Global, Regional and National Burden of Stroke among Perimenopausal Women: Trends from 1990 to 2021 and Projections up to 2044

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Utilizing GBD 2021 data, this study reveals that while global stroke prevalence, incidence, and DALY cases in perimenopausal women significantly increased from 1990–2021, age-standardized rates declined overall. Middle-high and high-SDI regions demonstrated polarized trends - the UAE saw the sharpest case increases while Estonia and Latvia achieved notable reductions. Joinpoint regression analysis of these 32-year trends suggests an impending reversal: projections indicate both disease burden rates and absolute case numbers will transition from historical declines to future increases by 2044. Despite previous improvements in age-adjusted metrics, the persistent rise in cases coupled with these forecasts underscores the critical need for targeted interventions to mitigate stroke's growing impact on perimenopausal women worldwide. Health sciences/Diseases Health sciences/Health care Health sciences/Neurology Stroke Perimenopausal women GBD Trend projection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Stroke is the second leading cause of human death worldwide after ischemic heart disease and the third leading contributor to disability - adjusted life years(DALYs) [1] , and it is one of the key diseases of concern in global public health. Although the age - standardized incidence, prevalence, mortality, and DALYs rate decreased between 1990 and 2019, the actual number of cases increased, and the global burden remains substantial [2] . In 2021, there were 757,234.61 age-standardized stroke cases worldwide, 8,720,000 DALYs, and 122,742 stroke-related fatalities [3] . The perimenopause in women usually occurs between the ages of 45 and 55. During this period, the ovaries gradually secrete less estrogen and progesterone, and it is a crucial stage of transition from sexual maturity to old age [4] . The perimenopause, which lasts until a year beyond menopause and is frequently accompanied by symptoms including mood swings, hot flashes, and night sweats, begins when a woman exhibits clinical indicators of irregular menstruation or hormonal changes, according to the World Health Organization (WHO). Studies have shown that in most age groups, the incidence and mortality of stroke in men are higher than those in women [5] , which may be related to the fact that smoking and alcohol consumption are more common in men than in women [6] . However, women have a higher lifetime risk of stroke than men [7] . In perimenopausal women, oxidative stress, vascular inflammation, and dysfunctions of ERα and eNOS lead to estrogen deficiency, accelerating vascular aging [8] , increasing the burden of cardiovascular and cerebrovascular diseases, and thus raising the incidence of stroke [9] . Currently, the majority of studies focusing on perimenopausal women mainly revolve around their vasomotor symptoms, mental health, and cognitive function, while research on stroke in perimenopausal women(SPW) is still insufficient. As a result, the attention given to stroke in this group of women and the resources of the healthcare system are inadequate, indirectly leading to a lack of standardized care for this population. Therefore, this article will comprehensively describe and analyze the overall incidence and changing trends of SPW from 1990 to 2021 based on the data of the GBD2021 study. In addition to analyzing temporal patterns and forecasting development trends from 2022 to 2044, it primarily contains the prevalence, incidence, and disability-adjusted life years of SPW at the national, regional, and worldwide levels from 1990 to 2021. Methods Data Source This study used data from the Global Burden of Disease 2021 (GBD2021) on SPW. Over 1,500 partner institutions worldwide are involved in this database, which is led by the Institute for Health Metrics and Evaluation (IHME) in the United States. It offers thorough data support for 369 diseases and injuries and 88 risk factors, and it includes 204 countries and regions in addition to dozens of sub-national locations. The GBD conducts multi - dimensional disease and risk factor analyses of global regions and is continuously updated with a long time - span. The timely update and forward - looking nature of its data can effectively guide global health policies, making it one of the most influential health data analysis projects globally. In this study, relevant data on SPW from 1990 to 2021 were extracted from the GBD, with a focus on indicators such as prevalence, incidence, and DALYs to analyze the disease burden and development trends of SPW. This study only involves data analysis, and the data are rigorous and publicly available without personal information, so there are no ethical requirements. Disease Definition Stroke is defined by the WHO as an acute brain dysfunction caused by cerebrovascular lesions, with symptoms lasting for 24 hours or more or resulting in death [10] . In the GBD, disease causes are classified into 4 levels. Ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage are the three subtypes of stroke, which is a level 2 cardiovascular condition and a level 3 cause. Research Subjects This study focuses on SPW. Data on this group's incidence, prevalence, and DALYs were taken from the GBD and used in the study. In the GBD, prevalence refers to the proportion of people in a population who have a certain disease, which can assess the static distribution of the disease burden; incidence refers to the proportion of new cases of a certain disease in a population, which can assess the dynamic risk of the disease burden; DALYs, or years of life lost owing to premature death (YLLs) plus years lived with disability (YLDs), or the entire loss of healthy life, are a measure of the overall disease burden [11] . DALYs = YLLs + YLDs Socio - Demographic Index The Socio-Demographic Index (SDI), which is based on per capita income, average years of education, and total fertility rate, is a comprehensive statistic for assessing the social development level of nations and regions. In GBD2021, the SDI was divided into 5 quintiles from low to high: low, low - middle, middle, middle - high, and high, representing 5 levels of regional comprehensive development. This study explored the linear relationship between the SDI and the disease prevalence, incidence, and DALYs to determine the impact of the SDI on the disease burden. Research Indicators 1. Estimated annual percentage change The Estimated annual percentage change (EAPC) refers to the average annual percentage change of a certain disease over a certain period. A positive value indicates an upward trend year by year, while a negative value indicates a downward trend. The calculation of EAPC takes time as a variable, fits the natural logarithm of each observation value into a straight line, and calculates based on the slope of this line [12] . Its 95% confidence interval (95%CI) is commonly used to represent its fluctuation range. EAPC =( eβ − 1)×100% 2. Percentage Change An indicator for calculating the relative difference between data at two points in time is the percentage change. To represent the changes in prevalence, incidence, and DALYs, the percentage change value was calculated in this study by dividing the difference between the number of cases in 1990 and 2021 by the number of cases in 1990. [11] 。 Percentage change=(2021cases-1990cases)/1990cases×100% 3. Age - Standardized Rate To guarantee comparability across various populations or geographical areas, the Age-Standardized Rate (ASR) is a metric that eliminates variations in the population's age distribution. This study used ASR to calculate the disease burden of stroke in perimenopausal women, and used APC to analyze key turning points and evaluate the change trend within a certain period; AAPC was used to analyze the weighted average annual change rate during the entire study period. From a statistical standpoint, an upward trend within the designated interval is indicated if the estimated value of APC or AAPC and the lower limit of its 95% CI surpass zero. On the other hand, a negative trend is indicated if the predicted value of APC or AAPC plus the upper limit of its 95% CI is less than zero. The trend is said to be stable when the 95% CI of APC or AAPC is zero. Prediction Analysis To predict the number of disease cases and prevalence from 2022 to 2044, this study used the Nordpred package in R language to establish a linear log - age - time - cohort model. This model effectively controlled the exponential growth trend and restricted the prediction of the linear trend, as well as the estimation method and parameter estimation results. The average weighted prevalence of the disease in 2044 was obtained, and combined with the population predictions of each country and region by the United Nations, the number of cases and ASR in 2044 were predicted [13] . Statistical Analysis In this study, R − 4.4.3 was used to clean, calculate, and plot the data from GBD2021, and the ggplot2 package was used for visual display. Joinpoint − 5.4.0 was used for piece - wise regression and time - series analysis, and its APC and AAPC were used to evaluate the internal and overall trends. Results Global Level At the global level, perimenopausal women saw a substantial rise in the prevalence, incidence, and DALYs of stroke cases. Of these, there was a minor decrease in the number of DALYs instances in areas with High-middle and High SDI (Fig. 1D). In perimenopausal women, for instance, the prevalence of stroke cases rose from 3,640,225 to 6,350,538 between 1990 and 2021, a 74% increase; incidence cases rose from 383,438 to 589,755, a 54% increase; and DALYs cases rose from 6,462,007 to 7,427,573, a 15% increase. Prevalence, incidence, and DALYs rates all trended lower despite the rise in cases; the EAPCs were − 0.7 (95%CI: − 0.73 - − 0.67), − 1.24 (95%CI: − 1.35 - − 1.13), and − 2.12 (95%CI: − 2.2 - − 2.03), respectively (Table 1, Table 2 , Table 3 , Fig. 1D). According to the development trends of the global prevalence rate, incidence rate, and DALYs rate from 1990 to 2021 in Fig. 1, the overall burden showed a downward trend (Fig. 1). However, it is worth noting that this downward trend is gradually slowing down, and there is an upward trend in some regions. SDI Region Level With 2,261,067 instances (95%UI: 2,051,507–2,487,375), 214,460 cases (95%UI: 168,005–266,654), and 2,817,137 cases (95%UI: 2,493,599–3,163,973), respectively, the middle-to-high SDI region had the greatest prevalence, incidence, and DALYs cases in 2021. About one-third of the similar instances worldwide were these cases. Followed by the number of prevalence and incidence cases in East Asia, which were 1,603,007 cases (95%UI: 1,422,796–1,793,184) and 155,573 cases (95%UI: 119,811 − 196,992) respectively. The region with the second - highest number of DALYs cases was the low - middle SDI region, with 2,091,928 cases (95%UI: 1,839,541–2,355,797) (Table 1, Table 2 , Table 3 ). The figure illustrates how, in 2021, the prevalence rate, incidence rate, and DALYs rate grew in inverse proportion to the decline in the SDI, which was accompanied by a decline in the comprehensive evaluation of per capita income, average years of schooling, and total fertility rate. Every SDI region's prevalence rate, incidence rate, and DALYs rate exhibited a declining trend when compared to 1990–2021, and all of the EAPC were negative, suggesting that the illness burden was declining in these areas(Fig. 1E). GBD Region Level Central Asia, Central Sub-Saharan Africa, and Oceania had the greatest rates of DALYs, incidence, and prevalence in 2021. With the exception of a few nations in Oceania, which may be directly tied to the pressures of local life, the majority of these locations are low-SDI or low-middle SDI. The regions with the largest decline in the prevalence rate and incidence rate were Tropical Latin America, with EAPCs of − 2.01 (95%CI: − 2.12 - − 1.89) and − 2.99 (95%CI: − 3.19 - − 2.79) respectively. The region with the largest decline in the DALYs rate was Central Europe, with an EAPC of − 3.65 (95%CI: − 3.87 - − 3.42). These regions usually belong to high - SDI or middle - high SDI regions (Table 1, Table 2 , Table 3 , Fig. 2E). The prevalence rate, incidence rate, and DALYs rate in 1990 were higher than those in 2021 in the remaining regions, and the EAPCs were all less than zero, with the exception of a slight increase in the prevalence rate in High-income North America (EAPC = 0.13, 95%CI: − 0.05–0.31) and the DALYs rate in Southern Sub-Saharan Africa (EAPC = 0.62, 95%CI: 0.19–1.06) (Table 1, Table 2 , Table 3 , Fig. 2A, Fig. 2B, Fig. 2C). Only a few regions, including Central Europe, Eastern Europe, and High-Income Asia Pacific, demonstrated a decline in DALYs, while the majority of regions had increases in prevalence, incidence, and DALYs instances (Fig. 2D). Table 1 TThe prevalence of cases and rates among SPW in 1990 and 2021 in regions, and the trends from 1990 to 2021 in regions. Location Prevalent cases Prevalent rate (per 100 000) 1990 (95%UI) 2021 (95%UI) Percentage change (100%) 1990 (95%UI) 2021 (95%UI) EAPC (95%CI) Global 3640225 (3318432–3968530) 6350538 (5831028–6908845) 0.74 1676.20 (1527.76-1827.45) 1391.16 (1277.26-1513.55) -0.7 (-0.73–0.67) Low SDI 286652 (266462–308835) 570008 (534301–605562) 0.99 1921.79 (1786.08-2070.87) 1572.53 (1474.00-1670.93) -0.76 (-0.79–0.72) Low-middle SDI 651800 (595454–711620) 1348918 (1247926–1460185) 1.07 1661.79 (1517.49-1814.54) 1490.32 (1378.69-1613.25) -0.42 (-0.44–0.4) Middle SDI 1112354 (1005291–1217876) 2261067 (2051507–2487375) 1.03 1768.88 (1598.19-1936.79) 1416.19 (1284.89-1557.99) -0.84 (-0.88–0.81) High-middle SDI 919236 (835199–1004617) 1287474 (1165361–1412575) 0.4 1779.27 (1616.94-1944.41) 1330.85 (1204.65-1460.16) -1.05 (-1.1–1) High SDI 666331 (610586–724426) 877914 (808638–946478) 0.32 1379.56 (1264.04-1499.74) 1204.98 (1109.91-1299.12) -0.53 (-0.59–0.47) Andean Latin America 21191 (20029–22318) 42064 (39857–44355) 0.98 1668.79 (1576.69-1757.67) 1221.78 (1157.59-1288.20) -1.15 (-1.2–1.1) Australasia 9307 (8687–9954) 15011 (14084–16028) 0.61 909.65 (849.01–972.70) 748.30 (702.08-799.02) -0.72 (-0.77–0.68) Caribbean 24801 (23372–26212) 42901 (40571–45131) 0.73 1731.84 (1632.18-1830.14) 1507.21 (1425.29-1585.55) -0.54 (-0.57–0.51) Central Asia 70324 (66303–74360) 104572 (98821–110403) 0.49 2536.75 (2392.17-2683.79) 2005.69 (1895.32-2117.31) -0.9 (-0.96–0.83) Central Europe 112624 (104113–120879) 91083 (84732–97140) -0.19 1596.87 (1476.34-1714.19) 1118.36 (1040.37-1192.59) -1.31 (-1.38–1.24) Central Latin America 92959 (85513–100743) 185195 (170947–199176) 0.99 1729.34 (1590.98-1874.26) 1235.48 (1140.52-1328.80) -1.26 (-1.34–1.19) Central Sub-Saharan Africa 36518 (34269–39007) 80693 (75958–85600) 1.21 2185.37 (2050.86-2334.47) 1834.65 (1726.67-1946.46) -0.64 (-0.67–0.61) East Asia 824292 (733226–914360) 1603007 (1422796–1793184) 0.94 1694.02 (1506.41-1879.29) 1348.81 (1197.61-1508.56) -0.88 (-0.92–0.84) Eastern Europe 298112 (259469–338468) 239362 (211872–267799) -0.2 2025.34 (1765.58-2297.76) 1665.12 (1473.70-1862.96) -0.73 (-0.79–0.67) Eastern Sub-Saharan Africa 116448 (107303–126131) 218505 (203474–234093) 0.88 2261.52 (2083.54-2449.73) 1701.29 (1583.62-1823.37) -1.05 (-1.09–1.01) High-income Asia Pacific 179159 (163250–196123) 176684 (161944–191722) -0.01 1638.28 (1492.84-1793.18) 1240.56 (1137.05-1346.15) -1 (-1.04–0.96) High-income North America 194010 (170881–218498) 341932 (305931–378991) 0.76 1360.16 (1198.10-1532.04) 1478.43 (1322.88-1638.57) 0.13 (-0.05-0.31) North Africa and Middle East 206451 (192785–220549) 472004 (444448–500635) 1.29 1940.17 (1811.47-2073.30) 1561.33 (1469.96-1656.39) -0.82 (-0.87–0.78) Oceania 3611 (3407–3811) 8495 (8047–8946) 1.35 1777.16 (1676.66-1875.98) 1493.67 (1414.75-1573.45) -0.62 (-0.69–0.55) South Asia 498266 (443245–556976) 1094439 (986281–1215843) 1.2 1348.00 (1198.87-1506.41) 1215.21 (1095.06-1349.71) -0.41 (-0.45–0.37) Southeast Asia 389154 (354573–427296) 808240 (738298–883823) 1.08 2262.61 (2060.99-2485.13) 1906.27 (1741.03-2084.52) -0.65 (-0.68–0.62) Southern Latin America 43633 (41154–46251) 51804 (48812–54899) 0.19 1823.60 (1719.75-1933.35) 1260.45 (1187.38-1335.90) -1.36 (-1.44–1.29) Southern Sub-Saharan Africa 37626 (32961–42752) 59185 (52996–66119) 0.57 2142.97 (1876.70-2435.52) 1551.02 (1388.53-1733.15) -1.22 (-1.32–1.12) Tropical Latin America 108114 (96397–120381) 155684 (139386–173191) 0.44 1863.87 (1660.78-2075.22) 1090.47 (976.10-1213.13) -2.01 (-2.12–1.89) Western Europe 257051 (235500–280283) 260406 (241414–280934) 0.01 1125.27 (1030.91-1226.96) 840.68 (779.25-906.93) -1 (-1.04–0.97) Western Sub-Saharan Africa 116574 (107176–126541) 299271 (276321–323807) 1.57 2233.95 (2053.46-2425.38) 1911.11 (1764.61-2068.55) -0.6 (-0.63–0.56) Table 2 The incidence of cases and rates among SPW in 1990 and 2021 in regions, and the trends from 1990 to 2021 in regions. Location Incident cases Incident rate (per 100 000) 1990 (95%UI) 2021 (95%UI) Percentage change (100%) 1990 (95%UI) 2021 (95%UI) EAPC (95%CI) Global 383438 (298359–476303) 589755 (463070–732733) 0.54 176.54 (137.48–219.10) 129.19 (101.51-160.42) -1.24 (-1.35–1.13) Low SDI 33917 (26691–41941) 60595 (48011–74785) 0.79 226.61 (178.54–279.80) 166.76 (132.56-205.37) -1.19 (-1.27–1.11) Low-middle SDI 74062 (57791–91554) 140274 (111467–172746) 0.89 188.54 (147.30-232.62) 154.83 (123.21-190.29) -0.75 (-0.79–0.7) Middle SDI 117675 (90960–146455) 214460 (168005–266654) 0.82 187.00 (144.70-232.40) 134.34 (105.28-166.99) -1.35 (-1.47–1.23) High-middle SDI 98986 (76637–123431) 114741 (88457–145526) 0.16 191.49 (148.11-239.01) 118.60 (91.42-150.43) -1.85 (-2.02–1.69) High SDI 58398 (45738–73417) 59212 (46322–74868) 0.01 121.01 (94.88-151.96) 81.28 (63.57–102.80) -1.54 (-1.68–1.4) Andean Latin America 1966 (1593–2406) 3237 (2534–4101) 0.65 154.02 (124.90-188.26) 93.77 (73.50-118.66) -1.82 (-1.94–1.71) Australasia 771 (616–952) 1132 (857–1433) 0.47 75.57 (60.42–93.04) 56.44 (42.74–71.45) -0.98 (-1.03–0.93) Caribbean 2568 (2107–3107) 4099 (3300–4998) 0.6 178.52 (146.64-215.92) 143.88 (115.88-175.41) -0.82 (-0.9–0.73) Central Asia 7574 (6158–9368) 10363 (8350–12875) 0.37 274.92 (223.11-340.39) 198.57 (160.08-246.65) -1.33 (-1.5–1.16) Central Europe 11763 (9526–14426) 7102 (5677–8870) -0.4 166.78 (135.00-204.57) 87.35 (69.89-109.01) -2.34 (-2.45–2.22) Central Latin America 8397 (6527–10583) 13684 (10533–17409) 0.63 155.75 (121.29–195.90) 91.21 (70.28-115.89) -2.09 (-2.27–1.91) Central Sub-Saharan Africa 4466 (3564–5554) 9432 (7523–11646) 1.11 266.18 (212.73-330.76) 213.23 (170.52-263.04) -0.82 (-0.87–0.77) East Asia 89252 (67335–112946) 155573 (119811–196992) 0.74 183.53 (138.56-232.04) 130.61 (100.42–165.60) -1.55 (-1.74–1.37) Eastern Europe 35513 (26893–45177) 22317 (17182–28529) -0.37 241.18 (181.36-308.25) 155.21 (119.65-198.22) -1.65 (-1.83–1.47) Eastern Sub-Saharan Africa 14089 (11097–17445) 23224 (18213–28921) 0.65 272.32 (214.78-336.64) 180.01 (141.75-223.75) -1.57 (-1.68–1.46) High-income Asia Pacific 18509 (14566–23052) 14667 (11411–18420) -0.21 169.14 (133.22-210.48) 102.96 (80.11-129.28) -2 (-2.16–1.84) High-income North America 13281 (9616–17815) 17133 (12776–22594) 0.29 93.59 (67.99–125.10) 74.11 (55.22–97.79) -0.96 (-1.1–0.83) North Africa and Middle East 18999 (15019–23404) 40289 (31803–50329) 1.12 178.67 (141.44–220.00) 133.50 (105.64–166.40) -1.08 (-1.15–1) Oceania 371 (297–449) 844 (687–1033) 1.27 181.93 (145.74-219.85) 148.08 (120.78-181.11) -0.76 (-0.85–0.67) South Asia 58041 (44334–73285) 111624 (87050–140311) 0.92 156.99 (120.13-197.63) 123.88 (96.74-155.36) -0.9 (-1.03–0.78) Southeast Asia 41503 (32486–51281) 83666 (66064–103409) 1.02 240.82 (188.69-297.24) 197.06 (155.75-243.37) -0.8 (-0.87–0.73) Southern Latin America 4672 (3770–5780) 4275 (3365–5419) -0.08 194.68 (157.14-240.78) 103.86 (81.77-131.58) -2.27 (-2.43–2.12) Southern Sub-Saharan Africa 3382 (2533–4331) 5207 (3984–6632) 0.54 192.27 (144.27–245.90) 136.49 (104.69-173.54) -1.2 (-1.32–1.08) Tropical Latin America 13619 (10383–17138) 15057 (11873–18678) 0.11 233.49 (178.28-293.48) 105.20 (83.02-130.41) -2.99 (-3.19–2.79) Western Europe 22654 (17659–28851) 19063 (15272–23290) -0.16 99.16 (77.28-126.31) 61.53 (49.27–75.23) -1.61 (-1.71–1.51) Western Sub-Saharan Africa 12048 (9374–15109) 27767 (21668–34565) 1.3 229.98 (179.45-287.76) 177.09 (138.66-220.05) -1.01 (-1.09–0.92) Table 3 ： The DALYs of cases and rates among SPW in 1990 and 2021 in regions, and the trends from 1990 to 2021 in regions. Location DALYs case DALYs rate 1990 (95%UI) 2021 (95%UI) Percentage change (100%) 1990 (95%UI) 2021 (95%UI) EAPC (95%CI) Global 6462007 (5934234–7039422) 7427573 (6742021–8121676) 0.15 2985.46 (2741.38-3252.41) 1629.86 (1479.39-1782.18) -2.12 (-2.2–2.03) Low SDI 575888 (487054–691943) 852374 (712813–998639) 0.48 3894.54 (3293.73-4679.66) 2375.38 (1985.54-2784.99) -1.81 (-1.93–1.69) Low-middle SDI 1378283 (1226126–1526965) 2091928 (1839541–2355797) 0.52 3539.55 (3146.32-3921.20) 2327.66 (2046.15-2621.57) -1.25 (-1.34–1.15) Middle SDI 2385011 (2132659–2698873) 2817137 (2493599–3163973) 0.18 3821.55 (3415.48-4326.62) 1765.94 (1563.03-1983.53) -2.65 (-2.74–2.56) High-middle SDI 1526690 (1374862–1707833) 1220708 (1060222–1393074) -0.2 2945.68 (2651.71-3295.90) 1261.61 (1095.77-1439.73) -3.21 (-3.53–2.87) High SDI 589288 (553139–622964) 438386 (398180–478382) -0.26 1221.23 (1146.33-1290.93) 601.76 (546.56–656.70) -2.42 (-2.5–2.33) Andean Latin America 28160 (23667–33999) 35003 (26788–44869) 0.24 2219.91 (1865.58-2682.18) 1018.88 (780.45-1304.77) -2.91 (-3.13–2.69) Australasia 7676 (6797–8645) 7044 (6079–8038) -0.08 755.00 (668.97-850.11) 351.14 (303.04-400.65) -2.19 (-2.43–1.95) Caribbean 45575 (38699–53128) 58175 (46257–72201) 0.28 3198.18 (2716.21-3729.23) 2043.96 (1625.31-2536.10) -1.41 (-1.5–1.31) Central Asia 100965 (94491–107595) 98160 (85529–112054) -0.03 3602.39 (3367.48-3841.13) 1889.84 (1646.86-2156.74) -2.82 (-3.23–2.4) Central Europe 168581 (159318–179110) 72034 (64192–79615) -0.57 2386.39 (2255.05-2535.40) 888.10 (791.36-981.38) -3.65 (-3.87–3.42) Central Latin America 88053 (83651–92562) 129097 (111648–148857) 0.47 1641.52 (1559.33-1725.27) 861.88 (745.71-993.45) -2.44 (-2.61–2.28) Central Sub-Saharan Africa 61446 (42791–87353) 112239 (76675–158211) 0.83 3687.25 (2567.72-5242.37) 2563.69 (1751.35-3606.15) -1.31 (-1.39–1.22) East Asia 2142335 (1753174–2599616) 1868768 (1502204–2303861) -0.13 4429.94 (3625.10-5375.92) 1564.57 (1257.67-1928.19) -3.64 (-3.83–3.45) Eastern Europe 383030 (360747–403974) 222421 (191205–252288) -0.42 2567.26 (2414.28-2711.95) 1549.42 (1331.57-1757.96) -2.64 (-3.18–2.1) Eastern Sub-Saharan Africa 212380 (174142–278194) 285168 (237259–339192) 0.34 4157.24 (3409.22-5441.85) 2240.64 (1863.69-2664.72) -2.31 (-2.45–2.16) High-income Asia Pacific 182394 (166214–198128) 87255 (78164–96628) -0.52 1668.45 (1520.49–1812.00) 612.52 (548.68-678.32) -3.35 (-3.52–3.19) High-income North America 126108 (117559–134946) 150231 (136738–164746) 0.19 885.12 (825.15-947.13) 649.27 (590.97-711.99) -1.07 (-1.15–1) North Africa and Middle East 374484 (305843–440376) 554494 (450843–661938) 0.48 3540.34 (2889.67-4162.17) 1846.71 (1502.69-2203.59) -2.31 (-2.42–2.21) Oceania 12480 (8668–17241) 25381 (18770–33486) 1.03 6185.00 (4306.58-8529.20) 4496.17 (3332.69-5917.63) -1.12 (-1.17–1.08) South Asia 938735 (808448–1067054) 1504585 (1272727–1741235) 0.6 2557.25 (2200.98-2905.55) 1678.76 (1419.23-1943.38) -1.2 (-1.46–0.94) Southeast Asia 891017 (782653–1001196) 1445739 (1212059–1729113) 0.62 5198.56 (4565.32-5840.35) 3415.59 (2864.56-4083.67) -1.39 (-1.45–1.34) Southern Latin America 57713 (52830–62444) 33077 (29371–37040) -0.43 2410.47 (2206.49-2607.92) 804.92 (715.13-901.23) -3.63 (-3.74–3.53) Southern Sub-Saharan Africa 42644 (36671–48609) 81308 (69413–95698) 0.91 2431.35 (2090.80-2770.93) 2147.29 (1835.28-2524.78) 0.62 (0.19–1.06) Tropical Latin America 210496 (202486–218896) 194836 (183998–206493) -0.07 3623.93 (3486.33-3767.63) 1363.14 (1287.33-1444.84) -3.57 (-3.7–3.44) Western Europe 185647 (173774–197411) 104899 (94418–115278) -0.43 812.75 (760.78-864.25) 338.60 (304.80-372.12) -2.87 (-2.95–2.78) Western Sub-Saharan Africa 202088 (163409–249018) 357658 (285257–452450) 0.77 3947.10 (3191.46-4858.52) 2325.68 (1853.74-2942.08) -1.78 (-1.86–1.71) Country Level Among the 204 countries studied, the number of prevalence, incidence, and DALYs cases increased in approximately two-thirds of the countries, with positive percentage change values. Among them, the United Arab Emirates, a high-SDI country, had the largest increase in the number of prevalence, incidence, or DALYs cases. In the other one-third of the countries, the number of cases decreased, with negative percentage change values. Estonia and Latvia had the largest decline, and both of these countries are in high-SDI regions. Thus, in high-SDI regions, the changes in the number of cases were polarized. Nevertheless, the disease burden in most countries was still decreasing (S1-3, Fig. 3A, Fig. 3B, Fig. 3C). Their prevalence rates, incidence rates, and DALYs rates all decreased, with negative EAPCs. With EAPCs of 0.85 (95% CI: 0.76–0.95), 0.72 (95% CI: 0.67–0.77), and 0.4 (95% CI: 0.36–0.44), respectively, the incidence rate only increased in a small number of middle- and low-middle-SDI nations, including Lesotho, Zimbabwe, and Turkmenistan. A small increase was also seen in a few high-SDI nations, including Guam, Samoa, and Libya. The incidence rate increased most in Zimbabwe, where the EAPC was 1.5 (95% CI: 1.28–1.71). With an EAPC of 0.4 (0.27–0.5), only a few nations, including Sierra Leone, saw an increase in the DALYs rate (S1-3, Fig. 3D, Fig. 3E, Fig. 3F). The Relationship between Disease Burden and SDI It can be seen from the figure that the disease burden of stroke in perimenopausal women is negatively correlated with the SDI. That is, as the SDI increases, the disease burden becomes smaller(Fig. 4). When the SDI ranges from 0.4 to 0.7, the prevalence and incidence are relatively stable. When the SDI is lower than 0.4 or higher than 0.7, both the prevalence and incidence fluctuate significantly. It can also be found that when the SDI is around 0.48, the prevalence and incidence in Central Asia reach their peaks (Fig. 4A, Fig. 4B).The DALYs rate tends to be stable when the SDI is lower than 0.4 and then decreases accordingly. When the SDI is 0.4, the DALYs rate in Oceania reaches its peak (Fig. 4C). While the illness burden is lower than anticipated in South Asia, Australasia, and Western Europe, it is higher than anticipated in Central Asia, Southeast Asia, Eastern Europe, and High-income Asia Pacific. It is evident that the illness burden and the declining trend are very similar, with the disease burden in Central Latin America and Andean Latin America being somewhat lower than anticipated. It is important to remember that Central and Southern Sub-Saharan Africa are low-SDI regions with high rates of DALYs, incidence, and prevalence. The low illness burden in high-SDI regions like Western Europe and Australasia contrasts sharply with this(Fig. 4). Time Joinpoint Analysis The age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), and age-standardized disability-adjusted life-year rate (ASDR) of SPW all significantly decreased globally between 1990 and 2021, according to the Joinpoint analysis (Fig. 5).The ASPR decreased significantly by 0.604% (95% CI: -0.6272% - -0.5809%, (P < 0.001)). The downward trend was the most significant from 2002 to 2011 ((APC = -0.9413%), 95% CI: -0.9633% - -0.9192%, (P < 0.001)). From 2019 to 2021, the trend reversed, showing a significant upward trend ((APC = 0.3843%), 95% CI: 0.1558% − 0.6133%, (P < 0.003)). The overall downward trend of the ASIR was obvious ((AAPC=-1.0234%), 95% CI: -1.0998% - -0.9469%, (P < 0.001)). The most significant decrease occurred from 2005 to 2014 ((APC=-2.0085%), 95% CI: -2.0944% - -1.9224%, (P < 0.001)). Conversely, from 2014 to 2019, the ASIR began to rise ((APC = 1.0781%), 95% CI: 0.8236% − 1.3333%, (P < 0.001)), and there was a slight decrease after 2019. From 1990 to 2021, the ASDR decreased significantly at an average annual rate of 1.9207%. The downward trends of the ASDR were sharp during the periods from 2004 to 2007 and from 2007 to 2011, with APC values of -3.9916% (95% CI: -5.1925% - -2.7755%, (P < 0.001)) and − 2.7698% (95% CI: -3.4043% - -2.131%, (P < 0.001)) respectively. After 2011, the decline became more stable (Table 4 ). Table 4 Time Joinpoint Analysis Figure 5 Start End val lower upper P measure ASPR 1990 2021 -0.604 -0.6272 -0.5809 0.000000 AAPC 1990 2002 -0.5088 -0.5223 -0.4953 0.000000 APC 2002 2011 -0.9413 -0.9633 -0.9192 0.000000 APC 2011 2015 -0.7447 -0.8416 -0.6477 0.000000 APC 2015 2019 -0.4806 -0.5835 -0.3776 0.000000 APC 2019 2021 0.3843 0.1558 0.6133 0.002357 APC ASIR 1990 2021 -1.0234 -1.0998 -0.9469 0.000000 AAPC 1990 1994 -0.6457 -0.9055 -0.3852 0.000061 APC 1994 2005 -1.2766 -1.3386 -1.2146 0.000000 APC 2005 2014 -2.0085 -2.0944 -1.9224 0.000000 APC 2014 2019 1.0781 0.8236 1.3333 0.000000 APC 2019 2021 -1.1266 -1.9498 -0.2964 0.010707 APC ASDR 1990 2021 -1.9207 -2.1154 -1.7256 0.000000 AAPC 1990 1995 -1.1035 -1.4615 -0.7443 0.000010 APC 1995 1998 -2.3164 -3.5877 -1.0283 0.001697 APC 1998 2004 -1.4156 -1.7042 -1.1262 0.000000 APC 2004 2007 -3.9916 -5.1925 -2.7755 0.000005 APC 2007 2011 -2.7698 -3.4043 -2.131 0.000000 APC 2011 2021 -1.5412 -1.6778 -1.4044 0.000000 APC Predicted Development Trends The total number of cases increased even while the disease burden declined between 1990 and 2021 (Fig. 6). According to predictions, the disease's global development trend will invert between 2022 and 2044, meaning that the number of cases will continue to rise and that ASPR, ASIR, and