Absolute and age-relative suicide-rates for women and men age 60 years and older, at the global, region, and nation level, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019 data

Absolute and age-relative suicide-rates for women and men age 60 years and older, at the global, region, and nation level, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019 data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Analysis Absolute and age-relative suicide-rates for women and men age 60 years and older, at the global, region, and nation level, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019 data Feng Sha, Qingsong Chang, Ziyi Zhao, Ziyi Cai, Bingyu Li, Donghui Wu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4365103/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Suicide-rates are highest among older adults. Yet, older-adult suicide has been under-studied, particularly in relation to suicide in other age-groups, and by sex and location. Age-standardized suicide-rates (ASSR) of older-adults (OA) (ages 60-years-and-older) and non-older-adults (NOA) (ages 10-59 years), and the ratio of OA-to-NOA ASSR, for the 1990-2019 period, were calculated based on 2019 Global-Burden-of-Disease (GBD) data. OA absolute and age-relative ASSR were examined by country/nation/territory Socio-Demographic Index (SDI). There was a significant negative-correlation between OA-to-NOA ASSR and SDI. OA-to-NOA ASSR-ratios were larger in women in many regions, though OA ASSR were lower among women. The finding that OA had higher age-relative suicide-rates in lower socioeconomic-position regions challenges the belief that OA-suicide is a problem of higher socioeconomic-position regions. The fact that in many regions OA age-relative suicide-rates were higher in women than in men challenge the belief that OA women are protected from suicide. Health sciences/Medical research/Epidemiology Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Around 750,000 individuals die by suicide worldwide yearly. 1 Between 1990 and 2019, the total number of suicides increased (19,941, 95% uncertainty interval 15,181 to 24,701), though age-specific suicide-rates decreased (-4.01; from 13.8–9.8% per 100,000), according to a study using Global-Burden-of-Disease (GBD) 2019-data. The increase varied by region’s income-level. Specifically, the lower-middle-income region saw a notable increase while the upper-middle-income region registered a substantial decline. 2 Between 1990 and 2019 suicide-rates were highest among individuals age 70-and-older in almost all world-regions. 2 In forthcoming decades the absolute number of older-adult (OA) suicides is expected to surge because the number of older adults and their proportion in the world population are growing. 2,3 Yet, OA suicide remains a neglected research area. 2,4 OA suicide-patterns have been studied using both absolute and age-relative suicide-rates. OA relative suicide-rates have typically been assessed by calculating the older-adult (OA) to non-older-adult (NOA) ratio of suicide-rates. 5–9 OA absolute and age-relative suicide-rates provide different information. According to 2015 GBD-data, the absolute suicide-rates of individuals age 60-years-and-older were around 28.0 per 100,000 in China, Austria, and Ukraine. By contrast, OA relative suicide-rates, that is, the ratio of OA-to-NOA suicide-rates were very different in China, Austria, and Ukraine (4.64, 2.29, and 1.30, respectively) because of different NOA suicide-rates in those countries. 9 The OA-to-NOA suicide-rates ratio allows better comparisons by region and country/nation/territory because it eliminates the influence, on base suicide-rates, of country/nation/territory differences and of data-quality differences. 9 Unlike the male-to-female ratio of suicide-rates, the OA-to-NOA ratio of suicide-rates has not been used to study older-adult suicide by sex, globally, by world-region, and by country/nation/territory. This study examined OA absolute and age-relative suicide-rates over nearly three decades, in women and men, globally, by world-region and by country/nation/territory, and in relation to countries/nations/territories socioeconomic position. Results Global absolute and age-relative patterns of older-adult suicide, 1990–2019 In 1990 there were 739,087 (95% uncertainty interval: 721,967 to 756,207) suicides among people age 60-years-and-older, globally. In 2019, the number of OA suicides increased by 2.71% (759,028; 95% uncertainty interval: 737,148 to 780,908). OA ASSR per 100,000 however declined by 33·89%, from 17·88 (17.47 to 18.30) in 1990, to 11.82 (11.48 to 12.16) in 2019 (see Table 1 ). Table 1 Number of OA suicides, ASSR per 100,000, percent-change from 1990 to 2019 with UI, and OA-NOA suicide ratios in 2019 for all regions. Location Number of OA suicides (95% UI) OA ASSR (95% UI) Percent change (95% UI) OA-NOA suicide-rates ratios 1990 2019 1990 2019 1990 2019 Global 739,087 (721,967, 756,208) 759,028 (737,148, 780,909) 17.88 (17.47, 18.3) 11.82 (11.48, 12.16) -33.89 (-33.97, -33.81) 1.93 1.87 Andean Latin America 1,541 (1,402, 1,679) 3,437 (3,141, 3,733) 5.55 (5.05, 6.05) 6.71 (6.13, 7.29) 20.94 (20.45, 21.43) 1.33 1.25 Australasia 2,815 (2,753, 2,878) 3,410 (3,279, 3,542) 16.36 (16, 16.73) 13.42 (12.91, 13.94) -17.96 (-18.07, -17.84) 1.02 0.88 Caribbean 4,204 (4,085, 4,323) 4,569 (4,292, 4,845) 15.34 (14.9, 15.77) 11.63 (10.92, 12.33) -24.19 (-24.36, -24.02) 2.25 2.19 Central Asia 6,903 (6,767, 7,038) 11,143 (10,622, 11,665) 13.39 (13.13, 13.65) 14.93 (14.23, 15.63) 11.5 (11.32, 11.69) 1.4 1.13 Central Europe 23,616 (23,311, 23,921) 17,408 (16,647, 18,169) 22.63 (22.34, 22.92) 16.96 (16.22, 17.7) -25.06 (-25.17, -24.95) 1.72 1.45 Central Latin America 6,827 (6,704, 6,950) 15,514 (14,738, 16,290) 5.68 (5.58, 5.79) 7.51 (7.13, 7.88) 32.07 (31.85, 32.29) 1.38 1.04 Central Sub-Saharan Africa 4,724 (4,286, 5,163) 9,199 (8,154, 10,244) 12.96 (11.75, 14.16) 10.06 (8.92, 11.2) -22.36 (-22.72, -22) 5.15 5.14 East Asia 219,531 (208,296, 230,766) 128,470 (121,011, 135,929) 22.02 (20.9, 23.15) 9.79 (9.22, 10.35) -55.57 (-55.68, -55.46) 2.78 3.58 Eastern Europe 63,778 (62,789, 64,768) 57,009 (53,997, 60,020) 33.3 (32.78, 33.82) 30.89 (29.26, 32.52) -7.24 (-7.4, -7.09) 1.25 0.95 Eastern Sub-Saharan Africa 15,312 (14,493, 16,132) 24,794 (23,219, 26,368) 12.27 (11.61, 12.92) 8.58 (8.04, 9.13) -30.01 (-30.2, -29.83) 5.75 6.11 High-income Asia Pacific 29,733 (28,122, 31,344) 39,837 (38,745, 40,928) 19.64 (18.58, 20.71) 23.15 (22.52, 23.79) 17.87 (17.65, 18.09) 2.3 1.53 High-income North America 38,103 (37,824, 38,382) 50,082 (49,423, 50,741) 15.95 (15.83, 16.06) 15.56 (15.36, 15.77) -2.41 (-2.46, -2.36) 1.28 1.15 North Africa and Middle East 16970 (15608, 18332) 25,803 (23,737, 27,868) 6.96 (6.41, 7.52) 5.27 (4.85, 5.69) -24.3 (-24.58, -24.03) 1.13 1.2 Oceania 448 (397, 500) 753 (656, 849) 9.69 (8.58, 10.81) 7.64 (6.67, 8.62) -21.14 (-21.55, -20.72) 1.34 1.47 South Asia 174,971 (166,138, 183,803) 223,958 (211,498, 236,419) 22.27 (21.14, 23.39) 15.25 (14.41, 16.1) -31.49 (-31.66, -31.33) 0.95 1.12 Southeast Asia 33,973 (32,240, 35,705) 37,100 (34,768, 39,432) 9.73 (9.23, 10.23) 6.6 (6.18, 7.01) -32.22 (-32.39, -32.05) 1.71 1.84 Southern Latin America 5,673 (5,537, 5,808) 7,702 (7,411, 7,993) 14.4 (14.06, 14.75) 13.56 (13.05, 14.08) -5.81 (-5.95, -5.68) 1.92 1.29 Southern Sub-Saharan Africa 8,431 (7,712, 9,149) 11,894 (10,881, 12,907) 21.89 (20.03, 23.76) 19.04 (17.42, 20.66) -13.04 (-13.38, -12.7) 1.23 1.32 Tropical Latin America 9,365 (9,171, 9,558) 13,931 (13,521, 14,340) 8.02 (7.86, 8.19) 7.3 (7.08, 7.51) -9.03 (-9.13, -8.93) 1.68 1.25 Western Europe 61,588 (60,995, 62,180) 49,755 (48,734, 50,776) 18.22 (18.04, 18.39) 12.72 (12.46, 12.98) -30.16 (-30.21, -30.11) 1.82 1.57 Western Sub-Saharan Africa 10,583 (9,677, 11,489) 23,263 (21,362, 25,164) 8.25 (7.54, 8.95) 7.33 (6.73, 7.93) -11.11 (-11.44, -10.79) 5.7 6.12 OA ASSR in 1990 and 2019, for each of eight OA age-groups (starting at age 60 years, at 5-year intervals), are shown in Table 2 and Fig. 1 . From 1990 to 2019, ASSR declined across all OA age-groups. In 2019, there was a 40·95% decrease in ASSR among OA ages 70-to-74-years, from 32.82 (29.7 to 35.65) per 100,000 in 1990 to 19·38 (17.12 to 21.49) per 100,000. This was the largest decline in global OA ASSR across all OA age-groups. In 2019 individuals ages 60-to-64-years had the lowest ASSR (14.49; 12.95 to 15.85 per 100,000) while individuals ages 85-to-89-years had the highest ASSR (35.55; 30.15 to 39.75 per 100,000). Table 2 ASSR and percent-change between 1990 and 2019 among individuals age 60 and older in 5-year age groups. Location ASSR ages 60 to 64 (95% UI) ASSR ages 65 to 69 (95% UI) ASSR ages 70 to 74 (95% UI) ASSR ages 75 to 79 (95% UI) 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change Global 24.28 (22.01, 26.26) 14.49 (12.95, 15.85) -40.35 (-40.88, -39.81) 26.7 (24.14, 28.98) 16.04 (14.37, 17.65) -39.92 (-40.47, -39.36) 32.82 (29.7, 35.65) 19.38 (17.12, 21.49) -40.95 (-41.51, -40.38) 39.82 (36.73, 42.91) 23.93 (21.05, 26.35) -39.91 (-40.42, -39.39) Andean Latin America 7.19 (5.73, 9.63) 7.67 (4.82, 9.96) -32.82 (-33.63, -32) 7.31 (5.79, 9.62) 7.69 (5.04, 9.95) -25.77 (-26.6, -24.94) 7.17 (5.64, 9.23) 8.21 (5.05, 10.48) -17.03 (-17.88, -16.17) 7.53 (5.99, 9.82) 8.69 (5.53, 11.09) -23.92 (-24.57, -23.27) Australasia 16.26 (14.91, 17.72) 12.22 (10.5, 14.18) 7.02 (6.68, 7.36) 15.63 (14.22, 17.1) 10.31 (8.81, 12.01) -9.82 (-10.13, -9.51) 17.41 (15.9, 18.96) 11.17 (9.46, 12.98) -18.94 (-19.25, -18.64) 17.74 (16.02, 19.64) 12.51 (10.49, 14.76) -24.8 (-25.19, -24.41) Caribbean 24.15 (21.91, 26.47) 16.57 (13.68, 20.02) -31.78 (-33.6, -29.97) 26.05 (24.08, 28.33) 17.44 (14.49, 21.01) -33.98 (-36.02, -31.93) 30.74 (28.37, 33.53) 21.05 (17.37, 25.49) -33.29 (-34.8, -31.78) 36.23 (33.01, 39.72) 25.35 (20.88, 30.5) -31.22 (-33.21, -29.22) Central Asia 17.45 (16.4, 18.65) 13.77 (11.85, 16.11) -26.73 (-27.5, -25.96) 16.38 (15.44, 17.54) 14.66 (12.84, 17.03) -36.77 (-37.39, -36.15) 17.48 (16.29, 18.76) 16.47 (14.48, 19.06) -40.51 (-41.13, -39.89) 20.9 (19.42, 22.39) 20.8 (18.26, 24) -43.74 (-44.3, -43.18) Central Europe 28.44 (27.21, 30.1) 20.84 (17.71, 24.1) -31.4 (-32.39, -30.41) 30.99 (29.57, 32.58) 19.59 (16.77, 22.66) -33.08 (-33.99, -32.17) 33.75 (32.15, 35.59) 20.08 (17.26, 23.28) -31.52 (-32.55, -30.49) 40.9 (38.79, 42.71) 23.01 (19.72, 26.56) -30.03 (-31.05, -29.01) Central Latin America 6.77 (6.44, 7.19) 6.97 (5.78, 8.37) -24.81 (-25.68, -23.94) 7.29 (6.92, 7.72) 7.49 (6.34, 8.89) -34.04 (-34.83, -33.24) 7.79 (7.34, 8.28) 7.57 (6.44, 8.94) -35.85 (-36.6, -35.1) 8.56 (8.01, 9.1) 7.91 (6.75, 9.24) -29.5 (-30.47, -28.53) Central Sub-Saharan Africa 40.38 (30.9, 52.69) 32.47 (23.43, 44.43) 6.68 (0.25, 13.11) 49.31 (36.52, 64.75) 39.32 (29.06, 52.72) 5.3 (-5.09, 15.69) 60.65 (44.93, 77.92) 46.18 (34.73, 60.59) 14.49 (-117.28, 146.27) 71.65 (52.59, 94.59) 53.04 (40.15, 68.78) 15.38 (10.29, 20.47) East Asia 33.38 (27.04, 38.83) 12.98 (10.76, 15.76) -61.13 (-61.91, -60.34) 40.58 (33.43, 47.19) 16.2 (13.39, 19.52) -60.07 (-60.83, -59.32) 59.94 (50.14, 68.74) 24.56 (20.3, 29.42) -59.03 (-59.74, -58.32) 77 (66.23, 87.05) 33.27 (27.66, 39.31) -56.8 (-57.44, -56.15) Eastern Europe 39.21 (38.05, 40.97) 26.34 (22.73, 31.45) -27.37 (-27.88, -26.86) 35.55 (34.43, 36.97) 26.39 (23.07, 31.17) -27.45 (-27.95, -26.94) 36.98 (35.79, 39.03) 30.69 (26.86, 35.5) -33.84 (-34.32, -33.36) 42.34 (40.61, 44) 32.22 (28.8, 36.25) -37.14 (-37.61, -36.67) Eastern Sub-Saharan Africa 40.67 (33.48, 48.51) 27.32 (22.69, 33.96) -21.07 (-21.93, -20.21) 50.27 (41.85, 60.47) 34.93 (29.25, 43.25) -10.53 (-11.53, -9.54) 61.95 (50.6, 74.32) 45.15 (38.07, 57.32) -5.78 (-6.8, -4.77) 71.4 (59.95, 86.41) 56.83 (48.14, 71.09) -0.48 (-1.51, 0.56) High-income Asia Pacific 26.87 (25.82, 30.31) 26.45 (21.41, 29.38) -32.16 (-32.8, -31.51) 28.19 (26.97, 31.59) 25.83 (21.78, 28.32) -35.3 (-35.96, -34.64) 36.18 (34.44, 39.74) 29.36 (24.87, 32.74) -38.13 (-38.78, -37.48) 47.72 (44.42, 51.2) 32.55 (26.8, 36.39) -34.84 (-35.54, -34.14) High-income North America 16.78 (16.36, 17.22) 17.95 (17.23, 18.74) -30.62 (-32.41, -28.83) 17.35 (16.88, 17.8) 15.65 (14.96, 16.39) -26.63 (-28.45, -24.81) 19.74 (19.17, 20.34) 16 (15.27, 16.87) -21.42 (-24.37, -18.48) 23.04 (22.04, 23.99) 17.33 (16.27, 18.58) -18.48 (-22.37, -14.58) North Africa and Middle East 7.62 (5.63, 9.45) 5.72 (4.44, 6.92) -7.43 (-9.43, -5.42) 7.47 (5.52, 9.06) 5.69 (4.36, 6.83) -5.48 (-7.57, -3.38) 7.82 (5.83, 9.33) 5.97 (4.73, 7.17) -11.21 (-13.73, -8.69) 7.09 (5.33, 8.59) 6 (4.65, 7.07) -10.53 (-12.91, -8.14) Oceania 11.88 (8.99, 14.67) 10.02 (7.46, 12.98) 2.89 (1.51, 4.28) 13.36 (10.12, 16.87) 11.44 (8.41, 14.76) 2.74 (1.51, 3.97) 11.82 (9.08, 14.51) 9.78 (7.23, 12.27) -2.86 (-4.05, -1.66) 12.36 (9.77, 14.8) 10.52 (8.2, 13.1) -7.54 (-8.62, -6.47) South Asia 19.58 (15.27, 24.42) 13.58 (10.73, 16.74) -19.6 (-27.06, -12.14) 20.89 (16.53, 26.1) 15.32 (12.2, 18.48) -20.28 (-25.55, -15) 19.36 (15.63, 24.48) 15.21 (12.14, 18.37) -23.86 (-26.82, -20.89) 26.1 (20.99, 33.38) 21.28 (17.16, 25.4) -25.98 (-33.45, -18.5) Southeast Asia 12.52 (9.57, 14.7) 8.54 (6.52, 10.6) -15.63 (-18.12, -13.14) 15.06 (11.37, 17.52) 9.94 (7.55, 11.95) -14.35 (-20.81, -7.88) 17.87 (13.64, 20.71) 11.92 (9.35, 14.49) -17.25 (-19.28, -15.21) 18.62 (13.99, 21.86) 12.81 (9.86, 15.48) -14.88 (-16.83, -12.92) Southern Latin America 21.46 (19.74, 23.2) 14.56 (12.73, 16.4) -1.54 (-3.02, -0.05) 23.49 (21.69, 25.2) 15.2 (13.29, 17.35) -8.4 (-9.54, -7.26) 24.91 (22.98, 26.95) 15.41 (13.64, 17.6) -18.85 (-19.76, -17.93) 28.37 (26.1, 30.84) 18.49 (15.98, 21.1) -31.79 (-32.61, -30.97) Southern Sub-Saharan Africa 22.01 (17.29, 26.37) 20.38 (16.56, 24.91) -10.54 (-18.03, -3.05) 23.96 (18.79, 28.57) 22.65 (17.91, 27.7) -4.89 (-20.02, 10.25) 27.62 (20.76, 32.57) 24.53 (19.85, 29.99) -1.51 (-12.4, 9.38) 31.39 (24.26, 37.9) 28.09 (22.86, 34.08) 1.85 (-48.41, 52.11) Tropical Latin America 11.12 (10.55, 11.7) 8.08 (7.46, 8.88) -24.91 (-27.05, -22.77) 11.73 (11.14, 12.33) 8.51 (7.9, 9.36) -23.81 (-26.85, -20.76) 13.53 (12.74, 14.3) 8.95 (8.25, 9.88) -23.6 (-25.77, -21.44) 14.74 (13.73, 15.7) 9.26 (8.44, 10.18) -15.46 (-17.79, -13.14) Western Europe 21.67 (20.98, 22.46) 14.23 (13.24, 15.32) -32.82 (-34.31, -31.33) 23.4 (22.59, 24.22) 13.79 (12.89, 14.79) -30.52 (-32.16, -28.89) 25.72 (24.74, 26.65) 15.05 (13.98, 16.22) -27.12 (-28.8, -25.44) 33.39 (31.6, 34.84) 18.13 (16.54, 19.66) -20.41 (-22.23, -18.59) Western Sub-Saharan Africa 24.66 (19.07, 33.22) 22.06 (17.17, 28.18) -34.36 (-34.7, -34.02) 30.95 (24.13, 41.92) 29.43 (22.92, 38.31) -41.06 (-41.35, -40.77) 38.81 (30.68, 52.94) 38.22 (30.25, 50.09) -41.51 (-41.82, -41.2) 46.15 (36.52, 62.38) 47.01 (38, 61.55) -45.69 (-46.04, -45.34) ASSR ages 80 to 84 (95% UI) ASSR ages 85 to 89 (95% UI) ASSR ages 90 to 94 (95% UI) ASSR ages 95 and older (95% UI) 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change Global 47.68 (43.17, 51.29) 29.99 (26.22, 33.35) -37.11 (-37.73, -36.5) 52.32 (46.45, 56.31) 35.55 (30.15, 39.75) -32.07 (-32.91, -31.23) 41.26 (35.27, 44.8) 30.06 (24.37, 33.79) -27.14 (-28.27, -26.02) 30.19 (24.66, 33.31) 25.85 (20.05, 29.55) -14.38 (-16.11, -12.66) Andean Latin America 6.36 (5.03, 8.23) 8.57 (5.52, 10.84) -27.02 (-27.65, -26.38) 7.32 (5.75, 9.3) 9.67 (6.53, 12.39) -20.73 (-21.65, -19.82) 6.45 (4.88, 8.18) 8.65 (5.71, 11.1) -15 (-16.28, -13.72) 9.15 (6.48, 12.32) 11.54 (7.54, 15.42) -7.07 (-8.85, -5.29) Australasia 18.06 (15.99, 20.08) 14.32 (11.73, 16.88) -20.83 (-21.46, -20.19) 15.44 (13.15, 17.73) 17.11 (13.89, 20.39) -7.39 (-8.44, -6.33) 12.38 (10.04, 14.62) 16.52 (12.86, 20.1) 2.54 (0.72, 4.36) 9.31 (7.37, 10.98) 12.94 (9.81, 15.44) 14.4 (11.73, 17.07) Caribbean 41.24 (36.56, 45.42) 30.92 (24.93, 37.23) -25.9 (-27.43, -24.37) 47.43 (40.93, 53.25) 37.16 (29.8, 45.32) -19.28 (-20.92, -17.64) 41.91 (34.36, 48.52) 28.97 (22.39, 34.73) -13.47 (-15.46, -11.48) 23.62 (19.07, 27.19) 28.11 (21.03, 34.42) -3.61 (-6.87, -0.35) Central Asia 20.73 (18.82, 22.48) 28.11 (24.52, 32.57) -45.76 (-46.29, -45.24) 19.1 (16.59, 21.08) 32.32 (27.94, 37.19) -45.31 (-46.01, -44.62) 18.29 (14.63, 20.65) 31.05 (25.28, 36.18) -41.14 (-42.06, -40.22) 17.41 (13.77, 20.82) 27.06 (20.94, 33.75) -30.02 (-31.42, -28.62) Central Europe 48.92 (45.44, 51.62) 26.53 (22.74, 30.38) -25.03 (-26.13, -23.94) 55.7 (49.35, 60.26) 30.46 (25.8, 34.91) -21.65 (-22.95, -20.35) 45.94 (39.49, 50.08) 27.04 (21.76, 30.75) -30.88 (-32.26, -29.51) 38.06 (30.88, 42.24) 26.63 (20.68, 30.74) 19.01 (16.34, 21.67) Central Latin America 7.79 (6.95, 8.48) 8.79 (7.42, 10.23) -20.7 (-21.81, -19.6) 10.72 (9.32, 11.73) 10.2 (8.31, 11.77) 10.86 (9.08, 12.64) 11.19 (9.06, 12.59) 10.55 (8.17, 12.38) 33.45 (30.82, 36.08) 19.31 (14.36, 22.85) 17.47 (12.71, 20.22) 39.01 (36.03, 42) Central Sub-Saharan Africa 84.9 (61.9, 110.31) 62.11 (46.71, 79.47) 34.78 (23.19, 46.37) 82.09 (60.59, 107.47) 67.35 (49.62, 88.03) 32.17 (27.74, 36.6) 71.79 (53.05, 92.07) 63.78 (46.83, 82.31) 34.1 (28.24, 39.97) 76.93 (56.77, 101.68) 84.76 (59, 113.62) 26.06 (16.03, 36.09) East Asia 106.74 (91.18, 120.91) 46.2 (38.15, 55.16) -56.71 (-57.43, -55.99) 136.4 (117.7, 154.28) 65.36 (55.14, 78.19) -52.08 (-52.86, -51.3) 120.64 (102.61, 137.38) 53.11 (43.18, 63.01) -55.98 (-56.67, -55.28) 92.59 (77.03, 105.59) 41.91 (32.3, 50.1) -54.74 (-55.65, -53.83) Eastern Europe 48.21 (44.9, 50.5) 35.19 (31.08, 39.65) -37.52 (-38.2, -36.83) 