Global burden of ischemic heart disease due to insufficient physical activity in middle-aged and elderly populations from 1990 to 2021 and projections for 2050

Global burden of ischemic heart disease due to insufficient physical activity in middle-aged and elderly populations from 1990 to 2021 and projections for 2050 | 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 Global burden of ischemic heart disease due to insufficient physical activity in middle-aged and elderly populations from 1990 to 2021 and projections for 2050 Xianjun Liu, Wenxue Yuan, Xinman Gao, Ziqi Zhao, Yibing Xia, Chuan He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6374014/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: With the consistent growth of aging population, ischemic heart disease (IHD) has become one of the primary causes of death among middle-aged and elderly individuals. This study analyzes the global, regional, and national trends and characteristics of IHD due to physical inactivity over the past 32 years. Methods: Using data from the 2021 Global Burden of Disease (GBD) study, we assessed the burden of IHD due to physical inactivity among middle-aged and elderly populations from 1990 to 2021. Key metrics included the number of disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), Death, and the age-standardized rates of DALYs (ASDALYR), Deaths (ASDR), LYDs (ASYLDR), and YLLS (ASYLLR). Trend analysis used the estimated annual percentage change (EAPC) method. Decomposition and equity analyses were conducted to evaluate the contributions of demographic and epidemiological factors to the observed changes in IHD burden. Autoregressive integrated moving average (ARIMA) model provides future projections. Results: Globally, the number of IHD health burden due to physical inactivity in the middle-aged and elderly population increased significantly from 1990 to 2021. The EAPCs of ASDALYR, ASYLDR, and ASYLLR were -0.35 (95% CI: -0.70 to -0.01), -0.25 (95% CI: -0.70 to 0.20), and -0.37 (95% CI: -0.72 to -0.03), respectively. The ASYLDR exhibited an upward trend, with an EAPC of 0.65(95% CI: 0.32 to 0.99). Globally, the burden increased with age, and in 2021, females bore a higher burden than males. Regional stratification by SDI showed that low-SDI and middle-SDI regions experienced the most notable increases. From 1990 to 2021, Denmark saw the greatest decline in IHD burden, while China exhibited the most substantial rise. Projections using the ARIMA model suggest a continued increase in IHD burden for both sexes by 2050. Conclusion: Marked disparities in the burden of IHD due to physical inactivity exist across regions, sexes, and age groups. This study provides critical evidence to support public health policymaking, with a view toward mitigating the long-term health risks associated with physical inactivity in aging populations. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Ischemic heart disease (IHD) remains one of the leading causes of mortality worldwide and is a major contributor to severe disability among middle-aged and elderly populations 1 . With the global aging population accelerating, the health burden of IHD has been increasing annually 2 . IHD represents the second-largest medical expenditure after cancer, resulting in an estimated $ 88 billion in direct economic losses each year 3 . While high-income countries have successfully reduced IHD-related mortality with advanced medical technologies and effective interventions, many developing countries, particularly low- and middle-income regions, continue to face rising IHD mortality rates due to insufficient physical activity and associated metabolic risk factors such as hypertension, diabetes, and obesity 4 Consequently, the health and economic burden of IHD has become a major global public health challenge, necessitating urgent and effective prevention and control measures 5 , 6 . Disease burden refers to the impact of diseases, disabilities, and premature mortality on human health. Assessing disease burden is crucial for evaluating disease controllability, setting public health priorities, and quantifying the associated economic implications. Before the 1990s, disease burden was typically evaluated using single metrics such as incidence, prevalence, and mortality rates. However, recent years have seen a shift towards a more comprehensive measure DALYs which provides a multidimensional and hierarchical assessment of disease impact on populations 7 . DALYs consist of two components: YLLs due to premature death and Years Lived with YLDs due to disease-related disability, enabling a holistic evaluation of the combined effects of mortality and morbidity on population health. These three indicators (DALYs, YLLs, and YLDs) are widely used in disease burden research to comprehensively assess the extent of health loss 8 . The burden of IHD is influenced by multiple risk factors, including smoking 9 , dietary patterns 10 , and environmental pollution 11 – 13 . Notably, insufficient physical activity has been recognized as an independent risk factor for increased IHD incidence and Deaths 14 , 15 . Therefore, based on data from the GBD Study, this research systematically analyzes the impact of insufficient physical activity on the IHD burden among middle-aged and elderly populations from 1990 to 2021, while also predicting trends by 2050. The findings provide critical theoretical evidence and data support for global IHD prevention and control efforts, particularly in guiding health management and disease prevention strategies for middle-aged and elderly populations. By elucidating the substantial impact of insufficient physical activity on the IHD burden, this study offers empirical evidence for the formulation of targeted public health policies and optimization of resource allocation. Moreover, it contributes to advancing global health intervention strategies for aging populations, ultimately aiming to reduce IHD incidence and mortality and alleviate the global socioeconomic burden associated with the disease. Materials and methods Ethical approvement Ethical approval was not required for this study because the analysis was based on publicly available, de-identified data from the Global Burden of Disease (GBD) database. All data were accessed and used in accordance with the GBD terms of use. Data Sources and Framework This study utilizes the GBD 2021 database, provided by the Institute for Health Metrics and Evaluation (IHME). The GBD 2021 follows a standardized methodology to estimate the global disease burden, covering 204 countries and regions, 371 diseases, 288 causes of death, and 88 risk factors. Additionally, the dataset includes subnational-level estimates for 21 countries and regions 16 .The GBD database integrates diverse data sources, including scientific literature, household surveys, epidemiological surveillance, disease registries, clinical informatics, and other relevant datasets. From this database, we extracted data from 1990 to 2021 on IHD cases attributable to insufficient physical activity in middle-aged and elderly populations, including DALYs, Deaths, YLDs, and YLLs, to evaluate temporal trends in the disease burden. Regional Classification This study employs the Socio-Demographic Index (SDI) from the GBD framework to classify countries. SDI is a composite metric that incorporates per capita income, the average years of education for individuals aged 15 and older, and the total fertility rate of women under 25. Based on SDI values, countries are categorized into five levels: low, lower-middle, middle, upper-middle, and high. Additionally, the GBD framework classifies the 204 countries and regions into 21 super-regions and 54 subregions based on geographical proximity and epidemiological characteristics. This classification accounts for economic stratification (World Bank income levels), regional divisions (WHO classifications), and healthcare system capacity. Furthermore, national health systems are further categorized as advanced, basic, limited, or minimal, providing a more refined classification of global health disparities. Estimation Models and Standardization Disease Modeling Meta-Regression, Version 2.1(Dis Mod-MR 2.1) is a Bayesian meta-regression framework used in the Global Burden of Disease (GBD) study for data modeling. This model simultaneously incorporates age, gender, geographic location, and time as key variables to ensure consistency across different data sources. By integrating global and regional data, Dis Mod-MR 2.1 enables the imputation of missing data and provides more precise estimates for various health burden indicators 17 . Furthermore, this model applies age standardization, facilitating comparability of health burdens across countries and regions, thereby offering a robust foundation for trend analysis and policy development. The GBD 2021 study employs a standardized tool, the Cause of Death Ensemble Model (CODEm), to estimate IHD-related mortality rates. CODEm is a highly integrated Bayesian geospatial regression analysis tool, commonly used for analyzing mortality rates or cause-specific death proportions associated with particular diseases. Measurement of Health Burden. YLLs metric quantifies the loss of healthy life years due to premature mortality and is calculated as follows: YLLs = N×L Wherein, N represents the number of deaths from IHD at a specific age group, and L denotes the difference between the age at death and the expected life expectancy for that age group. YLD metric quantifies health loss due to disease-related disability, using the following formula: YLDs = Duration of disability × Disability weight For a specific population, YLDs can be calculated as: YLDs = Number of IHD cases × Duration of disability×Disability weight The Disability-Adjusted Life Years (DALYs) metric serves as a comprehensive measure of disease burden, reflecting the cumulative impact of mortality, disability, age, and time on healthy life years lost. It is calculated as: DALYs = YLLs + YLDs One DALYs equates to the loss of one full year of healthy life 1 , 18 , 19 Additionally, this study includes the following age-standardized health burden indicators, all of which are standardized to the GBD reference population and expressed per 100,000 population to ensure comparability across regions and time periods: Additionally, this study includes the following age-standardized health burden indicators, all of which are standardized to the GBD reference population and expressed per 100,000 population to ensure comparability across regions and time periods: ASDALY, ASDR, ASYLDR, ASYLLR. Statistical Methods (1) The following statistical analyses were performed: ASR of DALYs, Deaths, YLDs, and YLLs were computed. ASR is derived by weighting age-specific observed data against the global standard population, ensuring comparability of health burden across different countries and regions. (2) Stratified Analysis: Stratified analyses were conducted based on age, gender, SDI, and geographic location to examine the disparities in DALYs, deaths, YLDs, and YLLs across different populations and regions. Age groups were categorized as 40 years and older, with further stratification in 5-year intervals. This analysis enables a deeper understanding of health burden disparities and identifies priority areas for health policy interventions. (3) Trend Analysis Using Log-Linear Regression: A log-linear regression model was employed to estimate EAPC, analyzing ASR trends from 1990 to 2021.This regression model provides a clear interpretation of annual percentage changes, yielding more reliable trend estimations. EAPC estimates were reported along with their 95% confidence intervals (CI) to assess statistical reliability. (4) Decomposition Analysis: To explore the contribution of different factors to changes in DALYs, Deaths, YLDs, and YLLs, decomposition analysis was applied. This approach quantifies the influence of various factors on overall health burden changes, providing data support for targeted health interventions. (5) Predictive Analysis: The ARIMA model was used to forecast gender-specific trends in health burden from 2022 to 2050.The ARIMA model, based on historical time-series data, incorporates seasonality and trend fluctuations to predict future trends. Model fitting and validation were performed to obtain future health burden projections for both men and women, with confidence intervals calculated to assess prediction accuracy and reliability. (6) Health Inequality Analysis: A health inequality analysis was conducted to evaluate disparities in health burden across different socioeconomic groups. This analysis focused on DALYs, Deaths, YLDs, and YLLs, considering income levels, education attainment, and geographic distribution. By identifying the socioeconomic stratification of disease burden, this analysis provides critical insights for addressing health inequities and informing strategies to reduce disparities in global health outcomes. Results Global Level According to the results of this study, in 2021, the number of global DALYs, Deaths, YLDs, and YLLs due to insufficient physical activity in middle-aged and elderly populations were 3,897,941 (95% UI: 1,767,043–6,116,373) (Fig. 1 B and Tables 1 ), 232,250 (95% UI: 103,518–371,375) (Fig. 1 A and Tables 2 ), 85,414 (95% UI: 36,522–147,216) (Fig. 1 C and Tables 3 ), and 3,812,527 (95% UI: 1,726,040–5,985,591) (Fig. 1 D and Tables 4 ). ASDALYR, ASDR, ASYLDR, and ASYLLR for 2021 were 46.63 (95% UI: 21.15–73.38) per 100,000 population, 2.88 (95% UI: 1.28–4.59), 1.01 (95% UI: 0.44–1.74), and 45.62 (95% UI: 20.68–71.84) (Tables 1 – 4 ). Table 1 The estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations DALYs and temporal trends by sex, age and SDI from 1990 to 2021 1990 2021 1990–2021 location name Number 95%UI ASR 95%UI Number 95%UI ASR 95%UI EAPC 95%CI Global 2248011 (1013161–3466301) 64.51 (28.78-101.42) 3897941 (1767043–6116373) 46.63 (21.15–73.38) -0.35 (-0.7–0.01) Sex Female 1462516 (657058–2257655) 73.85 (33-114.68) 2415790 (1072448–3832823) 51.88 (23.05–82.18) -1.22 (-1.26–1.18) Male 785494 (347068–1216683) 49.36 (21.34–78.43) 1482151 (693907–2337776) 39.41 (18.46–62.34) -0.83 (-0.91–0.75) Age 40–44 years 43588 (21330–66521) 15.22 (7.45–23.22) 68729 (34840–105065) 13.74 (6.96-21) -0.56 (-0.77–0.36) 45–49 years 71003 (34331–110358) 30.58 (14.79–47.53) 111386 (56552–172550) 23.52 (11.94–36.44) -1.05 (-1.28–0.82) 50–54 years 106643 (53178–166821) 50.17 (25.02–78.48) 171216 (81766–267383) 38.48 (18.38–60.1) -1.04 (-1.25–0.83) 55–59 years 166738 (74955–264609) 90.03 (40.47-142.88) 274743 (129631–443173) 69.43 (32.76-111.99) -1.05 (-1.26–0.84) 60–64 years 242671 (105496–383184) 151.09 (65.69-238.58) 344824 (157290–550555) 107.74 (49.15-172.02) -1.48 (-1.63–1.32) 65–69 years 280707 (125507–429881) 227.09 (101.54-347.77) 453445 (203708–710780) 164.39 (73.85-257.68) -1.29 (-1.39–1.19) 70–74 years 288192 (118389–464443) 340.41 (139.84-548.59) 536967 (238930–878240) 260.87 (116.08-426.66) -0.99 (-1.04–0.93) 75–79 years 333198 (146204–543012) 541.3 (237.51-882.15) 502828 (211457–852008) 381.26 (160.33-646.03) -1.04 (-1.09–0.99) 80–84 years 349484 (146302–572325) 987.91 (413.56-1617.84) 613267 (265595–1001891) 700.21 (303.25-1143.93) -1.09 (-1.19–1) 85–89 years 200580 (83496–334842) 1327.37 (552.55-2215.87) 423640 (184321–700204) 926.56 (403.14-1531.44) -1.15 (-1.22–1.09) 90–94 years 82676 (33713–136664) 1929.35 (786.74-3189.21) 234497 (100854–390675) 1310.81 (563.76-2183.84) -1.29 (-1.36–1.22) 95 + years 27934 (11105–48221) 2743.73 (1090.73-4736.42) 91721 (38268–154724) 1682.85 (702.12-2838.81) -1.72 (-1.83–1.62) SDI High-middle SDI 556615 (247968–850545) 66.73 (29.36–102.2) 959770 (416772–1545495) 49.51 (21.54–80.04) -1.12 (-1.26–0.98) High SDI 684673 (292480–1086985) 61.5 (26.52–97.76) 501160 (213062–805337) 21.77 (9.4-34.53) -3.64 (-3.84–3.44) Low-middle SDI 387119 (177224–586799) 68.84 (30.52-104.96) 869620 (400803–1343621) 65.79 (29.64-103.15) -0.13 (-0.18–0.08) Low SDI 71004 (31450–109892) 36.16 (15.55–56.12) 143136 (63927–223824) 32.83 (14.3-52.29) -0.23 (-0.36–0.09) Middle SDI 545231 (251302–838252) 61.61 (27.35–96.59) 1420318 (645352–2227648) 57.71 (26.11–90.69) -0.21 (-0.24–0.18) Abbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index Table 2 The estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations Deaths and temporal trends by sex, age and SDI from 1990 to 2021 1990 2021 1990–2021 location nam e Number 95%UI ASR 95%UI Number 95%UI ASR 95%UI EAPC 95%CI Global 125730 (55488–199339) 4.09 (1.78–6.56) 232250 (103518–371375) 2.88 (1.28–4.59) -0.25 (-0.7-0.2) Sex Female 87707 (38827–139152) 4.76 (2.11–7.66) 152305 (66250–248989) 3.24 (1.41–5.29) -1.29 (-1.32–1.25) Male 38023 (16293–60944) 2.88 (1.21–4.8) 79945 (36705–129067) 2.33 (1.07–3.82) -0.71 (-0.78–0.65) Age 40–44 years 901 (439–1378) 0.31 (0.15–0.48) 1415 (717–2174) 0.28 (0.14–0.43) -0.58 (-0.78–0.37) 45–49 years 1634 (788–2543) 0.7 (0.34–1.1) 2549 (1300–3945) 0.54 (0.27–0.83) -1.07 (-1.29–0.84) 50–54 years 2758 (1373–4310) 1.3 (0.65–2.03) 4402 (2091–6868) 0.99 (0.47–1.54) -1.06 (-1.27–0.85) 55–59 years 4915 (2209–7801) 2.65 (1.19–4.21) 8046 (3809–13017) 2.03 (0.96–3.29) -1.07 (-1.28–0.86) 60–64 years 8280 (3605–13082) 5.16 (2.24–8.15) 11688 (5312–18719) 3.65 (1.66–5.85) -1.5 (-1.66–1.35) 65–69 years 11320 (5062–17284) 9.16 (4.09–13.98) 18152 (8146–28459) 6.58 (2.95–10.32) -1.32 (-1.42–1.22) 70–74 years 14123 (5797–22662) 16.68 (6.85–26.77) 26077 (11564–42741) 12.67 (5.62–20.76) -1.01 (-1.07–0.95) 75–79 years 20524 (9038–33534) 33.34 (14.68–54.48) 30690 (12836–52009) 23.27 (9.73–39.44) -1.06 (-1.11–1) 80–84 years 27516 (11560–45247) 77.78 (32.68–127.9) 48130 (20869–78268) 54.95 (23.83–89.36) -1.11 (-1.21–1.01) 85–89 years 19914 (8297–33299) 131.78 (54.91-220.36) 41934 (18281–68925) 91.72 (39.98-150.75) -1.16 (-1.23–1.09) 90–94 years 9478 (3871–15683) 221.19 (90.33-365.98) 26769 (11441–44579) 149.64 (63.96-249.19) -1.3 (-1.37–1.23) 95 + years 3400 (1351–5881) 333.99 (132.69-577.69) 11151 (4654–18811) 204.59 (85.39-345.14) -1.71 (-1.81–1.61) SDI High-middle SDI 34525 (14974–53877) 4.69 (2-7.47) 66676 (28938–111391) 3.52 (1.53–5.88) -1.04 (-1.18–0.89) High SDI 44910 (18988–73978) 4.1 (1.73–6.79) 35921 (14372–58936) 1.4 (0.58–2.27) -3.75 (-3.89–3.61) Low-middle SDI 16972 (7429–25967) 3.6 (1.52–5.55) 43063 (19107–68468) 3.68 (1.61–5.89) 0.2 (0.12–0.29) Low SDI 3177 (1367–4926) 1.98 (0.82–3.14) 6875 (2975–11063) 1.9 (0.79–3.13) 0.08 (-0.12-0.28) Middle SDI 25947 (11467–40957) 3.62 (1.55–5.79) 79471 (35675–125589) 3.56 (1.59–5.66) 0.04 (-0.03-0.11) Abbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index Table 3 The estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations YLDs and temporal trends by sex, age and SDI from 1990 to 2021 1990 2021 1990–2021 location name Number 95%UI ASR 95%UI Number 95%UI ASR 95%UI EAPC 95%CI Global 36173 (15478–62617) 1.02 (0.43–1.77) 85414 (36522–147216) 1.01 (0.44–1.74) 0.65 (0.32–0.99) Sex Female 23564 (10079–40471) 1.16 (0.5-2) 54301 (23293–93131) 1.17 (0.5-2) -0.1 (-0.17–0.03) Male 12609 (5267–22124) 0.81 (0.34–1.42) 31113 (13037–55561) 0.82 (0.35–1.46) -0.04 (-0.14-0.06) Age 40–44 years 463 (203–852) 0.16 (0.07–0.3) 984 (439–1690) 0.2 (0.09–0.34) 0.71 (0.47–0.95) 45–49 years 804 (338–1463) 0.35 (0.15–0.63) 1832 (788–3225) 0.39 (0.17–0.68) 0.29 (0.02–0.55) 50–54 years 1307 (555–2377) 0.61 (0.26–1.12) 3029 (1248–5628) 0.68 (0.28–1.27) 0.2 (-0.05-0.45) 55–59 years 2216 (898–4085) 1.2 (0.48–2.21) 5020 (2004–9716) 1.27 (0.51–2.46) 0.06 (-0.13-0.24) 60–64 years 