ASDR will begin to rise(Fig. 6A, Fig. 6B, Fig. 6C). By 2044, it is predicted that there will be 9,088,013 stroke prevalence cases, 887,981 incidence cases, and 11,792,835 DALYs. Among them, the ASIR increased slightly from 2015 to 2019, and the number of cases also showed an increased growth trend. After 2019, it decreased sharply and then resumed a stable upward trend. This may be related to the outbreak of COVID − 19. It is worth noting that the disease burden of SPW was the lightest around 2021, and it will gradually increase thereafter (Fig. 6D, Fig. 6E, Fig. 6F). Discussion "Ensure healthy lives and promote well-being for all at all ages" is one of the Sustainable Development Goals (SDGs) of the UN that has been hampered by the rise in the real number of stroke cases [14] . Although stroke mostly occurs in the elderly, with the development of the economy and the increase in living pressure, stroke has become one of the major factors affecting the quality of life of various populations. Perimenopausal women bear various pressures from work, family, and personal physical changes, and they should be one of the key focuses of attention. Currently, both stroke and perimenopause are still hot topics in research. Nevertheless, the majority of research only looks at the disease burden in each region, ignoring the disease burden of SPW. Thus, this study examined the disease burden of SPW using data on stroke in perimenopausal women from GBD2021 in order to close this research gap. The study found that although the prevalence, incidence, and DALYs rates of SPW had all declined during the previous 32 years, the number of prevalence, incidence, and DALYs cases had nevertheless increased dramatically, with percentage changes of 74%, 54%, and 15%, respectively.This indicates that during this period, the growth trend of the number of stroke cases in SPW slowed down but did not reverse, which is consistent with the overall trend of the decrease in the age - standardized incidence, prevalence, and DALYs rates of stroke from 1990 to 2021 [15] . Such changes may be related to the rapid increase in the global population and the overall improvement of medical levels. It can also be observed that the higher the SDI, the lower the disease burden. Regions with a higher SDI have a more complete medical environment and higher - level medical care, and people have a relatively higher life happiness index, so the disease burden is relatively lower. From 1990 to 2021, the number of prevalence cases, incidence cases, and DALYs cases increased the most in the low - middle SDI region, which is consistent with our hypothesis. For example, in low - SDI regions, people may not be fully aware of the risks of stroke in SPW. Therefore, it is necessary to continuously monitor the quality of care and constantly strive to improve the broader sensitivity of healthcare workers and the community [16] . At the national level, among high - SDI countries, the United Arab Emirates, which had the largest increase in the number of cases, and Estonia and Latvia, which had the largest decrease, form a sharp contrast. The reason for this may be that in Middle Eastern countries represented by the United Arab Emirates, such as Saudi Arabia and Qatar, perimenopausal women face higher family - role constraints, gender pressures, and workplace challenges, as well as factors such as infertility and low social support, which lead to an increase in perimenopausal complications [17] , thus indirectly increasing the risk of stroke. Therefore, for such countries, it is necessary to enhance the attention to women, especially perimenopausal women. For example, starting from the perspective of religious beliefs, efforts can be made to change the idea that women place family needs above their own [18] . Society should also break the traditional patriarchal concept, emphasize equality, and enhance the social and family support for women [19] . The country should introduce corresponding policies, increase health institutions, and enhance the emphasis on the physical and mental health of perimenopausal women. On the contrary, in Nordic countries such as Estonia and Latvia, good social welfare, gender equality, and a sound medical policy have enhanced people's life happiness index. According to the World Happiness Report 2025, Nordic countries rank high in the happiness index. The incidence of perimenopausal complications in women is low. With the development of the economy and the significant improvement of medical levels, the emphasis on and prevention of stroke are also stronger. This may be one of the reasons why the number of stroke cases in SPW decreased the most in these countries. The overall trend of the illness burden of SPW has declined, according to the Joinpoint regression analysis, which is typically associated with the increasing attention. The release of additional policy documents and the European Society of Cardiology's (ESC) categorization of menopause as a separate risk factor for stroke in women suggest that SPW is gaining more attention, which has a significant effect on the risk trend of SPW. The synchronous decrease in ASPR, ASIR, and ASDR from 2002 to 2011 may be related to the research on the standardized use of hormone replacement therapy (HRT) by the Women's Health Initiative (WHI) globally in 2002 [20] . For ASPR, 2019 is an important inflection point. After 2019, the ASPR changed from a continuous decline to an upward trend, which may be related to the high social pressure and crowded medical resources during the COVID − 19 pandemic. This is different from the stable trend of the ASPR of stroke from 2019 to 2021 [21] , indicating that the increase in the prevalence rate of SPW requires more attention from society. The ASIR of SPW is generally in a downward trend. The upward trend from 2014 to 2019 may be related to the over - restriction and use risks of HRT [22] Moreover, some studies have shown that the use of hormones can also increase the risk of stroke [23] . The American Heart Association/American Stroke Association (AHA/ASA) stressed in 2018 that perimenopausal women should focus on high-risk factors for stroke rather than using hormone replacement therapy [24] , suggesting that stroke prevention has reached a plateau and that COVID-19's effects have caused volatility after 2019. Compared with ASPR and ASIR, the ASDR is more stable and is in a continuous downward state. The largest decline from 2004 to 2007 may also be related to the standardized use of HRT. Based on the existing data set prediction, the disease burden of SPW will gradually increase in the future. The prevalence rate, incidence rate, and DALYs rate will transition from a previous year-by-year decline to a current year-by-year increase, while the number of prevalence cases, incidence cases, and DALYs cases will shift from a previous slow increase to an accelerated increase, despite minor fluctuations between 2019 and 2021. Cardiovascular diseases have been identified as diseases with a relatively large proportion related to age and are deeply affected by age changes [25] . In the 20th century, the population aged rapidly, while the fertility rates of countries around the world decreased rapidly [26] . Therefore, population aging and a decrease in the fertility rate may be one of the reasons for the gradual increase in the disease burden. Thus, it is necessary to strengthen the attention to the aging of stroke patients, and perimenopausal women are part of this group. In addition, it is necessary to actively prevent stroke, control risks, and closely screen for high - risk factors. To this end, the Menopause Health Guideline (No. WHO/MNH/23.1) issued by WHO in 2023 clearly requires that menopausal women be classified as a high - risk group for stroke, and it is recommended to integrate blood pressure and blood lipid screening into menopause clinics. This study has the following limitations. First, the GBD data rely on the quality of reports from various countries. Therefore, there is uncertainty in the quality of reports from each country, and there may be underreporting in some low - income countries. Second, emerging risks such as climate change were not included in the prediction model, and the definition of perimenopause did not distinguish between natural menopause and surgical menopause. Therefore, to ensure the reliability of the research, more real - world studies are needed to verify the results. Conclusion Using data from GBD2021, this study examined the disease burden of SPW between 1990 and 2021. The findings indicate that while the number of patients has continued to rise during the last 32 years, the illness burden has usually declined, with only minor variations between 2019 and 2021. According to predictions, the illness burden will shift from a declining trend to an upward trend by 2044, and the number of cases would rise quickly. Therefore, the prevention of the disease and the screening of high - risk factors are of great importance. Regions and countries around the world should also formulate corresponding prevention policies according to local conditions. For example, a global perimenopausal health monitoring network can be established to promote transnational cooperation; countries can reform their health systems and incorporate stroke screening for perimenopausal women into basic public health services; at the same time, society as a whole should increase care for this group of people and improve social support. In short, the issue of SPW should be taken seriously. Declarations Competing interests The authors declare no competing interests. Author Contribution L: Task allocation, manuscript drafting.W: Conceptual guidance, manuscript revision.T: Figure preparation.X: Table preparation.Z: Data verification.ZJ: Conceptual guidance, manuscript revision. Data Availability The datasets analysed during the current study are available in the [Global Burden of Disease Study] repository, [https://ghdx.healthdata.org/gbd-2021/sources]. 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Burden of Stroke and its Risk Factors in China from 1990 to 2021: an Analysis for the Global Burden of Disease Study (GBD) 2021. (2024). Dylla, L. et al. Sex Differences in the Blood Metabolome During Acute Response to Ischemic Stroke. Journal of Women's Health, 33(10): pp. 1378–1384. (2024). Hildreth, K. L., Kohrt, W. M. & Moreau, K. L. Oxidative stress contributes to large elastic arterial stiffening across the stages of the menopausal transition. Menopause, 21(6). (2014). Gao, J. et al. Global trends, disparities, and future projections of ischemic stroke burden attributed to low-fiber diets: An analysis based on GBD 2021. J. Stroke Cerebrovasc. Dis. , 34 (6). (2025). Aho, K. et al. Cerebrovascular disease in the community: results of a WHO Collaborative Study*. Bull. World Health Organ. 58 (1), 113–130 (1980). Cen, J. et al. Global, regional, and national burden and trends of migraine among women of childbearing age from 1990 to 2021: insights from the Global Burden of Disease Study 2021. J. Headache Pain . 25 (1), 96 (2024). Li, S. et al. Global, regional, and national years lived with disability due to blindness and vision loss from 1990 to 2019: Findings from the Global Burden of Disease Study 2019. Front. Public. Health , Volume 10–2022. (2022). Møller, B. et al. Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches. Stat. Med. 22 (17), 2751–2766 (2003). Mählmann, L. et al. Policy Empowerment Public. Health Genomics , 20 (6): 312–320. (2018). Feigin, V.L., et al., Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology, 2021. 20(10): pp. 795–820. Matuja, S. S. et al. Implementing Acute Stroke Services in sub–Saharan Africa: Steps, Progress and Perspectives from the Tanzania Stroke Project (Cerebrovascular Diseases Extra, 2025). AlSwayied, G., Frost, R. & Hamilton, F. L. Menopause knowledge, attitudes and experiences of women in Saudi Arabia: a qualitative study. BMC Women's Health . 24 (1), 624 (2024). Vu, M. et al. Muslim women’s perspectives on designing mosque-based women’s health interventions—An exploratory qualitative study. Women Health . 58 (3), 334–346 (2018). Aloufi, B. & Hassanien, N. S. The Association of Menopausal Symptoms and Social Support Among Saudi Women at Primary Health Care Centers in Taif, Saudi Arabia. Cureus 14 (6), e26122 (2022). Writing, G. F. T. W. Risks and Benefits of Estrogen Plus Progestin in Healthy Postmenopausal WomenPrincipal Results From the Women's Health Initiative Randomized Controlled Trial. JAMA 288 (3), 321–333 (2002). Gao, Z. et al. The impact and projection of the COVID-19 pandemic on the burden of stroke at global, regional, and national levels: A comprehensive analysis for the Global Burden of Disease Study 2021. J. Stroke Cerebrovasc. Dis. , 34 (6). (2025). Abramson, B. L. et al. Guideline No. 422e: Menopause and Cardiovascular Disease. J. Obstet. Gynecol. Can. 43 (12), 1438–1443e1 (2021). Henderson, V. W. And Lobo, Hormone therapy and the risk of stroke: perspectives 10 years after the Women's Health Initiative trials. Climacteric 15 (3), 229–234 (2012). Powers, W. J. et al. Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke, 2018. 49(3): pp. e46-e99. (2018). Chang, A. Y. et al. Measuring population ageing: an analysis of the Global Burden of Disease Study 2017. Lancet Public. Health . 4 (3), e159–e167 (2019). Sander, M. et al. The challenges of human population ageing. Age ageing , 44. (2014). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.pdf 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6672476","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477199681,"identity":"da6d0da4-33ff-44cf-a357-6bccc7666b43","order_by":0,"name":"Ningning Luo","email":"","orcid":"","institution":"Shanxi Institute of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ningning","middleName":"","lastName":"Luo","suffix":""},{"id":477199682,"identity":"739a0ea2-e1ad-4380-8f10-7640da7873ef","order_by":1,"name":"Yang Wu","email":"","orcid":"","institution":"Nanjing University of Chinese 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12:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6672476/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6672476/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85834949,"identity":"439cf822-a110-4dcc-89fe-6a9c97b22ab4","added_by":"auto","created_at":"2025-07-02 08:13:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192973,"visible":true,"origin":"","legend":"\u003cp\u003eDisease burden of SPW in global and 5 territories. \u003cstrong\u003eA and B and C: \u003c/strong\u003eThe rates of prevalence(A), incidence(B) and DALYs(C) from 1990 to 2021. \u003cstrong\u003eD:\u003c/strong\u003e Percentage change in cases of prevalent, incident and DALYs in 1990 and 2021. \u003cstrong\u003eE: \u003c/strong\u003eThe EAPC of prevalence, incidence, and DALY rates from 1990 to 2021.