Age-period-cohort analysis of stroke incidence in China and India from 1990 to 2019 and predictions up to 2042

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Methods We downloaded the stroke incidence data of China and India residents from the GBD2019 database from 1990 to 2019 and fitted the trend of stroke incidence data of China city residents by using the Joinpoint regression model to calculate the annual percentage change (APC) and the average annual percentage change (AAPC). In addition, the effects of age, period, and birth cohort on the incidence of stroke were investigated by building an age-period-cohort model. Bayesian age-period-cohort models were used to predict stroke incidence by 2042. Results The overall trend in stroke incidence from 1990 to 2019 was downward in both China and India. Age-standardized incidence rates in China and India decreased from 221.51/100,000 and 121.35/100,000 in 1990 to 200.84/100,000 and 110.7/100,000 in 2019, respectively. Joinpoint regression models showed that stroke incidence in China declined by an average of 0.35% per year (AAPC = − 0.35%, P < 0.001), with the fastest decline occurring from 2005 to 2010 (AAPC = − 2.18%, P < 0.001), and that stroke incidence in India declined by an average of 0.32% per year (AAPC = − 0.32%, P < 0.001), with the fastest decline occurring from 1995 to 2000 (APC = − 1.57%, P < 0.001). Age-period-cohort models showed that the relative risk (RR) of stroke increased with age and period in both countries but decreased with birth cohort. Projections indicate a decreasing trend in the incidence of stroke in the Chinese population by 2042. The ASIR for men and women decreases to 186.87/100,000 and 161.97/100,000, respectively, while the incidence of stroke in the Indian population shows an upward trend, increasing to 133.85/100,000 and 209.16/100,000 for men and women, respectively. Conclusion The age-standardized incidence of stroke in both China and India showed a decreasing trend from 1990 to 2019. In both countries, the risk of stroke increased with increasing age and period and decreased with birth cohort. Increasing age is a key factor influencing stroke incidence in both countries, and stroke remains a major public health problem in both countries, especially because they are the two most populous countries in the world. Stroke Incidence Joinpoint regression Age-period-cohort Trend Prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Globally, stroke is the second-leading cause of death and the third-leading cause of death and disability [ 1 , 2 ]. It is a complex, multifactorial disease that affects people of all ages, genders, and races. The incidence of stroke varies widely across countries and regions, and China and India are no exception. Stroke is a major public health problem in China, with 940,000 new cases of stroke and 760,000 stroke deaths per three years, according to the Global Burden of Disease Study 2019 [ 3 ]. Stroke is also a major public health problem in India, where the incidence of stroke ranges from 105/100,000 to 152/100,000 per year [ 4 ], with an incidence that continues to increase year by year. Although the incidence of stroke in India is lower than that in China, it remains the leading cause of death and disability in the country [ 5 ]. This study provides a scientific basis for stroke prevention and control strategies in China and India by analyzing the effects of age, period, and birth cohort on stroke incidence in China and India and predicts the future incidence rate. Data And Methods 1.1. Data sources Our study data on stroke in China and India were obtained from the Global Health Data Exchange (GHDx) section of the results tool ( http://ghdx.healthdata.org/gbd-results-tool ). The data provide internally consistent estimates of age- and sex-specific all-cause and cause-specific mortality for 369 diseases and injuries across 204 countries and territories from 1990 to 2019, and the International Classification of Diseases (ICD-10) was used to classify the diseases studied, making the estimates more accurate, as described in the literature [ 6 , 7 ]. In this study, people between 0 and 95 years of age were selected as the study population. Specifically, people between 0 and 95 years of age were divided into 19 age groups in 5-year age increase intervals. Also, to avoid overlap of adjacent cohorts, six time periods (1990, 1995, 2000, 2005, 2010, and 2015) were selected for the study, and finally, 24 birth cohorts were obtained by subtracting the age from the period. The standardized population used in this study was derived from the world standard population compiled by GBD 2019, and its stroke incidence rate was standardized using the direct standardization method. This study did not require ethical approval because there was no direct participation of human subjects. 1.2. Time trend analysis Joinpoint regression models, which stem from the National Cancer Institute Surveillance Research Program, were log-transformed for ratios, and standard errors were calculated based on a binomial approximation using a Monte Carlo permutation test to determine the best-fit model [ 8 ]. The Joinpoint model was statistically analyzed with the indicators of the annual percentage change (APC), the average annual percentage change (AAPC), and a 95% confidence interval (CI) [ 9 ]. The APC was used to evaluate the trend of each period, and the AAPC was used to evaluate the trend of the entire period [ 10 ]. When the test level α was 0.05 and P < 0.05, the trend was statistically significant. 1.3. Age-period-cohort model Because of the linear relationship between age, period, and cohort, there may be non-identification problems, which can make it difficult to estimate a unique set of effects for each age group, period, and cohort [ 11 ]. The age-period-cohort (APC) model is a widely used statistical method for analyzing and understanding the effects of age, period, and cohort on various health outcomes, including disease incidence and mortality. In this study, the intrinsic estimation (IE) method was used to estimate the effect coefficients for age, period, and cohort, and the relative risk of incidence was obtained by natural logarithm transformation [ 12 ]. Relative risk (RR) RR = Exp (effect coefficient). 1.4. Bayesian age-period-cohort model In this study, the Bayesian age-period-cohort (BAPC) method was used to predict the incidence and number of strokes. The BAPC is a statistical model used to estimate the effects of age, period, and cohort on the response variables. The model was implemented using a hierarchical Bayesian framework, including separate regression models for age, period, and cohort, and estimated the effects of these three factors while considering their linear correlation. BAPC models are generally based on Poisson regression models, in which age effects are considered as row variables, period effects as column variables, and cohort effects as cross-effects of age and period effects. Three-factor models of age, period, and cohort were constructed and analyzed, and Markov chain Monte Carlo (MCMC) simulations were performed [ 13 ]. The population data required for the projections in this study are derived from the GBD2019 demographic data on the number of people by gender in China and India ( https://ghdx.healthdata.org/record/ihme-data/global-population-forecasts-2017-2100 ). 