3726 (1491–7081) 2.32 (0.93–4.41) 7304 (2904–13712) 2.28 (0.91–4.28) -0.26 (-0.42–0.1) 65–69 years 5447 (2242–9551) 4.41 (1.81–7.73) 12043 (4826–21901) 4.37 (1.75–7.94) -0.18 (-0.27–0.08) 70–74 years 5895 (2249–10913) 6.96 (2.66–12.89) 14816 (5799–27246) 7.2 (2.82–13.24) 0.02 (-0.05-0.1) 75–79 years 6047 (2437–11242) 9.82 (3.96–18.26) 12770 (4927–23478) 9.68 (3.74–17.8) -0.05 (-0.11-0.02) 80–84 years 5566 (2223–10463) 15.73 (6.28–29.58) 13184 (5282–24739) 15.05 (6.03–28.25) -0.2 (-0.22–0.17) 85–89 years 2933 (1254–5381) 19.41 (8.3-35.61) 8309 (3465–15269) 18.17 (7.58–33.4) -0.35 (-0.4–0.3) 90–94 years 978 (375–1795) 22.82 (8.75–41.89) 3811 (1544–6964) 21.3 (8.63–38.93) -0.44 (-0.51–0.38) 95 + years 251 (88–478) 24.68 (8.61–46.99) 1217 (478–2250) 22.32 (8.77–41.29) -0.51 (-0.57–0.45) SDI High-middle SDI 9353 (3809–15955) 1.05 (0.43–1.82) 21876 (9439–38504) 1.1 (0.48–1.95) 0.11 (0.06–0.17) High SDI 12384 (5323–22023) 1.1 (0.48–1.94) 18308 (7898–32148) 0.81 (0.35–1.41) -1.33 (-1.49–1.17) Low-middle SDI 5016 (2075–8800) 0.97 (0.4–1.69) 14091 (5779–24896) 1.1 (0.45–1.96) 0.41 (0.37–0.44) Low SDI 844 (343–1509) 0.46 (0.19–0.8) 2155 (881–3803) 0.51 (0.21–0.9) 0.29 (0.26–0.33) Middle SDI 8519 (3610–14728) 0.98 (0.42–1.69) 28886 (12075–50252) 1.15 (0.49–2.02) 0.53 (0.48–0.57) Abbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index Table 4 The estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations YLLs and temporal trends by sex, age and SDI from 1990 to 2021 1990 2021 1990–2021 location name Number 95%UI ASR 95%UI Number 95%UI ASR 95%UI EAPC 95%CI Global 2211838 (994931–3406072) 63.49 (28.24–99.92) 3812527(1726040–5985591) 45.62 (20.68–71.84) -0.37 (-0.72–0.03) Sex Female 1438953 (646043–2215065) 72.69 (32.55-112.58) 2361489 (1046160–3749396) 50.72 (22.49–80.4) -1.24 (-1.28–1.2) Male 772886 (339832–1196976) 48.56 (20.98–77.15) 1451038 (680569–2290024) 38.59 (18.08–60.95) -0.85 (-0.92–0.77) Age 40–44 years 43125 (21025–65940) 15.05 (7.34–23.02) 67745 (34306–104031) 13.54 (6.86–20.8) -0.58 (-0.79–0.37) 45–49 years 70199 (33860–109256) 30.23 (14.58–47.05) 109554 (55891–169597) 23.14 (11.8-35.82) -1.07 (-1.3–0.84) 50–54 years 105336 (52406–164607) 49.55 (24.65–77.44) 168187 (79896–262422) 37.8 (17.96–58.98) -1.06 (-1.27–0.85) 55–59 years 164522 (73936–261116) 88.84 (39.92-140.99) 269723 (127687–436402) 68.16 (32.27-110.28) -1.07 (-1.27–0.86) 60–64 years 238945 (104018–377623) 148.77 (64.76-235.12) 337520 (153450–540520) 105.46 (47.95-168.89) -1.5 (-1.65–1.35) 65–69 years 275260 (123063–420441) 222.69 (99.56-340.14) 441402 (198089–691881) 160.02 (71.81-250.82) -1.32 (-1.42–1.21) 70–74 years 282297 (115884–453002) 333.44 (136.88-535.08) 522152 (231587–855820) 253.67 (112.51-415.77) -1.01 (-1.07–0.95) 75–79 years 327151 (144062–534495) 531.47 (234.04-868.31) 490057 (205044–830687) 371.58 (155.47-629.86) -1.06 (-1.11–1.01) 80–84 years 343918 (144435–565491) 972.18 (408.29-1598.52) 600083 (260164–976006) 685.16 (297.05-1114.38) -1.11 (-1.21–1.01) 85–89 years 197647 (82333–330402) 1307.96(544.86-2186.49) 415331 (181072–682549) 908.39 (396.03-1492.83) -1.17 (-1.23–1.1) 90–94 years 81698 (33367–135171) 1906.53(778.66-3154.38) 230686 (98593–384157) 1289.51 (551.12-2147.41) -1.3 (-1.37–1.23) 95 + years 27682 (11013–47856) 2719.05(1081.7-4700.59) 90504 (37774–152736) 1660.53 (693.07-2802.33) -1.73 (-1.84–1.63) SDI High-middle SDI 547262 (243636–835171) 65.69 (28.83-100.72) 937894 (408742–1515369) 48.4 (21.14–78.49) -1.14 (-1.29–1) High SDI 672289 (287731–1065608) 60.41 (26.08–95.92) 482852 (206160–773191) 20.96 (9.08–33.1) -3.7 (-3.9–3.5) Low-middle SDI 382103 (174525–579386) 67.88 (30.01-103.49) 855528 (394032–1325175) 64.7 (29.12-101.52) -0.14 (-0.19–0.09) Low SDI 70160 (31089–108785) 35.69 (15.35–55.39) 140981 (62765–220795) 32.32 (14.04–51.6) -0.24 (-0.37–0.1) Middle SDI 536712 (247463–826135) 60.63 (26.95–94.8) 1391433 (631784–2183120) 56.55 (25.57–88.98) -0.22 (-0.26–0.19) Abbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index The number of health burden of IHD due to insufficient physical activity in middle-aged and elderly populations has shown a marked increase compared to 1990. However, the ASDALYR, ASDR, ASYLDR, and ASYLLR have shown a declining trend. The ASDALYR decreased from 64.51 (95% UI: 28.78–101.4) in 1990 to 46.63 (95% UI: 21.15–73.38) in 2021, with an Estimated Annual Percentage Change (EAPC) of -0.35 (95% CI: -0.7 to -0.01) (Tables 1 ). The ASDR dropped from 4.09 (95% UI: 1.78–6.56) in 1990 to 2.88 (95% UI: 1.28–4.59) in 2021, with an EAPC of -0.25 (95% CI: -0.7 to 0.2) Tables 2 ). The ASYLDR slightly decreased from 1.02 (95% UI: 0.43–1.77) in 1990 to 1.01 (95% UI: 0.44–1.74) in 2021, with an EAPC of 0.65 (95% CI: 0.32 to 0.99) (Tables 3 ). The ASYLLR decreased from 63.49 (95% UI: 28.24–99.92) in 1990 to 45.62 (95% UI: 20.68–71.84) in 2021, with an EAPC of -0.37 (95% CI: -0.72 to -0.03) (Tables 4 ). Global Trends by Gender and Age Group For 2021, females consistently exhibited higher number of DALYs, Deaths, YLDs, and YLLs (Appendix Fig. 1B), as well as ASR for these metrics (Appendix Fig. 1A), compared to males. Despite the downtrend in the indicators from 1990 to 2021, females, compared to males, experienced a more pronounced decline. For instance, female ASDALYR decreased from 73.85 (95% UI: 33–114.68) per 100,000 population in 1990 to 51.88 (95% UI: 23.05–82.18) in 2021, with an EAPC of -1.22 (95% CI: -1.26 to -1.18), while male ASDALYR decreased from 49.36 (95% UI: 21.34–78.43) in 1990 to 39.41 (95% UI: 18.46–62.34) in 2021, with an EAPC of -0.83 (95% CI: -0.91 to -0.75) (Fig. 2 A and Tables 1 ). Similarly, for females, ASDR decreased from 4.76 (95% UI: 2.11–7.66) in 1990 to 3.24 (95% UI: 1.41–5.29) in 2021, with an EAPC of -1.29 (95% CI: -1.32 to -1.25), ASYLDR decreased from 1.16 (95% UI: 0.5–2) in 1990 to 1.17 (95% UI: 0.5–2) in 2021, with an EAPC of -0.10 (95% CI: -0.17 to -0.03), and ASYLLR decreased from 72.69 (95% UI: 32.55–112.58) in 1990 to 50.72 (95% UI: 22.49–80.4) in 2021, with an EAPC of -1.24 (95% CI: -1.28 to -1.2) ; for males, ASDR decreased from 2.88 (95% UI: 1.21–4.8) in 1990 to 2.33 (95% UI: 1.07–3.82) in 2021, with an EAPC of -0.71 (95% CI: -0.78 to -0.65), ASYLDR decreased from 0.81 (95% UI: 0.34–1.42) in 1990 to 0.82 (95% UI: 0.35–1.46) in 2021, with an EAPC of -0.04 (95% CI: -0.14 to 0.06), and ASYLLR decreased from 48.56 (95% UI: 20.98–77.15) in 1990 to 38.59 (95% UI: 18.08–60.95) in 2021, with an EAPC of -0.85 (95% CI: -0.92 to -0.77) (Fig. 2 A, B and Tables 1 – 4 ). In 2021, ASDALYR, ASDR, ASYLDR, and ASYLLR increased with age (Fig. 3 A and Tables 1 – 4 ). The 80–84 age group exhibited the highest burden for DALYs, Deaths, YLDs, and YLLs as the following: DALYs: 613,267 (95% UI: 265,595–1,001,891), Deaths: 48,130 (95% UI: 20,869–78,268, YLDs: 13,184 (95% UI: 5,282–24,739), YLLs: 600,083 (95% UI: 260,164–976,006) (Fig. 3 B and Tables 1 – 4 ). Between 1990 and 2021, the EAPC for the 40–44 age group showed the least improvement as following: DALYs: -0.56 (95% CI: -0.77 to -0.36), Deaths: -0.58 (95% CI: -0.78 to -0.37), YLDs: 0.71 (95% CI: 0.47 to 0.95), YLLs: -0.58 (95% CI: -0.79 to -0.37) (Tables 1 – 4 ). In 2021, the burden of DALYs, Deaths, YLDs, and YLLs in females increased progressively from the age of 40–44 years, peaking at 80–84 years, and then gradually decreased. In contrast, males reached their peak at the age of 70–74 years, and then their burden declined slightly after reaching the maximum at 90–94 years (Fig. 4 and Tables 1 – 4 ). SDI Regional Level 2021 The heaviest number of DALYs, Death, and YLLs for IHD due to physical inactivity among middle-aged and older adults in different SDI regions was in the low and medium SDI regions and the lightest in the high SDI regions, and the heaviest burden of YLDs was in countries with medium SDI and the lightest was in countries with low SDI (Appendix Fig. 2B and Tables 1 – 4 ). In 2021, the ASDALYR and ASDR were highest in low-to-middle SDI countries, with 65.79 (95% UI: 29.64–104.96) per 100,000 population and 3.68 (95% UI: 1.61–5.89). Conversely, high SDI countries had the lightest burden, with 21.77 (95% UI: 9.40–34.53) and 1.40 (95% UI: 0.58–2.27), (Appendix Fig. 2A and Tables 1 , 2 ). The ASYLDR was highest in medium SDI countries at 1.15 (95% UI: 0.49–2.02), and lowest in low SDI countries at 0.51 (95% UI: 0.21–0.90) (Appendix Fig. 2Aand Tables 3 ). The ASYLLR was highest in low-to-middle SDI countries at 64.70 (95% UI: 29.12–101.52), and lowest in high SDI countries at 20.96 (95% UI: 9.08–33.10) Appendix Fig. 2A and Tables 4 ). From the overall trend between 1990 and 2021, the number of DALYRs, Deaths, and YLLs showed an increasing trend in all regions except high SDI regions (Fig. 5 B), YLDs exhibited an upward trend YLLs all showed increased (Fig. 5 A). ASDALYR and ASYLLR showed a declining trend across all SDI regions: High SDI countries experienced the fastest decline with EAPC values of -3.64 (95% CI: -3.84 to -3.44) and − 3.70 (95% CI: -3.90 to -3.50), while low-to-middle SDI countries experienced the slowest decline, with EAPC of -0.13 (95% CI: -0.18 to -0.08) and − 0.14 (95% CI: -0.19 to -0.09) (Tables 1 and Tables 4 ). The ASDR showed an upward trend in all regions except middle-to-high SDI and high SDI countries, with the highest increase observed in low-to-middle SDI countries at 0.20 (95% CI: 0.12–0.29) (Tables 2 ). The ASYLDR increased in all regions except high SDI countries, with the greatest increase seen in medium SDI countries at 0.53 (95% CI: 0.48–0.57) (Table 3 ). GBD Regional Situation In 2021, the GBD regions with the heaviest health burden in terms of age-standardized DALYs, Deaths, and YLLs were Northern Africa, including countries such as Egypt, Libya, Algeria, and Morocco, with 137.71 (95% UI: 224.48–60.74) per 100,000 population, 8.19 (95% UI: 13.63–3.49), and 136.02 (95% UI: 222.03–60.16). The regions with the lightest burden were Southern Latin America, with typical countries including Kenya, Tanzania, Uganda, and Ethiopia, with 7.77 (95% UI: 13.53–3.13), 0.46 (95% UI: 0.78–0.19), and 7.58 (95% UI: 13.22–3.05) (Fig. 6 A and Appendix Table s1 ). The ASYLDR burden was heaviest in Northern Africa and the Middle East, covering countries from Northern Africa to the Middle East, including Saudi Arabia, Iran, and the UAE, with 1.97 (95% UI: 3.38–0.84). The lightest burden was observed in Eastern Sub-Saharan Africa, with countries like Argentina, Chile, and Paraguay, with 7.58 (95% UI: 13.22–3.05) (Fig. 6 A and Appendix Table s1 ). In 2021, the regions with the highest number of DALYs, Deaths, YLDs, and YLLs were Asia, while Oceania had the lowest number (Fig. 6 B and Appendix Table s1 ).The regions with the greatest improvement in DALYs, Deaths, YLDs, and YLLs were Commonwealth High-Income countries, with EAPC of -4.52 (95% CI: -5.19 to -3.85), -4.22 (95% CI: -4.95 to -3.48), -1.73 (95% CI: -2.39 to -1.05), and − 4.59 (95% CI: -5.26 to -3.92) (Appendix Table s1 ). The regions with the greatest increase in age-standardized DALYs, deaths, YLDs, and YLLs were East Asia, with EAPC of 2.28 (95% CI: 1.70–2.85), 2.89 (95% CI: 2.08–3.71), 2.38 (95% CI: 1.94–2.82), and 2.27 (95% CI: 1.70–2.85) (Appendix Table s1 ). National Level Among 204 countries, Sudan had the highest ASDALYR, ASDR, ASYLDR and ASYLLR for IHD due to insufficient physical activity in middle-aged and elderly populations with 647.07 (95% UI: 293.16–1026.7) per 100,000 population, 7.94 (95% UI: 7.94–32.14), 18.1 (95% UI: 7.94–32.14), and 5.55 (95% UI: 2.4–9.66), while Tanzania had the lowest health burden, with 3.13 (95% UI: 1.16–6.28), 0.17 (95% UI: 0.06–0.36), 0.18 (95% UI: 0.07–0.33), and 1.00 (95% UI: 0–3.00) (Appendix Fig. 3 and Appendix Tables2). From the trend between 1990 and 2021, the countries with the largest reductions in DALYs, Deaths, and YLDs were Denmark, with EAPC values of -5.65 (95% CI: -6.51 to -4.79), -5.49 (95% CI: -6.4 to -4.56, and − 5.76 (95% CI: -6.62 to -4.9), while The countries with the largest increases in these indicators were China, with EAPC values of 2.33 (95% CI: 1.74–2.92, 2.95 (95% CI: 2.12–3.79), and 2.33 (95% CI: 1.74–2.92)(Fig. 7 A, Fig. 7 B and Fig. 7 C. For YLLs, the largest decrease in the EAPC was observed in the United Kingdom, with a reduction of -1.67 (95% CI: -2.45 to -0.88), while the largest increase was seen in Guam, with an EAPC of 2.91 (95% CI: 2.27–3.55) (Fig. 7 D) Decomposition Analysis Figure 8 presents the results of the decomposition analysis for DALYs, Deaths, YLDs, and YLLs globally, across five SDI regions, and by gender. In this study, the changes in the global burden of ischemic heart disease due to insufficient physical activity were primarily driven by a combination of epidemiological changes, population growth, and aging. The global DALYs burden increased from 2,248,010.85 in 1990 to 3,897,941 in 2021, with an increase of 1,649,930.14. The contribution of epidemiological changes to the DALYs burden was the largest (465.04%), while population growth and aging contributed − 414.92% and 29.88. In different SDI regions, epidemiological changes had the largest impact on the DALYs burden in high SDI countries (127.41%), while in low-to-middle SDI regions, population growth and aging showed a more significant influence. From a gender perspective, male DALYs were mainly influenced by population growth (506.47%) and aging (-41.08%), whereas female DALYs were more strongly impacted by epidemiological changes (385.25%). The contribution of epidemiological changes of Deaths was similarly dominant (-155.46%), with epidemiological changes having the largest impact on Deaths in middle-to-high SDI regions (194.92%). Changes in YLDs and YLLs were primarily driven by population growth, with global YLDs increasing from 36,172.75 to 85,413.64, an increase of 49,240.89, and global YLLs rising from 2,211,838.1 to 3,812,527.4, an increase of 1,600,689.25.At the gender level, women showed more significant changes across all health indicators, particularly in Deaths and YLL. Overall, epidemiological changes and population growth have had a profound impact on the health burden across different regions, particularly in low-to-middle SDI countries, where the effects of population growth and aging were more pronounced. ARIMA Model Forecast of Global Burden of IHD Due to Insufficient Physical Activity in Middle-Aged and Elderly Populations: 2022–2050 ARIMA model predicts rising trends in DALYs, Deaths, YLDs and YLLs due to insufficient physical activity for IHD in middle-aged and older populations globally from 2022–2050, but declining trends in these indicators under age-standardization for both men and women (Fig. 9 ) For men, the ADALYR and ASYLLR will show an increasing trend. ASDALYR will rise from 39.44 (per 100,000 population) in 2022 to 40.54 in 2050. ASYLLR will increase from 38.62 in 2022 to 39.49 in 2050. On the other hand, the ASDR will show a decreasing trend, dropping from 2.32 in 2022 to 1.82 in 2050. The ASYLDR will remain stable at 0.79 throughout the forecast period. The confidence intervals for the ASDALYR and ASYLLR are expected to gradually widen; for example, the confidence interval for ASDALYR will increase from 95% UI (38.81–40.08) to 95% UI (13.80–67.29). ASYLDR will consistently exhibit minor variability, with a confidence interval remaining at 95% UI (0.75–0.83), reflecting a high degree of precision. For women, ASDALYR, ASDR, and ASYLLR will show a significant decrease. From 2022 to 2050, DALYs will decrease from 51.17 to 31.33, deaths will decrease from 3.19 to 1.82, and YLLs will decrease from 50.01 to 30.16. YLDs will show an increasing trend, rising from 1.17 in 2022 to 1.35 in 2050. The confidence intervals for age-standardized DALYs, Deaths, YLDs, and YLLs will gradually widen; for example, the confidence interval for deaths will expand from 95% UI (3.13–3.25) in 2022 to 95% UI (1.47–2.16) in 2050. Cross-National Health Inequality Analysis The study results show the relative income inequality in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations, as measured by DALYs, Deaths, YLDs, and YLL. A comparison of data from 1990 and 2019 indicates a reduction in health inequality over this period. The concentration index for DALYs showed a small change, from − 0.16 in 1990 to -0.02 in 2021 (Fig. 10 A). However, the slope inequality index indicated that the health burden gap between low SDI and high SDI regions decreased from 58.66 in 1990 to 42.3 in 2021 (Fig. 10 B). For Deaths, the concentration index changed from − 0.26 in 1990 to -0.09 in 2021 (Fig. 10 C), and the slope index decreased from 3.76 in 1990 to 3.05 in 2021 (Fig. 10 D). The concentration index for YLDs decreased from − 0.21 in 1990 to -0.13 in 2021 (Fig. 10 E), while the slope inequality index for YLDs increased from 1.02 in 1990 to 1.68 in 2021 (Fig. 10 F). This phenomenon may be attributed to the significant health improvements in high-income groups, while low-income groups showed slower improvements, leading to an increased health gap. The concentration index for YLLs decreased from − 0.15 in 1990 to -0.01 in 2021 (Fig. 10 G), and the slope inequality index decreased from 57.6 in 1990 to 40.51 in 2021 (Fig. 10 H). All of these results suggest that the health inequality in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations has reduced. Discussion This study provides an in-depth analysis of the global trend in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations from 1990 to 2021, and predicts future trends using the ARIMA model. Furthermore, the study utilized decomposition analysis and health inequality analysis to examine the contributions of various factors to the changes in this health burden. The results show that, during this period, the number of DALYs, Deaths, YLDs, and YLLs due to insufficient physical activity have increased globally, while ASDALYR, ASDR, ASYLDR, ASYLLR have decreased. This phenomenon is likely closely related to advances in global diagnostics and treatment methods, such as the development of non-invasive imaging technologies 20 and the application of cardiovascular stem cell therapies 21 . However, despite improvements in treatment, the rise in DALYs suggests that many patients are still not fully benefiting from existing therapies. Additionally, the decomposition analysis reveals that population growth is a significant factor contributing to the absolute increase in disease burden 22 . Age stratification shows that the elderly population (especially those aged 80 and above) has seen a significant increase in the burden of IHD due to insufficient physical activity. This trend is the result of multiple factors. As individuals age, their cardiovascular system undergoes natural aging processes such as decreased vascular elasticity, arteriosclerosis, and reduced cardiac pumping efficiency, which reduce the ability to respond to pathological stimuli, thereby increasing the risk of disease. Insufficient physical activity further exacerbates these physiological changes, limiting cardiac and vascular function, and promoting the development of IHD, leading to a significant increase in health burden 23 . At the same time, the decline in muscle strength, endurance, and other physiological functions in the elderly reduces their ability to maintain adequate physical activity, creating a vicious cycle of insufficient exercise. This lack of exercise not only affects daily living abilities but also worsens cardiovascular health and exacerbates the impact of IHD. Therefore, to reduce the IHD burden in the elderly population due to insufficient physical activity, it