The Spatiotemporal Heterogeneity of Non-communicable Diseases Attributed to Low Physical Activity: Capturing Populations in Vulnerable Regions and Age Groups

The Spatiotemporal Heterogeneity of Non-communicable Diseases Attributed to Low Physical Activity: Capturing Populations in Vulnerable Regions and Age Groups | 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 The Spatiotemporal Heterogeneity of Non-communicable Diseases Attributed to Low Physical Activity: Capturing Populations in Vulnerable Regions and Age Groups Yuanxiang Shi, Xinwei Liu, Xiyu Zhang, Baoguo Shi, Xiaohe Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8156186/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background With the continuous rise in low physical activity (LPA) levels globally, the burden of non-communicable diseases (NCDs) caused by LPA has been increasing, posing a significant threat to public health. However, as a factor that can be actively improved by human intervention, the actual control effectiveness of LPA remains unsatisfactory. Therefore, this study aims to analyze the spatiotemporal trends of the disease burden of NCDs caused by LPA at the global, regional, national, and local levels, and to explore the spatiotemporal heterogeneity across gender, age, and various SDI groups. The findings are intended to provide evidence-based insights for formulating policies to improve LPA. Methods Data was extracted from the Global Burden of Disease 2021 database. Trends in mortality and disability-adjusted life years (DALYs) due to NCDs attributable to LPA from 1990 to 2021 were assessed using Estimated Annual Percentage Change (EAPC) and percentage change. An age-period-cohort (APC) model was employed to investigate spatiotemporal differences in age, period, and cohort effects across different SDI groups. Findings In 2021, the global DALYs due to NCDs caused by LPA were 15,475,981.4 (7,248,984.76 to 23,953,592.69), with an age-standardized DALY rate (ASDR) of 181.53 (83.95 to 280). The number of deaths was 649,308.59 (276,348.17 to 1,044,772.12), with an age-standardized mortality rate (ASMR) of 7.89 (3.35 to 12.79). The EAPC for ASDR was -0.49 (-0.55 to -0.44), and for ASMR was -0.9 (-0.94 to -0.86). The highest numbers of DALYs and deaths at the super-regional and national levels were recorded in Southeast Asia, East Asia, and Oceania regions and China, with 5,080,821.8 (2,282,693.81 to 7,996,695.8) and 216,436.64 (92,213.73 to 366,075.14), and 3,241,988.81 (1,377,392.22 to 5,307,582.63) and 147,725.7 (56,945.8 to 263,820.53), respectively. Females experienced higher diseases burden than males. In the 25–39 age group, the risk of mortality and disability from NCDs attributable to LPA in High SDI regions has increased, exhibiting a trend toward younger age groups. This is particularly pronounced in the case of diabetic kidney disease. Conclusions This study tracks the burden of NCDs attributable to LPA across different regions and SDI levels over a 32-year period. The overall inequality in the burden primarily stems from differences in population size. Furthermore, gender disparities are evident, with females being more vulnerable to the burden of LPA-related NCDs. Additionally, the risk of NCDs, particularly diabetic kidney disease, is increasing among younger populations globally, while the rising risk of cancer is more pronounced in regions with lower SDI levels. Low physical activities Non-communicabe diseases Spatiotemporal heterogeneity Age - Period - Cohort Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Physical activity is one of the most crucial measures for individuals of all age groups to improve their health 1 , 2 . According to relevant studies, physical activity is associated with a lower risk of mortality and morbidity, including cardiovascular diseases and cancer 3 , 4 . Low physical activity(LPA), defined as engaging in less than 3000 to 4500 metabolic equivalent (MET) minutes per week, exacerbates the incidence of obesity and increases the risk of related diseases such as cardiovascular diseases and type 2 diabetes 5 – 7 . Globally, 7.2% of all-cause mortality and 7.6% of cardiovascular disease mortality are attributed to LPA 8 . From a socioeconomic perspective, LPA imposes a significant economic burden worldwide. As early as 2013, LPA resulted in a global productivity loss of $ 13.7 billion 9 . According to the latest data from the World Health Organization (WHO), nearly one-third (31%) of adults worldwide (approximately 1.8 billion people) did not meet the recommended levels of physical activity in 2022, and this percentage is expected to rise to 35% by 2030 10 . Although LPA is a factor that can be subjectively controlled by humans, the actual control effectiveness is not ideal, and it remains a major public health threat. The impact of physical activity varies across different diseases, genders, and age groups. Compared to individuals with insufficient activity (total activity < 600 MET minutes/week), those in the highly active category (≥ 8000 MET minutes/week) experienced a 14% reduction in breast cancer risk, a 21% reduction in colon cancer risk, a 28% reduction in diabetes risk, a 25% reduction in ischemic heart disease risk, and a 26% reduction in ischemic stroke risk 11 . Additionally, in terms of psychological and cognitive health, appropriate physical activity significantly reduces the risk of depression and Alzheimer's disease 12 , 13 . Regarding gender differences, LPA levels have consistently been higher among females than males, exhibiting a "gender gap" that begins early in life and persists throughout adulthood 14 – 16 . Across all age groups, 81% of adolescents aged 11–17 do not meet the recommended levels of physical activity, while 31% of adults fail to meet these recommendations, with LPA levels increasing with age 17 . In recent years, LPA has led to a rapid increase in the share of medical expenses paid by individuals in many European countries. Higher out-of-pocket expenses directly impact household budgets, thereby limiting access to healthcare and exacerbating poverty 18 . Consequently, diseases caused by LPA not only reduce individuals' quality of life but also increase the economic burden on families, raise healthcare costs, and result in significant losses in social productivity. The widespread population affected and the substantial disease burden have created severe social losses, significantly impacting the health and quality of life of people in various countries. Comprehensive measures at the national, societal, and individual levels are urgently needed to address this issue. In 2018, the World Health Organization (WHO) launched the Global Action Plan on Physical Activity 2018–2030 , providing policy recommendations for countries and communities with the aim of reducing the global prevalence of physical inactivity by 15% by 2030 19 . However, due to insufficient consideration of factors such as economic levels, geographic environments, population characteristics, and cultural practices across different regions, the WHO's 2022 assessment of the global progress in implementing the Global Action Plan on Physical Activity 2018–2030 revealed limited advancements. Although more than three-quarters of countries have taken actions related to at least two policy indicators outlined in the plan, less than half reported implementing these policy indicators by 2021 20 . Current research on the disease burden of LPA is predominantly confined to specific regional populations or particular disease types, lacking a comprehensive depiction of the inequitable distribution of the overall disease burden attributable to LPA across countries with varying income levels 21 – 23 . Furthermore, physical activity spans the entire human life cycle, and its associated disease burden is influenced by a complex interplay of multiple factors, including regional economic levels, geographic environments, population characteristics, and cultural practices. Previous studies have largely focused on localized descriptions, lacking longitudinal tracking over time 24 – 26 . Therefore, this study adopts a global perspective, utilizing the latest Global Burden of Disease (GBD) 2021 data to delineate the disparities and inequities in the burden of chronic non-communicable diseases(NCDs) attributable to LPA across countries and regions with different economic development levels. Additionally, this study analyzes the relationships between age, period, and cohort effects to characterize the temporal trends in the burden of chronic NCDs caused by LPA. The aim of this research is to clarify the spatiotemporal distribution patterns of the disease burden attributable to LPA across countries, analyze the spatiotemporal heterogeneity of influencing factors, and provide adaptive strategies for addressing LPA in diverse regions and disease populations. Ultimately, this study seeks to progressively improve physical activity levels worldwide, enhancing the health and quality of life of populations globally. 2. Methods 2.1 Data source All data were sourced from GBD 2021, which offers the latest and most extensive assessments of 369 diseases and 87 associated risk factors globally, spanning from 1990 to 2021. Data on deaths, age-standardized death rates (ASMR), DALYs, and age-standardized DALY rates (ASDR) related to LPA in the context of NCDs were obtained from the online website ( https://vizhub.healthdata.org/gbd-results/).I n the GBD 2021 framework, NCDs caused by LPA comprise neoplasms, cardiovascular diseases, diabetes and kidney diseases 27 . We collected data on the death number and age-standardized death rate (ASMR), disability-adjusted life years (DALYs), and age-standardized DALY rate (ASDR) for NCDs caused by LPA from the GBD results tool, covering 204 countries and territories from 1990 to 2021. Geographically, the 204 countries or territories were divided into 21 regions and 7 super-regions 28 . Meanwhile, the 204 countries or territories were categorized into five groups based on the Socio-demographic Index (SDI) provided by GBD: High SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI. The SDI is a composite indicator of development status strongly correlated with health outcomes. It is the geometric mean of 0 to 1 indices of lag distributed income per capita, mean years of schooling for individuals 15 years and older, and total fertility rate for individuals younger than 25 years. A location with an SDI of 0 indicates a theoretical minimum level of development status relevant to health outcomes, while a location with an SDI of 1 indicates a theoretical maximum level 29 . 2.2 Statistical analysis 2.2.1 Descriptive statistics To analyze the temporal trends in data related to global, gender, SDI, super-region, and sub-region levels, we employed the Estimated Annual Percentage Change (EAPC). The EAPC is utilized to quantify trends in the ASMR and ASDR from 1990 to 2021. The logarithmic age-standardized indicators conform to a regression model represented by \(\:\text{ln}\left(y\right)=\alpha\:+\beta\:x+\epsilon\:\) , where \(\:y\) denotes the respective age-standardized indicators and \(\:x\) represents the calendar year. From this model, the EAPC and its 95% confidence interval (CI) are calculated as \(\:100\ast\:(\text{exp}\left(\beta\:\right)-1)\) .An increasing trend in the age-standardized indicators is indicated when the 95% CI of the estimated EAPC is greater than 0, while a decreasing trend is indicated if the 95% CI is less than 0. If the 95% CI includes 0, the trend is considered stable 30 .We calculated the EAPCs of ASMR and ASDR of NCDs caused by LPA to reflect their temporal trends. Additionally, we present the percentage change of these indicators from 1990 to 2021 across 204 countries or territories, calculated as the difference in the natural logarithm of the values at the beginning and end of the interval, divided by the number of years in that interval. Each indicator and its percentage change are accompanied by 95% uncertainty intervals (UI), representing the average estimate from 500 samples 28 . Data cleaning and integration for descriptive statistics were conducted using Excel 2016 and StataSE 15 software. The spatiotemporal characterization of the global burden of low physical activities and the creation of additional figures were performed using R version 4.4.1 and ArcGIS version 10.4.1. 2.2.2 Age-period-cohort effect model The APC model is a statistical analysis method widely employed in demography, sociology, economics, and epidemiology 31 . Our APC analysis identified three types of time-varying phenomena: age effects, period effects, and cohort effects. The age effect refers to variations associated with the biological and social ageing processes specific to individuals, typically indicating the differing risks faced by various age groups. In the APC model, the overall annual percentage change in the death number of low physical activities, DALYs is assessed through net drift, while local drift represents the annual percentage change in these rates for specific age groups relative to the net drift. The period effect denotes variations in risk across all age groups during different time periods. The cohort effect reflects the influence on individuals or groups due to differences in birth year or the duration of exposure to specific events. Given the characteristics of the model, the data structures for age and period groups must be consistent. Both age and period variables were categorized into five-year intervals. The APC analysis was conducted using the R-based web tool provided by the Division of Cancer Epidemiology and Genetics at the U.S. National Cancer Institute ( https://analysistools.cancer.gov/apc/ ). 