1.5. Statistical methods This study used Excel 2019 for data organization by using Joinpoint Regression Program 4.9.0.0 software for regression analysis, and the age-period-cohort model was constructed with Stata17.0 software. The BAPC model uses the BAPC and INLA packages in R 4.2.2 for prediction. Results 2.1. Trends in the incidence of stroke in China and India Figure 1 , Tables 1 and 2 show that the age-standardized incidence of stroke in both China and India showed a decreasing trend from 1990 to 2019, with gender differences in incidence between men and women. In China and India, the ASIR declined from 221.51/100,000 and 121.35/100,000 in 1990 to 200.84/100,000 and 110.7/100,000 in 2019, respectively. The Joinpoint regression model shows an average annual decline of 0.35% (AAPC = − 0.35%, P < 0.001) in stroke incidence in China, and the decline was fastest from 2005 to 2010 (APC = − 2.18%, P < 0.001), with an average annual decline of 0.30% (AAPC = − 0.3%, P < 0.001) for men and 0.38% (AAPC = − 0.38%, P < 0.001) for women. The average annual decline in stroke incidence in India was 0.32% (AAPC = − 0.32%, P < 0.001), with the fastest decline occurring from 1995 to 2000 (APC = − 1.57%, P < 0.001). The mean annual decline was 0.30% (AAPC = − 0.30%, P < 0.001) for men and 0.38% (AAPC = − 0.38%, P < 0.001) for women. Table 1 Overall trend of stroke incidence in China from 1990 to 2019 Gender Period APC 95%CI t P AAPC 95%CI t P “Male”-5 Joinpoints 1990–1996 0.97 0.86 ~ 1.09 18.51 < 0.001 -0.30 -0.40~-0.20 -6.09 < 0.001 “Male”-5 Joinpoints 1996–2005 -0.07 -0.14 ~ 0.00 -2.11 0.055 “Male”-5 Joinpoints 2005–2010 -2.96 -3.16~-2.75 -30.68 < 0.001 “Male”-5 Joinpoints 2010–2014 0.78 0.45 ~ 1.12 5.06 < 0.001 “Male”-5 Joinpoints 2014–2017 -0.99 -1.65~-0.32 -3.21 0.007 “Male”-5 Joinpoints 2017–2019 0.48 -0.19 ~ 1.15 1.54 0.148 “Female”-5 Joinpoints 1990–1996 0.42 0.32 ~ 0.53 8.56 < 0.001 -0.38 -0.47~-0.29 -8.09 < 0.001 “Female”-5 Joinpoints 1996–2001 -0.24 -0.44~-0.04 -2.63 0.021 “Female”-5 Joinpoints 2001–2010 -1.25 -1.32~-1.18 -39.37 < 0.001 “Female”-5 Joinpoints 2010–2014 0.09 -0.22 ~ 0.41 0.65 0.529 “Female”-5 Joinpoints 2014–2017 -0.88 -1.51~-0.25 -3.03 0.010 “Female”-5 Joinpoints 2017–2019 0.65 0.02 ~ 1.29 2.21 0.045 “Both”-5 Joinpoints 1990–1997 0.63 0.52 ~ 0.74 12.31 < 0.001 -0.35 -0.47~-0.23 -5.81 < 0.001 “Both”-5 Joinpoints 1997–2005 -0.50 -0.61~-0.39 -9.93 < 0.001 “Both”-5 Joinpoints 2005–2010 -2.18 -2.44~-1.93 -18.30 < 0.001 “Both”-5 Joinpoints 2010–2014 0.48 0.07 ~ 0.89 2.51 0.026 “Both”-5 Joinpoints 2014–2017 -0.89 -1.70~-0.07 -2.35 0.035 “Both”-5 Joinpoints 2017–2019 0.59 -0.23 ~ 1.42 1.55 0.146 APC, annual percentage change; AAPC, average annual percent change; CI, confidence interval. Table 2 Overall trend of stroke incidence in India from 1990 to 2019 Gender Period APC 95%CI t P AAPC 95%CI t P “Male”-5 Joinpoints 1990–1995 -0.42 -0.46~-0.37 -19.40 < 0.001 -0.33 -0.36~-0.31 -26.79 < 0.001 “Male”-5 Joinpoints 1995–2000 -1.42 -1.48~-1.35 -46.79 < 0.001 “Male”-5 Joinpoints 2000–2005 -0.13 -0.20~-0.07 -4.39 0.001 “Male”-5 Joinpoints 2005–2010 -0.86 -0.93~-0.80 -28.43 < 0.001 “Male”-5 Joinpoints 2010–2017 0.28 0.24 ~ 0.31 17.06 < 0.001 “Male”-5 Joinpoints 2017–2019 1.29 1.08 ~ 1.50 13.30 < 0.001 “Female”-5 Joinpoints 1990–1995 -0.60 -0.77~-0.43 -7.49 < 0.001 -0.31 -0.41~-0.21 -6.18 < 0.001 “Female”-5 Joinpoints 1995–2005 -1.54 -1.61~-1.48 -47.21 < 0.001 “Female”-5 Joinpoints 2005–2014 0.04 -0.04 ~ 0.13 1.12 0.277 “Female”-5 Joinpoints 2014–2017 2.43 1.65 ~ 3.22 6.66 < 0.001 “Female”-5 Joinpoints 2017–2019 1.02 0.25 ~ 1.80 2.81 0.012 “Both”-5 Joinpoints 1990–1995 -0.48 -0.60~-0.35 -8.14 < 0.001 -0.32 -0.37~-0.26 -12.03 < 0.001 “Both”-5 Joinpoints 1995–2000 -1.57 -1.74~-1.40 -19.10 < 0.001 “Both”-5 Joinpoints 2000–2009 -0.67 -0.73~-0.61 -23.61 < 0.001 “Both”-5 Joinpoints 2009–2014 0.17 -0.01 ~ 0.34 2.01 0.061 “Both”-5 Joinpoints 2014–2019 1.28 1.16 ~ 1.41 21.74 < 0.001 APC, annual percentage change; AAPC, average annual percent change; CI, confidence interval. 2.2. Trends in age, period, and birth cohort of stroke in China and India 2.2.1. Age-specific-period prevalence Figure 2 shows that for different periods, the incidence of stroke in China and India tends to increase with age, and in China, the increase in incidence among people aged 75 years or older slows down after the period 2005–2009. In India, the incidence rate across all six periods (1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014, and 2015–2019) showed a "J"-shaped increasing trend with age. 2.2.2. Age-specific-birth cohort prevalence Figure 3 shows that, in China, the trend of change in stroke incidence was not significant for all age groups except for the elderly group, where the incidence of stroke first increased and then decreased substantially. For India, the trend of incidence change was not significant, with all age groups showing a downward and then an upward trend. 2.3. Age-period-cohort incidence model analysis 2.3.1. Age effects Figure 4 , Supplementary Table 1, shows that the RR of stroke incidence increased with age in both China and India. Specifically, the incidence RR increased by 21.05, 16.47-fold, from the 0 to 4 years to the 90 to 94 years age groups in China for both men and women. In India, the incidence RR increased by 9.13, 8.68-fold, from the 0 to 4 years to the 90 to 94 years age group for both men and women. The incidence RR of stroke in China and India increased in parallel until the age group of 60–64 years, and the trend of an increasing incidence RR after the age group 60–64 years was higher in China than in India. In the 90–94 age group, the incidence RR was 1.49 times and 1.38 times higher in China than in India for men and women, respectively. 2.3.2. Period effect Figure 4 , Supplementary Table 1, shows the relationship between the incidence rate RR and period in China and India, with an increasing trend in both China and India. In China, the increasing trend of the incidence rate RR was rapid until 2000–2004, while a decreasing trend of the incidence rate RR was observed from 2000–2004 to 2005–2009, after which it started to increase slowly. In India, the incidence RR increased slowly until the period 2010–2014, after which the trend increased faster, and the incidence RR was 1.07 times and 1.15 times higher for men and women, respectively, in China during the period 2015–2019. 2.3.3. Cohort effect Figure 4 , Supplementary Table 1, shows that the RR of incidence declined substantially in China and India for Chinese men and women, with a risk reduction of 94.8% and 93.6% for Chinese men and women and 88.7% and 87.1% for Indian men and women during the periods 1990–1994 to 2015–2019. 2.4. Predicted incidence of stroke in China and India Figure 5 , Supplementary Table 2, shows that the incidence of stroke among the Chinese population is on a decreasing trend during 2019–2042, with a 10.82% decrease in ASIR among men (209.53/100,000–186.87/100,000) and a 15.75 decrease in ASIR among women (192.24/100,000–161.97/100,000). The future trend of stroke incidence among the Indian population is opposite to that of the Chinese population, with an increasing trend for both men and women and an increase of 20.03% in ASIR among men (109.69/100,000–133.85/100,000) and 84.03% in women (113.66/100,000–209.16/100,000). Discussion Stroke is a major health problem worldwide, and the burden of stroke is particularly acute in China and India. As the two most populous countries in the world, China and India face unique challenges in stroke prevention, treatment, and management, so it is essential to examine long-term trends in incidence in China and India. This study examines the effects of age, period, and birth cohort on stroke incidence in China and India, respectively, using the APC model. The age effect shows that the risk of stroke incidence increases with age in both China and India. Previous studies have also found that age is one of the most important factors influencing stroke incidence and is positively associated with stroke incidence, which is higher in older adults than in younger adults [ 14 , 15 ]. In China and India, the burden of stroke is particularly high due to an aging