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/ba19734d86587e7682bea2b9.png"},{"id":85837651,"identity":"51205843-04ba-4803-ad66-1d80ce46e2a1","added_by":"auto","created_at":"2025-07-02 08:29:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204879,"visible":true,"origin":"","legend":"\u003cp\u003eDisease burden of SPW in regions. \u003cstrong\u003eA and B and C\u003c/strong\u003e:Prevalence rate(A), incidence rate(B), and DALYs rate(C) per 100,000 population in 1990 and 2021. \u003cstrong\u003eD\u003c/strong\u003e:Percentage change in cases of prevalent, incident and DALYs in 1990 and 2021. \u003cstrong\u003eE\u003c/strong\u003e:The EAPC of prevalence, incidence, and DALY rates from 1990 to 2021.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/1dae8fd1011bbed62408d18d.png"},{"id":85837655,"identity":"2a0bc224-32db-40a9-9e52-c5a0dc4c6020","added_by":"auto","created_at":"2025-07-02 08:29:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":362835,"visible":true,"origin":"","legend":"\u003cp\u003eDisease burden of SPW in regions.\u003cstrong\u003e A and B and C\u003c/strong\u003e: \u0026nbsp;Percentage change in prevalent(A), incident(B) and DALYs(C) cases across 204 countries in 1990 and 2021.\u003cstrong\u003e D and E and F\u003c/strong\u003e: EAPC in prevalent(D), incident(E) and DALYs(F) rates across 204 countries from 1990 to 2021.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/2bc2d0e62d360e294cc4318e.png"},{"id":85834953,"identity":"6d380c52-6d7d-413b-a3a1-6e69af6eada2","added_by":"auto","created_at":"2025-07-02 08:13:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309695,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations between the SDI and disease burden of SPW in regions. \u003cstrong\u003eA and B and C\u003c/strong\u003e: The associations between the SDI and prevalent(A), incident(B) and DALYs(C) rates per 100,000 population of SPW in regions.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/0a9fec6d4ba46ab2c8aecf24.png"},{"id":85836196,"identity":"a32ef06b-51f0-4bbc-af2d-bd0d809d3a8f","added_by":"auto","created_at":"2025-07-02 08:21:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":156802,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint \u0026nbsp;\u0026nbsp;regression analysis of temporal trends in the disease burden of SPW. \u003cstrong\u003eA: \u003c/strong\u003eAge-standardized \u0026nbsp;\u0026nbsp;prevalence rates per 100,000 population from 1990 to 2021.\u003cstrong\u003e B:\u003c/strong\u003e \u0026nbsp;Age-standardized incidence rates per 100,000 population from 1990 to 2021. \u003cstrong\u003eC:\u003c/strong\u003e \u0026nbsp;Age-standardized DALYs rates per 100,000 population from 1990 to 2021.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/a62c647fdd7a69522a0a9b76.png"},{"id":85834959,"identity":"cf9c67fe-ca40-48b1-a891-4ade4ddce6f0","added_by":"auto","created_at":"2025-07-02 08:13:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":119911,"visible":true,"origin":"","legend":"\u003cp\u003eDisease prediction for SPW.\u003cstrong\u003eA and B and C\u003c/strong\u003e: The global change trends of ASR of prevalence(A), incidence(B) and DALYs(C) of SPW from 1990 to 2021, and its predicted trends between 2022 and 2044.\u003cstrong\u003e D and E and F\u003c/strong\u003e: The global change trends of case number of prevalence(D), incidence(E) and DALYs(F) of SPW from 1990 to 2021, and its predicted trends between 2022 and 2044.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/5db0e4bddf4301e2b3a5ab4f.png"},{"id":91582888,"identity":"3066f82a-3cd2-40d6-ac6e-cffee5c1cff3","added_by":"auto","created_at":"2025-09-18 04:53:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2793421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/9d3c5d46-5ead-4e78-b56d-f8678a09bf16.pdf"},{"id":85836187,"identity":"b540b9ad-c0ba-454d-9a8e-aac8803964d5","added_by":"auto","created_at":"2025-07-02 08:21:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1512893,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6672476/v1/5a236918ad0600a8a08194f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global, Regional and National Burden of Stroke among Perimenopausal Women: Trends from 1990 to 2021 and Projections up to 2044","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is the second leading cause of human death worldwide after ischemic heart disease and the third leading contributor to disability - adjusted life years(DALYs) \u003csup\u003e[1]\u003c/sup\u003e, and it is one of the key diseases of concern in global public health. Although the age - standardized incidence, prevalence, mortality, and DALYs rate decreased between 1990 and 2019, the actual number of cases increased, and the global burden remains substantial \u003csup\u003e[2]\u003c/sup\u003e. In 2021, there were 757,234.61 age-standardized stroke cases worldwide, 8,720,000 DALYs, and 122,742 stroke-related fatalities\u003csup\u003e[3]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe perimenopause in women usually occurs between the ages of 45 and 55. During this period, the ovaries gradually secrete less estrogen and progesterone, and it is a crucial stage of transition from sexual maturity to old age\u003csup\u003e[4]\u003c/sup\u003e. The perimenopause, which lasts until a year beyond menopause and is frequently accompanied by symptoms including mood swings, hot flashes, and night sweats, begins when a woman exhibits clinical indicators of irregular menstruation or hormonal changes, according to the World Health Organization (WHO).\u003c/p\u003e \u003cp\u003eStudies have shown that in most age groups, the incidence and mortality of stroke in men are higher than those in women \u003csup\u003e[5]\u003c/sup\u003e, which may be related to the fact that smoking and alcohol consumption are more common in men than in women\u003csup\u003e[6]\u003c/sup\u003e. However, women have a higher lifetime risk of stroke than men \u003csup\u003e[7]\u003c/sup\u003e. In perimenopausal women, oxidative stress, vascular inflammation, and dysfunctions of ERα and eNOS lead to estrogen deficiency, accelerating vascular aging \u003csup\u003e[8]\u003c/sup\u003e, increasing the burden of cardiovascular and cerebrovascular diseases, and thus raising the incidence of stroke\u003csup\u003e[9]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, the majority of studies focusing on perimenopausal women mainly revolve around their vasomotor symptoms, mental health, and cognitive function, while research on stroke in perimenopausal women(SPW) is still insufficient. As a result, the attention given to stroke in this group of women and the resources of the healthcare system are inadequate, indirectly leading to a lack of standardized care for this population. Therefore, this article will comprehensively describe and analyze the overall incidence and changing trends of SPW from 1990 to 2021 based on the data of the GBD2021 study. In addition to analyzing temporal patterns and forecasting development trends from 2022 to 2044, it primarily contains the prevalence, incidence, and disability-adjusted life years of SPW at the national, regional, and worldwide levels from 1990 to 2021.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData Source\u003c/h2\u003e\n \u003cp\u003eThis study used data from the Global Burden of Disease 2021 (GBD2021) on SPW. Over 1,500 partner institutions worldwide are involved in this database, which is led by the Institute for Health Metrics and Evaluation (IHME) in the United States. It offers thorough data support for 369 diseases and injuries and 88 risk factors, and it includes 204 countries and regions in addition to dozens of sub-national locations. The GBD conducts multi - dimensional disease and risk factor analyses of global regions and is continuously updated with a long time - span. The timely update and forward - looking nature of its data can effectively guide global health policies, making it one of the most influential health data analysis projects globally. In this study, relevant data on SPW from 1990 to 2021 were extracted from the GBD, with a focus on indicators such as prevalence, incidence, and DALYs to analyze the disease burden and development trends of SPW. This study only involves data analysis, and the data are rigorous and publicly available without personal information, so there are no ethical requirements.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDisease Definition\u003c/h3\u003e\n\u003cp\u003eStroke is defined by the WHO as an acute brain dysfunction caused by cerebrovascular lesions, with symptoms lasting for 24 hours or more or resulting in death\u003csup\u003e[10]\u003c/sup\u003e. In the GBD, disease causes are classified into 4 levels. Ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage are the three subtypes of stroke, which is a level 2 cardiovascular condition and a level 3 cause.\u003c/p\u003e\n\u003ch3\u003eResearch Subjects\u003c/h3\u003e\n\u003cp\u003eThis study focuses on SPW. Data on this group\u0026apos;s incidence, prevalence, and DALYs were taken from the GBD and used in the study. In the GBD, prevalence refers to the proportion of people in a population who have a certain disease, which can assess the static distribution of the disease burden; incidence refers to the proportion of new cases of a certain disease in a population, which can assess the dynamic risk of the disease burden; DALYs, or years of life lost owing to premature death (YLLs) plus years lived with disability (YLDs), or the entire loss of healthy life, are a measure of the overall disease burden \u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDALYs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eYLLs\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eYLDs\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eSocio - Demographic Index\u003c/h3\u003e\n\u003cp\u003eThe Socio-Demographic Index (SDI), which is based on per capita income, average years of education, and total fertility rate, is a comprehensive statistic for assessing the social development level of nations and regions. In GBD2021, the SDI was divided into 5 quintiles from low to high: low, low - middle, middle, middle - high, and high, representing 5 levels of regional comprehensive development. This study explored the linear relationship between the SDI and the disease prevalence, incidence, and DALYs to determine the impact of the SDI on the disease burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e1. Estimated annual percentage change\u003c/h3\u003e\n\u003cp\u003eThe Estimated annual percentage change (EAPC) refers to the average annual percentage change of a certain disease over a certain period. A positive value indicates an upward trend year by year, while a negative value indicates a downward trend. The calculation of EAPC takes time as a variable, fits the natural logarithm of each observation value into a straight line, and calculates based on the slope of this line\u003csup\u003e[12]\u003c/sup\u003e. Its 95% confidence interval (95%CI) is commonly used to represent its fluctuation range.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEAPC\u003c/em\u003e=(\u003cem\u003ee\u0026beta;\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026times;100%\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2. Percentage Change\u003c/h2\u003e\n \u003cp\u003eAn indicator for calculating the relative difference between data at two points in time is the percentage change. To represent the changes in prevalence, incidence, and DALYs, the percentage change value was calculated in this study by dividing the difference between the number of cases in 1990 and 2021 by the number of cases in 1990.\u003csup\u003e[11]\u003c/sup\u003e。\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ePercentage change=(2021cases-1990cases)/1990cases\u0026times;100%\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3. Age - Standardized Rate\u003c/h2\u003e\n \u003cp\u003eTo guarantee comparability across various populations or geographical areas, the Age-Standardized Rate (ASR) is a metric that eliminates variations in the population\u0026apos;s age distribution. This study used ASR to calculate the disease burden of stroke in perimenopausal women, and used APC to analyze key turning points and evaluate the change trend within a certain period; AAPC was used to analyze the weighted average annual change rate during the entire study period. From a statistical standpoint, an upward trend within the designated interval is indicated if the estimated value of APC or AAPC and the lower limit of its 95% CI surpass zero. On the other hand, a negative trend is indicated if the predicted value of APC or AAPC plus the upper limit of its 95% CI is less than zero. The trend is said to be stable when the 95% CI of APC or AAPC is zero.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePrediction Analysis\u003c/h2\u003e\n \u003cp\u003eTo predict the number of disease cases and prevalence from 2022 to 2044, this study used the Nordpred package in R language to establish a linear log - age - time - cohort model. This model effectively controlled the exponential growth trend and restricted the prediction of the linear trend, as well as the estimation method and parameter estimation results. The average weighted prevalence of the disease in 2044 was obtained, and combined with the population predictions of each country and region by the United Nations, the number of cases and ASR in 2044 were predicted\u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, R \u0026minus;\u0026thinsp;4.4.3 was used to clean, calculate, and plot the data from GBD2021, and the ggplot2 package was used for visual display. Joinpoint \u0026minus;\u0026thinsp;5.4.0 was used for piece - wise regression and time - series analysis, and its APC and AAPC were used to evaluate the internal and overall trends.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eGlobal Level\u003c/h2\u003e\n \u003cp\u003eAt the global level, perimenopausal women saw a substantial rise in the prevalence, incidence, and DALYs of stroke cases. Of these, there was a minor decrease in the number of DALYs instances in areas with High-middle and High SDI (Fig. 1D). In perimenopausal women, for instance, the prevalence of stroke cases rose from 3,640,225 to 6,350,538 between 1990 and 2021, a 74% increase; incidence cases rose from 383,438 to 589,755, a 54% increase; and DALYs cases rose from 6,462,007 to 7,427,573, a 15% increase. Prevalence, incidence, and DALYs rates all trended lower despite the rise in cases; the EAPCs were \u0026minus;\u0026thinsp;0.7 (95%CI: \u0026minus;\u0026thinsp;0.73 - \u0026minus;\u0026thinsp;0.67), \u0026minus;\u0026thinsp;1.24 (95%CI: \u0026minus;\u0026thinsp;1.35 - \u0026minus;\u0026thinsp;1.13), and \u0026minus;\u0026thinsp;2.12 (95%CI: \u0026minus;\u0026thinsp;2.2 - \u0026minus;\u0026thinsp;2.03), respectively (Table 1, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. 