3. Result 3.1 Spatial patterns of the burden of NCDs caused by Low physical activities at global, regional, and national levels. Table 2 DALYs and Death of Low physical activities by Gender, SDI, and Regions. DALYs Deaths 2021 1990–2021 2021 1990–2021 Characteristics Number (95% UI) ASDR per 100,000 (95% UI) EAPC of ASDR (95% CI) Number (95% UI) ASMR per 100,000 (95% UI) EAPC of ASMR (95% CI) Global 15475981.4 (7248984.76 to 23953592.69) 181.53 (83.95 to 280) -0.49 (-0.55 to -0.44) 649308.59 (276348.17 to 1044772.12) 7.89 (3.35 to 12.79) -0.9 (-0.94 to -0.86) Sex Male 6083300.51 (2864701.01 to 9482363.38) 155.91 (71.84 to 246.25) -0.28 (-0.34 to -0.22) 243372.05 (109914.75 to 396108.38) 6.84 (2.99 to 11.27) -0.53 (-0.56 to -0.49) Female 9392680.89 (4326915.27 to 14698836.54) 203.29 (93.46 to 317.73) -0.56 (-0.62 to -0.51) 405936.54 (168727.55 to 669703.19) 8.66 (3.6 to 14.29) -1.03 (-1.07 to -0.98) Sociodemographic index Low 695012.69 (310434.79 to 1075049.37) 147.16 (64.98 to 233.17) 0 (-0.07 to 0.07) 25267.44 (10639.96 to 39875.66) 6.53 (2.65 to 10.43) 0.02 (-0.09 to 0.14) Low-middle 3066142.93 (1411882.42 to 4764298.87) 224.1 (101.98 to 354.21) 0.35 (0.32 to 0.39) 119794.41 (52630.59 to 191296) 9.95 (4.2 to 16.03) 0.39 (0.34 to 0.44) Middle 5493578.59 (2561409.32 to 8404361.82) 211.41 (96.06 to 322.3) -0.03 (-0.06 to 0) 220433.77 (95234.71 to 352739.08) 9.36 (3.89 to 15.14) -0.12 (-0.17 to -0.07) High-middle 3483913.04 (1558566.32 to 5509733.48) 177.05 (79.15 to 279.93) -0.83 (-0.93 to -0.73) 165811.11 (69043.81 to 274974.67) 8.59 (3.53 to 14.38) -1.1 (-1.23 to -0.98) High 2719894.01 (1239492.2 to 4198715.57) 129.07 (59.59 to 196.56) -1.44 (-1.6 to -1.28) 117268.8 (49149.25 to 191410.55) 4.75 (2.02 to 7.62) -2.52 (-2.63 to -2.42) Super Region Central Europe, Eastern Europe, and Central Asia 1245368.28 (536296.39 to 1974043.81) 187.35 (81.24 to 296.23) -1.19 (-1.39 to -0.98) 65779.25 (25381.19 to 114016.12) 9.85 (3.82 to 17.07) -1.37 (-1.6 to -1.13) High-income 2616750.46 (1190714.5 to 4077392.22) 116.39 (53.27 to 178.67) -1.63 (-1.77 to -1.49) 117335.88 (49398.44 to 192014.33) 4.36 (1.86 to 7) -2.68 (-2.78 to -2.59) Latin America and Caribbean 1208928.54 (541643.66 to 1857528.78) 195.8 (87.61 to 298.8) -0.54 (-0.66 to -0.42) 45545.8 (20241.92 to 70603.22) 7.63 (3.38 to 11.78) -0.96 (-1.05 to -0.86) North Africa and Middle East 1771238.38 (835733.76 to 2733902.85) 392.81 (183.75 to 614.27) -0.26 (-0.32 to -0.21) 60096.5 (26428.1 to 96409.26) 15.98 (6.65 to 26.13) -0.55 (-0.63 to -0.48) South Asia 2785781.3 (1278126.12 to 4389335.21) 202.69 (92.18 to 323.99) 0.25 (0.16 to 0.35) 116217.85 (52658.09 to 185983.83) 9.66 (4.19 to 15.73) 0.51 (0.38 to 0.65) Southeast Asia, East Asia, and Oceania 5080821.8 (2282693.81 to 7996695.8) 186.25 (82.41 to 299.78) 0.14 (0.05 to 0.24) 216436.64 (92213.73 to 366075.14) 8.8 (3.64 to 15.27) 0.21 (0.04 to 0.39) Sub-Saharan Africa 767092.62 (337346.77 to 1163571.92) 173.62 (75.81 to 271.09) 0.34 (0.25 to 0.44) 27896.67 (11840.55 to 44219.42) 7.8 (3.11 to 12.65) 0.29 (0.16 to 0.41) Region Central Asia 113178.25 (49235.68 to 181908.48) 154.72 (66.35 to 253.99) -0.61 (-0.84 to -0.39) 4846.15 (1946.73 to 8286.94) 7.67 (2.99 to 13.47) -0.75 (-0.97 to -0.53) Central Europe 429177.5 (195428.72 to 667759.12) 185.41 (84.17 to 288.21) -1.29 (-1.36 to -1.23) 21911.36 (8851.2 to 36961.42) 9.05 (3.7 to 15.23) -1.61 (-1.69 to -1.53) Eastern Europe 703012.54 (287106.25 to 1133648.71) 194.34 (80.91 to 314.36) -1.19 (-1.49 to -0.88) 39021.74 (13154.32 to 68779.92) 10.71 (3.75 to 18.79) -1.3 (-1.64 to -0.96) Australasia 67444.1 (32860.34 to 105179.08) 124.17 (59.49 to 192.03) -1.98 (-2.1 to -1.85) 3132.71 (1406.12 to 5241.57) 5.08 (2.35 to 8.35) -2.84 (-2.91 to -2.77) High-income Asia Pacific 517545.89 (229151.88 to 811374.47) 118.91 (53.93 to 183.64) -1.17 (-1.25 to -1.09) 19250.87 (8315.56 to 32899.33) 3.19 (1.48 to 5.23) -2.46 (-2.55 to -2.37) High-income North America 848804.57 (352795.48 to 1359053.44) 127.69 (54.07 to 203.32) -0.55 (-0.75 to -0.35) 33853.35 (12750.96 to 56181.17) 4.68 (1.82 to 7.68) -1.81 (-1.97 to -1.64) Southern Latin America 75475.41 (33095.88 to 121395.21) 85.37 (37.54 to 136.82) -0.9 (-0.95 to -0.85) 3161.86 (1285 to 5219.07) 3.45 (1.41 to 5.7) -1.41 (-1.51 to -1.31) Western Europe 1107480.49 (521017.49 to 1725703.61) 111.79 (51.76 to 172.78) -2.25 (-2.37 to -2.13) 57937.09 (24688.53 to 95312.67) 4.82 (2.11 to 7.79) -2.97 (-3.05 to -2.89) Andean Latin America 63362.62 (28851.21 to 99881.24) 108.67 (49.26 to 172.06) 0.08 (0.01 to 0.16) 2491.1 (1082.18 to 3944.7) 4.42 (1.91 to 7.02) -0.37 (-0.48 to -0.27) Caribbean 136335.52 (62883.68 to 215379.45) 252.66 (116.62 to 398.86) -0.29 (-0.36 to -0.22) 5110.49 (2322.29 to 8378.05) 9.36 (4.26 to 15.34) -0.94 (-1.02 to -0.85) Central Latin America 441716.46 (195704.84 to 686181.84) 176.5 (78.54 to 274.24) -0.07 (-0.32 to 0.18) 15767.87 (7281.48 to 24958.9) 6.6 (3 to 10.5) -0.43 (-0.67 to -0.19) Tropical Latin America 567513.93 (247987.48 to 887996.16) 222.49 (97.73 to 349.25) -0.87 (-0.94 to -0.8) 22176.33 (9573.61 to 35564.71) 8.99 (3.9 to 14.5) -1.26 (-1.33 to -1.2) North Africa and Middle East 1771238.38 (835733.76 to 2733902.85) 392.81 (183.75 to 614.27) -0.26 (-0.32 to -0.21) 60096.5 (26428.1 to 96409.26) 15.98 (6.65 to 26.13) -0.55 (-0.63 to -0.48) South Asia 2785781.3 (1278126.12 to 4389335.21) 202.69 (92.18 to 323.99) 0.25 (0.16 to 0.35) 116217.85 (52658.09 to 185983.83) 9.66 (4.19 to 15.73) 0.51 (0.38 to 0.65) East Asia 3375908 (1462201.25 to 5507286.71) 162.3 (67.18 to 267.59) -0.06 (-0.18 to 0.07) 152720.28 (59631.5 to 270802.07) 8.09 (2.99 to 14.67) 0.07 (-0.13 to 0.28) Oceania 32943.16 (13777.34 to 52463.11) 403.68 (168.46 to 649.78) 0.21 (0.14 to 0.28) 920.88 (381.29 to 1504.48) 14.15 (5.74 to 22.72) -0.06 (-0.13 to 0.01) Southeast Asia 1671970.65 (751294.02 to 2536645.77) 265.45 (115.35 to 408.61) 0.55 (0.5 to 0.61) 62795.48 (26948.79 to 100297.85) 11.25 (4.58 to 18.44) 0.44 (0.34 to 0.55) Central Sub-Saharan Africa 95402.36 (39798.43 to 157338.53) 200.68 (84.59 to 335.89) -0.2 (-0.29 to -0.1) 3411.33 (1402.22 to 5760.62) 9.28 (3.73 to 16.4) -0.31 (-0.4 to -0.23) Eastern Sub-Saharan Africa 138104.06 (60227.76 to 222451.09) 87.27 (37.83 to 143.62) -0.24 (-0.3 to -0.17) 5014 (2097.96 to 8427.69) 3.89 (1.51 to 6.62) -0.22 (-0.28 to -0.16) Southern Sub-Saharan Africa 222249.36 (100737.47 to 338879.36) 406.85 (180.26 to 623.84) 1.28 (0.97 to 1.59) 8503.02 (3869.3 to 13248.03) 18.19 (7.74 to 28.97) 1.4 (1.02 to 1.78) Western Sub-Saharan Africa 311336.84 (132164.42 to 480516.51) 167.6 (69.36 to 262.49) 0.27 (0.24 to 0.3) 10968.31 (4388.69 to 17855.75) 7.35 (2.74 to 12.63) 0.16 (0.12 to 0.2) In 2021, the global DALYs due to NCDs caused by LPA reached 15.48 million (7.24 to 23.95), with an ASDR of 181.53 (83.95 to 280). Deaths numbered 0.65 million (0.28 to 1.04), with an ASMR of 7.89 (3.35 to 12.79). Females had an obviously higher global disease burden in death number,DALYs,ASMR and ASDR compared to females. From 1990 to 2021, despite a downward trend in ASMR and ASDR globally, with EAPC of -0.9% (-0.94 to -0.86) and − 0.49%(-0.55 to -0.44), respectively, the burden of low physical activity remained significant, resulting in 15.48 million DALYs and 649308.59 deaths in 2021 (Table 2 ). Appendix Table S1 presents the quantities of death and DALYs for the year 1990, along with their percentage changes up to 2021. In 2021, by region, deaths exceeded 100,000 in both East Asia and South Asia, with East Asia having the highest number of deaths at 152,720 (59,632 to 270,80), followed by South Asia with 116,218 (52,658 to 185,984). Meanwhile, East Asia and South Asia were also the regions with the highest number of DALYs, with 3,375,908 (1,462,201 to 5,507,287) and 2,785,781 (1,278,126 to 4,389,335), respectively. After age-standardization, the region with the highest ASMR was Southern Sub-Saharan Africa, at 18.19 million (7.74 to 28.97 ), followed by North Africa and the Middle East, and Oceania, with 15.98 million (6.65 to 26.13)and 14.15 million (5.74 to 22.72), respectively. The region with the highest ASDR was Southern Sub-Saharan Africa, at 406.85 million(180.26 to 623.84), followed by Oceania and North Africa and the Middle East, with 403.68 million (168.46 to 649.78) and 392.81 million(183.75 to 614.27), respectively. Between 1990 and 2021, the four regions, Southern Sub-Saharan Africa, Southeast Asia, Western Sub-Saharan Africa and South Asia showed increases in both ASMR and ASDR, with EAPCs of 1.4% (1.02 to 1.78) and 1.28%(0.97 to 1.59) for Southern Sub-Saharan Africa,0.44%(0.34 to 0.55) and 0.55% (0.5 to 0.61) for Southeast Asia, 0.16% (0.12 to 0.2)and 0.27% (0.24 to 0.3)for Western Sub-Saharan Africa, and0.51% (0.38 to 0.65)and 0.25% (0.16 to 0.35) for South Asia. There was significant heterogeneity in DALYs and death numbers across regions, with Southern Sub-Saharan Africa, North Africa, Middle East and Oceania having notably higher ASDR and Age-standardized DALY rates compared to other regions.(Table 2 ) Nationally (Fig. 1 and Appendix Table S2), China recorded the highest number of death number and DALYs in 2021, with 147725.7 (56945.8 to 263820.53) and 3241988.81 (1377392.22 to 5307582.63), respectively. Marshall Islands reported the highest ASMR and ASDR at 50.14 (21.81 to 79.7) and 1553.22 (689.15 to 2427.66), respectively. Between 1990 and 2021(Appendix Table S3), Guatemala experienced the largest percent increase in death numbers at 5.11% (2.82 to 8.74), and Cabo Verde had the largest percent increase in ASMR at 1.07%(0.23 to 2.63). Qatar had the largest percent increase in DALYs at 6.44%(4.39 to 9.02), while Lesotho had the largest percent increase in ASDR at 0.94 (0.19 to 2.22).Compared to 1990, 123 countries saw a decrease in ASMR by 2021, with Singapore experiencing the largest decrease at -0.7%(-0.8 to -0.54). ASDR decreased in 102 countries, with Ireland seeing the largest decline at -0.58% (-0.69 to -0.44). Both death numbers and DALYs decreased in 19 countries, with United Kingdom showing the largest declines at -0.44%(-0.55 to -0.3) and-0.33% (-0.45 to -0.18), respectively. Despite declines in ASMR and ASDR in most countries, the number of death and DALYs continued to rise, increasing the disease burden, especially in South Asia, Southeast Asia, Central Latin America, Andean Latin America and Southern Sub-Saharan Africa. 3.2 Trends in the disease burden of NCDs caused by LPA across SDI quintiles. In the five SDI groups (Fig. 2 ), the ASMR and ASDR in High SDI regions declined rapidly between 1990 and 2010 but the ASDR rose slightly from 2015 to 2021, with the ASMR keeping on declining. In High-middle SDI regions, the ASMR and ASDR rose slightly in the beginning and peaked in 1995, but after 1995 the High-middle SDI regions showed a rapidly downward trend apart from the stable period experienced from 2000 to 2005. The overall trend of Middle-SDI regions and Low-SDI regions is relatively stable. In Middle-SDI regions, both ASMR and ASDR showed a slight decline around 2005, and after 2015, ASMR showed a slow decline while ASDR showed a slight increase. The ASMR and ASDR in Low-SDI regions declined slowly between 1995 and 2010, but rose after 2010 and then declined again after peaking in 2015. Middle-SDI regions showed a wavy rise in both the ASMR and ASDR.The spatiotemporal variation of ASMR and ASDR in low physical activities showed significant heterogeneity among different SDI groups, with the largest increase in the Low-middle SDI regions and the largest decrease in the High-SDI regions, demonstrating a polarization trend. 3.3 The disease burden of NCDs caused by LPA among different gender and age groups in global and five SDI regions. In 2021, within the disease burden of NCDs attributed to LPA, the number of male deaths stood at 243,372.05 (109,914.75 to 396,108.38), with an ASMR of 6.84 (2.99 to 11.27); the DALYs for men amounted to 6,083,300.51 (2,864,701.01 to 9,482,363.38), and the ASDR was 155.91 (71.84 to 246.25). For females, the death toll was 405,936.54 (168,727.55 to 669,703.19), with an ASMR of 8.66 (3.6 to 14.29); the DALYs were 9,392,680.89 (4,326,915.27 to 14,698,836.54), and the ASDR was 203.29 (93.46 to 317.73). In 1990, the number of DALYs for females was 75.56% higher than that for males, while the number of deaths was 100% higher. However, by 2021, this trend had declined. The number of DALYs for females was 54.40% higher than that for males, and the number of deaths was 66.80% higher. During the period from 1990 to 2021, the levels of ASMR and ASDR for both males and females, resulting from NCDs caused by LPA, exhibited a downward trend. Notably, the decline was relatively more significant for females. The EAPC of ASMR for males and females were − 0.53 (-0.56 to -0.49) and − 1.03 (-1.07 to -0.98) respectively, while the EAPC of ASDR were − 21.31 − 0.28 (-0.34 to -0.22) and − 0.56 (-0.62 to -0.51). During the period from 1990 to 2021, the disease burden of NCDs caused by LPA displayed heterogeneity between males and females and manifested a distinct gender gap. Figure 3 illustrates the disparities in the disease burden of NCDs attributable to LPA across different genders and age groups in 2021. In terms of age groups, it is evident that the 45–49 age range serves as a critical inflection point. The number of deaths and the total DALYs due to non-communicable diseases exhibit divergent trends before and after this age group. Between the ages of 20–49, there is a gradual upward trend, which accelerates sharply after the age of 49. Specifically, mortality peaks in the 80–84 age group, while DALYs peak in the 70–74 age group. Subsequently, both indicators show a rapid decline, while mortality and disability rates continue to rise. Regarding gender differences, females consistently exhibit higher mortality rates, DALY counts, and corresponding rate values than males across all age groups. These differences follow an age-related pattern, where younger and middle-aged women have slightly higher figures than men, and the gap widens significantly in the older age groups. 3.4 Comparison between 1990 and 2021 on disease burdens of NCDs caused by LPA among different age groups in global and five SDI regions. Figure 4 presents the changes in age-specific DALYs rates globally and across different SDI regions in 1990 and 2021. From a global perspective, there was a notable delay in the disease burden among older age groups in 2021. However, significant heterogeneity exists across different SDI regions. In High SDI regions, the disease burden in older age groups showed a clear delay, whereas in the younger age group of 25–49 years, the DALYs rate in 2021 was higher than in 1990, indicating a tendency towards younger age groups. A similar pattern was observed in High-middle SDI regions, with the shift towards younger age groups concentrated in the 25–39 year range. In Middle SDI countries, while DALYs rates across all age groups in 1990 and 2021 remained relatively similar, there was no delay observed in older age groups. In fact, a reversal occurred, with a worsening trend in DALYs rates. Additionally, the increase in DALYs rates among the 25–49 age group signals a potential shift towards younger populations in Middle SDI regions. In Low-middle SDI and Low SDI regions, the majority of age groups in 2021 had higher DALYs rates compared to 1990, particularly in older age groups, where there was a marked increase in DALYs rates. 