population and a generally high prevalence of risk factors [ 16 ]. China is the most populous country in the world, and its proportion of the elderly population is rapidly increasing. According to the latest census of China, conducted in 2020, the proportion of Chinese people aged 65 years and older is close to 14%, which indicates that China is becoming an aging society [ 17 ]. The higher incidence of stroke among the elderly population can also be attributed to a range of risk factors, including hypertension, diabetes mellitus, and high cholesterol [ 18 – 20 ]. Previous studies have shown that, in China, the prevalence of hypertension, diabetes mellitus, and hypercholesterolemia in residents older than 60 years of age was 58.3%, 19.4%, and 10.5%, respectively, and that 75.8% of residents had at least one chronic disease [ 21 ]. Similarly, age is a major factor influencing the incidence of stroke in India, where increasing longevity and declining fertility rates have led to an increasingly aging society, with the population over 60 years of age expected to account for 19.1% of the total population by 2050 [ 22 ]. Furthermore, diseases, such as hypertension and cardiac arrhythmias, and low physical activity are increasing with age and also represent important factors contributing to the risk of stroke [ 23 ]. A period effect is the risk that a particular social environment or natural condition will lead to a change in incidence after controlling for age and cohort effects. The period effect shows an increasing trend in the risk of stroke incidence over time in both China and India that can be attributed to a few factors. One of the main probable factors is population aging, which has led to an increase in the number of stroke patients among the elderly. Another factor that may increase the incidence of stroke in both countries is the increased prevalence of chronic diseases. Also contributing to the increased risk of stroke are unhealthy lifestyles, including smoking, which is a common behavior in India and China and a frequently reported risk factor for stroke [ 24 ]. Some studies have further shown that China and India are the two countries with the highest tobacco use in the world [ 25 ]. It is also noteworthy that, in China, the risk of stroke showed a decreasing trend from 2004 to 2009, which then gradually started to increase again. The reasons for this may be improved lifestyles, dietary changes, increased physical activity, better control of hypertension, and increased use of statins in the Chinese population from 2004 to 2009, which led to a decrease in the risk of stroke [ 26 , 27 ]. For example, the cure rate and control of hypertension in China effectively improved from 2002 to 2012, while the daily salt intake among adolescents was effectively controlled during the same period [ 3 ]. The cohort effect refers to the exposure of different birth cohorts to different factors after controlling for age and period effects. The present study showed that the risk of stroke decreased gradually with each birth cohort in both China and India. Possible explanations for these decreasing birth cohort effects in China and India include the following. One is the improvement in healthcare and public health interventions in both countries [ 28 – 30 ]. For example, the implementation of the international essential drug system and the gradual expansion of health insurance coverage in China have led to better healthcare measures for the population while also reducing the cost burden for stroke patients; thus, these systems may have led to more effective control of stroke incidence. Another aspect is the increased awareness of disease prevention, which may lead to a lower risk of stroke as the younger generation becomes more educated and more aware of stroke risk factors and prevention strategies [ 31 ]. Advances in medical technology may also make stroke treatment potentially more effective, thereby reducing the risk of disability and death due to stroke, which may, in turn, positively impact the risk of stroke in future birth cohorts. Finally, smoking rates have been declining in both China and India in recent years, especially among the younger generations [ 32 , 33 ]. Since smoking is a major risk factor for stroke, this decrease may help reduce the risk of stroke in future birth cohorts [ 34 ]. Predictions indicate that the incidence of stroke in China will gradually decline over the coming period. This decline is likely due to significant progress in stroke prevention and treatment, increased investment in healthcare infrastructure, and overall improved public health in China. Combined with increasing awareness and education regarding stroke, this has led to a decrease in the incidence of stroke. Unlike China, the incidence of stroke in India will be on the rise in the coming period, especially among women. Therefore, there is a need to improve the prevention and management of stroke in women and to improve healthcare services in rural areas to increase stroke awareness and education in India. From the above, it can be concluded that the risk of stroke increases with age and period in both countries and decreases with the birth cohort, indicating that older men are at high risk of developing stroke and that the risk of the disease is higher the earlier the birth cohort is. Although the age-standardized incidence of stroke is showing a downward trend in both countries, stroke remains a major public health challenge in both China and India, and further efforts are needed to prevent and manage stroke in the population. This includes public health campaigns, improved healthcare infrastructure and services, and effective management of risk factors, such as hypertension, diabetes, and smoking. Another point worth noting is that the projections show an increasing trend in the incidence of stroke in India over the next period, especially among women. Therefore, India should focus on the elderly and women to reduce the disease burden of stroke. There are some limitations in this paper; the data analyzed in this study are from the data provided by GBD 2019 and, since the data in this database are simulated using mathematical models, some bias is inevitable. In addition, the GBD 2019 database lacks rural and urban prevalence data for China and India, so the gap between rural and urban stroke prevalence cannot be analyzed. Finally, the standardized population used in this study is derived from the data published in GBD 2019, which is useful for comparison with other countries but does not accurately reflect the prevalence in each country because of the large difference in the number of people in each age group in China and India. Abbreviations GBD Global Burden of Disease RR Relative risk ASIR Age-standardized incidence rate APC Annual percentage change AAPC Average annual percent change CI Confidence interval Declarations Acknowledgements The authors would like to thank the study participants for their cooperation. Author contributions MY designed the research. Xincan Ji participated in data collection and analysis and drafted the manuscript. H-YG, WW, PW, LJ, MT, HY, HP helped analyze data and manuscript development. MY provided research funding and software support. All authors read and approved the final manuscript. Funding This work was supported by the Humanities and Social Sciences Research Planning Foundation of Ministry of Education, China (NO. 2022AH010075), the Academic Support Project for Top-notch Talents in Disciplines (Majors) of Universities in Anhui Province, China (NO. gxbjZD2022042), Research Fund for Young and Middle-aged Researchers of Wannan Medical College (WKS2022F03). Availability of data and materials The GBD 2019 data that support the findings of this study are available from the GBD Data Tool repository via the website of the Institute of Health Metrics and Evaluation (http://ghdx.healthdata.org/gbd-results-tool). Ethics approval and consent to participate. 