 to 68·66) 77·42 (77·01 to 77·86) 16·34 (16·1 to 16·6) 21·64 (21·33 to 21·96) 22 (21 to 23) 20 (19 to 20) Russia 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·29 (0·29 to 0·30) 0·11 (0·11 to 0·11) 66·75 (66·63 to 66·89) 77·24 (77·12 to 77·36) 16·43 (16·36 to 16·5) 21·7 (21·62 to 21·78) 919 (911 to 926) 916 (907 to 925) Ukraine 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·33 (0·31 to 0·35) 0·11 (0·10 to 0·12) 64·65 (63·86 to 65·44) 76·52 (75·78 to 77·19) 15·24 (14·84 to 15·64) 21·2 (20·69 to 21·66) 355 (337 to 373) 335 (317 to 357) High income 0·01 (0·01 to 0·01) <0·01 (<0·01 to 0·01) 0·10 (0·10 to 0·10) 0·06 (0·06 to 0·06) 78·43 (78·25 to 78·61) 83·56 (83·38 to 83·74) 22·51 (22·38 to 22·63) 26·18 (26·04 to 26·32) 4885 (4812 to 4959) 4784 (4705 to 4866) Australasia <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·08 (0·07 to 0·09) 0·05 (0·04 to 0·05) 80·13 (79·05 to 81·23) 84·42 (83·44 to 85·37) 23·52 (22·73 to 24·34) 26·58 (25·79 to 27·37) 106 (96 to 117) 98 (88 to 108) Australia <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·08 (0·07 to 0·09) 0·05 (0·04 to 0·05) 80·21 (78·94 to 81·49) 84·58 (83·42 to 85·74) 23·56 (22·66 to 24·48) 26·69 (25·74 to 27·64) 89 (79 to 100) 82 (72 to 92) New Zealand <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·09 (0·08 to 0·09) 0·05 (0·05 to 0·06) 79·65 (79·03 to 80·29) 83·57 (82·98 to 84·16) 23·33 (22·88 to 23·81) 26·03 (25·53 to 26·5) 17 (16 to 18) 16 (15 to 17) High-income Asia Pacific <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·07 to 0·08) 0·04 (0·03 to 0·04) 80·76 (80·49 to 81·03) 86·93 (86·71 to 87·15) 23·62 (23·42 to 23·82) 28·67 (28·49 to 28·84) 879 (858 to 901) 814 (796 to 832) Brunei 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·15 (0·13 to 0·16) 0·10 (0·09 to 0·11) 73·35 (72·31 to 74·39) 77·5 (76·63 to 78·43) 18·9 (17·88 to 19·83) 21·58 (21 to 22·21) 1 (1 to 1) 1 (1 to 1) Japan <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·07 to 0·07) 0·04 (0·03 to 0·04) 81·08 (80·80 to 81·34) 87·21 (86·96 to 87·44) 23·8 (23·59 to 24) 28·93 (28·73 to 29·11) 704 (688 to 722) 668 (652 to 685) Singapore <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·06 (0·06 to 0·07) 0·03 (0·03 to 0·04) 81·93 (81·24 to 82·61) 87·55 (86·9 to 88·08) 24·28 (23·72 to 24·83) 29·13 (28·58 to 29·55) 11 (11 to 12) 9 (8 to 9) South Korea <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·08 (0·07 to 0·09) 0·04 (0·03 to 0·04) 79·52 (78·74 to 80·29) 85·48 (84·89 to 86·11) 22·64 (22·06 to 23·21) 27·23 (26·75 to 27·74) 163 (151 to 176) 136 (127 to 146) High-income North America 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·13 (0·13 to 0·14) 0·08 (0·08 to 0·08) 76·46 (76·15 to 76·76) 81·38 (81·11 to 81·66) 21·9 (21·7 to 22·1) 24·87 (24·68 to 25·08) 1603 (1566 to 1642) 1534 (1498 to 1569) Canada 0·01 (0·01 to 0·01) 0·01 (<0·01 to 0·01) 0·09 (0·08 to 0·09) 0·05 (0·05 to 0·06) 79·86 (79·2 to 80·53) 83·99 (83·36 to 84·57) 23·5 (23·02 to 24) 26·47 (25·96 to 26·92) 141 (133 to 150) 138 (131 to 147) Greenland 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·18 (0·17 to 0·19) 0·11 (0·10 to 0·12) 70·84 (70·33 to 71·38) 77·15 (76·2 to 78·04) 17·5 (17·3 to 18·32) 22·05 (21·34 to 22·7) <1 (<1 to <1) <1 (<1 to <1) USA 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·14 (0·14 to 0·14) 0·08 (0·08 to 0·08) 76·09 (75·76 to 76·42) 81·09 (80·80 to 81·38) 21·72 (21·51 to 21·94) 24·69 (24·48 to 24·91) 1461 (1424 to 1499) 1396 (1361 to 1431) Southern Latin America 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·13 (0·12 to 0·15) 0·07 (0·07 to 0·08) 74·51 (73·32 to 75·55) 80·36 (79·33 to 81·28) 19·86 (19·06 to 20·58) 23·77 (22·98 to 24·48) 248 (228 to 272) 230 (211 to 253) Argentina 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·14 (0·12 to 0·16) 0·08 (0·07 to 0·09) 73·57 (71·97 to 74·97) 79·67 (78·33 to 80·99) 19·2 (18·14 to 20·16) 23·35 (22·37 to 24·34) 172 (154 to 195) 160 (142 to 180) Chile 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·11 (0·10 to 0·13) 0·06 (0·05 to 0·07) 77·19 (75·73 to 78·67) 82·11 (80·81 to 83·42) 21·76 (20·72 to 22·84) 24·85 (23·81 to 25·92) 59 (52 to 66) 54 (47 to 61) Uruguay 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·15 (0·13 to 0·17) 0·08 (0·07 to 0·09) 73·51 (72·07 to 75·02) 80·43 (79·03 to 81·87) 19·26 (18·3 to 20·27) 23·94 (22·92 to 25·03) 17 (15 to 19) 17 (15 to 19) Western Europe <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·08 (0·08 to 0·08) 0·05 (0·04 to 0·05) 79·53 (79·19 to 79·84) 84·21 (83·9 to 84·51) 22·65 (22·4 to 22·89) 26·21 (25·96 to 26·46) 2049 (1992 to 2111) 2108 (2046 to 2174) Andorra <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·08 (0·06 to 0·09) 0·04 (0·04 to 0·05) 80·55 (79·43 to 81·68) 85·06 (83·58 to 86·74) 23·48 (22·79 to 24·22) 26·85 (25·59 to 28·34) <1 (<1 to <1) <1 (<1 to <1) Austria <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·08 (0·07 to 0·09) 0·04 (0·04 to 0·05) 79·4 (78·75 to 80·07) 84·03 (83·4 to 84·62) 22·41 (21·91 to 22·92) 25·94 (25·42 to 26·43) 39 (37 to 42) 42 (40 to 45) Belgium <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·09 (0·08 to 0·09) 0·05 (0·05 to 0·05) 78·87 (78·22 to 79·55) 83·82 (83·14 to 84·45) 22·16 (21·66 to 22·66) 25·97 (25·43 to 26·48) 54 (51 to 57) 55 (52 to 59) Cyprus <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·08 (0·07 to 0·09) 0·04 (0·03 to 0·04) 78·45 (77·41 to 79·47) 85·21 (84·33 to 85·98) 21·46 (20·70 to 22·23) 26·96 (26·23 to 27·56) 5 (4 to 5) 3 (3 to 4) Denmark <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·08 (0·08 to 0·09) 0·05 (0·05 to 0·06) 78·81 (78·12 to 79·48) 82·69 (81·91 to 83·37) 21·86 (21·34 to 22·36) 24·83 (24·2 to 25·4) 27 (26 to 29) 27 (25 to 29) Finland <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·10 (0·09 to 0·10) 0·04 (0·04 to 0·05) 78·55 (77·77 to 79·23) 84·28 (83·58 to 84·94) 22·1 (21·54 to 22·59) 26·15 (25·58 to 26·7) 28 (26 to 30) 27 (26 to 29) France <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·09 (0·09 to 0·10) 0·05 (0·04 to 0·05) 79·82 (79·18 to 80·43) 85·72 (85·15 to 86·29) 23·38 (22·91 to 23·83) 27·84 (27·38 to 28·29) 289 (274 to 306) 290 (274 to 307) Germany <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·09 (0·08 to 0·11) 0·05 (0·04 to 0·06) 78·24 (76·91 to 79·49) 83·01 (81·82 to 84·2) 21·61 (20·63 to 22·55) 25·11 (24·14 to 26·09) 464 (415 to 520) 484 (429 to 544) Greece <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·10 (0·09 to 0·10) 0·05 (0·04 to 0·05) 78·44 (77·79 to 79·15) 83·56 (82·96 to 84·21) 22·12 (21·64 to 22·64) 25·67 (25·16 to 26·18) 63 (59 to 66) 57 (54 to 61) Iceland <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·07 to 0·07) 0·04 (0·03 to 0·04) 79·83 (79·4 to 80·25) 85·94 (85·45 to 86·42) 22·63 (22·31 to 22·95) 27·57 (27·16 to 27·98) 1 (1 to 1) 1 (1 to 1) Ireland <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·06 to 0·08) 0·05 (0·04 to 0·05) 80 (79·31 to 80·71) 83·68 (82·92 to 84·35) 22·83 (22·32 to 23·38) 25·6 (24·97 to 26·16) 16 (15 to 17) 15 (14 to 16) Israel <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·06 to 0·07) 0·04 (0·03 to 0·04) 81·27 (80·60 to 81·92) 84·58 (83·93 to 85·25) 24·02 (23·5 to 24·52) 26·33 (25·77 to 26·9) 23 (21 to 24) 23 (22 to 25) Italy <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·06 (0·06 to 0·07) 0·04 (0·03 to 0·04) 80·85 (80·22 to 81·43) 85·31 (84·72 to 85·91) 23·39 (22·91 to 23·84) 26·99 (26·5 to 27·5) 299 (282 to 317) 324 (303 to 344) Luxembourg <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·06 to 0·08) 0·05 (0·04 to 0·05) 80·03 (78·91 to 81·2) 83·25 (82·31 to 84·22) 22·83 (21·99 to 23·73) 25·22 (24·44 to 26·02) 2 (2 to 2) 2 (2 to 3) Malta 0·01 (0·01 to 0·01) 0·01 (<0·01 to 0·01) 0·07 (0·07 to 0·08) 0·04 (0·04 to 0·04) 78·91 (78·42 to 79·45) 83·02 (82·42 to 83·6) 21·99 (21·64 to 22·4) 25 (24·49 to 25·5) 2 (2 to 2) 2 (2 to 2) Netherlands <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·06 to 0·07) 0·05 (0·05 to 0·06) 79·89 (79·25 to 80·50) 83·06 (82·42 to 83·71) 22·44 (21·95 to 22·92) 25·21 (24·68 to 25·74) 74 (69 to 79) 78 (73 to 84) Norway <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·06 to 0·07) 0·04 (0·04 to 0·04) 80·46 (80·25 to 80·69) 84·17 (83·95 to 84·39) 23·11 (22·95 to 23·28) 25·94 (25·77 to 26·12) 20 (20 to 21) 21 (21 to 22) Portugal <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·10 (0·09 to 0·11) 0·04 (0·04 to 0·05) 78·51 (77·86 to 79·23) 84·22 (83·6 to 84·82) 22·15 (21·68 to 22·68) 26·11 (25·62 to 26·61) 57 (53 to 60) 57 (53 to 61) Spain <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·07 (0·07 to 0·08) 0·04 (0·03 to 0·04) 80·21 (79·65 to 80·80) 85·82 (85·31 to 86·34) 23·03 (22·6 to 23·48) 27·53 (27·11 to 27·97) 211 (200 to 222) 206 (195 to 218) Sweden <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·06 (0·06 to 0·07) 0·04 (0·04 to 0·04) 80·79 (80·22 to 81·35) 84·18 (83·65 to 84·71) 23·36 (22·92 to 23·8) 25·91 (25·47 to 26·36) 45 (42 to 47) 47 (45 to 50) Switzerland <0·01 (<0·01 to <0·01) <0·01 (<0·01 to <0·01) 0·05 (0·05 to 0·06) 0·03 (0·03 to 0·04) 82·12 (81·53 to 82·78) 85·66 (85·09 to 86·27) 24·46 (23·99 to 24·98) 27·32 (26·84 to 27·84) 31 (29 to 33) 34 (31 to 36) UK <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·08 (0·08 to 0·08) 0·05 (0·05 to 0·06) 79·18 (79·05 to 79·32) 82·72 (82·59 to 82·85) 22·5 (22·42 to 22·6) 25·05 (24·95 to 25·14) 299 (295 to 302) 310 (306 to 313) England <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·08 (0·08 to 0·08) 0·05 (0·05 to 0·05) 79·49 (79·39 to 79·59) 82·91 (82·83 to 