Analysis and comparison of the trends in burden of injury in China and ASEAN countries from 1990 to 2021 and its association with the socio-demographic index

Analysis and comparison of the trends in burden of injury in China and ASEAN countries from 1990 to 2021 and its association with the socio-demographic index | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis and comparison of the trends in burden of injury in China and ASEAN countries from 1990 to 2021 and its association with the socio-demographic index Feiyang Nie, Xinyu Bai, Wenjie Liang, Zhenyu Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5825013/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background As China and the Association of Southeast Asian Nations (ASEAN) experience rapid economic growth and urbanization, injuries have become a significant public health issue. This study aims to analyze and compare the injury burden trends in these regions from 1990 to 2021, while examining the correlation with the Socio-Demographic Index (SDI). Methods Data from the Global Burden of Disease Study 2021 (GBD 2021) was used to assess injury burden metrics such as incidence, prevalence, mortality, and disability-adjusted life years (DALYs). Joinpoint regression analysis identified trends, while frontier analysis assessed the optimal scenario for managing injury burden relative to the SDI. Health inequality was analyzed using the Slope Index of Inequality (SII) and Concentration Index (CI). Results The study revealed a mixed picture of injury burden trends. While overall trends showed a decrease in injury incidence, prevalence, mortality, and DALYs, certain periods and countries experienced increases. Unintentional injuries remained the predominant cause. The injury burden shifted to older adults, particularly those aged 70 and above, reflecting the demographic shift towards an aging population, with males bearing a higher burden compared to females. The injury burden was strongly correlated with the SDI, indicating a decrease as countries develop. In the frontier analysis examining the correlation between injury burden and the SDI, countries furthest from the global frontier fit line were predominantly those with middle to high SDI rankings. This finding suggests that countries with higher SDI levels exhibit a more substantial potential for advancing health burden mitigation efforts. The SII for DALYs decreased from − 2407.96 in 1990 to -1159.885 in 2021, indicating a reduction in the disparity of age-standardized injury burden between high-income and low-income countries. Conclusions The study provides valuable insights into the injury burden trends and disparities in China and ASEAN countries. It underscores the importance of addressing social determinants of injury risk and emphasizes the need for tailored interventions considering regional disparities and evolving age distributions. By leveraging these findings, policymakers and stakeholders can develop more effective strategies to reduce the socio-economic burden of injuries, contributing to the achievement of Sustainable Development Goals related to injury prevention. Injury burden China ASEAN Socio-Demographic Index Trend analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background The member states of the Association of Southeast Asian Nations (ASEAN), encompassing Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, are interconnected both economically and socially[ 1 ]. Nevertheless, given the diverse political systems, multiple faiths, varying stages of economic progress, and, crucially, the distinct models of healthcare systems among them, the member countries of ASEAN still face obstacles in attaining regional collaboration for the prevention and management of injuries[ 2 ]. China and ASEAN, as major economies in the Asia-Pacific region and each other's largest trading partners, have built a dialogue relationship spanning more than three decades. This positions them to play a pivotal role in shaping the trajectory of health affairs in Asia and on a worldwide scale. Injury poses a global health issue affecting all nations, impacting individuals of varying ages and genders. Over the past 30 years in China, the swift growth of the economy, the process of urbanization, the increase in motor vehicle usage, the aging population, and shifts in environmental and lifestyle factors have resulted in injuries becoming the fifth most common cause of mortality, following cancers, strokes, respiratory illnesses, and heart attacks[ 3 ]. Injuries result in a yearly productivity deficit of 12.6 million years in China, surpassing the loss from respiratory, cardiovascular, infectious, and cancer diseases combined[ 4 ]. The immediate healthcare expenses related to injuries in China are approximated at 65 billion Yuan or $ 10 billion annually. Hence, injury is increasingly viewed as a significant public health issue in China, potentially leading to substantial economic costs. Research and collaboration on a global scale focusing on monitoring, intervention, educational initiatives, promotional activities, and advocacy efforts aimed at various injury types and vulnerable groups have resulted in the formulation and enforcement of numerous policies, rules, and legislative measures in China's injury prevention and control sectors[ 5 ]. Nonetheless, there remains a gap in comprehensive data that captures the overall impact of injuries at both the national and regional levels. Additionally, there is a growing interest in tracking the trends of major injuries over time and pinpointing key areas for injury prevention efforts. In the Sustainable Development Goals (SDGs), there is a specific target dedicated to reducing injuries, namely SDG 3.6. This target is to halve the global mortality rate from road traffic injuries by 2030. To achieve this, a series of comprehensive measures must be implemented to decrease both the frequency and severity of road traffic accidents. These measures are expected to lower the number of deaths from road traffic injuries and to comprehensively enhance global road traffic safety. The SDGs stress the importance of reducing injuries and incidents of violence, which take more than 5 million lives each year, constituting 9% of all global deaths. Worldwide, injuries are among the leading causes of death and disability. However, progress in the field of injury prevention remains inadequate. Estimates suggest that the probability of meeting the specific injury-related targets in the SDGs is less than 5%.[ 6 ] This situation indicates that the economic impact of injuries is likely to continue to rise. For China and ASEAN countries, injury prevention is of great significance in achieving the SDGs. These countries face challenges from road traffic accidents and other unintentional injuries as they undergo economic development and urbanization. In China and ASEAN countries, strong measures are needed to reduce these injuries. This is not only to fulfill the SDGs but also to reduce the socio-economic burden of injuries, which is particularly severe for groups with lower socio-economic status. Therefore, these countries need to increase their investment and efforts in injury prevention to achieve the relevant SDG targets. This study aims to examine and contrast the current situation and changing trends of injury burden in China and ASEAN countries from 1990 to 2021, leveraging data from the Global Burden of Disease Study 2021 (GBD 2021). Meanwhile, this study will also focus on exploring the inherent relationships between these changes and the Socio-demographic Index (SDI) of each country. The findings of this study offer a powerful perspective for a comprehensive examination of the trend characteristics and differences in the injury burden of China and ASEAN countries, and are of evidential reference value for promoting the formulation of more targeted and effective injury prevention strategies on a global scale. Methods Data source In this observational inquiry, we draw upon the findings yielded by the GBD 2021 result instruments. The GBD 2021 offers an exhaustive examination of the health detriments arising from 369 distinct diseases, injuries, and impairments, as well as 88 contributory risk factors. Encompassing a global spectrum of 204 countries and territories, the study avails itself of the most current epidemiological datasets and enhanced, standardized analytical approaches[7]. We procured the estimations for incidence, prevalence, mortality, and disability-adjusted life years (DALYs) pertaining to injury, complete with their corresponding 95% uncertainty intervals (95% UI), from the GBD 2021 dataset. Additionally, to provide a nuanced analysis of our findings, we incorporated the SDI. The SDI is a multifaceted indicator that mirrors a country's progress in terms of health outcomes, calculated by averaging three components: fertility rate among those under 25, educational attainment for individuals aged 15 and above, and per capita income, with an adjustment for time lag. An SDI score of 0 signifies the lowest level of development for health, while a score of 1 represents the highest[8]. Burden description A systematic evaluation was undertaken to ascertain the burden of injury in China and ASEAN countries, encompassing its epidemiological landscape, including prevalence, incidence, mortality, and DALYs. Furthermore, this study probed the demographic determinants shaping the injury burden, analyzing the differential distribution of the disease’s load among various age strata and between sexes. Statistics analysis We meticulously examined the incidence, prevalence, mortality, DALYs, and their respective age-standardized incidence rates (ASIR), age-standardized prevalence rates (ASPR), age-standardized mortality rates (ASMR), and age-standardized DALY rates (ASDR) of injuries in China, ASEAN countries, and globally, utilizing data from the GBD2021 database. Additionally, we computed the crude incidence rates (CIR), crude prevalence rates (CPR), crude mortality rates (CMR), and crude DALY rates (CDR) for different age groups. The projections of incidence, prevalence, mortality, and DALYs are presented per 100,000 individuals, complete with 95% UI, to reflect the variability in the estimates. In this investigation, we utilized the Joinpoint regression analysis model, a methodology frequently employed in epidemiological studies to evaluate temporal trends in injury incidence, prevalence, mortality, and DALYs[9]. This sophisticated model adeptly detects and quantitatively describes significant change points in the time-series data of injury rates across China, ASEAN countries and global contexts. The analysis enabled the estimation of the annual percent change (APC) and its corresponding 95% confidence interval (95%CI), thereby delineating prevalence trends over specified periods. Furthermore, to comprehensively assess the observed trends, we also computed the average annual percent change (AAPC), encapsulating the aggregated trend data over the study duration from 1990 to 2021. In a sophisticated analytical approach, the age-standardized indicators are modelled using a logarithmic regression framework, expressed as ln(y) = α + βx + ε. Here, y denotes the age-standardized indicator of interest, while x signifies the chronological year. The AAPC is computed as 100 × (exp(β) − 1), and the 95% CI can also be calculated from the model. Statistically, an APC or AAPC estimate with a 95% CI lower bound greater than zero indicates an upward trend within the given interval. Conversely, an APC or AAPC estimate with a 95% CI upper bound less than zero suggests a downward trend. When the 95% CI for the APC or AAPC includes zero, it suggests that the trend has remained stable over time. In our pursuit to delineate the intricate relationship between injury burden and sociodemographic advancement, we have employed frontier analysis to gain further insights. This method generates a nonlinear frontier, which signifies the minimum attainable burden in relation to a given level of development. Utilizing non-parametric data envelope analysis, we adhered to the meticulous methodologies outlined in prior research[10, 11]. The discrepancy between a nation’s observed burden rates and the frontier, termed as the effective difference, quantifies the unattained health benefits that could be realized within the context of the country’s or region’s current developmental stage. In our pursuit to quantify health inequality, we have extracted data on total DALYs and ASDRs for in-depth inequality analysis. Adhering to the guidelines set forth by the World Health Organization, we have employed two canonical measures — the Slope Index of Inequality (SII) and the Concentration Index (CI) — to evaluate both absolute and relative income-related inequalities across countries[12]. The SII encapsulates the gradient of the regression line that correlates a country’s ASDR for injury with its weighted socioeconomic ranking. To normalize for differing levels of burden, we divide the SII by the global ASDR, thus deriving the Relative Index of Inequality (RII). Meanwhile, the CI serves as an instrument to gauge the relative disparity in the injury burden among countries by constructing the Lorenz concentration curve, which is based on cumulative DALYs and population data. The CI is quantified as the area under this curve, ranging from -1 to 1. A negative CI value signifies a disproportionate concentration of the injury burden among populations in countries with lower SDI values. All statistical manipulations and graphical representations were conducted using R version 4.4.1. Statistical significance was assigned to results with a P value of less than 0.05. Results Description of the burden of injury in China and ASEAN countries Incidence of injury in China and ASEAN countries Incidence of injury in China and the ASEAN countries collectively accounted for 18.61% of the global tally in 1990, which had marginally increased to 19.61% by 2021. Meanwhile, globally, the incidence increased from 554,872,072 (95% UI: 520,239,997-592,107,015) in 1990 to 607,789,604 (95% UI: 574,661,712-644,552,987) in 2021, representing a cumulative increase of 9.54%. In 2021, China bore the brunt of injury occurrences with a staggering 83.71 million cases, trailed by Indonesia, which reported 12.32 million, and Vietnam with 6.16 million. The global ASIR exhibited a decline from 10,264.62 per 100,000 population (95% UI: 9,647.30-10,944.94) in 1990 to 7,705.75 per 100,000 population (95% UI: 7,271.61-8,171.19) by 2021. Within China, the ASIR diminished from 6,001.81 per 100,000 population (95% UI: 5,546.05-6,583.71) in 1990 to 5,840.52 per 100,000 population (95% UI: 5,395.03-6,400.14) in 2021. The overall injury ASIRs for both China and ASEAN countries were below the global average in 2021. Notably, only two countries, Brunei and Singapore, recorded higher injury ASIRs than the worldwide norm. Brunei topped the list with an ASIR of 13,921.83 per 100,000, closely followed by Singapore with 12,656.07 per 100,000. Myanmar held the third position with an ASIR of 7,532.78 per 100,000. Concurrently, the AAPC in the global incidence rate registered a decrease of 0.98% (95% CI: -1.23 to -0.73) from 1990 to 2021, with Indonesia leading the decline at a rate of 1.12% (95% CI: -1.35 to -0.89), succeeded by Thailand at a rate of 1.07% (95% CI: -1.23 to -0.90). (Table 1 and Figure 1). Prevalence of injury in China and ASEAN countries In terms of prevalence, the number of injury cases in China and ASEAN countries collectively accounted for 24.42% of the global tally in 1990, which incremented to 25.47% by 2021. Globally, the prevalence of injuries escalated from 1,030,712,875 (95% UI: 980,087,458-1,086,397,210) in 1990 to 1,456,350,420 (95% UI: 1,385,784,930-1,535,575,907) in 2021, marking a cumulative increase of 41.30%. The global ASPR witnessed a decline from 21,445.74 per 100,000 population (95% UI: 20,428.04-22,536.24) in 1990 to 17,531.20 per 100,000 population (95% UI: 16,677.62-18,485.66) in 2021. In China, the ASPR decreased from 17,035.57 per 100,000 population (95% UI: 16,112.16-18,086.07) in 1990 to 16,266.69 per 100,000 population (95% UI: 15,325.96-17,338.10) in 2021. By 2021, both China and the ASEAN countries exhibited ASPRs below the global average. Notably, only Brunei and Singapore reported injury ASPRs surpassing the global norm. Brunei topped the rankings with an ASPR of 22,983.99 per 100,000, closely followed by Singapore with 21,147.39 per 100,000. Cambodia ranked third with an ASPR of 17,007.83 per 100,000. Concurrently, the AAPC in the global prevalence rate recorded a decrement of 0.64% (95% CI: -0.68 to -0.81) from 1990 to 2021. The decline in injury prevalence rates was led by Cambodia, Laos, and Indonesia, each exhibiting an AAPC of 0.81%. Specifically, Cambodia’s AAPC was -0.81% (95% CI: -0.87 to -0.75), Laos’s AAPC was -0.81% (95% CI: -0.83 to -0.79), and Indonesia’s AAPC was -0.81% (95% CI: -0.89 to -0.72). (Table 1, Figure 1). Mortality of injury in China and ASEAN countries In the domain of injury mortality, China and ASEAN countries collectively Shouldered a significant burden, accounting for 29.30% of the global injury deaths in 1990. However, by 2021, this proportion had experienced a modest decline to 23.16%. Concurrently, on a global scale, the number of injury deaths saw a slight uptick, ascending from 4,185,780 (95% UI: 3,974,301-4,373,502) in 1990 to 4,343,698 (95% UI: 3,984,365-4,631,455) in 2021, marking a cumulative increase of 3.77%. This global trend of increment stands in contrast to the notable reduction observed in both the absolute numbers and proportional contribution of injury deaths in China and the ASEAN countries. The global ASMR exhibited a decline from 84.86 per 100,000 population (95% UI: 80.77-88.56) in 1990 to 53.66 per 100,000 population (95% UI: 49.18-57.28) by 2021. Within China, the ASMR diminished from 89.81 per 100,000 population (95% UI: 81.37-99.45) in 1990 to 41.82 per 100,000 population (95% UI: 35.01-49.61) in 2021. In the annals of 2021, the ASMRs for China and ASEAN countries exhibited a mixed pattern when juxtaposed with the global average. Notably, five of these countries surpassed the global average, while a total of six fell beneath it. Vietnam topped the list with an ASMR of 68.49 per 100,000, closely followed by Cambodia with 67.13 per 100,000. Thailand held the third position with an ASMR of 65.9 per 100,000. Concurrently, the AAPC in the global mortality rate registered a decrease of 1.50% (95% CI: -1.67 to -1.33) from 1990 to 2021, with Singapore leading the decline at a rate of 3.33% (95% CI: -3.61 to -3.04), succeeded by China at a rate of 2.41% (95% CI: -2.87 to -1.95). (Table 1, Figure 1). DALYs of injury in China and ASEAN countries DALYs of injury in China and ASEAN countries collectively accounted for 29.37% of the global tally