1D). According to the development trends of the global prevalence rate, incidence rate, and DALYs rate from 1990 to 2021 in Fig. 1, the overall burden showed a downward trend (Fig. 1). However, it is worth noting that this downward trend is gradually slowing down, and there is an upward trend in some regions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eSDI Region Level\u003c/h2\u003e\n \u003cp\u003eWith 2,261,067 instances (95%UI: 2,051,507\u0026ndash;2,487,375), 214,460 cases (95%UI: 168,005\u0026ndash;266,654), and 2,817,137 cases (95%UI: 2,493,599\u0026ndash;3,163,973), respectively, the middle-to-high SDI region had the greatest prevalence, incidence, and DALYs cases in 2021. About one-third of the similar instances worldwide were these cases. Followed by the number of prevalence and incidence cases in East Asia, which were 1,603,007 cases (95%UI: 1,422,796\u0026ndash;1,793,184) and 155,573 cases (95%UI: 119,811\u0026thinsp;\u0026minus;\u0026thinsp;196,992) respectively. The region with the second - highest number of DALYs cases was the low - middle SDI region, with 2,091,928 cases (95%UI: 1,839,541\u0026ndash;2,355,797) (Table 1, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The figure illustrates how, in 2021, the prevalence rate, incidence rate, and DALYs rate grew in inverse proportion to the decline in the SDI, which was accompanied by a decline in the comprehensive evaluation of per capita income, average years of schooling, and total fertility rate. Every SDI region\u0026apos;s prevalence rate, incidence rate, and DALYs rate exhibited a declining trend when compared to 1990\u0026ndash;2021, and all of the EAPC were negative, suggesting that the illness burden was declining in these areas(Fig.\u0026nbsp;1E).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eGBD Region Level\u003c/h2\u003e\n \u003cp\u003eCentral Asia, Central Sub-Saharan Africa, and Oceania had the greatest rates of DALYs, incidence, and prevalence in 2021. With the exception of a few nations in Oceania, which may be directly tied to the pressures of local life, the majority of these locations are low-SDI or low-middle SDI. The regions with the largest decline in the prevalence rate and incidence rate were Tropical Latin America, with EAPCs of \u0026minus;\u0026thinsp;2.01 (95%CI: \u0026minus;\u0026thinsp;2.12 - \u0026minus;\u0026thinsp;1.89) and \u0026minus;\u0026thinsp;2.99 (95%CI: \u0026minus;\u0026thinsp;3.19 - \u0026minus;\u0026thinsp;2.79) respectively. The region with the largest decline in the DALYs rate was Central Europe, with an EAPC of \u0026minus;\u0026thinsp;3.65 (95%CI: \u0026minus;\u0026thinsp;3.87 - \u0026minus;\u0026thinsp;3.42). These regions usually belong to high - SDI or middle - high SDI regions (Table 1, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. 2E). The prevalence rate, incidence rate, and DALYs rate in 1990 were higher than those in 2021 in the remaining regions, and the EAPCs were all less than zero, with the exception of a slight increase in the prevalence rate in High-income North America (EAPC\u0026thinsp;=\u0026thinsp;0.13, 95%CI: \u0026minus;\u0026thinsp;0.05\u0026ndash;0.31) and the DALYs rate in Southern Sub-Saharan Africa (EAPC\u0026thinsp;=\u0026thinsp;0.62, 95%CI: 0.19\u0026ndash;1.06) (Table 1, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. 2A, Fig. 2B, Fig. 2C). Only a few regions, including Central Europe, Eastern Europe, and High-Income Asia Pacific, demonstrated a decline in DALYs, while the majority of regions had increases in prevalence, incidence, and DALYs instances (Fig. 2D).\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTThe prevalence of cases and rates among SPW in 1990 and 2021 in regions, and the trends from 1990 to 2021 in regions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePrevalent cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePrevalent rate (per 100 000)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage change (100%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3640225 (3318432\u0026ndash;3968530)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6350538 (5831028\u0026ndash;6908845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1676.20 (1527.76-1827.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1391.16 (1277.26-1513.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7 (-0.73\u0026ndash;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286652 (266462\u0026ndash;308835)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e570008 (534301\u0026ndash;605562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1921.79 (1786.08-2070.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1572.53 (1474.00-1670.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.76 (-0.79\u0026ndash;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651800 (595454\u0026ndash;711620)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1348918 (1247926\u0026ndash;1460185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1661.79 (1517.49-1814.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1490.32 (1378.69-1613.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.42 (-0.44\u0026ndash;0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1112354 (1005291\u0026ndash;1217876)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2261067 (2051507\u0026ndash;2487375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1768.88 (1598.19-1936.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1416.19 (1284.89-1557.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.84 (-0.88\u0026ndash;0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e919236 (835199\u0026ndash;1004617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1287474 (1165361\u0026ndash;1412575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1779.27 (1616.94-1944.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1330.85 (1204.65-1460.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05 (-1.1\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e666331 (610586\u0026ndash;724426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877914 (808638\u0026ndash;946478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1379.56 (1264.04-1499.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1204.98 (1109.91-1299.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.53 (-0.59\u0026ndash;0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21191 (20029\u0026ndash;22318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42064 (39857\u0026ndash;44355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1668.79 (1576.69-1757.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1221.78 (1157.59-1288.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.15 (-1.2\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9307 (8687\u0026ndash;9954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15011 (14084\u0026ndash;16028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e909.65 (849.01\u0026ndash;972.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e748.30 (702.08-799.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.72 (-0.77\u0026ndash;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24801 (23372\u0026ndash;26212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42901 (40571\u0026ndash;45131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1731.84 (1632.18-1830.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1507.21 (1425.29-1585.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.54 (-0.57\u0026ndash;0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70324 (66303\u0026ndash;74360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104572 (98821\u0026ndash;110403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2536.75 (2392.17-2683.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005.69 (1895.32-2117.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9 (-0.96\u0026ndash;0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112624 (104113\u0026ndash;120879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91083 (84732\u0026ndash;97140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1596.87 (1476.34-1714.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1118.36 (1040.37-1192.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.31 (-1.38\u0026ndash;1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92959 (85513\u0026ndash;100743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185195 (170947\u0026ndash;199176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1729.34 (1590.98-1874.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1235.48 (1140.52-1328.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.26 (-1.34\u0026ndash;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36518 (34269\u0026ndash;39007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80693 (75958\u0026ndash;85600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2185.37 (2050.86-2334.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1834.65 (1726.67-1946.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.64 (-0.67\u0026ndash;0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e824292 (733226\u0026ndash;914360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1603007 (1422796\u0026ndash;1793184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1694.02 (1506.41-1879.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1348.81 (1197.61-1508.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.88 (-0.92\u0026ndash;0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298112 (259469\u0026ndash;338468)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239362 (211872\u0026ndash;267799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2025.34 (1765.58-2297.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1665.12 (1473.70-1862.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.73 (-0.79\u0026ndash;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116448 (107303\u0026ndash;126131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218505 (203474\u0026ndash;234093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2261.52 (2083.54-2449.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1701.29 (1583.62-1823.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05 (-1.09\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179159 (163250\u0026ndash;196123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176684 (161944\u0026ndash;191722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1638.28 (1492.84-1793.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1240.56 (1137.05-1346.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1 (-1.04\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194010 (170881\u0026ndash;218498)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e341932 (305931\u0026ndash;378991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1360.16 (1198.10-1532.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1478.43 (1322.88-1638.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (-0.05-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206451 (192785\u0026ndash;220549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e472004 (444448\u0026ndash;500635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1940.17 (1811.47-2073.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1561.33 (1469.96-1656.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.82 (-0.87\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3611 (3407\u0026ndash;3811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8495 (8047\u0026ndash;8946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1777.16 (1676.66-1875.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1493.67 (1414.75-1573.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.62 (-0.69\u0026ndash;0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e498266 (443245\u0026ndash;556976)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1094439 (986281\u0026ndash;1215843)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1348.00 (1198.87-1506.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1215.21 (1095.06-1349.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.41 (-0.45\u0026ndash;0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e389154 (354573\u0026ndash;427296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e808240 (738298\u0026ndash;883823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2262.61 (2060.99-2485.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1906.27 (1741.03-2084.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.65 (-0.68\u0026ndash;0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43633 (41154\u0026ndash;46251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51804 (48812\u0026ndash;54899)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1823.60 (1719.75-1933.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1260.45 (1187.38-1335.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.36 (-1.44\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37626 (32961\u0026ndash;42752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59185 (52996\u0026ndash;66119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2142.97 (1876.70-2435.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1551.02 (1388.53-1733.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.22 (-1.32\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108114 (96397\u0026ndash;120381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155684 (139386\u0026ndash;173191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1863.87 (1660.78-2075.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1090.47 (976.10-1213.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.01 (-2.12\u0026ndash;1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257051 (235500\u0026ndash;280283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260406 (241414\u0026ndash;280934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1125.27 (1030.91-1226.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e840.68 (779.25-906.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1 (-1.04\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116574 (107176\u0026ndash;126541)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299271 (276321\u0026ndash;323807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2233.95 (2053.46-2425.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1911.11 (1764.61-2068.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6 (-0.63\u0026ndash;0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe incidence of cases and rates among SPW in 1990 and 2021 in regions, and the trends from 1990 to 2021 in regions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eIncident cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eIncident rate (per 100 000)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage change (100%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e383438 (298359\u0026ndash;476303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e589755 (463070\u0026ndash;732733)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.54 (137.48\u0026ndash;219.