53.26 (48.04, 56.24) 42.22 (35.96, 48.21) -43.18 (-43.97, -42.38) 49.91 (42.76, 54.47) 42.43 (34.54, 48.56) -47.18 (-48.25, -46.12) 43.27 (35.41, 47.49) 40.21 (31.42, 46.21) -39.37 (-40.86, -37.88) Eastern Sub-Saharan Africa 77.94 (64.33, 94.38) 69.12 (57.47, 86.3) 35.61 (34.01, 37.22) 76.41 (62.15, 94.86) 73.99 (59.73, 93.36) 69.18 (67, 71.36) 67.34 (54.86, 84.36) 69.2 (54.04, 87.29) 69.81 (66.62, 72.99) 81.04 (62.66, 99.2) 90.66 (67.33, 109.1) 55.4 (51.5, 59.29) High-income Asia Pacific 63.33 (57.3, 67.21) 35.73 (28.75, 40.88) -25.22 (-26.08, -24.37) 80.47 (70.09, 86.01) 39.15 (30.51, 45.46) -22.04 (-23.03, -21.05) 72.7 (60.92, 79.08) 33.79 (24.27, 39.74) -9.88 (-11.48, -8.28) 46.65 (36.82, 51.71) 27.26 (18.87, 32.29) 9.78 (7.49, 12.08) High-income North America 24.61 (22.56, 25.89) 19.49 (17.54, 21.1) -5.38 (-8.97, -1.79) 22.32 (19.66, 23.9) 20.67 (17.78, 22.66) -8.87 (-11.95, -5.78) 17.63 (14.62, 19.23) 18.07 (14.87, 20.06) 7.21 (-62.77, 77.18) 13.39 (10.62, 15.05) 15.32 (12.14, 19.02) 23.64 (-88.59, 135.87) North Africa and Middle East 9.37 (7.08, 11.32) 7.73 (6, 9.11) -4.23 (-6.36, -2.09) 11.78 (8.79, 14.19) 10.95 (8.55, 13.05) -5.55 (-7.49, -3.62) 11.65 (8.73, 14.48) 12.16 (9.33, 14.81) 2.72 (0.91, 4.52) 16.28 (12.08, 20.13) 19.77 (13.68, 26.11) 5.79 (3.43, 8.15) Oceania 15.49 (12.58, 18.91) 13.25 (10.73, 16.53) 12.81 (11.33, 14.29) 19.41 (15.95, 23.5) 16.4 (13.23, 20.1) -4.85 (-6.18, -3.52) 34.41 (27.79, 40.66) 28.34 (22.16, 34.25) -5.73 (-7.7, -3.76) 19.66 (14.81, 24.89) 25.89 (19.27, 32.72) -9.55 (-14.43, -4.66) South Asia 30.07 (23.49, 39.16) 28.45 (22.42, 33.68) -26.85 (-29.59, -24.11) 31.93 (24.39, 41.17) 29.1 (22.62, 34.43) -17.96 (-21.82, -14.1) 27.7 (20.44, 37.27) 29.7 (22.04, 35.31) -11.16 (-17.17, -5.16) 23.02 (16.63, 33.99) 28.47 (21.06, 37.84) 10.17 (6.06, 14.28) Southeast Asia 20.86 (16.67, 24.44) 15.46 (12.18, 18.5) -14.43 (-16.39, -12.47) 25.82 (20.61, 30.75) 20.85 (16.65, 25.29) -15.54 (-17.35, -13.73) 23.07 (18.3, 27.59) 19.97 (15.69, 24.18) -17.63 (-19.34, -15.92) 25.57 (19.14, 32.09) 24.65 (18.16, 33.91) 31.68 (27.83, 35.53) Southern Latin America 28.03 (25.31, 30.79) 20.96 (18.12, 24.27) -43.58 (-44.34, -42.83) 29.77 (26.05, 33.57) 23.21 (19.58, 26.9) -51.34 (-52.16, -50.52) 21.48 (17.7, 24.59) 19.36 (15.59, 22.25) -53.52 (-54.56, -52.48) 23.57 (18.94, 27.27) 25.88 (20.07, 29.95) -41.58 (-43.06, -40.09) Southern Sub-Saharan Africa 33.67 (27.2, 40.4) 32.25 (26.78, 39.07) 10.31 (1.68, 18.94) 42.76 (35.51, 50.34) 40.38 (34.12, 49.88) 17.51 (1.47, 33.55) 42.25 (34.67, 49.94) 43.4 (35.34, 51.4) 23.27 (-2.31, 48.86) 70.73 (55.52, 84.12) 74.83 (58.61, 90.39) 20.42 (15.55, 25.28) Tropical Latin America 15.57 (13.87, 16.83) 9.73 (8.46, 10.69) -17.49 (-19.72, -15.25) 18.22 (15.81, 19.92) 10.35 (8.62, 11.59) -7.05 (-9.47, -4.63) 17.07 (14.05, 18.88) 9.02 (6.93, 10.29) 4.41 (-2.3, 11.12) 20.11 (15.9, 22.4) 12.19 (8.84, 14.21) 21.4 (17.26, 25.54) Western Europe 39.91 (36.54, 42.15) 22.06 (19.39, 24.2) -11.32 (-13.36, -9.27) 45.81 (40.42, 49.35) 26.81 (22.6, 30.04) -3.16 (-5.8, -0.51) 39.5 (33.1, 43.15) 27.1 (22, 30.65) 2.77 (-5.83, 11.37) 28.77 (22.95, 31.96) 23.97 (18.8, 27.18) 11.88 (8.77, 14.99) Western Sub-Saharan Africa 56.24 (43.88, 75.42) 62.04 (49.6, 81.19) -44.72 (-45.24, -44.21) 58.51 (45.65, 78.98) 68.75 (53.33, 91.92) -41.46 (-42.25, -40.67) 47.27 (35.5, 63.63) 58.27 (43.06, 78.46) -31.4 (-32.55, -30.25) 62.17 (44.36, 82.4) 74.86 (52.21, 97.69) -16.7 (-18.44, -14.96) Table 1 and Fig. 2 show that globally, the ratio of OA-to-NOA ASSR decreased, from 1.93 in 1990 to 1.87 in 2019. However, this ratio was higher at each older OA-age-group (see Fig. 3 ). Across the eight OA age-groups, the ratio of OA-to-NOA ASSR for OA ages 95-years-and-older was highest (2.49) in 2019—a 33·13% increase from 1.87 in 1990 (see Table 3 ). Table 3 ASSR ratio for individuals age 60 and older by 5-year groups relative to individuals ages 10 to 59 years as well as the percent change in ASSR ratios between 1990 and 2019. Location 60 to 64 vs 59 and below ASSR ratio 65 to 69 vs 59 and below ASSR ratio 70 to 74 vs 59 and below ASSR ratio 75 to 79 vs 59 and below ASSR ratio 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change Global 1.51 1.40 -7.24 1.66 1.55 -6.57 2.03 1.87 -8.18 2.47 2.31 -6.56 Andean Latin America 1.33 1.18 -11.40 1.35 1.18 -12.55 1.33 1.26 -4.91 1.40 1.34 -4.17 Australasia 1.00 0.88 -11.29 0.96 0.75 -22.17 1.07 0.81 -24.31 1.09 0.90 -16.82 Caribbean 1.81 1.70 -5.70 1.95 1.79 -8.01 2.3 2.16 -5.87 2.71 2.61 -3.81 Central Asia 1.36 0.94 -31.19 1.28 1.00 -22.00 1.36 1.12 -17.86 1.63 1.41 -13.23 Central Europe 1.42 1.39 -2.63 1.55 1.3 -15.97 1.69 1.34 -20.94 2.05 1.53 -25.23 Central Latin America 1.23 0.93 -23.99 1.32 1.00 -24.11 1.41 1.01 -28.24 1.55 1.06 -31.70 Central Sub-Saharan Africa 4.02 4.04 0.67 4.9 4.90 -0.17 6.03 5.75 -4.66 7.13 6.61 -7.31 East Asia 1.80 2.02 12.25 2.19 2.52 15.29 3.23 3.82 18.31 4.15 5.18 24.75 Eastern Europe 1.23 0.84 -31.83 1.12 0.84 -24.68 1.16 0.98 -15.81 1.33 1.03 -22.8 Eastern Sub-Saharan Africa 4.38 4.18 -4.59 5.41 5.34 -1.33 6.67 6.9 3.50 7.68 8.69 13.03 High-income Asia Pacific 1.67 1.34 -19.62 1.75 1.31 -25.23 2.24 1.49 -33.75 2.96 1.65 -44.32 High-income North America 1.11 1.20 7.97 1.15 1.04 -9.02 1.31 1.07 -18.23 1.52 1.16 -24.13 North Africa and Middle East 1.11 1.11 0.13 1.09 1.10 1.61 1.14 1.16 1.88 1.03 1.16 12.73 Oceania 1.26 1.36 8.12 1.41 1.55 9.76 1.25 1.32 6.05 1.31 1.42 9.08 South Asia 0.88 0.90 3.06 0.93 1.02 8.98 0.87 1.01 16.72 1.17 1.41 21.09 Southeast Asia 1.36 1.43 5.17 1.64 1.67 1.80 1.95 2.00 2.85 2.03 2.15 6.05 Southern Latin America 1.70 1.13 -33.27 1.86 1.18 -36.36 1.97 1.20 -39.15 2.24 1.44 -35.91 Southern Sub-Saharan Africa 1.03 1.11 8.02 1.12 1.23 10.30 1.29 1.33 3.61 1.46 1.53 4.40 Tropical Latin America 1.47 1.15 -21.96 1.55 1.21 -22.04 1.79 1.27 -28.92 1.95 1.32 -32.46 Western Europe 1.41 1.31 -7.31 1.52 1.27 -16.77 1.67 1.38 -17.41 2.17 1.66 -23.31 Western Sub-Saharan Africa 4.10 4.00 -2.43 5.14 5.33 3.73 6.45 6.93 7.42 7.67 8.52 11.08 Location 80 to 84 vs 59 and below ASSR ratio 85 to 89 vs 59 and below ASSR ratio 90 to 94 vs 59 and below ASSR ratio 95 and above vs 59 and below ASSR ratio 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change 1990 2019 Percent change Global 2.96 2.89 -2.22 3.24 3.43 5.63 2.56 2.9 13.29 1.87 2.49 33.13 Andean Latin America 1.18 1.32 11.93 1.36 1.49 9.76 1.20 1.33 11.37 1.70 1.78 4.69 Australasia 1.11 1.04 -6.44 0.95 1.24 30.80 0.76 1.19 57.45 0.57 0.94 64.02 Caribbean 3.08 3.18 3.05 3.55 3.82 7.70 3.13 2.98 -4.99 1.77 2.89 63.59 Central Asia 1.62 1.91 18.23 1.49 2.20 47.5 1.43 2.11 48.04 1.36 1.84 35.48 Central Europe 2.45 1.77 -27.92 2.79 2.03 -27.32 2.30 1.80 -21.78 1.91 1.77 -7.00 Central Latin America 1.41 1.18 -16.67 1.94 1.37 -29.71 2.03 1.41 -30.36 3.50 2.34 -33.18 Central Sub-Saharan Africa 8.44 7.74 -8.41 8.16 8.39 2.73 7.14 7.94 11.24 7.65 10.56 37.95 East Asia 5.75 7.19 24.99 7.35 10.18 38.37 6.50 8.27 27.12 4.99 6.52 30.69 Eastern Europe 1.52 1.12 -25.95 1.68 1.35 -19.57 1.57 1.36 -13.75 1.36 1.28 -5.71 Eastern Sub-Saharan Africa 8.39 10.56 25.95 8.22 11.31 37.54 7.25 10.58 45.95 8.72 13.86 58.89 High-income Asia Pacific 3.93 1.81 -53.95 4.99 1.98 -60.28 4.51 1.71 -62.06 2.89 1.38 -52.31 High-income North America 1.63 1.30 -20.13 1.48 1.38 -6.57 1.17 1.21 3.45 0.89 1.02 15.41 North Africa and Middle East 1.36 1.50 10.03 1.71 2.12 23.95 1.69 2.35 39.24 2.36 3.83 61.89 Oceania 1.64 1.79 9.65 2.05 2.22 8.23 3.64 3.84 5.56 2.08 3.51 68.74 South Asia 1.35 1.89 40.55 1.43 1.93 35.37 1.24 1.97 59.24 1.03 1.89 83.65 Southeast Asia 2.27 2.59 14.24 2.81 3.50 24.46 2.51 3.35 33.41 2.78 4.14 48.62 Southern Latin America 2.22 1.63 -26.46 2.35 1.80 -23.32 1.70 1.51 -11.36 1.86 2.01 7.97 Southern Sub-Saharan Africa 1.57 1.75 11.76 1.99 2.19 10.21 1.97 2.36 19.86 3.29 4.07 23.44 Tropical Latin America 2.06 1.38 -32.86 2.41 1.47 -38.95 2.26 1.28 -43.25 2.66 1.74 -34.86 Western Europe 2.59 2.02 -21.95 2.98 2.46 -17.34 2.57 2.49 -3.14 1.87 2.20 17.62 Western Sub-Saharan Africa 9.35 11.24 20.30 9.72 12.46 28.16 7.86 10.56 34.44 10.33 13.57 31.33 Regional patterns of older-adult suicide, 1990–2019 In 2019, South Asia had the greatest number of OA-suicides (223,958, 211,497 to 236,418), followed by East Asia (128,469, 121,010 to 135,929) and East Europe (57,009, 53,997 to 60,020) (see Table 1 ). East Europe had the highest OA ASSR in 2019 (30.89 per 100 000, 29.26 to 32.52), followed by the high-income Asia-Pacific (23.15 per 100 000, 22.52 to 23.79) and the Southern Sub-Saharan African regions (19.04 per 100 000, 17.42 to 20.66). East Asia (-55.57%, -55.68% to -55.46%), Southeast Asia (-32.22%, -32.39% to -32.05%), and South Asia (-31.49%, -31.66% to -31.33%) were the regions with the greatest percent-decline in OA ASSR. The regions with greatest OA-ASSR increases were Central Latin America (32.07%, 31.85–32.29%), Andean Latin America (20.94%, 20.45–21.43%) and the high-income Asia-Pacific region (17.87%, 17.65–18.09%). There was variability in the ratio of OA-to-NOA ASSR across regions and by year. In 2019 the ratios were lowest in in Australasia (0.88) and East Europe (0.95) and much greater than 1 in Western Sub-Saharan Africa (6.12), East Sub-Saharan Africa (6.11) and Central Sub-Saharan Africa (5.14). Western Sub-Saharan Africa had the highest regional-ratio (5.7) in 1990. The first graph of Fig. 2 displays OA-to-NOA ASSR ratios across GBD regions from 1990 to 2019. The last two graphs illustrate OA-to-NOA ASSR ratios, from 1990 to 2019, by sex. The OA-to-NOA ASSR-ratios of Sub-Saharan Africa, Southeast Asia, East Asia, and Oceania were significantly higher than the global OA-to-NOA ASSR-ratio. From 1990 to 2019 OA-to-NOA ASSR-ratios increased in Sub-Saharan Africa, where the ratio exceeded 5 after 2012. By contrast, in Southeast Asia, East Asia, and Oceania, OA-to-NOA ASSR-ratios declined after 2011, after increases from 2000 to 2006. The OA-to-NOA ASSR-ratios of high-income countries and of Latin America and the Caribbean, North Africa and the Middle East, Central Europe, East Europe, and Central Asia decreased slightly or remained constant from 1990 to 2019. The older-adult to non- OA-to-NOA ASSR-ratio of South Asia increased slowly though it was the lowest on average of all regions. Country/nation/territory absolute and age-relative patterns of older-adult suicide, 1990–2019 Between 1990 and 2019 the largest increase in OA ASSR occurred in Armenia (181·09%, 180.25–181.93%), the Republic of Korea (119.33%, 116.87–121.80%), Lesotho (97.17%, 95.48–98.86%), Jamaica (95.32%, 94.54–96.09%), and Taiwan (a province of China) (76.39%, 95% uncertainty interval, 75.85–76.93%). The largest increase in OA ASSR were recorded in Denmark (-61.47%, -61.54%, -61.39%), Equatorial Guinea (-58.91%, -59.26% to -58.57%), Ethiopia (-57.49%, 95% uncertainty interval: -57.65% to -57.32%), China (-57.11%, -57.22% to -57.01%), and Hungary (-53.41%, -53.53% to -53.30%) (see Appendix, Fig. 2 and Table S1 ). In 2019 several Sub-Saharan African countries had the highest OA-to-NOA ASSR-ratios: Ethiopia (7.44), Nigeria (7.07), South Sudan (6.87), Chad (6.80), and Uganda (6.79) (see Appendix Table S1 ). Mongolia (0.51) Mauritius (0.62), Kiribati (0.69), New Zealand (0.76), Uzbekistan (0.77) are the countries with the lowest OA-to-NOA ASSR-ratios (see Table S1 ). Older-adult absolute and age-relative suicide-patterns by sex Figure 1 shows patterns of OA age-specific suicide-rates, from the youngest to the oldest OA age-group, across GBD regions, by sex. Women’s age-specific suicide-rates were significantly lower than men’s age-specific suicide-rates across GBD regions. As shown in Table 3 , OA-to-NOA ASSR-ratios tended to be higher in older OA-groups. Across OA age-groups women’s age-specific OA-to-NOA ASSR-ratios were significantly higher than men’s in Central Europe, East Europe, Central Asia, Southeast Asia, East Asia, and Oceania, and Sub-Saharan Africa. This female-male difference was particularly pronounced among OA ages 95-years-and-older. Figure 3 displays OA-to-NOA ASSR-ratios by sex, across GBD regions in 2019. Women’s OA-to-NOA ASSR-ratios were higher than men’s across OA age-groups. OA-to-NOA ASSR-ratios were highest in Sub-Saharan Africa and in Southeast Asia, East Asia, and Oceania, relative to other GBD regions. OA-to-NOA ASSR-ratios increased with increasing age for women and men, until ages 84-to-89-years, and then they decreased, except among OA women in Sub-Saharan Africa. Absolute and age-relative patterns of older-adult suicide by the sociodemographic position of countries/nations Figure 4 displays a scatterplot of the correlations between OA-to-NOA ASSR-ratios and countries/nations/territories’ sociodemographic position, measured via the SDI, across seven GBD regions, in 2019. The OA-to-NOA ASSR-ratio was negatively correlated with the SDI (correlation coefficient r = -0.64, p-value < 0.05). This correlation suggests the OA-to-NOA ASSR-ratio is higher in lower sociodemographic-position countries/nations/territories. Lower-SDI Sub-Saharan African countries/nations/territories stood out for their higher OA-to-NOA ASSR-ratios. By contrast, high-income, Asian and European countries/nations had both higher SDI and lower OA-to-NOA ASSR-ratios. Figure 5 illustrates changes in OA ASSR across eight time-periods between 1990 to 2019, and across five SDI-levels. It shows the percent-change in OA ASSR across four 8-year intervals (1990–1997, 1998–2005, 2006–2012; 2013–2019), by quintiles of SDI (Low, Low-Middle, Middle, High-Middle, High), and across three OA age-groups (ages 60-to-69-years, ages 70-to-79-years, and ages 80-years-and-older). Most distributions had well-balanced bell-shapes. There were not significant variations in distribution of percent-change among the three OA age-groups. Over the years, distributions-variability shrunk–which made the bell-shaped distributions center toward zero. The median percent-change of the last three time-periods (1998–2005, 2006–2012, and 2013–2019) across the three OA age-groups was negative and approached zero. This indicates that in more than half of the 204 countries/nations/territories, OA ASSR declined. In the first three 7-year intervals (1990–1997, 1998–2005, 2006–2012), countries/nations/territories with above the middle-value of SDI tended to have declining OA ASSR. However, in the last 7-year interval (2013–2019), there were equal number of countries where OA ASSR declined and where the OA ASSR increased, across rising SDI levels. Discussion To our knowledge, this is the first study to examine, by sex, and at global, region, and country/nation/territory levels, and over nearly three decades, both absolute and age-relative patterns of OA suicide. Based on past studies, we expected age-relative and absolute rates of OA suicide to provide different kinds of information. Our expectation was supported. Between 1990 and 2019 the absolute number of OA suicides increased globally, even though both OA ASSR and the OA-to-NOA ASSR-ratio decreased substantially. These global patterns may be due to population growth and to changes in the population age-structure. Because the number of older adults and their proportion in the population are growing, the number of OA suicides will likely continue to grow. 2 To better understand the patterns described above, a direction for future studies is to examine changes in the leading causes of OA death by country/nation/territory. There was considerable variation in OA absolute and age-relative suicide-rates by world-region, country/nation/territory, by sex, and over time. This variability in OA-suicide-patterns, together with the substantial decrease in older-adult absolute and age-relative suicide-rates, challenges simplistic theories of older-adult-suicide, including the Anglophone-countries-based theory that OA suicide is a relatively understandable, if not an inevitable response to aging-related adversities (e.g., increases in illnesses and disabilities). 14 Between 1990 and 2019 OA age-relative suicide-rates were highest in Sub-Saharan Africa and East Asia. OA age-relative suicide-rates were generally higher in countries with a lower socioeconomic-position. These findings challenge the dominant belief that OA suicide is a problem of high-socioeconomic-position countries. OA absolute suicide-rates were significantly lower among women than among men in all regions. At the same time, OA age-relative suicide-rates were significantly higher in women than in men in many GBD regions. Also, in Southeast Asia, East Asia, and Oceania, OA women’s age-relative suicide-rates increased monotonously while OA men’s started to drop after 2010. Furthermore, in Sub-Saharan Africa, OA women’s age-relative suicide-rates kept growing, though with large fluctuation, while OA men’s age-relative suicide-rates increased monotonously. These findings indicate that in many GBD regions, particularly in lower socioeconomic-position regions, women’s likelihood of suicide is greater in older adulthood than prior to older adulthood; and also that, in those regions, OA women’s relative suicide-risk is on the increase. There are several possible explanations for these patterns. One is that there have been improvements in the recording of female suicide. Female suicide is less likely to be recognized as such, and/or to be reported than male suicide, 15 especially in communities where women’s agency is systemically-restricted by men, and where female suicide is viewed as a form of defiance of male de-jure or de-facto ownership of women. 