in 1990, which had marginally decreased to 21.57% by 2021. Meanwhile, globally, the DALYs decreased from 278,725,499 (95% UI: 262,114,155-298,335,835) in 1990 to 247,843,924 (95% UI: 226,826,042-272,347,784) in 2021, representing a cumulative decrease of 11.08%. The global ASDR exhibited a decline from 5,221.14 per 100,000 population (95% UI: 4,898.12-5,617.65) in 1990 to 3,101.29 per 100,000 population (95% UI: 2,839.62-3,408.68) by 2021. Within China, the ASDR diminished from 5,354.92 per 100,000 population (95% UI: 4,846.13-5,930.71) in 1990 to 2,284.18 per 100,000 population (95% UI: 1,991.95-2,605.37) in 2021. In terms of injury ASDRs for 2021, a distinctive dichotomy pattern emerged within the China and ASEAN countries’ cohort: whereas four nations bore a heavier burden with ASDRs exceeding the global average, a majority of seven countries demonstrated a comparative leniency, with their ASDRs falling below the global average. Thailand topped the list with an ASDR of 3,818.34 per 100,000, closely followed by Myanmar with 3,552.06 per 100,000. Cambodia held the third position with an ASDR of 3,506.18 per 100,000. Concurrently, the AAPC of the DALYs globally registered a decrease of 1.71% (95% CI: -1.89 to -1.53) from 1990 to 2021, with China leading the decline at a rate of 2.92 % (95% CI: -3.13 to -2.71), succeeded by Singapore at a rate of 2.25% (95% CI: -2.38 to -2.12). (Table 1, Figure 1). Joinpoint regression analysis of the burden of injury in China, ASEAN countries and global Despite an overall downward trend in the burden of injury across most countries from 1990 to 2021, certain periods exhibited an increase, as indicated by joinpoint regression analyses. Notably, the ASIR of injury demonstrated an ascending trend from 2011 to 2021 in China (APC = 2.29%, 95% CI 1.63–2.96%). Same uptrends were also found in Cambodia (from 1990 to 1996, APC = 2.00%, 95% CI 0.58–3.45%; from 1999 to 2011, APC = 0.68%, 95% CI 0.17–1.20%), Singapore (from 1990 to 2000, APC = 0.04%, 95% CI 0.01–0.07%). In recent years, the ASPR of injury showed an uptrend from 2010 to 2021 in China (APC = 0.91%, 95% CI 0.80–1.02%). Same uptrends were also found in Malaysia (from 2019 to 2021, APC = 0.23%, 95% CI 0.13–0.32%), Laos (from 2018 to 2021, APC = 0.12%, 95% CI 0.03–0.21%). The ASMR and ASDR of injury in China and ASEAN countries had not shown uptrends in recent years except Brunei, which showed an uptrend of injury ASMR from 2008 to 2016 (APC = 0.58%, 95% CI 0.31–0.84%). (Table 2, Supplementary figure 1, Supplementary figure 2, Supplementary figure 3, Supplementary figure 4). Trends in the burden of injury in China, ASEAN countries and global The ASIR of injury in China and global has demonstrated a progressive downturn from 1990 to 2021. Nonetheless, there have been notable anomalies in certain years, with specific countries experiencing substantial increases in their ASIR. Notably, in 2004, both Indonesia and Thailand observed marked deviations from the norm, as did Myanmar and China in 2008, the Philippines in 2013 and 2016, and Myanmar once more in 2017. With respect to the ASPR, an overall declining trajectory has been evident across countries and on a global scale, save for a deviation in Myanmar in 2008. Regarding the ASMR, the predominant trend for most countries and global at large has been a decrease, albeit with intermittent rises in specific years. In particular, countries such as Indonesia, Myanmar, Philippines, and Thailand have exhibited a pattern of fluctuation in their ASMR. This ebb and flow is similarly mirrored in the ASDR. (Figure 2). Evolving Proportions of Transport Injuries, Unintentional Injuries, Self-Harm and Interpersonal Violence in the Burden of China and ASEAN Countries in 1990 and 2021 Overall, the burden of injury across various nations, whether in 1990 or 2021, has consistently been most pronounced due to unintentional injuries. However, the proportion of the total injury burden attributable to this category has undergone some changes from 1990 to 2021. When examining ASIR, there is a marked reduction in the proportion of self-harm and interpersonal violence in China, Laos, Philippines, Thailand, and Cambodia, whereas in Myanmar, the proportion of self-harm and interpersonal violence has increased. Additionally, a notable reduction in the proportion of transport injuries is evident across China, Myanmar, Thailand, the Philippines, and Indonesia. In terms of ASPR, a notable shift is observed in Myanmar, Vietnam, and Indonesia, where the proportion of transport injuries has significantly declined. From the perspective of ASMR, countries such as China, Laos, and Cambodia exhibit little change in the proportion of unintentional injuries. However, there is a concurrent decrease in the proportion of self-harm and interpersonal violence, alongside an increase in the proportion of transport injuries. When considering ASDR, a pronounced reduction in the proportion of unintentional injuries is evident in Myanmar, Vietnam, and Brunei. (Figure 3, Figure 4). Burden of injury in different age groups in China, ASEAN countries and global in 1990 and 2021 Figure 5 exhibited a comparison of the incidence, prevalence, mortality, and DALYs of injury in different age groups in China, ASEAN countries and global in 1990 and 2021, along with their corresponding crude rates. Based on the incidence data, the occurrence of injury cases in 1990 was predominantly concentrated among individuals under the age of 45. Notably, the number of cases in the < 5 age group was significant, second only to the 20-24 age group. The overall trend exhibited an approximately right-skewed distribution. In contrast, by 2021, the age distribution of injury cases had shifted, with the highest incidence observed among individuals aged 70+. Other age groups displayed a roughly normal distribution, centered around the 30-34 age group. In both 1990 and 2021, the CIR of injury demonstrated an increasing trend from the < 5 age group to the 20-24 age group, followed by a decreasing trend from the age of 55-59. Subsequently, in 1990, the CIR continued to decline until the 65-69 age group before experiencing a marked increase. In 2021, however, the CIR began to rise after the 55-59 age group. The highest incidence peak was consistently observed among individuals aged 70 and over in both years. It is worth noting that, in both 1990 and 2021, the CIR for most age groups in China and ASEAN countries was below the global average. However, Brunei and Singapore had CIRs that were predominantly higher across all age groups in 1990, a pattern that persisted in 2021, along with Myanmar for the first six age groups. Drawing upon prevalence data, the burden of injury in 1990 was largely shouldered by individuals aged 20-39, with a notably lower prevalence observed among those under the age of 14. Fast forward to 2021, the prevalence of injury had shifted, with a concentration in the individuals aged 30-59, and the highest prevalence found among the 70+ demographic. Examining the CPR, a smooth ascending trend from the <5 age group to the 65-69 age group was evident in both 1990 and 2021 for countries other than Cambodia. Subsequent to this, a more rapid and pronounced increase was observed. In contrast, Cambodia exhibited a distinct decline in the 25-39 age group in 1990, and a similar downward trend was apparent in the 55-69 age group in 2021. Regarding deaths, the age group with the greatest number of injury deaths was the <5 group in 1990, transitioning to the 70+ demographic by 2021. The CMR for injury in these two years exhibited a descending pattern from the <5 age group to the 10-14 age group, followed by a steady ascent until the 20-24 age group, after which a decline was noted until the 30-34 age group, where an upswing in mortality with advancing age became apparent. The age group with the peak mortality rate was consistently the 70+ group in both 1990 and 2021. In terms of DALYs, the zenith of DALYs was observed among the <5 age group in 1990, shifting to the 70+ demographic by 2021, mirroring the distribution pattern observed in injury deaths figures. Examining the CDR, the peak in 1990 was registered in the <5 age group, whereas in 2021, it was evident in the 70+ age group. Across both time points, the trajectory of CDR exhibited a general alignment with the patterns observed in the CMR, indicating a consistent correlation between the burden of disability and mortality due to injury. Sex differences of injury in China and ASEAN countries As depicted in Figure6, the incidence of injury consistently exhibited a higher frequency among males compared to females annually from 1990 to 2021, within both China and ASEAN countries. In 2021, a total of 45.30 million females and 73.88 million males suffered from injury in these regions. The prevalence of injury remained relatively equitable between genders from 1990 to 2021 in China and ASEAN countries. However, the number of injury deaths and the cumulative DALYs for males substantially exceeded those for females over the same period. Additionally, a peculiar phenomenon was noted: despite an annual increase in the prevalence of injury among both males and females, the DALYs attributed to injury paradoxically decreased year by year. Frontier analysis for the association between injury burden and SDI To delve into the optimal scenario where countries can manage the injury burden in alignment with their respective SDI statuses for each year, a frontier analysis has been conducted (Figure 7). The outcomes of this analysis reveal that the three countries that are in closest proximity to the global frontier fit line are highlighted in blue, whereas the three nations that lie at the greatest distance from this line are denoted in red. With respect to injury incidence, the countries that are most removed from the frontier line are Brunei, Singapore, and Myanmar. In the realm of injury prevalence, Brunei, Singapore, and Cambodia emerge as the furthest from the frontier line. When examining injury mortality, Vietnam, Thailand, and Malaysia are found to be the most distant. Lastly, in terms of injury DALYs, Thailand, Vietnam, and Myanmar are the countries that fall farthest from the frontier line. With sociodemographic development, effective difference tends to increase, the burden of injury showing an appreciable decrease at the outset. Nonetheless, as the SDI continues to rise, the rate of decline in the burden of injury slows, with the countries furthest from the frontier fit line predominantly belonging to the middle to high SDI strata. This observation indicates that nations or regions with higher SDI levels exhibit a more substantial potential for advancing health burden mitigation efforts. Cross-national injury health inequality In 1990 and 2021, the SII (per 100,000 population) for DALYs were -2407.96 and -1159.885 respectively. These figures denote a negative correlation between ASDRs and the SDI. (Figure 8). This significant decline marked reduction signifies a lessening in the disparity of the age-standardized burden of injury across high-income and low-income countries over this time span. Between 1990 and 2021, the concentration index for DALYs has exhibited a downward trajectory. While the regional disparity in the burden of injury has diminished between economically disparate countries, inequality persists. This observation underscores that despite the narrowing of wealth disparities in certain regions, global inequality in the incidence of injury remains an enduring challenge. (Figure 9). Discussion To the best of our knowledge, this is the first comprehensive effort to describe the injury burden in China and ASEAN countries, also estimating their long-term trends throughout the past 32 years, and accessing the association of the injury burden with the socio-demographic index. Our findings reveal a complex landscape, characterized by both progress and challenges. The burden of injury, encompassing incidence, prevalence, mortality, and DALYs, has exhibited a downward trend in both China and ASEAN countries over the study period, as indicated by the decline in age-standardized rates. This reduction aligns with the global injury trends. The transformation is unsurprising within the context of China, where the burden of fatal injury outcomes may have been mitigated by the profound socioeconomic and political advancements that have transpired over the course of the past 32 years[5, 13]. In spite of the noted decline in the comprehensive injury burden, unintentional injuries continue to dominate as the primary contributor to the injury burden within China and ASEAN countries. In a concerted effort to curtail the incidence of accidental harm, China has implemented a series of proactive measures. Since 2007, the Ministry of Education has annually issued directives highlighting the perils of drowning and advocating for adult supervision in proximity to aquatic environments. Additionally, China has been dedicated to the enhancement of its infrastructure, which has, to some extent, diminished exposure to water bodies. In the ASEAN countries, the importance of inter-sectoral collaboration for drowning prevention is also starkly highlighted[14]. This underscores the critical need for ongoing prioritization of preventive strategies, particularly those designed to attenuate the hazards associated with falls, drowning, and other prevalent causes of unintentional injuries. In China and certain ASEAN countries, there has been an increase in the proportion of transport injuries' ASMR relative to the overall injury ASMR, alongside a decrease in the proportion of transport injuries' ASIR compared to the total injury ASIR. Existing research indicates that economic development is intricately linked with transport injuries, with reports suggesting that transport injury mortalities tend to rise in the early stages of economic progress, in alignment with our findings. Nonetheless, concurrent developments such as enhanced road construction, the broadening of road safety initiatives, and the advancement of motor vehicle safety technologies have progressed in tandem with economic growth. These improvements may account for the observed phenomenon. In the realm of self-harm and interpersonal violence, a decline in the proportion of ASIR and ASMR relative to total injury rates has been noted in countries such as China, Laos, and Cambodia. Among the pivotal strategies for self-harm prevention has been the restriction of access to means of self-harm[15]. Illustratively, the Chinese government has enacted stringent legislation to control firearms, pesticides, and other agricultural chemicals, as well as to enhance the regulation of anesthetic and psychotropic substances, thereby mitigating the risk of self-harm[16–18]. Additionally, in recognition of the significant proportion of the self-harm population suffering from mental illness in China[19], which exceeds half of the affected population, the formulation and enforcement of the Mental Health Law has facilitated the advancement of mental health services[20, 21]. Concurrently, extant research has elucidated a correlation between psychological distress and substance use, both of which are linked to one or multiple types of injuries[22–24]. This observation lends heightened support to the implementation of individual injury interventions that encompass socio-psychological concerns, as well as issues related to the use of both legal and illicit substances[24]. The decline in the burden of interpersonal violence is consonant with nations' adherence to the World Health Organization's recommendations in enacting pertinent legislation and policies, signifying that these regulatory advancements are yielding positive outcomes towards a more civilized, progressive, and stable society. Taking China as a case in point[25], the legal framework for the prevention of violence encompasses a suite of statutes including the Criminal Law, the Anti-Domestic Violence Law, the Security Administration Punishment Act, the Law on the Protection of Minors, and the Law on Preventing Juvenile Delinquency, among others. Moreover, a raft of policies is aimed at forestalling and mitigating risk factors for violence, such as assistance programs for impoverished students, affordable housing policies, the National Plan for Children’s Development, the National Plan for Women’s Development, and the plan for elderly services and the construction of a senior care system. The reduced burden of interpersonal violence may be attributed to the collective advancements in legal development, economic conditions, facilitation of interpersonal communication, and the enhancement of healthcare services within China and ASEAN countries. The demographic distribution of injury cases has trended towards older adults, with a notable increase among individuals aged 70 and above. A study conducted in Malaysia has revealed that older age individuals exhibit a two-fold mortality rate when compared to the younger age group of victims involved in injury accidents[26]. This demographic shift mirrors the aging population dynamics within both China and ASEAN countries, underscoring the critical need for the development of age-specific injury prevention strategies. Concurrently, this shift also illuminates the proactive governmental initiatives, exemplified by the Chinese government, which have bolstered injury prevention awareness among the populace, with a particular emphasis on the prevention of child injuries[5, 27]. Child safety measures have garnered widespread recognition and public endorsement, demonstrating a commitment to safeguarding the youngest members of society. The injury burden persists at a higher level in males relative to females across nearly all age categories. Existing research has also documented that males have historically exhibited higher rates of hazardous driving and associated injuries, with a propensity to be involved in crashes that lead to harm or fatality, and a more pronounced engagement in risk behaviors such as drunk driving, speeding, and aggressive driving that precipitate such incidents[28, 29]. However, the gap in DALYs attributed to injury between genders has narrowed, potentially due to improved treatment and recovery outcomes for both sexes. Despite an overall downward trend, certain nations within the study sample have experienced intermittent increases in the burden of injury. These fluctuations necessitate in-depth investigation to ascertain the root causes and to devise precision intervention strategies. Such anomalous oscillations are commonly attributed to natural disasters, social dynamics, economic conditions, and policy influences. For instance, in the realm of natural disasters, the 2004 Indian Ocean tsunami, the 2008 Cyclone Nargis in Myanmar, the Wenchuan earthquake in China, and the landfall of Typhoon Haiyan in the Philippines in 2013, among