3.5 Temporal trends in the burden of NCDs caused by LPA Based on the Age-Period-Cohort model. Net drift represents the overall annual percentage change in mortality or DALYs rates over the entire study period, while local drift indicates the annual percentage change in mortality or DALYs rates for each age group relative to the net drift. In this study, the annual percentage changes in mortality due to diabetes and kidney diseases, as well as cancers attributable to LPA, demonstrated significant heterogeneity across different SDI regions (Fig. 5 ). The annual percentage change in mortality due to diabetes and kidney diseases was highest in the Low-middle SDI region (0.8576% [95% CI, 0.7046 to 1.0108]), reflecting a substantial increase in mortality from these conditions attributable to LPA in this region over the study period. For cancers, the annual percentage changes in mortality in the Low-middle SDI, Low SDI, and Middle SDI regions indicated an upward trend, with the Low-middle SDI region experiencing the fastest increase (0.8005% [95% CI, 0.6064 to 0.9948]). Among the diseases attributable to LPA, the Low-middle SDI region exhibited the fastest rise in mortality due to diabetes and kidney diseases (0.8576% [95% CI, 0.7046 to 1.0108]), while the High SDI region showed the fastest decline in cardiovascular diseases mortality (-2.6905% [95% CI, -2.8221 to -2.5587]). Globally, the annual percentage change in mortality due to diabetes and kidney diseases was greater than 0 (0.404% [95% CI, 0.3136 to 0.4944]), whereas the annual percentage changes in mortality due to cardiovascular diseases and cancers were both less than 0. In contrast to the mortality trends, the DALYs rates attributable to LPA showed the most significant increase in the Low-middle SDI region (0.3079% [95% CI, 0.2291 to 0.3868]). Among the three categories of NCDs attributable to LPA, cardiovascular diseases DALYs rates declined across all studied regions, while DALYs rates for diabetes and kidney diseases increased in all regions. Notably, the annual increase in DALYs rates for diabetes and kidney diseases was relatively higher in regions with higher SDI levels. However, in the Low-middle SDI region, the annual increase in DALYs rates for these conditions was unexpectedly high (1.4091% [95% CI, 1.3425 to 1.4757]). The trends in mortality due to NCDs attributable to LPA varied significantly across different age groups and regions. In the 25–39 age group, the annual percentage change in mortality exceeded 0 in High SDI regions, indicating a trend toward younger age groups in these regions. In the Low-middle SDI, Low SDI, and Middle SDI regions, the annual percentage change in mortality due to NCDs attributable to LPA was significantly higher among older adults compared to younger individuals. This trend aligns with the patterns observed for cardiovascular diseases mortality attributable to LPA in these regions. Additionally, the annual percentage change in mortality among younger individuals was greater than 0 across all studied regions, clearly outlining a trend toward younger age groups in mortality due to diabetes and kidney diseases, particularly in High SDI regions. The trends in cancer mortality attributable to LPA across different age groups in all regions exhibited a "middle-low, both ends high" distribution. In the studied regions, the annual percentage change in DALYs rates due to NCDs attributable to LPA among younger populations showed a positive growth trend. For cardiovascular diseases DALYs rates attributable to LPA, only the High-middle SDI regions exhibited a declining trend in the 25–34 age group. Regarding DALYs rates for diabetes and kidney diseases attributable to LPA, the annual percentage changes were greater than 0 in all regions. Notably, the inflection point for DALYs rates in High SDI regions occurred in the 75–79 age group, while in the Low-middle and Low SDI regions, it occurred in the 60–64 age group, with the former being 15 years earlier than the latter. For cancer DALYs rates attributable to LPA, the annual percentage changes across age groups exhibited a "U-shaped" distribution, with the Low-middle SDI regions maintaining annual percentage changes greater than 0 across all age groups. Panel A displays the net and local drift of the death of NCDs caused by LPA, while panel B shows the net and local drift of DALYs. In Figs. 6 and 7 , the estimated effects of age, period, and cohort on the mortality and DALYs rates of NCDs attributable to LPA reveal the fluctuating trends in the burden of LPA-related NCDs across populations. The age effect, represented by longitudinal age curves, illustrates the natural history of LPA-related NCDs associated with aging. In this study, both the mortality and DALYs rates of NCDs attributable to LPA increased with age, with significant regional disparities emerging after the age of 45. Additionally, the burden of cancer attributable to LPA was relatively lower, with higher levels observed in regions with higher SDI. The period effect, expressed as the relative mortality risk by period, tracks the progression of mortality and DALYs rates of NCDs attributable to LPA over time. Between 1992 and 2021, the mortality risk of NCDs attributable to LPA exhibited heterogeneous trends across regions with different SDI levels. High SDI, High-middle SDI, Middle SDI regions, and the global average all showed a continuous decline in mortality risk. However, in Low-middle SDI regions, a turning point occurred between 2002 and 2006, characterized by an initial decline followed by an increase. For specific disease types, the mortality risk of cardiovascular diseases attributable to LPA showed an overall declining trend, although Low SDI regions experienced fluctuations between 2007 and 2021, with an initial rise followed by a decline. For diabetes and kidney diseases, Middle SDI regions, the global average, and Low-middle SDI regions exhibited an upward trend, while Low SDI regions experienced a turning point between 2007 and 2011. Regarding cancer mortality risk, Low SDI, Middle SDI, and Low-middle SDI regions showed an increasing trend, whereas High SDI, Global, and High-middle SDI regions demonstrated a continuous decline. In terms of disability risk, High SDI, High-middle SDI regions, and the Global average exhibited a declining trend, although High SDI regions and the Global average experienced a slight increase between 2012 and 2021. In contrast, Low-middle SDI, Middle SDI, and Low SDI regions showed an increasing trend in disability risk, with a more pronounced rise between 2012 and 2021. Notably, for diabetes and kidney diseases, all studied regions exhibited an upward trend in disability risk throughout the study period. The cohort effect, represented as the relative mortality risk by birth cohort, tracks changes in mortality and DALYs rates of NCDs attributable to LPA across different birth cohorts. In terms of mortality risk of NCDs attributable to LPA, High SDI regions showed the most significant decline, while Low SDI and Low-middle SDI regions exhibited a fluctuating upward trend. For cardiovascular disease mortality risk, all regions demonstrated a declining trend, with the magnitude of decline decreasing as SDI levels decreased. For diabetes and kidney diseases attributable to LPA, all studied regions showed an upward trend, although High SDI regions displayed a "U-shaped" pattern. Regarding cancer mortality risk attributable to LPA, High SDI, Global, and High-middle SDI regions exhibited a declining trend, while Middle SDI, Low SDI, and Low-middle SDI regions showed an increasing trend. For disability risk of NCDs attributable to LPA, High SDI, High-middle SDI regions, and the Global average demonstrated a "U-shaped" distribution, indicating that earlier birth cohorts had relatively higher disability risks, which declined in intermediate birth cohorts but increased again in later birth cohorts. In contrast, Middle SDI, Low SDI, and Low-middle SDI regions exhibited a different trend, with disability risks of NCDs attributable to LPA showing an upward trajectory. This suggests that in regions with lower SDI levels, later birth cohorts face a higher disability risk due to LPA-related NCDs. 4. Discussion NCDs represent a significant global public health challenge, with LPA identified as a modifiable risk factor for NCDs. Improving LPA is widely recognized as a highly cost-effective intervention for the prevention and control of NCDs 32 . Our study reveals that over the past 32 years, although the ASMR and ASDR attributable to LPA have shown a declining trend globally, the absolute number of deaths has nearly doubled due to the large and continuously growing global population. Therefore, it is imperative to conduct disease burden studies on NCD-related deaths attributable to LPA across global regions. This study is the first to utilize the latest GBD 2021 data and the age-period-cohort (APC) model to analyze the disease burden of NCDs attributable to LPA over a 32-year period. Our findings indicate significant spatiotemporal inequalities in the disease burden of NCDs attributable to LPA across different regions and countries. Furthermore, notable disparities exist between genders and age groups. From the perspective of the SDI, regions with higher SDI levels exhibit a trend of younger age groups bearing a greater disease burden, while regions with lower SDI levels continue to experience an overall increase in disease burden. 4.1 Spatiotemporal inequality in the burden of non-communicable diseases caused by low physical activities from 1990 to 2021. From 1990 to 2021, the global ASMR and ASDR showed a significant downward trend. However, the absolute burden of NCDs attributable to LPA continued to grow steadily, with the number of deaths increasing by 94.88% and DALYs rising by 105.65%. The population affected by these conditions nearly doubled during this period. The increase in the disease burden of NCDs attributable to LPA exhibited notable regional heterogeneity. This study found that Southeast Asia, East Asia, Oceania, and South Asia had the highest number of deaths and DALYs. The high population density, accelerated urbanization, and lifestyle changes in these regions have increased exposure to LPA and other NCD risk factors. Coupled with weak chronic disease health information systems in most countries within these regions, these factors have contributed to the heaviest absolute burden of NCDs attributable to LPA 8 , 23 , 33 . Meanwhile, North Africa and the Middle East recorded the highest ASMR and ASDR. This may be attributed to poor socioeconomic status, challenging environmental conditions, and underdeveloped healthcare systems in these regions, which limit access to high-quality medical care and preventive treatments, thereby exacerbating the relative burden of NCDs attributable to LPA 34 . At the national level, China had the highest number of deaths and DALYs attributable to LPA-related NCDs, followed by India, Indonesia, and the United States. These four countries are among the world's most populous nations, highlighting population size as a critical factor influencing the absolute disease burden. As the two most populous countries, China and India reported 147,725.7 (56,945.8 to 263,820.53) and 97,782.63 (45,267.12 to 155,348.67) deaths, respectively. Since the beginning of the 21st century, China has experienced rapid urbanization, with the urban population proportion rising from 18% in 1978 to nearly 65% in 2020. This shift has been accompanied by dietary pattern changes, transitioning from traditional plant-based diets to Western-style diets, while physical activity levels have lagged. The imbalance between energy intake and expenditure has led to over half of the adult population being overweight or obese as of 2020 35,36 . In India, a society with significant wealth disparity, low physical activity is primarily observed in urban areas with higher socioeconomic status, where sedentary lifestyles and high-calorie diets are major contributors to the disease burden. In contrast, malnutrition remains a primary issue in regions with lower socioeconomic status 37 – 39 . Although the United States and Indonesia have similar population sizes, Indonesia's ASMR is significantly higher than that of the United States, largely due to the latter's early focus on monitoring and interventions to promote physical activity 40 . Therefore, countries with a heavy absolute disease burden should address unreasonable population growth while aligning with the WHO's Global Action Plan on Physical Activity 2018–2030 . These nations should create environments and systems conducive to physical activity, foster populations that embrace active lifestyles, and ultimately build vibrant societies to delay or reduce the disease burden of NCDs attributable to LPA. 4.2 The differences between gender and age groups in the disease burden of NCDs caused by LPA across five SDI regions. This study found that the disease burden of NCDs attributable to LPA is significantly higher among females than males. In 2021, the number of deaths among females was 66.80% higher than that among males, indicating that females lag behind males in improving LPA levels, particularly in the 70–89 age group. Although it is an indisputable fact that males exhibit higher physical activity levels across all age groups, studies have consistently shown that females derive greater survival benefits from the same level of physical activity compared to males. The gender gap in physical activity is primarily observed in higher-intensity activities 41 – 43 . Therefore, addressing the issue of low LPA levels among females requires gender-specific approaches, including enhanced individual risk assessments and tailored exercise prescriptions, to improve female participation in physical activity. Regarding age, based on comparisons between 1990 and 2021 and the results from the APC model, we observed that in regions with higher SDI levels, the disease burden of NCDs attributable to LPA has been delayed in older age groups but shows a trend of younger onset in younger age groups. In contrast, regions with lower SDI levels exhibit an overall increase in the disease burden across all age groups, particularly among older adults. This suggests that older adults in higher SDI regions are increasingly engaging in physical activities to improve their health 44 . Among younger populations, the disease burden of NCDs attributable to LPA is trending toward younger age groups, likely due to industrialization, urbanization, and lifestyle changes in higher SDI regions. The shift from physically demanding jobs to sedentary occupations has significantly reduced physical activity levels. Additionally, the mechanization and automation of daily transportation have further limited opportunities for physical activity, with few individuals compensating for this reduction through leisure-time physical activity 45 – 48 . In lower SDI regions, the increased disease burden among older adults may be attributed to poorer balance and a higher risk of falls, as well as a lack of opportunities and facilities for physical activity, particularly the absence of safe community infrastructure, which poses significant barriers to physical activity for older adults 2 , 49 , 50 . Therefore, higher SDI regions should focus on encouraging leisure-time recreational physical activity to counteract the decline in occupational and daily physical activity. In lower SDI regions, efforts should prioritize the construction of safe sports facilities for older adults, along with functional balance and strength training to enhance physical capacity and prevent falls, as well as differentiated medication management for older populations. Based on the net drift and local drift results from the APC model, it is evident that the risk of diabetes and kidney diseases among younger populations in High SDI regions is significantly higher than that among middle-aged individuals. This may be partly due to a decline in physical activity during the transition from adolescence to young adulthood 51 , 52 . Compared to adolescents, young adults face fewer opportunities for physical activity as they transition from school to the workplace, a critical period of life changes. Additionally, the transition to parenthood may further reduce leisure time available for physical activity 53 , 54 . On the other hand, LPA among young adults can lead to early-onset obesity, and prolonged obesity may cause additional metabolic changes, contributing to the development and progression of diabetes 50 , 55 . The age effect indicates that the mortality burden of NCDs attributable to LPA increases with age, suggesting that older adults are more vulnerable to the adverse effects of physical inactivity. This is due to age-related loss of muscle mass, declining physiological function, and increasing frailty, which limit the ability of older adults to perform essential daily activities, thereby exacerbating the disease burden of NCDs attributable to LPA 56 . From the combined perspective of period and cohort effects, regions with Low SDI, Low-middle SDI, and Middle SDI levels have experienced an increase in mortality and disability risks for diabetes, kidney diseases, and cancers since 2012. For later-born cohorts, the risks of mortality and disability due to diabetes and kidney diseases have continued to rise across Low SDI, Low-middle SDI, Middle SDI, and High SDI regions. This may be attributed to lifestyle changes, including excessive consumption of alcohol, tobacco, and coffee, as well as sedentary behaviors, which negatively impact insulin sensitivity and blood glucose control 57 . Furthermore, the rising cancer risk in Low SDI, Low-middle SDI, Middle SDI regions may be due to inadequate socioeconomic conditions and underdeveloped cancer screening and monitoring systems. Younger populations in these regions may neglect such screenings, leading to increased mortality and risk 2 , 8 . Therefore, lower SDI countries need to invest in cancer surveillance systems to curb the rising risk among younger populations. 