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Circ Res . 2017;120(3):472-495. doi:10.1161/CIRCRESAHA.116.308398 Hoffman SJ, Mammone J, Rogers Van Katwyk S, et al. Cigarette consumption estimates for 71 countries from 1970 to 2015: systematic collection of comparable data to facilitate quasi-experimental evaluations of national and global tobacco control interventions. BMJ . 2019;365:l2231. Published 2019 Jun 19. doi:10.1136/bmj.l2231 Xu T, Yu X, Ou S, Liu X, Yuan J, Chen Y. Statin Adherence and the Risk of Stroke: A Dose-Response Meta-Analysis. CNS Drugs . 2017;31(4):263-271. doi:10.1007/s40263-017-0420-5 Zhao W, Xiao ZJ, Zhao SP. The Benefits and Risks of Statin Therapy in Ischemic Stroke: A Review of the Literature. Neurol India . 2019;67(4):983-992. doi:10.4103/0028-3886.266274 Yip W, Fu H, Chen AT, et al. 10 years of health-care reform in China: progress and gaps in Universal Health Coverage. Lancet . 2019;394(10204):1192-1204. doi:10.1016/S0140-6736(19)32136-1 Liang Z, Wu D, Guo C, Gu J. Temporal trend of population structure, burden of diseases, healthcare resources and expenditure in China, 2000-2019. BMJ Open . 2023;13(1):e062091. Published 2023 Jan 18. doi:10.1136/bmjopen-2022-062091 Patel V, Parikh R, Nandraj S, et al. Assuring health coverage for all in India. Lancet . 2015;386(10011):2422-2435. doi:10.1016/S0140-6736(15)00955-1 Jha RP, Shri N, Patel P, Dhamnetiya D, Bhattacharyya K, Singh M. Trends in the diabetes incidence and mortality in India from 1990 to 2019: a joinpoint and age-period-cohort analysis [published correction appears in J Diabetes Metab Disord. 2021 Aug 12;20(2):1741]. J Diabetes Metab Disord. 2021;20(2):1725-1740. Published 2021 Jul 5. doi:10.1007/s40200-021-00834-y Jha P. Smoking cessation and e-cigarettes in China and India. BMJ . 2019;367:l6016. Published 2019 Oct 18. doi:10.1136/bmj.l6016 Xi B, Magnussen CG. Smoking control in China: A need for comprehensive national legislation. PLoS Med. 2022;19(8):e1004065. Published 2022 Aug 25. doi:10.1371/journal.pmed.1004065 Pan B, Jin X, Jun L, Qiu S, Zheng Q, Pan M. The relationship between smoking and stroke: A meta-analysis. Medicine (Baltimore). 2019;98(12):e14872. doi:10.1097/MD.0000000000014872 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-2648089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":183017071,"identity":"e29db187-b0b8-4b07-86dc-9597b98bcb0a","order_by":0,"name":"Xincan Ji","email":"","orcid":"","institution":"School of Public Health, Wannan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xincan","middleName":"","lastName":"Ji","suffix":""},{"id":183017072,"identity":"475a4a35-8c24-4297-ac20-8532d2797998","order_by":1,"name":"Mengjun Tao","email":"","orcid":"","institution":"Health management Center, The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengjun","middleName":"","lastName":"Tao","suffix":""},{"id":183017073,"identity":"ac70869a-4472-40d9-a4a9-ad2a0d8c8add","order_by":2,"name":"Hao-Yang Guo","email":"","orcid":"","institution":"School of Public Health, Wannan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hao-Yang","middleName":"","lastName":"Guo","suffix":""},{"id":183017074,"identity":"455456c6-9481-46e4-b38b-df9099445a3e","order_by":3,"name":"Wei Wang","email":"","orcid":"","institution":"School of Public Health, Wannan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":183017076,"identity":"a2a9e346-d177-47a7-97a9-856f9edc3d62","order_by":4,"name":"Peipei Wang","email":"","orcid":"","institution":"School of Medical Information, Wannan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Wang","suffix":""},{"id":183017078,"identity":"6de5a7b7-4648-4ab3-97b0-2486aa283d03","order_by":5,"name":"Lairun Jin","email":"","orcid":"","institution":"School of Public Health Southeast University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lairun","middleName":"","lastName":"Jin","suffix":""},{"id":183017080,"identity":"6e7c5aa2-7313-4b83-b391-b1b33c8067c0","order_by":6,"name":"Hui Yuan","email":"","orcid":"","institution":"School of Public Health, Wannan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yuan","suffix":""},{"id":183017081,"identity":"a60323ac-900b-4b04-8730-5de767a4e6bb","order_by":7,"name":"Hui Peng","email":"","orcid":"","institution":"Department of Science and Technology, The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Peng","suffix":""},{"id":183017082,"identity":"8d8f19d5-7188-4e8f-b4cc-ff7aafe6deb7","order_by":8,"name":"Mingquan Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYDADfmbmww9I0yLZzpZmQJoWg/M8ChLEqbx2eAMz745t8saHeRgMGGpsoglruZ1WwMx75rbhtsO8Bx4wHEvLbSCsJceAObftdoLZYb4EA8aGwyRoMW7mMZAgTYsBM7FaJEF++dt223DGYWAgJxDjF77byRsYZ7bdlufvP3z4wYcaG8JaFA4wmP+A8xIIKQcB+QYGEuN8FIyCUTAKRh4AAN05Ppc6/d6iAAAAAElFTkSuQmCC","orcid":"","institution":"School of Medical Information, Wannan Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mingquan","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2023-03-02 15:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2648089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2648089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34315575,"identity":"cd165c50-a2d4-4714-b12a-df3f31ae7dd2","added_by":"auto","created_at":"2023-03-15 19:28:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145677,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of age-standardized incidence rates (ASIR) of stroke in China and India, male and female, from 1990 to 2019.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2648089/v1/122119b642bb8629b5bf7331.png"},{"id":34315306,"identity":"740e83e3-0eb7-4046-bb8c-c0323e2801e7","added_by":"auto","created_at":"2023-03-15 19:20:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113606,"visible":true,"origin":"","legend":"\u003cp\u003eAge-period trends in prevalence among residents of China (A) and India (B)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2648089/v1/d2152d3370f7f9761811aa45.png"},{"id":34315576,"identity":"e0efb997-0bf0-4f17-bb96-dc4221bc3d65","added_by":"auto","created_at":"2023-03-15 19:28:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184748,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in China (A) and India (B) residents-incidence birth cohort-age\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2648089/v1/3bbf966988543be8f7d819e1.png"},{"id":34315311,"identity":"59e3bd43-c98a-4465-80a0-8deb02a0bf2e","added_by":"auto","created_at":"2023-03-15 19:20:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141489,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of age (A)-period (B)-birth cohort (C) effects by sex in China and India\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2648089/v1/8404f937213a0fdb96459ba5.png"},{"id":34315309,"identity":"f14437b8-17e3-478f-8db0-47d6207347b0","added_by":"auto","created_at":"2023-03-15 19:20:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":279785,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted stroke incidence in China and India. A (Chinese man); B (Chinese women); C (Indian man); D (Indian women)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2648089/v1/5188c2ea4b28d14b403bd295.png"},{"id":35472300,"identity":"64eb46e7-d46a-47e5-a27a-450c7072f7e4","added_by":"auto","created_at":"2023-04-08 05:14:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1289947,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2648089/v1/d3ae1961-6ba5-454e-ad9d-3a8d1a89d15b.pdf"},{"id":34315308,"identity":"a703f9cd-f1de-4be2-9a15-d4211fc3b085","added_by":"auto","created_at":"2023-03-15 19:20:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32763,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-2648089/v1/ed27da8a9fb43c630e390764.