others, have had profound impacts. In the aftermath of earthquakes and tsunamis, the immediate surge in mortality and morbidity is evident, while survivors also face heightened risks of infectious diseases or deteriorating health conditions, thereby adding to the injury burden. Social factors, such as societal unrest and conflict, persist, exemplified by the Rohingya crisis in Myanmar. These social issues can precipitate acts of violence and casualties, thereby augmenting the burden of injury. The efficacy of robust emergency response plans and disaster preparedness strategies is crucial in mitigating the disease burden associated with such calamities. Moreover, regional disparities persist, necessitating tailored approaches that address the unique challenges faced by each nation. This study reveals a robust correlation between the burden of injury and the SDI. As nations ascend the SDI hierarchy, they generally witness enhancements in healthcare systems, infrastructure, education, and economic stability, all of which are conducive to a decrease in the injury burden, in alignment with existing research findings[30]. However, the non-linear relationship identified in our study suggests that the advantages of SDI progression may be most evident in the initial phases of development, with a subsequent decline in the incremental benefits as the SDI advances. Strategic policies and interventions must be customized to the unique SDI context of each country. As countries progress along the SDI spectrum, the emphasis should transition to addressing the intricate health challenges associated with higher SDI levels, including the management of an aging population, which can also affect injury risk. Moreover, the enduring global disparity in injury rates highlights the imperative for focused interventions that tackle the social determinants of health. This encompasses improving living standards, advancing educational opportunities, and narrowing socioeconomic gaps, all of which can play a direct role in injury prevention. International cooperation and the dissemination of best practices are instrumental in promoting the adoption of effective injury prevention strategies across diverse SDI settings. By synthesizing theoretical insights with empirical data, policymakers can craft more sophisticated and impactful strategies to mitigate the burden of injury throughout the various stages of sociodemographic development. Our study was subject to certain limitations. While we delineated the comprehensive landscape of injury in China and ASEAN countries, as well as the disease burden associated with three distinct injury categories, we did not delve into a more nuanced analysis, omitting a further subdivision within these categories to explore more specific injury types. The estimation of injury burden utilizing GBD results is based on several models, potentially leading to an underestimation of the true magnitude of the issue. Furthermore, the study's focus on China and ASEAN countries alone limits its ability to capture the full spectrum of global injury burden diversity. The assumption of linear trends and a single optimal "frontier" state in our analytical approach may not accurately reflect the complexities of real-world dynamics. Additionally, the temporal scope of the study, spanning from 1990 to 2021, may not account for recent changes in injury patterns. Despite these limitations that may affect the generalizability and precision of our findings, our research underscores the critical need for regional collaboration in injury prevention and control, emphasizing the urgency of crafting targeted interventions to mitigate the risks within each injury category and effectively diminish the injury burden across these regions. Conclusions This study provides valuable insights into the injury burden trends and disparities in China and ASEAN countries. It underscores the importance of addressing social determinants of injury risk and emphasizes the need for tailored interventions considering regional disparities and evolving age distributions. By leveraging these findings, policymakers and stakeholders can develop more effective strategies to reduce the socio-economic burden of injuries, contributing to the achievement of Sustainable Development Goals related to injury prevention. Abbreviations ASEAN Association of Southeast Asian Nations SDI Socio-Demographic Index GBD 2021 Global Burden of Disease Study 2021 DALYs Disability-adjusted life years SII Slope Index of Inequality CI Concentration Index SDGs Sustainable Development Goals 95%UI 95% uncertainty interval ASIR Age-standardized incidence rates ASPR Age-standardized prevalence rates ASMR Age-standardized mortality rates ASDR Age-standardized DALY rates CIR Crude incidence rates CPR Crude prevalence rates CMR Crude mortality rates CDR Crude DALY rates APC Annual percent change 95%CI 95% confidence interval AAPC Average annual percent change RII Relative Index of Inequality Declarations Ethics approval and consent to participate The Institutional Review Board of the University of Washington examined and authorized a consent waiver for the GBD research. In-depth details regarding the ethical guidelines are available on the official portal at http://www. healthdata.org/gbd. Consent for publication Not applicable. Availability of data and materials All original data is available at http://www.healthdata.org/gbd. In the study, we followed the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License and Section 7 of the University of Washington’s Website Terms and Conditions of Use. Competing interests: The authors have filled out the ICMJE Disclosure of Interest Form and declare that they have no conflicts of interest. The form can be obtained by contacting the corresponding author. Funding: This research is sponsored by the Research Institute of Innovative think-tank at Guangxi Medical University. Authors' contributions: Study design: Ma ZY. Data collection: Nie FY, Bai XY. Data analysis: Nie FY. Figures: Nie FY. Manuscript writing: Nie FY. Manuscript proofing: Liang WJ. Acknowledgements: We owe a debt of gratitude to those who have taken part in this study or have contributed to the preparation of this article. References Liu Q, Yan W, Qin C, Du M, Wang Y, Liu M, et al. Incidence and mortality trends of neglected tropical diseases and malaria in China and ASEAN countries from 1990 to 2019 and its association with the socio-demographic index. Global Health Res Policy. 2023;8:22. Teerawattananon Y, Dabak SV, Isaranuwatchai W, Lertwilairatanapong T, Shafie AA, Suwantika AA, et al. What can we learn from others to develop a regional centre for infectious diseases in ASEAN? Comment on “operationalising regional cooperation for infectious disease control: a scoping review of regional disease control bodies and networks.” Int J Health Policy Manag. 2022;:1. Leilei D, Pengpeng Y, Haagsma JA, Ye J, Yuan W, Yuliang E, et al. The burden of injury in China, 1990-2017: findings from the Global Burden of Disease Study 2017. Lancet Public Health. 2019;4:e449–61. Zhou Y, Baker TD, Rao K, Li G. Productivity losses from injury in China. Inj Prev. 2003;9:124–7. Li L, Yang J. Injury prevention in China: government-supported initiatives on the leading causes of injury-related deaths. Am J Public Health. 2019;109:557–8. Ma T, Peden AE, Peden M, Hyder AA, Jagnoor J, Duan L, et al. Out of the silos: embedding injury prevention into the sustainable development goals. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2021;27:166–71. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1204–22. Global burden of disease study 2021 (GBD 2021) socio-demographic index (SDI) 1950–2021 | GHDx. https://ghdx.healthdata.org/record/global-burden-disease-study-2021-gbd-2021-socio-demographic-index-sdi-1950%E2%80%932021. Accessed 13 Nov 2024. Joinpoint regression program. https://surveillance.cancer.gov/joinpoint/. Accessed 15 Nov 2024. Barber RM, Fullman N, Sorensen RJD, Bollyky T, McKee M, Nolte E, et al. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of Disease Study 2015. The Lancet. 2017;390:231–66. Xie Y, Bowe B, Xian H, Balasubramanian S, Al-Aly Z. Rate of Kidney Function Decline and Risk of Hospitalizations in Stage 3A CKD. Clin J Am Soc Nephrol. 2015;10:1946–55. World Health Organization. Handbook on health inequality monitoring with a special focus on low- and middle-income countries. Geneva: World Health Organization; 2013. Ozanne-Smith J, Li Q. A social change perspective on injury prevention in China. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2018;24 Suppl 1:i25–31. Peden AE, Scarr J, Doan Minh T, Latif R, Le Thi Anh D, Chong TL, et al. Drowning prevention challenges and opportunities: an exploratory study of perspectives of delegates from ASEAN nations. PLOS One. 2024;19:e0304138. Florentine JB, Crane C. Suicide prevention by limiting access to methods: a review of theory and practice. Soc Sci Med (1982). 2010;70:1626–32. National People’s Congress. Law of the people’s republic of China on control of guns. 2015. State Council of China. Pesticide regulations. 2017. State Council of China. Regulations on Narcotic Drugs and Psychotropic Substances. 2016. Phillips MR, Yang G, Zhang Y, Wang L, Ji H, Zhou M. Risk factors for suicide in China: a national case-control psychological autopsy study. Lancet (Lond Engl). 2002;360:1728–36. Zhu Y, Zhang W, Wang Y, Cai J. Providing free treatment for severe mental disorders in China. Shanghai Arch Psychiatry. 2014;26:101–2. Standing Committee of the National People’s Congress. Mental Health Law of the People’s Republic of China. 2018. Peltzer K, Pengpid S. Unintentional injuries and psychosocial correlates among in-school adolescents in Malaysia. Int J Environ Res Public Health. 2015;12:14936–47. Street EJ, Jacobsen KH. Injury incidence among middle school students aged 13-15 years in 47 low-income and middle-income countries. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2016;22:432–6. Asbridge M, Azagba S, Langille DB, Rasic D. Elevated depressive symptoms and adolescent injury: examining associations by injury frequency, injury type, and gender. BMC Public Health. 2014;14:190. Gao X, Ye P, Er Y, Jin Y, Wang L, Duan L. Violence prevalence and prevention status in China. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2019;25:67–73. Tan Chor Lip H, Tan JH, Mohamad Y, Ariffin AC, Imran R, Azmah Tuan Mat TN. Clinical characteristics of 1653 injured motorcyclists and factors that predict mortality from motorcycle crashes in Malaysia. Chin j traumatol = Zhonghua chuang shang za zhi. 2019;22:69–74. Duan L, Ye P, Wang L. Future challenges and solutions for safety in China: china CDC’s exploration of injury prevention strategies. Global Health Journal. 2018;2:14–23. Lonczak HS, Neighbors C, Donovan DM. Predicting risky and angry driving as a function of gender. Accid; Anal Prev. 2007;39:536–45. Zador PL, Krawchuk SA, Voas RB. Alcohol-related relative risk of driver fatalities and driver involvement in fatal crashes in relation to driver age and gender: an update using 1996 data. J Stud Alcohol. 2000;61:387–95. Haagsma JA, James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, et al. Burden of injury along the development spectrum: associations between the socio-demographic index and disability-adjusted life year estimates from the global burden of disease study 2017. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2020;26 Supp 1:i12–26. Tables Table 1. All-age cases and age-standardized incidence, prevalence, mortality, and disability-adjusted life years (DALYs) rates and corresponding average annual percent change (AAPC) of injury in China, Association of Southeast Asian Nations (ASEAN) countries and global in 1990 and 2021 All-ages cases Age-standardized rate (per 100,000) 1990–2021 AAPC 1990 2021 1990 2021 Incidence Global 554,872,072 (520,239,997 to 592,107,015) 607,789,604 (574,661,712 to 644,552,987) 10,264.62 (9,647.30 to 10,944.94) 7,705.75 (7,271.61 to 8,171.19) -0.98 (-1.23 to -0.73) China 71,430,993 (65,753,645 to 78,559,510) 83,709,567 (77,025,678 to 92,494,934) 6,001.81 (5,546.05 to 6,583.71) 5,840.52 (5,395.03 to 6,400.14) -0.08 (-1.02 to 0.88) Brunei 47,019 (43,448 to 50,460) 64,009 (59,509 to 68,389) 17,004.17 (15,810.03 to 18,199.30) 13,921.83 (12,911.81 to 14,897.50) -0.64 (-0.68 to -0.60) Cambodia 734,599 (690,860 to 786,580) 947,719 (894,742 to 1,001,283) 6,811.44 (6,444.41 to 7,204.40) 5,589.20 (5,287.53 to 5,892.65) -0.59 (-1.38 to 0.20) Indonesia 12,192,941 (11,217,826 to 13,205,385) 12,317,952 (11,475,111 to 13,217,218) 6,324.20 (5,851.76 to 6,798.94) 4,523.94 (4,214.30 to 4,846.67) -1.12 (-1.35 to -0.89) Laos 332,783 (291,964 to 402,500) 350,497 (330,896 to 371,721) 7,609.53 (6,758.00 to 9,044.21) 4,632.51 (4,389.06 to 4,899.28) -1.03 (-1.11 to -0.94) Malaysia 938,953 (867,760 to 1,009,571) 1,510,238 (1,419,261 to 1,607,930) 5,239.24 (4,867.79 to 5,585.62) 4,675.03 (4,398.80 to 4,968.42) -0.43 (-0.46 to -0.40) Myanmar 3,272,013 (3,075,580 to 3,467,928) 4,312,844 (3,963,132 to 4,786,272) 7,659.78 (7,250.67 to 8,069.44) 7,532.78 (6,927.21 to 8,338.34) -0.89 (-1.49 to -0.28) Philippines 4,839,948 (4,525,881 to 5,169,259) 5,497,525 (5,146,653 to 5,876,532) 7,308.96 (6,876.02 to 7,788.32) 4,752.72 (4,465.96 to 5,058.03) -0.90 (-1.19 to -0.62) Singapore 491,792 (453,637 to 530,505) 633,618 (580,473 to 688,379) 15,108.16 (13,871.18 to 16,282.40) 12,656.07 (11,406.79 to 13,880.68) -0.57 (-0.62 to -0.51) Thailand 4,450,471 (4,192,969 to 4,716,653) 3,673,114 (3,505,006 to 3,845,453) 7,398.97 (6,993.12 to 7,797.12) 5,673.96 (5,358.79 to 5,981.75) -1.07 (-1.23 to -0.90) Viet Nam 4,548,084 (4,217,570 to 4,888,034) 6,162,990 (5,827,286 to 6,497,524) 6,679.74 (6,238.67 to 7,159.67) 6,234.82 (5,887.23 to 6,564.22) -0.23 (-0.52 to 0.06) Prevalence Global 1,030,712,875 (980,087,458 to 1,086,397,210) 1,456,350,420 (1,385,784,930 to 1,535,575,907) 21,445.74 (20,428.04 to 22,536.24) 17,531.20 (16,677.62 to 18,485.66) -0.64 (-0.68 to -0.61) China 191,930,723 (180,576,902 to 205,168,088) 277,962,514 (263,402,632 to 295,114,044) 17,035.57 (16,112.16 to 18,086.07) 16,266.69 (15,325.96 to 17,338.10) -0.15 (-0.27 to -0.02) Brunei 59,002 (56,349 to 62,446) 107,214 (101,836 to 113,539) 28,386.24 (27,258.49 to 29,761.95) 22,983.99 (21,935.52 to 24,228.71) -0.68 (-0.71 to -0.66) Cambodia 1,836,921 (1,342,620 to 2,991,039) 2,645,427 (2,285,810 to 3,388,319) 21,953.73 (17,034.22 to 33,249.26) 17,007.83 (14,648.24 to 22,005.74) -0.81 (-0.87 to -0.75) Indonesia 24,052,762 (22,579,994 to 26,030,025) 34,548,833 (32,598,107 to 36,714,937) 15,532.01 (14,684.61 to 16,552.19) 12,167.50 (11,510.62 to 12,865.21) -0.81 (-0.89 to -0.72) Laos 485,784 (465,332 to 509,082) 796,391 (758,047 to 837,386) 15,429.79 (14,796.41 to 16,051.64) 11,992.52 (11,442.03 to 12,547.53) -0.81 (-0.83 to -0.79) Malaysia 1,886,401 (1,798,058 to 1,988,710) 3,842,960 (3,657,516 to 4,047,225) 13,124.05 (12,540.49 to 13,760.63) 11,862.79 (11,305.94 to 12,472.76) -0.33 (-0.34 to -0.31) Myanmar 5,939,531 (5,376,997 to 7,324,033) 9,000,475 (8,276,763 to 10,234,411) 17,244.30 (15,871.45 to 20,350.45) 16,135.29 (14,878.76 to 18,253.78) -0.34 (-0.62 to -0.05) Philippines 8,110,695 (7,494,664 to 9,118,350) 13,632,959 (12,745,851 to 14,681,170) 16,083.75 (15,079.54 to 17,548.31) 12,850.57 (12,050.65 to 13,813.81) -0.74 (-0.80 to -0.67) Singapore 736,867 (696,604 to 782,656) 1,537,287 (1,449,786 to 1,639,905) 23,917.26 (22,673.88 to 25,328.03) 21,147.39 (19,983.69 to 22,590.89) -0.39 (-0.47 to -0.31) Thailand 8,589,824 (8,212,888 to 8,978,840) 11,974,553 (11,409,145 to 12,611,671) 16,715.11 (16,049.25 to 17,381.50) 13,944.25 (13,298.84 to 14,702.57) -0.59 (-0.66 to -0.52) Viet Nam 8,034,567 (7,672,492 to 8,459,635) 14,831,076 (14,153,845 to 15,535,661) 14,847.78 (14,171.44 to 15,543.16) 14,160.72 (13,533.39 to 14,823.98) -0.16 (-0.26 to -0.06) Deaths Global 4,185,780 (3,974,301 to 4,373,502) 4,343,698 (3,984,365 to 4,631,455) 84.86 (80.77 to 88.56) 53.66 (49.18 to 57.28) -1.50 (-1.67 to -1.33) China 929,473 (841,757 to 1,028,156) 688,564 (568,572 to 826,236) 89.81 (81.37 to 99.45) 41.82 (35.01 to 49.61) -2.41 (-2.87 to -1.95) Brunei 129 (117 to 142) 122 (109 to 135) 62.62 (56.21 to 68.50) 29.94 (26.83 to 33.06) -2.34 (-2.51 to -2.17) Cambodia 8,161 (7,061 to 9,237) 9,415 (7,280 to 12,004) 95.88 (83.51 to 109.62) 67.13 (53.09 to 83.95) -1.06 (-1.24 to -0.88) Indonesia 93,472 (83,396 to 103,181) 92,498 (78,958 to 111,405) 59.06 (51.84 to 65.68) 38.37 (32.61 to 45.59) -1.50 (-2.61 to -0.39) Laos 4,023 (3,300 to 4,931) 3,521 (2,715 to 4,479) 106.85 (88.91 to 130.75) 54.44 (42.36 to 68.13) -2.02 (-2.18 to -1.85) Malaysia 8,768 (8,280 to 9,237) 14,765 (13,970 to 15,650) 62.10 (58.00 to 65.98) 47.72 (45.00 to 50.80) -0.81 (-1.62 to 0.01) Myanmar 38,948 (31,410 to 47,134) 30,900 (25,808 to 37,292) 105.13 (85.47 to 126.51) 58.68 (49.71 to 70.36) -1.76 (-4.11 to 0.64) Philippines 39,889 (37,376 to 42,563) 48,586 (41,740 to 56,274) 70.29 (65.64 to 75.51) 46.43 (39.89 to 53.80) -0.86 (-1.17 to -0.55) Singapore 1,122 (1,098 to 1,147) 938 (883 to 984) 38.30 (37.34 to 39.20) 13.43 (12.69 to 14.08) -3.33 (-3.61 to -3.04) Thailand 48,740 (43,541 to 54,139) 50,802 (40,169 to 62,677) 91.69 (81.47 to 102.06) 65.90 (52.82 to 80.87) -1.19 (-1.71 to -0.66) Viet Nam 53,587 (44,882 to 64,633) 65,844 (53,132 to 79,315) 95.19 (78.36 to 116.83) 68.49 (54.40 to 81.87) -1.05 (-1.18 to -0.91) DALYs Global 278,725,499 (262,114,155 to 298,335,835) 247,843,924 (226,826,042 to 272,347,784) 5,221.14 (4,898.12 to 5,617.65) 3,101.29 (2,839.62 to 3,408.68) -1.71 (-1.89 to -1.53) China 61,496,972 (55,696,155 to 68,063,372) 35,048,515 (30,180,468 to 40,547,676) 5,354.92 (4,846.13 to 5,930.71) 2,284.18 (1,991.95 to 2,605.37) -2.92 (-3.13 to -2.71) Brunei 10,046 (9,020 to 11,120) 9,747 (8,435 to 11,399) 4,153.50 (3,701.17 to 4,681.18) 2,108.15 (1,826.17 to 2,459.01) -2.13 (-2.30 to -1.96) Cambodia 625,324 (536,379 to 734,946) 570,989 (465,534 to 720,442) 5,935.99 (5,096.81 to 6,955.68) 3,506.18 (2,887.81 to 4,388.83) -1.65 (-1.87 to -1.43) Indonesia 6,818,031 (6,151,599 to 7,544,704) 5,765,573 (4,996,302 to 6,717,030) 3,688.91 (3,350.34 to 4,096.12) 2,124.88 (1,860.03 to 2,456.60) -1.76 (-2.72 to -0.78) Laos 280,500 (232,356 to 341,963) 221,599 (175,206 to 277,582) 6,441.76 (5,387.78 to 7,724.02) 3,028.08 (2,414.81 to 3,766.63) -2.25 (-2.44 to -2.06) Malaysia 538,626 (507,237 to 572,640) 790,197 (744,662 to 849,016) 3,283.28 (3,068.28 to 3,496.49) 2,395.83 (2,255.45 to 2,576.98) -1.01 (-1.82 to -0.19) Myanmar 2,741,334 (2,241,832 to 3,293,471) 1,997,987 (1,712,265 to 2,362,582) 6,477.17 (5,334.41 to 7,675.94) 3,552.06 (3,051.93 to 4,192.48) -2.02 (-3.90 to -0.10) Philippines 2,852,002 (2,662,425 to 3,063,580) 3,050,392 (2,675,491 to 3,452,582) 4,438.67 (4,130.33 to 4,794.37) 2,709.32 (2,370.54 to 3,066.20) -1.23 (-1.49 to -0.97) Singapore 82,201 (73,942 to 92,728) 86,868 (70,276 to 108,177) 2,581.96 (2,309.66 to 2,926.34) 1,280.59 (1,059.01 to 1,573.23) -2.25 (-2.38 to -2.12) Thailand 3,123,334 (2,822,292 to 3,460,897) 2,666,000 (2,213,246 to 3,212,989) 5,358.80 (4,830.04 to 5,962.06) 3,818.34 (3,204.77 to 4,577.14) -1.17 (-1.67 to -0.66) Viet Nam 3,305,713 (2,827,689 to 3,894,761) 3,257,690 (2,688,122 to 3,860,112) 4,961.83 (4,222.82 to 5,908.60) 3,239.88 (2,697.65 to 3,798.31) -1.37 (-1.51 to -1.23) Table 2. Joinpoint regression results, periods with uptrends of the burden of injury in China and ASEAN countries Start year End year APC (95% CI) (%) P Value ASIR China 2011 2021 2.29 (1.63 to 2.96) <0.01 Cambodia 1990 1996 2.00 (0.58 to 3.45) 0.01 1999 2011 0.68 (0.17 to 1.20) 0.01 Singapore 1990 2000 0.04 (0.01 to 0.07) 0.02 ASPR China 2010 2021 0.91 (0.80 to 1.02) <0.01 Viet Nam 2005 2010 0.88 (0.63 to 1.13) <0.01 Malaysia 2019 2021 0.23 (0.13 to 0.32) <0.01 Laos 2018 2021 0.12 (0.03 to 0.21) 0.01 Myanmar 2006 2009 3.75 (0.72 to 6.87) 0.02 Singapore 1990 2001 0.04 (0.01 to 0.07) 0.03 Thailand 1990 1994 0.14 (0.01 to 0.27) 0.04 ASMR Brunei 2008 2016 0.58 (0.31 to 0.84) <0.01 Cambodia 1990 1994 1.64 (0.55 to 2.75) <0.01 Thailand 1990 1995 3.34 (1.79 to 4.91) <0.01 ASDR Thailand 1990 1995 3.50 (1.96 to 5.06) <0.01 Additional Declarations No competing interests reported. 