83·01) 22·67 (22·61 to 22·75) 25·18 (25·11 to 25·26) 245 (243 to 247) 256 (254 to 258) Northern Ireland 0·01 (<0·01 to 0·01) <0·01 (<0·01 to 0·01) 0·09 (0·08 to 0·1) 0·06 (0·05 to 0·06) 78·74 (77·74 to 79·77) 82·48 (81·45 to 83·44) 22·39 (21·68 to 23·15) 24·92 (24·11 to 25·69) 8 (7 to 8) 8 (7 to 9) Scotland <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·12 (0·1 to 0·13) 0·07 (0·06 to 0·08) 76·91 (75·95 to 77·96) 81·2 (80·33 to 82·12) 21·29 (20·63 to 22·02) 23·99 (23·34 to 24·73) 29 (26 to 31) 29 (27 to 32) Wales <0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·1 (0·09 to 0·1) 0·06 (0·05 to 0·06) 78·27 (77·52 to 79·1) 82·47 (81·67 to 83·23) 22 (21·45 to 22·59) 24·88 (24·24 to 25·49) 17 (16 to 18) 16 (15 to 18) Latin America and Caribbean 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·17 (0·17 to 0·18) 0·09 (0·09 to 0·09) 72·79 (72·44 to 73·16) 78·94 (78·63 to 79·23) 20·94 (20·80 to 21·09) 23·72 (23·57 to 23·87) 1895 (1863 to 1928) 1501 (1475 to 1527) Andean Latin America 0·02 (0·02 to 0·03) 0·02 (0·01 to 0·02) 0·12 (0·11 to 0·13) 0·08 (0·07 to 0·09) 76·18 (74·95 to 77·35) 79·49 (78·39 to 80·58) 22·62 (21·77 to 23·44) 24·19 (23·37 to 25) 159 (146 to 174) 137 (124 to 150) Bolivia 0·03 (0·03 to 0·04) 0·03 (0·02 to 0·03) 0·14 (0·11 to 0·18) 0·12 (0·09 to 0·15) 71·3 (68·76 to 73·93) 74·15 (72·08 to 76·58) 18·45 (16·43 to 20·49) 20·36 (19·01 to 22·22) 35 (28 to 43) 32 (26 to 37) Ecuador 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·15 (0·13 to 0·17) 0·09 (0·08 to 0·10) 74·77 (73·32 to 76·08) 78·72 (77·48 to 79·92) 22·26 (21·44 to 23·06) 23·5 (22·63 to 24·36) 48 (43 to 53) 40 (36 to 44) Peru 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·10 (0·08 to 0·12) 0·07 (0·06 to 0·08) 78·74 (76·78 to 80·79) 81·89 (80·05 to 83·73) 24·28 (22·95 to 25·69) 25·91 (24·48 to 27·31) 77 (65 to 89) 65 (55 to 77) Caribbean 0·04 (0·03 to 0·05) 0·03 (0·03 to 0·04) 0·18 (0·17 to 0·20) 0·12 (0·11 to 0·14) 70·35 (69·35 to 71·43) 75·39 (74·36 to 76·39) 19·78 (19·25 to 20·34) 22·63 (22·02 to 23·23) 196 (185 to 208) 164 (154 to 175) Antigua and Barbuda 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·13 (0·12 to 0·14) 0·09 (0·08 to 0·09) 75·28 (74·4 to 76·15) 78·74 (78·13 to 79·36) 20·97 (20·38 to 21·55) 22·48 (21·97 to 23·07) <1 (<1 to <1) <1 (<1 to <1) The Bahamas 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·22 (0·20 to 0·24) 0·13 (0·12 to 0·14) 70·84 (69·58 to 72·12) 76·58 (75·41 to 77·89) 19·81 (19·09 to 20·56) 22·35 (21·54 to 23·25) 1 (1 to 1) 1 (1 to 1) Barbados 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·13 (0·12 to 0·15) 0·09 (0·08 to 0·10) 75·49 (74·44 to 76·64) 78·63 (77·73 to 79·62) 21·28 (20·59 to 22·02) 23·23 (22·63 to 23·97) 1 (1 to 1) 1 (1 to 2) Belize 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·21 (0·21 to 0·22) 0·12 (0·11 to 0·12) 71·25 (70·67 to 71·84) 77·4 (76·87 to 77·94) 20·73 (20·42 to 21·07) 23·02 (22·68 to 23·38) 1 (1 to 1) 1 (1 to 1) Bermuda 0·01 (<0·01 to 0·01) <0·01 (<0·01 to 0·01) 0·11 (0·10 to 0·12) 0·04 (0·03 to 0·04) 77·05 (76·42 to 77·6) 85·67 (84·82 to 86·53) 21·11 (20·59 to 21·45) 27·61 (26·92 to 28·31) <1 (<1 to <1) <1 (<1 to <1) Cuba 0·01 (<0·01 to 0·01) <0·01 (<0·01 to 0·01) 0·12 (0·11 to 0·14) 0·07 (0·06 to 0·08) 76·18 (74·64 to 77·65) 80·71 (79·32 to 82·1) 20·76 (19·7 to 21·79) 23·68 (22·6 to 24·79) 55 (49 to 62) 46 (41 to 53) Dominica 0·03 (0·03 to 0·04) 0·03 (0·02 to 0·03) 0·17 (0·16 to 0·19) 0·10 (0·09 to 0·11) 70·42 (69·42 to 71·4) 75·36 (74·33 to 76·4) 19·01 (18·46 to 19·53) 21·55 (20·89 to 22·3) <1 (<1 to <1) <1 (<1 to <1) Dominican Republic 0·03 (0·03 to 0·04) 0·03 (0·02 to 0·03) 0·21 (0·18 to 0·24) 0·11 (0·10 to 0·13) 69·78 (67·83 to 71·9) 76·77 (75·17 to 78·47) 19·48 (18·33 to 20·86) 23·12 (22·08 to 24·32) 40 (35 to 45) 27 (24 to 31) Grenada 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·18 (0·17 to 0·19) 0·12 (0·11 to 0·13) 72·99 (72·31 to 73·65) 75·41 (74·68 to 76·15) 20·11 (19·67 to 20·53) 20·53 (19·98 to 21·14) 1 (1 to 1) 1 (1 to 1) Guyana 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·27 (0·24 to 0·31) 0·17 (0·15 to 0·20) 66·36 (64·55 to 68·16) 72·16 (70·49 to 73·9) 17·01 (16 to 18·04) 19·56 (18·49 to 20·70) 3 (3 to 3) 2 (2 to 3) Haiti 0·06 (0·05 to 0·08) 0·05 (0·05 to 0·06) 0·25 (0·20 to 0·30) 0·22 (0·18 to 0·27) 63·83 (61·44 to 66·42) 65·96 (63·27 to 68·75) 16·05 (15 to 17·52) 16·95 (15·36 to 18·92) 45 (39 to 52) 43 (36 to 52) Jamaica 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·18 (0·15 to 0·21) 0·11 (0·09 to 0·13) 71·96 (69·85 to 74·14) 77·48 (75·41 to 79·4) 19·14 (17·88 to 20·50) 22·3 (20·77 to 23·72) 11 (9 to 13) 9 (8 to 11) Puerto Rico 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·17 (0·16 to 0·18) 0·08 (0·07 to 0·08) 74·52 (73·69 to 75·39) 81·6 (80·88 to 82·32) 22·38 (21·84 to 22·94) 25·76 (25·2 to 26·32) 18 (17 to 20) 16 (15 to 17) Saint Lucia 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·18 (0·16 to 0·19) 0·10 (0·09 to 0·11) 73·12 (72·24 to 74) 78·08 (77·2 to 78·93) 20·57 (20·04 to 21·13) 22·62 (21·98 to 23·2) 1 (1 to 1) 1 (1 to 1) Saint Vincent and the Grenadines 0·02 (0·02 to 0·02) 0·01 (0·01 to 0·02) 0·21 (0·20 to 0·22) 0·13 (0·12 to 0·14) 69·65 (68·86 to 70·38) 75·41 (74·56 to 76·29) 18·08 (17·66 to 18·49) 21·18 (20·59 to 21·74) 1 (1 to 1) <1 (<1 to <1) Suriname 0·03 (0·03 to 0·04) 0·03 (0·02 to 0·03) 0·22 (0·19 to 0·24) 0·12 (0·11 to 0·14) 68·95 (67·25 to 70·72) 75·28 (73·98 to 76·61) 18·37 (17·36 to 19·42) 21·97 (21·05 to 22·9) 2 (2 to 3) 2 (2 to 2) Trinidad and Tobago 0·02 (0·02 to 0·02) 0·01 (0·01 to 0·02) 0·19 (0·15 to 0·24) 0·11 (0·08 to 0·14) 71·13 (68·45 to 73·95) 77·55 (74·82 to 80·33) 19·1 (17·43 to 20·85) 22·73 (20·81 to 24·81) 6 (5 to 8) 5 (4 to 6) Virgin Islands 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·22 (0·19 to 0·25) 0·09 (0·08 to 0·11) 69·49 (67·94 to 71·76) 78·78 (77·23 to 80·05) 16·61 (15·8 to 18·7) 22·72 (21·66 to 23·66) 1 (1 to 1) 1 (<1 to 1) Central Latin America 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·17 (0·17 to 0·18) 0·09 (0·08 to 0·09) 73·3 (72·79 to 73·82) 79·42 (79·01 to 79·82) 21·44 (21·17 to 21·72) 23·87 (23·6 to 24·14) 766 (742 to 792) 593 (575 to 611) Colombia 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·12 (0·11 to 0·14) 0·06 (0·05 to 0·07) 77·44 (75·94 to 79·03) 82·68 (81·36 to 83·95) 24·03 (23·03 to 25·03) 26·31 (25·27 to 27·32) 127 (113 to 143) 107 (95 to 121) Costa Rica 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·13 (0·12 to 0·14) 0·06 (0·06 to 0·07) 76·31 (75·53 to 77·13) 82·67 (81·86 to 83·4) 21·83 (21·33 to 22·36) 25·68 (25·04 to 26·25) 14 (13 to 14) 10 (9 to 11) El Salvador 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·26 (0·21 to 0·30) 0·10 (0·08 to 0·13) 69·29 (66·66 to 72·05) 78·3 (75·98 to 80·41) 19·96 (18·64 to 21·41) 22·92 (21·25 to 24·51) 23 (20 to 27) 18 (15 to 22) Guatemala 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·03) 0·23 (0·20 to 0·25) 0·12 (0·10 to 0·14) 69·14 (67·44 to 70·76) 75·99 (74·53 to 77·38) 19·72 (18·87 to 20·60) 22·14 (21·17 to 23·16) 52 (46 to 58) 38 (33 to 42) Honduras 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·17 (0·13 to 0·21) 0·14 (0·10 to 0·17) 72·88 (70·17 to 75·6) 74·96 (72·41 to 78·18) 20·52 (19·01 to 22·22) 20·80 (19·3 to 23·23) 23 (19 to 27) 22 (17 to 26) Mexico 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·19 (0·18 to 0·19) 0·09 (0·09 to 0·09) 72·56 (72·27 to 72·85) 78·5 (78·22 to 78·76) 20·77 (20·64 to 20·89) 22·98 (22·84 to 23·13) 401 (396 to 407) 310 (305 to 315) Nicaragua 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·14 (0·12 to 0·16) 0·07 (0·06 to 0·09) 76·92 (75·26 to 78·41) 80·64 (79·36 to 82·04) 23·58 (22·5 to 24·57) 24·64 (23·71 to 25·7) 12 (11 to 14) 11 (10 to 12) Panama 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·12 (0·11 to 0·13) 0·07 (0·06 to 0·07) 77·01 (76·17 to 77·93) 81·7 (80·93 to 82·47) 23·58 (23·07 to 24·15) 25·89 (25·35 to 26·46) 11 (10 to 11) 8 (8 to 9) Venezuela 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·20 (0·17 to 0·23) 0·08 (0·07 to 0·10) 71·23 (68·89 to 73·7) 79·6 (77·73 to 81·49) 20·41 (19·11 to 21·8) 24·03 (22·63 to 25·52) 104 (87 to 121) 69 (58 to 81) Tropical Latin America 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·19 (0·18 to 0·19) 0·09 (0·09 to 0·09) 72·03 (71·75 to 72·29) 79·07 (78·81 to 79·28) 20·35 (20·28 to 20·43) 23·72 (23·65 to 23·8) 774 (767 to 781) 608 (602 to 614) Brazil 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·19 (0·18 to 0·19) 0·09 (0·09 to 0·09) 71·98 (71·71 to 72·23) 79·06 (78·81 to 79·27) 20·36 (20·30 to 20·43) 23·74 (23·66 to 23·81) 755 (749 to 761) 594 (588 to 599) Paraguay 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·16 (0·12 to 0·19) 0·09 (0·07 to 0·11) 73·44 (70·99 to 75·98) 78·93 (76·76 to 81·19) 20·04 (18·49 to 21·72) 23·24 (21·66 to 24·95) 19 (15 to 22) 14 (11 to 17) North Africa and Middle East 0·03 (0·03 to 0·03) 0·02 (0·02 to 0·03) 0·15 (0·14 to 0·15) 0·10 (0·09 to 0·10) 72 (71·53 to 72·49) 76·85 (76·4 to 77·32) 19·32 (19·02 to 19·64) 22·53 (22·21 to 22·86) 1684 (1628 to 1742) 1179 (1135 to 1224) Afghanistan 0·06 (0·05 to 0·06) 0·05 (0·04 to 0·06) 0·27 (0·21 to 0·32) 0·28 (0·23 to 0·34) 63·56 (61·28 to 65·89) 63·18 (60·63 to 65·85) 15·43 (14·64 to 16·33) 15·07 (14·18 to 16·33) 115 (100 to 131) 112 (96 to 130) Algeria 0·02 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·09 (0·09 to 0·10) 0·07 (0·07 to 0·08) 77·03 (76·39 to 77·61) 78·48 (77·89 to 79·06) 22·46 (22 to 22·88) 23·05 (22·61 to 23·5) 90 (85 to 95) 78 (74 to 83) Bahrain 