16 Another explanation is that there has been an increase in female suicide in lower-socioeconomic-position regions. A study found that the relatively-high suicide-rates of women in low- and middle-income countries, as compared to the suicide-rates of women in high-income countries, are predicted by the greater institutional-discrimination that women experience in low- and middle-income countries–including restricted access to productive and financial assets and justice, and lesser family-law rights. 17 Because the effects of institutional discrimination accumulate over the lifespan, institutional discrimination often weighs heavier on OA women. In an increasingly-connected world, recent generations of women living in low socioeconomic-position countries with high levels of institutional discrimination may be more aware than earlier generations of women of the human-rights violations that they experience, with suicide being their desperate protest against the human-rights abuses, at least, in countries/nations/territories where female suicide follows the protest-script. 16,17 The findings that, in many GBD regions, OA women’s age-relative suicide-rates were significantly higher than OA men’s age-relative suicide-rates challenge the dominant belief that in late-adulthood, suicide is men’s problem. This belief is supported by the absolute OA suicide-rates—with OA men having higher absolute suicide-rates. The belief that suicide is men’s problem has become dominant also because men, particularly men of European descent, have the highest suicide-rates in high-income, Anglophone countries, 14 given that studies from high-income, Anglophone countries are over-represented in the scientific literature. This study’s examination of both age-relative and absolute OA suicide-rates, across countries/nations/territories, regions, and globally, provides a window on OA women’s and men’s different ways of suicide-vulnerability. Interpretations of this study’s findings require consideration of its method’s strengths and weaknesses. A strength is that it used GBD data that are comparable across country/nation/territory and region, and over time. Another strength is that it examined absolute and age-relative OA suicide-data by sex, across different scales of location. Limitations of this study result from GBD-2019 data-gaps and data-quality variability. Data-gaps and data-quality variations are more likely in lower-socioeconomic-position countries more than in higher-socioeconomic-position countries. For example, many Sub-Saharan countries have limited vital-registration data. Data from neighboring countries are used to impute the missing data, leading to more homogenous estimates in Sub-Saharan Africa than in other regions. 9 Another issue is selective suicide-underreporting and misclassification, by country/nation/territory. While suicide-underreporting and misclassification occur in all countries, they are more common in countries/nations/territories where suicide is subject to more negative cultural and religious sanctions. 18 Also, as noted earlier, underreporting and misclassification tend to be more of a problem in terms of women’s suicides, 15 especially in cultures where there is a strong prohibition of women’s suicide. 16 For these reasons, the fact that the GBD definition of suicide encompasses both suicide and intentional self-harm-deaths is an asset of this study. Other limitations have to do with construct operationalization. We set age 60 as the older-adulthood threshold, across countries. This decision was necessary but problematic. One reason is that there is substantial variability in longevity, by sex and by country/nation/territory. Another reason is that being age 60-and-older has different meanings and involves different experiences, depending on sex and culture, with implications for suicide. Using the SDI as a measure of country/nation/territory socioeconomic-position is a limitation. The SDI is a composite measure of total fertility-rate for persons > 25-years, mean education-years for persons > 15-years, and per-capita income. Given the variability in women’s and men’s education, paid work, and income, by country/nation/territory and region, due to discrimination against women that varies by location, the SDI provides different information about women’s and men’s socioeconomic position, depending on location and culture. It is also a limitation to use, in a study of late-adulthood suicide, a socioeconomic-position measure that includes fertility as one of its three indices. Methods Overview This study used estimates from the 2019 GBD-study to assess OA absolute and age-relative suicide-rates by sex, region and country/nation/territory, from 1990 to 2019. The threshold for being classified OA was age 60-years-and-older. NOA was defined as ages 10–59 years. Data on people younger than age 10-years were not included in this study because of the very low numbers of suicides reported, in most locations, among individuals under the age of 10, and also because of the difficulty in determining suicide intent in young children. 10 OA age-relative suicide-rates were the ratios of OA-to-NOA age-standardized suicide-rates (ASSR). OA ASSR and OA-to-NOA ASSR-ratios were correlated with country/nation/territory’s socioeconomic position using the Socio-Demographic Index (SDI). The 2019 GBD-study includes mortality estimates for 363 death-causes by sex, age-group, country/nation/territory, and region, in 204 countries/nations/territories, from 1990 to 2019. The 2019 GBD-study uses the International-Classification-of-Disease (ICD) definition of suicide as a death by intentional self-harm by a diversity of means (ICD-10 codes X60-X84). Data and measures Suicide data and cause-of-death (COD) estimates Estimated suicide numbers and rates by sex, age-group, region, and country/nation/territory were downloaded via the GBD-results query-tool developed by the Institute of Health Metric and Evaluation (IHME) of Washington University, Seattle, USA. Cause-of-death (COD) information was extracted from the raw data collected by the IHME or its collaborators through vital autopsy (VA) and vital registration (VR) systems. VA-systems were utilized to collect the COD information when data from VR-systems were unavailable. The GBD 2019 implemented methods for data quality (e.g., star-rating systems) and regression modelling to standardize and re-classify cases with inappropriate or ambiguous death-codes or without specific causes of death. Details about the GBD rating-systems and modeling techniques are in Mohsen et al,. 10 The 2019 GBD-study implemented a weighted, COD-ensemble-models framework to estimate suicide-rates. These models adjust each component-model weight for death estimates from countries with low data-quality, through adjacent regions or time periods. Weighted, COD-ensemble models generate better COD estimates than other models. 11 Socio-Demographic Index (SDI) The SDI is calculated through the geometric mean of total fertility-rate for persons under the age of 25 (TFU 15), and lag-distributed income (LDI) per capita. These three factors were regressed against life expectancy. The three SDI-variables values, that related to minimum and maximum life-expectancy value, were rescaled to the SDI index that ranged from lowest (0) to highest value ( 1 ). 12 Analytic strategies Age-standardized suicide-rates In the GBD 2019-data ASSR were based on age-standardized world-populations. Age-standardized world-populations were calculated using non-weighted means of 2019 age-specific proportional distributions from GBD 2019 population-estimates for countries/nations/territories with a population greater than 5 million people in 2019. 12 Older-adult to non-older-adult ratios of age-standardized suicide-rates. OA-to-NOA ASSR-ratios were calculated by dividing the OA ASSR by the NOA ASSR. Higher OA-to-NOA ASSR-ratios indicate that OA have higher suicide-rates than NOA. Temporal change of suicide mortality In 1990 and 2019, the total-population suicide-rate at each age-group was calculated through dividing the number of suicides by the ASSR. The number of suicides across 5-year age-groups above age 10-years was summed up. The ASSR for all age-groups was obtained by dividing the total number of suicides by total population. The difference in ASSR between 1990 and 2019 was calculated and divided by the ASSR in 1990, to obtain the changing proportion. Uncertainty analyses To estimate suicide-rates, and their 95% uncertainty interval (UI), 1000 simulated suicide-rates draws were generated from the posterior suicide-rates distribution conditional on age, sex, regions, countries/nations/territory, and year. The 95% uncertainty intervals for 1000 draws were determined using the 2.5th and 97.5th percentiles. The suicide-rates point-estimates were estimated from the mean of these draws. In the analysis of suicide-rates temporal-changes, a criterion for statistical significance was employed. A change was deemed statistically significant if the uncertainty interval of the percentage-change did not intersect zero. Analyses All analyses complied with the Guidelines for Accurate and Transparent Health Estimates Reporting of the World Health Organization. 13 Research Ethics This study was reviewed and approved by the University of Washington’s Institutional-Review-Board, Seattle, USA. It was deemed exempt because it used de-identified, aggregate, 2019 GBD-study data. Conclusions This study demonstrates the value of examining both absolute and age-relative rates of OA suicide, globally, and by region and by country/nation/territory. This study’s findings show that absolute and age-relative rates of OA suicide provide different information. This study contributes to the growing evidence that suicide is not a homogenous phenomenon. 2 Specifically, it expands to older adults the evidence that absolute and relative suicide-rates vary by sex, and depending on culture and time-period. The variability in OA suicide-patterns challenges the presumed universality of dominant, Anglophone-countries-based theories of OA suicide. The fact that OA suicide is not a uniform phenomenon means that OA suicide theory, research, and prevention require an intersectional approach, 19 that, at a minimum, considers sex, culture, and time-period. This study’s findings challenge two other myths of OA suicide, with implications for prevention. One is that women are protected from suicide. The other is that OA suicide is a problem of high socioeconomic-position countries/nations/territories. This study’s findings call attention to OA women (particularly OA women living in Sub-Saharan Africa, Southeast Asia, East Asia, and Oceania) a so-far unnoticed, suicide-vulnerable population. Declarations Authors’ Contributions QC and FS conceptualized the study. QC, ZZ, ZC, SC and FS provided critical methodological input. ZZ and ZC curated the data and contributed to the formal analysis with the guidance of SF. QC, ZZ, SC and SF wrote the first draft and the other authors made critical revisions of the manuscript. SC, BL, DW, HL, XY and PY contributed to the clinical interpretation. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. Availability of data and materials The datasets analyzed during the current study are available in the the GBD website (https://www.healthdata.org/research-analysis/gbd). Conflict of Interest Statements We declare no competing interests. Ethics Committee approval This study was reviewed and approved by the University of Washington’s Institutional-Review-Board, Seattle, USA. It was deemed exempt because it used de-identified, aggregate, 2019 GBD-study data. Role of Funding Source This study was partly supported by the National Social Science Fund of China (grant No. 21CSH057), the Strategic Priority Research Program of Chinese Academy of Sciences (grant No. XDB 38040200), Shenzhen Science and Technology Program (grant No. KQTD20190929172835662) and Xiamen One Heart Charity (grant No.HX2023027). The funder had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. 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International psychogeriatrics 2007; 19(6): 1141–52. Van Orden K, Conwell Y. Suicides in late life. Current psychiatry reports 2011; 13(3): 234–41. Conwell Y. Suicide Later in Life: Challenges and Priorities for Prevention. American Journal of Preventive Medicine 2014; 47(3, Supplement 2): S244–S50. Chang Q, Conwell Y, Wu D, Guo Y, Yip PSF. A study on household headship, living arrangement, and recipient of pension among the older adults in association with suicidal risks. Journal of affective disorders 2019; 256: 618–26. Mohsen N. Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016. BMJ 2019; 364: l94. Foreman KJ, Lozano R, Lopez AD, Murray CJL. Modeling causes of death: an integrated approach using CODEm. Population Health Metrics 2012; 10(1): 1. Dicker D, Nguyen G, Abate D, et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 2018; 392(10159): 1684–735. Stevens GA, Alkema L, Black RE, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. The Lancet 2016; 388(10062): e19–e23. Canetto SS. Suicide: Why are older men so vulnerable? Men and Masculinities 2017; 20(1): 49–70. Rockett IRH, Caine ED, Connery HS, et al. Unrecognised self-injury mortality (SIM) trends among racial/ethnic minorities and women in the USA. Injury prevention: journal of the International Society for Child and Adolescent Injury Prevention 2020; 26(5): 439–47. Canetto SS. Suicidal behaviors among Muslim women: Patterns, pathways, meanings, and prevention. Crisis: The Journal of Crisis Intervention and Suicide Prevention 2015; 36(6): 447–58. Cai Z, Canetto SS, Chang Q, Yip PSF. Women's suicide in low-, middle-, and high-income countries: Do laws discriminating against women matter? Social science & medicine (1982) 2021; 282: 114035. Pritchard C, Amanullah S. An analysis of suicide and undetermined deaths in 17 predominantly Islamic countries contrasted with the UK. Psychological Medicine 2007; 37(3): 421–30. Canetto SS. Language, culture, gender, and intersectionalities in suicide theory, research, and prevention: Challenges and changes. Suicide & life-threatening behavior 2021; 51(6): 1045–54. Additional Declarations There is NO Competing Interest. Supplementary Files Appendix050324.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Yip","email":"","orcid":"https://orcid.org/0000-0003-1596-4120","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"S.F.","lastName":"Yip","suffix":""},{"id":305241665,"identity":"ab71a918-46f6-4dda-8771-ad6a596f4cfb","order_by":8,"name":"Silvia Canetto","email":"","orcid":"","institution":"Colorado State University","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Canetto","suffix":""}],"badges":[],"createdAt":"2024-05-03 16:01:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4365103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4365103/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56943533,"identity":"3fa3f8fc-220e-4b39-8ecb-794b61a98514","added_by":"auto","created_at":"2024-05-22 12:49:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48636,"visible":true,"origin":"","legend":"\u003cp\u003eOA ASSR by super regions, age groups and gender in 2019\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4365103/v1/7d377f7b017ea65f66d4acca.png"},{"id":56943528,"identity":"1831e963-20d4-4e11-98c5-d7aa332c107a","added_by":"auto","created_at":"2024-05-22 12:49:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":303607,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends of OA-NOA suicide ratios by super regions and gender from 1990 to 2019.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4365103/v1/2da57060d5ff2555f6a58900.png"},{"id":56943524,"identity":"636c12fc-d481-4d76-b1b1-90a11e322e08","added_by":"auto","created_at":"2024-05-22 12:49:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88978,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific OA-NOA suicide ratio by super region, age groups, and gender in 2019.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4365103/v1/23d91b4a5d24d8c424c82689.png"},{"id":56943532,"identity":"eabac004-b2c9-4dd4-ab40-7751d6e22323","added_by":"auto","created_at":"2024-05-22 12:49:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199926,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized OA-NOA suicide ratio by SDI across super regions in 2019.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4365103/v1/817cb2978eba73665a1fe6b1.png"},{"id":56944150,"identity":"e36e13fa-dec5-4bcb-a314-28bc07bebdf5","added_by":"auto","created_at":"2024-05-22 12:57:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138446,"visible":true,"origin":"","legend":"\u003cp\u003ePercent change of ASSR by country SDI, in five OA age-groups.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4365103/v1/53e757e2fb95ff69678b93df.png"},{"id":75652584,"identity":"df7c62f2-aa28-445c-98a2-e50e07c359b1","added_by":"auto","created_at":"2025-02-06 18:23:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2881103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4365103/v1/870c7e3b-19dc-488c-abe6-a1391eb87ab2.pdf"},{"id":56944149,"identity":"c5615ea3-89d2-4da3-bcb6-a16e8260948d","added_by":"auto","created_at":"2024-05-22 12:57:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1062747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Appendix050324.docx","url":"https://assets-eu.researchsquare.com/files/rs-4365103/v1/c54954f23bf5b8b3b82dc380.