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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-5825013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402461094,"identity":"f5f89061-8263-4133-bdee-e0afb97ff5dc","order_by":0,"name":"Feiyang Nie","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feiyang","middleName":"","lastName":"Nie","suffix":""},{"id":402461095,"identity":"658ad579-6c2d-467c-91fa-e090ec406244","order_by":1,"name":"Xinyu Bai","email":"","orcid":"","institution":"Guangxi Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Bai","suffix":""},{"id":402461096,"identity":"0ac94c6f-d889-4d2f-95dc-1f4af7525552","order_by":2,"name":"Wenjie Liang","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Liang","suffix":""},{"id":402461097,"identity":"2be8dd47-01a2-4d21-b4cd-ae136571eb82","order_by":3,"name":"Zhenyu Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYJACZjDJ3gDlHiBaCw9MKfFaJBKI1KLbfoD5c0HFncQNN59fk7rZxiDHdyOB8XMBHi1mZxIYjGeceZa44XZOsXFuG4Ox5I0EZukZ+LQcSGBI5m07DNKS+BioJXHDjQQ2Zh58Ws4/YDjM+w+o5eaZhMNALfWEtQBd3szbANRyg/0gyJYEA8JaHjAz8xw7bDzzTA6zcc45CcOZZx42S+N3WALzZ56aw7J9x48/k84ps5HnO5588DM+LQwM/B9ApGMDA48BkJYAYsYGvBpgwB6YYh4QpXIUjIJRMApGHgAAkFNSmyJsOQ8AAAAASUVORK5CYII=","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-01-14 07:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5825013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5825013/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74029603,"identity":"8af1904c-4923-44bf-8930-2d6364d78021","added_by":"auto","created_at":"2025-01-17 06:48:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135237,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall average annual percent change (AAPC) of age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) in China, Association of Southeast Asian Nations (ASEAN) countries and global\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/7261be6acf2c1d5ba0f3142a.png"},{"id":74029602,"identity":"ab19de39-66a0-4a18-b3aa-e14b74969a9b","added_by":"auto","created_at":"2025-01-17 06:48:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2866890,"visible":true,"origin":"","legend":"\u003cp\u003eTrend comparison of age-standardized incidence rates (ASIR), age-standardized prevalence rates (ASPR), age-standardized mortality rates (ASMR), and age-standardized DALY rates (ASDR) of injury in China, Association of Southeast Asian Nations (ASEAN) countries and global from 1990 to 2021. The Myanmar line fits the right Y axis, and other lines fit the left axis in A, C and D. (A) ASIR; (B) ASPR; (C) ASMR; (D) ASDR\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/03c87b957a69582c571d4bd1.png"},{"id":74031254,"identity":"4eb7cdb4-ca7a-44bb-bf43-00a970c55c8a","added_by":"auto","created_at":"2025-01-17 06:56:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":624093,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized incidence rates (ASIR) and age-standardized prevalence rates (ASPR) for transport injuries, unintentional injuries, self-harm and interpersonal violence in China and Association of Southeast Asian Nations (ASEAN) countries in 1990 and 2021. (A) ASIR in 1990(B) ASIR in 2021 (C) ASPR in 1990 (D) ASPR in 2021\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/6f8c8f60b694453d83ad42f7.png"},{"id":74029645,"identity":"2a8e1b17-b1c2-4593-a45b-de2c5de8e735","added_by":"auto","created_at":"2025-01-17 06:48:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":757729,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized mortality rates (ASMR) and age-standardized DALY rates (ASDR) for transport injuries, unintentional injuries, self-harm and interpersonal violence in China and Association of Southeast Asian Nations (ASEAN) countries in 1990 and 2021. (A) ASMR in 1990 (B) ASMR in 2021 (C) ASDR in 1990 (D) ASDR in 2021\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/493acbc4a96c9d453fc511c4.png"},{"id":74029618,"identity":"d7f01384-8ca7-42b0-a7f7-0fb1797275a8","added_by":"auto","created_at":"2025-01-17 06:48:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3198063,"visible":true,"origin":"","legend":"\u003cp\u003eComparative of the incidence, prevalence, deaths, and disability-adjusted life years (DALYs) counts, along with their crude rates, by age group in China, Association of Southeast Asian Nations (ASEAN) countries and global,1990 and 2021. (A) Incident cases and crude incidence rates (CIR) in 1990; (B) Incident cases and CIR in 2021; (C) Prevalent cases and crude prevalence rates (CPR) in 1990; (D) Prevalent cases and CPR in 2021; (E) Death cases and crude mortality rates (CMR) in 1990; (F) Death cases and CMR in 2021; (G) DALYs counts and crude DALY rates (CDR) in 1990; (H) DALYs counts and CDR in 2021; Bar charts represent counts; lines represent crude rates\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/bd5c832d8ec2a01690be7bc3.png"},{"id":74031462,"identity":"c7076c94-1061-47a1-bcdc-89ca41864b79","added_by":"auto","created_at":"2025-01-17 07:04:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1452565,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the number of incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of injuryin males and females in different years in China and Association of Southeast Asian Nations (ASEAN) countries. (A) Incidence; (B) Prevalence; (C) Mortality; (D) DALYs\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/197adf08e0563a4bd130e088.png"},{"id":74029657,"identity":"778e6276-e120-40ec-a1f2-6066b1d30853","added_by":"auto","created_at":"2025-01-17 06:48:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":219304,"visible":true,"origin":"","legend":"\u003cp\u003eFrontier analysis based on Socio-Demographic Index (SDI) and injury burden in China and Association of Southeast Asian Nations (ASEAN) countries. (\u003cstrong\u003eA,B\u003c/strong\u003e) association of age-standardized incidence rates (ASIR) with SDI; (\u003cstrong\u003eC,D)\u003c/strong\u003e association of age-standardized prevalence rates (ASPR) with SDI; (\u003cstrong\u003eE,F)\u003c/strong\u003eassociation of age-standardized mortality rates (ASMR) with SDI; (\u003cstrong\u003eG,H)\u003c/strong\u003eassociation of age-standardized DALY rates (ASDR) with SDI;\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/42ef09a4499e0aeb8621f162.png"},{"id":74029607,"identity":"8fefddbb-b6d5-44de-8873-9a6d74050458","added_by":"auto","created_at":"2025-01-17 06:48:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":87532,"visible":true,"origin":"","legend":"\u003cp\u003eSlope Index of Inequality (SII) analysis. (A) Absolute income-related healthy inequality in injuryburden, presented using regression lines, 1990 vs. 2021. (B) Trendline demonstrates the trend in SII from 1990 to 2021\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/69c25295526344ad233894be.png"},{"id":74031262,"identity":"e093fee4-3e12-42b3-b2c8-d57fcb3db588","added_by":"auto","created_at":"2025-01-17 06:56:34","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":182217,"visible":true,"origin":"","legend":"\u003cp\u003eConcentration index analysis. Relative income-related healthy inequality in injury burden, presented using concentration curves, 1990 vs. 2021.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/5d39e0531fc309973bf84c9c.png"},{"id":75304404,"identity":"631c9135-d020-4f1e-8741-e189dd4e8c12","added_by":"auto","created_at":"2025-02-03 08:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8882431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/5296aff2-1857-4774-a696-9f019ce36913.pdf"},{"id":74031252,"identity":"cba78e5e-3c55-4c91-9a9c-714cbdfca5f7","added_by":"auto","created_at":"2025-01-17 06:56:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1208835,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-5825013/v1/bcc1e9dae9f4d82471b83764.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis and comparison of the trends in burden of injury in China and ASEAN countries from 1990 to 2021 and its association with the socio-demographic index","fulltext":[{"header":"Background","content":"\u003cp\u003eThe member states of the Association of Southeast Asian Nations (ASEAN), encompassing Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, are interconnected both economically and socially[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nevertheless, given the diverse political systems, multiple faiths, varying stages of economic progress, and, crucially, the distinct models of healthcare systems among them, the member countries of ASEAN still face obstacles in attaining regional collaboration for the prevention and management of injuries[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. China and ASEAN, as major economies in the Asia-Pacific region and each other's largest trading partners, have built a dialogue relationship spanning more than three decades. This positions them to play a pivotal role in shaping the trajectory of health affairs in Asia and on a worldwide scale.\u003c/p\u003e \u003cp\u003eInjury poses a global health issue affecting all nations, impacting individuals of varying ages and genders. Over the past 30 years in China, the swift growth of the economy, the process of urbanization, the increase in motor vehicle usage, the aging population, and shifts in environmental and lifestyle factors have resulted in injuries becoming the fifth most common cause of mortality, following cancers, strokes, respiratory illnesses, and heart attacks[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Injuries result in a yearly productivity deficit of 12.6\u0026nbsp;million years in China, surpassing the loss from respiratory, cardiovascular, infectious, and cancer diseases combined[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The immediate healthcare expenses related to injuries in China are approximated at 65\u0026nbsp;billion Yuan or \u003cspan\u003e$\u003c/span\u003e10\u0026nbsp;billion annually. Hence, injury is increasingly viewed as a significant public health issue in China, potentially leading to substantial economic costs.\u003c/p\u003e \u003cp\u003eResearch and collaboration on a global scale focusing on monitoring, intervention, educational initiatives, promotional activities, and advocacy efforts aimed at various injury types and vulnerable groups have resulted in the formulation and enforcement of numerous policies, rules, and legislative measures in China's injury prevention and control sectors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nonetheless, there remains a gap in comprehensive data that captures the overall impact of injuries at both the national and regional levels. Additionally, there is a growing interest in tracking the trends of major injuries over time and pinpointing key areas for injury prevention efforts.\u003c/p\u003e \u003cp\u003eIn the Sustainable Development Goals (SDGs), there is a specific target dedicated to reducing injuries, namely SDG 3.6. This target is to halve the global mortality rate from road traffic injuries by 2030. To achieve this, a series of comprehensive measures must be implemented to decrease both the frequency and severity of road traffic accidents. These measures are expected to lower the number of deaths from road traffic injuries and to comprehensively enhance global road traffic safety. The SDGs stress the importance of reducing injuries and incidents of violence, which take more than 5\u0026nbsp;million lives each year, constituting 9% of all global deaths. Worldwide, injuries are among the leading causes of death and disability. However, progress in the field of injury prevention remains inadequate. Estimates suggest that the probability of meeting the specific injury-related targets in the SDGs is less than 5%.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] This situation indicates that the economic impact of injuries is likely to continue to rise. For China and ASEAN countries, injury prevention is of great significance in achieving the SDGs. These countries face challenges from road traffic accidents and other unintentional injuries as they undergo economic development and urbanization. In China and ASEAN countries, strong measures are needed to reduce these injuries. This is not only to fulfill the SDGs but also to reduce the socio-economic burden of injuries, which is particularly severe for groups with lower socio-economic status. Therefore, these countries need to increase their investment and efforts in injury prevention to achieve the relevant SDG targets.\u003c/p\u003e \u003cp\u003eThis study aims to examine and contrast the current situation and changing trends of injury burden in China and ASEAN countries from 1990 to 2021, leveraging data from the Global Burden of Disease Study 2021 (GBD 2021). Meanwhile, this study will also focus on exploring the inherent relationships between these changes and the Socio-demographic Index (SDI) of each country. The findings of this study offer a powerful perspective for a comprehensive examination of the trend characteristics and differences in the injury burden of China and ASEAN countries, and are of evidential reference value for promoting the formulation of more targeted and effective injury prevention strategies on a global scale.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this observational inquiry, we draw upon the findings yielded by the GBD 2021 result instruments. The GBD 2021 offers an exhaustive examination of the health detriments arising from 369 distinct diseases, injuries, and impairments, as well as 88 contributory risk factors. Encompassing a global spectrum of 204 countries and territories, the study avails itself of the most current epidemiological datasets and enhanced, standardized analytical approaches[7]. We procured the estimations for incidence, prevalence, mortality, and disability-adjusted life years (DALYs) pertaining to injury, complete with their corresponding 95% uncertainty intervals (95% UI), from the GBD 2021 dataset. Additionally, to provide a nuanced analysis of our findings, we incorporated the SDI. The SDI is a multifaceted indicator that mirrors a country\u0026apos;s progress in terms of health outcomes, calculated by averaging three components: fertility rate among those under 25, educational attainment for individuals aged 15 and above, and per capita income, with an adjustment for time lag. An SDI score of 0 signifies the lowest level of development for health, while a score of 1 represents the highest[8].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBurden description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA systematic evaluation was undertaken to ascertain the burden of injury in China and ASEAN countries, encompassing its epidemiological landscape, including prevalence, incidence, mortality, and DALYs. Furthermore, this study probed the demographic determinants shaping the injury burden, analyzing the differential distribution of the disease\u0026rsquo;s load among various age strata and between sexes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics analysis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe meticulously examined the incidence, prevalence, mortality, DALYs, and their respective age-standardized incidence rates (ASIR), age-standardized prevalence rates (ASPR), age-standardized mortality rates (ASMR), and age-standardized DALY rates (ASDR) of injuries in China, ASEAN countries, and globally, utilizing data from the GBD2021 database. Additionally, we computed the crude incidence rates (CIR), crude prevalence rates (CPR), crude mortality rates (CMR), and crude DALY rates (CDR) for different age groups. The projections of incidence, prevalence, mortality, and DALYs are presented per 100,000 individuals, complete with 95% UI, to reflect the variability in the estimates.\u003c/p\u003e\n\u003cp\u003eIn this investigation, we utilized the Joinpoint regression analysis model, a methodology frequently employed in epidemiological studies to evaluate temporal trends in injury incidence, prevalence, mortality, and DALYs[9]. This sophisticated model adeptly detects and quantitatively describes significant change points in the time-series data of injury rates across China, ASEAN countries and global contexts. The analysis enabled the estimation of the annual percent change (APC) and its corresponding 95% confidence interval (95%CI), thereby delineating prevalence trends over specified periods. Furthermore, to comprehensively assess the observed trends, we also computed the average annual percent change (AAPC), encapsulating the aggregated trend data over the study duration from 1990 to 2021. In a sophisticated analytical approach, the age-standardized indicators are modelled using a logarithmic regression framework, expressed as ln(y) = \u0026alpha; + \u0026beta;x + \u0026epsilon;. Here, y denotes the age-standardized indicator of interest, while x signifies the chronological year. The AAPC is computed as 100 \u0026times; (exp(\u0026beta;) \u0026minus; 1), and the 95% CI can also be calculated from the model. Statistically, an APC or AAPC estimate with a 95% CI lower bound greater than zero indicates an upward trend within the given interval. Conversely, an APC or AAPC estimate with a 95% CI upper bound less than zero suggests a downward trend. When the 95% CI for the APC or AAPC includes zero, it suggests that the trend has remained stable over time.