is essential to strengthen physical activity interventions, particularly cardiovascular health exercises. Moreover, personalized health management plans should be developed, combining metabolic risk factor monitoring and intervention, to ensure improvements in the elderly population's quality of life and health status. Globally, the health burden of IHD due to insufficient physical activity is significantly higher in women than in men, a phenomenon likely related to women's longer life expectancy 24 . Longer life expectancy makes women more susceptible to age-related diseases, and this risk significantly increases after menopause due to the decline in estrogen levels 25 . Moreover, socioeconomic factors play a crucial role in the cardiovascular health burden in female. Women with lower socioeconomic status often have poorer access to health resources and higher health risks, especially in low-income countries and regions, where the health burden for women is even more severe. Cultural and social factors also influence women's physical activity, as traditional gender roles restrict women's opportunities for exercise, further exacerbating the burden of cardiovascular diseases 26 . In contrast, the decline in health burden in men is more gradual, which may be associated with their higher baseline health burden and lower engagement in health management. Men often have lower participation in cardiovascular health management, and in many cultures, they bear more social and economic responsibilities, leading to health issues being neglected. When facing insufficient physical activity and poor health, men often fail to take effective measures for improvement, resulting in a slower reduction in health burden compared to women. This gender difference underscores the importance of gender-specific interventions, particularly in addressing the relationship between physical activity and cardiovascular health, and highlights the need for targeted interventions for women. Therefore, gender-specific health strategies should be implemented, with a focus on increasing women's participation in physical activities, while also improving health resource access for socioeconomically disadvantaged groups. Social and cultural factors should be considered when designing physical activity programs that facilitate participation from different groups, ensuring the accessibility and sustainability of health interventions. The SDI analysis results indicate significant disparities in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations across countries with different development levels. The total health burden in high SDI countries has significantly decreased 27 , while countries with other development levels have shown increasing trends, with medium SDI countries exhibiting the most noticeable rise. In high SDI countries, age-standardized DALYs, deaths, YLDs, and YLLs have shown significant declines, and middle-high SDI countries also demonstrated a downward trend. However, medium, low-medium, and low SDI countries showed fluctuating patterns. In high SDI regions, the burden of IHD due to insufficient physical activity in middle-aged and elderly populations has significantly decreased, which may be attributed to advanced healthcare systems, improved health management systems, the widespread adoption of healthy dietary habits, and the development of the sports industry. In particular, the improved treatment levels for acute myocardial infarction (AMI) and its complications have greatly improved disease prognosis 28 , 29 . Moreover, government policy interventions, infrastructure development, and the booming sports industry have promoted daily physical activity levels and contributed to the dissemination of fitness and health culture, all of which have worked together to reduce the burden of cardiovascular diseases. However, as population aging intensifies, some groups (such as the elderly and low-income populations) have not fully benefited from these health interventions and resources, and continue to face higher health burdens. Therefore, in high SDI regions, ensuring equitable distribution of health resources and expanding health service coverage is critical to further reducing the health burden. In medium SDI and low-medium SDI countries, the burden of IHD due to insufficient physical activity in middle-aged and elderly populations has significantly increased. This trend is likely closely related to the rapid transformation of economic systems in developing countries over the past 30 years. The deepening processes of industrialization, urbanization, and globalization have drastically changed people's lifestyles and dietary habits, which have, in turn, contributed to the rise of cardiovascular metabolic diseases 30 – 34 . Moreover, these regions have lower disease awareness, delayed diagnoses, and insufficient basic healthcare facilities and funding, further exacerbating the health burden 35 . For example, after IHD onset, lipid-lowering treatment is an effective intervention that can reduce the risk of cardiovascular events (CVE) recurrence. Typically, achieving an LDL-c level of 100 mg/dL or 70 mg/dL (2.59 mmol/L and 1.81 mmol/L) is the ideal therapeutic goal. A health and nutrition survey of 18,656 high-risk U.S. individuals from 1999 to 2008 showed that the proportion of IHD patients achieving LDL-c < 100 mg/dL increased from 27–65%, and those achieving LDL-c < 70 mg/dL rose from 3–21% 36 . In contrast, a similar study in India, following 17,236 MI survivors from 2004–2009, found that only 4.5% of patients achieved the LDL-c < 97 mg/dL goal 37 . In addition to medical resource limitations, these regions generally have low physical activity levels, poor health awareness, and insufficient exercise habits, further increasing the incidence of cardiovascular diseases. Socioeconomic factors also play a critical role: lower socioeconomic status limits residents' access to health resources, exacerbating health inequalities, particularly among low-income groups. Furthermore, the Westernization of lifestyles, the spread of unhealthy dietary habits, and the increased consumption of high-calorie foods have further increased the burden of metabolic diseases, thus raising the incidence of IHD. Additionally, the accelerating pace of urbanization in these countries has brought greater living pressures, environmental pollution, and a lack of sufficient opportunities for physical activity, all of which have contributed to the rising disease burden. In low SDI countries, the overall health burden is relatively low, but changes are minimal. This may be due to the labor structure in these countries, where many residents engage in manual labor such as agriculture and construction. These labor-intensive jobs promote higher daily activity levels, thereby reducing the risk of IHD due to insufficient physical activity. However, these countries face serious limitations in healthcare resources and health management systems, making effective intervention and treatment of chronic diseases difficult. Poor medical infrastructure, limited health service coverage, and low health education levels result in significant challenges in cardiovascular disease prevention and treatment. Moreover, as urbanization progresses, sedentary occupations are increasing in low SDI countries, further reducing physical activity levels, which restricts improvements in health burden. Therefore, despite the protective effect of manual labor, the lack of healthcare and health management systems has resulted in little change in the health burden of these countries. The study finds that Northern Africa was the region with the highest health burden in 2021, a result consistent with other studies 38 . Sudan was identified as the country with the heaviest health burden. The high burden in Sudan and Northern Africa may be attributed to similar factors. Political instability has intensified the fragility of health management systems, and these factors, combined with a lack of effective public health interventions and health education, have contributed to the rising health burden. Other North African countries, such as Libya and Morocco, also face similar challenges. The disruption of government services, damage to infrastructure, and the unequal distribution of health resources have exacerbated the burden of cardiovascular diseases and other metabolic diseases. Meanwhile, these countries also face the impact of air pollution and extreme climate conditions 39 , which not only disrupt agricultural production and food security, but also limit outdoor activities, further increasing the health burden of cardiovascular diseases due to insufficient physical activity. Southern Latin America has the lowest health burden for IHD due to insufficient physical activity, with Tanzania identified as the country with the lowest health burden. This is attributed to the higher participation in physical labor, which helps maintain high levels of daily physical activity. Agriculture and animal husbandry are labor-intensive industries that encourage frequent physical activity, thus reducing the cardiovascular risk associated with sedentary behavior. The dietary patterns in these regions are relatively traditional and healthy, focusing on natural foods and reducing the negative impact of high-fat and high-sugar diets on metabolic diseases. Although these regions have limited healthcare resources, health management and public health measures have gradually improved, leading to better disease prevention and reducing the health burden. East Asia, especially China, has seen a significant increase in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations in recent years. This trend is linked to the acceleration of urbanization, the gradual reduction of traditional physical labor, and changes in work and lifestyle. It may also be related to increased social pressure, particularly in the fast-paced urban lifestyle driven by economic competition, where many people neglect the importance of health management and physical activity. Although China has made improvements in health management and public health policies, urban-rural disparities and unequal socioeconomic development continue to challenge the distribution and access to health resources. Furthermore, despite the growing popularity of the sports industry and fitness culture in China, the coverage is still insufficient to cope with the growing health burden. These combined factors have contributed to the significant increase in the burden of IHD due to insufficient physical activity in East Asia, especially China. In Commonwealth high-income countries, the burden of IHD has been significantly reduced, due to advanced medical facilities, strong public health policies, and well-established health management systems. These countries benefit from world-leading cardiovascular disease treatment technologies, such as percutaneous coronary intervention (PCI), cardiac resynchronization therapy (CRT), and implantable cardioverter-defibrillators (ICDs), which play a critical role in early diagnosis and precision treatment, significantly reducing the mortality and disability rates. Additionally, government policy interventions, such as anti-smoking, healthy eating, and promotion of physical exercise, have effectively reduced the risk factors associated with metabolic diseases, such as obesity, hypertension, and hyperglycemia, further decreasing the incidence of IHD. High-income countries generally implement universal health screenings and regular checkups, ensuring early diagnosis and treatment for high-risk populations, thanks to government investment and resource allocation. The comprehensive impact of these policies has enabled high-income countries to lead the world in reducing the burden of IHD, significantly outperforming other regions. Based on our findings, we propose the following measures to alleviate the global burden of IHD attributable to physical inactivity in middle-aged and older adults. First, we recommend the establishment of a universal hypertension screening program by placing free blood pressure monitoring stations in community pharmacies worldwide 40 . In addition, physical activity assessments—including questionnaires, pedometer tracking, or cardiopulmonary endurance tests—should be integrated into routine health examinations for middle-aged and elderly individuals. These evaluations would help identify activity levels and support the provision of personalized exercise prescriptions. Second, governments should introduce physical activity incentive policies targeting older adults, such as subsidies for senior fitness programs, reduced fees for access to public sports facilities, or the inclusion of exercise interventions in national health insurance systems. At the community level, free or low-cost outdoor fitness zones should be developed, accompanied by regular health education seminars focused on physical activity for the elderly to enhance health awareness. Third, reducing work-related stress and promoting healthy lifestyles can also lessen the disease burden. Employers should implement flexible work schedules, workplace wellness programs, and psychological support services to help middle-aged employees manage stress and improve quality of life. Lifestyle improvement efforts should aim to cultivate regular sleep patterns, a balanced diet, and consistent exercise habits. Physical activity should be seamlessly integrated into daily routines—for example, encouraging walking or cycling to work, choosing stairs over elevators, and incorporating micro-exercises such as standing desks, household chores, or brisk walking during lunch breaks. These strategies can effectively reduce the health risks associated with chronic stress and prolonged sedentary behavior. Limitations This study has several limitations. First, Data quality and availability are the primary considerations. In low- and middle-income countries, the incompleteness of epidemiological data and underreporting of IHD cases may result in an underestimation of the actual disease burden. Therefore, any assessments of health burdens in these regions should be interpreted with caution, given that data gaps may impact the accuracy of the conclusions. Second, on the assumptions and modeling methods used in the GBD study, despite rigorous statistical modeling and data adjustment strategies the study has employs to minimize bias, the uncertainty remains. As a modeling tool that heavily relies on assumptions, GBD provides the relatively dependable estimates based on the available data, which should be treated as approximations rather than absolute values. Future research should approach the application of the GBD results with caution and remains aware of the underlying assumptions and potential sources of error. Conclusion Over the past 32 years, the global burden of IHD attributable to physical inactivity among middle-aged and elderly individuals has significantly increased. However, age-standardized health burden indicators have shown a downward trend, with females experiencing a more pronounced decrease compared to males. Meanwhile, the IHD burden among the elderly, particularly those aged 80 and above, has risen significantly, reflecting that population aging has become a critical challenge for global cardiovascular health. The burden of IHD varies across countries with different SDI levels, with high-SDI countries exhibiting the lowest burden and middle-SDI countries bearing the highest burden. Although health equity has improved, targeted interventions are still needed to further reduce the IHD health risks related to physical inactivity in middle-aged and elderly populations. This study highlights the critical role of physical inactivity in the IHD health burden and provides important data to support global cardiovascular disease prevention efforts. Future health management strategies should focus more on personalized interventions, not only increasing physical activity but also developing targeted measures based on each country's specific needs and health context. Such approach can more effectively reduce the IHD burden, alleviate socioeconomic pressures, and provide scientific evidence for developing regional and global prevention strategies, further emphasizing the core role of improving physical activity levels in mitigating the IHD disease burden. Abbreviations IHD Ischemic heart disease GBD Global Burden of Disease SDI Sociodemographic Index DALY Annual Disability-Adjusted Life Years YLD Years Lived with Disability YLL Years of Life Lost ASR Age-standardized rate ASDALYR Age-standardized DALY rate ASDR Age-standardized Death rate ASYLDR Age-standardized YLD rate ASYLLR Age-standardized YLL rate EAPC Estimated annual percentage change CODEm Cause of Death Ensemble Model ARIMA Autoregressive integrated moving average UI Uncertainty Interval CI Confidence Interval Declarations Acknowledgments The authors acknowledge the Global Burden of Disease (GBD) Collaborative Network for making the aggregated dataset publicly available. Authors’ Contributions Xianjun Liu and Xuewen Yuan contributed to the conceptualization of this research. Xin man Gao collected the data. Yibing Xia performed the data analyses. Chuan He wrote the original draft. Ziqi Zhao reviewed and edited the manuscript. All authors read the manuscript and agreed to publish this version. Funding No funding was received for this study. Institutional Review Board Statement This study utilized publicly available, fully de-identified data from the Global Burden of Disease (GBD) Study 2021.formal ethical approval is not required for analyses of anonymized secondary data. The study complies with the Declaration of Helsinki principles for ethical research. Informed Consent Statement Informed consent was not applicable for this study. The GBD database contains strictly de-identified, population-level health metrics that preclude identification of individual participants. Data access and usage comply with the GBD Collaborative Institutional Use Agreement. Clinical trial number Not applicable Consent for publication Not Applicable. Availability of data and materials The data can be accessed and downloaded through the official website of the Institute for Health Metrics and Evaluation (IHME) at http://ghdx.health data.org. Given the open-access nature of this database and the absence of personally identifiable information, our study is in compliance with the ethical standards for the use of public data. Competing interests No conflicts of interest among authors need to be disclosed. References Global burden. of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. 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J Am Coll Cardiol. 2022;80(19):1818–28. Campbell NRC, Ordunez P, Giraldo G, Rodriguez Morales YA, Lombardi C, Khan T, Padwal R, Tsuyuki RT, Varghese C. WHO HEARTS: A Global Program to Reduce Cardiovascular Disease Burden: Experience Implementing in the Americas and Opportunities in Canada. Can J Cardiol. 2021;37(5):744–55. Additional Declarations No competing interests reported. Supplementary Files AppendixTable.xlsx AppendixFig.