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-period-cohort analysis of stroke incidence in China and India from 1990 to 2019 and predictions up to 2042","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, stroke is the second-leading cause of death and the third-leading cause of death and disability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is a complex, multifactorial disease that affects people of all ages, genders, and races. The incidence of stroke varies widely across countries and regions, and China and India are no exception. Stroke is a major public health problem in China, with 940,000 new cases of stroke and 760,000 stroke deaths per three years, according to the Global Burden of Disease Study 2019 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Stroke is also a major public health problem in India, where the incidence of stroke ranges from 105/100,000 to 152/100,000 per year [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with an incidence that continues to increase year by year. Although the incidence of stroke in India is lower than that in China, it remains the leading cause of death and disability in the country [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This study provides a scientific basis for stroke prevention and control strategies in China and India by analyzing the effects of age, period, and birth cohort on stroke incidence in China and India and predicts the future incidence rate.\u003c/p\u003e"},{"header":"Data And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Data sources\u003c/h2\u003e \u003cp\u003eOur study data on stroke in China and India were obtained from the Global Health Data Exchange (GHDx) section of the results tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdata.org/gbd-results-tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data provide internally consistent estimates of age- and sex-specific all-cause and cause-specific mortality for 369 diseases and injuries across 204 countries and territories from 1990 to 2019, and the International Classification of Diseases (ICD-10) was used to classify the diseases studied, making the estimates more accurate, as described in the literature [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this study, people between 0 and 95 years of age were selected as the study population. Specifically, people between 0 and 95 years of age were divided into 19 age groups in 5-year age increase intervals. Also, to avoid overlap of adjacent cohorts, six time periods (1990, 1995, 2000, 2005, 2010, and 2015) were selected for the study, and finally, 24 birth cohorts were obtained by subtracting the age from the period. The standardized population used in this study was derived from the world standard population compiled by GBD 2019, and its stroke incidence rate was standardized using the direct standardization method. This study did not require ethical approval because there was no direct participation of human subjects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Time trend analysis\u003c/h2\u003e \u003cp\u003eJoinpoint regression models, which stem from the National Cancer Institute Surveillance Research Program, were log-transformed for ratios, and standard errors were calculated based on a binomial approximation using a Monte Carlo permutation test to determine the best-fit model [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The Joinpoint model was statistically analyzed with the indicators of the annual percentage change (APC), the average annual percentage change (AAPC), and a 95% confidence interval (CI) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The APC was used to evaluate the trend of each period, and the AAPC was used to evaluate the trend of the entire period [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. When the test level α was 0.05 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the trend was statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Age-period-cohort model\u003c/h2\u003e \u003cp\u003eBecause of the linear relationship between age, period, and cohort, there may be non-identification problems, which can make it difficult to estimate a unique set of effects for each age group, period, and cohort [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The age-period-cohort (APC) model is a widely used statistical method for analyzing and understanding the effects of age, period, and cohort on various health outcomes, including disease incidence and mortality. In this study, the intrinsic estimation (IE) method was used to estimate the effect coefficients for age, period, and cohort, and the relative risk of incidence was obtained by natural logarithm transformation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Relative risk (RR) RR\u0026thinsp;=\u0026thinsp;Exp (effect coefficient).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Bayesian age-period-cohort model\u003c/h2\u003e \u003cp\u003eIn this study, the Bayesian age-period-cohort (BAPC) method was used to predict the incidence and number of strokes. The BAPC is a statistical model used to estimate the effects of age, period, and cohort on the response variables. The model was implemented using a hierarchical Bayesian framework, including separate regression models for age, period, and cohort, and estimated the effects of these three factors while considering their linear correlation. BAPC models are generally based on Poisson regression models, in which age effects are considered as row variables, period effects as column variables, and cohort effects as cross-effects of age and period effects. Three-factor models of age, period, and cohort were constructed and analyzed, and Markov chain Monte Carlo (MCMC) simulations were performed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The population data required for the projections in this study are derived from the GBD2019 demographic data on the number of people by gender in China and India (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/record/ihme-data/global-population-forecasts-2017-2100\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/record/ihme-data/global-population-forecasts-2017-2100\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.5. Statistical methods\u003c/h2\u003e \u003cp\u003eThis study used Excel 2019 for data organization by using Joinpoint Regression Program 4.9.0.0 software for regression analysis, and the age-period-cohort model was constructed with Stata17.0 software. The BAPC model uses the BAPC and INLA packages in R 4.2.2 for prediction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Trends in the incidence of stroke in China and India\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that the age-standardized incidence of stroke in both China and India showed a decreasing trend from 1990 to 2019, with gender differences in incidence between men and women. In China and India, the ASIR declined from 221.51/100,000 and 121.35/100,000 in 1990 to 200.84/100,000 and 110.7/100,000 in 2019, respectively. The Joinpoint regression model shows an average annual decline of 0.35% (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.35%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in stroke incidence in China, and the decline was fastest from 2005 to 2010 (APC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.18%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an average annual decline of 0.30% (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for men and 0.38% (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.38%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for women. The average annual decline in stroke incidence in India was 0.32% (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.32%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the fastest decline occurring from 1995 to 2000 (APC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.57%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean annual decline was 0.30% (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.30%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for men and 0.38% (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.38%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for women.