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·06 (0·06 to 0·07) 0·05 (0·05 to 0·06) 78·8 (77·81 to 79·84) 80·44 (79·49 to 81·38) 21·67 (20·87 to 22·51) 22·82 (22·02 to 23·63) 2 (2 to 2) 1 (1 to 1) Egypt 0·02 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·20 (0·18 to 0·23) 0·13 (0·11 to 0·14) 67·96 (66·61 to 69·31) 74·33 (72·88 to 75·79) 15·16 (14·39 to 15·97) 19·98 (18·99 to 20·97) 316 (284 to 353) 183 (161 to 206) Iran 0·02 (0·02 to 0·02) 0·01 (0·01 to 0·01) 0·12 (0·12 to 0·12) 0·06 (0·06 to 0·06) 75·47 (75·38 to 75·55) 79·36 (79·28 to 79·46) 21·2 (21·15 to 21·25) 22·79 (22·73 to 22·85) 219 (217 to 220) 161 (160 to 162) Iraq 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·03) 0·15 (0·14 to 0·16) 0·08 (0·07 to 0·08) 74·79 (73·85 to 75·6) 78·83 (78·06 to 79·65) 23·58 (22·92 to 24·2) 24·42 (23·81 to 25·01) 92 (87 to 97) 60 (56 to 65) Jordan 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·08 (0·07 to 0·10) 0·05 (0·04 to 0·06) 77·85 (76·34 to 79·18) 81·07 (79·84 to 82·31) 22·16 (20·89 to 23·22) 24·07 (23·12 to 25·09) 16 (15 to 18) 11 (10 to 13) Kuwait 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·08 (0·07 to 0·08) 0·03 (0·03 to 0·03) 80·66 (79·98 to 81·35) 87·18 (86·69 to 87·67) 24·31 (23·81 to 24·83) 29·22 (28·81 to 29·62) 6 (5 to 6) 2 (2 to 2) Lebanon 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·12 (0·11 to 0·13) 0·07 (0·06 to 0·08) 75·8 (75·05 to 76·38) 79·95 (79·37 to 80·71) 20·51 (19·82 to 20·99) 23·14 (22·7 to 23·74) 17 (16 to 19) 16 (15 to 17) Libya 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·18 (0·15 to 0·21) 0·12 (0·10 to 0·14) 71·14 (69·36 to 73·18) 74·97 (73·27 to 76·87) 18·82 (17·7 to 20·13) 20·21 (18·97 to 21·62) 20 (17 to 23) 14 (12 to 17) Morocco 0·02 (0·02 to 0·03) 0·02 (0·01 to 0·02) 0·13 (0·10 to 0·16) 0·12 (0·10 to 0·15) 73·23 (71 to 75·48) 74·7 (72·66 to 76·8) 19·48 (17·91 to 21·12) 20·19 (18·78 to 21·61) 113 (95 to 136) 107 (90 to 128) Oman 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·11 (0·08 to 0·13) 0·07 (0·06 to 0·09) 75·47 (73·25 to 77·89) 79·44 (78·21 to 81·24) 20·10 (18·48 to 21·94) 22·87 (22·05 to 24·28) 7 (6 to 9) 4 (3 to 4) Palestine 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·11 (0·10 to 0·11) 0·07 (0·06 to 0·07) 75·62 (74·72 to 76·43) 78 (77·32 to 78·85) 20·39 (19·58 to 21·09) 21·25 (20·70 to 21·96) 7 (7 to 8) 7 (7 to 8) Qatar 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·07 (0·05 to 0·08) 0·05 (0·04 to 0·06) 79·55 (77·69 to 81·55) 81·66 (79·84 to 83·51) 22·73 (21·29 to 24·29) 23·98 (22·46 to 25·58) 3 (2 to 4) 1 (1 to 1) Saudi Arabia 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·13 (0·10 to 0·15) 0·08 (0·07 to 0·10) 75·29 (73·87 to 76·57) 79·43 (78·04 to 80·23) 20·31 (19·39 to 20·94) 23·08 (22·24 to 23·7) 64 (55 to 74) 30 (27 to 35) Sudan 0·05 (0·05 to 0·06) 0·04 (0·04 to 0·05) 0·16 (0·13 to 0·21) 0·14 (0·11 to 0·18) 68·85 (66·37 to 71·45) 72·02 (69·54 to 74·68) 18·26 (16·45 to 20·17) 20·15 (18·44 to 21·92) 120 (101 to 140) 89 (75 to 104) Syria 0·02 (0·02 to 0·02) 0·02 (0·02 to 0·02) 0·28 (0·25 to 0·30) 0·13 (0·11 to 0·14) 65·49 (63·79 to 67·19) 75·04 (73·98 to 76·31) 18·57 (17·16 to 20·12) 22·37 (21·8 to 23·31) 76 (68 to 85) 39 (35 to 42) Tunisia 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·10 (0·08 to 0·13) 0·06 (0·04 to 0·07) 76·09 (73·66 to 78·57) 80·72 (78·47 to 83·03) 20·57 (18·86 to 22·46) 23·62 (21·84 to 25·54) 38 (30 to 47) 28 (22 to 35) Turkey 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·02) 0·11 (0·10 to 0·12) 0·05 (0·05 to 0·06) 75·2 (74·14 to 76·25) 83·04 (82·04 to 84·04) 20·02 (19·24 to 20·81) 26·17 (25·35 to 26·99) 246 (225 to 269) 156 (141 to 172) United Arab Emirates 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·15 (0·12 to 0·19) 0·09 (0·07 to 0·12) 71·65 (69·35 to 74·05) 76·94 (74·73 to 79·19) 16·74 (15·15 to 18·43) 20·33 (18·63 to 22·11) 22 (17 to 27) 5 (4 to 6) Yemen 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·22 (0·18 to 0·27) 0·16 (0·13 to 0·20) 65·98 (63·58 to 68·33) 70·27 (67·58 to 72·72) 16·81 (15·48 to 18·48) 19·2 (17·29 to 20·83) 93 (77 to 113) 72 (58 to 89) South Asia 0·04 (0·04 to 0·05) 0·05 (0·04 to 0·05) 0·20 (0·20 to 0·21) 0·15 (0·15 to 0·16) 67·91 (67·4 to 68·45) 70·21 (69·66 to 70·74) 17·41 (17·19 to 17·64) 18·77 (18·52 to 19·02) 6587 (6400 to 6768) 5813 (5652 to 5982) Bangladesh 0·03 (0·03 to 0·04) 0·03 (0·03 to 0·04) 0·15 (0·13 to 0·17) 0·11 (0·10 to 0·13) 71·8 (70·29 to 73·34) 74·6 (73·05 to 76·03) 19·49 (18·45 to 20·55) 21·24 (20·19 to 22·32) 503 (447 to 562) 384 (341 to 433) Bhutan 0·03 (0·02 to 0·04) 0·03 (0·02 to 0·03) 0·13 (0·11 to 0·16) 0·10 (0·08 to 0·12) 72·34 (69·83 to 74·79) 76·04 (73·93 to 78·11) 19·08 (17·08 to 20·80) 21·84 (20·23 to 23·39) 2 (2 to 3) 2 (1 to 2) India 0·04 (0·04 to 0·05) 0·04 (0·04 to 0·05) 0·21 (0·20 to 0·22) 0·15 (0·15 to 0·16) 67·81 (67·25 to 68·33) 70·18 (69·53 to 70·76) 17·2 (17 to 17·39) 18·6 (18·37 to 18·82) 5230 (5115 to 5360) 4680 (4564 to 4798) Nepal 0·03 (0·03 to 0·04) 0·03 (0·03 to 0·04) 0·18 (0·15 to 0·22) 0·13 (0·11 to 0·16) 68·72 (67·24 to 70·56) 73·28 (71·54 to 75·11) 16·58 (16·02 to 18) 20·04 (18·89 to 21·23) 103 (89 to 113) 80 (70 to 92) Pakistan 0·06 (0·05 to 0·07) 0·06 (0·05 to 0·07) 0·21 (0·17 to 0·25) 0·18 (0·14 to 0·21) 66·31 (63·8 to 69·1) 67·41 (65·07 to 70·12) 17·33 (15·77 to 18·97) 17·99 (16·41 to 19·74) 749 (633 to 880) 667 (566 to 775) Southeast Asia, east Asia, and Oceania 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·14 (0·13 to 0·14) 0·07 (0·07 to 0·07) 72·91 (72·54 to 73·33) 78·56 (78·21 to 78·9) 19·01 (18·75 to 19·28) 22·48 (22·22 to 22·75) 8837 (8562 to 9099) 6574 (6370 to 6782) East Asia 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·12 (0·11 to 0·12) 0·06 (0·05 to 0·06) 74·46 (73·98 to 74·94) 79·88 (79·43 to 80·30) 19·4 (19·08 to 19·74) 22·82 (22·49 to 23·15) 6375 (6121 to 6624) 4670 (4483 to 4866) China 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·11 (0·11 to 0·12) 0·05 (0·05 to 0·06) 74·52 (74·05 to 75·01) 79·92 (79·44 to 80·36) 19·39 (19·07 to 19·74) 22·81 (22·47 to 23·16) 6052 (5802 to 6297) 4400 (4214 to 4591) North Korea 0·02 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·20 (0·16 to 0·24) 0·11 (0·09 to 0·14) 68·64 (67·1 to 70·21) 75·05 (72·91 to 77·17) 16·45 (15·92 to 17·04) 20·54 (19·1 to 22·07) 113 (101 to 126) 122 (101 to 146) Taiwan (province of China) 0·01 (<0·01 to 0·01) <0·01 (<0·01 to <0·01) 0·13 (0·12 to 0·14) 0·05 (0·05 to 0·06) 76·82 (76·1 to 77·51) 83·26 (82·63 to 83·87) 21·77 (21·29 to 22·23) 25·67 (25·18 to 26·17) 106 (100 to 112) 73 (69 to 78) Oceania 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·41 (0·35 to 0·47) 0·30 (0·25 to 0·36) 58·2 (55·92 to 60·60) 63·38 (61·1 to 65·54) 13·41 (12·71 to 14·17) 15·71 (15 to 16·41) 65 (56 to 74) 45 (39 to 52) American Samoa 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·21 (0·19 to 0·23) 0·15 (0·14 to 0·17) 69·99 (68·51 to 71·65) 73·8 (72·94 to 74·78) 17·11 (15·92 to 18·64) 19·66 (19·24 to 20·19) <1 (<1 to <1) <1 (<1 to <1) Federated States of Micronesia 0·02 (0·02 to 0·02) 0·02 (0·01 to 0·02) 0·30 (0·25 to 0·35) 0·22 (0·17 to 0·27) 64·98 (62·8 to 67·25) 69·58 (67·15 to 71·68) 15·12 (14·39 to 15·83) 17·66 (16·48 to 18·51) <1 (<1 to <1) <1 (<1 to <1) Fiji 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·03) 0·26 (0·22 to 0·29) 0·18 (0·15 to 0·21) 65·9 (64·17 to 67·7) 70·40 (68·44 to 72·51) 14·94 (13·93 to 16·02) 17·56 (16·27 to 19·02) 4 (3 to 5) 3 (3 to 4) Guam 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·23 (0·21 to 0·25) 0·12 (0·11 to 0·13) 70·23 (69·19 to 71·34) 76·4 (75·31 to 77·46) 18·82 (18·21 to 19·49) 21·5 (20·72 to 22·26) 1 (1 to 1) 1 (<1 to 1) Kiribati 0·05 (0·04 to 0·05) 0·04 (0·03 to 0·05) 0·41 (0·35 to 0·47) 0·23 (0·19 to 0·28) 58·59 (56·21 to 61·05) 66·31 (63·94 to 68·86) 13·14 (12·39 to 14·07) 16·2 (15·27 to 17·67) 1 (<1 to 1) <1 (<1 to 1) Marshall Islands 0·02 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·33 (0·29 to 0·39) 0·27 (0·22 to 0·31) 62·57 (60·56 to 64·61) 66·82 (64·55 to 68·96) 13·46 (12·53 to 14·46) 16·44 (15·33 to 17·38) <1 (<1 to <1) <1 (<1 to <1) Northern Mariana Islands 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·15 (0·13 to 0·18) 0·09 (0·07 to 0·10) 73·59 (72·32 to 75·01) 79·25 (78·02 to 80·15) 19·45 (18·3 to 20·42) 22·96 (22·21 to 23·71) <1 (<1 to <1) <1 (<1 to <1) Papua New Guinea 0·06 (0·05 to 0·07) 0·05 (0·04 to 0·06) 0·45 (0·38 to 0·52) 0·34 (0·28 to 0·40) 56·23 (53·56 to 59·16) 61·23 (58·55 to 63·85) 12·6 (11·72 to 13·62) 14·49 (13·56 to 15·38) 50 (42 to 59) 34 (29 to 41) Samoa 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·16 (0·13 to 0·19) 0·13 (0·11 to 0·16) 71·28 (70·03 to 72·69) 74·49 (72·89 to 76·7) 17·43 (17·07 to 18·21) 19·95 (18·96 to 21·52) 1 (1 to 1) 1 (<1 to 1) Solomon Islands 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·03) 0·30 (0·25 to 0·35) 0·24 (0·20 to 0·29) 64·12 (62 to 66·31) 67·52 (65·39 to 69·43) 14·93 (14·1 to 15·81) 16·7 (15·85 to 17·37) 2 (2 to 3) 2 (2 to 2) Tonga 0·02 (0·02 to 0·02) 0·01 (0·01 to 0·02) 0·22 (0·18 to 0·26) 0·12 (0·10 to 0·15) 68·62 (66·74 to 70·06) 75·14 (73·33 to 77·21) 16·57 (15·63 to 17·2) 20·35 (19·17 to 21·83) <1 (<1 to <1) <1 (<1 to <1) Vanuatu 0·03 (0·03 to 0·04) 0·03 (0·02 to 0·03) 0·34 (0·28 to 0·42) 0·23 (0·18 to 0·29) 62·11 (59·17 to 64·96) 67·75 (65·02 to 70·22) 14·21 (13·05 to 15·32) 16·67 (15·66 to 17·82) 1 (1 to 2) 1 (1 to 1) Southeast Asia 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·19 (0·18 to 0·20) 0·11 (0·11 to 0·12) 69·45 (68·87 to 70·02) 75·76 (75·18 