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6374014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453372157,"identity":"47a5ed61-741c-4baf-804c-40e0606e6117","order_by":0,"name":"Xianjun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACPgaGBAaGCmYZMI+HGC1sYC1nmHlALKK1MDAwtpGkRSLhmXThPGseg/sNjA/etjHImxPUwnMgTXrmtnQeg2MMzIZz2xgMdzYQ0sLekCbNu+0wSAubNG8bQ4LBAUJamBmAWuaAtbD/Jk4L2JYGiC3MxGnhOZBszXMsnUfyWGKz5JxzEoYbCGnhl8hJvM1TYy3Hd/jwwQ9vymzkCdoCjIsEKIOxAUhIEFQPBOyETR0Fo2AUjIIRDgABxTPSp5b9DQAAAABJRU5ErkJggg==","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Xianjun","middleName":"","lastName":"Liu","suffix":""},{"id":453372158,"identity":"23e7798e-36ce-45d7-bd1b-c258ed876dd2","order_by":1,"name":"Wenxue Yuan","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenxue","middleName":"","lastName":"Yuan","suffix":""},{"id":453372159,"identity":"7255ee7f-a418-4293-8fac-bf33e5197638","order_by":2,"name":"Xinman Gao","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinman","middleName":"","lastName":"Gao","suffix":""},{"id":453372160,"identity":"6330b876-4cd6-402a-a5c6-d59580b74b8a","order_by":3,"name":"Ziqi Zhao","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Zhao","suffix":""},{"id":453372161,"identity":"54556d6a-6cf6-4b9f-93b2-30756a57c7e0","order_by":4,"name":"Yibing Xia","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yibing","middleName":"","lastName":"Xia","suffix":""},{"id":453372162,"identity":"fd1a9323-5778-410c-a999-c25661cf26b3","order_by":5,"name":"Chuan He","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-04-04 07:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6374014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6374014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82394832,"identity":"be2db7ba-3846-40a8-9fc5-66fa2d428300","added_by":"auto","created_at":"2025-05-09 19:55:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":371548,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal burden of IHD due to insufficient physical activity in middle-aged and elderly populations 1990 to 2021: Age-standardized rates and case numbers. (A)Age-standardized rate and number of Deaths case. (B) Age-standardized rate and number of DALYs case. (C) Age-standardized rate and number of YLDs case (D) Age-standardized rate and number of YLLs case.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/a6a40caf6494274984a954c7.jpg"},{"id":82395000,"identity":"286588f4-b365-451e-b2da-e371a8d7fb51","added_by":"auto","created_at":"2025-05-09 20:03:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal burden of IHD due to insufficient physical activity in middle-aged and elderly populations by gender from 1990 to 2021: Age-standardized rates and case numbers. (A) Age-standardized rates of DALYs, Death, YLDs, YLLs by gender from 1990 to 2021. (B) Number of DALYs, Death, YLDs, YLLs by gender from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/9275ea1a37a0cb1da5f6e156.jpg"},{"id":82395001,"identity":"da4e167e-e00c-4270-8011-43a34e0c9829","added_by":"auto","created_at":"2025-05-09 20:03:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal burden of IHD due to insufficient physical activity in middle-aged and elderly populations by age in 2021: Age-standardized rates and case numbers. (A) Age-standardized rates of DALYs, Death, YLDs, YLLs by age in 2021. (B) Number of DALYs, Death, YLDs, YLLs by age in 2021\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/16fa6627be434e877c758d66.jpg"},{"id":82394837,"identity":"e8318314-79bb-49a5-af07-e43dd1c9c5d0","added_by":"auto","created_at":"2025-05-09 19:55:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal burden of IHD due to insufficient physical activity in middle-aged and elderly populations by sex and age group: number of cases and age-standardized rates of DALYs(A), Deaths (B), YLDs(C), and YLLs(D)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/37fd18fa193c87f260971ddb.jpg"},{"id":82395002,"identity":"eb0d78ad-d31b-44f4-8043-3f4ec3f2eb92","added_by":"auto","created_at":"2025-05-09 20:03:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":234317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal burden of IHD due to insufficient physical activity in middle-aged and elderly populations by SDI from 1990 to 2021: Age-standardized rates and case numbers. (A) Age-standardized rates of DALYs, Death, YLDs, YLLs by gender from 1990 to 2021. (B) Number of DALYs, Death, YLDs, YLLs by gender from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/c9297ec64504431ea4708ea3.jpg"},{"id":82395003,"identity":"47c66780-3215-413d-9cce-b0b99e1681be","added_by":"auto","created_at":"2025-05-09 20:03:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":431002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal burden of IHD due to insufficient physical activity in middle-aged and elderly populations by GBD region in 2021: Age-standardized rates and case numbers. (A) Age-standardized rates of DALYs, Death, YLDs, YLLs by gender from 1990 to 2021. (B) Number of DALYs, Death, YLDs, YLLs by gender from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/cc98fc0068117138b49e264e.jpg"},{"id":82394841,"identity":"fe56461b-43b1-44ca-b034-8e01eb93166c","added_by":"auto","created_at":"2025-05-09 19:55:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":280598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal burden of IHD due to insufficient physical activity in middle-aged and elderly populations by 204 countries from 1990 to 2021. EAPC for age-standardized (A) DALYs, (B) Death, (C) YLDs, (D) YLLs.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/cb5795e1bd52d9246124d682.jpg"},{"id":82394842,"identity":"d9325908-8228-4867-a17d-d854e3efa6a0","added_by":"auto","created_at":"2025-05-09 19:55:25","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":138154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferent in DALYs, Death, YLDs, YLLs of Global burden of IHD due to insufficient physical activity in middle-aged and elderly populations by three population-level determinants: aging, population and epidemiological change.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/cd26106dddd59751330161f3.jpg"},{"id":82395005,"identity":"70c4498b-510b-49a9-b0dc-6e481fc8d756","added_by":"auto","created_at":"2025-05-09 20:03:25","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":483029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eARIMA Model projections of IHD due to insufficient physical activity in middle-aged and elderly populations from 2022-2050 by gender (A)Future trend in number of DALYs and age-standardized rates of DALYs, (B Future trend in number of Deaths and age-standardized rates of Deaths, (C) Future trend in number of YLDs and age-standardized rate of YLDs, (D) Future trend in number of YLLs and age-standardized rates of YLLs.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/45a2fe7fbbbc518887a61043.jpg"},{"id":82394848,"identity":"ebb594c9-00b6-45a3-9358-6ff321aa7cfb","added_by":"auto","created_at":"2025-05-09 19:55:25","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":202090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealth inequality regression curves and concentration curves for the DALYs (A and B), Deaths (C and D), YLDs (E and F), YLLs (G and H) of IHD due to insufficient physical activity in middle-aged and elderly populations from 1990-2021.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/e0ce0abdf143cba493c20f13.jpg"},{"id":104401345,"identity":"fb93bd6e-5c3a-4c88-ac79-7922e4ab4973","added_by":"auto","created_at":"2026-03-11 12:12:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5380893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/cafc34f8-8112-4e20-bbfd-43028f42bffa.pdf"},{"id":82394836,"identity":"2a6bc1fb-7364-4598-b1f9-ce8603db07c6","added_by":"auto","created_at":"2025-05-09 19:55:24","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":108329,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/7e2412c0a94b1e481eb47bc2.xlsx"},{"id":82394840,"identity":"d9caef84-f8a7-4191-9d38-20bf23efd9d2","added_by":"auto","created_at":"2025-05-09 19:55:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":674708,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixFig.docx","url":"https://assets-eu.researchsquare.com/files/rs-6374014/v1/e40151492ad3be4befa7a582.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global burden of ischemic heart disease due to insufficient physical activity in middle-aged and elderly populations from 1990 to 2021 and projections for 2050","fulltext":[{"header":"Introduction","content":"\u003cp\u003e Ischemic heart disease (IHD) remains one of the leading causes of mortality worldwide and is a major contributor to severe disability among middle-aged and elderly populations\u003csup\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003c/sup\u003e. With the global aging population accelerating, the health burden of IHD has been increasing annually\u003csup\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003c/sup\u003e. IHD represents the second-largest medical expenditure after cancer, resulting in an estimated \u003cspan\u003e$\u003c/span\u003e88\u0026nbsp;billion in direct economic losses each year\u003csup\u003e \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e \u003c/sup\u003e. While high-income countries have successfully reduced IHD-related mortality with advanced medical technologies and effective interventions, many developing countries, particularly low- and middle-income regions, continue to face rising IHD mortality rates due to insufficient physical activity and associated metabolic risk factors such as hypertension, diabetes, and obesity\u003csup\u003e \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003c/sup\u003e Consequently, the health and economic burden of IHD has become a major global public health challenge, necessitating urgent and effective prevention and control measures\u003csup\u003e \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e \u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDisease burden refers to the impact of diseases, disabilities, and premature mortality on human health. Assessing disease burden is crucial for evaluating disease controllability, setting public health priorities, and quantifying the associated economic implications. Before the 1990s, disease burden was typically evaluated using single metrics such as incidence, prevalence, and mortality rates. However, recent years have seen a shift towards a more comprehensive measure DALYs which provides a multidimensional and hierarchical assessment of disease impact on populations\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. DALYs consist of two components: YLLs due to premature death and Years Lived with YLDs due to disease-related disability, enabling a holistic evaluation of the combined effects of mortality and morbidity on population health. These three indicators (DALYs, YLLs, and YLDs) are widely used in disease burden research to comprehensively assess the extent of health loss\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe burden of IHD is influenced by multiple risk factors, including smoking\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, dietary patterns\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and environmental pollution\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Notably, insufficient physical activity has been recognized as an independent risk factor for increased IHD incidence and Deaths\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Therefore, based on data from the GBD Study, this research systematically analyzes the impact of insufficient physical activity on the IHD burden among middle-aged and elderly populations from 1990 to 2021, while also predicting trends by 2050. The findings provide critical theoretical evidence and data support for global IHD prevention and control efforts, particularly in guiding health management and disease prevention strategies for middle-aged and elderly populations. By elucidating the substantial impact of insufficient physical activity on the IHD burden, this study offers empirical evidence for the formulation of targeted public health policies and optimization of resource allocation. Moreover, it contributes to advancing global health intervention strategies for aging populations, ultimately aiming to reduce IHD incidence and mortality and alleviate the global socioeconomic burden associated with the disease.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eEthical approvement\u003c/p\u003e \u003cp\u003e Ethical approval was not required for this study because the analysis was based on publicly available, de-identified data from the Global Burden of Disease (GBD) database. All data were accessed and used in accordance with the GBD terms of use.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources and Framework\u003c/h2\u003e \u003cp\u003eThis study utilizes the GBD 2021 database, provided by the Institute for Health Metrics and Evaluation (IHME). The GBD 2021 follows a standardized methodology to estimate the global disease burden, covering 204 countries and regions, 371 diseases, 288 causes of death, and 88 risk factors. Additionally, the dataset includes subnational-level estimates for 21 countries and regions\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.The GBD database integrates diverse data sources, including scientific literature, household surveys, epidemiological surveillance, disease registries, clinical informatics, and other relevant datasets. From this database, we extracted data from 1990 to 2021 on IHD cases attributable to insufficient physical activity in middle-aged and elderly populations, including DALYs, Deaths, YLDs, and YLLs, to evaluate temporal trends in the disease burden.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRegional Classification\u003c/h3\u003e\n\u003cp\u003eThis study employs the Socio-Demographic Index (SDI) from the GBD framework to classify countries. SDI is a composite metric that incorporates per capita income, the average years of education for individuals aged 15 and older, and the total fertility rate of women under 25. Based on SDI values, countries are categorized into five levels: low, lower-middle, middle, upper-middle, and high. Additionally, the GBD framework classifies the 204 countries and regions into 21 super-regions and 54 subregions based on geographical proximity and epidemiological characteristics. This classification accounts for economic stratification (World Bank income levels), regional divisions (WHO classifications), and healthcare system capacity. Furthermore, national health systems are further categorized as advanced, basic, limited, or minimal, providing a more refined classification of global health disparities.\u003c/p\u003e\n\u003ch3\u003eEstimation Models and Standardization\u003c/h3\u003e\n\u003cp\u003eDisease Modeling Meta-Regression, Version 2.1(Dis Mod-MR 2.1) is a Bayesian meta-regression framework used in the Global Burden of Disease (GBD) study for data modeling. This model simultaneously incorporates age, gender, geographic location, and time as key variables to ensure consistency across different data sources. By integrating global and regional data, Dis Mod-MR 2.1 enables the imputation of missing data and provides more precise estimates for various health burden indicators\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Furthermore, this model applies age standardization, facilitating comparability of health burdens across countries and regions, thereby offering a robust foundation for trend analysis and policy development.\u003c/p\u003e \u003cp\u003eThe GBD 2021 study employs a standardized tool, the Cause of Death Ensemble Model (CODEm), to estimate IHD-related mortality rates. CODEm is a highly integrated Bayesian geospatial regression analysis tool, commonly used for analyzing mortality rates or cause-specific death proportions associated with particular diseases. Measurement of Health Burden. YLLs metric quantifies the loss of healthy life years due to premature mortality and is calculated as follows:\u003c/p\u003e\n\u003ch3\u003eYLLs = N×L\u003c/h3\u003e\n\u003cp\u003eWherein, N represents the number of deaths from IHD at a specific age group, and L denotes the difference between the age at death and the expected life expectancy for that age group. YLD metric quantifies health loss due to disease-related disability, using the following formula:\u003c/p\u003e\n\u003ch3\u003eYLDs = Duration of disability × Disability weight\u003c/h3\u003e\n\u003cp\u003eFor a specific population, YLDs can be calculated as:\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eYLDs\u0026thinsp;=\u0026thinsp;Number of IHD cases \u0026times; Duration of disability\u0026times;Disability weight\u003c/h2\u003e \u003cp\u003eThe Disability-Adjusted Life Years (DALYs) metric serves as a comprehensive measure of disease burden, reflecting the cumulative impact of mortality, disability, age, and time on healthy life years lost. It is calculated as:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDALYs = YLLs + YLDs\u003c/h3\u003e\n\u003cp\u003eOne DALYs equates to the loss of one full year of healthy life\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAdditionally, this study includes the following age-standardized health burden indicators, all of which are standardized to the GBD reference population and expressed per 100,000 population to ensure comparability across regions and time periods: Additionally, this study includes the following age-standardized health burden indicators, all of which are standardized to the GBD reference population and expressed per 100,000 population to ensure comparability across regions and time periods: ASDALY, ASDR, ASYLDR, ASYLLR.\u003c/p\u003e\n\u003ch3\u003eStatistical Methods\u003c/h3\u003e\n\u003cp\u003e(1) The following statistical analyses were performed: ASR of DALYs, Deaths, YLDs, and YLLs were computed. ASR is derived by weighting age-specific observed data against the global standard population, ensuring comparability of health burden across different countries and regions. (2) Stratified Analysis: Stratified analyses were conducted based on age, gender, SDI, and geographic location to examine the disparities in DALYs, deaths, YLDs, and YLLs across different populations and regions. Age groups were categorized as 40 years and older, with further stratification in 5-year intervals. This analysis enables a deeper understanding of health burden disparities and identifies priority areas for health policy interventions. (3) Trend Analysis Using Log-Linear Regression: A log-linear regression model was employed to estimate EAPC, analyzing ASR trends from 1990 to 2021.This regression model provides a clear interpretation of annual percentage changes, yielding more reliable trend estimations. EAPC estimates were reported along with their 95% confidence intervals (CI) to assess statistical reliability. (4) Decomposition Analysis: To explore the contribution of different factors to changes in DALYs, Deaths, YLDs, and YLLs, decomposition analysis was applied. This approach quantifies the influence of various factors on overall health burden changes, providing data support for targeted health interventions. (5) Predictive Analysis: The ARIMA model was used to forecast gender-specific trends in health burden from 2022 to 2050.The ARIMA model, based on historical time-series data, incorporates seasonality and trend fluctuations to predict future trends. Model fitting and validation were performed to obtain future health burden projections for both men and women, with confidence intervals calculated to assess prediction accuracy and reliability. (6) Health Inequality Analysis: A health inequality analysis was conducted to evaluate disparities in health burden across different socioeconomic groups. This analysis focused on DALYs, Deaths, YLDs, and YLLs, considering income levels, education attainment, and geographic distribution. By identifying the socioeconomic stratification of disease burden, this analysis provides critical insights for addressing health inequities and informing strategies to reduce disparities in global health outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Level\u003c/h2\u003e \u003cp\u003eAccording to the results of this study, in 2021, the number of global DALYs, Deaths, YLDs, and YLLs due to insufficient physical activity in middle-aged and elderly populations were 3,897,941 (95% UI: 1,767,043\u0026ndash;6,116,373) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 232,250 (95% UI: 103,518\u0026ndash;371,375) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), 85,414 (95% UI: 36,522\u0026ndash;147,216) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and 3,812,527 (95% UI: 1,726,040\u0026ndash;5,985,591) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). ASDALYR, ASDR, ASYLDR, and ASYLLR for 2021 were 46.63 (95% UI: 21.15\u0026ndash;73.38) per 100,000 population, 2.88 (95% UI: 1.28\u0026ndash;4.59), 1.01 (95% UI: 0.44\u0026ndash;1.74), and 45.62 (95% UI: 20.68\u0026ndash;71.84) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations DALYs and temporal trends by sex, age and SDI from 1990 to 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocation name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASR 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASR 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAPC 95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2248011 (1013161\u0026ndash;3466301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.51 (28.78-101.