5. Conclusion This study found that from 1990 to 2021, the disease burden of NCDs attributable to LPA has steadily increased, with the number of deaths nearly doubling. At the national level, only 19 countries experienced a decline in the number of deaths, while the remaining 185 countries showed an upward trend. Regions with large population bases, such as Southeast Asia, East Asia, Oceania, and South Asia, exhibited a higher absolute disease burden, whereas regions with lower socioeconomic development levels, such as North Africa and the Middle East, demonstrated a higher relative disease burden. Additionally, the disease burden among females was greater than that among males across all age groups. From an age perspective, while the global disease burden showed a delay in progression after the age of 75 in 2021, the APC model results revealed a trend of younger onset in the 25–39 age group in High SDI regions, particularly for diabetes and kidney diseases. Furthermore, regions with lower SDI levels need to address the rising cancer risk among younger populations. Therefore, it is imperative for countries worldwide to adopt differentiated strategies based on their specific LPA profiles to reduce the disease burden of NCDs attributable to LPA, bridge existing gaps, and achieve sustainable development goals. Declarations Contributors Y.L.(Ye Li) conceived the entire research program and was responsible for overseeing the implementation of the research plan and reviewing the results. X.L. and Y.L.(Yongqiang Lai) carried out the data collection, data cleaning and data analysis, and wrote the first draft based on comments from other authors. Y.S. provided technical support during data processing and analysis. X.Z. performed data collection and cleaning and provided technical support to produce figures and tables in the manuscript. B.S., X.W.,C.X. and J.M. provided academic support for the overall writing of this paper. All authors performed the final review of the manuscript and unanimously agreed to submit it. Ethics approval and consent to participate Not applicable. Data sharing The data on death and disability of NCDs attributable to LPA used in this study can be obtained from the Global Burden of Disease 2021 database (https://vizhub.healthdata.org/gbd-results/). The data on factors influencing the incidence of edentulism can be accessed from the GBD 2021 covariates database (https://cloud.ihme.washington.edu/s/d8S5F48dzpm8pnp?path=%2F). Declaration of interests We declare no competing interests. Funding Declaration We thank the Global Burden of Disease Study 2021 for providing publicly available data. 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Supplementary Files SupplementaryAppendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviews received at journal 05 Jan, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 23 Nov, 2025 Submission checks completed at journal 23 Nov, 2025 First submitted to journal 19 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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08:31:43","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161281,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/f7783681edc7fc232ace3a2a.html"},{"id":98427701,"identity":"ffeec9f4-5118-4d95-b3b5-c168c07e7717","added_by":"auto","created_at":"2025-12-17 16:41:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":645316,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of Death numbers and DALYs from NCDs caused by LPA in 204 countries or territories in 2021. Panel (A) shows the Death numbers of 204 countries or regions in 2021; Panel (B) shows the ASMR in 204 countries or territories in 2021; Panel (C) shows the number of DALYs of 204 countries or regions in 2021; Panel (D) shows ASDR in 204 countries or regions in 2021.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/6af1ee40ada4e0cffcd599ae.png"},{"id":98048030,"identity":"03dc9859-30c6-4c39-9883-4f5cb7835955","added_by":"auto","created_at":"2025-12-12 08:31:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169842,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the disease burden of NCDs caused by LPA across SDI quintiles. Panels A, B show the trends in Age-standardized Death rate and Age-standardized DALYs rate for each SDI quintile from 1990 to 2021.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/fd9b0d45217bd062ac4d14b3.png"},{"id":98428018,"identity":"5e925b9d-9ee2-43af-afdb-3d422f815cec","added_by":"auto","created_at":"2025-12-17 16:41:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115908,"visible":true,"origin":"","legend":"\u003cp\u003eThe disease burden of NCDs caused by LPA among different gender and age groups. The left panel shows the burden in the number of deaths across different genders and age groups in 2021. The right panel shows the burden in the number of DALYs across different genders and age groups in 2021\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/34fc54a4533186e55dcb980c.png"},{"id":98427267,"identity":"0ba50672-c680-46b6-9a47-2cf6d5702a4c","added_by":"auto","created_at":"2025-12-17 16:40:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118113,"visible":true,"origin":"","legend":"\u003cp\u003eThe rate of DALYs of NCDs caused by LPA in global and five SDI regions among different age groups in 1990 and 2021.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/f66229f9f1a69b20249eb357.png"},{"id":98427751,"identity":"79e98a29-6da8-4f6a-bba6-15d60972e642","added_by":"auto","created_at":"2025-12-17 16:41:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":302983,"visible":true,"origin":"","legend":"\u003cp\u003eLocal drift and net drift values for NCDs caused by LPA in NCDs and three different disease subtype (Neoplasms, Cardiovascular diseases, Diabetes and kidney diseases) across global, high SDI, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions from 1992 to 2021. Panel A displays the net and local drift of the death of NCDs caused by LPA, while panel B shows the net and local drift of DALYs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/c3641b0bb56fcf3c98618582.png"},{"id":98048041,"identity":"aa7a8625-66a8-4341-a4d0-bf54de55bd67","added_by":"auto","created_at":"2025-12-12 08:31:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":322923,"visible":true,"origin":"","legend":"\u003cp\u003eParameter estimates of age, period, and cohort effects on NCDs caused by LPA and its three different disease subtype (Neoplasms, Cardiovascular diseases, Diabetes and kidney diseases) on Age-standardized death rate across global, high SDI, high-middle SDI, middle SDI, low-middle SDI, and low SDI from 1992 to 2021.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/50593f4947cbb4a5af71da28.png"},{"id":98048038,"identity":"0678960d-accb-436d-9ed9-28fc1e80edb6","added_by":"auto","created_at":"2025-12-12 08:31:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":308279,"visible":true,"origin":"","legend":"\u003cp\u003eParameter estimates of age, period, and cohort effects on NCDs caused by LPA and its three different disease subtype (Neoplasms, Cardiovascular diseases, Diabetes and kidney diseases) on Age-standardized DALYs rate across global, high SDI, high-middle SDI, middle SDI, low-middle SDI, and low SDI from 1992 to 2021.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/ff9bea644164aaeb61f300a9.png"},{"id":98444644,"identity":"bd71e84f-dfe9-48f9-838f-fb78ec070f5b","added_by":"auto","created_at":"2025-12-17 17:16:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2755114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/9c36e126-aaf3-4e4a-b11a-e0ed6ada52c5.pdf"},{"id":98048036,"identity":"7a6d5e62-df6a-489e-a7f5-0e7376b2a6c9","added_by":"auto","created_at":"2025-12-12 08:31:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":97588,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8156186/v1/d4048c7509a3e61ee71ec6d8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Spatiotemporal Heterogeneity of Non-communicable Diseases Attributed to Low Physical Activity: Capturing Populations in Vulnerable Regions and Age Groups","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePhysical activity is one of the most crucial measures for individuals of all age groups to improve their health\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. According to relevant studies, physical activity is associated with a lower risk of mortality and morbidity, including cardiovascular diseases and cancer\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Low physical activity(LPA), defined as engaging in less than 3000 to 4500 metabolic equivalent (MET) minutes per week, exacerbates the incidence of obesity and increases the risk of related diseases such as cardiovascular diseases and type 2 diabetes\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Globally, 7.2% of all-cause mortality and 7.6% of cardiovascular disease mortality are attributed to LPA\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. From a socioeconomic perspective, LPA imposes a significant economic burden worldwide. As early as 2013, LPA resulted in a global productivity loss of \u003cspan\u003e$\u003c/span\u003e13.7 billion\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. According to the latest data from the World Health Organization (WHO), nearly one-third (31%) of adults worldwide (approximately 1.8\u0026nbsp;billion people) did not meet the recommended levels of physical activity in 2022, and this percentage is expected to rise to 35% by 2030\u003csup\u003e10\u003c/sup\u003e. Although LPA is a factor that can be subjectively controlled by humans, the actual control effectiveness is not ideal, and it remains a major public health threat.\u003c/p\u003e\u003cp\u003eThe impact of physical activity varies across different diseases, genders, and age groups. Compared to individuals with insufficient activity (total activity\u0026thinsp;\u0026lt;\u0026thinsp;600 MET minutes/week), those in the highly active category (\u0026ge;\u0026thinsp;8000 MET minutes/week) experienced a 14% reduction in breast cancer risk, a 21% reduction in colon cancer risk, a 28% reduction in diabetes risk, a 25% reduction in ischemic heart disease risk, and a 26% reduction in ischemic stroke risk\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Additionally, in terms of psychological and cognitive health, appropriate physical activity significantly reduces the risk of depression and Alzheimer's disease\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Regarding gender differences, LPA levels have consistently been higher among females than males, exhibiting a \"gender gap\" that begins early in life and persists throughout adulthood\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Across all age groups, 81% of adolescents aged 11\u0026ndash;17 do not meet the recommended levels of physical activity, while 31% of adults fail to meet these recommendations, with LPA levels increasing with age\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In recent years, LPA has led to a rapid increase in the share of medical expenses paid by individuals in many European countries. Higher out-of-pocket expenses directly impact household budgets, thereby limiting access to healthcare and exacerbating poverty\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Consequently, diseases caused by LPA not only reduce individuals' quality of life but also increase the economic burden on families, raise healthcare costs, and result in significant losses in social productivity. The widespread population affected and the substantial disease burden have created severe social losses, significantly impacting the health and quality of life of people in various countries. Comprehensive measures at the national, societal, and individual levels are urgently needed to address this issue.\u003c/p\u003e\u003cp\u003eIn 2018, the World Health Organization (WHO) launched the \u003cb\u003eGlobal Action Plan on Physical Activity 2018\u0026ndash;2030\u003c/b\u003e, providing policy recommendations for countries and communities with the aim of reducing the global prevalence of physical inactivity by 15% by 2030\u003csup\u003e19\u003c/sup\u003e. However, due to insufficient consideration of factors such as economic levels, geographic environments, population characteristics, and cultural practices across different regions, the WHO's 2022 assessment of the global progress in implementing the \u003cb\u003eGlobal Action Plan on Physical Activity 2018\u0026ndash;2030\u003c/b\u003e revealed limited advancements. Although more than three-quarters of countries have taken actions related to at least two policy indicators outlined in the plan, less than half reported implementing these policy indicators by 2021\u003csup\u003e20\u003c/sup\u003e. Current research on the disease burden of LPA is predominantly confined to specific regional populations or particular disease types, lacking a comprehensive depiction of the inequitable distribution of the overall disease burden attributable to LPA across countries with varying income levels\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Furthermore, physical activity spans the entire human life cycle, and its associated disease burden is influenced by a complex interplay of multiple factors, including regional economic levels, geographic environments, population characteristics, and cultural practices. Previous studies have largely focused on localized descriptions, lacking longitudinal tracking over time\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTherefore, this study adopts a global perspective, utilizing the latest Global Burden of Disease (GBD) 2021 data to delineate the disparities and inequities in the burden of chronic non-communicable diseases(NCDs) attributable to LPA across countries and regions with different economic development levels. Additionally, this study analyzes the relationships between age, period, and cohort effects to characterize the temporal trends in the burden of chronic NCDs caused by LPA. The aim of this research is to clarify the spatiotemporal distribution patterns of the disease burden attributable to LPA across countries, analyze the spatiotemporal heterogeneity of influencing factors, and provide adaptive strategies for addressing LPA in diverse regions and disease populations. Ultimately, this study seeks to progressively improve physical activity levels worldwide, enhancing the health and quality of life of populations globally.