\u003c/p\u003e\n\u003cp\u003eIn our pursuit to delineate the intricate relationship between injury burden and sociodemographic advancement, we have employed frontier analysis to gain further insights. This method generates a nonlinear frontier, which signifies the minimum attainable burden in relation to a given level of development. Utilizing non-parametric data envelope analysis, we adhered to the meticulous methodologies outlined in prior research[10, 11]. The discrepancy between a nation\u0026rsquo;s observed burden rates and the frontier, termed as the effective difference, quantifies the unattained health benefits that could be realized within the context of the country\u0026rsquo;s or region\u0026rsquo;s current developmental stage.\u003c/p\u003e\n\u003cp\u003eIn our pursuit to quantify health inequality, we have extracted data on total DALYs and ASDRs for in-depth inequality analysis. Adhering to the guidelines set forth by the World Health Organization, we have employed two canonical measures \u0026mdash; the Slope Index of Inequality (SII) and the Concentration Index (CI) \u0026mdash; to evaluate both absolute and relative income-related inequalities across countries[12]. The SII encapsulates the gradient of the regression line that correlates a country\u0026rsquo;s ASDR for injury with its weighted socioeconomic ranking. To normalize for differing levels of burden, we divide the SII by the global ASDR, thus deriving the Relative Index of Inequality (RII). Meanwhile, the CI serves as an instrument to gauge the relative disparity in the injury burden among countries by constructing the Lorenz concentration curve, which is based on cumulative DALYs and population data. The CI is quantified as the area under this curve, ranging from -1 to 1. A negative CI value signifies a disproportionate concentration of the injury burden among populations in countries with lower SDI values.\u003c/p\u003e\n\u003cp\u003eAll statistical manipulations and graphical representations were conducted using R version 4.4.1. Statistical significance was assigned to results with a P value of less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescription of the burden of injury in China and ASEAN countries\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIncidence of injury in China and ASEAN countries\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncidence of injury in China and the ASEAN countries collectively accounted for 18.61% of the global tally in 1990, which had marginally increased to 19.61% by 2021. Meanwhile, globally, the incidence increased from 554,872,072 (95% UI: 520,239,997-592,107,015) in 1990 to 607,789,604 (95% UI: 574,661,712-644,552,987) in 2021, representing a cumulative increase of 9.54%. In 2021, China bore the brunt of injury occurrences with a staggering 83.71 million cases, trailed by Indonesia, which reported 12.32 million, and Vietnam with 6.16 million. The global ASIR exhibited a decline from 10,264.62 per 100,000 population (95% UI: 9,647.30-10,944.94) in 1990 to 7,705.75 per 100,000 population (95% UI: 7,271.61-8,171.19) by 2021. Within China, the ASIR diminished from 6,001.81 per 100,000 population (95% UI: 5,546.05-6,583.71) in 1990 to 5,840.52 per 100,000 population (95% UI: 5,395.03-6,400.14) in 2021. The overall injury ASIRs for both China and ASEAN countries were below the global average in 2021. Notably, only two countries, Brunei and Singapore, recorded higher injury ASIRs than the worldwide norm. Brunei topped the list with an ASIR of 13,921.83 per 100,000, closely followed by Singapore with 12,656.07 per 100,000. Myanmar held the third position with an ASIR of 7,532.78 per 100,000. Concurrently, the AAPC in the global incidence rate registered a decrease of 0.98% (95% CI: -1.23 to -0.73) from 1990 to 2021, with Indonesia leading the decline at a rate of 1.12% (95% CI: -1.35 to -0.89), succeeded by Thailand at a rate of 1.07% (95% CI: -1.23 to -0.90). (Table 1 and Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrevalence of injury in China and ASEAN countries\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of prevalence, the number of injury cases in China and ASEAN countries collectively accounted for 24.42% of the global tally in 1990, which incremented to 25.47% by 2021. Globally, the prevalence of injuries escalated from 1,030,712,875 (95% UI: 980,087,458-1,086,397,210) in 1990 to 1,456,350,420 (95% UI: 1,385,784,930-1,535,575,907) in 2021, marking a cumulative increase of 41.30%. The global ASPR witnessed a decline from 21,445.74 per 100,000 population (95% UI: 20,428.04-22,536.24) in 1990 to 17,531.20 per 100,000 population (95% UI: 16,677.62-18,485.66) in 2021. In China, the ASPR decreased from 17,035.57 per 100,000 population (95% UI: 16,112.16-18,086.07) in 1990 to 16,266.69 per 100,000 population (95% UI: 15,325.96-17,338.10) in 2021. By 2021, both China and the ASEAN countries exhibited ASPRs below the global average. Notably, only Brunei and Singapore reported injury ASPRs surpassing the global norm. Brunei topped the rankings with an ASPR of 22,983.99 per 100,000, closely followed by Singapore with 21,147.39 per 100,000. Cambodia ranked third with an ASPR of 17,007.83 per 100,000. Concurrently, the AAPC in the global prevalence rate recorded a decrement of 0.64% (95% CI: -0.68 to -0.81) from 1990 to 2021. The decline in injury prevalence rates was led by Cambodia, Laos, and Indonesia, each exhibiting an AAPC of 0.81%. Specifically, Cambodia\u0026rsquo;s AAPC was -0.81% (95% CI: -0.87 to -0.75), Laos\u0026rsquo;s AAPC was -0.81% (95% CI: -0.83 to -0.79), and Indonesia\u0026rsquo;s AAPC was -0.81% (95% CI: -0.89 to -0.72). (Table 1, Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMortality of injury in China and ASEAN countries\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the domain of injury mortality, China and ASEAN countries collectively Shouldered a significant burden, accounting for 29.30% of the global injury deaths in 1990. However, by 2021, this proportion had experienced a modest decline to 23.16%. Concurrently, on a global scale, the number of injury deaths saw a slight uptick, ascending from 4,185,780 (95% UI: 3,974,301-4,373,502) in 1990 to 4,343,698 (95% UI: 3,984,365-4,631,455) in 2021, marking a cumulative increase of 3.77%. This global trend of increment stands in contrast to the notable reduction observed in both the absolute numbers and proportional contribution of injury deaths in China and the ASEAN countries. The global ASMR exhibited a decline from 84.86 per 100,000 population (95% UI: 80.77-88.56) in 1990 to 53.66 per 100,000 population (95% UI: 49.18-57.28) by 2021. Within China, the ASMR diminished from 89.81 per 100,000 population (95% UI: 81.37-99.45) in 1990 to 41.82 per 100,000 population (95% UI: 35.01-49.61) in 2021. In the annals of 2021, the ASMRs for China and ASEAN countries exhibited a mixed pattern when juxtaposed with the global average. Notably, five of these countries surpassed the global average, while a total of six fell beneath it. Vietnam topped the list with an ASMR of 68.49 per 100,000, closely followed by Cambodia with 67.13 per 100,000. Thailand held the third position with an ASMR of 65.9 per 100,000. Concurrently, the AAPC in the global mortality rate registered a decrease of 1.50% (95% CI: -1.67 to -1.33) from 1990 to 2021, with Singapore leading the decline at a rate of 3.33% (95% CI: -3.61 to -3.04), succeeded by China at a rate of 2.41% (95% CI: -2.87 to -1.95). (Table 1, Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDALYs of injury in China and ASEAN countries\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDALYs of injury in China and ASEAN countries collectively accounted for 29.37% of the global tally in 1990, which had marginally decreased to 21.57% by 2021. Meanwhile, globally, the DALYs decreased from 278,725,499 (95% UI: 262,114,155-298,335,835) in 1990 to 247,843,924 (95% UI: 226,826,042-272,347,784) in 2021, representing a cumulative decrease of 11.08%. The global ASDR exhibited a decline from 5,221.14 per 100,000 population (95% UI: 4,898.12-5,617.65) in 1990 to 3,101.29 per 100,000 population (95% UI: 2,839.62-3,408.68) by 2021. Within China, the ASDR diminished from 5,354.92 per 100,000 population (95% UI: 4,846.13-5,930.71) in 1990 to 2,284.18 per 100,000 population (95% UI: 1,991.95-2,605.37) in 2021. In terms of injury ASDRs for 2021, a distinctive dichotomy pattern emerged within the China and ASEAN countries\u0026rsquo; cohort: whereas four nations bore a heavier burden with ASDRs exceeding the global average, a majority of seven countries demonstrated a comparative leniency, with their ASDRs falling below the global average. Thailand topped the list with an ASDR of 3,818.34 per 100,000, closely followed by Myanmar with 3,552.06 per 100,000. Cambodia held the third position with an ASDR of 3,506.18 per 100,000. Concurrently, the AAPC of the DALYs globally registered a decrease of 1.71% (95% CI: -1.89 to -1.53) from 1990 to 2021, with China leading the decline at a rate of 2.92 % (95% CI: -3.13 to -2.71), succeeded by Singapore at a rate of 2.25% (95% CI: -2.38 to -2.12). (Table 1, Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoinpoint regression analysis of the burden of injury in China, ASEAN countries and global \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite an overall downward trend in the burden of injury across most countries from 1990 to 2021, certain periods exhibited an increase, as indicated by joinpoint regression analyses. Notably, the ASIR of injury demonstrated an ascending trend from 2011 to 2021 in China (APC = 2.29%, 95% CI 1.63\u0026ndash;2.96%). Same uptrends were also found in Cambodia (from 1990 to 1996, APC = 2.00%, 95% CI 0.58\u0026ndash;3.45%; from 1999 to 2011, APC = 0.68%, 95% CI 0.17\u0026ndash;1.20%), Singapore (from 1990 to 2000, APC = 0.04%, 95% CI 0.01\u0026ndash;0.07%). In recent years, the ASPR of injury showed an uptrend from 2010 to 2021 in China (APC = 0.91%, 95% CI 0.80\u0026ndash;1.02%). Same uptrends were also found in Malaysia (from 2019 to 2021, APC = 0.23%, 95% CI 0.13\u0026ndash;0.32%),\u0026nbsp;Laos (from 2018 to 2021, APC = 0.12%, 95% CI 0.03\u0026ndash;0.21%). The ASMR and ASDR of injury in China and ASEAN countries had not shown uptrends in recent years except Brunei, which showed an uptrend of injury ASMR from 2008 to 2016 (APC = 0.58%, 95% CI 0.31\u0026ndash;0.84%). (Table 2, Supplementary figure 1, Supplementary figure 2, Supplementary figure 3, Supplementary figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends in the burden of injury in China, ASEAN countries and global \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ASIR of injury in China and global has demonstrated a progressive downturn from 1990 to 2021. Nonetheless, there have been notable anomalies in certain years, with specific countries experiencing substantial increases in their ASIR. Notably, in 2004, both Indonesia and Thailand observed marked deviations from the norm, as did Myanmar and China in 2008, the Philippines in 2013 and 2016, and Myanmar once more in 2017. With respect to the ASPR, an overall declining trajectory has been evident across countries and on a global scale, save for a deviation in Myanmar in 2008. Regarding the ASMR, the predominant trend for most countries and global at large has been a decrease, albeit with intermittent rises in specific years. In particular, countries such as Indonesia, Myanmar, Philippines, and Thailand have exhibited a pattern of fluctuation in their ASMR. This ebb and flow is similarly mirrored in the ASDR. (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvolving Proportions of Transport Injuries, Unintentional Injuries, Self-Harm and Interpersonal Violence in the Burden of China and ASEAN Countries in 1990 and 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the burden of injury across various nations, whether in 1990 or 2021, has consistently been most pronounced due to unintentional injuries. However, the proportion of the total injury burden attributable to this category has undergone some changes from 1990 to 2021. When examining ASIR, there is a marked reduction in the proportion of self-harm and interpersonal violence in China, Laos, Philippines, Thailand, and Cambodia, whereas in Myanmar, the proportion of self-harm and interpersonal violence has increased. Additionally, a notable reduction in the proportion of transport injuries is evident across China, Myanmar, Thailand, the Philippines, and Indonesia. In terms of ASPR, a notable shift is observed in Myanmar, Vietnam, and Indonesia, where the proportion of transport injuries has significantly declined. From the perspective of ASMR, countries such as China, Laos, and Cambodia exhibit little change in the proportion of unintentional injuries. However, there is a concurrent decrease in the proportion of self-harm and interpersonal violence, alongside an increase in the proportion of transport injuries. When considering ASDR, a pronounced reduction in the proportion of unintentional injuries is evident in Myanmar, Vietnam, and Brunei. (Figure 3, Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBurden of injury in different age groups in\u0026nbsp;China, ASEAN countries and global in 1990 and 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 exhibited a comparison of the incidence, prevalence, mortality, and DALYs of injury in different age groups in China, ASEAN countries and global in 1990 and 2021, along with their corresponding crude rates. Based on the incidence data, the occurrence of injury cases in 1990 was predominantly concentrated among individuals under the age of 45. Notably, the number of cases in the \u0026lt; 5 age group was significant, second only to the 20-24 age group. The overall trend exhibited an approximately right-skewed distribution. In contrast, by 2021, the age distribution of injury cases had shifted, with the highest incidence observed among individuals aged 70+. Other age groups displayed a roughly normal distribution, centered around the 30-34 age group. In both 1990 and 2021, the CIR of injury demonstrated an increasing trend from the \u0026lt; 5 age group to the 20-24 age group, followed by a decreasing trend from the age of 55-59. Subsequently, in 1990, the CIR continued to decline until the 65-69 age group before experiencing a marked increase. In 2021, however, the CIR began to rise after the 55-59 age group. The highest incidence peak was consistently observed among individuals aged 70 and over in both years. It is worth noting that, in both 1990 and 2021, the CIR for most age groups in China and ASEAN countries was below the global average. However, Brunei and Singapore had CIRs that were predominantly higher across all age groups in 1990, a pattern that persisted in 2021, along with Myanmar for the first six age groups.\u003c/p\u003e\n\u003cp\u003eDrawing upon prevalence data, the burden of injury in 1990 was largely shouldered by individuals aged 20-39, with a notably lower prevalence observed among those under the age of 14. Fast forward to 2021, the prevalence of injury had shifted, with a concentration in the individuals aged 30-59, and the highest prevalence found among the 70+ demographic. Examining the CPR, a smooth ascending trend from the \u0026lt;5 age group to the 65-69 age group was evident in both 1990 and 2021 for countries other than Cambodia. Subsequent to this, a more rapid and pronounced increase was observed. In contrast, Cambodia exhibited a distinct decline in the 25-39 age group in 1990, and a similar downward trend was apparent in the 55-69 age group in 2021.\u003c/p\u003e\n\u003cp\u003eRegarding deaths, the age group with the greatest number of injury deaths was the \u0026lt;5 group in 1990, transitioning to the 70+ demographic by 2021. The CMR for injury in these two years exhibited a descending pattern from the \u0026lt;5 age group to the 10-14 age group, followed by a steady ascent until the 20-24 age group, after which a decline was noted until the 30-34 age group, where an upswing in mortality with advancing age became apparent. The age group with the peak mortality rate was consistently the 70+ group in both 1990 and 2021.\u003c/p\u003e\n\u003cp\u003eIn terms of DALYs, the zenith of DALYs was observed among the \u0026lt;5 age group in 1990, shifting to the 70+ demographic by 2021, mirroring the distribution pattern observed in injury deaths figures. Examining the CDR, the peak in 1990 was registered in the \u0026lt;5 age group, whereas in 2021, it was evident in the 70+ age group. Across both time points, the trajectory of CDR exhibited a general alignment with the patterns observed in the CMR, indicating a consistent correlation between the burden of disability and mortality due to injury.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSex differences of injury in China and ASEAN countries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs depicted in Figure6, the incidence of injury consistently exhibited a higher frequency among males compared to females annually from 1990 to 2021, within both China and ASEAN countries. In 2021, a total of 45.30 million females and 73.88 million males suffered from injury in these regions. The prevalence of injury remained relatively equitable between genders from 1990 to 2021 in China and ASEAN countries. However, the number of injury deaths and the cumulative DALYs for males substantially exceeded those for females over the same period. Additionally, a peculiar phenomenon was noted: despite an annual increase in the prevalence of injury among both males and females, the DALYs attributed to injury paradoxically decreased year by year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrontier analysis for the association between injury burden and SDI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo delve into the optimal scenario where countries can manage the injury burden in alignment with their respective SDI statuses for each year, a frontier analysis has been conducted (Figure 7). The outcomes of this analysis reveal that the three countries that are in closest proximity to the global frontier fit line are highlighted in blue, whereas the three nations that lie at the greatest distance from this line are denoted in red. With respect to injury incidence, the countries that are most removed from the frontier line are Brunei, Singapore, and Myanmar. In the realm of injury prevalence, Brunei, Singapore, and Cambodia emerge as the furthest from the frontier line. When examining injury mortality, Vietnam, Thailand, and Malaysia are found to be the most distant. Lastly, in terms of injury DALYs, Thailand, Vietnam, and Myanmar are the countries that fall farthest from the frontier line. With sociodemographic development, effective difference tends to increase, the burden of injury showing an appreciable decrease at the outset. Nonetheless, as the SDI continues to rise, the rate of decline in the burden of injury slows, with the countries furthest from the frontier fit line predominantly belonging to the middle to high SDI strata. This observation indicates that nations or regions with higher SDI levels exhibit a more substantial potential for advancing health burden mitigation efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-national injury health inequality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 1990 and 2021, the SII (per 100,000 population) for DALYs were -2407.96 and -1159.885 respectively. These figures denote a negative correlation between ASDRs and the SDI. (Figure 8). This significant decline marked reduction signifies a lessening in the disparity of the age-standardized burden of injury across high-income and low-income countries over this time span. Between 1990 and 2021, the concentration index for DALYs has exhibited a downward trajectory. While the regional disparity in the burden of injury has diminished between economically disparate countries, inequality persists. This observation underscores that despite the narrowing of wealth disparities in certain regions, global inequality in the incidence of injury remains an enduring challenge. (Figure 9).