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3897941 (1767043\u0026ndash;6116373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.63 (21.15\u0026ndash;73.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.35 (-0.7\u0026ndash;0.01)\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 \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\u003e1462516 (657058\u0026ndash;2257655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.85 (33-114.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2415790 (1072448\u0026ndash;3832823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.88 (23.05\u0026ndash;82.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.22 (-1.26\u0026ndash;1.18)\u003c/p\u003e \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\u003e785494 (347068\u0026ndash;1216683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.36 (21.34\u0026ndash;78.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1482151 (693907\u0026ndash;2337776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.41 (18.46\u0026ndash;62.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.83 (-0.91\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43588 (21330\u0026ndash;66521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.22 (7.45\u0026ndash;23.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68729 (34840\u0026ndash;105065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.74 (6.96-21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.56 (-0.77\u0026ndash;0.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71003 (34331\u0026ndash;110358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.58 (14.79\u0026ndash;47.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111386 (56552\u0026ndash;172550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.52 (11.94\u0026ndash;36.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.05 (-1.28\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106643 (53178\u0026ndash;166821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.17 (25.02\u0026ndash;78.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171216 (81766\u0026ndash;267383)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.48 (18.38\u0026ndash;60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.04 (-1.25\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166738 (74955\u0026ndash;264609)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.03 (40.47-142.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274743 (129631\u0026ndash;443173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.43 (32.76-111.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.05 (-1.26\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242671 (105496\u0026ndash;383184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151.09 (65.69-238.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344824 (157290\u0026ndash;550555)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.74 (49.15-172.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.48 (-1.63\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280707 (125507\u0026ndash;429881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227.09 (101.54-347.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e453445 (203708\u0026ndash;710780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164.39 (73.85-257.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.29 (-1.39\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288192 (118389\u0026ndash;464443)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340.41 (139.84-548.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e536967 (238930\u0026ndash;878240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e260.87 (116.08-426.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.99 (-1.04\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333198 (146204\u0026ndash;543012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e541.3 (237.51-882.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e502828 (211457\u0026ndash;852008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e381.26 (160.33-646.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.04 (-1.09\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;84 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349484 (146302\u0026ndash;572325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e987.91 (413.56-1617.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e613267 (265595\u0026ndash;1001891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e700.21 (303.25-1143.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.09 (-1.19\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u0026ndash;89 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200580 (83496\u0026ndash;334842)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1327.37 (552.55-2215.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423640 (184321\u0026ndash;700204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e926.56 (403.14-1531.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.15 (-1.22\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u0026ndash;94 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82676 (33713\u0026ndash;136664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1929.35 (786.74-3189.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234497 (100854\u0026ndash;390675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1310.81 (563.76-2183.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.29 (-1.36\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27934 (11105\u0026ndash;48221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2743.73 (1090.73-4736.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91721 (38268\u0026ndash;154724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1682.85 (702.12-2838.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.72 (-1.83\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e556615 (247968\u0026ndash;850545)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.73 (29.36\u0026ndash;102.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e959770 (416772\u0026ndash;1545495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.51 (21.54\u0026ndash;80.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.12 (-1.26\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e684673 (292480\u0026ndash;1086985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5 (26.52\u0026ndash;97.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e501160 (213062\u0026ndash;805337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.77 (9.4-34.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.64 (-3.84\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387119 (177224\u0026ndash;586799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.84 (30.52-104.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e869620 (400803\u0026ndash;1343621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.79 (29.64-103.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.13 (-0.18\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71004 (31450\u0026ndash;109892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.16 (15.55\u0026ndash;56.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143136 (63927\u0026ndash;223824)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.83 (14.3-52.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.23 (-0.36\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e545231 (251302\u0026ndash;838252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.61 (27.35\u0026ndash;96.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1420318 (645352\u0026ndash;2227648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.71 (26.11\u0026ndash;90.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.21 (-0.24\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations Deaths and temporal trends by sex, age and SDI from 1990 to 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003e\u003cb\u003elocation nam\u003c/b\u003ee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNumber 95%UI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eASR 95%UI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNumber 95%UI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eASR 95%UI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEAPC 95%CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125730 (55488\u0026ndash;199339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.09 (1.78\u0026ndash;6.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232250 (103518\u0026ndash;371375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88 (1.28\u0026ndash;4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25 (-0.7-0.2)\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 \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\u003e87707 (38827\u0026ndash;139152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76 (2.11\u0026ndash;7.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152305 (66250\u0026ndash;248989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.24 (1.41\u0026ndash;5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.29 (-1.32\u0026ndash;1.25)\u003c/p\u003e \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\u003e38023 (16293\u0026ndash;60944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88 (1.21\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79945 (36705\u0026ndash;129067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33 (1.07\u0026ndash;3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.71 (-0.78\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901 (439\u0026ndash;1378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.15\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1415 (717\u0026ndash;2174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28 (0.14\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.58 (-0.78\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1634 (788\u0026ndash;2543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (0.34\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2549 (1300\u0026ndash;3945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54 (0.27\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.07 (-1.29\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2758 (1373\u0026ndash;4310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.65\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4402 (2091\u0026ndash;6868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.47\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.06 (-1.27\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4915 (2209\u0026ndash;7801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65 (1.19\u0026ndash;4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8046 (3809\u0026ndash;13017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.03 (0.96\u0026ndash;3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.07 (-1.28\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8280 (3605\u0026ndash;13082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.16 (2.24\u0026ndash;8.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11688 (5312\u0026ndash;18719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.65 (1.66\u0026ndash;5.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.5 (-1.66\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11320 (5062\u0026ndash;17284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.16 (4.09\u0026ndash;13.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18152 (8146\u0026ndash;28459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.58 (2.95\u0026ndash;10.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.32 (-1.42\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14123 (5797\u0026ndash;22662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.68 (6.85\u0026ndash;26.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26077 (11564\u0026ndash;42741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.67 (5.62\u0026ndash;20.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.01 (-1.07\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20524 (9038\u0026ndash;33534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.34 (14.68\u0026ndash;54.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30690 (12836\u0026ndash;52009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.27 (9.73\u0026ndash;39.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.06 (-1.11\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;84 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27516 (11560\u0026ndash;45247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.78 (32.68\u0026ndash;127.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48130 (20869\u0026ndash;78268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.95 (23.83\u0026ndash;89.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11 (-1.21\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u0026ndash;89 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19914 (8297\u0026ndash;33299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.78 (54.91-220.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41934 (18281\u0026ndash;68925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.72 (39.98-150.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.16 (-1.23\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u0026ndash;94 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9478 (3871\u0026ndash;15683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221.19 (90.33-365.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26769 (11441\u0026ndash;44579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149.64 (63.96-249.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.3 (-1.37\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3400 (1351\u0026ndash;5881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.99 (132.69-577.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11151 (4654\u0026ndash;18811)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e204.59 (85.39-345.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.71 (-1.81\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34525 (14974\u0026ndash;53877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.69 (2-7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66676 (28938\u0026ndash;111391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52 (1.53\u0026ndash;5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.04 (-1.18\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44910 (18988\u0026ndash;73978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 (1.73\u0026ndash;6.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35921 (14372\u0026ndash;58936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4 (0.58\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.75 (-3.89\u0026ndash;3.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16972 (7429\u0026ndash;25967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (1.52\u0026ndash;5.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43063 (19107\u0026ndash;68468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.68 (1.61\u0026ndash;5.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 (0.12\u0026ndash;0.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3177 (1367\u0026ndash;4926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98 (0.82\u0026ndash;3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6875 (2975\u0026ndash;11063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9 (0.79\u0026ndash;3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08 (-0.12-0.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25947 (11467\u0026ndash;40957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62 (1.55\u0026ndash;5.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79471 (35675\u0026ndash;125589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.56 (1.59\u0026ndash;5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04 (-0.03-0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations YLDs and temporal trends by sex, age and SDI from 1990 to 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocation name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASR 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASR 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAPC 95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36173 (15478\u0026ndash;62617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.43\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85414 (36522\u0026ndash;147216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.44\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65 (0.32\u0026ndash;0.99)\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 \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\u003e23564 (10079\u0026ndash;40471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.5-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54301 (23293\u0026ndash;93131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (0.5-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1 (-0.17\u0026ndash;0.03)\u003c/p\u003e \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\u003e12609 (5267\u0026ndash;22124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.34\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31113 (13037\u0026ndash;55561)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82 (0.35\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04 (-0.14-0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463 (203\u0026ndash;852)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.07\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e984 (439\u0026ndash;1690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 (0.09\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71 (0.47\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e804 (338\u0026ndash;1463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.15\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1832 (788\u0026ndash;3225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39 (0.17\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29 (0.02\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1307 (555\u0026ndash;2377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.26\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3029 (1248\u0026ndash;5628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68 (0.28\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 (-0.05-0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2216 (898\u0026ndash;4085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.48\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5020 (2004\u0026ndash;9716)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27 (0.51\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06 (-0.13-0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3726 (1491\u0026ndash;7081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32 (0.93\u0026ndash;4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7304 (2904\u0026ndash;13712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28 (0.91\u0026ndash;4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26 (-0.42\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5447 (2242\u0026ndash;9551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.41 (1.81\u0026ndash;7.