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.19 (101.51-160.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.24 (-1.35\u0026ndash;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33917 (26691\u0026ndash;41941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60595 (48011\u0026ndash;74785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226.61 (178.54\u0026ndash;279.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.76 (132.56-205.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.19 (-1.27\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74062 (57791\u0026ndash;91554)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140274 (111467\u0026ndash;172746)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.54 (147.30-232.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.83 (123.21-190.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.75 (-0.79\u0026ndash;0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117675 (90960\u0026ndash;146455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214460 (168005\u0026ndash;266654)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.00 (144.70-232.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.34 (105.28-166.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.35 (-1.47\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98986 (76637\u0026ndash;123431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114741 (88457\u0026ndash;145526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191.49 (148.11-239.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.60 (91.42-150.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.85 (-2.02\u0026ndash;1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58398 (45738\u0026ndash;73417)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59212 (46322\u0026ndash;74868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.01 (94.88-151.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.28 (63.57\u0026ndash;102.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.54 (-1.68\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1966 (1593\u0026ndash;2406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3237 (2534\u0026ndash;4101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.02 (124.90-188.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.77 (73.50-118.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.82 (-1.94\u0026ndash;1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e771 (616\u0026ndash;952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1132 (857\u0026ndash;1433)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.57 (60.42\u0026ndash;93.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.44 (42.74\u0026ndash;71.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.98 (-1.03\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2568 (2107\u0026ndash;3107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4099 (3300\u0026ndash;4998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.52 (146.64-215.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.88 (115.88-175.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.82 (-0.9\u0026ndash;0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7574 (6158\u0026ndash;9368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10363 (8350\u0026ndash;12875)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274.92 (223.11-340.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198.57 (160.08-246.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.33 (-1.5\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11763 (9526\u0026ndash;14426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7102 (5677\u0026ndash;8870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.78 (135.00-204.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.35 (69.89-109.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.34 (-2.45\u0026ndash;2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8397 (6527\u0026ndash;10583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13684 (10533\u0026ndash;17409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.75 (121.29\u0026ndash;195.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.21 (70.28-115.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.09 (-2.27\u0026ndash;1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4466 (3564\u0026ndash;5554)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9432 (7523\u0026ndash;11646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266.18 (212.73-330.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213.23 (170.52-263.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.82 (-0.87\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89252 (67335\u0026ndash;112946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155573 (119811\u0026ndash;196992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.53 (138.56-232.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.61 (100.42\u0026ndash;165.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.55 (-1.74\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35513 (26893\u0026ndash;45177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22317 (17182\u0026ndash;28529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241.18 (181.36-308.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.21 (119.65-198.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.65 (-1.83\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14089 (11097\u0026ndash;17445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23224 (18213\u0026ndash;28921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272.32 (214.78-336.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.01 (141.75-223.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.57 (-1.68\u0026ndash;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18509 (14566\u0026ndash;23052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14667 (11411\u0026ndash;18420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169.14 (133.22-210.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.96 (80.11-129.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2 (-2.16\u0026ndash;1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13281 (9616\u0026ndash;17815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17133 (12776\u0026ndash;22594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.59 (67.99\u0026ndash;125.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.11 (55.22\u0026ndash;97.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.96 (-1.1\u0026ndash;0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18999 (15019\u0026ndash;23404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40289 (31803\u0026ndash;50329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.67 (141.44\u0026ndash;220.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.50 (105.64\u0026ndash;166.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.08 (-1.15\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e371 (297\u0026ndash;449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844 (687\u0026ndash;1033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181.93 (145.74-219.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.08 (120.78-181.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.76 (-0.85\u0026ndash;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58041 (44334\u0026ndash;73285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111624 (87050\u0026ndash;140311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156.99 (120.13-197.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.88 (96.74-155.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9 (-1.03\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41503 (32486\u0026ndash;51281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83666 (66064\u0026ndash;103409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240.82 (188.69-297.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197.06 (155.75-243.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8 (-0.87\u0026ndash;0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4672 (3770\u0026ndash;5780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4275 (3365\u0026ndash;5419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.68 (157.14-240.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.86 (81.77-131.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.27 (-2.43\u0026ndash;2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3382 (2533\u0026ndash;4331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5207 (3984\u0026ndash;6632)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.27 (144.27\u0026ndash;245.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.49 (104.69-173.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2 (-1.32\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13619 (10383\u0026ndash;17138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15057 (11873\u0026ndash;18678)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233.49 (178.28-293.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.20 (83.02-130.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.99 (-3.19\u0026ndash;2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22654 (17659\u0026ndash;28851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19063 (15272\u0026ndash;23290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.16 (77.28-126.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.53 (49.27\u0026ndash;75.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.61 (-1.71\u0026ndash;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12048 (9374\u0026ndash;15109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27767 (21668\u0026ndash;34565)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.98 (179.45-287.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177.09 (138.66-220.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.01 (-1.09\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe DALYs of cases and rates among SPW in 1990 and 2021 in regions, and the trends from 1990 to 2021 in regions.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eDALYs case\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eDALYs rate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage change (100%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003cp\u003e(95%UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6462007 (5934234\u0026ndash;7039422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7427573 (6742021\u0026ndash;8121676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2985.46 (2741.38-3252.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1629.86 (1479.39-1782.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.12 (-2.2\u0026ndash;2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575888 (487054\u0026ndash;691943)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e852374 (712813\u0026ndash;998639)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3894.54 (3293.73-4679.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2375.38 (1985.54-2784.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.81 (-1.93\u0026ndash;1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1378283 (1226126\u0026ndash;1526965)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2091928 (1839541\u0026ndash;2355797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3539.55 (3146.32-3921.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2327.66 (2046.15-2621.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.25 (-1.34\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2385011 (2132659\u0026ndash;2698873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2817137 (2493599\u0026ndash;3163973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3821.55 (3415.48-4326.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1765.94 (1563.03-1983.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.65 (-2.74\u0026ndash;2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1526690 (1374862\u0026ndash;1707833)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1220708 (1060222\u0026ndash;1393074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2945.68 (2651.71-3295.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1261.61 (1095.77-1439.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.21 (-3.53\u0026ndash;2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e589288 (553139\u0026ndash;622964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438386 (398180\u0026ndash;478382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1221.23 (1146.33-1290.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e601.76 (546.56\u0026ndash;656.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.42 (-2.5\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28160 (23667\u0026ndash;33999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35003 (26788\u0026ndash;44869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2219.91 (1865.58-2682.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1018.88 (780.45-1304.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.91 (-3.13\u0026ndash;2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7676 (6797\u0026ndash;8645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7044 (6079\u0026ndash;8038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e755.00 (668.97-850.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351.14 (303.04-400.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.19 (-2.43\u0026ndash;1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45575 (38699\u0026ndash;53128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58175 (46257\u0026ndash;72201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3198.18 (2716.21-3729.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2043.96 (1625.31-2536.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.41 (-1.5\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100965 (94491\u0026ndash;107595)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98160 (85529\u0026ndash;112054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3602.39 (3367.48-3841.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1889.84 (1646.86-2156.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.82 (-3.23\u0026ndash;2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168581 (159318\u0026ndash;179110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72034 (64192\u0026ndash;79615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2386.39 (2255.05-2535.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e888.10 (791.36-981.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.65 (-3.87\u0026ndash;3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88053 (83651\u0026ndash;92562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129097 (111648\u0026ndash;148857)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1641.52 (1559.33-1725.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e861.88 (745.71-993.