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first comprehensive effort to describe the injury burden in China and ASEAN countries, also estimating their long-term trends throughout the past 32 years, and accessing the association of the injury burden with the socio-demographic index. Our findings reveal a complex landscape, characterized by both progress and challenges.\u003c/p\u003e\n\u003cp\u003eThe burden of injury, encompassing incidence, prevalence, mortality, and DALYs, has exhibited a downward trend in both China and ASEAN countries over the study period, as indicated by the decline in age-standardized rates. This reduction aligns with the global injury trends. The transformation is unsurprising within the context of China, where the burden of fatal injury outcomes may have been mitigated by the profound socioeconomic and political advancements that have transpired over the course of the past 32 years[5, 13].\u003c/p\u003e\n\u003cp\u003eIn spite of the noted decline in the comprehensive injury burden, unintentional injuries continue to dominate as the primary contributor to the injury burden within China and ASEAN countries. In a concerted effort to curtail the incidence of accidental harm, China has implemented a series of proactive measures. Since 2007, the Ministry of Education has annually issued directives highlighting the perils of drowning and advocating for adult supervision in proximity to aquatic environments. Additionally, China has been dedicated to the enhancement of its infrastructure, which has, to some extent, diminished exposure to water bodies. In the ASEAN countries, the importance of inter-sectoral collaboration for drowning prevention is also starkly highlighted[14]. This underscores the critical need for ongoing prioritization of preventive strategies, particularly those designed to attenuate the hazards associated with falls, drowning, and other prevalent causes of unintentional injuries. In China and certain ASEAN countries, there has been an increase in the proportion of transport injuries\u0026apos; ASMR relative to the overall injury ASMR, alongside a decrease in the proportion of transport injuries\u0026apos; ASIR compared to the total injury ASIR. Existing research indicates that economic development is intricately linked with transport injuries, with reports suggesting that transport injury mortalities tend to rise in the early stages of economic progress, in alignment with our findings. Nonetheless, concurrent developments such as enhanced road construction, the broadening of road safety initiatives, and the advancement of motor vehicle safety technologies have progressed in tandem with economic growth. These improvements may account for the observed phenomenon. In the realm of self-harm and interpersonal violence, a decline in the proportion of ASIR and ASMR relative to total injury rates has been noted in countries such as China, Laos, and Cambodia. Among the pivotal strategies for self-harm prevention has been the restriction of access to means of self-harm[15]. Illustratively, the Chinese government has enacted stringent legislation to control firearms, pesticides, and other agricultural chemicals, as well as to enhance the regulation of anesthetic and psychotropic substances, thereby mitigating the risk of self-harm[16\u0026ndash;18]. Additionally, in recognition of the significant proportion of the self-harm population suffering from mental illness in China[19], which exceeds half of the affected population, the formulation and enforcement of the Mental Health Law has facilitated the advancement of mental health services[20, 21].\u0026nbsp;Concurrently, extant research has elucidated a correlation between psychological distress and substance use, both of which are linked to one or multiple types of injuries[22\u0026ndash;24]. This observation lends heightened support to the implementation of individual injury interventions that encompass socio-psychological concerns, as well as issues related to the use of both legal and illicit substances[24]. The decline in the burden of interpersonal violence is consonant with nations\u0026apos; adherence to the World Health Organization\u0026apos;s recommendations in enacting pertinent legislation and policies, signifying that these regulatory advancements are yielding positive outcomes towards a more civilized, progressive, and stable society. Taking China as a case in point[25], the legal framework for the prevention of violence encompasses a suite of statutes including the Criminal Law, the Anti-Domestic Violence Law, the Security Administration Punishment Act, the Law on the Protection of Minors, and the Law on Preventing Juvenile Delinquency, among others. Moreover, a raft of policies is aimed at forestalling and mitigating risk factors for violence, such as assistance programs for impoverished students, affordable housing policies, the National Plan for Children\u0026rsquo;s Development, the National Plan for Women\u0026rsquo;s Development, and the plan for elderly services and the construction of a senior care system. The reduced burden of interpersonal violence may be attributed to the collective advancements in legal development, economic conditions, facilitation of interpersonal communication, and the enhancement of healthcare services within China and ASEAN countries.\u003c/p\u003e\n\u003cp\u003eThe demographic distribution of injury cases has trended towards older adults, with a notable increase among individuals aged 70 and above. A study conducted in Malaysia has revealed that older age individuals exhibit a two-fold mortality rate when compared to the younger age group of victims involved in injury accidents[26]. This demographic shift mirrors the aging population dynamics within both China and ASEAN countries, underscoring the critical need for the development of age-specific injury prevention strategies. Concurrently, this shift also illuminates the proactive governmental initiatives, exemplified by the Chinese government, which have bolstered injury prevention awareness among the populace, with a particular emphasis on the prevention of child injuries[5, 27]. Child safety measures have garnered widespread recognition and public endorsement, demonstrating a commitment to safeguarding the youngest members of society.\u003c/p\u003e\n\u003cp\u003eThe injury burden persists at a higher level in males relative to females across nearly all age categories. Existing research has also documented that males have historically exhibited higher rates of hazardous driving and associated injuries, with a propensity to be involved in crashes that lead to harm or fatality, and a more pronounced engagement in risk behaviors such as drunk driving, speeding, and aggressive driving that precipitate such incidents[28, 29]. However, the gap in DALYs attributed to injury between genders has narrowed, potentially due to improved treatment and recovery outcomes for both sexes.\u003c/p\u003e\n\u003cp\u003eDespite an overall downward trend, certain nations within the study sample have experienced intermittent increases in the burden of injury. These fluctuations necessitate in-depth investigation to ascertain the root causes and to devise precision intervention strategies. Such anomalous oscillations are commonly attributed to natural disasters, social dynamics, economic conditions, and policy influences. For instance, in the realm of natural disasters, the 2004 Indian Ocean tsunami, the 2008 Cyclone Nargis in Myanmar, the Wenchuan earthquake in China, and the landfall of Typhoon Haiyan in the Philippines in 2013, among others, have had profound impacts. In the aftermath of earthquakes and tsunamis, the immediate surge in mortality and morbidity is evident, while survivors also face heightened risks of infectious diseases or deteriorating health conditions, thereby adding to the injury burden. Social factors, such as societal unrest and conflict, persist, exemplified by the Rohingya crisis in Myanmar. These social issues can precipitate acts of violence and casualties, thereby augmenting the burden of injury. The efficacy of robust emergency response plans and disaster preparedness strategies is crucial in mitigating the disease burden associated with such calamities. Moreover, regional disparities persist, necessitating tailored approaches that address the unique challenges faced by each nation.\u003c/p\u003e\n\u003cp\u003eThis study reveals a robust correlation between the burden of injury and the SDI. As nations ascend the SDI hierarchy, they generally witness enhancements in healthcare systems, infrastructure, education, and economic stability, all of which are conducive to a decrease in the injury burden, in alignment with existing research findings[30]. However, the non-linear relationship identified in our study suggests that the advantages of SDI progression may be most evident in the initial phases of development, with a subsequent decline in the incremental benefits as the SDI advances. Strategic policies and interventions must be customized to the unique SDI context of each country. As countries progress along the SDI spectrum, the emphasis should transition to addressing the intricate health challenges associated with higher SDI levels, including the management of an aging population, which can also affect injury risk. Moreover, the enduring global disparity in injury rates highlights the imperative for focused interventions that tackle the social determinants of health. This encompasses improving living standards, advancing educational opportunities, and narrowing socioeconomic gaps, all of which can play a direct role in injury prevention. International cooperation and the dissemination of best practices are instrumental in promoting the adoption of effective injury prevention strategies across diverse SDI settings. By synthesizing theoretical insights with empirical data, policymakers can craft more sophisticated and impactful strategies to mitigate the burden of injury throughout the various stages of sociodemographic development.\u003c/p\u003e\n\u003cp\u003eOur study was subject to certain limitations. While we delineated the comprehensive landscape of injury in China and ASEAN countries, as well as the disease burden associated with three distinct injury categories, we did not delve into a more nuanced analysis, omitting a further subdivision within these categories to explore more specific injury types. The estimation of injury burden utilizing GBD results is based on several models, potentially leading to an underestimation of the true magnitude of the issue. Furthermore, the study\u0026apos;s focus on China and ASEAN countries alone limits its ability to capture the full spectrum of global injury burden diversity. The assumption of linear trends and a single optimal \u0026quot;frontier\u0026quot; state in our analytical approach may not accurately reflect the complexities of real-world dynamics. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Additionally, the temporal scope of the study, spanning from 1990 to 2021, may not account for recent changes in injury patterns. Despite these limitations that may affect the generalizability and precision of our findings, our research underscores the critical need for regional collaboration in injury prevention and control, emphasizing the urgency of crafting targeted interventions to mitigate the risks within each injury category and effectively diminish the injury burden across these regions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides valuable insights into the injury burden trends and disparities in China and ASEAN countries. It underscores the importance of addressing social determinants of injury risk and emphasizes the need for tailored interventions considering regional disparities and evolving age distributions. By leveraging these findings, policymakers and stakeholders can develop more effective strategies to reduce the socio-economic burden of injuries, contributing to the achievement of Sustainable Development Goals related to injury prevention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eASEAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAssociation of Southeast Asian Nations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSocio-Demographic Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eGBD 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eGlobal Burden of Disease Study 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eDisability-adjusted life years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSlope Index of Inequality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eConcentration Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSDGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSustainable Development Goals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e95%UI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e95% uncertainty interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eASIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge-standardized incidence rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eASPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge-standardized prevalence rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eASMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge-standardized mortality rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eASDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge-standardized DALY rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCrude incidence rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCrude prevalence rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCrude mortality rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCrude DALY rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAnnual percent change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e95% confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAverage annual percent change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eRII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eRelative Index of Inequality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board of the University of Washington examined and authorized a consent waiver for the GBD research. In-depth details regarding the ethical guidelines are available on the official portal at http://www. healthdata.org/gbd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll original data is available at http://www.healthdata.org/gbd. In the study, we followed the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License and Section 7 of the University of Washington\u0026rsquo;s Website Terms and Conditions of Use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors have filled out the ICMJE Disclosure of Interest Form and declare that they have no conflicts of interest. The form can be obtained by contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research is sponsored by the Research Institute of Innovative think-tank at Guangxi Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e Study design: Ma ZY. Data collection: Nie FY, Bai XY. Data analysis: Nie FY. Figures: Nie FY. Manuscript writing: Nie FY. Manuscript proofing: Liang WJ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We owe a debt of gratitude to those who have taken part in this study or have contributed to the preparation of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu Q, Yan W, Qin C, Du M, Wang Y, Liu M, et al. Incidence and mortality trends of neglected tropical diseases and malaria in China and ASEAN countries from 1990 to 2019 and its association with the socio-demographic index. Global Health Res Policy. 2023;8:22.\u003c/li\u003e\n\u003cli\u003eTeerawattananon Y, Dabak SV, Isaranuwatchai W, Lertwilairatanapong T, Shafie AA, Suwantika AA, et al. What can we learn from others to develop a regional centre for infectious diseases in ASEAN? Comment on \u0026ldquo;operationalising regional cooperation for infectious disease control: a scoping review of regional disease control bodies and networks.\u0026rdquo; Int J Health Policy Manag. 2022;:1.\u003c/li\u003e\n\u003cli\u003eLeilei D, Pengpeng Y, Haagsma JA, Ye J, Yuan W, Yuliang E, et al. The burden of injury in China, 1990-2017: findings from the Global Burden of Disease Study 2017. Lancet Public Health. 2019;4:e449\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eZhou Y, Baker TD, Rao K, Li G. Productivity losses from injury in China. Inj Prev. 2003;9:124\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLi L, Yang J. Injury prevention in China: government-supported initiatives on the leading causes of injury-related deaths. Am J Public Health. 2019;109:557\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMa T, Peden AE, Peden M, Hyder AA, Jagnoor J, Duan L, et al. Out of the silos: embedding injury prevention into the sustainable development goals. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2021;27:166\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1204\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eGlobal burden of disease study 2021 (GBD 2021) socio-demographic index (SDI) 1950\u0026ndash;2021 | GHDx. https://ghdx.healthdata.org/record/global-burden-disease-study-2021-gbd-2021-socio-demographic-index-sdi-1950%E2%80%932021. Accessed 13 Nov 2024.\u003c/li\u003e\n\u003cli\u003eJoinpoint regression program. https://surveillance.cancer.gov/joinpoint/. Accessed 15 Nov 2024.\u003c/li\u003e\n\u003cli\u003eBarber RM, Fullman N, Sorensen RJD, Bollyky T, McKee M, Nolte E, et al. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990\u0026ndash;2015: a novel analysis from the Global Burden of Disease Study 2015. The Lancet. 2017;390:231\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eXie Y, Bowe B, Xian H, Balasubramanian S, Al-Aly Z. Rate of Kidney Function Decline and Risk of Hospitalizations in Stage 3A CKD. Clin J Am Soc Nephrol. 