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data source\u003c/h2\u003e\u003cp\u003eAll data were sourced from GBD 2021, which offers the latest and most extensive assessments of 369 diseases and 87 associated risk factors globally, spanning from 1990 to 2021. Data on deaths, age-standardized death rates (ASMR), DALYs, and age-standardized DALY rates (ASDR) related to LPA in the context of NCDs were obtained from the online website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/).I\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/).I\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003en the GBD 2021 framework, NCDs caused by LPA comprise neoplasms, cardiovascular diseases, diabetes and kidney diseases\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We collected data on the death number and age-standardized death rate (ASMR), disability-adjusted life years (DALYs), and age-standardized DALY rate (ASDR) for NCDs caused by LPA from the GBD results tool, covering 204 countries and territories from 1990 to 2021.\u003c/p\u003e\u003cp\u003eGeographically, the 204 countries or territories were divided into 21 regions and 7 super-regions\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the 204 countries or territories were categorized into five groups based on the Socio-demographic Index (SDI) provided by GBD: High SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI. The SDI is a composite indicator of development status strongly correlated with health outcomes. It is the geometric mean of 0 to 1 indices of lag distributed income per capita, mean years of schooling for individuals 15 years and older, and total fertility rate for individuals younger than 25 years. A location with an SDI of 0 indicates a theoretical minimum level of development status relevant to health outcomes, while a location with an SDI of 1 indicates a theoretical maximum level\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Descriptive statistics\u003c/h2\u003e\u003cp\u003eTo analyze the temporal trends in data related to global, gender, SDI, super-region, and sub-region levels, we employed the Estimated Annual Percentage Change (EAPC). The EAPC is utilized to quantify trends in the ASMR and ASDR from 1990 to 2021. The logarithmic age-standardized indicators conform to a regression model represented by\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ln}\\left(y\\right)=\\alpha\\:+\\beta\\:x+\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y\\)\u003c/span\u003e\u003c/span\u003e denotes the respective age-standardized indicators and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e represents the calendar year. From this model, the EAPC and its 95% confidence interval (CI) are calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:100\\ast\\:(\\text{exp}\\left(\\beta\\:\\right)-1)\\)\u003c/span\u003e\u003c/span\u003e.An increasing trend in the age-standardized indicators is indicated when the 95% CI of the estimated EAPC is greater than 0, while a decreasing trend is indicated if the 95% CI is less than 0. If the 95% CI includes 0, the trend is considered stable\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.We calculated the EAPCs of ASMR and ASDR of NCDs caused by LPA to reflect their temporal trends.\u003c/p\u003e\u003cp\u003eAdditionally, we present the percentage change of these indicators from 1990 to 2021 across 204 countries or territories, calculated as the difference in the natural logarithm of the values at the beginning and end of the interval, divided by the number of years in that interval. Each indicator and its percentage change are accompanied by 95% uncertainty intervals (UI), representing the average estimate from 500 samples\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eData cleaning and integration for descriptive statistics were conducted using Excel 2016 and StataSE 15 software. The spatiotemporal characterization of the global burden of low physical activities and the creation of additional figures were performed using R version 4.4.1 and ArcGIS version 10.4.1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Age-period-cohort effect model\u003c/h2\u003e\u003cp\u003eThe APC model is a statistical analysis method widely employed in demography, sociology, economics, and epidemiology\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our APC analysis identified three types of time-varying phenomena: age effects, period effects, and cohort effects. The age effect refers to variations associated with the biological and social ageing processes specific to individuals, typically indicating the differing risks faced by various age groups. In the APC model, the overall annual percentage change in the death number of low physical activities, DALYs is assessed through net drift, while local drift represents the annual percentage change in these rates for specific age groups relative to the net drift.\u003c/p\u003e\u003cp\u003eThe period effect denotes variations in risk across all age groups during different time periods. The cohort effect reflects the influence on individuals or groups due to differences in birth year or the duration of exposure to specific events. Given the characteristics of the model, the data structures for age and period groups must be consistent. Both age and period variables were categorized into five-year intervals. The APC analysis was conducted using the R-based web tool provided by the Division of Cancer Epidemiology and Genetics at the U.S. National Cancer Institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://analysistools.cancer.gov/apc/\u003c/span\u003e\u003cspan address=\"https://analysistools.cancer.gov/apc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003e3.1 Spatial patterns of the burden of NCDs caused by Low physical activities at global, regional, and national levels.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDALYs and Death of Low physical activities by Gender, SDI, and Regions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber (95% UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASDR per 100,000 (95% UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEAPC of ASDR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNumber (95% UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eASMR per 100,000 (95% UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEAPC of ASMR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15475981.4 (7248984.76 to 23953592.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181.53 (83.95 to 280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.49 (-0.55 to -0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e649308.59 (276348.17 to 1044772.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.89 (3.35 to 12.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.9 (-0.94 to -0.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6083300.51 (2864701.01 to 9482363.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155.91 (71.84 to 246.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.28 (-0.34 to -0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e243372.05 (109914.75 to 396108.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.84 (2.99 to 11.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.53 (-0.56 to -0.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9392680.89 (4326915.27 to 14698836.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203.29 (93.46 to 317.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.56 (-0.62 to -0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e405936.54 (168727.55 to 669703.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.66 (3.6 to 14.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.03 (-1.07 to -0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSociodemographic index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e695012.69 (310434.79 to 1075049.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147.16 (64.98 to 233.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (-0.07 to 0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25267.44 (10639.96 to 39875.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.53 (2.65 to 10.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02 (-0.09 to 0.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3066142.93 (1411882.42 to 4764298.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224.1 (101.98 to 354.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35 (0.32 to 0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e119794.41 (52630.59 to 191296)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.95 (4.2 to 16.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39 (0.34 to 0.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5493578.59 (2561409.32 to 8404361.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211.41 (96.06 to 322.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03 (-0.06 to 0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e220433.77 (95234.71 to 352739.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.36 (3.89 to 15.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.12 (-0.17 to -0.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3483913.04 (1558566.32 to 5509733.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177.05 (79.15 to 279.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.83 (-0.93 to -0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e165811.11 (69043.81 to 274974.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.59 (3.53 to 14.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.1 (-1.23 to -0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2719894.01 (1239492.2 to 4198715.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.07 (59.59 to 196.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.44 (-1.6 to -1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e117268.8 (49149.25 to 191410.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.75 (2.02 to 7.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.52 (-2.63 to -2.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSuper Region\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Europe, Eastern Europe, and Central Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1245368.28 (536296.39 to 1974043.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187.35 (81.24 to 296.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.19 (-1.39 to -0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65779.25 (25381.19 to 114016.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.85 (3.82 to 17.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.37 (-1.6 to -1.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2616750.46 (1190714.5 to 4077392.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116.39 (53.27 to 178.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.63 (-1.77 to -1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e117335.88 (49398.44 to 192014.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.36 (1.86 to 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.68 (-2.78 to -2.59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatin America and Caribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1208928.54 (541643.66 to 1857528.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195.8 (87.61 to 298.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.54 (-0.66 to -0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45545.8 (20241.92 to 70603.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.63 (3.38 to 11.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.96 (-1.05 to -0.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1771238.38 (835733.76 to 2733902.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e392.81 (183.75 to 614.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.26 (-0.32 to -0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60096.5 (26428.1 to 96409.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.98 (6.65 to 26.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.55 (-0.63 to -0.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2785781.3 (1278126.12 to 4389335.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202.69 (92.18 to 323.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25 (0.16 to 0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e116217.85 (52658.09 to 185983.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.66 (4.19 to 15.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51 (0.38 to 0.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia, East Asia, and Oceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5080821.8 (2282693.81 to 7996695.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186.25 (82.41 to 299.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14 (0.05 to 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e216436.64 (92213.73 to 366075.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.8 (3.64 to 15.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21 (0.04 to 0.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e767092.62 (337346.77 to 1163571.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173.62 (75.81 to 271.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34 (0.25 to 0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27896.67 (11840.55 to 44219.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.8 (3.11 to 12.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29 (0.16 to 0.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113178.25 (49235.68 to 181908.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154.72 (66.35 to 253.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.61 (-0.84 to -0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4846.15 (1946.73 to 8286.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.67 (2.99 to 13.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.75 (-0.97 to -0.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e429177.5 (195428.72 to 667759.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185.41 (84.17 to 288.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.29 (-1.36 to -1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21911.36 (8851.2 to 36961.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.05 (3.7 to 15.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.61 (-1.69 to -1.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e703012.54 (287106.25 to 1133648.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194.34 (80.91 to 314.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.19 (-1.49 to -0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39021.74 (13154.32 to 68779.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.71 (3.75 to 18.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.3 (-1.64 to -0.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralasia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67444.1 (32860.34 to 105179.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.17 (59.49 to 192.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.98 (-2.1 to -1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3132.71 (1406.12 to 5241.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.08 (2.35 to 8.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.84 (-2.91 to -2.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e517545.89 (229151.88 to 811374.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118.91 (53.93 to 183.