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.44 (-2.61\u0026ndash;2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61446 (42791\u0026ndash;87353)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112239 (76675\u0026ndash;158211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3687.25 (2567.72-5242.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2563.69 (1751.35-3606.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.31 (-1.39\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2142335 (1753174\u0026ndash;2599616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1868768 (1502204\u0026ndash;2303861)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4429.94 (3625.10-5375.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1564.57 (1257.67-1928.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.64 (-3.83\u0026ndash;3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e383030 (360747\u0026ndash;403974)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222421 (191205\u0026ndash;252288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2567.26 (2414.28-2711.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1549.42 (1331.57-1757.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.64 (-3.18\u0026ndash;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212380 (174142\u0026ndash;278194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285168 (237259\u0026ndash;339192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4157.24 (3409.22-5441.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2240.64 (1863.69-2664.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.31 (-2.45\u0026ndash;2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182394 (166214\u0026ndash;198128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87255 (78164\u0026ndash;96628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1668.45 (1520.49\u0026ndash;1812.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612.52 (548.68-678.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.35 (-3.52\u0026ndash;3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126108 (117559\u0026ndash;134946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150231 (136738\u0026ndash;164746)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e885.12 (825.15-947.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e649.27 (590.97-711.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.07 (-1.15\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374484 (305843\u0026ndash;440376)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e554494 (450843\u0026ndash;661938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3540.34 (2889.67-4162.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1846.71 (1502.69-2203.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.31 (-2.42\u0026ndash;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12480 (8668\u0026ndash;17241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25381 (18770\u0026ndash;33486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6185.00 (4306.58-8529.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4496.17 (3332.69-5917.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.12 (-1.17\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e938735 (808448\u0026ndash;1067054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504585 (1272727\u0026ndash;1741235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2557.25 (2200.98-2905.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1678.76 (1419.23-1943.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2 (-1.46\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891017 (782653\u0026ndash;1001196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1445739 (1212059\u0026ndash;1729113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5198.56 (4565.32-5840.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3415.59 (2864.56-4083.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.39 (-1.45\u0026ndash;1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57713 (52830\u0026ndash;62444)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33077 (29371\u0026ndash;37040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2410.47 (2206.49-2607.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e804.92 (715.13-901.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.63 (-3.74\u0026ndash;3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42644 (36671\u0026ndash;48609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81308 (69413\u0026ndash;95698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2431.35 (2090.80-2770.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2147.29 (1835.28-2524.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 (0.19\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210496 (202486\u0026ndash;218896)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194836 (183998\u0026ndash;206493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3623.93 (3486.33-3767.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1363.14 (1287.33-1444.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.57 (-3.7\u0026ndash;3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185647 (173774\u0026ndash;197411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104899 (94418\u0026ndash;115278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e812.75 (760.78-864.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338.60 (304.80-372.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.87 (-2.95\u0026ndash;2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202088 (163409\u0026ndash;249018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e357658 (285257\u0026ndash;452450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3947.10 (3191.46-4858.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2325.68 (1853.74-2942.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.78 (-1.86\u0026ndash;1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eCountry Level\u003c/h2\u003e\n \u003cp\u003eAmong the 204 countries studied, the number of prevalence, incidence, and DALYs cases increased in approximately two-thirds of the countries, with positive percentage change values. Among them, the United Arab Emirates, a high-SDI country, had the largest increase in the number of prevalence, incidence, or DALYs cases. In the other one-third of the countries, the number of cases decreased, with negative percentage change values. Estonia and Latvia had the largest decline, and both of these countries are in high-SDI regions. Thus, in high-SDI regions, the changes in the number of cases were polarized. Nevertheless, the disease burden in most countries was still decreasing (S1-3, Fig.\u0026nbsp;3A, Fig.\u0026nbsp;3B, Fig.\u0026nbsp;3C). Their prevalence rates, incidence rates, and DALYs rates all decreased, with negative EAPCs. With EAPCs of 0.85 (95% CI: 0.76\u0026ndash;0.95), 0.72 (95% CI: 0.67\u0026ndash;0.77), and 0.4 (95% CI: 0.36\u0026ndash;0.44), respectively, the incidence rate only increased in a small number of middle- and low-middle-SDI nations, including Lesotho, Zimbabwe, and Turkmenistan. A small increase was also seen in a few high-SDI nations, including Guam, Samoa, and Libya. The incidence rate increased most in Zimbabwe, where the EAPC was 1.5 (95% CI: 1.28\u0026ndash;1.71). With an EAPC of 0.4 (0.27\u0026ndash;0.5), only a few nations, including Sierra Leone, saw an increase in the DALYs rate (S1-3, Fig.\u0026nbsp;3D, Fig.\u0026nbsp;3E, Fig.\u0026nbsp;3F).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eThe Relationship between Disease Burden and SDI\u003c/h2\u003e\n \u003cp\u003eIt can be seen from the figure that the disease burden of stroke in perimenopausal women is negatively correlated with the SDI. That is, as the SDI increases, the disease burden becomes smaller(Fig. 4). When the SDI ranges from 0.4 to 0.7, the prevalence and incidence are relatively stable. When the SDI is lower than 0.4 or higher than 0.7, both the prevalence and incidence fluctuate significantly. It can also be found that when the SDI is around 0.48, the prevalence and incidence in Central Asia reach their peaks (Fig. 4A, Fig. 4B).The DALYs rate tends to be stable when the SDI is lower than 0.4 and then decreases accordingly. When the SDI is 0.4, the DALYs rate in Oceania reaches its peak (Fig. 4C). While the illness burden is lower than anticipated in South Asia, Australasia, and Western Europe, it is higher than anticipated in Central Asia, Southeast Asia, Eastern Europe, and High-income Asia Pacific. It is evident that the illness burden and the declining trend are very similar, with the disease burden in Central Latin America and Andean Latin America being somewhat lower than anticipated. It is important to remember that Central and Southern Sub-Saharan Africa are low-SDI regions with high rates of DALYs, incidence, and prevalence. The low illness burden in high-SDI regions like Western Europe and Australasia contrasts sharply with this(Fig. 4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eTime Joinpoint Analysis\u003c/h2\u003e\n \u003cp\u003eThe age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), and age-standardized disability-adjusted life-year rate (ASDR) of SPW all significantly decreased globally between 1990 and 2021, according to the Joinpoint analysis (Fig. 5).The ASPR decreased significantly by 0.604% (95% CI: -0.6272% - -0.5809%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)). The downward trend was the most significant from 2002 to 2011 ((APC = -0.9413%), 95% CI: -0.9633% - -0.9192%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)). From 2019 to 2021, the trend reversed, showing a significant upward trend ((APC\u0026thinsp;=\u0026thinsp;0.3843%), 95% CI: 0.1558% \u0026minus;\u0026thinsp;0.6133%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.003)). The overall downward trend of the ASIR was obvious ((AAPC=-1.0234%), 95% CI: -1.0998% - -0.9469%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)). The most significant decrease occurred from 2005 to 2014 ((APC=-2.0085%), 95% CI: -2.0944% - -1.9224%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)). Conversely, from 2014 to 2019, the ASIR began to rise ((APC\u0026thinsp;=\u0026thinsp;1.0781%), 95% CI: 0.8236% \u0026minus;\u0026thinsp;1.3333%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)), and there was a slight decrease after 2019. From 1990 to 2021, the ASDR decreased significantly at an average annual rate of 1.9207%. The downward trends of the ASDR were sharp during the periods from 2004 to 2007 and from 2007 to 2011, with APC values of -3.9916% (95% CI: -5.1925% - -2.7755%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)) and \u0026minus;\u0026thinsp;2.7698% (95% CI: -3.4043% - -2.131%, (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)) respectively. After 2011, the decline became more stable (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTime Joinpoint Analysis Figure\u0026nbsp;5\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003elower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eupper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003emeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eASPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.6272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.5809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.5088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.5223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.4953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.9413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.9633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.9192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.7447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.8416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.6477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.4806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.5835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.3776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.3843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.1558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.6133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.002357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eASIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.0234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.0998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.9469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.6457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.9055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.3852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.2766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.3386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.2146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.0085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.0944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.9224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.0781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.8236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.3333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.1266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.9498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.2964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.010707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eASDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.9207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.1154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.7256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.1035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.4615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-0.7443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.3164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.5877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.0283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.001697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.4156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.7042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.1262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.9916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-5.1925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.7755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.7698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.4043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.5412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.6778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.4044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003ePredicted Development Trends\u003c/h2\u003e\n \u003cp\u003eThe total number of cases increased even while the disease burden declined between 1990 and 2021 (Fig. 6). According to predictions, the disease\u0026apos;s global development trend will invert between 2022 and 2044, meaning that the number of cases will continue to rise and that ASPR, ASIR, and ASDR will begin to rise(Fig. 6A, Fig. 6B, Fig. 6C). By 2044, it is predicted that there will be 9,088,013 stroke prevalence cases, 887,981 incidence cases, and 11,792,835 DALYs. Among them, the ASIR increased slightly from 2015 to 2019, and the number of cases also showed an increased growth trend. After 2019, it decreased sharply and then resumed a stable upward trend. This may be related to the outbreak of COVID \u0026minus;\u0026thinsp;19. It is worth noting that the disease burden of SPW was the lightest around 2021, and it will gradually increase thereafter (Fig. 6D, Fig. 6E, Fig. 6F).