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Absolute and age-relative suicide-rates for women and men age 60 years and older, at the global, region, and nation level, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019 data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAround 750,000 individuals die by suicide worldwide yearly.\u003csup\u003e1\u003c/sup\u003e Between 1990 and 2019, the total number of suicides increased (19,941, 95% uncertainty interval 15,181 to 24,701), though age-specific suicide-rates decreased (-4.01; from 13.8\u0026ndash;9.8% per 100,000), according to a study using Global-Burden-of-Disease (GBD) 2019-data. The increase varied by region\u0026rsquo;s income-level. Specifically, the lower-middle-income region saw a notable increase while the upper-middle-income region registered a substantial decline.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBetween 1990 and 2019 suicide-rates were highest among individuals age 70-and-older in almost all world-regions.\u003csup\u003e2\u003c/sup\u003e In forthcoming decades the absolute number of older-adult (OA) suicides is expected to surge because the number of older adults and their proportion in the world population are growing.\u003csup\u003e2,3\u003c/sup\u003e Yet, OA suicide remains a neglected research area.\u003csup\u003e2,4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOA suicide-patterns have been studied using both absolute and age-relative suicide-rates. OA relative suicide-rates have typically been assessed by calculating the older-adult (OA) to non-older-adult (NOA) ratio of suicide-rates.\u003csup\u003e5\u0026ndash;9\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOA absolute and age-relative suicide-rates provide different information. According to 2015 GBD-data, the absolute suicide-rates of individuals age 60-years-and-older were around 28.0 per 100,000 in China, Austria, and Ukraine. By contrast, OA relative suicide-rates, that is, the ratio of OA-to-NOA suicide-rates were very different in China, Austria, and Ukraine (4.64, 2.29, and 1.30, respectively) because of different NOA suicide-rates in those countries.\u003csup\u003e9\u003c/sup\u003e The OA-to-NOA suicide-rates ratio allows better comparisons by region and country/nation/territory because it eliminates the influence, on base suicide-rates, of country/nation/territory differences and of data-quality differences.\u003csup\u003e9\u003c/sup\u003e Unlike the male-to-female ratio of suicide-rates, the OA-to-NOA ratio of suicide-rates has not been used to study older-adult suicide by sex, globally, by world-region, and by country/nation/territory.\u003c/p\u003e \u003cp\u003eThis study examined OA absolute and age-relative suicide-rates over nearly three decades, in women and men, globally, by world-region and by country/nation/territory, and in relation to countries/nations/territories socioeconomic position.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGlobal absolute and age-relative patterns of older-adult suicide, 1990\u0026ndash;2019\u003c/h2\u003e \u003cp\u003eIn 1990 there were 739,087 (95% uncertainty interval: 721,967 to 756,207) suicides among people age 60-years-and-older, globally. In 2019, the number of OA suicides increased by 2.71% (759,028; 95% uncertainty interval: 737,148 to 780,908). OA ASSR per 100,000 however declined by 33\u0026middot;89%, from 17\u0026middot;88 (17.47 to 18.30) in 1990, to 11.82 (11.48 to 12.16) in 2019 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eNumber of OA suicides, ASSR per 100,000, percent-change from 1990 to 2019 with UI, and OA-NOA suicide ratios in 2019 for all regions.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNumber of OA suicides (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOA ASSR (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePercent change (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eOA-NOA suicide-rates ratios\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e739,087 (721,967, 756,208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e759,028 (737,148, 780,909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.88 (17.47, 18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.82 (11.48, 12.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-33.89 (-33.97, -33.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,541 (1,402, 1,679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,437 (3,141, 3,733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.55 (5.05, 6.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.71 (6.13, 7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.94 (20.45, 21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,815 (2,753, 2,878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,410 (3,279, 3,542)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.36 (16, 16.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.42 (12.91, 13.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17.96 (-18.07, -17.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,204 (4,085, 4,323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,569 (4,292, 4,845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.34 (14.9, 15.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.63 (10.92, 12.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.19 (-24.36, -24.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,903 (6,767, 7,038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,143 (10,622, 11,665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.39 (13.13, 13.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.93 (14.23, 15.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.5 (11.32, 11.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,616 (23,311, 23,921)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,408 (16,647, 18,169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.63 (22.34, 22.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.96 (16.22, 17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.06 (-25.17, -24.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,827 (6,704, 6,950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,514 (14,738, 16,290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68 (5.58, 5.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.51 (7.13, 7.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.07 (31.85, 32.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,724 (4,286, 5,163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,199 (8,154, 10,244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.96 (11.75, 14.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.06 (8.92, 11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.36 (-22.72, -22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219,531 (208,296, 230,766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128,470 (121,011, 135,929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.02 (20.9, 23.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.79 (9.22, 10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-55.57 (-55.68, -55.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63,778 (62,789, 64,768)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57,009 (53,997, 60,020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3 (32.78, 33.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.89 (29.26, 32.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.24 (-7.4, -7.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,312 (14,493, 16,132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,794 (23,219, 26,368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.27 (11.61, 12.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.58 (8.04, 9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-30.01 (-30.2, -29.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29,733 (28,122, 31,344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,837 (38,745, 40,928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.64 (18.58, 20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.15 (22.52, 23.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.87 (17.65, 18.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38,103 (37,824, 38,382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50,082 (49,423, 50,741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.95 (15.83, 16.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.56 (15.36, 15.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.41 (-2.46, -2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16970 (15608, 18332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,803 (23,737, 27,868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.96 (6.41, 7.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.27 (4.85, 5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.3 (-24.58, -24.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e448 (397, 500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e753 (656, 849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.69 (8.58, 10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.64 (6.67, 8.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-21.14 (-21.55, -20.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174,971 (166,138, 183,803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223,958 (211,498, 236,419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.27 (21.14, 23.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.25 (14.41, 16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.49 (-31.66, -31.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,973 (32,240, 35,705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37,100 (34,768, 39,432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.73 (9.23, 10.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.6 (6.18, 7.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.22 (-32.39, -32.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,673 (5,537, 5,808)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,702 (7,411, 7,993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.4 (14.06, 14.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.56 (13.05, 14.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.81 (-5.95, -5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,431 (7,712, 9,149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,894 (10,881, 12,907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.89 (20.03, 23.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.04 (17.42, 20.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.04 (-13.38, -12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTropical Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,365 (9,171, 9,558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,931 (13,521, 14,340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.02 (7.86, 8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.3 (7.08, 7.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.03 (-9.13, -8.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61,588 (60,995, 62,180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49,755 (48,734, 50,776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.22 (18.04, 18.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.72 (12.46, 12.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-30.16 (-30.21, -30.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,583 (9,677, 11,489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,263 (21,362, 25,164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.25 (7.54, 8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.33 (6.73, 7.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.11 (-11.44, -10.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOA ASSR in 1990 and 2019, for each of eight OA age-groups (starting at age 60 years, at 5-year intervals), are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. From 1990 to 2019, ASSR declined across all OA age-groups. In 2019, there was a 40\u0026middot;95% decrease in ASSR among OA ages 70-to-74-years, from 32.82 (29.7 to 35.65) per 100,000 in 1990 to 19\u0026middot;38 (17.12 to 21.49) per 100,000. This was the largest decline in global OA ASSR across all OA age-groups. In 2019 individuals ages 60-to-64-years had the lowest ASSR (14.49; 12.95 to 15.85 per 100,000) while individuals ages 85-to-89-years had the highest ASSR (35.55; 30.15 to 39.75 per 100,000).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eASSR and percent-change between 1990 and 2019 among individuals age 60 and older in 5-year age groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"25\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eASSR ages 60 to 64 (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e \u003cp\u003eASSR ages 65 to 69 (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c20\" namest=\"c15\"\u003e \u003cp\u003eASSR ages 70 to 74 (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c25\" namest=\"c21\"\u003e \u003cp\u003eASSR ages 75 to 79 (95% UI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c25\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e24.28 (22.01, 26.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14.49 (12.95, 15.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-40.35 (-40.88, -39.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e26.7 (24.14, 28.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e16.04 (14.37, 17.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-39.92 (-40.47, -39.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e32.82 (29.7, 35.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e19.38 (17.12, 21.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-40.95 (-41.51, -40.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e39.82 (36.73, 42.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e23.93 (21.05, 26.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-39.91 (-40.42, -39.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.19 (5.73, 9.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7.67 (4.82, 9.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-32.82 (-33.63, -32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e7.31 (5.79, 9.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e7.69 (5.04, 9.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-25.77 (-26.6, -24.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e7.17 (5.64, 9.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e8.21 (5.05, 10.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-17.03 (-17.88, -16.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e7.53 (5.99, 9.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e8.69 (5.53, 11.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-23.92 (-24.57, -23.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAustralasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.26 (14.91, 17.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12.22 (10.5, 14.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7.02 (6.68, 7.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e15.63 (14.22, 17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e10.31 (8.81, 12.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-9.82 (-10.13, -9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e17.41 (15.9, 18.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e11.17 (9.46, 12.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-18.94 (-19.25, -18.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e17.74 (16.02, 19.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e12.51 (10.49, 14.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-24.8 (-25.19, -24.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCaribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e24.15 (21.91, 26.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e16.57 (13.68, 20.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-31.78 (-33.6, -29.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e26.05 (24.08, 28.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e17.44 (14.49, 21.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-33.98 (-36.02, -31.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e30.74 (28.37, 33.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e21.05 (17.37, 25.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-33.29 (-34.8, -31.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e36.23 (33.01, 39.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e25.35 (20.88, 30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-31.22 (-33.21, -29.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCentral Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17.45 (16.4, 18.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13.77 (11.85, 16.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-26.73 (-27.5, -25.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e16.38 (15.44, 17.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e14.66 (12.84, 17.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-36.77 (-37.39, -36.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e17.48 (16.29, 18.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e16.47 (14.48, 19.