to 76·29) 17·57 (17·23 to 17·91) 21·4 (20·99 to 21·78) 2397 (2302 to 2496) 1859 (1781 to 1947) Cambodia 0·03 (0·03 to 0·04) 0·03 (0·02 to 0·03) 0·23 (0·19 to 0·27) 0·14 (0·12 to 0·17) 66·77 (65·28 to 68·26) 72·7 (70·59 to 74·24) 16·06 (15·66 to 16·45) 19·6 (18·19 to 20·56) 54 (49 to 60) 48 (43 to 56) Indonesia 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·03) 0·18 (0·17 to 0·19) 0·13 (0·12 to 0·14) 69·21 (68·39 to 70·07) 73·87 (73·03 to 74·67) 16·69 (16·18 to 17·31) 19·89 (19·29 to 20·43) 904 (850 to 957) 738 (694 to 791) Laos 0·06 (0·05 to 0·08) 0·05 (0·04 to 0·06) 0·22 (0·19 to 0·26) 0·15 (0·12 to 0·18) 65·05 (62·98 to 67·11) 70·32 (68·26 to 72·28) 16·31 (15·3 to 17·53) 19·32 (17·86 to 20·59) 26 (23 to 29) 20 (17 to 23) Malaysia 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·16 (0·15 to 0·18) 0·09 (0·08 to 0·10) 72·4 (71·26 to 73·48) 77·34 (76·36 to 78·35) 18·2 (17·44 to 18·96) 20·80 (20·05 to 21·61) 96 (88 to 105) 69 (62 to 76) Maldives 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·07 (0·06 to 0·07) 0·05 (0·04 to 0·05) 79·93 (79·22 to 80·62) 83·37 (82·62 to 84·15) 23·06 (22·52 to 23·6) 25·73 (25·07 to 26·4) 1 (1 to 1) <1 (<1 to <1) Mauritius 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·19 (0·17 to 0·20) 0·09 (0·08 to 0·10) 71·54 (70·65 to 72·46) 78·1 (77·23 to 78·96) 18·65 (18·11 to 19·22) 22·27 (21·63 to 22·91) 6 (5 to 6) 5 (4 to 5) Myanmar 0·05 (0·04 to 0·06) 0·04 (0·03 to 0·04) 0·25 (0·21 to 0·29) 0·14 (0·11 to 0·17) 64·86 (63·15 to 66·71) 72·15 (70·26 to 74·22) 15·86 (15·44 to 16·65) 20·04 (18·76 to 21·46) 229 (204 to 251) 181 (155 to 209) Philippines 0·03 (0·02 to 0·04) 0·02 (0·02 to 0·03) 0·24 (0·20 to 0·28) 0·13 (0·11 to 0·16) 66·58 (64·65 to 68·61) 73·1 (71·16 to 74·95) 15·87 (14·71 to 17·14) 19·55 (18·26 to 20·78) 380 (327 to 437) 287 (247 to 334) Sri Lanka 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·15 (0·12 to 0·18) 0·06 (0·05 to 0·08) 73·83 (71·67 to 75·96) 81·05 (79·55 to 83·32) 19·49 (18·2 to 20·83) 23·97 (22·83 to 25·89) 73 (61 to 87) 53 (41 to 61) Seychelles 0·01 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·21 (0·20 to 0·22) 0·10 (0·09 to 0·11) 70·11 (69·49 to 70·74) 77·69 (76·95 to 78·44) 17·57 (17·21 to 17·94) 22·12 (21·61 to 22·66) <1 (<1 to <1) <1 (<1 to <1) Thailand 0·01 (0·01 to 0·01) 0·01 (0·01 to 0·01) 0·18 (0·16 to 0·20) 0·08 (0·07 to 0·08) 74·32 (72·91 to 75·92) 81·96 (80·85 to 83·14) 22·15 (21·29 to 23·15) 25·83 (24·97 to 26·75) 273 (244 to 301) 195 (174 to 215) Timor-Leste 0·04 (0·03 to 0·05) 0·03 (0·03 to 0·04) 0·17 (0·15 to 0·20) 0·13 (0·11 to 0·16) 68·83 (67·27 to 70·67) 73·02 (71·29 to 74·76) 17·09 (16·19 to 18·45) 19·98 (18·85 to 21·11) 4 (4 to 5) 3 (3 to 3) Vietnam 0·02 (0·01 to 0·02) 0·01 (0·01 to 0·01) 0·20 (0·17 to 0·23) 0·08 (0·06 to 0·10) 69·98 (68·33 to 71·23) 79·16 (77·84 to 80·89) 17·15 (16·06 to 17·85) 22·79 (21·92 to 24·12) 349 (319 to 401) 258 (221 to 286) Sub-Saharan Africa 0·08 (0·08 to 0·09) 0·07 (0·07 to 0·08) 0·28 (0·27 to 0·29) 0·21 (0·20 to 0·22) 61·65 (60·79 to 62·42) 66·24 (65·38 to 67·02) 16·43 (16·06 to 16·74) 18·91 (18·47 to 19·35) 4072 (3922 to 4265) 3404 (3268 to 3563) Central sub-Saharan Africa 0·08 (0·07 to 0·10) 0·07 (0·06 to 0·08) 0·30 (0·27 to 0·33) 0·23 (0·21 to 0·26) 60·29 (58·66 to 62) 64·41 (62·7 to 65·98) 14·94 (14·3 to 15·86) 17·13 (16·04 to 18·17) 505 (460 to 556) 443 (404 to 488) Angola 0·07 (0·06 to 0·08) 0·06 (0·05 to 0·07) 0·29 (0·24 to 0·33) 0·22 (0·18 to 0·25) 61·67 (59·67 to 63·96) 66·68 (64·5 to 68·9) 15·21 (14·48 to 16·44) 18·44 (16·91 to 19·96) 100 (88 to 115) 84 (73 to 96) Central African Republic 0·13 (0·11 to 0·16) 0·12 (0·10 to 0·14) 0·52 (0·45 to 0·58) 0·38 (0·31 to 0·45) 49·11 (46·48 to 51·72) 54·91 (51·97 to 58·02) 11·92 (11·07 to 12·96) 14·24 (12·84 to 16·34) 36 (31 to 42) 28 (24 to 33) Congo (Brazzaville) 0·06 (0·05 to 0·07) 0·05 (0·04 to 0·06) 0·29 (0·24 to 0·34) 0·31 (0·26 to 0·36) 62·55 (60·39 to 64·81) 62·7 (60·20 to 65·63) 15·6 (14·92 to 16·79) 15·87 (14·81 to 17·61) 18 (16 to 21) 19 (16 to 23) Democratic Republic of the Congo 0·09 (0·07 to 0·10) 0·08 (0·07 to 0·09) 0·29 (0·25 to 0·34) 0·23 (0·19 to 0·27) 60·36 (58·19 to 62·67) 64·32 (62·01 to 66·69) 14·98 (14·08 to 16·33) 16·97 (15·45 to 18·43) 340 (298 to 389) 303 (266 to 345) Equatorial Guinea 0·06 (0·05 to 0·07) 0·05 (0·04 to 0·06) 0·26 (0·20 to 0·32) 0·26 (0·20 to 0·32) 64·26 (61·26 to 67·1) 66·42 (62·61 to 70·52) 16·85 (15·17 to 18·74) 19·35 (16·38 to 22·62) 4 (3 to 5) 4 (3 to 5) Gabon 0·04 (0·03 to 0·05) 0·03 (0·03 to 0·04) 0·26 (0·22 to 0·30) 0·16 (0·13 to 0·20) 65·08 (63·3 to 66·7) 72·07 (69·79 to 74·39) 15·84 (15·31 to 16·23) 19·96 (18·39 to 21·71) 6 (6 to 7) 5 (4 to 5) Eastern sub-Saharan Africa 0·07 (0·06 to 0·08) 0·06 (0·05 to 0·06) 0·28 (0·27 to 0·29) 0·20 (0·19 to 0·21) 62·51 (61·74 to 63·26) 67·43 (66·77 to 68·11) 16·04 (15·76 to 16·32) 18·74 (18·38 to 19·09) 1412 (1365 to 1460) 1126 (1085 to 1165) Burundi 0·09 (0·07 to 0·10) 0·07 (0·07 to 0·08) 0·31 (0·26 to 0·36) 0·24 (0·20 to 0·29) 59·69 (57·35 to 62·16) 63·58 (61·3 to 65·88) 14·74 (13·86 to 16·19) 16·31 (14·96 to 17·97) 46 (40 to 52) 36 (32 to 41) Comoros 0·05 (0·04 to 0·06) 0·05 (0·04 to 0·05) 0·20 (0·16 to 0·23) 0·16 (0·13 to 0·20) 67·1 (65·04 to 69·21) 70·04 (67·84 to 72·28) 16·7 (15·69 to 18·04) 18·76 (17·22 to 20·23) 2 (2 to 3) 2 (2 to 3) Djibouti 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·23 (0·18 to 0·29) 0·20 (0·16 to 0·26) 66·05 (63·13 to 68·79) 68·86 (65·27 to 72·01) 16·62 (15·14 to 18·43) 18·85 (16·52 to 21·08) 4 (3 to 5) 3 (2 to 4) Eritrea 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·38 (0·32 to 0·45) 0·24 (0·19 to 0·30) 59·17 (56·42 to 61·93) 65·92 (63·4 to 68·97) 13·58 (12·66 to 14·79) 16·39 (15·29 to 18·17) 23 (19 to 27) 19 (16 to 22) Ethiopia 0·06 (0·05 to 0·07) 0·05 (0·05 to 0·06) 0·20 (0·18 to 0·21) 0·15 (0·14 to 0·17) 66·66 (65·57 to 67·74) 70·38 (69·3 to 71·51) 17·37 (16·74 to 17·96) 19·66 (18·99 to 20·35) 308 (291 to 328) 229 (214 to 244) Kenya 0·05 (0·04 to 0·06) 0·04 (0·03 to 0·05) 0·29 (0·28 to 0·31) 0·21 (0·20 to 0·23) 63·21 (62·44 to 63·94) 68·75 (67·94 to 69·55) 15·78 (15·53 to 16·06) 19·46 (19·05 to 19·9) 162 (156 to 167) 127 (123 to 132) Madagascar 0·08 (0·07 to 0·09) 0·07 (0·06 to 0·08) 0·26 (0·21 to 0·32) 0·22 (0·18 to 0·27) 62·17 (59·75 to 64·82) 64·81 (62·28 to 67·54) 15·51 (14·38 to 17·14) 16·67 (15·16 to 18·48) 97 (81 to 116) 87 (74 to 103) Malawi 0·07 (0·06 to 0·08) 0·06 (0·05 to 0·07) 0·34 (0·30 to 0·38) 0·22 (0·19 to 0·25) 59·6 (57·93 to 61·5) 66·93 (64·87 to 68·98) 15·09 (14·51 to 15·98) 19·42 (17·86 to 20·77) 72 (65 to 80) 57 (51 to 64) Mozambique 0·08 (0·07 to 0·09) 0·07 (0·06 to 0·08) 0·44 (0·40 to 0·50) 0·29 (0·25 to 0·34) 54·82 (52·67 to 57·04) 61·99 (59·39 to 64·45) 13·77 (12·85 to 14·69) 17·25 (15·74 to 19·04) 142 (126 to 160) 114 (100 to 130) Rwanda 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·22 (0·19 to 0·26) 0·16 (0·13 to 0·18) 65·75 (64·04 to 67·64) 70·83 (69·06 to 72·73) 16·22 (15·47 to 17·42) 19·61 (18·41 to 20·88) 36 (32 to 40) 32 (29 to 37) Somalia 0·11 (0·09 to 0·14) 0·09 (0·08 to 0·11) 0·34 (0·28 to 0·42) 0·27 (0·22 to 0·34) 56·52 (53·67 to 59·32) 60·59 (57·74 to 63·27) 14·08 (12·83 to 15·66) 15·34 (13·65 to 17·05) 80 (63 to 103) 65 (52 to 83) South Sudan 0·11 (0·09 to 0·13) 0·10 (0·08 to 0·11) 0·33 (0·27 to 0·41) 0·24 (0·19 to 0·32) 56·94 (53·94 to 59·97) 61·83 (58·63 to 65·14) 14·88 (13·34 to 16·51) 16·9 (14·82 to 18·87) 56 (47 to 67) 42 (35 to 50) Tanzania 0·07 (0·05 to 0·08) 0·06 (0·05 to 0·07) 0·24 (0·21 to 0·27) 0·18 (0·15 to 0·20) 64·62 (62·89 to 66·27) 68·88 (67·18 to 70·58) 17·08 (16·07 to 17·92) 19·57 (18·5 to 20·56) 185 (166 to 207) 157 (141 to 177) Uganda 0·07 (0·06 to 0·08) 0·05 (0·05 to 0·06) 0·28 (0·24 to 0·32) 0·17 (0·15 to 0·20) 62·28 (60·50 to 64·15) 69·17 (67·2 to 71·13) 15·71 (14·9 to 17·01) 19·68 (18·34 to 20·93) 131 (119 to 146) 102 (91 to 115) Zambia 0·07 (0·06 to 0·09) 0·05 (0·05 to 0·06) 0·32 (0·29 to 0·36) 0·23 (0·20 to 0·26) 60·36 (58·52 to 62·34) 66·28 (64·46 to 68·35) 15·33 (14·76 to 16·29) 18·38 (17·08 to 19·86) 68 (60 to 76) 51 (45 to 57) Southern sub-Saharan Africa 0·04 (0·04 to 0·05) 0·04 (0·03 to 0·04) 0·37 (0·35 to 0·38) 0·25 (0·23 to 0·27) 61·5 (60·75 to 62·18) 68·49 (67·57 to 69·33) 16·81 (16·59 to 17·02) 20·98 (20·65 to 21·28) 355 (343 to 368) 305 (292 to 319) Botswana 0·03 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·28 (0·23 to 0·35) 0·21 (0·18 to 0·26) 67·03 (64·14 to 69·19) 70·97 (68·75 to 72·48) 18·15 (16·59 to 18·94) 20·01 (18·92 to 20·85) 7 (6 to 9) 7 (6 to 8) Lesotho 0·08 (0·07 to 0·09) 0·06 (0·05 to 0·07) 0·57 (0·51 to 0·62) 0·37 (0·31 to 0·43) 50·27 (48·13 to 52·65) 59·32 (56·33 to 62·67) 12·21 (11·47 to 13·06) 16·64 (14·87 to 19·24) 14 (12 to 16) 11 (9 to 14) Namibia 0·04 (0·04 to 0·05) 0·03 (0·03 to 0·04) 0·33 (0·28 to 0·38) 0·21 (0·16 to 0·27) 62·33 (60·28 to 64·31) 70·70 (67·46 to 73·54) 15·73 (15·17 to 16·2) 21·46 (19·66 to 23·14) 10 (9 to 11) 7 (6 to 9) South Africa 0·04 (0·03 to 0·04) 0·03 (0·03 to 0·04) 0·36 (0·34 to 0·38) 0·25 (0·23 to 0·27) 62·8 (61·99 to 63·56) 69·69 (68·6 to 70·62) 17·51 (17·35 to 17·66) 21·88 (21·69 to 22·06) 255 (245 to 266) 221 (210 to 233) Swaziland (eSwatini) 0·05 (0·04 to 0·06) 0·04 (0·03 to 0·05) 0·49 (0·43 to 0·55) 0·28 (0·23 to 0·33) 54·92 (52·57 to 57·56) 65·15 (62·13 to 68·35) 13·22 (12·38 to 14·44) 18·52 (16·35 to 20·95) 6 (5 to 7) 4 (4 to 5) Zimbabwe 0·06 (0·05 to 0·07) 0·05 (0·04 to 0·06) 0·40 (0·35 to 0·44) 0·27 (0·23 to 0·31) 58·15 (56·31 to 60·10) 64·39 (62·13 to 66·6) 14·13 (13·2 to 15·09) 16·97 (15·55 to 18·57) 64 (57 to 71) 54 (47 to 62) Western sub-Saharan Africa 0·10 (0·09 to 0·11) 0·09 (0·08 to 0·10) 0·25 (0·23 to 0·28) 0·20 (0·18 to 0·23) 61·7 (60·16 to 62·94) 65·33 (63·57 to 66·85) 17·07 (16·26 to 17·69) 18·87 (17·81 to 19·87) 1801 (1674 to 1972) 1531 (1414 to 1681) Benin 0·09 (0·08 to 0·11) 0·08 (0·07 to 0·09) 0·24 (0·19 to 0·29) 0·18 (0·14 to 0·22) 62·61 (60·09 to 65·03) 66·63 (64·19 to 69·09) 16·4 (14·89 to 17·69) 18·4 (16·74 to 20·04) 43 (37 to 51) 37 (32 to 43) Burkina Faso 0·12 (0·10 to 0·14) 0·10 (0·08 to 0·11) 0·28 (0·25 to 0·32) 0·19 (0·17 to 0·22) 58·94 (56·92 to 61·04) 64·38 (62·57 to 66·3) 15·41 (14·42 to 16·4) 17·74 (16·61 to 18·89) 100 (88 to 117) 82 (73 to 94) Cameroon 0·08 (0·07 to 0·09) 0·07 (0·06 to 0·08) 0·30 (0·25 to 0·35) 0·24 (0·20 to 0·28) 60·97 (58·62 to 63·46) 65·1 (62·69 to 67·82) 15·7 (14·45 to 17·31) 18·22 (16·36 to 20) 104 (90 to 119) 87 (75 to 101) Cape Verde 0·02 (0·02 to 0·03) 0·02 (0·02 to 0·02) 0·19 (0·17 to 0·21) 0·09 (0·08 to 0·10) 72·52 (71·26 to 73·75) 79·01 (78·23 to 80·06) 21·18 (20·49 to 21·95) 23·98 (23·63 to 24·72) 2 (1 to 2) 1 (1 to 1) Chad 0·12 (0·11 to 0·14) 0·11 (0·10 to 0·13) 0·28 (0·24 to 0·33) 0·23 (0·19 to 0·27) 58·6 (56·43 to 60·82) 61·64 (59·19 to 64·23) 15·8 (14·55 to 16·93) 17·08 (15·68 to 18·51) 80 (71 to 91) 67 (59 to 75) Côte d'Ivoire 0·09 (0·08 to 0·11) 0·07 (0·06 to 0·08) 0·30 (0·26 to 0·34) 0·22 (0·19 to 0·26) 60·10 (57·82 to 62·32) 65·31 (62·84 to 67·7) 15·83 (14·5 to 17·06) 18·06 (16·59 to 19·68) 108 (95 to 122) 77 (67 to 87) The Gambia 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·27 (0·23 to 0·32) 0·21 (0·17 to 0·25) 63·78 (62·03 to 65·79) 67·87 (65·62 to 70·16) 15·67 (15·19 to 16·42) 17·88 (16·38 to 19·43) 7 (7 to 8) 6 (5 to 7) Ghana 0·06 (0·05 to 0·07) 0·05 (0·04 to 0·06) 0·28 (0·25 to 0·32) 0·20 (0·17 to 0·23) 62·59 (60·95 to 64·33) 68·4 (66·65 to 70·28) 15·36 (14·83 to 16·2) 18·64 (17·37 to 19·94) 111 (100 to 122) 91 (80 to 102) Guinea 0·10 (0·09 to 0·13) 0·09 (0·08 to 0·10) 0·29 (0·26 to 0·33) 0·25 (0·21 to 0·28) 59·26 (57·22 to 61·36) 62·23 (60·32 to 64·18) 15·09 (14·07 to 16·27) 16·28 (15·14 to 17·53) 58 (52 to 65) 51 (46 to 57) Guinea-Bissau 0·08 (0·07 to 0·10) 0·07 (0·06 to 0·07) 0·38 (0·32 to 0·43) 0·28 (0·24 to 0·32) 57·36 (55·12 to 59·67) 62·63 (60·33 to 64·94) 13·99 (12·98 to 14·89) 16·1 (14·72 to 17·86) 8 (7 to 10) 7 (6 to 8) Liberia 0·08 (0·07 to 0·10) 0·07 (0·06 to 0·08) 0·24 (0·20 to 0·28) 0·22 (0·18 to 0·26) 63·7 (61·52 to 65·79) 65·11 (63·13 to 67·43) 16·89 (15·5 to 17·99) 17·4 (15·96 to 19) 16 (14 to 19) 15 (13 to 17) Mali 0·13 (0·11 to 0·15) 0·11 (0·10 to 0·12) 0·22 (0·19 to 0·26) 0·21 (0·17 to 0·24) 60·96 (58·73 to 63·17) 62·98 (61·06 to 64·87) 17·37 (16·36 to 18·6) 17·89 (16·66 to 19·17) 101 (87 to 117) 86 (76 to 97) Mauritania 0·05 (0·04 to 0·06) 0·04 (0·04 to 0·05) 0·15 (0·12 to 0·18) 0·15 (0·12 to 0·19) 70·04 (68·03 to 72·26) 71·01 (68·91 to 73·02) 18·64 (17·39 to 20·26) 19·11 (17·65 to 20·48) 10 (9 to 12) 10 (8 to 11) Niger 0·11 (0·09 to 0·14) 0·10 (0·09 to 0·12) 0·24 (0·20 to 0·28) 0·20 (0·16 to 0·24) 61·13 (58·83 to 63·48) 63·59 (61·39 to 65·95) 16·56 (15·47 to 17·7) 17·63 (16·22 to 19·06) 92 (79 to 108) 81 (71 to 92) Nigeria 0·11 (0·10 to 0·12) 0·10 (0·08 to 0·11) 0·23 (0·19 to 0·28) 0·19 (0·15 to 0·25) 62·76 (59·7 to 65·2) 65·82 (62·32 to 69·11) 18·55 (16·62 to 19·91) 20·09 (17·61 to 22·73) 847 (724 to 1015) 736 (623 to 879) São Tomé and Príncipe 0·03 (0·03 to 0·04) 0·02 (0·02 to 0·03) 0·20 (0·17 to 0·23) 0·16 (0·13 to 0·18) 68·09 (66·51 to 69·83) 71·77 (70·06 to 73·78) 16·77 (16·09 to 17·78) 18·83 (17·7 to 20·26) 1 (<1 to 1) <1 (<1 to 1) Senegal 0·05 (0·05 to 0·06) 0·04 (0·04 to 0·05) 0·21 (0·18 to 0·25) 0·17 (0·14 to 0·20) 66·14 (64·5 to 67·87) 70·05 (68·32 to 71·93) 16·45 (15·54 to 17·51) 18·83 (17·58 to 20·03) 48 (43 to 54) 40 (35 to 45) Sierra Leone 0·12 (0·10 to 0·14) 0·10 (0·09 to 0·12) 0·27 (0·22 to 0·31) 0·24 (0·20 to 0·29) 59·47 (57·21 to 61·72) 61·38 (59·4 to 63·73) 16·04 (14·86 to 17·16) 16·45 (15·14 to 17·89) 37 (32 to 43) 34 (30 to 38) Togo 0·07 (0·06 to 0·09) 0·06 (0·05 to 0·07) 0·30 (0·25 to 0·35) 0·20 (0·16 to 0·24) 61·37 (59·06 to 63·8) 67·23 (64·96 to 69·62) 15·36 (14·41 to 16·76) 18·54 (16·93 to 20·16) 27 (24 to 31) 23 (20 to 27) Data in parentheses are 95% uncertainty intervals. Super-regions, regions, and countries are listed in alphabetical order. SDI=Socio-demographic Index. The most important changes in GBD 2017 are the independent estimation of population and a comprehensive update on fertility, which are described in a separate paper. There are several countries with significant differences in population size between the UNPOP estimates and the new GBD estimates. Since population is the denominator for mortality calculations, this leads to substantial changes in life expectancy and age-specific mortality rates in several countries. There were four major data additions and improvements that related to the estimation of mortality. First, for the estimation of population size, we systematically searched for census data and found data from 1257 censuses, which are now used in the analysis and which enabled an extended analysis of completeness using death distribution methods in more locations than previous iterations. Second, in the estimation of adult mortality, we included data from 31 Demographic Surveillance Sites (DSS) which were adjusted based on the relationship between DSS under-5 death rates and national under-5 death rates. Third, we used published sources to create a database of fatal discontinuities from conflicts and natural disasters that extends back to 1950; each fatal discontinuity has been given a unique ID that tags the reported deaths to a location, date, and type of discontinuity. Fourth, GBD 2017 included an additional 622 data sources that were not available for GBD 2016 and which do not fall into the three categories already described. The main methodological improvements fall into two categories: the first category is enhancements to the modelling framework, which improved the estimation of both child mortality, defined as the probability of death below the age of 5 years, and adult mortality, a term we use to refer to the probability of death between ages 15 and 60 years. For child mortality, we standardised hyperparameter selection for the spatiotemporal Gaussian process regression models, which enhances the comparability of results between locations and across time. For adult mortality, we also standardised hyperparameter selection and added child mortality as a covariate to the model. These changes had minimal effect on the mean estimate but changed the width of the uncertainty intervals in small populations and locations with sparse data. The second category encompasses three substantial improvements to the GBD model life table system: first, we revised the entire database to reflect the change in population counts. Second, each life table in the database was assigned a quality score using explicit criteria related to the variance in the slope of the death rate with respect to age, reductions in mortality at older ages compared with younger ages (age >60 years), and other unexpected crossovers. On the basis of these quality scores, life tables have been assigned to three categories: high quality for universal use, acceptable quality for use in the creation of location-specific standards, and unacceptable quality. Third, we estimated complete single-year life tables for each sex, location, and year instead of abridged life tables as in previous iterations of the GBD. In GBD 2017, for the first time, we are reporting a complete time series of trends in age-specific mortality and life expectancy since 1950. The extension of the analysis back in time provides the opportunity to analyse and report on longer-term trends in age-specific mortality. Implications of all the available evidence By using internally consistent estimates of deaths, births, and population over time, this analysis of trends in age-sex-specific death rates and summary measures such as life expectancy provides important perspectives on how mortality has been evolving since 1950. The findings of this study highlight global successes, such as the remarkable decline in under-5 mortality. This great success story reflects significant local, national, and global commitment and investment over several decades, a commitment that has intensified since the turn of the century. At the same time, our findings also bring attention to mortality patterns that are cause for concern, particularly among men aged 20–45 years and, to a lesser extent, women aged 20–45 years. In these groups, our findings show mortality rates that have stagnated over the time period covered by this study, and in some cases, are increasing. Comparing levels of mortality to those expected on the basis of development status, as measured with the Socio-demographic Index, provides insights into which countries have achieved lower and which countries are experiencing higher mortality rates than would be expected based on their level of development. Our findings show enormous variation in progress achieved across locations and ages, with countries that are performing better than expected in all regions of the world. Our results also highlight that greater emphasis needs to be placed on understanding the drivers of success for countries that have performed better than expected and that urgent attention needs to be brought to those countries that are lagging behind.