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\"Ensure healthy lives and promote well-being for all at all ages\" is one of the Sustainable Development Goals (SDGs) of the UN that has been hampered by the rise in the real number of stroke cases\u003csup\u003e[14]\u003c/sup\u003e. Although stroke mostly occurs in the elderly, with the development of the economy and the increase in living pressure, stroke has become one of the major factors affecting the quality of life of various populations. Perimenopausal women bear various pressures from work, family, and personal physical changes, and they should be one of the key focuses of attention. Currently, both stroke and perimenopause are still hot topics in research. Nevertheless, the majority of research only looks at the disease burden in each region, ignoring the disease burden of SPW. Thus, this study examined the disease burden of SPW using data on stroke in perimenopausal women from GBD2021 in order to close this research gap.\u003c/p\u003e \u003cp\u003eThe study found that although the prevalence, incidence, and DALYs rates of SPW had all declined during the previous 32 years, the number of prevalence, incidence, and DALYs cases had nevertheless increased dramatically, with percentage changes of 74%, 54%, and 15%, respectively.This indicates that during this period, the growth trend of the number of stroke cases in SPW slowed down but did not reverse, which is consistent with the overall trend of the decrease in the age - standardized incidence, prevalence, and DALYs rates of stroke from 1990 to 2021\u003csup\u003e[15]\u003c/sup\u003e. Such changes may be related to the rapid increase in the global population and the overall improvement of medical levels. It can also be observed that the higher the SDI, the lower the disease burden. Regions with a higher SDI have a more complete medical environment and higher - level medical care, and people have a relatively higher life happiness index, so the disease burden is relatively lower. From 1990 to 2021, the number of prevalence cases, incidence cases, and DALYs cases increased the most in the low - middle SDI region, which is consistent with our hypothesis. For example, in low - SDI regions, people may not be fully aware of the risks of stroke in SPW. Therefore, it is necessary to continuously monitor the quality of care and constantly strive to improve the broader sensitivity of healthcare workers and the community \u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the national level, among high - SDI countries, the United Arab Emirates, which had the largest increase in the number of cases, and Estonia and Latvia, which had the largest decrease, form a sharp contrast. The reason for this may be that in Middle Eastern countries represented by the United Arab Emirates, such as Saudi Arabia and Qatar, perimenopausal women face higher family - role constraints, gender pressures, and workplace challenges, as well as factors such as infertility and low social support, which lead to an increase in perimenopausal complications\u003csup\u003e[17]\u003c/sup\u003e, thus indirectly increasing the risk of stroke. Therefore, for such countries, it is necessary to enhance the attention to women, especially perimenopausal women. For example, starting from the perspective of religious beliefs, efforts can be made to change the idea that women place family needs above their own \u003csup\u003e[18]\u003c/sup\u003e. Society should also break the traditional patriarchal concept, emphasize equality, and enhance the social and family support for women\u003csup\u003e[19]\u003c/sup\u003e. The country should introduce corresponding policies, increase health institutions, and enhance the emphasis on the physical and mental health of perimenopausal women. On the contrary, in Nordic countries such as Estonia and Latvia, good social welfare, gender equality, and a sound medical policy have enhanced people's life happiness index. According to the World Happiness Report 2025, Nordic countries rank high in the happiness index. The incidence of perimenopausal complications in women is low. With the development of the economy and the significant improvement of medical levels, the emphasis on and prevention of stroke are also stronger. This may be one of the reasons why the number of stroke cases in SPW decreased the most in these countries.\u003c/p\u003e \u003cp\u003eThe overall trend of the illness burden of SPW has declined, according to the Joinpoint regression analysis, which is typically associated with the increasing attention. The release of additional policy documents and the European Society of Cardiology's (ESC) categorization of menopause as a separate risk factor for stroke in women suggest that SPW is gaining more attention, which has a significant effect on the risk trend of SPW. The synchronous decrease in ASPR, ASIR, and ASDR from 2002 to 2011 may be related to the research on the standardized use of hormone replacement therapy (HRT) by the Women's Health Initiative (WHI) globally in 2002\u003csup\u003e[20]\u003c/sup\u003e. For ASPR, 2019 is an important inflection point. After 2019, the ASPR changed from a continuous decline to an upward trend, which may be related to the high social pressure and crowded medical resources during the COVID \u0026minus;\u0026thinsp;19 pandemic. This is different from the stable trend of the ASPR of stroke from 2019 to 2021\u003csup\u003e[21]\u003c/sup\u003e, indicating that the increase in the prevalence rate of SPW requires more attention from society. The ASIR of SPW is generally in a downward trend. The upward trend from 2014 to 2019 may be related to the over - restriction and use risks of HRT\u003csup\u003e[22]\u003c/sup\u003e Moreover, some studies have shown that the use of hormones can also increase the risk of stroke\u003csup\u003e[23]\u003c/sup\u003e. The American Heart Association/American Stroke Association (AHA/ASA) stressed in 2018 that perimenopausal women should focus on high-risk factors for stroke rather than using hormone replacement therapy\u003csup\u003e[24]\u003c/sup\u003e, suggesting that stroke prevention has reached a plateau and that COVID-19's effects have caused volatility after 2019. Compared with ASPR and ASIR, the ASDR is more stable and is in a continuous downward state. The largest decline from 2004 to 2007 may also be related to the standardized use of HRT.\u003c/p\u003e \u003cp\u003eBased on the existing data set prediction, the disease burden of SPW will gradually increase in the future. The prevalence rate, incidence rate, and DALYs rate will transition from a previous year-by-year decline to a current year-by-year increase, while the number of prevalence cases, incidence cases, and DALYs cases will shift from a previous slow increase to an accelerated increase, despite minor fluctuations between 2019 and 2021. Cardiovascular diseases have been identified as diseases with a relatively large proportion related to age and are deeply affected by age changes\u003csup\u003e[25]\u003c/sup\u003e. In the 20th century, the population aged rapidly, while the fertility rates of countries around the world decreased rapidly \u003csup\u003e[26]\u003c/sup\u003e. Therefore, population aging and a decrease in the fertility rate may be one of the reasons for the gradual increase in the disease burden. Thus, it is necessary to strengthen the attention to the aging of stroke patients, and perimenopausal women are part of this group. In addition, it is necessary to actively prevent stroke, control risks, and closely screen for high - risk factors. To this end, the Menopause Health Guideline (No. WHO/MNH/23.1) issued by WHO in 2023 clearly requires that menopausal women be classified as a high - risk group for stroke, and it is recommended to integrate blood pressure and blood lipid screening into menopause clinics.\u003c/p\u003e \u003cp\u003eThis study has the following limitations. First, the GBD data rely on the quality of reports from various countries. Therefore, there is uncertainty in the quality of reports from each country, and there may be underreporting in some low - income countries. Second, emerging risks such as climate change were not included in the prediction model, and the definition of perimenopause did not distinguish between natural menopause and surgical menopause. Therefore, to ensure the reliability of the research, more real - world studies are needed to verify the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing data from GBD2021, this study examined the disease burden of SPW between 1990 and 2021. The findings indicate that while the number of patients has continued to rise during the last 32 years, the illness burden has usually declined, with only minor variations between 2019 and 2021. According to predictions, the illness burden will shift from a declining trend to an upward trend by 2044, and the number of cases would rise quickly. Therefore, the prevention of the disease and the screening of high - risk factors are of great importance. Regions and countries around the world should also formulate corresponding prevention policies according to local conditions. For example, a global perimenopausal health monitoring network can be established to promote transnational cooperation; countries can reform their health systems and incorporate stroke screening for perimenopausal women into basic public health services; at the same time, society as a whole should increase care for this group of people and improve social support. In short, the issue of SPW should be taken seriously.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL: Task allocation, manuscript drafting.W: Conceptual guidance, manuscript revision.T: Figure preparation.X: Table preparation.Z: Data verification.ZJ: Conceptual guidance, manuscript revision.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed during the current study are available in the [Global Burden of Disease Study] repository, [https://ghdx.healthdata.org/gbd-2021/sources].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e Vos, T., et al., Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 2020. 396(10258): pp. 1204\u0026ndash;1222..\u003c/li\u003e\n\u003cli\u003eSun, T. et al. Trends and patterns in the global burden of intracerebral hemorrhage: a comprehensive analysis from 1990 to 2019. Frontiers in Neurology, Volume 14\u0026ndash;2023. (2023).\u003c/li\u003e\n\u003cli\u003eYu, Q. et al. Global, regional, and national burden and trends of stroke among youths and young adults aged 15\u0026ndash;39 years from 1990 to 2021: findings from the Global Burden of Disease study 2021. \u003cem\u003eFront. Neurol.\u003c/em\u003e, Volume 16\u0026ndash;2025. (2025).\u003c/li\u003e\n\u003cli\u003eWang, X. Y. et al. Association of menopausal status and symptoms with depressive symptoms in middle-aged Chinese women. Climacteric, 25(5): pp. 453\u0026ndash;459. (2022).\u003c/li\u003e\n\u003cli\u003e Li, W., et al., Global, regional and national trends in the burden of intracranial hemorrhage, 1990\u0026ndash;2021: Results from the Global Burden of Disease study. Heliyon,2025. 11(4)..\u003c/li\u003e\n\u003cli\u003eYan, S. et al. Burden of Stroke and its Risk Factors in China from 1990 to 2021: an Analysis for the Global Burden of Disease Study (GBD) 2021. (2024).\u003c/li\u003e\n\u003cli\u003eDylla, L. et al. Sex Differences in the Blood Metabolome During Acute Response to Ischemic Stroke. Journal of Women's Health, 33(10): pp. 1378\u0026ndash;1384. (2024).\u003c/li\u003e\n\u003cli\u003eHildreth, K. L., Kohrt, W. M. \u0026amp; Moreau, K. L. Oxidative stress contributes to large elastic arterial stiffening across the stages of the menopausal transition. Menopause, 21(6). (2014).\u003c/li\u003e\n\u003cli\u003eGao, J. et al. Global trends, disparities, and future projections of ischemic stroke burden attributed to low-fiber diets: An analysis based on GBD 2021. \u003cem\u003eJ. Stroke Cerebrovasc. Dis.\u003c/em\u003e, \u003cstrong\u003e34\u003c/strong\u003e(6). (2025).\u003c/li\u003e\n\u003cli\u003eAho, K. et al. 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(2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Stroke, Perimenopausal women, GBD, Trend projection","lastPublishedDoi":"10.21203/rs.3.rs-6672476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6672476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStroke, the second leading global cause of death, disproportionately affects perimenopausal women, though research on this population remains limited. Utilizing GBD 2021 data, this study reveals that while global stroke prevalence, incidence, and DALY cases in perimenopausal women significantly increased from 1990\u0026ndash;2021, age-standardized rates declined overall. Middle-high and high-SDI regions demonstrated polarized trends - the UAE saw the sharpest case increases while Estonia and Latvia achieved notable reductions. Joinpoint regression analysis of these 32-year trends suggests an impending reversal: projections indicate both disease burden rates and absolute case numbers will transition from historical declines to future increases by 2044. Despite previous improvements in age-adjusted metrics, the persistent rise in cases coupled with these forecasts underscores the critical need for targeted interventions to mitigate stroke's growing impact on perimenopausal women worldwide.\u003c/p\u003e","manuscriptTitle":"Global, Regional and National Burden of Stroke among Perimenopausal Women: Trends from 1990 to 2021 and Projections up to 2044","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 08:13:38","doi":"10.21203/rs.3.rs-6672476/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":"39a0e615-fc6d-4867-b7e8-4148eb469bfc","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50673585,"name":"Health sciences/Diseases"},{"id":50673586,"name":"Health sciences/Health care"},{"id":50673587,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-09-18T04:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 08:13:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6672476","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6672476","identity":"rs-6672476","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}