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall trend of stroke incidence in China from 1990 to 2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u0026thinsp;~\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e-0.40~-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1996\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u0026thinsp;~\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.16~-2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-30.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u0026thinsp;~\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.65~-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.19\u0026thinsp;~\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u0026thinsp;~\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e-0.47~-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1996\u0026ndash;2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.44~-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.32~-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-39.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.22\u0026thinsp;~\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.51~-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u0026thinsp;~\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u0026thinsp;~\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e-0.47~-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1997\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.61~-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.44~-1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-18.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u0026thinsp;~\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.70~-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u0026thinsp;~\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAPC, annual percentage change; AAPC, average annual percent change; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall trend of stroke incidence in India from 1990 to 2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.46~-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-19.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e-0.36~-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-26.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.48~-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-46.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20~-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.93~-0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-28.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u0026thinsp;~\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Male\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u0026thinsp;~\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.77~-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e-0.41~-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.61~-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-47.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u0026thinsp;~\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u0026thinsp;~\u0026thinsp;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Female\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026thinsp;~\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.60~-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e-0.37~-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.74~-1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-19.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.73~-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-23.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2009\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026thinsp;~\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ldquo;Both\u0026rdquo;-5 Joinpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026thinsp;~\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAPC, annual percentage change; AAPC, average annual percent change; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Trends in age, period, and birth cohort of stroke in China and India\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Age-specific-period prevalence\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that for different periods, the incidence of stroke in China and India tends to increase with age, and in China, the increase in incidence among people aged 75 years or older slows down after the period 2005\u0026ndash;2009. In India, the incidence rate across all six periods (1990\u0026ndash;1994, 1995\u0026ndash;1999, 2000\u0026ndash;2004, 2005\u0026ndash;2009, 2010\u0026ndash;2014, and 2015\u0026ndash;2019) showed a \"J\"-shaped increasing trend with age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Age-specific-birth cohort prevalence\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that, in China, the trend of change in stroke incidence was not significant for all age groups except for the elderly group, where the incidence of stroke first increased and then decreased substantially. For India, the trend of incidence change was not significant, with all age groups showing a downward and then an upward trend.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Age-period-cohort incidence model analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Age effects\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;1, shows that the RR of stroke incidence increased with age in both China and India. Specifically, the incidence RR increased by 21.05, 16.47-fold, from the 0 to 4 years to the 90 to 94 years age groups in China for both men and women. In India, the incidence RR increased by 9.13, 8.68-fold, from the 0 to 4 years to the 90 to 94 years age group for both men and women. The incidence RR of stroke in China and India increased in parallel until the age group of 60\u0026ndash;64 years, and the trend of an increasing incidence RR after the age group 60\u0026ndash;64 years was higher in China than in India. In the 90\u0026ndash;94 age group, the incidence RR was 1.49 times and 1.38 times higher in China than in India for men and women, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Period effect\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;1, shows the relationship between the incidence rate RR and period in China and India, with an increasing trend in both China and India. In China, the increasing trend of the incidence rate RR was rapid until 2000\u0026ndash;2004, while a decreasing trend of the incidence rate RR was observed from 2000\u0026ndash;2004 to 2005\u0026ndash;2009, after which it started to increase slowly. In India, the incidence RR increased slowly until the period 2010\u0026ndash;2014, after which the trend increased faster, and the incidence RR was 1.07 times and 1.15 times higher for men and women, respectively, in China during the period 2015\u0026ndash;2019.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Cohort effect\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;1, shows that the RR of incidence declined substantially in China and India for Chinese men and women, with a risk reduction of 94.8% and 93.6% for Chinese men and women and 88.7% and 87.1% for Indian men and women during the periods 1990\u0026ndash;1994 to 2015\u0026ndash;2019.