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12043 (4826\u0026ndash;21901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.37 (1.75\u0026ndash;7.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18 (-0.27\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5895 (2249\u0026ndash;10913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.96 (2.66\u0026ndash;12.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14816 (5799\u0026ndash;27246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2 (2.82\u0026ndash;13.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (-0.05-0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6047 (2437\u0026ndash;11242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.82 (3.96\u0026ndash;18.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12770 (4927\u0026ndash;23478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.68 (3.74\u0026ndash;17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05 (-0.11-0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;84 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5566 (2223\u0026ndash;10463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.73 (6.28\u0026ndash;29.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13184 (5282\u0026ndash;24739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.05 (6.03\u0026ndash;28.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2 (-0.22\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u0026ndash;89 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2933 (1254\u0026ndash;5381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.41 (8.3-35.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8309 (3465\u0026ndash;15269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.17 (7.58\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.35 (-0.4\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u0026ndash;94 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e978 (375\u0026ndash;1795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.82 (8.75\u0026ndash;41.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3811 (1544\u0026ndash;6964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.3 (8.63\u0026ndash;38.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.44 (-0.51\u0026ndash;0.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e251 (88\u0026ndash;478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.68 (8.61\u0026ndash;46.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1217 (478\u0026ndash;2250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.32 (8.77\u0026ndash;41.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51 (-0.57\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9353 (3809\u0026ndash;15955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.43\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21876 (9439\u0026ndash;38504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.48\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11 (0.06\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12384 (5323\u0026ndash;22023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.48\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18308 (7898\u0026ndash;32148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81 (0.35\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.33 (-1.49\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5016 (2075\u0026ndash;8800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.4\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14091 (5779\u0026ndash;24896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.45\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (0.37\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e844 (343\u0026ndash;1509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.19\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2155 (881\u0026ndash;3803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51 (0.21\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29 (0.26\u0026ndash;0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8519 (3610\u0026ndash;14728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.42\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28886 (12075\u0026ndash;50252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15 (0.49\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53 (0.48\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe estimated number and age-standardized rates (per 100,00 population population) of IHD due to insufficient physical activity in middle-aged and elderly populations YLLs and temporal trends by sex, age and SDI from 1990 to 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocation name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASR 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASR 95%UI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAPC 95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2211838 (994931\u0026ndash;3406072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.49 (28.24\u0026ndash;99.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3812527(1726040\u0026ndash;5985591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.62 (20.68\u0026ndash;71.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.37 (-0.72\u0026ndash;0.03)\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 \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\u003e1438953 (646043\u0026ndash;2215065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.69 (32.55-112.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2361489 (1046160\u0026ndash;3749396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.72 (22.49\u0026ndash;80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.24 (-1.28\u0026ndash;1.2)\u003c/p\u003e \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\u003e772886 (339832\u0026ndash;1196976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.56 (20.98\u0026ndash;77.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1451038 (680569\u0026ndash;2290024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.59 (18.08\u0026ndash;60.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.85 (-0.92\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43125 (21025\u0026ndash;65940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.05 (7.34\u0026ndash;23.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67745 (34306\u0026ndash;104031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.54 (6.86\u0026ndash;20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.58 (-0.79\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70199 (33860\u0026ndash;109256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.23 (14.58\u0026ndash;47.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109554 (55891\u0026ndash;169597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.14 (11.8-35.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.07 (-1.3\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105336 (52406\u0026ndash;164607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.55 (24.65\u0026ndash;77.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168187 (79896\u0026ndash;262422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.8 (17.96\u0026ndash;58.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.06 (-1.27\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164522 (73936\u0026ndash;261116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.84 (39.92-140.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269723 (127687\u0026ndash;436402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.16 (32.27-110.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.07 (-1.27\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238945 (104018\u0026ndash;377623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.77 (64.76-235.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337520 (153450\u0026ndash;540520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.46 (47.95-168.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.5 (-1.65\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275260 (123063\u0026ndash;420441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222.69 (99.56-340.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e441402 (198089\u0026ndash;691881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160.02 (71.81-250.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.32 (-1.42\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282297 (115884\u0026ndash;453002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.44 (136.88-535.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e522152 (231587\u0026ndash;855820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253.67 (112.51-415.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.01 (-1.07\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327151 (144062\u0026ndash;534495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e531.47 (234.04-868.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e490057 (205044\u0026ndash;830687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e371.58 (155.47-629.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.06 (-1.11\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;84 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343918 (144435\u0026ndash;565491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e972.18 (408.29-1598.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e600083 (260164\u0026ndash;976006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e685.16 (297.05-1114.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11 (-1.21\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u0026ndash;89 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197647 (82333\u0026ndash;330402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1307.96(544.86-2186.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415331 (181072\u0026ndash;682549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e908.39 (396.03-1492.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.17 (-1.23\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u0026ndash;94 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81698 (33367\u0026ndash;135171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1906.53(778.66-3154.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230686 (98593\u0026ndash;384157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1289.51 (551.12-2147.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.3 (-1.37\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27682 (11013\u0026ndash;47856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2719.05(1081.7-4700.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90504 (37774\u0026ndash;152736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1660.53 (693.07-2802.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.73 (-1.84\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547262 (243636\u0026ndash;835171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.69 (28.83-100.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e937894 (408742\u0026ndash;1515369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.4 (21.14\u0026ndash;78.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.14 (-1.29\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e672289 (287731\u0026ndash;1065608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.41 (26.08\u0026ndash;95.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e482852 (206160\u0026ndash;773191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.96 (9.08\u0026ndash;33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.7 (-3.9\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382103 (174525\u0026ndash;579386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.88 (30.01-103.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e855528 (394032\u0026ndash;1325175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.7 (29.12-101.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14 (-0.19\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70160 (31089\u0026ndash;108785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.69 (15.35\u0026ndash;55.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140981 (62765\u0026ndash;220795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.32 (14.04\u0026ndash;51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.24 (-0.37\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e536712 (247463\u0026ndash;826135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.63 (26.95\u0026ndash;94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1391433 (631784\u0026ndash;2183120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.55 (25.57\u0026ndash;88.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.22 (-0.26\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: EAPC: Estimated Annual Percentage Change; UI: Uncertainty Interval; CI: Confidence Interval; SDI: Sociodemographic Index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe number of health burden of IHD due to insufficient physical activity in middle-aged and elderly populations has shown a marked increase compared to 1990. However, the ASDALYR, ASDR, ASYLDR, and ASYLLR have shown a declining trend. The ASDALYR decreased from 64.51 (95% UI: 28.78\u0026ndash;101.4) in 1990 to 46.63 (95% UI: 21.15\u0026ndash;73.38) in 2021, with an Estimated Annual Percentage Change (EAPC) of -0.35 (95% CI: -0.7 to -0.01) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ASDR dropped from 4.09 (95% UI: 1.78\u0026ndash;6.56) in 1990 to 2.88 (95% UI: 1.28\u0026ndash;4.59) in 2021, with an EAPC of -0.25 (95% CI: -0.7 to 0.2) Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ASYLDR slightly decreased from 1.02 (95% UI: 0.43\u0026ndash;1.77) in 1990 to 1.01 (95% UI: 0.44\u0026ndash;1.74) in 2021, with an EAPC of 0.65 (95% CI: 0.32 to 0.99) (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The ASYLLR decreased from 63.49 (95% UI: 28.24\u0026ndash;99.92) in 1990 to 45.62 (95% UI: 20.68\u0026ndash;71.84) in 2021, with an EAPC of -0.37 (95% CI: -0.72 to -0.03) (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Trends by Gender and Age Group\u003c/h2\u003e \u003cp\u003eFor 2021, females consistently exhibited higher number of DALYs, Deaths, YLDs, and YLLs (Appendix Fig.\u0026nbsp;1B), as well as ASR for these metrics (Appendix Fig.\u0026nbsp;1A), compared to males. Despite the downtrend in the indicators from 1990 to 2021, females, compared to males, experienced a more pronounced decline. For instance, female ASDALYR decreased from 73.85 (95% UI: 33\u0026ndash;114.68) per 100,000 population in 1990 to 51.88 (95% UI: 23.05\u0026ndash;82.18) in 2021, with an EAPC of -1.22 (95% CI: -1.26 to -1.18), while male ASDALYR decreased from 49.36 (95% UI: 21.34\u0026ndash;78.43) in 1990 to 39.41 (95% UI: 18.46\u0026ndash;62.34) in 2021, with an EAPC of -0.83 (95% CI: -0.91 to -0.75) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, for females, ASDR decreased from 4.76 (95% UI: 2.11\u0026ndash;7.66) in 1990 to 3.24 (95% UI: 1.41\u0026ndash;5.29) in 2021, with an EAPC of -1.29 (95% CI: -1.32 to -1.25), ASYLDR decreased from 1.16 (95% UI: 0.5\u0026ndash;2) in 1990 to 1.17 (95% UI: 0.5\u0026ndash;2) in 2021, with an EAPC of -0.10 (95% CI: -0.17 to -0.03), and ASYLLR decreased from 72.69 (95% UI: 32.55\u0026ndash;112.58) in 1990 to 50.72 (95% UI: 22.49\u0026ndash;80.4) in 2021, with an EAPC of -1.24 (95% CI: -1.28 to -1.2) ; for males, ASDR decreased from 2.88 (95% UI: 1.21\u0026ndash;4.8) in 1990 to 2.33 (95% UI: 1.07\u0026ndash;3.82) in 2021, with an EAPC of -0.71 (95% CI: -0.78 to -0.65), ASYLDR decreased from 0.81 (95% UI: 0.34\u0026ndash;1.42) in 1990 to 0.82 (95% UI: 0.35\u0026ndash;1.46) in 2021, with an EAPC of -0.04 (95% CI: -0.14 to 0.06), and ASYLLR decreased from 48.56 (95% UI: 20.98\u0026ndash;77.15) in 1990 to 38.59 (95% UI: 18.08\u0026ndash;60.95) in 2021, with an EAPC of -0.85 (95% CI: -0.92 to -0.77) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2021, ASDALYR, ASDR, ASYLDR, and ASYLLR increased with age (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The 80\u0026ndash;84 age group exhibited the highest burden for DALYs, Deaths, YLDs, and YLLs as the following: DALYs: 613,267 (95% UI: 265,595\u0026ndash;1,001,891), Deaths: 48,130 (95% UI: 20,869\u0026ndash;78,268, YLDs: 13,184 (95% UI: 5,282\u0026ndash;24,739), YLLs: 600,083 (95% UI: 260,164\u0026ndash;976,006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBetween 1990 and 2021, the EAPC for the 40\u0026ndash;44 age group showed the least improvement as following: DALYs: -0.56 (95% CI: -0.77 to -0.36), Deaths: -0.58 (95% CI: -0.78 to -0.37), YLDs: 0.71 (95% CI: 0.47 to 0.95), YLLs: -0.58 (95% CI: -0.79 to -0.37) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In 2021, the burden of DALYs, Deaths, YLDs, and YLLs in females increased progressively from the age of 40\u0026ndash;44 years, peaking at 80\u0026ndash;84 years, and then gradually decreased. In contrast, males reached their peak at the age of 70\u0026ndash;74 years, and then their burden declined slightly after reaching the maximum at 90\u0026ndash;94 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSDI Regional Level\u003c/h2\u003e \u003cp\u003e2021 The heaviest number of DALYs, Death, and YLLs for IHD due to physical inactivity among middle-aged and older adults in different SDI regions was in the low and medium SDI regions and the lightest in the high SDI regions, and the heaviest burden of YLDs was in countries with medium SDI and the lightest was in countries with low SDI (Appendix Fig.\u0026nbsp;2B and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2021, the ASDALYR and ASDR were highest in low-to-middle SDI countries, with 65.79 (95% UI: 29.64\u0026ndash;104.96) per 100,000 population and 3.68 (95% UI: 1.61\u0026ndash;5.89). Conversely, high SDI countries had the lightest burden, with 21.77 (95% UI: 9.40\u0026ndash;34.53) and 1.40 (95% UI: 0.58\u0026ndash;2.27), (Appendix Fig.\u0026nbsp;2A and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ASYLDR was highest in medium SDI countries at 1.15 (95% UI: 0.49\u0026ndash;2.02), and lowest in low SDI countries at 0.51 (95% UI: 0.21\u0026ndash;0.90) (Appendix Fig.