2015;10:1946\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Handbook on health inequality monitoring with a special focus on low- and middle-income countries. Geneva: World Health Organization; 2013.\u003c/li\u003e\n\u003cli\u003eOzanne-Smith J, Li Q. A social change perspective on injury prevention in China. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2018;24 Suppl 1:i25\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003ePeden AE, Scarr J, Doan Minh T, Latif R, Le Thi Anh D, Chong TL, et al. Drowning prevention challenges and opportunities: an exploratory study of perspectives of delegates from ASEAN nations. PLOS One. 2024;19:e0304138.\u003c/li\u003e\n\u003cli\u003eFlorentine JB, Crane C. Suicide prevention by limiting access to methods: a review of theory and practice. Soc Sci Med (1982). 2010;70:1626\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eNational People\u0026rsquo;s Congress. Law of the people\u0026rsquo;s republic of China on control of guns. 2015.\u003c/li\u003e\n\u003cli\u003eState Council of China. Pesticide regulations. 2017.\u003c/li\u003e\n\u003cli\u003eState Council of China. Regulations on Narcotic Drugs and Psychotropic Substances. 2016.\u003c/li\u003e\n\u003cli\u003ePhillips MR, Yang G, Zhang Y, Wang L, Ji H, Zhou M. Risk factors for suicide in China: a national case-control psychological autopsy study. Lancet (Lond Engl). 2002;360:1728\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eZhu Y, Zhang W, Wang Y, Cai J. Providing free treatment for severe mental disorders in China. Shanghai Arch Psychiatry. 2014;26:101\u0026ndash;2.\u003c/li\u003e\n\u003cli\u003eStanding Committee of the National People\u0026rsquo;s Congress. Mental Health Law of the People\u0026rsquo;s Republic of China. 2018.\u003c/li\u003e\n\u003cli\u003ePeltzer K, Pengpid S. Unintentional injuries and psychosocial correlates among in-school adolescents in Malaysia. Int J Environ Res Public Health. 2015;12:14936\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eStreet EJ, Jacobsen KH. Injury incidence among middle school students aged 13-15 years in 47 low-income and middle-income countries. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2016;22:432\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eAsbridge M, Azagba S, Langille DB, Rasic D. Elevated depressive symptoms and adolescent injury: examining associations by injury frequency, injury type, and gender. BMC Public Health. 2014;14:190.\u003c/li\u003e\n\u003cli\u003eGao X, Ye P, Er Y, Jin Y, Wang L, Duan L. Violence prevalence and prevention status in China. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2019;25:67\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eTan Chor Lip H, Tan JH, Mohamad Y, Ariffin AC, Imran R, Azmah Tuan Mat TN. Clinical characteristics of 1653 injured motorcyclists and factors that predict mortality from motorcycle crashes in Malaysia. Chin j traumatol = Zhonghua chuang shang za zhi. 2019;22:69\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eDuan L, Ye P, Wang L. Future challenges and solutions for safety in China: china CDC\u0026rsquo;s exploration of injury prevention strategies. Global Health Journal. 2018;2:14\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eLonczak HS, Neighbors C, Donovan DM. Predicting risky and angry driving as a function of gender. Accid; Anal Prev. 2007;39:536\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eZador PL, Krawchuk SA, Voas RB. Alcohol-related relative risk of driver fatalities and driver involvement in fatal crashes in relation to driver age and gender: an update using 1996 data. J Stud Alcohol. 2000;61:387\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eHaagsma JA, James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, et al. Burden of injury along the development spectrum: associations between the socio-demographic index and disability-adjusted life year estimates from the global burden of disease study 2017. Inj Prev: J Int Soc Child Adolesc Inj Prev. 2020;26 Supp 1:i12\u0026ndash;26.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eAll-age cases and age-standardized incidence, prevalence, mortality, and disability-adjusted life years (DALYs) rates and corresponding average annual percent change (AAPC) of injury in China, Association of Southeast Asian Nations (ASEAN) countries and global in 1990 and 2021\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"859\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 358px;\"\u003e\n \u003cp\u003eAll-ages cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 322px;\"\u003e\n \u003cp\u003eAge-standardized rate (per 100,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1990\u0026ndash;2021\u0026nbsp;\u003cbr\u003e\u0026nbsp;AAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIncidence\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e554,872,072\u003cbr\u003e\u0026nbsp;(520,239,997 to 592,107,015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e607,789,604\u003cbr\u003e\u0026nbsp;(574,661,712 to 644,552,987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e10,264.62\u0026nbsp;\u003cbr\u003e\u0026nbsp;(9,647.30 to 10,944.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7,705.75\u0026nbsp;\u003cbr\u003e\u0026nbsp;(7,271.61 to 8,171.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.98\u003cbr\u003e\u0026nbsp;(-1.23 to -0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e71,430,993\u003cbr\u003e\u0026nbsp;(65,753,645 to 78,559,510)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e83,709,567\u003cbr\u003e\u0026nbsp;(77,025,678 to 92,494,934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6,001.81\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,546.05 to 6,583.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5,840.52\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,395.03 to 6,400.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.08\u003cbr\u003e\u0026nbsp;(-1.02 to 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eBrunei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e47,019\u003cbr\u003e\u0026nbsp;(43,448 to 50,460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e64,009\u003cbr\u003e\u0026nbsp;(59,509 to 68,389)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e17,004.17\u0026nbsp;\u003cbr\u003e\u0026nbsp;(15,810.03 to 18,199.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e13,921.83\u0026nbsp;\u003cbr\u003e\u0026nbsp;(12,911.81 to 14,897.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.64\u003cbr\u003e\u0026nbsp;(-0.68 to -0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCambodia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e734,599\u003cbr\u003e\u0026nbsp;(690,860 to 786,580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e947,719\u003cbr\u003e\u0026nbsp;(894,742 to 1,001,283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6,811.44\u0026nbsp;\u003cbr\u003e\u0026nbsp;(6,444.41 to 7,204.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5,589.20\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,287.53 to 5,892.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.59\u003cbr\u003e\u0026nbsp;(-1.38 to 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e12,192,941\u003cbr\u003e\u0026nbsp;(11,217,826 to 13,205,385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e12,317,952\u003cbr\u003e\u0026nbsp;(11,475,111 to 13,217,218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6,324.20\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,851.76 to 6,798.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4,523.94\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,214.30 to 4,846.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.12\u003cbr\u003e\u0026nbsp;(-1.35 to -0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLaos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e332,783\u003cbr\u003e\u0026nbsp;(291,964 to 402,500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e350,497\u003cbr\u003e\u0026nbsp;(330,896 to 371,721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e7,609.53\u0026nbsp;\u003cbr\u003e\u0026nbsp;(6,758.00 to 9,044.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4,632.51\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,389.06 to 4,899.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.03\u003cbr\u003e\u0026nbsp;(-1.11 to -0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e938,953\u003cbr\u003e\u0026nbsp;(867,760 to 1,009,571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e1,510,238\u003cbr\u003e\u0026nbsp;(1,419,261 to 1,607,930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e5,239.24\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,867.79 to 5,585.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4,675.03\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,398.80 to 4,968.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.43\u003cbr\u003e\u0026nbsp;(-0.46 to -0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMyanmar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e3,272,013\u003cbr\u003e\u0026nbsp;(3,075,580 to 3,467,928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e4,312,844\u003cbr\u003e\u0026nbsp;(3,963,132 to 4,786,272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e7,659.78\u0026nbsp;\u003cbr\u003e\u0026nbsp;(7,250.67 to 8,069.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7,532.78\u0026nbsp;\u003cbr\u003e\u0026nbsp;(6,927.21 to 8,338.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.89\u003cbr\u003e\u0026nbsp;(-1.49 to -0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e4,839,948\u003cbr\u003e\u0026nbsp;(4,525,881 to 5,169,259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e5,497,525\u003cbr\u003e\u0026nbsp;(5,146,653 to 5,876,532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e7,308.96\u0026nbsp;\u003cbr\u003e\u0026nbsp;(6,876.02 to 7,788.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4,752.72\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,465.96 to 5,058.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.90\u003cbr\u003e\u0026nbsp;(-1.19 to -0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e491,792\u003cbr\u003e\u0026nbsp;(453,637 to 530,505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e633,618\u003cbr\u003e\u0026nbsp;(580,473 to 688,379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e15,108.16\u0026nbsp;\u003cbr\u003e\u0026nbsp;(13,871.18 to 16,282.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e12,656.07\u0026nbsp;\u003cbr\u003e\u0026nbsp;(11,406.79 to 13,880.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.57\u003cbr\u003e\u0026nbsp;(-0.62 to -0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e4,450,471\u003cbr\u003e\u0026nbsp;(4,192,969 to 4,716,653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e3,673,114\u003cbr\u003e\u0026nbsp;(3,505,006 to 3,845,453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e7,398.97\u0026nbsp;\u003cbr\u003e\u0026nbsp;(6,993.12 to 7,797.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5,673.96\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,358.79 to 5,981.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.07\u003cbr\u003e\u0026nbsp;(-1.23 to -0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eViet Nam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e4,548,084\u003cbr\u003e\u0026nbsp;(4,217,570 to 4,888,034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e6,162,990\u003cbr\u003e\u0026nbsp;(5,827,286 to 6,497,524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6,679.74\u0026nbsp;\u003cbr\u003e\u0026nbsp;(6,238.67 to 7,159.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6,234.82\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,887.23 to 6,564.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.23\u003cbr\u003e\u0026nbsp;(-0.52 to 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrevalence\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1,030,712,875\u003cbr\u003e\u0026nbsp;(980,087,458 to 1,086,397,210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e1,456,350,420\u003cbr\u003e\u0026nbsp;(1,385,784,930 to 1,535,575,907)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e21,445.74\u0026nbsp;\u003cbr\u003e\u0026nbsp;(20,428.04 to 22,536.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e17,531.20\u0026nbsp;\u003cbr\u003e\u0026nbsp;(16,677.62 to 18,485.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.64\u003cbr\u003e\u0026nbsp;(-0.68 to -0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e191,930,723\u003cbr\u003e\u0026nbsp;(180,576,902 to 205,168,088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e277,962,514\u003cbr\u003e\u0026nbsp;(263,402,632 to 295,114,044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e17,035.57\u0026nbsp;\u003cbr\u003e\u0026nbsp;(16,112.16 to 18,086.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e16,266.69\u0026nbsp;\u003cbr\u003e\u0026nbsp;(15,325.96 to 17,338.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.15\u003cbr\u003e\u0026nbsp;(-0.27 to -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eBrunei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e59,002\u003cbr\u003e\u0026nbsp;(56,349 to 62,446)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e107,214\u003cbr\u003e\u0026nbsp;(101,836 to 113,539)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e28,386.24\u0026nbsp;\u003cbr\u003e\u0026nbsp;(27,258.49 to 29,761.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e22,983.99\u0026nbsp;\u003cbr\u003e\u0026nbsp;(21,935.52 to 24,228.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.68\u003cbr\u003e\u0026nbsp;(-0.71 to -0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCambodia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1,836,921\u003cbr\u003e\u0026nbsp;(1,342,620 to 2,991,039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e2,645,427\u003cbr\u003e\u0026nbsp;(2,285,810 to 3,388,319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e21,953.73\u0026nbsp;\u003cbr\u003e\u0026nbsp;(17,034.22 to 33,249.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e17,007.83\u0026nbsp;\u003cbr\u003e\u0026nbsp;(14,648.24 to 22,005.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.81\u003cbr\u003e\u0026nbsp;(-0.87 to -0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e24,052,762\u003cbr\u003e\u0026nbsp;(22,579,994 to 26,030,025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e34,548,833\u003cbr\u003e\u0026nbsp;(32,598,107 to 36,714,937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e15,532.01\u0026nbsp;\u003cbr\u003e\u0026nbsp;(14,684.61 to 16,552.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e12,167.50\u0026nbsp;\u003cbr\u003e\u0026nbsp;(11,510.62 to 12,865.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.81\u003cbr\u003e\u0026nbsp;(-0.89 to -0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLaos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e485,784\u003cbr\u003e\u0026nbsp;(465,332 to 509,082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e796,391\u003cbr\u003e\u0026nbsp;(758,047 to 837,386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e15,429.79\u0026nbsp;\u003cbr\u003e\u0026nbsp;(14,796.41 to 16,051.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e11,992.52\u0026nbsp;\u003cbr\u003e\u0026nbsp;(11,442.03 to 12,547.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.81\u003cbr\u003e\u0026nbsp;(-0.83 to -0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1,886,401\u003cbr\u003e\u0026nbsp;(1,798,058 to 1,988,710)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e3,842,960\u003cbr\u003e\u0026nbsp;(3,657,516 to 4,047,225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e13,124.05\u0026nbsp;\u003cbr\u003e\u0026nbsp;(12,540.49 to 13,760.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e11,862.79\u0026nbsp;\u003cbr\u003e\u0026nbsp;(11,305.94 to 12,472.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.33\u003cbr\u003e\u0026nbsp;(-0.34 to -0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMyanmar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e5,939,531\u003cbr\u003e\u0026nbsp;(5,376,997 to 7,324,033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e9,000,475\u003cbr\u003e\u0026nbsp;(8,276,763 to 10,234,411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e17,244.30\u0026nbsp;\u003cbr\u003e\u0026nbsp;(15,871.45 to 20,350.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e16,135.29\u0026nbsp;\u003cbr\u003e\u0026nbsp;(14,878.76 to 18,253.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.34\u003cbr\u003e\u0026nbsp;(-0.62 to -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e8,110,695\u003cbr\u003e\u0026nbsp;(7,494,664 to 9,118,350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e13,632,959\u003cbr\u003e\u0026nbsp;(12,745,851 to 14,681,170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e16,083.75\u0026nbsp;\u003cbr\u003e\u0026nbsp;(15,079.54 to 17,548.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e12,850.57\u0026nbsp;\u003cbr\u003e\u0026nbsp;(12,050.65 to 13,813.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.74\u003cbr\u003e\u0026nbsp;(-0.80 to -0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e736,867\u003cbr\u003e\u0026nbsp;(696,604 to 782,656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e1,537,287\u003cbr\u003e\u0026nbsp;(1,449,786 to 1,639,905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e23,917.26\u0026nbsp;\u003cbr\u003e\u0026nbsp;(22,673.88 to 25,328.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e21,147.39\u0026nbsp;\u003cbr\u003e\u0026nbsp;(19,983.69 to 22,590.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.39\u003cbr\u003e\u0026nbsp;(-0.47 to -0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e8,589,824\u003cbr\u003e\u0026nbsp;(8,212,888 to 8,978,840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e11,974,553\u003cbr\u003e\u0026nbsp;(11,409,145 to 12,611,671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e16,715.11\u0026nbsp;\u003cbr\u003e\u0026nbsp;(16,049.25 to 17,381.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e13,944.25\u0026nbsp;\u003cbr\u003e\u0026nbsp;(13,298.84 to 14,702.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.59\u003cbr\u003e\u0026nbsp;(-0.66 to -0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eViet Nam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e8,034,567\u003cbr\u003e\u0026nbsp;(7,672,492 to 8,459,635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e14,831,076\u003cbr\u003e\u0026nbsp;(14,153,845 to 15,535,661)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e14,847.78\u0026nbsp;\u003cbr\u003e\u0026nbsp;(14,171.44 to 15,543.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e14,160.72\u0026nbsp;\u003cbr\u003e\u0026nbsp;(13,533.39 to 14,823.