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-40.51 (-41.13, -39.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e20.9 (19.42, 22.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e20.8 (18.26, 24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-43.74 (-44.3, -43.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCentral Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e28.44 (27.21, 30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20.84 (17.71, 24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-31.4 (-32.39, -30.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e30.99 (29.57, 32.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e19.59 (16.77, 22.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-33.08 (-33.99, -32.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e33.75 (32.15, 35.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e20.08 (17.26, 23.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-31.52 (-32.55, -30.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e40.9 (38.79, 42.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e23.01 (19.72, 26.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-30.03 (-31.05, -29.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCentral Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.77 (6.44, 7.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.97 (5.78, 8.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-24.81 (-25.68, -23.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e7.29 (6.92, 7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e7.49 (6.34, 8.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-34.04 (-34.83, -33.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e7.79 (7.34, 8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e7.57 (6.44, 8.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-35.85 (-36.6, -35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e8.56 (8.01, 9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e7.91 (6.75, 9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-29.5 (-30.47, -28.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40.38 (30.9, 52.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e32.47 (23.43, 44.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6.68 (0.25, 13.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e49.31 (36.52, 64.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e39.32 (29.06, 52.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e5.3 (-5.09, 15.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e60.65 (44.93, 77.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e46.18 (34.73, 60.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e14.49 (-117.28, 146.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e71.65 (52.59, 94.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e53.04 (40.15, 68.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e15.38 (10.29, 20.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e33.38 (27.04, 38.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12.98 (10.76, 15.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-61.13 (-61.91, -60.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e40.58 (33.43, 47.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e16.2 (13.39, 19.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-60.07 (-60.83, -59.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e59.94 (50.14, 68.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e24.56 (20.3, 29.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-59.03 (-59.74, -58.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e77 (66.23, 87.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e33.27 (27.66, 39.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-56.8 (-57.44, -56.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e39.21 (38.05, 40.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e26.34 (22.73, 31.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-27.37 (-27.88, -26.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e35.55 (34.43, 36.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e26.39 (23.07, 31.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-27.45 (-27.95, -26.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e36.98 (35.79, 39.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e30.69 (26.86, 35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-33.84 (-34.32, -33.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e42.34 (40.61, 44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e32.22 (28.8, 36.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-37.14 (-37.61, -36.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40.67 (33.48, 48.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e27.32 (22.69, 33.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-21.07 (-21.93, -20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e50.27 (41.85, 60.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e34.93 (29.25, 43.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-10.53 (-11.53, -9.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e61.95 (50.6, 74.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e45.15 (38.07, 57.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-5.78 (-6.8, -4.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e71.4 (59.95, 86.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e56.83 (48.14, 71.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-0.48 (-1.51, 0.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e26.87 (25.82, 30.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e26.45 (21.41, 29.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-32.16 (-32.8, -31.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e28.19 (26.97, 31.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e25.83 (21.78, 28.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-35.3 (-35.96, -34.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e36.18 (34.44, 39.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e29.36 (24.87, 32.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-38.13 (-38.78, -37.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e47.72 (44.42, 51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e32.55 (26.8, 36.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-34.84 (-35.54, -34.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHigh-income North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.78 (16.36, 17.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e17.95 (17.23, 18.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-30.62 (-32.41, -28.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e17.35 (16.88, 17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e15.65 (14.96, 16.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-26.63 (-28.45, -24.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e19.74 (19.17, 20.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e16 (15.27, 16.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-21.42 (-24.37, -18.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e23.04 (22.04, 23.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e17.33 (16.27, 18.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-18.48 (-22.37, -14.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.62 (5.63, 9.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.72 (4.44, 6.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-7.43 (-9.43, -5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e7.47 (5.52, 9.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e5.69 (4.36, 6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-5.48 (-7.57, -3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e7.82 (5.83, 9.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e5.97 (4.73, 7.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-11.21 (-13.73, -8.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e7.09 (5.33, 8.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e6 (4.65, 7.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-10.53 (-12.91, -8.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11.88 (8.99, 14.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10.02 (7.46, 12.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.89 (1.51, 4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e13.36 (10.12, 16.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e11.44 (8.41, 14.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e2.74 (1.51, 3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e11.82 (9.08, 14.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e9.78 (7.23, 12.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-2.86 (-4.05, -1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e12.36 (9.77, 14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e10.52 (8.2, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-7.54 (-8.62, -6.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19.58 (15.27, 24.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13.58 (10.73, 16.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-19.6 (-27.06, -12.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e20.89 (16.53, 26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e15.32 (12.2, 18.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-20.28 (-25.55, -15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e19.36 (15.63, 24.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e15.21 (12.14, 18.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-23.86 (-26.82, -20.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e26.1 (20.99, 33.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e21.28 (17.16, 25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-25.98 (-33.45, -18.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e12.52 (9.57, 14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8.54 (6.52, 10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-15.63 (-18.12, -13.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e15.06 (11.37, 17.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e9.94 (7.55, 11.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-14.35 (-20.81, -7.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e17.87 (13.64, 20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e11.92 (9.35, 14.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-17.25 (-19.28, -15.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e18.62 (13.99, 21.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e12.81 (9.86, 15.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-14.88 (-16.83, -12.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSouthern Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21.46 (19.74, 23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14.56 (12.73, 16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-1.54 (-3.02, -0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e23.49 (21.69, 25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e15.2 (13.29, 17.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-8.4 (-9.54, -7.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e24.91 (22.98, 26.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e15.41 (13.64, 17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-18.85 (-19.76, -17.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e28.37 (26.1, 30.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e18.49 (15.98, 21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-31.79 (-32.61, -30.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e22.01 (17.29, 26.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20.38 (16.56, 24.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-10.54 (-18.03, -3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e23.96 (18.79, 28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e22.65 (17.91, 27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-4.89 (-20.02, 10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e27.62 (20.76, 32.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e24.53 (19.85, 29.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-1.51 (-12.4, 9.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e31.39 (24.26, 37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e28.09 (22.86, 34.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e1.85 (-48.41, 52.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTropical Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11.12 (10.55, 11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8.08 (7.46, 8.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-24.91 (-27.05, -22.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e11.73 (11.14, 12.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e8.51 (7.9, 9.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-23.81 (-26.85, -20.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e13.53 (12.74, 14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e8.95 (8.25, 9.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-23.6 (-25.77, -21.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e14.74 (13.73, 15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e9.26 (8.44, 10.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-15.46 (-17.79, -13.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21.67 (20.98, 22.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14.23 (13.24, 15.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-32.82 (-34.31, -31.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e23.4 (22.59, 24.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e13.79 (12.89, 14.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-30.52 (-32.16, -28.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e25.72 (24.74, 26.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e15.05 (13.98, 16.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-27.12 (-28.8, -25.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e33.39 (31.6, 34.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e18.13 (16.54, 19.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-20.41 (-22.23, -18.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e24.66 (19.07, 33.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e22.06 (17.17, 28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-34.36 (-34.7, -34.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e30.95 (24.13, 41.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e29.43 (22.92, 38.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e-41.06 (-41.35, -40.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e38.81 (30.68, 52.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e38.22 (30.25, 50.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-41.51 (-41.82, -41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e46.15 (36.52, 62.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e47.01 (38, 61.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e-45.69 (-46.04, -45.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eASSR ages 80 to 84 (95% UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eASSR ages 85 to 89 (95% UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e \u003cp\u003eASSR ages 90 to 94 (95% UI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c25\" namest=\"c20\"\u003e \u003cp\u003eASSR ages 95 and older (95% UI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003cp\u003echange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e47.68 (43.17, 51.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29.99 (26.22, 33.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-37.11 (-37.73, -36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e52.32 (46.45, 56.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e35.55 (30.15, 39.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-32.07 (-32.91, -31.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e41.26 (35.27, 44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e30.06 (24.37, 33.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-27.14 (-28.27, -26.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e30.19 (24.66, 33.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e25.85 (20.05, 29.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-14.38 (-16.11, -12.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6.36 (5.03, 8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8.57 (5.52, 10.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-27.02 (-27.65, -26.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.32 (5.75, 9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e9.67 (6.53, 12.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-20.73 (-21.65, -19.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e6.45 (4.88, 8.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e8.65 (5.71, 11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-15 (-16.28, -13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e9.15 (6.48, 12.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e11.54 (7.54, 15.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-7.07 (-8.85, -5.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18.06 (15.99, 20.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e14.32 (11.73, 16.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-20.83 (-21.46, -20.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e15.44 (13.15, 17.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e17.11 (13.89, 20.