\u0026nbsp;2Aand Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The ASYLLR was highest in low-to-middle SDI countries at 64.70 (95% UI: 29.12\u0026ndash;101.52), and lowest in high SDI countries at 20.96 (95% UI: 9.08\u0026ndash;33.10) Appendix Fig.\u0026nbsp;2A and Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom the overall trend between 1990 and 2021, the number of DALYRs, Deaths, and YLLs showed an increasing trend in all regions except high SDI regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), YLDs exhibited an upward trend YLLs all showed increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). ASDALYR and ASYLLR showed a declining trend across all SDI regions: High SDI countries experienced the fastest decline with EAPC values of -3.64 (95% CI: -3.84 to -3.44) and \u0026minus;\u0026thinsp;3.70 (95% CI: -3.90 to -3.50), while low-to-middle SDI countries experienced the slowest decline, with EAPC of -0.13 (95% CI: -0.18 to -0.08) and \u0026minus;\u0026thinsp;0.14 (95% CI: -0.19 to -0.09) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The ASDR showed an upward trend in all regions except middle-to-high SDI and high SDI countries, with the highest increase observed in low-to-middle SDI countries at 0.20 (95% CI: 0.12\u0026ndash;0.29) (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ASYLDR increased in all regions except high SDI countries, with the greatest increase seen in medium SDI countries at 0.53 (95% CI: 0.48\u0026ndash;0.57) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGBD Regional Situation\u003c/h2\u003e \u003cp\u003eIn 2021, the GBD regions with the heaviest health burden in terms of age-standardized DALYs, Deaths, and YLLs were Northern Africa, including countries such as Egypt, Libya, Algeria, and Morocco, with 137.71 (95% UI: 224.48\u0026ndash;60.74) per 100,000 population, 8.19 (95% UI: 13.63\u0026ndash;3.49), and 136.02 (95% UI: 222.03\u0026ndash;60.16). The regions with the lightest burden were Southern Latin America, with typical countries including Kenya, Tanzania, Uganda, and Ethiopia, with 7.77 (95% UI: 13.53\u0026ndash;3.13), 0.46 (95% UI: 0.78\u0026ndash;0.19), and 7.58 (95% UI: 13.22\u0026ndash;3.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and Appendix Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e). The ASYLDR burden was heaviest in Northern Africa and the Middle East, covering countries from Northern Africa to the Middle East, including Saudi Arabia, Iran, and the UAE, with 1.97 (95% UI: 3.38\u0026ndash;0.84). The lightest burden was observed in Eastern Sub-Saharan Africa, with countries like Argentina, Chile, and Paraguay, with 7.58 (95% UI: 13.22\u0026ndash;3.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and Appendix Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2021, the regions with the highest number of DALYs, Deaths, YLDs, and YLLs were Asia, while Oceania had the lowest number (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and Appendix Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e).The regions with the greatest improvement in DALYs, Deaths, YLDs, and YLLs were Commonwealth High-Income countries, with EAPC of -4.52 (95% CI: -5.19 to -3.85), -4.22 (95% CI: -4.95 to -3.48), -1.73 (95% CI: -2.39 to -1.05), and \u0026minus;\u0026thinsp;4.59 (95% CI: -5.26 to -3.92) (Appendix Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e). The regions with the greatest increase in age-standardized DALYs, deaths, YLDs, and YLLs were East Asia, with EAPC of 2.28 (95% CI: 1.70\u0026ndash;2.85), 2.89 (95% CI: 2.08\u0026ndash;3.71), 2.38 (95% CI: 1.94\u0026ndash;2.82), and 2.27 (95% CI: 1.70\u0026ndash;2.85) (Appendix Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNational Level\u003c/h2\u003e \u003cp\u003eAmong 204 countries, Sudan had the highest ASDALYR, ASDR, ASYLDR and ASYLLR for IHD due to insufficient physical activity in middle-aged and elderly populations with 647.07 (95% UI: 293.16\u0026ndash;1026.7) per 100,000 population, 7.94 (95% UI: 7.94\u0026ndash;32.14), 18.1 (95% UI: 7.94\u0026ndash;32.14), and 5.55 (95% UI: 2.4\u0026ndash;9.66), while Tanzania had the lowest health burden, with 3.13 (95% UI: 1.16\u0026ndash;6.28), 0.17 (95% UI: 0.06\u0026ndash;0.36), 0.18 (95% UI: 0.07\u0026ndash;0.33), and 1.00 (95% UI: 0\u0026ndash;3.00) (Appendix Fig.\u0026nbsp;3 and Appendix Tables2).\u003c/p\u003e \u003cp\u003eFrom the trend between 1990 and 2021, the countries with the largest reductions in DALYs, Deaths, and YLDs were Denmark, with EAPC values of -5.65 (95% CI: -6.51 to -4.79), -5.49 (95% CI: -6.4 to -4.56, and \u0026minus;\u0026thinsp;5.76 (95% CI: -6.62 to -4.9), while The countries with the largest increases in these indicators were China, with EAPC values of 2.33 (95% CI: 1.74\u0026ndash;2.92, 2.95 (95% CI: 2.12\u0026ndash;3.79), and 2.33 (95% CI: 1.74\u0026ndash;2.92)(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eB and Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eC. For YLLs, the largest decrease in the EAPC was observed in the United Kingdom, with a reduction of -1.67 (95% CI: -2.45 to -0.88), while the largest increase was seen in Guam, with an EAPC of 2.91 (95% CI: 2.27\u0026ndash;3.55) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eD)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the results of the decomposition analysis for DALYs, Deaths, YLDs, and YLLs globally, across five SDI regions, and by gender. In this study, the changes in the global burden of ischemic heart disease due to insufficient physical activity were primarily driven by a combination of epidemiological changes, population growth, and aging. The global DALYs burden increased from 2,248,010.85 in 1990 to 3,897,941 in 2021, with an increase of 1,649,930.14. The contribution of epidemiological changes to the DALYs burden was the largest (465.04%), while population growth and aging contributed \u0026minus;\u0026thinsp;414.92% and 29.88. In different SDI regions, epidemiological changes had the largest impact on the DALYs burden in high SDI countries (127.41%), while in low-to-middle SDI regions, population growth and aging showed a more significant influence. From a gender perspective, male DALYs were mainly influenced by population growth (506.47%) and aging (-41.08%), whereas female DALYs were more strongly impacted by epidemiological changes (385.25%). The contribution of epidemiological changes of Deaths was similarly dominant (-155.46%), with epidemiological changes having the largest impact on Deaths in middle-to-high SDI regions (194.92%). Changes in YLDs and YLLs were primarily driven by population growth, with global YLDs increasing from 36,172.75 to 85,413.64, an increase of 49,240.89, and global YLLs rising from 2,211,838.1 to 3,812,527.4, an increase of 1,600,689.25.At the gender level, women showed more significant changes across all health indicators, particularly in Deaths and YLL. Overall, epidemiological changes and population growth have had a profound impact on the health burden across different regions, particularly in low-to-middle SDI countries, where the effects of population growth and aging were more pronounced.\u003c/p\u003e \u003cp\u003e \u003cb\u003eARIMA Model Forecast of Global Burden of IHD Due to Insufficient Physical Activity in Middle-Aged and Elderly Populations: 2022\u0026ndash;2050\u003c/b\u003e \u003c/p\u003e \u003cp\u003eARIMA model predicts rising trends in DALYs, Deaths, YLDs and YLLs due to insufficient physical activity for IHD in middle-aged and older populations globally from 2022\u0026ndash;2050, but declining trends in these indicators under age-standardization for both men and women (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e) For men, the ADALYR and ASYLLR will show an increasing trend. ASDALYR will rise from 39.44 (per 100,000 population) in 2022 to 40.54 in 2050. ASYLLR will increase from 38.62 in 2022 to 39.49 in 2050. On the other hand, the ASDR will show a decreasing trend, dropping from 2.32 in 2022 to 1.82 in 2050. The ASYLDR will remain stable at 0.79 throughout the forecast period. The confidence intervals for the ASDALYR and ASYLLR are expected to gradually widen; for example, the confidence interval for ASDALYR will increase from 95% UI (38.81\u0026ndash;40.08) to 95% UI (13.80\u0026ndash;67.29). ASYLDR will consistently exhibit minor variability, with a confidence interval remaining at 95% UI (0.75\u0026ndash;0.83), reflecting a high degree of precision. For women, ASDALYR, ASDR, and ASYLLR will show a significant decrease. From 2022 to 2050, DALYs will decrease from 51.17 to 31.33, deaths will decrease from 3.19 to 1.82, and YLLs will decrease from 50.01 to 30.16. YLDs will show an increasing trend, rising from 1.17 in 2022 to 1.35 in 2050. The confidence intervals for age-standardized DALYs, Deaths, YLDs, and YLLs will gradually widen; for example, the confidence interval for deaths will expand from 95% UI (3.13\u0026ndash;3.25) in 2022 to 95% UI (1.47\u0026ndash;2.16) in 2050.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCross-National Health Inequality Analysis\u003c/h2\u003e \u003cp\u003eThe study results show the relative income inequality in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations, as measured by DALYs, Deaths, YLDs, and YLL. A comparison of data from 1990 and 2019 indicates a reduction in health inequality over this period. The concentration index for DALYs showed a small change, from \u0026minus;\u0026thinsp;0.16 in 1990 to -0.02 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). However, the slope inequality index indicated that the health burden gap between low SDI and high SDI regions decreased from 58.66 in 1990 to 42.3 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). For Deaths, the concentration index changed from \u0026minus;\u0026thinsp;0.26 in 1990 to -0.09 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eC), and the slope index decreased from 3.76 in 1990 to 3.05 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). The concentration index for YLDs decreased from \u0026minus;\u0026thinsp;0.21 in 1990 to -0.13 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eE), while the slope inequality index for YLDs increased from 1.02 in 1990 to 1.68 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eF). This phenomenon may be attributed to the significant health improvements in high-income groups, while low-income groups showed slower improvements, leading to an increased health gap. The concentration index for YLLs decreased from \u0026minus;\u0026thinsp;0.15 in 1990 to -0.01 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eG), and the slope inequality index decreased from 57.6 in 1990 to 40.51 in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eH). All of these results suggest that the health inequality in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations has reduced.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides an in-depth analysis of the global trend in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations from 1990 to 2021, and predicts future trends using the ARIMA model. Furthermore, the study utilized decomposition analysis and health inequality analysis to examine the contributions of various factors to the changes in this health burden. The results show that, during this period, the number of DALYs, Deaths, YLDs, and YLLs due to insufficient physical activity have increased globally, while ASDALYR, ASDR, ASYLDR, ASYLLR have decreased. This phenomenon is likely closely related to advances in global diagnostics and treatment methods, such as the development of non-invasive imaging technologies\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and the application of cardiovascular stem cell therapies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, despite improvements in treatment, the rise in DALYs suggests that many patients are still not fully benefiting from existing therapies. Additionally, the decomposition analysis reveals that population growth is a significant factor contributing to the absolute increase in disease burden\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAge stratification shows that the elderly population (especially those aged 80 and above) has seen a significant increase in the burden of IHD due to insufficient physical activity. This trend is the result of multiple factors. As individuals age, their cardiovascular system undergoes natural aging processes such as decreased vascular elasticity, arteriosclerosis, and reduced cardiac pumping efficiency, which reduce the ability to respond to pathological stimuli, thereby increasing the risk of disease. Insufficient physical activity further exacerbates these physiological changes, limiting cardiac and vascular function, and promoting the development of IHD, leading to a significant increase in health burden\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. At the same time, the decline in muscle strength, endurance, and other physiological functions in the elderly reduces their ability to maintain adequate physical activity, creating a vicious cycle of insufficient exercise. This lack of exercise not only affects daily living abilities but also worsens cardiovascular health and exacerbates the impact of IHD. Therefore, to reduce the IHD burden in the elderly population due to insufficient physical activity, it is essential to strengthen physical activity interventions, particularly cardiovascular health exercises. Moreover, personalized health management plans should be developed, combining metabolic risk factor monitoring and intervention, to ensure improvements in the elderly population's quality of life and health status.\u003c/p\u003e \u003cp\u003eGlobally, the health burden of IHD due to insufficient physical activity is significantly higher in women than in men, a phenomenon likely related to women's longer life expectancy\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Longer life expectancy makes women more susceptible to age-related diseases, and this risk significantly increases after menopause due to the decline in estrogen levels\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Moreover, socioeconomic factors play a crucial role in the cardiovascular health burden in female. Women with lower socioeconomic status often have poorer access to health resources and higher health risks, especially in low-income countries and regions, where the health burden for women is even more severe. Cultural and social factors also influence women's physical activity, as traditional gender roles restrict women's opportunities for exercise, further exacerbating the burden of cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In contrast, the decline in health burden in men is more gradual, which may be associated with their higher baseline health burden and lower engagement in health management. Men often have lower participation in cardiovascular health management, and in many cultures, they bear more social and economic responsibilities, leading to health issues being neglected. When facing insufficient physical activity and poor health, men often fail to take effective measures for improvement, resulting in a slower reduction in health burden compared to women. This gender difference underscores the importance of gender-specific interventions, particularly in addressing the relationship between physical activity and cardiovascular health, and highlights the need for targeted interventions for women. Therefore, gender-specific health strategies should be implemented, with a focus on increasing women's participation in physical activities, while also improving health resource access for socioeconomically disadvantaged groups. Social and cultural factors should be considered when designing physical activity programs that facilitate participation from different groups, ensuring the accessibility and sustainability of health interventions.\u003c/p\u003e \u003cp\u003eThe SDI analysis results indicate significant disparities in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations across countries with different development levels. The total health burden in high SDI countries has significantly decreased\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, while countries with other development levels have shown increasing trends, with medium SDI countries exhibiting the most noticeable rise. In high SDI countries, age-standardized DALYs, deaths, YLDs, and YLLs have shown significant declines, and middle-high SDI countries also demonstrated a downward trend. However, medium, low-medium, and low SDI countries showed fluctuating patterns. In high SDI regions, the burden of IHD due to insufficient physical activity in middle-aged and elderly populations has significantly decreased, which may be attributed to advanced healthcare systems, improved health management systems, the widespread adoption of healthy dietary habits, and the development of the sports industry. In particular, the improved treatment levels for acute myocardial infarction (AMI) and its complications have greatly improved disease prognosis\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Moreover, government policy interventions, infrastructure development, and the booming sports industry have promoted daily physical activity levels and contributed to the dissemination of fitness and health culture, all of which have worked together to reduce the burden of cardiovascular diseases. However, as population aging intensifies, some groups (such as the elderly and low-income populations) have not fully benefited from these health interventions and resources, and continue to face higher health burdens. Therefore, in high SDI regions, ensuring equitable distribution of health resources and expanding health service coverage is critical to further reducing the health burden.