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.17 (-1.25 to -1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19250.87 (8315.56 to 32899.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.19 (1.48 to 5.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.46 (-2.55 to -2.37)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e848804.57 (352795.48 to 1359053.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127.69 (54.07 to 203.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.55 (-0.75 to -0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33853.35 (12750.96 to 56181.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.68 (1.82 to 7.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.81 (-1.97 to -1.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75475.41 (33095.88 to 121395.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.37 (37.54 to 136.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.9 (-0.95 to -0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3161.86 (1285 to 5219.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.45 (1.41 to 5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.41 (-1.51 to -1.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1107480.49 (521017.49 to 1725703.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.79 (51.76 to 172.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.25 (-2.37 to -2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57937.09 (24688.53 to 95312.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.82 (2.11 to 7.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.97 (-3.05 to -2.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndean Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63362.62 (28851.21 to 99881.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.67 (49.26 to 172.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08 (0.01 to 0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2491.1 (1082.18 to 3944.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.42 (1.91 to 7.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.37 (-0.48 to -0.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136335.52 (62883.68 to 215379.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e252.66 (116.62 to 398.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.29 (-0.36 to -0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5110.49 (2322.29 to 8378.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.36 (4.26 to 15.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.94 (-1.02 to -0.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e441716.46 (195704.84 to 686181.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176.5 (78.54 to 274.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.07 (-0.32 to 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15767.87 (7281.48 to 24958.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.6 (3 to 10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.43 (-0.67 to -0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e567513.93 (247987.48 to 887996.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e222.49 (97.73 to 349.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.87 (-0.94 to -0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22176.33 (9573.61 to 35564.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.99 (3.9 to 14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.26 (-1.33 to -1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1771238.38 (835733.76 to 2733902.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e392.81 (183.75 to 614.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.26 (-0.32 to -0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60096.5 (26428.1 to 96409.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.98 (6.65 to 26.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.55 (-0.63 to -0.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2785781.3 (1278126.12 to 4389335.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202.69 (92.18 to 323.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25 (0.16 to 0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e116217.85 (52658.09 to 185983.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.66 (4.19 to 15.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51 (0.38 to 0.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3375908 (1462201.25 to 5507286.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162.3 (67.18 to 267.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.06 (-0.18 to 0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e152720.28 (59631.5 to 270802.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.09 (2.99 to 14.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07 (-0.13 to 0.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32943.16 (13777.34 to 52463.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e403.68 (168.46 to 649.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21 (0.14 to 0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e920.88 (381.29 to 1504.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.15 (5.74 to 22.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.06 (-0.13 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1671970.65 (751294.02 to 2536645.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265.45 (115.35 to 408.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55 (0.5 to 0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62795.48 (26948.79 to 100297.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.25 (4.58 to 18.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44 (0.34 to 0.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95402.36 (39798.43 to 157338.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200.68 (84.59 to 335.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2 (-0.29 to -0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3411.33 (1402.22 to 5760.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.28 (3.73 to 16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.31 (-0.4 to -0.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138104.06 (60227.76 to 222451.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.27 (37.83 to 143.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.24 (-0.3 to -0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5014 (2097.96 to 8427.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.89 (1.51 to 6.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.22 (-0.28 to -0.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e222249.36 (100737.47 to 338879.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e406.85 (180.26 to 623.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28 (0.97 to 1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8503.02 (3869.3 to 13248.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.19 (7.74 to 28.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.4 (1.02 to 1.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e311336.84 (132164.42 to 480516.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167.6 (69.36 to 262.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27 (0.24 to 0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10968.31 (4388.69 to 17855.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.35 (2.74 to 12.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16 (0.12 to 0.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the global DALYs due to NCDs caused by LPA reached 15.48\u0026nbsp;million (7.24 to 23.95), with an ASDR of 181.53 (83.95 to 280). Deaths numbered 0.65\u0026nbsp;million (0.28 to 1.04), with an ASMR of 7.89 (3.35 to 12.79). Females had an obviously higher global disease burden in death number,DALYs,ASMR and ASDR compared to females. From 1990 to 2021, despite a downward trend in ASMR and ASDR globally, with EAPC of -0.9% (-0.94 to -0.86) and \u0026minus;\u0026thinsp;0.49%(-0.55 to -0.44), respectively, the burden of low physical activity remained significant, resulting in 15.48\u0026nbsp;million DALYs and 649308.59 deaths in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Appendix Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e presents the quantities of death and DALYs for the year 1990, along with their percentage changes up to 2021.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn 2021, by region, deaths exceeded 100,000 in both East Asia and South Asia, with East Asia having the highest number of deaths at 152,720 (59,632 to 270,80), followed by South Asia with 116,218 (52,658 to 185,984). Meanwhile, East Asia and South Asia were also the regions with the highest number of DALYs, with 3,375,908 (1,462,201 to 5,507,287) and 2,785,781 (1,278,126 to 4,389,335), respectively. After age-standardization, the region with the highest ASMR was Southern Sub-Saharan Africa, at 18.19\u0026nbsp;million (7.74 to 28.97 ), followed by North Africa and the Middle East, and Oceania, with 15.98\u0026nbsp;million (6.65 to 26.13)and 14.15\u0026nbsp;million (5.74 to 22.72), respectively. The region with the highest ASDR was Southern Sub-Saharan Africa, at 406.85\u0026nbsp;million(180.26 to 623.84), followed by Oceania and North Africa and the Middle East, with 403.68\u0026nbsp;million (168.46 to 649.78) and 392.81\u0026nbsp;million(183.75 to 614.27), respectively. Between 1990 and 2021, the four regions, Southern Sub-Saharan Africa, Southeast Asia, Western Sub-Saharan Africa and South Asia showed increases in both ASMR and ASDR, with EAPCs of 1.4% (1.02 to 1.78) and 1.28%(0.97 to 1.59) for Southern Sub-Saharan Africa,0.44%(0.34 to 0.55) and 0.55% (0.5 to 0.61) for Southeast Asia, 0.16% (0.12 to 0.2)and 0.27% (0.24 to 0.3)for Western Sub-Saharan Africa, and0.51% (0.38 to 0.65)and 0.25% (0.16 to 0.35) for South Asia. There was significant heterogeneity in DALYs and death numbers across regions, with Southern Sub-Saharan Africa, North Africa, Middle East and Oceania having notably higher ASDR and Age-standardized DALY rates compared to other regions.(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eNationally (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Appendix Table S2), China recorded the highest number of death number and DALYs in 2021, with 147725.7 (56945.8 to 263820.53) and 3241988.81 (1377392.22 to 5307582.63), respectively. Marshall Islands reported the highest ASMR and ASDR at 50.14 (21.81 to 79.7) and 1553.22 (689.15 to 2427.66), respectively. Between 1990 and 2021(Appendix Table S3), Guatemala experienced the largest percent increase in death numbers at 5.11% (2.82 to 8.74), and Cabo Verde had the largest percent increase in ASMR at 1.07%(0.23 to 2.63). Qatar had the largest percent increase in DALYs at 6.44%(4.39 to 9.02), while Lesotho had the largest percent increase in ASDR at 0.94 (0.19 to 2.22).Compared to 1990, 123 countries saw a decrease in ASMR by 2021, with Singapore experiencing the largest decrease at -0.7%(-0.8 to -0.54). ASDR decreased in 102 countries, with Ireland seeing the largest decline at -0.58% (-0.69 to -0.44). Both death numbers and DALYs decreased in 19 countries, with United Kingdom showing the largest declines at -0.44%(-0.55 to -0.3) and-0.33% (-0.45 to -0.18), respectively. Despite declines in ASMR and ASDR in most countries, the number of death and DALYs continued to rise, increasing the disease burden, especially in South Asia, Southeast Asia, Central Latin America, Andean Latin America and Southern Sub-Saharan Africa.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Trends in the disease burden of NCDs caused by LPA across SDI quintiles.\u003c/h2\u003e\u003cp\u003eIn the five SDI groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the ASMR and ASDR in High SDI regions declined rapidly between 1990 and 2010 but the ASDR rose slightly from 2015 to 2021, with the ASMR keeping on declining. In High-middle SDI regions, the ASMR and ASDR rose slightly in the beginning and peaked in 1995, but after 1995 the High-middle SDI regions showed a rapidly downward trend apart from the stable period experienced from 2000 to 2005. The overall trend of Middle-SDI regions and Low-SDI regions is relatively stable. In Middle-SDI regions, both ASMR and ASDR showed a slight decline around 2005, and after 2015, ASMR showed a slow decline while ASDR showed a slight increase. The ASMR and ASDR in Low-SDI regions declined slowly between 1995 and 2010, but rose after 2010 and then declined again after peaking in 2015. Middle-SDI regions showed a wavy rise in both the ASMR and ASDR.The spatiotemporal variation of ASMR and ASDR in low physical activities showed significant heterogeneity among different SDI groups, with the largest increase in the Low-middle SDI regions and the largest decrease in the High-SDI regions, demonstrating a polarization trend.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e3.3 The disease burden of NCDs caused by LPA among different gender and age groups in global and five SDI regions.\u003c/p\u003e\u003cp\u003eIn 2021, within the disease burden of NCDs attributed to LPA, the number of male deaths stood at 243,372.05 (109,914.75 to 396,108.38), with an ASMR of 6.84 (2.99 to 11.27); the DALYs for men amounted to 6,083,300.51 (2,864,701.01 to 9,482,363.38), and the ASDR was 155.91 (71.84 to 246.25). For females, the death toll was 405,936.54 (168,727.55 to 669,703.19), with an ASMR of 8.66 (3.6 to 14.29); the DALYs were 9,392,680.89 (4,326,915.27 to 14,698,836.54), and the ASDR was 203.29 (93.46 to 317.73). In 1990, the number of DALYs for females was 75.56% higher than that for males, while the number of deaths was 100% higher. However, by 2021, this trend had declined. The number of DALYs for females was 54.40% higher than that for males, and the number of deaths was 66.80% higher. During the period from 1990 to 2021, the levels of ASMR and ASDR for both males and females, resulting from NCDs caused by LPA, exhibited a downward trend. Notably, the decline was relatively more significant for females. The EAPC of ASMR for males and females were \u0026minus;\u0026thinsp;0.53 (-0.56 to -0.49) and \u0026minus;\u0026thinsp;1.03 (-1.07 to -0.98) respectively, while the EAPC of ASDR were \u0026minus;\u0026thinsp;21.31\u0026thinsp;\u0026minus;\u0026thinsp;0.28 (-0.34 to -0.22) and \u0026minus;\u0026thinsp;0.56 (-0.62 to -0.51). During the period from 1990 to 2021, the disease burden of NCDs caused by LPA displayed heterogeneity between males and females and manifested a distinct gender gap.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the disparities in the disease burden of NCDs attributable to LPA across different genders and age groups in 2021. In terms of age groups, it is evident that the 45\u0026ndash;49 age range serves as a critical inflection point. The number of deaths and the total DALYs due to non-communicable diseases exhibit divergent trends before and after this age group. Between the ages of 20\u0026ndash;49, there is a gradual upward trend, which accelerates sharply after the age of 49. Specifically, mortality peaks in the 80\u0026ndash;84 age group, while DALYs peak in the 70\u0026ndash;74 age group. Subsequently, both indicators show a rapid decline, while mortality and disability rates continue to rise. Regarding gender differences, females consistently exhibit higher mortality rates, DALY counts, and corresponding rate values than males across all age groups. These differences follow an age-related pattern, where younger and middle-aged women have slightly higher figures than men, and the gap widens significantly in the older age groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e3.4 Comparison between 1990 and 2021 on disease burdens of NCDs caused by LPA among different age groups in global and five SDI regions.