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.16\u003cbr\u003e\u0026nbsp;(-0.26 to -0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeaths\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e4,185,780\u003cbr\u003e\u0026nbsp;(3,974,301 to 4,373,502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e4,343,698\u003cbr\u003e\u0026nbsp;(3,984,365 to 4,631,455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e84.86\u0026nbsp;\u003cbr\u003e\u0026nbsp;(80.77 to 88.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e53.66\u0026nbsp;\u003cbr\u003e\u0026nbsp;(49.18 to 57.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.50\u003cbr\u003e\u0026nbsp;(-1.67 to -1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e929,473\u003cbr\u003e\u0026nbsp;(841,757 to 1,028,156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e688,564\u003cbr\u003e\u0026nbsp;(568,572 to 826,236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e89.81\u0026nbsp;\u003cbr\u003e\u0026nbsp;(81.37 to 99.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e41.82\u0026nbsp;\u003cbr\u003e\u0026nbsp;(35.01 to 49.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.41\u003cbr\u003e\u0026nbsp;(-2.87 to -1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eBrunei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e129\u003cbr\u003e\u0026nbsp;(117 to 142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e122\u003cbr\u003e\u0026nbsp;(109 to 135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e62.62\u0026nbsp;\u003cbr\u003e\u0026nbsp;(56.21 to 68.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e29.94\u0026nbsp;\u003cbr\u003e\u0026nbsp;(26.83 to 33.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.34\u003cbr\u003e\u0026nbsp;(-2.51 to -2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCambodia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e8,161\u003cbr\u003e\u0026nbsp;(7,061 to 9,237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e9,415\u003cbr\u003e\u0026nbsp;(7,280 to 12,004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e95.88\u0026nbsp;\u003cbr\u003e\u0026nbsp;(83.51 to 109.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e67.13\u0026nbsp;\u003cbr\u003e\u0026nbsp;(53.09 to 83.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.06\u003cbr\u003e\u0026nbsp;(-1.24 to -0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e93,472\u003cbr\u003e\u0026nbsp;(83,396 to 103,181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e92,498\u003cbr\u003e\u0026nbsp;(78,958 to 111,405)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e59.06\u0026nbsp;\u003cbr\u003e\u0026nbsp;(51.84 to 65.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e38.37\u0026nbsp;\u003cbr\u003e\u0026nbsp;(32.61 to 45.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.50\u003cbr\u003e\u0026nbsp;(-2.61 to -0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLaos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e4,023\u003cbr\u003e\u0026nbsp;(3,300 to 4,931)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e3,521\u003cbr\u003e\u0026nbsp;(2,715 to 4,479)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e106.85\u0026nbsp;\u003cbr\u003e\u0026nbsp;(88.91 to 130.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e54.44\u0026nbsp;\u003cbr\u003e\u0026nbsp;(42.36 to 68.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.02\u003cbr\u003e\u0026nbsp;(-2.18 to -1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e8,768\u003cbr\u003e\u0026nbsp;(8,280 to 9,237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e14,765\u003cbr\u003e\u0026nbsp;(13,970 to 15,650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e62.10\u0026nbsp;\u003cbr\u003e\u0026nbsp;(58.00 to 65.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e47.72\u0026nbsp;\u003cbr\u003e\u0026nbsp;(45.00 to 50.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.81\u003cbr\u003e\u0026nbsp;(-1.62 to 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMyanmar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e38,948\u003cbr\u003e\u0026nbsp;(31,410 to 47,134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e30,900\u003cbr\u003e\u0026nbsp;(25,808 to 37,292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e105.13\u0026nbsp;\u003cbr\u003e\u0026nbsp;(85.47 to 126.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e58.68\u0026nbsp;\u003cbr\u003e\u0026nbsp;(49.71 to 70.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.76\u003cbr\u003e\u0026nbsp;(-4.11 to 0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e39,889\u003cbr\u003e\u0026nbsp;(37,376 to 42,563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e48,586\u003cbr\u003e\u0026nbsp;(41,740 to 56,274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e70.29\u0026nbsp;\u003cbr\u003e\u0026nbsp;(65.64 to 75.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e46.43\u0026nbsp;\u003cbr\u003e\u0026nbsp;(39.89 to 53.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.86\u003cbr\u003e\u0026nbsp;(-1.17 to -0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1,122\u003cbr\u003e\u0026nbsp;(1,098 to 1,147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e938\u003cbr\u003e\u0026nbsp;(883 to 984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e38.30\u0026nbsp;\u003cbr\u003e\u0026nbsp;(37.34 to 39.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e13.43\u0026nbsp;\u003cbr\u003e\u0026nbsp;(12.69 to 14.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-3.33\u003cbr\u003e\u0026nbsp;(-3.61 to -3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e48,740\u003cbr\u003e\u0026nbsp;(43,541 to 54,139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e50,802\u003cbr\u003e\u0026nbsp;(40,169 to 62,677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e91.69\u0026nbsp;\u003cbr\u003e\u0026nbsp;(81.47 to 102.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e65.90\u0026nbsp;\u003cbr\u003e\u0026nbsp;(52.82 to 80.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.19\u003cbr\u003e\u0026nbsp;(-1.71 to -0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eViet Nam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e53,587\u003cbr\u003e\u0026nbsp;(44,882 to 64,633)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e65,844\u003cbr\u003e\u0026nbsp;(53,132 to 79,315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e95.19\u0026nbsp;\u003cbr\u003e\u0026nbsp;(78.36 to 116.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e68.49\u0026nbsp;\u003cbr\u003e\u0026nbsp;(54.40 to 81.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.05\u003cbr\u003e\u0026nbsp;(-1.18 to -0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDALYs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e278,725,499\u003cbr\u003e\u0026nbsp;(262,114,155 to 298,335,835)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e247,843,924\u003cbr\u003e\u0026nbsp;(226,826,042 to 272,347,784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e5,221.14\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,898.12 to 5,617.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3,101.29\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2,839.62 to 3,408.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.71\u003cbr\u003e\u0026nbsp;(-1.89 to -1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e61,496,972\u003cbr\u003e\u0026nbsp;(55,696,155 to 68,063,372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e35,048,515\u003cbr\u003e\u0026nbsp;(30,180,468 to 40,547,676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e5,354.92\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,846.13 to 5,930.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2,284.18\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1,991.95 to 2,605.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.92\u003cbr\u003e\u0026nbsp;(-3.13 to -2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eBrunei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e10,046\u003cbr\u003e\u0026nbsp;(9,020 to 11,120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e9,747\u003cbr\u003e\u0026nbsp;(8,435 to 11,399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e4,153.50\u0026nbsp;\u003cbr\u003e\u0026nbsp;(3,701.17 to 4,681.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2,108.15\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1,826.17 to 2,459.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.13\u003cbr\u003e\u0026nbsp;(-2.30 to -1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCambodia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e625,324\u003cbr\u003e\u0026nbsp;(536,379 to 734,946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e570,989\u003cbr\u003e\u0026nbsp;(465,534 to 720,442)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e5,935.99\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,096.81 to 6,955.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3,506.18\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2,887.81 to 4,388.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.65\u003cbr\u003e\u0026nbsp;(-1.87 to -1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e6,818,031\u003cbr\u003e\u0026nbsp;(6,151,599 to 7,544,704)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e5,765,573\u003cbr\u003e\u0026nbsp;(4,996,302 to 6,717,030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e3,688.91\u0026nbsp;\u003cbr\u003e\u0026nbsp;(3,350.34 to 4,096.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2,124.88\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1,860.03 to 2,456.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.76\u003cbr\u003e\u0026nbsp;(-2.72 to -0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLaos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e280,500\u003cbr\u003e\u0026nbsp;(232,356 to 341,963)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e221,599\u003cbr\u003e\u0026nbsp;(175,206 to 277,582)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6,441.76\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,387.78 to 7,724.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3,028.08\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2,414.81 to 3,766.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.25\u003cbr\u003e\u0026nbsp;(-2.44 to -2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e538,626\u003cbr\u003e\u0026nbsp;(507,237 to 572,640)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e790,197\u003cbr\u003e\u0026nbsp;(744,662 to 849,016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e3,283.28\u0026nbsp;\u003cbr\u003e\u0026nbsp;(3,068.28 to 3,496.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2,395.83\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2,255.45 to 2,576.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.01\u003cbr\u003e\u0026nbsp;(-1.82 to -0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMyanmar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e2,741,334\u003cbr\u003e\u0026nbsp;(2,241,832 to 3,293,471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e1,997,987\u003cbr\u003e\u0026nbsp;(1,712,265 to 2,362,582)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6,477.17\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5,334.41 to 7,675.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3,552.06\u0026nbsp;\u003cbr\u003e\u0026nbsp;(3,051.93 to 4,192.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.02\u003cbr\u003e\u0026nbsp;(-3.90 to -0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e2,852,002\u003cbr\u003e\u0026nbsp;(2,662,425 to 3,063,580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e3,050,392\u003cbr\u003e\u0026nbsp;(2,675,491 to 3,452,582)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e4,438.67\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,130.33 to 4,794.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2,709.32\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2,370.54 to 3,066.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.23\u003cbr\u003e\u0026nbsp;(-1.49 to -0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e82,201\u003cbr\u003e\u0026nbsp;(73,942 to 92,728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e86,868\u003cbr\u003e\u0026nbsp;(70,276 to 108,177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e2,581.96\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2,309.66 to 2,926.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1,280.59\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1,059.01 to 1,573.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.25\u003cbr\u003e\u0026nbsp;(-2.38 to -2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e3,123,334\u003cbr\u003e\u0026nbsp;(2,822,292 to 3,460,897)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e2,666,000\u003cbr\u003e\u0026nbsp;(2,213,246 to 3,212,989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e5,358.80\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,830.04 to 5,962.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3,818.34\u0026nbsp;\u003cbr\u003e\u0026nbsp;(3,204.77 to 4,577.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.17\u003cbr\u003e\u0026nbsp;(-1.67 to -0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eViet Nam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e3,305,713\u003cbr\u003e\u0026nbsp;(2,827,689 to 3,894,761)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e3,257,690\u003cbr\u003e\u0026nbsp;(2,688,122 to 3,860,112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e4,961.83\u0026nbsp;\u003cbr\u003e\u0026nbsp;(4,222.82 to 5,908.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3,239.88\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2,697.65 to 3,798.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.37\u003cbr\u003e\u0026nbsp;(-1.51 to -1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Joinpoint regression results, periods with uptrends of the burden of injury in China and ASEAN countries\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPC (95% CI) (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eASIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.29 (1.63 to 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eCambodia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.00 (0.58 to 3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.68 (0.17 to 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.04 (0.01 to 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eASPR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.91 (0.80 to 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eViet Nam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.88 (0.63 to 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.23 (0.13 to 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eLaos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.12 (0.03 to 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eMyanmar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.75 (0.72 to 6.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.04 (0.01 to 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.14 (0.01 to 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eASMR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eBrunei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.58 (0.31 to 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eCambodia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.64 (0.55 to 2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.34 (1.79 to 4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eASDR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.50 (1.96 to 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"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":"Injury burden, China, ASEAN, Socio-Demographic Index, Trend analysis","lastPublishedDoi":"10.21203/rs.3.rs-5825013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5825013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs China and the Association of Southeast Asian Nations (ASEAN) experience rapid economic growth and urbanization, injuries have become a significant public health issue. This study aims to analyze and compare the injury burden trends in these regions from 1990 to 2021, while examining the correlation with the Socio-Demographic Index (SDI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from the Global Burden of Disease Study 2021 (GBD 2021) was used to assess injury burden metrics such as incidence, prevalence, mortality, and disability-adjusted life years (DALYs). Joinpoint regression analysis identified trends, while frontier analysis assessed the optimal scenario for managing injury burden relative to the SDI. Health inequality was analyzed using the Slope Index of Inequality (SII) and Concentration Index (CI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study revealed a mixed picture of injury burden trends. While overall trends showed a decrease in injury incidence, prevalence, mortality, and DALYs, certain periods and countries experienced increases. Unintentional injuries remained the predominant cause. The injury burden shifted to older adults, particularly those aged 70 and above, reflecting the demographic shift towards an aging population, with males bearing a higher burden compared to females. The injury burden was strongly correlated with the SDI, indicating a decrease as countries develop. In the frontier analysis examining the correlation between injury burden and the SDI, countries furthest from the global frontier fit line were predominantly those with middle to high SDI rankings. This finding suggests that countries with higher SDI levels exhibit a more substantial potential for advancing health burden mitigation efforts. The SII for DALYs decreased from \u0026minus;\u0026thinsp;2407.96 in 1990 to -1159.885 in 2021, indicating a reduction in the disparity of age-standardized injury burden between high-income and low-income countries.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study provides valuable insights into the injury burden trends and disparities in China and ASEAN countries. It underscores the importance of addressing social determinants of injury risk and emphasizes the need for tailored interventions considering regional disparities and evolving age distributions. By leveraging these findings, policymakers and stakeholders can develop more effective strategies to reduce the socio-economic burden of injuries, contributing to the achievement of Sustainable Development Goals related to injury prevention.\u003c/p\u003e","manuscriptTitle":"Analysis and comparison of the trends in burden of injury in China and ASEAN countries from 1990 to 2021 and its association with the socio-demographic index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 06:48:28","doi":"10.21203/rs.3.rs-5825013/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":"39f30d92-27cb-467d-bd6e-5c63da42b93a","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T08:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-17 06:48:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5825013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5825013","identity":"rs-5825013","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}