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Predicted incidence of stroke in China and India\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Table\u0026nbsp;2, shows that the incidence of stroke among the Chinese population is on a decreasing trend during 2019\u0026ndash;2042, with a 10.82% decrease in ASIR among men (209.53/100,000\u0026ndash;186.87/100,000) and a 15.75 decrease in ASIR among women (192.24/100,000\u0026ndash;161.97/100,000). The future trend of stroke incidence among the Indian population is opposite to that of the Chinese population, with an increasing trend for both men and women and an increase of 20.03% in ASIR among men (109.69/100,000\u0026ndash;133.85/100,000) and 84.03% in women (113.66/100,000\u0026ndash;209.16/100,000).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eStroke is a major health problem worldwide, and the burden of stroke is particularly acute in China and India. As the two most populous countries in the world, China and India face unique challenges in stroke prevention, treatment, and management, so it is essential to examine long-term trends in incidence in China and India. This study examines the effects of age, period, and birth cohort on stroke incidence in China and India, respectively, using the APC model.\u003c/p\u003e \u003cp\u003eThe age effect shows that the risk of stroke incidence increases with age in both China and India. Previous studies have also found that age is one of the most important factors influencing stroke incidence and is positively associated with stroke incidence, which is higher in older adults than in younger adults [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In China and India, the burden of stroke is particularly high due to an aging population and a generally high prevalence of risk factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChina is the most populous country in the world, and its proportion of the elderly population is rapidly increasing. According to the latest census of China, conducted in 2020, the proportion of Chinese people aged 65 years and older is close to 14%, which indicates that China is becoming an aging society [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The higher incidence of stroke among the elderly population can also be attributed to a range of risk factors, including hypertension, diabetes mellitus, and high cholesterol [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Previous studies have shown that, in China, the prevalence of hypertension, diabetes mellitus, and hypercholesterolemia in residents older than 60 years of age was 58.3%, 19.4%, and 10.5%, respectively, and that 75.8% of residents had at least one chronic disease [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, age is a major factor influencing the incidence of stroke in India, where increasing longevity and declining fertility rates have led to an increasingly aging society, with the population over 60 years of age expected to account for 19.1% of the total population by 2050 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, diseases, such as hypertension and cardiac arrhythmias, and low physical activity are increasing with age and also represent important factors contributing to the risk of stroke [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA period effect is the risk that a particular social environment or natural condition will lead to a change in incidence after controlling for age and cohort effects. The period effect shows an increasing trend in the risk of stroke incidence over time in both China and India that can be attributed to a few factors. One of the main probable factors is population aging, which has led to an increase in the number of stroke patients among the elderly. Another factor that may increase the incidence of stroke in both countries is the increased prevalence of chronic diseases. Also contributing to the increased risk of stroke are unhealthy lifestyles, including smoking, which is a common behavior in India and China and a frequently reported risk factor for stroke [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Some studies have further shown that China and India are the two countries with the highest tobacco use in the world [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is also noteworthy that, in China, the risk of stroke showed a decreasing trend from 2004 to 2009, which then gradually started to increase again. The reasons for this may be improved lifestyles, dietary changes, increased physical activity, better control of hypertension, and increased use of statins in the Chinese population from 2004 to 2009, which led to a decrease in the risk of stroke [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For example, the cure rate and control of hypertension in China effectively improved from 2002 to 2012, while the daily salt intake among adolescents was effectively controlled during the same period [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cohort effect refers to the exposure of different birth cohorts to different factors after controlling for age and period effects. The present study showed that the risk of stroke decreased gradually with each birth cohort in both China and India. Possible explanations for these decreasing birth cohort effects in China and India include the following. One is the improvement in healthcare and public health interventions in both countries [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For example, the implementation of the international essential drug system and the gradual expansion of health insurance coverage in China have led to better healthcare measures for the population while also reducing the cost burden for stroke patients; thus, these systems may have led to more effective control of stroke incidence. Another aspect is the increased awareness of disease prevention, which may lead to a lower risk of stroke as the younger generation becomes more educated and more aware of stroke risk factors and prevention strategies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Advances in medical technology may also make stroke treatment potentially more effective, thereby reducing the risk of disability and death due to stroke, which may, in turn, positively impact the risk of stroke in future birth cohorts. Finally, smoking rates have been declining in both China and India in recent years, especially among the younger generations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Since smoking is a major risk factor for stroke, this decrease may help reduce the risk of stroke in future birth cohorts [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePredictions indicate that the incidence of stroke in China will gradually decline over the coming period. This decline is likely due to significant progress in stroke prevention and treatment, increased investment in healthcare infrastructure, and overall improved public health in China. Combined with increasing awareness and education regarding stroke, this has led to a decrease in the incidence of stroke. Unlike China, the incidence of stroke in India will be on the rise in the coming period, especially among women. Therefore, there is a need to improve the prevention and management of stroke in women and to improve healthcare services in rural areas to increase stroke awareness and education in India.