Introduction 1 Harkness AG Age at marriage and at death in the Roman Empire. , 2 Scheidel W Disease and death in the ancient city of Rome. 3 UN

Sustainable development knowledge platform. Measurement of mortality has always been crucial for populations, and mortality is a quantity that societies have attempted to track since ancient times.More recently, its relevance and importance have been highlighted in the global agenda in the form of the health-related Sustainable Development Goals (SDGs), which not only include two indicators expressly focused on all-cause mortality (SDG indicators 3.2.1, under-5 mortality, and 3.2.2, neonatal mortality), but also death registration (SDG indicator 17.19.2c) and ten indicators of cause-specific or risk-attributable mortality.The prominence of mortality among the health-related SDGs intensifies the need for comparable, robust measurements of mortality that can be used for monitoring progress on mortality levels and trends across countries. National governments and international agencies alike need reliable evidence to identify and then prioritise addressing the largest challenges in improving survival, particularly during the SDG era. 4 GBD 2016 Mortality Collaborators

Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 5 Ahmad OB

Lopez AD

Inoue M The decline in child mortality: a reappraisal. , 6 You D

Jin NR

Wardlaw T Levels & trends in child mortality. , 7 Centers for Disease Control and Prevention (CDC)

Trends in aging—United States and worldwide. , 8 Roser M Life expectancy. Our World in Data. 9 US Burden of Disease Collaborators

The State of US health, 1990–2016: burden of disease, injuries, and risk factors among US States. , 10 Dwyer-Lindgren L

Bertozzi-Villa A

Stubbs RW

et al. Inequalities in life expectancy among US counties, 1980 to 2014: temporal trends and key drivers. , 11 Kochanek KD

Murphy SL

Xu J

Arias E Mortality in the United States, 2016. NCHS Data Brief No. 293. , 12 Newton JN

Briggs ADM

Wolfe CDA Changes in health in England, with analysis by English regions and areas of deprivation, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. , 13 Fransham M

Dorling D Have mortality improvements stalled in England?. , 14 Wang H

Abajobir AA

Abate KH

et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. 14 Wang H

Abajobir AA

Abate KH

et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 15 Mokdad AH

Forouzanfar MH

Daoud F

et al. Health in times of uncertainty in the eastern Mediterranean region, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. 9 US Burden of Disease Collaborators

The State of US health, 1990–2016: burden of disease, injuries, and risk factors among US States. , 10 Dwyer-Lindgren L

Bertozzi-Villa A

Stubbs RW

et al. Inequalities in life expectancy among US counties, 1980 to 2014: temporal trends and key drivers. , 11 Kochanek KD

Murphy SL

Xu J

Arias E Mortality in the United States, 2016. NCHS Data Brief No. 293. , 16 Gómez-Dantés H

Fullman N

Lamadrid-Figueroa H

et al. Dissonant health transition in the states of Mexico, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. 17 Preston SH

Vierboom YC

Stokes A The role of obesity in exceptionally slow US mortality improvement. , 18 Walls HL

Backholer K

Proietto J

McNeil JJ Obesity and trends in life expectancy. , 19 Angelantonio ED

Bhupathiraju SN

Wormser D

et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. 20 Groenewald P

Nannan N

Bourne D

Laubscher R

Bradshaw D Identifying deaths from AIDS in South Africa. , 21 Kahn K

Garenne ML

Collinson MA

Tollman SM Mortality trends in a new South Africa: hard to make a fresh start. , 22 National Research Council (US) Committee on Population , 23 National Research Council (US) Committee on Population Amid global gains in life expectancy and significant reductions in child mortality over the past few decades, concerning trends have surfaced in several countries and demographic groups, which have been attributed to a wide range of determinants of health.For example, although many high-income countries, including the USA and the UK, experienced large gains in life expectancy for many decades, the pace of progress has stalled in recent years, particularly in the past decade, and within-country inequalities in life expectancy have widened.For other countries, such as Syria and Yemen, civil war has effectively erased—and reversed—years of steady gains.In Mexico, studies have highlighted a combination of surging interpersonal violence and non-communicable diseases (NCDs) as the main factors underlying rising age-specific mortality among adult men, while in the USA, drug use disorders, suicide, cirrhosis, and diabetes are considered to be among the main culprits for plateaued mortality improvements among men.Increasing rates of obesity are also viewed as a probable factor underlying the slowing of progress in female life expectancy in various countries.Changes in age-specific mortality rates and life expectancy can be used to track the impact of population-wide health threats, such as the HIV epidemic in sub-Saharan Africa, and also to quantify uncharacteristically high mortality experiences, such as the excess adult male mortality in central and eastern European countries during 1990s.Accurate monitoring of levels and trends of mortality on a timely basis can provide crucial information for deploying resources and effective interventions at the population level. 24 UN Department of Economic and Social Affairs