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-7.39 (-8.44, -6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e12.38 (10.04, 14.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e16.52 (12.86, 20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e2.54 (0.72, 4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e9.31 (7.37, 10.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e12.94 (9.81, 15.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e14.4 (11.73, 17.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e41.24 (36.56, 45.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e30.92 (24.93, 37.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-25.9 (-27.43, -24.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e47.43 (40.93, 53.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e37.16 (29.8, 45.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-19.28 (-20.92, -17.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e41.91 (34.36, 48.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e28.97 (22.39, 34.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-13.47 (-15.46, -11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e23.62 (19.07, 27.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e28.11 (21.03, 34.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-3.61 (-6.87, -0.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e20.73 (18.82, 22.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e28.11 (24.52, 32.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-45.76 (-46.29, -45.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e19.1 (16.59, 21.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e32.32 (27.94, 37.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-45.31 (-46.01, -44.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e18.29 (14.63, 20.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e31.05 (25.28, 36.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-41.14 (-42.06, -40.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e17.41 (13.77, 20.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e27.06 (20.94, 33.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-30.02 (-31.42, -28.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e48.92 (45.44, 51.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e26.53 (22.74, 30.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-25.03 (-26.13, -23.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e55.7 (49.35, 60.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e30.46 (25.8, 34.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-21.65 (-22.95, -20.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e45.94 (39.49, 50.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e27.04 (21.76, 30.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-30.88 (-32.26, -29.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e38.06 (30.88, 42.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e26.63 (20.68, 30.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e19.01 (16.34, 21.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7.79 (6.95, 8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8.79 (7.42, 10.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-20.7 (-21.81, -19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e10.72 (9.32, 11.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e10.2 (8.31, 11.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e10.86 (9.08, 12.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e11.19 (9.06, 12.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e10.55 (8.17, 12.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e33.45 (30.82, 36.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e19.31 (14.36, 22.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e17.47 (12.71, 20.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e39.01 (36.03, 42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e84.9 (61.9, 110.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e62.11 (46.71, 79.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e34.78 (23.19, 46.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e82.09 (60.59, 107.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e67.35 (49.62, 88.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e32.17 (27.74, 36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e71.79 (53.05, 92.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e63.78 (46.83, 82.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e34.1 (28.24, 39.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e76.93 (56.77, 101.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e84.76 (59, 113.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e26.06 (16.03, 36.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e106.74 (91.18, 120.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e46.2 (38.15, 55.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-56.71 (-57.43, -55.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e136.4 (117.7, 154.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e65.36 (55.14, 78.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-52.08 (-52.86, -51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e120.64 (102.61, 137.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e53.11 (43.18, 63.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-55.98 (-56.67, -55.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e92.59 (77.03, 105.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e41.91 (32.3, 50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-54.74 (-55.65, -53.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e48.21 (44.9, 50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e35.19 (31.08, 39.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-37.52 (-38.2, -36.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e53.26 (48.04, 56.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e42.22 (35.96, 48.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-43.18 (-43.97, -42.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e49.91 (42.76, 54.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e42.43 (34.54, 48.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-47.18 (-48.25, -46.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e43.27 (35.41, 47.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e40.21 (31.42, 46.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-39.37 (-40.86, -37.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e77.94 (64.33, 94.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e69.12 (57.47, 86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e35.61 (34.01, 37.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e76.41 (62.15, 94.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e73.99 (59.73, 93.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e69.18 (67, 71.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e67.34 (54.86, 84.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e69.2 (54.04, 87.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e69.81 (66.62, 72.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e81.04 (62.66, 99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e90.66 (67.33, 109.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e55.4 (51.5, 59.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e63.33 (57.3, 67.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e35.73 (28.75, 40.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-25.22 (-26.08, -24.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e80.47 (70.09, 86.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e39.15 (30.51, 45.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-22.04 (-23.03, -21.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e72.7 (60.92, 79.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e33.79 (24.27, 39.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-9.88 (-11.48, -8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e46.65 (36.82, 51.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e27.26 (18.87, 32.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e9.78 (7.49, 12.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e24.61 (22.56, 25.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e19.49 (17.54, 21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-5.38 (-8.97, -1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e22.32 (19.66, 23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e20.67 (17.78, 22.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-8.87 (-11.95, -5.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e17.63 (14.62, 19.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e18.07 (14.87, 20.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e7.21 (-62.77, 77.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e13.39 (10.62, 15.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e15.32 (12.14, 19.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e23.64 (-88.59, 135.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9.37 (7.08, 11.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7.73 (6, 9.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-4.23 (-6.36, -2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11.78 (8.79, 14.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e10.95 (8.55, 13.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-5.55 (-7.49, -3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e11.65 (8.73, 14.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e12.16 (9.33, 14.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e2.72 (0.91, 4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e16.28 (12.08, 20.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e19.77 (13.68, 26.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e5.79 (3.43, 8.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15.49 (12.58, 18.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e13.25 (10.73, 16.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e12.81 (11.33, 14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e19.41 (15.95, 23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e16.4 (13.23, 20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-4.85 (-6.18, -3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e34.41 (27.79, 40.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e28.34 (22.16, 34.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-5.73 (-7.7, -3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e19.66 (14.81, 24.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e25.89 (19.27, 32.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-9.55 (-14.43, -4.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e30.07 (23.49, 39.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e28.45 (22.42, 33.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-26.85 (-29.59, -24.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e31.93 (24.39, 41.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e29.1 (22.62, 34.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-17.96 (-21.82, -14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e27.7 (20.44, 37.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e29.7 (22.04, 35.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-11.16 (-17.17, -5.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e23.02 (16.63, 33.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e28.47 (21.06, 37.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e10.17 (6.06, 14.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e20.86 (16.67, 24.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15.46 (12.18, 18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-14.43 (-16.39, -12.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e25.82 (20.61, 30.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e20.85 (16.65, 25.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-15.54 (-17.35, -13.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e23.07 (18.3, 27.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e19.97 (15.69, 24.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-17.63 (-19.34, -15.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e25.57 (19.14, 32.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e24.65 (18.16, 33.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e31.68 (27.83, 35.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e28.03 (25.31, 30.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20.96 (18.12, 24.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-43.58 (-44.34, -42.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e29.77 (26.05, 33.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e23.21 (19.58, 26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-51.34 (-52.16, -50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e21.48 (17.7, 24.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e19.36 (15.59, 22.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-53.52 (-54.56, -52.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e23.57 (18.94, 27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e25.88 (20.07, 29.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-41.58 (-43.06, -40.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e33.67 (27.2, 40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e32.25 (26.78, 39.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e10.31 (1.68, 18.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e42.76 (35.51, 50.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e40.38 (34.12, 49.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e17.51 (1.47, 33.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e42.25 (34.67, 49.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e43.4 (35.34, 51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e23.27 (-2.31, 48.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e70.73 (55.52, 84.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e74.83 (58.61, 90.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e20.42 (15.55, 25.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTropical Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15.57 (13.87, 16.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9.73 (8.46, 10.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-17.49 (-19.72, -15.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e18.22 (15.81, 19.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e10.35 (8.62, 11.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-7.05 (-9.47, -4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e17.07 (14.05, 18.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e9.02 (6.93, 10.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e4.41 (-2.3, 11.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e20.11 (15.9, 22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e12.19 (8.84, 14.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e21.4 (17.26, 25.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39.91 (36.54, 42.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.06 (19.39, 24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-11.32 (-13.36, -9.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e45.81 (40.42, 49.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e26.81 (22.6, 30.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-3.16 (-5.8, -0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e39.5 (33.1, 43.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e27.1 (22, 30.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e2.77 (-5.83, 11.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e28.77 (22.95, 31.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e23.97 (18.8, 27.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e11.88 (8.77, 14.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e56.24 (43.88, 75.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e62.04 (49.6, 81.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-44.72 (-45.24, -44.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e58.51 (45.65, 78.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e68.75 (53.33, 91.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-41.46 (-42.25, -40.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e47.27 (35.5, 63.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e58.27 (43.06, 78.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-31.4 (-32.55, -30.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e62.17 (44.36, 82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e74.86 (52.21, 97.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-16.7 (-18.44, -14.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that globally, the ratio of OA-to-NOA ASSR decreased, from 1.93 in 1990 to 1.87 in 2019. However, this ratio was higher at each older OA-age-group (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Across the eight OA age-groups, the ratio of OA-to-NOA ASSR for OA ages 95-years-and-older was highest (2.49) in 2019\u0026mdash;a 33\u0026middot;13% increase from 1.87 in 1990 (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eASSR ratio for individuals age 60 and older by 5-year groups relative to individuals ages 10 to 59 years as well as the percent change in ASSR ratios between 1990 and 2019.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e60 to 64 vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e65 to 69 vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e70 to 74 vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e75 to 79 vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-6.