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the changes in age-specific DALYs rates globally and across different SDI regions in 1990 and 2021. From a global perspective, there was a notable delay in the disease burden among older age groups in 2021. However, significant heterogeneity exists across different SDI regions. In High SDI regions, the disease burden in older age groups showed a clear delay, whereas in the younger age group of 25\u0026ndash;49 years, the DALYs rate in 2021 was higher than in 1990, indicating a tendency towards younger age groups. A similar pattern was observed in High-middle SDI regions, with the shift towards younger age groups concentrated in the 25\u0026ndash;39 year range. In Middle SDI countries, while DALYs rates across all age groups in 1990 and 2021 remained relatively similar, there was no delay observed in older age groups. In fact, a reversal occurred, with a worsening trend in DALYs rates. Additionally, the increase in DALYs rates among the 25\u0026ndash;49 age group signals a potential shift towards younger populations in Middle SDI regions. In Low-middle SDI and Low SDI regions, the majority of age groups in 2021 had higher DALYs rates compared to 1990, particularly in older age groups, where there was a marked increase in DALYs rates.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Temporal trends in the burden of NCDs caused by LPA Based on the Age-Period-Cohort model.\u003c/h2\u003e\u003cp\u003eNet drift represents the overall annual percentage change in mortality or DALYs rates over the entire study period, while local drift indicates the annual percentage change in mortality or DALYs rates for each age group relative to the net drift. In this study, the annual percentage changes in mortality due to diabetes and kidney diseases, as well as cancers attributable to LPA, demonstrated significant heterogeneity across different SDI regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The annual percentage change in mortality due to diabetes and kidney diseases was highest in the Low-middle SDI region (0.8576% [95% CI, 0.7046 to 1.0108]), reflecting a substantial increase in mortality from these conditions attributable to LPA in this region over the study period. For cancers, the annual percentage changes in mortality in the Low-middle SDI, Low SDI, and Middle SDI regions indicated an upward trend, with the Low-middle SDI region experiencing the fastest increase (0.8005% [95% CI, 0.6064 to 0.9948]). Among the diseases attributable to LPA, the Low-middle SDI region exhibited the fastest rise in mortality due to diabetes and kidney diseases (0.8576% [95% CI, 0.7046 to 1.0108]), while the High SDI region showed the fastest decline in cardiovascular diseases mortality (-2.6905% [95% CI, -2.8221 to -2.5587]). Globally, the annual percentage change in mortality due to diabetes and kidney diseases was greater than 0 (0.404% [95% CI, 0.3136 to 0.4944]), whereas the annual percentage changes in mortality due to cardiovascular diseases and cancers were both less than 0.\u003c/p\u003e\u003cp\u003eIn contrast to the mortality trends, the DALYs rates attributable to LPA showed the most significant increase in the Low-middle SDI region (0.3079% [95% CI, 0.2291 to 0.3868]). Among the three categories of NCDs attributable to LPA, cardiovascular diseases DALYs rates declined across all studied regions, while DALYs rates for diabetes and kidney diseases increased in all regions. Notably, the annual increase in DALYs rates for diabetes and kidney diseases was relatively higher in regions with higher SDI levels. However, in the Low-middle SDI region, the annual increase in DALYs rates for these conditions was unexpectedly high (1.4091% [95% CI, 1.3425 to 1.4757]).\u003c/p\u003e\u003cp\u003eThe trends in mortality due to NCDs attributable to LPA varied significantly across different age groups and regions. In the 25\u0026ndash;39 age group, the annual percentage change in mortality exceeded 0 in High SDI regions, indicating a trend toward younger age groups in these regions. In the Low-middle SDI, Low SDI, and Middle SDI regions, the annual percentage change in mortality due to NCDs attributable to LPA was significantly higher among older adults compared to younger individuals. This trend aligns with the patterns observed for cardiovascular diseases mortality attributable to LPA in these regions. Additionally, the annual percentage change in mortality among younger individuals was greater than 0 across all studied regions, clearly outlining a trend toward younger age groups in mortality due to diabetes and kidney diseases, particularly in High SDI regions. The trends in cancer mortality attributable to LPA across different age groups in all regions exhibited a \"middle-low, both ends high\" distribution.\u003c/p\u003e\u003cp\u003eIn the studied regions, the annual percentage change in DALYs rates due to NCDs attributable to LPA among younger populations showed a positive growth trend. For cardiovascular diseases DALYs rates attributable to LPA, only the High-middle SDI regions exhibited a declining trend in the 25\u0026ndash;34 age group. Regarding DALYs rates for diabetes and kidney diseases attributable to LPA, the annual percentage changes were greater than 0 in all regions. Notably, the inflection point for DALYs rates in High SDI regions occurred in the 75\u0026ndash;79 age group, while in the Low-middle and Low SDI regions, it occurred in the 60\u0026ndash;64 age group, with the former being 15 years earlier than the latter. For cancer DALYs rates attributable to LPA, the annual percentage changes across age groups exhibited a \"U-shaped\" distribution, with the Low-middle SDI regions maintaining annual percentage changes greater than 0 across all age groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePanel A displays the net and local drift of the death of NCDs caused by LPA, while panel B shows the net and local drift of DALYs.\u003c/p\u003e\u003cp\u003eIn Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the estimated effects of age, period, and cohort on the mortality and DALYs rates of NCDs attributable to LPA reveal the fluctuating trends in the burden of LPA-related NCDs across populations. The age effect, represented by longitudinal age curves, illustrates the natural history of LPA-related NCDs associated with aging. In this study, both the mortality and DALYs rates of NCDs attributable to LPA increased with age, with significant regional disparities emerging after the age of 45. Additionally, the burden of cancer attributable to LPA was relatively lower, with higher levels observed in regions with higher SDI.\u003c/p\u003e\u003cp\u003eThe period effect, expressed as the relative mortality risk by period, tracks the progression of mortality and DALYs rates of NCDs attributable to LPA over time. Between 1992 and 2021, the mortality risk of NCDs attributable to LPA exhibited heterogeneous trends across regions with different SDI levels. High SDI, High-middle SDI, Middle SDI regions, and the global average all showed a continuous decline in mortality risk. However, in Low-middle SDI regions, a turning point occurred between 2002 and 2006, characterized by an initial decline followed by an increase. For specific disease types, the mortality risk of cardiovascular diseases attributable to LPA showed an overall declining trend, although Low SDI regions experienced fluctuations between 2007 and 2021, with an initial rise followed by a decline. For diabetes and kidney diseases, Middle SDI regions, the global average, and Low-middle SDI regions exhibited an upward trend, while Low SDI regions experienced a turning point between 2007 and 2011. Regarding cancer mortality risk, Low SDI, Middle SDI, and Low-middle SDI regions showed an increasing trend, whereas High SDI, Global, and High-middle SDI regions demonstrated a continuous decline. In terms of disability risk, High SDI, High-middle SDI regions, and the Global average exhibited a declining trend, although High SDI regions and the Global average experienced a slight increase between 2012 and 2021. In contrast, Low-middle SDI, Middle SDI, and Low SDI regions showed an increasing trend in disability risk, with a more pronounced rise between 2012 and 2021. Notably, for diabetes and kidney diseases, all studied regions exhibited an upward trend in disability risk throughout the study period.\u003c/p\u003e\u003cp\u003eThe cohort effect, represented as the relative mortality risk by birth cohort, tracks changes in mortality and DALYs rates of NCDs attributable to LPA across different birth cohorts. In terms of mortality risk of NCDs attributable to LPA, High SDI regions showed the most significant decline, while Low SDI and Low-middle SDI regions exhibited a fluctuating upward trend. For cardiovascular disease mortality risk, all regions demonstrated a declining trend, with the magnitude of decline decreasing as SDI levels decreased. For diabetes and kidney diseases attributable to LPA, all studied regions showed an upward trend, although High SDI regions displayed a \"U-shaped\" pattern. Regarding cancer mortality risk attributable to LPA, High SDI, Global, and High-middle SDI regions exhibited a declining trend, while Middle SDI, Low SDI, and Low-middle SDI regions showed an increasing trend. For disability risk of NCDs attributable to LPA, High SDI, High-middle SDI regions, and the Global average demonstrated a \"U-shaped\" distribution, indicating that earlier birth cohorts had relatively higher disability risks, which declined in intermediate birth cohorts but increased again in later birth cohorts. In contrast, Middle SDI, Low SDI, and Low-middle SDI regions exhibited a different trend, with disability risks of NCDs attributable to LPA showing an upward trajectory. This suggests that in regions with lower SDI levels, later birth cohorts face a higher disability risk due to LPA-related NCDs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNCDs represent a significant global public health challenge, with LPA identified as a modifiable risk factor for NCDs. Improving LPA is widely recognized as a highly cost-effective intervention for the prevention and control of NCDs\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our study reveals that over the past 32 years, although the ASMR and ASDR attributable to LPA have shown a declining trend globally, the absolute number of deaths has nearly doubled due to the large and continuously growing global population. Therefore, it is imperative to conduct disease burden studies on NCD-related deaths attributable to LPA across global regions. This study is the first to utilize the latest GBD 2021 data and the age-period-cohort (APC) model to analyze the disease burden of NCDs attributable to LPA over a 32-year period. Our findings indicate significant spatiotemporal inequalities in the disease burden of NCDs attributable to LPA across different regions and countries. Furthermore, notable disparities exist between genders and age groups. From the perspective of the SDI, regions with higher SDI levels exhibit a trend of younger age groups bearing a greater disease burden, while regions with lower SDI levels continue to experience an overall increase in disease burden.\u003c/p\u003e\u003cp\u003e4.1 Spatiotemporal inequality in the burden of non-communicable diseases caused by low physical activities from 1990 to 2021.\u003c/p\u003e\u003cp\u003eFrom 1990 to 2021, the global ASMR and ASDR showed a significant downward trend. However, the absolute burden of NCDs attributable to LPA continued to grow steadily, with the number of deaths increasing by 94.88% and DALYs rising by 105.65%. The population affected by these conditions nearly doubled during this period.\u003c/p\u003e\u003cp\u003eThe increase in the disease burden of NCDs attributable to LPA exhibited notable regional heterogeneity. This study found that Southeast Asia, East Asia, Oceania, and South Asia had the highest number of deaths and DALYs. The high population density, accelerated urbanization, and lifestyle changes in these regions have increased exposure to LPA and other NCD risk factors. Coupled with weak chronic disease health information systems in most countries within these regions, these factors have contributed to the heaviest absolute burden of NCDs attributable to LPA\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Meanwhile, North Africa and the Middle East recorded the highest ASMR and ASDR. This may be attributed to poor socioeconomic status, challenging environmental conditions, and underdeveloped healthcare systems in these regions, which limit access to high-quality medical care and preventive treatments, thereby exacerbating the relative burden of NCDs attributable to LPA\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt the national level, China had the highest number of deaths and DALYs attributable to LPA-related NCDs, followed by India, Indonesia, and the United States. These four countries are among the world's most populous nations, highlighting population size as a critical factor influencing the absolute disease burden. As the two most populous countries, China and India reported 147,725.7 (56,945.8 to 263,820.53) and 97,782.63 (45,267.12 to 155,348.67) deaths, respectively. Since the beginning of the 21st century, China has experienced rapid urbanization, with the urban population proportion rising from 18% in 1978 to nearly 65% in 2020. This shift has been accompanied by dietary pattern changes, transitioning from traditional plant-based diets to Western-style diets, while physical activity levels have lagged. The imbalance between energy intake and expenditure has led to over half of the adult population being overweight or obese as of 2020\u003csup\u003e35,36\u003c/sup\u003e. In India, a society with significant wealth disparity, low physical activity is primarily observed in urban areas with higher socioeconomic status, where sedentary lifestyles and high-calorie diets are major contributors to the disease burden. In contrast, malnutrition remains a primary issue in regions with lower socioeconomic status\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Although the United States and Indonesia have similar population sizes, Indonesia's ASMR is significantly higher than that of the United States, largely due to the latter's early focus on monitoring and interventions to promote physical activity\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTherefore, countries with a heavy absolute disease burden should address unreasonable population growth while aligning with the WHO's \u003cb\u003eGlobal Action Plan on Physical Activity 2018\u0026ndash;2030\u003c/b\u003e. These nations should create environments and systems conducive to physical activity, foster populations that embrace active lifestyles, and ultimately build vibrant societies to delay or reduce the disease burden of NCDs attributable to LPA.\u003c/p\u003e\u003cp\u003e4.2 The differences between gender and age groups in the disease burden of NCDs caused by LPA across five SDI regions.