\u003c/p\u003e \u003cp\u003eFrom the above, it can be concluded that the risk of stroke increases with age and period in both countries and decreases with the birth cohort, indicating that older men are at high risk of developing stroke and that the risk of the disease is higher the earlier the birth cohort is. Although the age-standardized incidence of stroke is showing a downward trend in both countries, stroke remains a major public health challenge in both China and India, and further efforts are needed to prevent and manage stroke in the population. This includes public health campaigns, improved healthcare infrastructure and services, and effective management of risk factors, such as hypertension, diabetes, and smoking. Another point worth noting is that the projections show an increasing trend in the incidence of stroke in India over the next period, especially among women. Therefore, India should focus on the elderly and women to reduce the disease burden of stroke.\u003c/p\u003e \u003cp\u003eThere are some limitations in this paper; the data analyzed in this study are from the data provided by GBD 2019 and, since the data in this database are simulated using mathematical models, some bias is inevitable. In addition, the GBD 2019 database lacks rural and urban prevalence data for China and India, so the gap between rural and urban stroke prevalence cannot be analyzed. Finally, the standardized population used in this study is derived from the data published in GBD 2019, which is useful for comparison with other countries but does not accurately reflect the prevalence in each country because of the large difference in the number of people in each age group in China and India.\u003c/p\u003e"},{"header":"Abbreviations ","content":"\u003cp\u003eGBD \u0026nbsp; \u0026nbsp; \u0026nbsp;Global Burden of Disease\u003c/p\u003e\n\u003cp\u003eRR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Relative risk\u003c/p\u003e\n\u003cp\u003eASIR \u0026nbsp; \u0026nbsp; Age-standardized incidence rate\u003c/p\u003e\n\u003cp\u003eAPC \u0026nbsp; \u0026nbsp; \u0026nbsp; Annual percentage change\u003c/p\u003e\n\u003cp\u003eAAPC \u0026nbsp; Average annual percent change\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the study participants for their cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMY designed the research. Xincan Ji participated in data collection and analysis and drafted the manuscript. H-YG, WW, PW, LJ, MT, HY, HP helped analyze data and manuscript development. MY provided research funding and software support. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Humanities and Social Sciences Research Planning Foundation of Ministry of Education, China (NO. 2022AH010075), the Academic Support Project for Top-notch Talents in Disciplines (Majors) of Universities in Anhui Province, China (NO. gxbjZD2022042), Research Fund for Young and Middle-aged Researchers of Wannan Medical College (WKS2022F03).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GBD 2019 data that support the findings of this study are available from the GBD Data Tool repository via the website of the Institute of Health Metrics and Evaluation (http://ghdx.healthdata.org/gbd-results-tool).\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate.\u003c/p\u003e\n\u003cp\u003eThis study is based on public data and does not involve any individual information. Ethical approval is not required.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePandian JD, Sebastian IA. Integrated approach to stroke burden: are we doing enough? \u003cem\u003eLancet Neurol\u003c/em\u003e. 2021;20(10):774-775. doi:10.1016/S1474-4422(21)00287-8\u003c/li\u003e\n\u003cli\u003eThayabaranathan T, Kim J, Cadilhac DA, et al. Global stroke statistics 2022. \u003cem\u003eInt J Stroke\u003c/em\u003e. 2022;17(9):946-956. doi:10.1177/17474930221123175\u003c/li\u003e\n\u003cli\u003eWang YJ, Li ZX, Gu HQ, et al. 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Published 2022 Aug 25. doi:10.1371/journal.pmed.1004065\u003c/li\u003e\n\u003cli\u003ePan B, Jin X, Jun L, Qiu S, Zheng Q, Pan M. The relationship between smoking and stroke: A meta-analysis. Medicine (Baltimore). 2019;98(12):e14872. doi:10.1097/MD.0000000000014872\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, Incidence, Joinpoint regression, Age-period-cohort, Trend, Prediction","lastPublishedDoi":"10.21203/rs.3.rs-2648089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2648089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo analyze the trend of stroke incidence in Chinese and Indian residents from 1990 to 2019, to discuss the effects of age, period, and birth cohort factors on the incidence of stroke in China and India, respectively, and to predict the future incidence trends to provide scientific reference for stroke prevention and control measures in China and India.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe downloaded the stroke incidence data of China and India residents from the GBD2019 database from 1990 to 2019 and fitted the trend of stroke incidence data of China city residents by using the Joinpoint regression model to calculate the annual percentage change (APC) and the average annual percentage change (AAPC). In addition, the effects of age, period, and birth cohort on the incidence of stroke were investigated by building an age-period-cohort model. Bayesian age-period-cohort models were used to predict stroke incidence by 2042.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall trend in stroke incidence from 1990 to 2019 was downward in both China and India. Age-standardized incidence rates in China and India decreased from 221.51/100,000 and 121.35/100,000 in 1990 to 200.84/100,000 and 110.7/100,000 in 2019, respectively. Joinpoint regression models showed that stroke incidence in China declined by an average of 0.35% per year (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.35%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the fastest decline occurring from 2005 to 2010 (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.18%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and that stroke incidence in India declined by an average of 0.32% per year (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.32%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the fastest decline occurring from 1995 to 2000 (APC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.57%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age-period-cohort models showed that the relative risk (RR) of stroke increased with age and period in both countries but decreased with birth cohort. Projections indicate a decreasing trend in the incidence of stroke in the Chinese population by 2042. The ASIR for men and women decreases to 186.87/100,000 and 161.97/100,000, respectively, while the incidence of stroke in the Indian population shows an upward trend, increasing to 133.85/100,000 and 209.16/100,000 for men and women, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe age-standardized incidence of stroke in both China and India showed a decreasing trend from 1990 to 2019. In both countries, the risk of stroke increased with increasing age and period and decreased with birth cohort. Increasing age is a key factor influencing stroke incidence in both countries, and stroke remains a major public health problem in both countries, especially because they are the two most populous countries in the world.\u003c/p\u003e","manuscriptTitle":"Age-period-cohort analysis of stroke incidence in China and India from 1990 to 2019 and predictions up to 2042","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-15 19:19:56","doi":"10.21203/rs.3.rs-2648089/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":"d739b23f-e4ba-4a5b-ab91-dbbc79ac8ad2","owner":[],"postedDate":"March 15th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-04-08T05:14:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-15 19:19:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2648089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2648089","identity":"rs-2648089","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}