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-22.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-24.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-16.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-31.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-17.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-13.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-15.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-20.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-25.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-24.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-28.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-31.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-7.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e24.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-31.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-24.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-19.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-25.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-33.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-44.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-18.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-24.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-33.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-36.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-39.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-35.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTropical Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-22.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-28.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-32.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-23.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e80 to 84 vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e85 to 89 vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e90 to 94 vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e95 and above vs 59 and below ASSR ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePercent change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePercent\u0026nbsp; change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e33.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e57.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e64.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e63.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e35.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-27.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-27.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-21.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-29.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-30.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-33.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e37.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e30.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-19.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-13.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-5.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e58.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-53.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-60.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-62.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-52.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e61.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e68.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e59.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e83.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e48.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-26.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-23.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e23.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTropical Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-32.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-38.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-43.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-34.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-17.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e17.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e31.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRegional patterns of older-adult suicide, 1990\u0026ndash;2019\u003c/h2\u003e \u003cp\u003eIn 2019, South Asia had the greatest number of OA-suicides (223,958, 211,497 to 236,418), followed by East Asia (128,469, 121,010 to 135,929) and East Europe (57,009, 53,997 to 60,020) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). East Europe had the highest OA ASSR in 2019 (30.89 per 100 000, 29.26 to 32.52), followed by the high-income Asia-Pacific (23.15 per 100 000, 22.52 to 23.79) and the Southern Sub-Saharan African regions (19.04 per 100 000, 17.42 to 20.66). East Asia (-55.57%, -55.68% to -55.46%), Southeast Asia (-32.22%, -32.39% to -32.05%), and South Asia (-31.49%, -31.66% to -31.33%) were the regions with the greatest percent-decline in OA ASSR. The regions with greatest OA-ASSR increases were Central Latin America (32.07%, 31.85\u0026ndash;32.29%), Andean Latin America (20.94%, 20.45\u0026ndash;21.43%) and the high-income Asia-Pacific region (17.87%, 17.65\u0026ndash;18.09%). There was variability in the ratio of OA-to-NOA ASSR across regions and by year. In 2019 the ratios were lowest in in Australasia (0.88) and East Europe (0.95) and much greater than 1 in Western Sub-Saharan Africa (6.12), East Sub-Saharan Africa (6.11) and Central Sub-Saharan Africa (5.14). Western Sub-Saharan Africa had the highest regional-ratio (5.7) in 1990.\u003c/p\u003e \u003cp\u003eThe first graph of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays OA-to-NOA ASSR ratios across GBD regions from 1990 to 2019. The last two graphs illustrate OA-to-NOA ASSR ratios, from 1990 to 2019, by sex. The OA-to-NOA ASSR-ratios of Sub-Saharan Africa, Southeast Asia, East Asia, and Oceania were significantly higher than the global OA-to-NOA ASSR-ratio. From 1990 to 2019 OA-to-NOA ASSR-ratios increased in Sub-Saharan Africa, where the ratio exceeded 5 after 2012. By contrast, in Southeast Asia, East Asia, and Oceania, OA-to-NOA ASSR-ratios declined after 2011, after increases from 2000 to 2006.\u003c/p\u003e \u003cp\u003eThe OA-to-NOA ASSR-ratios of high-income countries and of Latin America and the Caribbean, North Africa and the Middle East, Central Europe, East Europe, and Central Asia decreased slightly or remained constant from 1990 to 2019. The older-adult to non- OA-to-NOA ASSR-ratio of South Asia increased slowly though it was the lowest on average of all regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCountry/nation/territory absolute and age-relative patterns of older-adult suicide, 1990\u0026ndash;2019\u003c/h2\u003e \u003cp\u003eBetween 1990 and 2019 the largest increase in OA ASSR occurred in Armenia (181\u0026middot;09%, 180.25\u0026ndash;181.93%), the Republic of Korea (119.33%, 116.87\u0026ndash;121.80%), Lesotho (97.17%, 95.48\u0026ndash;98.86%), Jamaica (95.32%, 94.54\u0026ndash;96.09%), and Taiwan (a province of China) (76.39%, 95% uncertainty interval, 75.85\u0026ndash;76.93%). The largest increase in OA ASSR were recorded in Denmark (-61.47%, -61.54%, -61.39%), Equatorial Guinea (-58.91%, -59.26% to -58.57%), Ethiopia (-57.49%, 95% uncertainty interval: -57.65% to -57.32%), China (-57.11%, -57.22% to -57.01%), and Hungary (-53.41%, -53.53% to -53.30%) (see Appendix, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2019 several Sub-Saharan African countries had the highest OA-to-NOA ASSR-ratios: Ethiopia (7.44), Nigeria (7.07), South Sudan (6.87), Chad (6.80), and Uganda (6.79) (see Appendix Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Mongolia (0.51) Mauritius (0.62), Kiribati (0.69), New Zealand (0.76), Uzbekistan (0.77) are the countries with the lowest OA-to-NOA ASSR-ratios (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOlder-adult absolute and age-relative suicide-patterns by sex\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows patterns of OA age-specific suicide-rates, from the youngest to the oldest OA age-group, across GBD regions, by sex. Women\u0026rsquo;s age-specific suicide-rates were significantly lower than men\u0026rsquo;s age-specific suicide-rates across GBD regions. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, OA-to-NOA ASSR-ratios tended to be higher in older OA-groups. Across OA age-groups women\u0026rsquo;s age-specific OA-to-NOA ASSR-ratios were significantly higher than men\u0026rsquo;s in Central Europe, East Europe, Central Asia, Southeast Asia, East Asia, and Oceania, and Sub-Saharan Africa. This female-male difference was particularly pronounced among OA ages 95-years-and-older.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays OA-to-NOA ASSR-ratios by sex, across GBD regions in 2019. Women\u0026rsquo;s OA-to-NOA ASSR-ratios were higher than men\u0026rsquo;s across OA age-groups. OA-to-NOA ASSR-ratios were highest in Sub-Saharan Africa and in Southeast Asia, East Asia, and Oceania, relative to other GBD regions. OA-to-NOA ASSR-ratios increased with increasing age for women and men, until ages 84-to-89-years, and then they decreased, except among OA women in Sub-Saharan Africa.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAbsolute and age-relative patterns of older-adult suicide by the sociodemographic position of countries/nations\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays a scatterplot of the correlations between OA-to-NOA ASSR-ratios and countries/nations/territories\u0026rsquo; sociodemographic position, measured via the SDI, across seven GBD regions, in 2019. The OA-to-NOA ASSR-ratio was negatively correlated with the SDI (correlation coefficient r = -0.64, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This correlation suggests the OA-to-NOA ASSR-ratio is higher in lower sociodemographic-position countries/nations/territories. Lower-SDI Sub-Saharan African countries/nations/territories stood out for their higher OA-to-NOA ASSR-ratios. By contrast, high-income, Asian and European countries/nations had both higher SDI and lower OA-to-NOA ASSR-ratios.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates changes in OA ASSR across eight time-periods between 1990 to 2019, and across five SDI-levels. It shows the percent-change in OA ASSR across four 8-year intervals (1990\u0026ndash;1997, 1998\u0026ndash;2005, 2006\u0026ndash;2012; 2013\u0026ndash;2019), by quintiles of SDI (Low, Low-Middle, Middle, High-Middle, High), and across three OA age-groups (ages 60-to-69-years, ages 70-to-79-years, and ages 80-years-and-older). Most distributions had well-balanced bell-shapes. There were not significant variations in distribution of percent-change among the three OA age-groups. Over the years, distributions-variability shrunk\u0026ndash;which made the bell-shaped distributions center toward zero. The median percent-change of the last three time-periods (1998\u0026ndash;2005, 2006\u0026ndash;2012, and 2013\u0026ndash;2019) across the three OA age-groups was negative and approached zero. This indicates that in more than half of the 204 countries/nations/territories, OA ASSR declined. In the first three 7-year intervals (1990\u0026ndash;1997, 1998\u0026ndash;2005, 2006\u0026ndash;2012), countries/nations/territories with above the middle-value of SDI tended to have declining OA ASSR. However, in the last 7-year interval (2013\u0026ndash;2019), there were equal number of countries where OA ASSR declined and where the OA ASSR increased, across rising SDI levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to examine, by sex, and at global, region, and country/nation/territory levels, and over nearly three decades, both absolute and age-relative patterns of OA suicide. Based on past studies, we expected age-relative and absolute rates of OA suicide to provide different kinds of information. Our expectation was supported.\u003c/p\u003e\n\u003cp\u003eBetween 1990 and 2019 the absolute number of OA suicides increased globally, even though both OA ASSR and the OA-to-NOA ASSR-ratio decreased substantially. These global patterns may be due to population growth and to changes in the population age-structure. Because the number of older adults and their proportion in the population are growing, the number of OA suicides will likely continue to grow.\u003csup\u003e2\u003c/sup\u003e To better understand the patterns described above, a direction for future studies is to examine changes in the leading causes of OA death by country/nation/territory.\u003c/p\u003e\n\u003cp\u003eThere was considerable variation in OA absolute and age-relative suicide-rates by world-region, country/nation/territory, by sex, and over time. This variability in OA-suicide-patterns, together with the substantial decrease in older-adult absolute and age-relative suicide-rates, challenges simplistic theories of older-adult-suicide, including the Anglophone-countries-based theory that OA suicide is a relatively understandable, if not an inevitable response to aging-related adversities (e.g., increases in illnesses and disabilities).\u003csup\u003e14\u003c/sup\u003e Between 1990 and 2019 OA age-relative suicide-rates were highest in Sub-Saharan Africa and East Asia. OA age-relative suicide-rates were generally higher in countries with a lower socioeconomic-position. These findings challenge the dominant belief that OA suicide is a problem of high-socioeconomic-position countries.\u003c/p\u003e\n\u003cp\u003eOA absolute suicide-rates were significantly lower among women than among men in all regions. At the same time, OA age-relative suicide-rates were significantly higher in women than in men in many GBD regions. Also, in Southeast Asia, East Asia, and Oceania, OA women\u0026rsquo;s age-relative suicide-rates increased monotonously while OA men\u0026rsquo;s started to drop after 2010. Furthermore, in Sub-Saharan Africa, OA women\u0026rsquo;s age-relative suicide-rates kept growing, though with large fluctuation, while OA men\u0026rsquo;s age-relative suicide-rates increased monotonously. These findings indicate that in many GBD regions, particularly in lower socioeconomic-position regions, women\u0026rsquo;s likelihood of suicide is greater in older adulthood than prior to older adulthood; and also that, in those regions, OA women\u0026rsquo;s relative suicide-risk is on the increase. There are several possible explanations for these patterns. One is that there have been improvements in the recording of female suicide. Female suicide is less likely to be recognized as such, and/or to be reported than male suicide,\u003csup\u003e15\u003c/sup\u003e especially in communities where women\u0026rsquo;s agency is systemically-restricted by men, and where female suicide is viewed as a form of defiance of male de-jure or de-facto ownership of women.\u003csup\u003e16\u003c/sup\u003e Another explanation is that there has been an increase in female suicide in lower-socioeconomic-position regions. A study found that the relatively-high suicide-rates of women in low- and middle-income countries, as compared to the suicide-rates of women in high-income countries, are predicted by the greater institutional-discrimination that women experience in low- and middle-income countries\u0026ndash;including restricted access to productive and financial assets and justice, and lesser family-law rights.\u003csup\u003e17\u003c/sup\u003e Because the effects of institutional discrimination accumulate over the lifespan, institutional discrimination often weighs heavier on OA women. In an increasingly-connected world, recent generations of women living in low socioeconomic-position countries with high levels of institutional discrimination may be more aware than earlier generations of women of the human-rights violations that they experience, with suicide being their desperate protest against the human-rights abuses, at least, in countries/nations/territories where female suicide follows the protest-script.\u003csup\u003e16,17\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe findings that, in many GBD regions, OA women\u0026rsquo;s age-relative suicide-rates were significantly higher than OA men\u0026rsquo;s age-relative suicide-rates challenge the dominant belief that in late-adulthood, suicide is men\u0026rsquo;s problem. This belief is supported by the absolute OA suicide-rates\u0026mdash;with OA men having higher absolute suicide-rates. The belief that suicide is men\u0026rsquo;s problem has become dominant also because men, particularly men of European descent, have the highest suicide-rates in high-income, Anglophone countries,\u003csup\u003e14\u003c/sup\u003e given that studies from high-income, Anglophone countries are over-represented in the scientific literature. This study\u0026rsquo;s examination of both age-relative and absolute OA suicide-rates, across countries/nations/territories, regions, and globally, provides a window on OA women\u0026rsquo;s and men\u0026rsquo;s different ways of suicide-vulnerability.