\u003c/p\u003e\u003cp\u003eThis study found that the disease burden of NCDs attributable to LPA is significantly higher among females than males. In 2021, the number of deaths among females was 66.80% higher than that among males, indicating that females lag behind males in improving LPA levels, particularly in the 70\u0026ndash;89 age group. Although it is an indisputable fact that males exhibit higher physical activity levels across all age groups, studies have consistently shown that females derive greater survival benefits from the same level of physical activity compared to males. The gender gap in physical activity is primarily observed in higher-intensity activities\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Therefore, addressing the issue of low LPA levels among females requires gender-specific approaches, including enhanced individual risk assessments and tailored exercise prescriptions, to improve female participation in physical activity.\u003c/p\u003e\u003cp\u003eRegarding age, based on comparisons between 1990 and 2021 and the results from the APC model, we observed that in regions with higher SDI levels, the disease burden of NCDs attributable to LPA has been delayed in older age groups but shows a trend of younger onset in younger age groups. In contrast, regions with lower SDI levels exhibit an overall increase in the disease burden across all age groups, particularly among older adults. This suggests that older adults in higher SDI regions are increasingly engaging in physical activities to improve their health\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Among younger populations, the disease burden of NCDs attributable to LPA is trending toward younger age groups, likely due to industrialization, urbanization, and lifestyle changes in higher SDI regions. The shift from physically demanding jobs to sedentary occupations has significantly reduced physical activity levels. Additionally, the mechanization and automation of daily transportation have further limited opportunities for physical activity, with few individuals compensating for this reduction through leisure-time physical activity\u003csup\u003e\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In lower SDI regions, the increased disease burden among older adults may be attributed to poorer balance and a higher risk of falls, as well as a lack of opportunities and facilities for physical activity, particularly the absence of safe community infrastructure, which poses significant barriers to physical activity for older adults\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Therefore, higher SDI regions should focus on encouraging leisure-time recreational physical activity to counteract the decline in occupational and daily physical activity. In lower SDI regions, efforts should prioritize the construction of safe sports facilities for older adults, along with functional balance and strength training to enhance physical capacity and prevent falls, as well as differentiated medication management for older populations.\u003c/p\u003e\u003cp\u003eBased on the net drift and local drift results from the APC model, it is evident that the risk of diabetes and kidney diseases among younger populations in High SDI regions is significantly higher than that among middle-aged individuals. This may be partly due to a decline in physical activity during the transition from adolescence to young adulthood\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Compared to adolescents, young adults face fewer opportunities for physical activity as they transition from school to the workplace, a critical period of life changes. Additionally, the transition to parenthood may further reduce leisure time available for physical activity\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. On the other hand, LPA among young adults can lead to early-onset obesity, and prolonged obesity may cause additional metabolic changes, contributing to the development and progression of diabetes\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The age effect indicates that the mortality burden of NCDs attributable to LPA increases with age, suggesting that older adults are more vulnerable to the adverse effects of physical inactivity. This is due to age-related loss of muscle mass, declining physiological function, and increasing frailty, which limit the ability of older adults to perform essential daily activities, thereby exacerbating the disease burden of NCDs attributable to LPA\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom the combined perspective of period and cohort effects, regions with Low SDI, Low-middle SDI, and Middle SDI levels have experienced an increase in mortality and disability risks for diabetes, kidney diseases, and cancers since 2012. For later-born cohorts, the risks of mortality and disability due to diabetes and kidney diseases have continued to rise across Low SDI, Low-middle SDI, Middle SDI, and High SDI regions. This may be attributed to lifestyle changes, including excessive consumption of alcohol, tobacco, and coffee, as well as sedentary behaviors, which negatively impact insulin sensitivity and blood glucose control\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Furthermore, the rising cancer risk in Low SDI, Low-middle SDI, Middle SDI regions may be due to inadequate socioeconomic conditions and underdeveloped cancer screening and monitoring systems. Younger populations in these regions may neglect such screenings, leading to increased mortality and risk\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, lower SDI countries need to invest in cancer surveillance systems to curb the rising risk among younger populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study found that from 1990 to 2021, the disease burden of NCDs attributable to LPA has steadily increased, with the number of deaths nearly doubling. At the national level, only 19 countries experienced a decline in the number of deaths, while the remaining 185 countries showed an upward trend. Regions with large population bases, such as Southeast Asia, East Asia, Oceania, and South Asia, exhibited a higher absolute disease burden, whereas regions with lower socioeconomic development levels, such as North Africa and the Middle East, demonstrated a higher relative disease burden. Additionally, the disease burden among females was greater than that among males across all age groups. From an age perspective, while the global disease burden showed a delay in progression after the age of 75 in 2021, the APC model results revealed a trend of younger onset in the 25\u0026ndash;39 age group in High SDI regions, particularly for diabetes and kidney diseases. Furthermore, regions with lower SDI levels need to address the rising cancer risk among younger populations. Therefore, it is imperative for countries worldwide to adopt differentiated strategies based on their specific LPA profiles to reduce the disease burden of NCDs attributable to LPA, bridge existing gaps, and achieve sustainable development goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L.(Ye Li) conceived the entire research program and was responsible for overseeing the implementation of the research plan and reviewing the results. X.L. and Y.L.(Yongqiang Lai) carried out the data collection, data cleaning and data analysis, and wrote the first draft based on comments from other authors. Y.S. provided technical support during data processing and analysis. X.Z. performed data collection and cleaning and provided technical support to produce figures and tables in the manuscript. B.S., X.W.,C.X. and J.M. provided academic support for the overall writing of this paper. All authors performed the final review of the manuscript and unanimously agreed to submit it.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eData sharing\u003c/p\u003e\n\u003cp\u003eThe data on death and disability of NCDs attributable to LPA used in this study can be obtained from the Global Burden of Disease 2021 database (https://vizhub.healthdata.org/gbd-results/). The data on factors influencing the incidence of edentulism can be accessed from the GBD 2021 covariates database (https://cloud.ihme.washington.edu/s/d8S5F48dzpm8pnp?path=%2F).\u003c/p\u003e\n\u003cp\u003eDeclaration of interests\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding Declaration\u003c/p\u003e\n\u003cp\u003eWe thank the Global Burden of Disease Study 2021 for providing publicly available data. This work was supported by the National Natural Science Foundation of China (Grant No. 72174047, 71874045).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDing, D. et al. Physical activity guidelines 2020: comprehensive and inclusive recommendations to activate populations. Lancet 396, 1780\u0026ndash;1782 (2020).\u003c/li\u003e\n\u003cli\u003eBull, F. C. et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. 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The Lancet 380, 247\u0026ndash;257 (2012).\u003c/li\u003e\n\u003cli\u003eFord, N. D., Patel, S. A. \u0026amp; Narayan, K. M. V. Obesity in Low- and Middle-Income Countries: Burden, Drivers, and Emerging Challenges. Annu. Rev. Public Health 38, 145\u0026ndash;164 (2017).\u003c/li\u003e\n\u003cli\u003eGough, C., Weber, H., George, S., Maeder, A. \u0026amp; Lewis, L. Location monitoring of physical activity and participation in community dwelling older people: a scoping review. Disability and Rehabilitation 43, 270\u0026ndash;283 (2021).\u003c/li\u003e\n\u003cli\u003eMisra, S. et al. Current insights and emerging trends in early-onset type 2 diabetes. The Lancet Diabetes \u0026amp; Endocrinology 11, 768\u0026ndash;782 (2023).\u003c/li\u003e\n\u003cli\u003eKwon, S., Janz, K. F., Letuchy, E. M., Burns, T. L. \u0026amp; Levy, S. M. Developmental Trajectories of Physical Activity, Sports, and Television Viewing During Childhood to Young Adulthood: Iowa Bone Development Study. JAMA Pediatr 169, 666 (2015).\u003c/li\u003e\n\u003cli\u003eLi, K. et al. Changes in Moderate-to-Vigorous Physical Activity Among Older Adolescents. Pediatrics 138, e20161372 (2016).\u003c/li\u003e\n\u003cli\u003eKwan, M. Y., Cairney, J., Faulkner, G. E. \u0026amp; Pullenayegum, E. E. Physical Activity and Other Health-Risk Behaviors During the Transition Into Early Adulthood. American Journal of Preventive Medicine 42, 14\u0026ndash;20 (2012).\u003c/li\u003e\n\u003cli\u003eBellows-Riecken, K. H. \u0026amp; Rhodes, R. E. A birth of inactivity? A review of physical activity and parenthood. Preventive Medicine 46, 99\u0026ndash;110 (2008).\u003c/li\u003e\n\u003cli\u003eKahn, S. E., Hull, R. L. \u0026amp; Utzschneider, K. M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840\u0026ndash;846 (2006).\u003c/li\u003e\n\u003cli\u003eMcPhee, J. S. et al. Physical activity in older age: perspectives for healthy ageing and frailty. Biogerontology 17, 567\u0026ndash;580 (2016).\u003c/li\u003e\n\u003cli\u003eKolb, H. \u0026amp; Martin, S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med 15, 131 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Low physical activities, Non-communicabe diseases, Spatiotemporal heterogeneity, Age - Period - Cohort","lastPublishedDoi":"10.21203/rs.3.rs-8156186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8156186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eWith the continuous rise in low physical activity (LPA) levels globally, the burden of non-communicable diseases (NCDs) caused by LPA has been increasing, posing a significant threat to public health. However, as a factor that can be actively improved by human intervention, the actual control effectiveness of LPA remains unsatisfactory. Therefore, this study aims to analyze the spatiotemporal trends of the disease burden of NCDs caused by LPA at the global, regional, national, and local levels, and to explore the spatiotemporal heterogeneity across gender, age, and various SDI groups. The findings are intended to provide evidence-based insights for formulating policies to improve LPA.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eData was extracted from the Global Burden of Disease 2021 database. Trends in mortality and disability-adjusted life years (DALYs) due to NCDs attributable to LPA from 1990 to 2021 were assessed using Estimated Annual Percentage Change (EAPC) and percentage change. An age-period-cohort (APC) model was employed to investigate spatiotemporal differences in age, period, and cohort effects across different SDI groups.\u003c/p\u003e\n\u003cp\u003eFindings\u003c/p\u003e\n\u003cp\u003eIn 2021, the global DALYs due to NCDs caused by LPA were 15,475,981.4 (7,248,984.76 to 23,953,592.69), with an age-standardized DALY rate (ASDR) of 181.53 (83.95 to 280). The number of deaths was 649,308.59 (276,348.17 to 1,044,772.12), with an age-standardized mortality rate (ASMR) of 7.89 (3.35 to 12.79). The EAPC for ASDR was -0.49 (-0.55 to -0.44), and for ASMR was -0.9 (-0.94 to -0.86). The highest numbers of DALYs and deaths at the super-regional and national levels were recorded in Southeast Asia, East Asia, and Oceania regions and China, with 5,080,821.8 (2,282,693.81 to 7,996,695.8) and 216,436.64 (92,213.73 to 366,075.14), and 3,241,988.81 (1,377,392.22 to 5,307,582.63) and 147,725.7 (56,945.8 to 263,820.53), respectively. Females experienced higher diseases burden than males. In the 25–39 age group, the risk of mortality and disability from NCDs attributable to LPA in High SDI regions has increased, exhibiting a trend toward younger age groups. This is particularly pronounced in the case of diabetic kidney disease.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eThis study tracks the burden of NCDs attributable to LPA across different regions and SDI levels over a 32-year period. The overall inequality in the burden primarily stems from differences in population size. Furthermore, gender disparities are evident, with females being more vulnerable to the burden of LPA-related NCDs. Additionally, the risk of NCDs, particularly diabetic kidney disease, is increasing among younger populations globally, while the rising risk of cancer is more pronounced in regions with lower SDI levels.\u003c/p\u003e","manuscriptTitle":"The Spatiotemporal Heterogeneity of Non-communicable Diseases Attributed to Low Physical Activity: Capturing Populations in Vulnerable Regions and Age Groups","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 08:31:38","doi":"10.21203/rs.3.rs-8156186/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T15:19:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T04:07:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138076344612300708205534016977061654977","date":"2026-02-24T08:31:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T07:37:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156739324294769680946923600353881388792","date":"2025-12-24T04:42:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-08T15:33:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T04:25:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T04:24:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Health Geographics","date":"2025-11-19T13:59:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b2e5b3d-0848-481e-92f8-5c01e801f6f1","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T15:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 08:31:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8156186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8156186","identity":"rs-8156186","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}