World population prospects: the 2017 revision. 25 United States Census Bureau

International data base. 26 WHO

WHO methods and data sources for global burden of disease estimates. 27 National Bureau of Statistics of China

National data. , 28 Government of India

Mortality. Open government data (OGD) platform India. , 29 National Bureau of statistics (Nigeria)

Population and vital statistics.pdf. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides the only source of annually updated age-sex-specific mortality for countries across the world. Three other analytical efforts exist that provide estimates of age-specific mortality for a broad set of countries; however, we believe that these are not as comprehensive or timely as the GBD. The United Nations Population Division, Department of Economics and Social Affairs (UNPOP) has reported on life expectancy and age-specific mortality for 5-year calendar intervals by age, sex, and country since 2005 and for 201 countries. Their estimates are updated biannually; however, the estimates are not reported with uncertainty intervals (UIs).The US Census Bureau analyses only 15–25 countries per year and updates demographic estimates for them.WHO estimates of mortality are largely based on UNPOP estimates that have been interpolated to single years with some modifications for countries with complete vital registration (VR).In addition to these cross-national efforts, many countries produce their own estimates of age-specific mortality, which often differ from the international assessments. 14 Wang H

Abajobir AA

Abate KH

et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 30 GBD 2015 Mortality and Causes of Death Collaborators

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. 31 GBD 2017 Population and Fertility collaborators

Population and fertility by age and sex for 195 countries and territories 1950–2017: a systematic analysis for the Global Burden of Disease 2017. GBD 2017 represents the third iteration of the annual updates of the GBD.This version of the GBD reports on trends in age-specific mortality and summary measures of mortality, such as life expectancy, with four main improvements. First, new data sources that have been released or reported since GBD 2016 have been incorporated. Second, for the first time, estimates of age-sex-specific population generated in the GBD are used in the estimation of all-cause mortality, whereas previous efforts by the GBD used the UN Population Division estimates of population by age and sex.Third, statistical methods used in different components of the analysis have been further standardised and improved. Lastly, we have extended the analysis and reporting of age-specific mortality back to 1950 to further contribute to research and analyses of long-term trends in mortality and life expectancy.

Methods Overview 32 Stevens GA

Alkema L

Black RE

et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. As with GBD 2016, this analysis adheres to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) standards developed by WHO and others. table detailing our adherence to GATHER is included in appendix 1 ; statistical code used in the entire process is publicly available online . Analyses were done with Python versions 2.5.4 and 2.7.3, Stata version 13.1, and R version 3.1.2. The methods used to produce estimates of age-specific mortality remain similar to those used in GBD 2016. Here we provide a broad overview and highlight the major changes since GBD 2016. All other details are included in appendix 1 Geographical units and time periods The GBD is hierarchically organised by geographic units or locations, with seven super-regions, 21 regions nested within those super-regions, and 195 countries or territories within the 21 regions. Each year, GBD includes subnational analyses for a few new countries and continues to provide subnational estimates for countries that were added in previous cycles. Subnational estimation in GBD 2017 includes five new countries (Ethiopia, Iran, New Zealand, Norway, Russia) and countries previously estimated at subnational levels (GBD 2013: China, Mexico, and the UK [regional level]; GBD 2015: Brazil, India, Japan, Kenya, South Africa, Sweden, and the USA; GBD 2016: Indonesia and the UK [local government authority level]). All analyses are at the first level of administrative organisation within each country except for New Zealand (by Māori ethnicity), Sweden (by Stockholm and non-Stockholm), and the UK (by local government authorities). All subnational estimates for these countries were incorporated into model development and evaluation as part of GBD 2017. To meet data use requirements, in this publication we present all subnational estimates excluding those pending publication (Brazil, India, Japan, Kenya, Mexico, Sweden, the UK, and the USA); these results are presented in appendix tables and figures appendix 2 ). Subnational estimates for countries with populations larger than 200 million (as measured with our most recent year of published estimates) that have not yet been published elsewhere are presented wherever estimates are illustrated with maps, but are not included in data tables. Data and data processing In the estimation of age-specific mortality for GBD 2017, we used five types of data. These were data from VR systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites (DSS). 33 Murray CJ

Rajaratnam JK

Marcus J

Laakso T

Lopez AD What can we conclude from death registration? Improved methods for evaluating completeness. , 34 Brass W Demographic data analysis in less developed countries: 1946–1996. , 35 Hill K Estimating census and death registration completeness. , 36 Vincent P La mortalité des vieillards. , 37 Bennett NG

Horiuch S Estimating the completeness of death registration in a closed population. , 38 Hill K

You D

Choi Y Death distribution methods for estimating adult mortality: Sensitivity analysis with simulated data errors. 33 Murray CJ

Rajaratnam JK

Marcus J

Laakso T

Lopez AD What can we conclude from death registration? Improved methods for evaluating completeness. , 38 Hill K

You D

Choi Y Death distribution methods for estimating adult mortality: Sensitivity analysis with simulated data errors. 31 GBD 2017 Population and Fertility collaborators

Population and fertility by age and sex for 195 countries and territories 1950–2017: a systematic analysis for the Global Burden of Disease 2017. The most robust source for estimating age-specific mortality is a VR system that records all deaths by age, sex, and location. Our analysis of mortality starts with collating all publicly available VR data plus data shared directly by governments or GBD collaborators from VR systems. We evaluate the completeness of VR data separately for deaths under the age of 5 years and deaths over the age of 15 years. For under-5 deaths, we statistically compare VR-based death rates with those recorded in censuses or surveys. For deaths over the age of 15 years, we apply three methods for detecting under-registration: generalised growth balance, synthetic extinct generations, and a hybrid method that uses both methods.These methods are collectively described as death distribution methods because they use the demographic balance equation to infer completeness of registration. Age misreporting and migration affect these methods.We used the spatiotemporal regression framework with the results of these methods for all intercensal intervals to produce a coherent time series of completeness for each location. For this step, the first stage of the model uses completeness of child death registration as a covariate and then applies time and space weights on the residuals to produce a smoothed result. In some countries, sample registration systems are operated wherein events are recorded in detail for a representative sample of communities within those countries. We used the same death distribution methods to evaluate the completeness of these sources as for VR; sample registration death counts were scaled in the death distribution methods analysis to the national level. This study considers a country to have complete VR when it used a civil registration system, vital statistics, or sample registration system that captures at least 95% of all deaths within the country. When calculating death rates for under-5 mortality, adult mortality, or empirical life tables, we used the GBD population estimates by age, sex, location, and year as the denominator. 39 Rajaratnam JK

Tran LN

Lopez AD

Murray CJL Measuring under-five mortality: validation of new low-cost methods. In addition to VR data, for the estimation of under-5 death rates, we use data from complete birth histories collected through household survey programmes, including the World Fertility Survey, Demographic and Health Surveys, some Multiple Indicator Cluster Surveys, and various other national surveys. A wider set of surveys and many censuses also collect data on the number of livebirths for a woman and the number of these children who are still surviving. This information is called a summary birth history and can yield an unbiased assessment of the trend in the under-5 death rate. 40 Obermeyer Z

Rajaratnam JK

Park CH

et al. Measuring adult mortality using sibling survival: a new analytical method and new results for 44 countries, 1974–2006. 41 Department of International Economic and Social Affairs

Manual X. Indirect techniques for demographic estimation: a collaboration of the Population Division of the Department of International Economic and Social Affairs of the United Nations Secretariat with the Committee on Population and Demography of the National Research Council, United States National Academy of Sciences. Assessments of adult mortality, in addition to VR and sample registration data, use survey data collected on sibling histories. A sibling history means that a respondent is asked to report on the survival or death of each of their siblings; in other words, the respondent provides a complete birth history for their mother. Sibling histories are subject to survivor bias and recall bias. Sibling history data are processed for GBD using methods that address these limitations.Some surveys and some censuses also use information on deaths in a household over some recent time interval—for example, the past 12 months. Studies suggest that respondents can over-report or under-report deaths of household members.We apply death distribution methods to assess completeness, which can be greater than 100% due to telescoping of event reporting, which happens when a respondent reports an event that happened before the recall period as if it happened during the recall period. For GBD 2017, we also included DSS data on adult mortality for the first time, specifically on the probability of death between the ages of 15 and 60 years (45q15), from local communities that are under direct surveillance. Because these DSS communities are not nationally representative, we adjusted the level of 45q15 based on the ratio of the probability of death from birth to age 5 years (5q0) from the DSS to the national 5q0, taking into account that the relationship between 5q0 and 45q15 changes as the level of 5q0 declines because, on average, there are larger declines in 5q0 than in 45q15 over time. New data for GBD 2017 compared to GBD 2016 In GBD 2017, we have added 458 location-years of VR data at the national level and 9 location-years of VR data at the subnational level compared with GBD 2016. We also included an additional 62 complete birth history sources at the national level, 12 complete birth history sources at the subnational level, 72 national summary birth history sources, and 16 subnational summary birth history data sources. 11 national and seven subnational sibling history surveys were also added. We included 1529 datapoints from DSSs in 15 countries. The total numbers of datapoints used were 181 625 for under-5 mortality estimation and 63 234 for adult mortality estimation. We also used 35 177 empirical life tables in the all-cause mortality database for GBD 2017. Appendix 1 provides complete lists of data availability and data sources by location; these are also available using our online source tool, the Global Health Data Exchange . The addition of these data has provided increasingly accurate mortality metrics in many countries over all years estimated in GBD. Estimating under-5 mortality and more detailed age intervals below 5 years 42 Global Burden of Disease Health Financing Collaborator Network

Trends in future health financing and coverage: future health spending and universal health coverage in 188 countries, 2016–40. , 43 Gakidou E

Cowling K

Lozano R

Murray CJ Increased educational attainment and its effect on child mortality in 175 countries between 1970 and 2009: a systematic analysis. , 44 Murray CJL

Ortblad KF

Guinovart C

et al. Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Using all the VR, complete birth history, and summary birth history data available for each country, we estimate the time trends from 1950 to 2017 for each location. We use spatiotemporal Gaussian process regression (ST-GPR) to estimate time trends. This model has four components. First, it includes three covariates: lag-distributed income (LDI) per capita, average years of schooling for women aged 15–49 years, and the crude rate of death from HIV/AIDS.Second, it includes random effects for each source of data in each country, where a source refers to a particular survey or census. Using the random effects, data are adjusted to the reference source for each country. The reference source is VR in countries with complete VR and complete birth histories in countries without complete VR. In some locations, reference sources are selected on the basis of expert knowledge of a country and its data sources provided by GBD collaborators. The third component of the model borrows strength over space and time by smoothing the residuals; the degree of smoothing is controlled by three hyperparameters. These hyperparameters are a time weight (lambda), a space weight (zeta), and a temporal correlation weight (scale). Additional details on the selection of the hyperparameters are included in appendix 1 section 2.2 . The fourth component of the model uses the output after the first three components have been run as the mean prior in a Gaussian process regression. Gaussian process regression also includes four hyperparameters, lambda, zeta, scale, and an additional hyperparameter, amplitude. Details on these hyperparameters are included in appendix 1 . In GBD 2017, to standardise our analysis further, we have opted to use the same amplitude for all locations. The value for amplitude is based on the analysis of variation over time in countries with complete VR that is not explained by the covariates. We use a multiphase approach to generate age-specific and age-sex-specific under-5 mortality. We first model the ratio of male to female 5q0. Next, we run separate models to estimate the probability of death for each sex and age group, specifically early neonatal (0–6 days), late neonatal (7–27 days), postneonatal (28–364 days), infant mortality (<1 year), and childhood mortality (between 1 and 5 years). These are run to take advantage of greater data density for both the ratio of male to female mortality and the split between infant mortality and childhood mortality as compared with the split of infant mortality into the components of early neonatal, late neonatal, and postneonatal. Each is modelled using ST-GPR. Results of the sex-ratio model are first applied to derive sex-specific under-5 death rates (U5MR). Next, the probability of death from birth to the exact age of 1 year and from age 1 year to the exact age of 5 years are transformed to conditional probabilities and scaled to the sex-specific U5MR estimates. This is done to ensure that the value of 1 minus the probabilities from birth to the exact age of 1 year and from age 1 year to the exact age of 5 years equals the probability of death between birth and the exact age of 5 years. Lastly, early neonatal, late neonatal, and postneonatal model results are transformed to conditional probabilities and scaled in the same manner to equal the sex-specific probability of death from birth to the exact age of 1 year. More information on the models, model hyperparameters, and scaling can be found in appendix 1 section 2.2 Estimating the probability of death between ages 15 and 60 years 42 Global Burden of Disease Health Financing Collaborator Network