\u003c/p\u003e\n\u003cp\u003eInterpretations of this study\u0026rsquo;s findings require consideration of its method\u0026rsquo;s strengths and weaknesses. A strength is that it used GBD data that are comparable across country/nation/territory and region, and over time. Another strength is that it examined absolute and age-relative OA suicide-data by sex, across different scales of location.\u003c/p\u003e\n\u003cp\u003eLimitations of this study result from GBD-2019 data-gaps and data-quality variability. Data-gaps and data-quality variations are more likely in lower-socioeconomic-position countries more than in higher-socioeconomic-position countries. For example, many Sub-Saharan countries have limited vital-registration data. Data from neighboring countries are used to impute the missing data, leading to more homogenous estimates in Sub-Saharan Africa than in other regions.\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAnother issue is selective suicide-underreporting and misclassification, by country/nation/territory. While suicide-underreporting and misclassification occur in all countries, they are more common in countries/nations/territories where suicide is subject to more negative cultural and religious sanctions.\u003csup\u003e18\u003c/sup\u003e Also, as noted earlier, underreporting and misclassification tend to be more of a problem in terms of women\u0026rsquo;s suicides,\u003csup\u003e15\u003c/sup\u003e especially in cultures where there is a strong prohibition of women\u0026rsquo;s suicide.\u003csup\u003e16\u003c/sup\u003e For these reasons, the fact that the GBD definition of suicide encompasses both suicide and intentional self-harm-deaths is an asset of this study.\u003c/p\u003e\n\u003cp\u003eOther limitations have to do with construct operationalization. We set age 60 as the older-adulthood threshold, across countries. This decision was necessary but problematic. One reason is that there is substantial variability in longevity, by sex and by country/nation/territory. Another reason is that being age 60-and-older has different meanings and involves different experiences, depending on sex and culture, with implications for suicide.\u003c/p\u003e\n\u003cp\u003eUsing the SDI as a measure of country/nation/territory socioeconomic-position is a limitation. The SDI is a composite measure of total fertility-rate for persons\u0026thinsp;\u0026gt;\u0026thinsp;25-years, mean education-years for persons\u0026thinsp;\u0026gt;\u0026thinsp;15-years, and per-capita income. Given the variability in women\u0026rsquo;s and men\u0026rsquo;s education, paid work, and income, by country/nation/territory and region, due to discrimination against women that varies by location, the SDI provides different information about women\u0026rsquo;s and men\u0026rsquo;s socioeconomic position, depending on location and culture. It is also a limitation to use, in a study of late-adulthood suicide, a socioeconomic-position measure that includes fertility as one of its three indices.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eOverview\u003c/h2\u003e\u003cp\u003eThis study used estimates from the 2019 GBD-study to assess OA absolute and age-relative suicide-rates by sex, region and country/nation/territory, from 1990 to 2019. The threshold for being classified OA was age 60-years-and-older. NOA was defined as ages 10–59 years. Data on people younger than age 10-years were not included in this study because of the very low numbers of suicides reported, in most locations, among individuals under the age of 10, and also because of the difficulty in determining suicide intent in young children.\u003csup\u003e10\u003c/sup\u003e OA age-relative suicide-rates were the ratios of OA-to-NOA age-standardized suicide-rates (ASSR). OA ASSR and OA-to-NOA ASSR-ratios were correlated with country/nation/territory’s socioeconomic position using the Socio-Demographic Index (SDI). The 2019 GBD-study includes mortality estimates for 363 death-causes by sex, age-group, country/nation/territory, and region, in 204 countries/nations/territories, from 1990 to 2019. The 2019 GBD-study uses the International-Classification-of-Disease (ICD) definition of suicide as a death by intentional self-harm by a diversity of means (ICD-10 codes X60-X84).\u003c/p\u003e\u003ch2\u003eData and measures\u003c/h2\u003e\u003ch2\u003eSuicide data and cause-of-death (COD) estimates\u003c/h2\u003e\u003cp\u003eEstimated suicide numbers and rates by sex, age-group, region, and country/nation/territory were downloaded via the GBD-results query-tool developed by the Institute of Health Metric and Evaluation (IHME) of Washington University, Seattle, USA. Cause-of-death (COD) information was extracted from the raw data collected by the IHME or its collaborators through vital autopsy (VA) and vital registration (VR) systems. VA-systems were utilized to collect the COD information when data from VR-systems were unavailable. The GBD 2019 implemented methods for data quality (e.g., star-rating systems) and regression modelling to standardize and re-classify cases with inappropriate or ambiguous death-codes or without specific causes of death. Details about the GBD rating-systems and modeling techniques are in Mohsen et al,.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe 2019 GBD-study implemented a weighted, COD-ensemble-models framework to estimate suicide-rates. These models adjust each component-model weight for death estimates from countries with low data-quality, through adjacent regions or time periods. Weighted, COD-ensemble models generate better COD estimates than other models.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eSocio-Demographic Index (SDI)\u003c/h2\u003e\u003cp\u003eThe SDI is calculated through the geometric mean of total fertility-rate for persons under the age of 25 (TFU \u0026lt; 25), mean years of education for persons older than age 15 (EDU \u0026gt; 15), and lag-distributed income (LDI) per capita. These three factors were regressed against life expectancy. The three SDI-variables values, that related to minimum and maximum life-expectancy value, were rescaled to the SDI index that ranged from lowest (0) to highest value (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eAnalytic strategies\u003c/h2\u003e\u003ch2\u003eAge-standardized suicide-rates\u003c/h2\u003e\u003cp\u003eIn the GBD 2019-data ASSR were based on age-standardized world-populations. Age-standardized world-populations were calculated using non-weighted means of 2019 age-specific proportional distributions from GBD 2019 population-estimates for countries/nations/territories with a population greater than 5\u0026nbsp;million people in 2019.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e \u003cem\u003eOlder-adult to non-older-adult ratios of age-standardized suicide-rates.\u003c/em\u003e \u003c/p\u003e\u003cp\u003eOA-to-NOA ASSR-ratios were calculated by dividing the OA ASSR by the NOA ASSR. Higher OA-to-NOA ASSR-ratios indicate that OA have higher suicide-rates than NOA.\u003c/p\u003e\u003ch2\u003eTemporal change of suicide mortality\u003c/h2\u003e\u003cp\u003eIn 1990 and 2019, the total-population suicide-rate at each age-group was calculated through dividing the number of suicides by the ASSR. The number of suicides across 5-year age-groups above age 10-years was summed up. The ASSR for all age-groups was obtained by dividing the total number of suicides by total population. The difference in ASSR between 1990 and 2019 was calculated and divided by the ASSR in 1990, to obtain the changing proportion.\u003c/p\u003e\u003ch2\u003eUncertainty analyses\u003c/h2\u003e\u003cp\u003eTo estimate suicide-rates, and their 95% uncertainty interval (UI), 1000 simulated suicide-rates draws were generated from the posterior suicide-rates distribution conditional on age, sex, regions, countries/nations/territory, and year. The 95% uncertainty intervals for 1000 draws were determined using the 2.5th and 97.5th percentiles. The suicide-rates point-estimates were estimated from the mean of these draws. In the analysis of suicide-rates temporal-changes, a criterion for statistical significance was employed. A change was deemed statistically significant if the uncertainty interval of the percentage-change did not intersect zero.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eAnalyses\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAll analyses complied with the Guidelines for Accurate and Transparent Health Estimates Reporting of the World Health Organization.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eResearch Ethics\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThis study was reviewed and approved by the University of Washington’s Institutional-Review-Board, Seattle, USA. It was deemed exempt because it used de-identified, aggregate, 2019 GBD-study data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates the value of examining both absolute and age-relative rates of OA suicide, globally, and by region and by country/nation/territory. This study’s findings show that absolute and age-relative rates of OA suicide provide different information.\u003c/p\u003e\n\u003cp\u003eThis study contributes to the growing evidence that suicide is not a homogenous phenomenon.\u003csup\u003e2\u003c/sup\u003e Specifically, it expands to older adults the evidence that absolute and relative suicide-rates vary by sex, and depending on culture and time-period. The variability in OA suicide-patterns challenges the presumed universality of dominant, Anglophone-countries-based theories of OA suicide. The fact that OA suicide is not a uniform phenomenon means that OA suicide theory, research, and prevention require an intersectional approach,\u003csup\u003e19\u003c/sup\u003e that, at a minimum, considers sex, culture, and time-period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study’s findings challenge two other myths of OA suicide, with implications for prevention. One is that women are protected from suicide. The other is that OA suicide is a problem of high socioeconomic-position countries/nations/territories. This study’s findings call attention to OA women (particularly OA women living in Sub-Saharan Africa, Southeast Asia, East Asia, and Oceania) a so-far unnoticed, suicide-vulnerable population.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQC and FS conceptualized the study. QC, ZZ, ZC, SC and FS provided critical methodological input. ZZ and ZC curated the data and contributed to the formal analysis with the guidance of SF. QC, ZZ, SC and SF wrote the first draft and the other authors made critical revisions of the manuscript. SC, BL, DW, HL, XY and PY contributed to the clinical interpretation. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the the GBD website (https://www.healthdata.org/research-analysis/gbd).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Committee approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the University of Washington’s Institutional-Review-Board, Seattle, USA. It was deemed exempt because it used de-identified, aggregate, 2019 GBD-study data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of Funding Source\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partly supported by the National Social Science Fund of China (grant No. 21CSH057), the Strategic Priority Research Program of Chinese Academy of Sciences (grant No. XDB 38040200), Shenzhen Science and Technology Program (grant No. KQTD20190929172835662) and Xiamen One Heart Charity (grant No.HX2023027).\u0026nbsp;The funder had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization (WHO). Suicide. 2024/08/23 2023. https://www.who.int/news-room/fact-sheets/detail/suicide (accessed Feb 5th 2024).\u003c/li\u003e\n \u003cli\u003eYip PSF, Zheng Y, Wong C. Demographic and epidemiological decomposition analysis of global changes in suicide rates and numbers over the period 1990-2019. Injury prevention: journal of the International Society for Child and Adolescent Injury Prevention 2022; 28(2): 117\u0026ndash;24.\u003c/li\u003e\n \u003cli\u003eChristensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. Lancet (London, England) 2009; 374(9696): 1196\u0026ndash;208.\u003c/li\u003e\n \u003cli\u003eCanetto SS. Gender and suicide in the elderly. Suicide and Life-Threatening Behavior 1992; 22(1): 80\u0026ndash;97.\u003c/li\u003e\n \u003cli\u003eDe Leo D, Spathonis K. Suicide and Suicidal Behaviour in Late Life. Suicidal behaviour: Theories and research findings. Ashland, OH, US: Hogrefe \u0026amp; Huber Publishers; 2004: 253\u0026ndash;86.\u003c/li\u003e\n \u003cli\u003eShah A. The relationship between suicide rates and age: an analysis of multinational data from the World Health Organization. International psychogeriatrics 2007; 19(6): 1141\u0026ndash;52.\u003c/li\u003e\n \u003cli\u003eVan Orden K, Conwell Y. Suicides in late life. Current psychiatry reports 2011; 13(3): 234\u0026ndash;41.\u003c/li\u003e\n \u003cli\u003eConwell Y. Suicide Later in Life: Challenges and Priorities for Prevention. American Journal of Preventive Medicine 2014; 47(3, Supplement 2): S244\u0026ndash;S50.\u003c/li\u003e\n \u003cli\u003eChang Q, Conwell Y, Wu D, Guo Y, Yip PSF. A study on household headship, living arrangement, and recipient of pension among the older adults in association with suicidal risks. Journal of affective disorders 2019; 256: 618\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eMohsen N. Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016. BMJ 2019; 364: l94.\u003c/li\u003e\n \u003cli\u003eForeman KJ, Lozano R, Lopez AD, Murray CJL. Modeling causes of death: an integrated approach using CODEm. Population Health Metrics 2012; 10(1): 1.\u003c/li\u003e\n \u003cli\u003eDicker D, Nguyen G, Abate D, et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 2018; 392(10159): 1684\u0026ndash;735.\u003c/li\u003e\n \u003cli\u003eStevens GA, Alkema L, Black RE, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. The Lancet 2016; 388(10062): e19\u0026ndash;e23.\u003c/li\u003e\n \u003cli\u003eCanetto SS. Suicide: Why are older men so vulnerable? Men and Masculinities 2017; 20(1): 49\u0026ndash;70.\u003c/li\u003e\n \u003cli\u003eRockett IRH, Caine ED, Connery HS, et al. Unrecognised self-injury mortality (SIM) trends among racial/ethnic minorities and women in the USA. Injury prevention: journal of the International Society for Child and Adolescent Injury Prevention 2020; 26(5): 439\u0026ndash;47.\u003c/li\u003e\n \u003cli\u003eCanetto SS. Suicidal behaviors among Muslim women: Patterns, pathways, meanings, and prevention. Crisis: The Journal of Crisis Intervention and Suicide Prevention 2015; 36(6): 447\u0026ndash;58.\u003c/li\u003e\n \u003cli\u003eCai Z, Canetto SS, Chang Q, Yip PSF. Women\u0026apos;s suicide in low-, middle-, and high-income countries: Do laws discriminating against women matter? Social science \u0026amp; medicine (1982) 2021; 282: 114035.\u003c/li\u003e\n \u003cli\u003ePritchard C, Amanullah S. An analysis of suicide and undetermined deaths in 17 predominantly Islamic countries contrasted with the UK. Psychological Medicine 2007; 37(3): 421\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eCanetto SS. Language, culture, gender, and intersectionalities in suicide theory, research, and prevention: Challenges and changes. Suicide \u0026amp; life-threatening behavior 2021; 51(6): 1045\u0026ndash;54.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4365103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4365103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSuicide-rates are highest among older adults. Yet, older-adult suicide has been under-studied, particularly in relation to suicide in other age-groups, and by sex and location. Age-standardized suicide-rates (ASSR) of older-adults (OA) (ages 60-years-and-older) and non-older-adults (NOA) (ages 10-59 years), and the ratio of OA-to-NOA ASSR, for the 1990-2019 period, were calculated based on 2019 Global-Burden-of-Disease (GBD) data. OA absolute and age-relative ASSR were examined by country/nation/territory Socio-Demographic Index (SDI). There was a significant negative-correlation between OA-to-NOA ASSR and SDI. OA-to-NOA ASSR-ratios were larger in women in many regions, though OA ASSR were lower among women. The finding that OA had higher age-relative suicide-rates in lower socioeconomic-position regions challenges the belief that OA-suicide is a problem of higher socioeconomic-position regions. The fact that in many regions OA age-relative suicide-rates were higher in women than in men challenge the belief that OA women are protected from suicide.\u003c/p\u003e","manuscriptTitle":"Absolute and age-relative suicide-rates for women and men age 60 years and older, at the global, region, and nation level, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019 data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 12:49:31","doi":"10.21203/rs.3.rs-4365103/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac43f930-0829-44f3-8f4a-0afa490e823b","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32228816,"name":"Health sciences/Medical research/Epidemiology"},{"id":32228817,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2025-07-11T09:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-22 12:49:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4365103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4365103","identity":"rs-4365103","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}