\u003c/p\u003e \u003cp\u003eIn medium SDI and low-medium SDI countries, the burden of IHD due to insufficient physical activity in middle-aged and elderly populations has significantly increased. This trend is likely closely related to the rapid transformation of economic systems in developing countries over the past 30 years. The deepening processes of industrialization, urbanization, and globalization have drastically changed people's lifestyles and dietary habits, which have, in turn, contributed to the rise of cardiovascular metabolic diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Moreover, these regions have lower disease awareness, delayed diagnoses, and insufficient basic healthcare facilities and funding, further exacerbating the health burden\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. For example, after IHD onset, lipid-lowering treatment is an effective intervention that can reduce the risk of cardiovascular events (CVE) recurrence. Typically, achieving an LDL-c level of 100 mg/dL or 70 mg/dL (2.59 mmol/L and 1.81 mmol/L) is the ideal therapeutic goal. A health and nutrition survey of 18,656 high-risk U.S. individuals from 1999 to 2008 showed that the proportion of IHD patients achieving LDL-c\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL increased from 27\u0026ndash;65%, and those achieving LDL-c\u0026thinsp;\u0026lt;\u0026thinsp;70 mg/dL rose from 3\u0026ndash;21%\u003csup\u003e36\u003c/sup\u003e. In contrast, a similar study in India, following 17,236 MI survivors from 2004\u0026ndash;2009, found that only 4.5% of patients achieved the LDL-c\u0026thinsp;\u0026lt;\u0026thinsp;97 mg/dL goal\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In addition to medical resource limitations, these regions generally have low physical activity levels, poor health awareness, and insufficient exercise habits, further increasing the incidence of cardiovascular diseases. Socioeconomic factors also play a critical role: lower socioeconomic status limits residents' access to health resources, exacerbating health inequalities, particularly among low-income groups. Furthermore, the Westernization of lifestyles, the spread of unhealthy dietary habits, and the increased consumption of high-calorie foods have further increased the burden of metabolic diseases, thus raising the incidence of IHD. Additionally, the accelerating pace of urbanization in these countries has brought greater living pressures, environmental pollution, and a lack of sufficient opportunities for physical activity, all of which have contributed to the rising disease burden. In low SDI countries, the overall health burden is relatively low, but changes are minimal. This may be due to the labor structure in these countries, where many residents engage in manual labor such as agriculture and construction. These labor-intensive jobs promote higher daily activity levels, thereby reducing the risk of IHD due to insufficient physical activity. However, these countries face serious limitations in healthcare resources and health management systems, making effective intervention and treatment of chronic diseases difficult. Poor medical infrastructure, limited health service coverage, and low health education levels result in significant challenges in cardiovascular disease prevention and treatment. Moreover, as urbanization progresses, sedentary occupations are increasing in low SDI countries, further reducing physical activity levels, which restricts improvements in health burden. Therefore, despite the protective effect of manual labor, the lack of healthcare and health management systems has resulted in little change in the health burden of these countries.\u003c/p\u003e \u003cp\u003eThe study finds that Northern Africa was the region with the highest health burden in 2021, a result consistent with other studies\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Sudan was identified as the country with the heaviest health burden. The high burden in Sudan and Northern Africa may be attributed to similar factors. Political instability has intensified the fragility of health management systems, and these factors, combined with a lack of effective public health interventions and health education, have contributed to the rising health burden. Other North African countries, such as Libya and Morocco, also face similar challenges. The disruption of government services, damage to infrastructure, and the unequal distribution of health resources have exacerbated the burden of cardiovascular diseases and other metabolic diseases. Meanwhile, these countries also face the impact of air pollution and extreme climate conditions\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, which not only disrupt agricultural production and food security, but also limit outdoor activities, further increasing the health burden of cardiovascular diseases due to insufficient physical activity. Southern Latin America has the lowest health burden for IHD due to insufficient physical activity, with Tanzania identified as the country with the lowest health burden. This is attributed to the higher participation in physical labor, which helps maintain high levels of daily physical activity. Agriculture and animal husbandry are labor-intensive industries that encourage frequent physical activity, thus reducing the cardiovascular risk associated with sedentary behavior. The dietary patterns in these regions are relatively traditional and healthy, focusing on natural foods and reducing the negative impact of high-fat and high-sugar diets on metabolic diseases. Although these regions have limited healthcare resources, health management and public health measures have gradually improved, leading to better disease prevention and reducing the health burden. East Asia, especially China, has seen a significant increase in the burden of IHD due to insufficient physical activity in middle-aged and elderly populations in recent years. This trend is linked to the acceleration of urbanization, the gradual reduction of traditional physical labor, and changes in work and lifestyle. It may also be related to increased social pressure, particularly in the fast-paced urban lifestyle driven by economic competition, where many people neglect the importance of health management and physical activity. Although China has made improvements in health management and public health policies, urban-rural disparities and unequal socioeconomic development continue to challenge the distribution and access to health resources. Furthermore, despite the growing popularity of the sports industry and fitness culture in China, the coverage is still insufficient to cope with the growing health burden. These combined factors have contributed to the significant increase in the burden of IHD due to insufficient physical activity in East Asia, especially China. In Commonwealth high-income countries, the burden of IHD has been significantly reduced, due to advanced medical facilities, strong public health policies, and well-established health management systems. These countries benefit from world-leading cardiovascular disease treatment technologies, such as percutaneous coronary intervention (PCI), cardiac resynchronization therapy (CRT), and implantable cardioverter-defibrillators (ICDs), which play a critical role in early diagnosis and precision treatment, significantly reducing the mortality and disability rates. Additionally, government policy interventions, such as anti-smoking, healthy eating, and promotion of physical exercise, have effectively reduced the risk factors associated with metabolic diseases, such as obesity, hypertension, and hyperglycemia, further decreasing the incidence of IHD. High-income countries generally implement universal health screenings and regular checkups, ensuring early diagnosis and treatment for high-risk populations, thanks to government investment and resource allocation. The comprehensive impact of these policies has enabled high-income countries to lead the world in reducing the burden of IHD, significantly outperforming other regions.\u003c/p\u003e \u003cp\u003eBased on our findings, we propose the following measures to alleviate the global burden of IHD attributable to physical inactivity in middle-aged and older adults. First, we recommend the establishment of a universal hypertension screening program by placing free blood pressure monitoring stations in community pharmacies worldwide\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In addition, physical activity assessments\u0026mdash;including questionnaires, pedometer tracking, or cardiopulmonary endurance tests\u0026mdash;should be integrated into routine health examinations for middle-aged and elderly individuals. These evaluations would help identify activity levels and support the provision of personalized exercise prescriptions. Second, governments should introduce physical activity incentive policies targeting older adults, such as subsidies for senior fitness programs, reduced fees for access to public sports facilities, or the inclusion of exercise interventions in national health insurance systems. At the community level, free or low-cost outdoor fitness zones should be developed, accompanied by regular health education seminars focused on physical activity for the elderly to enhance health awareness. Third, reducing work-related stress and promoting healthy lifestyles can also lessen the disease burden. Employers should implement flexible work schedules, workplace wellness programs, and psychological support services to help middle-aged employees manage stress and improve quality of life. Lifestyle improvement efforts should aim to cultivate regular sleep patterns, a balanced diet, and consistent exercise habits. Physical activity should be seamlessly integrated into daily routines\u0026mdash;for example, encouraging walking or cycling to work, choosing stairs over elevators, and incorporating micro-exercises such as standing desks, household chores, or brisk walking during lunch breaks. These strategies can effectively reduce the health risks associated with chronic stress and prolonged sedentary behavior.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, Data quality and availability are the primary considerations. In low- and middle-income countries, the incompleteness of epidemiological data and underreporting of IHD cases may result in an underestimation of the actual disease burden. Therefore, any assessments of health burdens in these regions should be interpreted with caution, given that data gaps may impact the accuracy of the conclusions.\u003c/p\u003e \u003cp\u003eSecond, on the assumptions and modeling methods used in the GBD study, despite rigorous statistical modeling and data adjustment strategies the study has employs to minimize bias, the uncertainty remains. As a modeling tool that heavily relies on assumptions, GBD provides the relatively dependable estimates based on the available data, which should be treated as approximations rather than absolute values. Future research should approach the application of the GBD results with caution and remains aware of the underlying assumptions and potential sources of error.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOver the past 32 years, the global burden of IHD attributable to physical inactivity among middle-aged and elderly individuals has significantly increased. However, age-standardized health burden indicators have shown a downward trend, with females experiencing a more pronounced decrease compared to males. Meanwhile, the IHD burden among the elderly, particularly those aged 80 and above, has risen significantly, reflecting that population aging has become a critical challenge for global cardiovascular health. The burden of IHD varies across countries with different SDI levels, with high-SDI countries exhibiting the lowest burden and middle-SDI countries bearing the highest burden. Although health equity has improved, targeted interventions are still needed to further reduce the IHD health risks related to physical inactivity in middle-aged and elderly populations.\u003c/p\u003e \u003cp\u003eThis study highlights the critical role of physical inactivity in the IHD health burden and provides important data to support global cardiovascular disease prevention efforts. Future health management strategies should focus more on personalized interventions, not only increasing physical activity but also developing targeted measures based on each country's specific needs and health context. Such approach can more effectively reduce the IHD burden, alleviate socioeconomic pressures, and provide scientific evidence for developing regional and global prevention strategies, further emphasizing the core role of improving physical activity levels in mitigating the IHD disease burden.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIHD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ischemic heart disease\u003c/p\u003e\n\u003cp\u003eGBD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Global Burden of Disease\u003c/p\u003e\n\u003cp\u003eSDI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sociodemographic Index\u003c/p\u003e\n\u003cp\u003eDALY\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Annual Disability-Adjusted Life Years\u003c/p\u003e\n\u003cp\u003eYLD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Years Lived with Disability\u003c/p\u003e\n\u003cp\u003eYLL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Years of Life Lost\u003c/p\u003e\n\u003cp\u003eASR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Age-standardized rate\u003c/p\u003e\n\u003cp\u003eASDALYR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age-standardized DALY rate\u003c/p\u003e\n\u003cp\u003eASDR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age-standardized Death rate\u003c/p\u003e\n\u003cp\u003eASYLDR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age-standardized YLD rate\u003c/p\u003e\n\u003cp\u003eASYLLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Age-standardized YLL rate\u003c/p\u003e\n\u003cp\u003eEAPC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Estimated annual percentage change\u003c/p\u003e\n\u003cp\u003eCODEm \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cause of Death Ensemble Model\u003c/p\u003e\n\u003cp\u003eARIMA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Autoregressive integrated moving average\u003c/p\u003e\n\u003cp\u003eUI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Uncertainty Interval\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence Interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Global Burden of Disease (GBD) Collaborative Network for making the aggregated dataset publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXianjun Liu and Xuewen Yuan contributed to the conceptualization of this research. Xin man Gao collected the data. Yibing Xia performed the data analyses. Chuan He wrote the original draft. Ziqi Zhao reviewed and edited the manuscript. All authors read the manuscript and agreed to publish this version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available, fully de-identified data from the Global Burden of Disease (GBD) Study 2021.formal ethical approval is not required for analyses of anonymized secondary data. The study complies with the Declaration of Helsinki principles for ethical research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was not applicable for this study. The GBD database contains strictly de-identified, population-level health metrics that preclude identification of individual participants. Data access and usage comply with the GBD Collaborative Institutional Use Agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be accessed and downloaded through the official website of the Institute for Health Metrics and Evaluation (IHME) at http://ghdx.health data.org. Given the open-access nature of this database and the absence of personally identifiable information, our study is in compliance with the ethical standards for the use of public data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest among authors need to be disclosed.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal burden. of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. 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Epidemiological profile and management patterns of acute myocardial infarction in very young patients from a tertiary care center. Indian Heart J. 2020;72(1):32\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalki N, Koupil I, Eloranta S, Weibull CE, Tiikkaja S, Ingelsson E, Spar\u0026eacute;n P. Temporal trends in incidence of myocardial infarction and ischemic stroke by socioeconomic position in Sweden 1987\u0026ndash;2010. PLoS ONE. 2014;9(8):e105279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah NS, Huffman MD, Ning H, Lloyd-Jones DM. Trends in myocardial infarction secondary prevention: The National Health and Nutrition Examination Surveys (NHANES), 1999\u0026ndash;2012. J Am Heart Assoc 2015, 4(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAminorroaya A, Saeedi Moghaddam S, Tavolinejad H, Aryan Z, Heidari B, Ebrahimi H, Naderian M, Shobeiri P, Ghanbari A, Rezaei N, et al. Burden of Ischemic Heart Disease and Its Attributable Risk Factors in North Africa and the Middle East, 1990 to 2019: Results From the GBD Study 2019. J Am Heart Assoc. 2024;13(2):e030165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamilli M, Russo M, Rinaldi R, Caff\u0026egrave; A, La Vecchia G, Bonanni A, Iannaccone G, Basile M, Vergallo R, Aurigemma C, et al. Air Pollution and Coronary Vasomotor Disorders in Patients With Myocardial Ischemia and Unobstructed Coronary Arteries. J Am Coll Cardiol. 2022;80(19):1818\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell NRC, Ordunez P, Giraldo G, Rodriguez Morales YA, Lombardi C, Khan T, Padwal R, Tsuyuki RT, Varghese C. WHO HEARTS: A Global Program to Reduce Cardiovascular Disease Burden: Experience Implementing in the Americas and Opportunities in Canada. Can J Cardiol. 2021;37(5):744\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6374014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6374014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eWith the consistent growth of aging population, ischemic heart disease (IHD) has become one of the primary causes of death among middle-aged and elderly individuals. This study analyzes the global, regional, and national trends and characteristics of IHD due to physical inactivity over the past 32 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Using data from the 2021 Global Burden of Disease (GBD) study, we assessed the burden of IHD due to physical inactivity among middle-aged and elderly populations from 1990 to 2021. Key metrics included the number of disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), Death, and the age-standardized rates of DALYs (ASDALYR), Deaths (ASDR), LYDs (ASYLDR), and YLLS (ASYLLR). Trend analysis used the estimated annual percentage change (EAPC) method. Decomposition and equity analyses were conducted to evaluate the contributions of demographic and epidemiological factors to the observed changes in IHD burden.\u003cstrong\u003e \u003c/strong\u003eAutoregressive integrated moving average (ARIMA) model provides future projections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eGlobally, the number of IHD health burden due to physical inactivity in the middle-aged and elderly population increased significantly from 1990 to 2021. The EAPCs of ASDALYR, ASYLDR, and ASYLLR were -0.35 (95% CI: -0.70 to -0.01), -0.25 (95% CI: -0.70 to 0.20), and -0.37 (95% CI: -0.72 to -0.03), respectively. The ASYLDR exhibited an upward trend, with an EAPC of 0.65(95% CI: 0.32 to 0.99). Globally, the burden increased with age, and in 2021, females bore a higher burden than males. Regional stratification by SDI showed that low-SDI and middle-SDI regions experienced the most notable increases. From 1990 to 2021, Denmark saw the greatest decline in IHD burden, while China exhibited the most substantial rise. Projections using the ARIMA model suggest a continued increase in IHD burden for both sexes by 2050.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eMarked disparities in the burden of IHD due to physical inactivity exist across regions, sexes, and age groups. This study provides critical evidence to support public health policymaking, with a view toward mitigating the long-term health risks associated with physical inactivity in aging populations.\u003c/p\u003e","manuscriptTitle":"Global burden of ischemic heart disease due to insufficient physical activity in middle-aged and elderly populations from 1990 to 2021 and projections for 2050","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 19:55:20","doi":"10.21203/rs.3.rs-6374014/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f26b3673-fbc9-4b05-9e49-47b3df4100af","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T10:41:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 19:55:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6374014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6374014","identity":"rs-6374014","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}