Trends in future health financing and coverage: future health spending and universal health coverage in 188 countries, 2016–40. , 43 Gakidou E

Cowling K

Lozano R

Murray CJ Increased educational attainment and its effect on child mortality in 175 countries between 1970 and 2009: a systematic analysis. , 44 Murray CJL

Ortblad KF

Guinovart C

et al. Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Data on the probability of death between the ages of 15 and 60 years are also modelled using ST-GPR. In the first stage model, we use LDI per capita, average years of schooling for the population aged 15–59 years, the crude rate of death from HIV/AIDS, and the under-5 mortality rate as covariates.Under-5 mortality rate was not used as a covariate in GBD 2016, but we found that the model, which is now estimating for a longer time period going back to 1950, performs better when this covariate is included. We model the data for males and females together and include a dummy variable for sex in the model. In GBD 2016, we had run separate models for males and females, but this had yielded implausible sex ratios of adult mortality rates in specific location-years. More details, including hyperparameters for the ST-GPR model, are described in appendix 1 section 2.3 GBD model life table system and the database of empirical life tables 14 Wang H

Abajobir AA

Abate KH

et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. To produce a complete set of age-specific mortality rates (an abridged life table ) for each location, we used the GBD model life table system, which identifies a reference life table for each location, year, and sex, on the basis of the nearest matches found in our empirical life table database.As we have revised the population denominators used to create the empirical life tables in GBD 2017, we have substantially updated and revised the database of empirical life tables as well. In previous GBD iterations, we excluded life tables based on implausible patterns of variation in death rates in the age groups older than 40 years. As with previous GBD cycles, we have two sets of life tables that meet inclusion criteria: a universal set that is used for all locations to identify matches and a location-specific set that is used for each location along with the universal set. We have formalised the inclusion criteria for life tables for both the location-specific and the universal set, and those are listed in section 2.4 of appendix 1 . Life tables that meet all of the general inclusion criteria but not all of the universal life table inclusion criteria are categorised as location-specific life tables. For each life table, within each location, we sort life tables by year and generate smoothed life tables using moving averages of widths 3, 5, and 7 adjacent years within each location. This smoothing helps to address jumps or drops in age-specific mortality in locations where small numbers of deaths resulted in high variability of mortality patterns across age. After separately categorising each life table, we keep the least-smoothed of the candidate life tables within each life table set. The smoothing process and inclusion criteria help to address implausible age patterns from countries with small populations, unstable death rates, or poor data quality. We have also set the number of matches searched for in the databases to be 100 for all locations; to ensure that locations with high-quality data primarily rely on their own age patterns of mortality, we have modified the space-time weighting scheme through a 25-fold increase in the country-specific weights compared with GBD 2016, with an additional 15-fold increase in 0-year and 1-year lag country-specific weights and a three-fold increase in the 2-year, 3-year, and 4-year lag country-specific weights. We also generated a new geographical strata of life table weights for subnational locations that are within the same country, which were assigned the same value as the original GBD 2016 country-specific weights. For both all-cause mortality and cause-specific mortality analyses in GBD, we amassed a comprehensive database on human mortality from full VR systems and sample VR systems such as the Sample Registration System (SRS) from India and the Disease Surveillance Point system from China. These data sources provided a total of 42 138 empirical life tables, which also include subnational locations. After applying inclusion criteria, we use 35 177 life tables, of which 10 885 are universal and 24 292 location-specific. The GBD model life tables varied in quality in accordance with the coverage of a location's VR: for locations where VR coverage was high, the standard was overwhelmingly derived from observed mortality patterns, whereas in locations where VR coverage was low, the standard was based on locations with similar under-5 and adult mortality rates, with more weight given to life tables that were closer geographically and temporally. The selection of geographically and temporally similar locations helped to capture differences in mortality patterns by age due to specific causes of death. Single-year life tables 31 GBD 2017 Population and Fertility collaborators

Population and fertility by age and sex for 195 countries and territories 1950–2017: a systematic analysis for the Global Burden of Disease 2017. To support the estimation of single-year population for each location-age-sex-year, we have also generated single-year life tables for all locations from the abridged life tables after the HIV/AIDS mortality reconcilliation process and the addition of fatal discontinuities. Our method for generating single-year probabilities of death that are consistent with the abridged life table probabilities of death and known data on single-year patterns is described in the GBD 2017 population and fertility publication. Fatal discontinuities Fatal discontinuities are idiosyncratic increases in mortality that would affect long-term mortality trends if modelled using the all-cause mortality estimation process, and as a result, are estimated separately. Events categorised as fatal discontinuities are epidemics (such as Ebola virus disease or cholera); natural disasters, major technological or transport accidents, and war and terrorism. The specific data sources used to compile fatal discontinuities can be explored using the online source tool, the Global Health Data Exchange, and are described in detail in appendix 1 section 4 . Estimates from high-quality VR systems were included instead of estimates from other sources in the event that conflicting sources were identified for a fatal discontinuity, with few exceptions when there was evidence to suggest that the VR system was compromised by the event. Regional, cause-specific UIs were used to estimate uncertainty for events where only point-estimate mortality data were available. For GBD 2017, we have recoded the locations of all events using a new suite of software developed in-house to match differently coded locations in the fatal discontinuities database to GBD locations, taking advantage of detailed location information that was presented in non-standardised ways—eg, sources that included the name of a city or village instead of latitude and longitude. We first overlaid the portions of the database with latitude-longitude coordinates to the most detailed GBD location. When coordinates were not available, we used three web-based geocoding services—the Google Maps, OpenStreetMap, and Geonames geocoding application programming interfaces—to get a set of possible latitude and longitude coordinates from the location, overlaid those coordinates on to GBD locations, and then used the most common result from the three services to assign a GBD location. Since discontinuities for recent years are not well tracked in the available databases, we have supplemented these databases with online searches. For GBD 2017, we systematised the identification of events missing from our database by mining Twitter accounts of major news providers for common terms associated with such events, like “earthquake” and “casualties.” This provided 62 events. Once events were identified, news reports of death totals, location, and date were used. The age pattern of deaths is rarely identified in databases of fatal discontinuities. In order to estimate an age and sex distribution, events were first assigned to a GBD cause. Events were then split based on both the global age and sex distribution of that cause of death and the age and sex distribution of the population in the GBD location of the event, following the GBD causes of death age-sex-splitting algorithm. The main effect of this effort is that we are much less likely to miss shocks or allocate them to the wrong subnational location. HIV/AIDS in countries with large epidemics and incomplete VR 45 Ghys PD

Brown T

Grassly NC

et al. The UNAIDS Estimation and Projection Package: a software package to estimate and project national HIV epidemics. We produced estimates of adult HIV/AIDS incidence and prevalence using the estimation and projection package (EPP), a Bayesian model developed by UNAIDS.Our implementation of EPP made use of GBD-estimated demographic parameters, mortality rates for people on and off antiretroviral therapy, and CD4 progression rates to fit a model to HIV/AIDS prevalence data from surveillance sites and representative surveys. EPP-generated incidence and prevalence time series were used as inputs into Spectrum, a compartmental HIV/AIDS progression model originally developed by UNAIDS. Spectrum generated a full set of age-sex-specific HIV/AIDS mortality rates using detailed demographic parameters that align with those used for EPP. In countries with VR data, we adjusted age-specific and sex-specific incidence rates to produce mortality estimates that better fit observed deaths. In parallel, the GBD model life table process produced a separate set of HIV/AIDS death estimates, which were reconciled with Spectrum outputs to produce final mortality estimates. For countries with high-quality VR systems, mortality estimates were generated using ST-GPR on VR data. Analysing the relationship between age-specific mortality rates and development status 31 GBD 2017 Population and Fertility collaborators

Population and fertility by age and sex for 195 countries and territories 1950–2017: a systematic analysis for the Global Burden of Disease 2017. To characterise development status, we used the Socio-demographic Index (SDI), a composite measure based on the total fertility under the age of 25 years (TFU25), average educational attainment in those aged 15 years or older, and LDI. Compared with GBD 2016, the SDI calculation in GBD 2017 has been refined to use TFU25 instead of the total fertility rate because TFU25 does not show a U-shaped pattern with development at higher levels of development status and is a better proxy for the status of women in society.Aggregate SDI groupings were generated by applying quintile cutoffs from the distribution of national-level SDI for countries with populations greater than 1 million in 2017 to estimates of SDI for all GBD locations in 2017. The SDI analysis is described in further detail in appendix 1 (section 3) ; additional detail on correlation for the weighted scores is also provided. To evaluate the average relationship between SDI and all-cause mortality, we fit a generalised additive model with a Loess smoother on SDI by age and sex group using GBD 2017 estimates from 1950 to 2017. The expected value is based solely on SDI status and does not vary over time. Examination of how the ratio of observed death rates to expected death rates changes over time allows us to explore the impact of how the relationships are changing over time. The expected age-sex-specific mortality rates were subsequently used to generate a complete life table expected on the basis of SDI alone. Uncertainty analysis We estimate uncertainty systematically throughout the all-cause mortality estimation process. We generated 1000 draws for each all-cause mortality metric, and 95% UIs are calculated using the 2·5th and 97·5th percentiles of the draw-level values. Analytical steps are connected at the draw level, and the uncertainty of key mortality metrics is propagated throughout the all-cause mortality estimation process. Uncertainty in under-5 mortality and adult mortality rate estimation and completeness synthesis are estimated using non-sampling error and sampling error by data source. For the model life table step and HIV/AIDS-specific mortality calculations, uncertainty was estimated from uncertainty in the life table standard and from the regression parameters and sampling error in the EPP, respectively. Role of the funding source The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to the data in the study and had final responsibility for the decision to submit for publication.

Discussion Main findings This study represents the first comprehensive analysis of age-sex-specific death rates for single calendar years and single-year age groups for 195 countries and territories from 1950 to 2017. Our results show the remarkable variation in mortality rates over time and across countries. The decline in death rates has been the greatest in the age groups younger than 5 years, followed by young adults, and has been slower among older adults. Rising death rates have occurred in conflicts, natural disasters, large HIV epidemics, and in several locations such as eastern Europe, some countries in southeast Asia and Latin America, and most recently, in the USA. Increases in adult mortality rates even as child death rates fall are stark reminders that the drivers of adult mortality can be complex. Because of the celebrated progress in many locations, many people have come to expect age-specific death rates to always decline; however, there is nothing inevitable about the trajectory of death rates, particularly in adults. Cross-cutting themes 46 McKeown T The role of medicine: dream, mirage, or nemesis?. 47 Preston SH The changing relation between mortality and level of economic development. 9 US Burden of Disease Collaborators

The State of US health, 1990–2016: burden of disease, injuries, and risk factors among US States. , 10 Dwyer-Lindgren L

Bertozzi-Villa A

Stubbs RW

et al. Inequalities in life expectancy among US counties, 1980 to 2014: temporal trends and key drivers. , 11 Kochanek KD

Murphy SL

Xu J

Arias E Mortality in the United States, 2016. NCHS Data Brief No. 293. , 16 Gómez-Dantés H

Fullman N

Lamadrid-Figueroa H

et al. Dissonant health transition in the states of Mexico, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. A long-running theme in the demographic literature has been the balance between development and technology innovation as determinants of mortality change. Thomas McKeown,examining declines in mortality in the UK in the first half of the 20th century, argued that health technology played little role and mortality decline in that setting was driven by improvements in the standard of living. Samuel Preston,in a series of classic studies, roughly assigned one-third of life expectancy improvement to rising income per capita, one-third to improvements in educational attainment, and one-third to changes correlated with time, which he assigned to technology improvement. Given the wide array of drugs, vaccines, and procedures and understanding of risk factors that have emerged in the past 50 years, often with strong causal evidence such as randomised trials, major temporal shifts unexplained by development should be expected.