Download:
-
Cardiovascular disease (CVD) is a major cause of premature mortality and a significant factor in escalating healthcare costs. With the lower birth rates and increased life expectancy, population aging and it's associated CVD burden have emerged as a critical social challenge. This study sought to systematically assess the effect of population aging on the disability-adjusted life years (DALYs) associated with CVD, utilizing data from the Global Burden of Disease Study (GBD) 2021. The findings reveal that global DALYs for CVD reached 428.3 million in 2021, marking a 44.0% increase since 1990, largely driven by aging populations. Notably, the impact of population aging on DALYs varied significantly across different countries, territories, and types of CVD. Public health professionals are urged to focus on tailored preventive and treatment strategies to address the impending challenges of population aging and its influence on CVD burden.
Data were obtained from the GBD 2021 online database (http://ghdx.healthdata.org/gbd-results-tool), encompassing DALYs, age-standardized DALY rate (ASDR), and population sizes spanning 1990 to 2021. Age-standardized rates facilitated the comparison of DALY rates across nations or regions with diverse age structures and demographic profiles. The analysis included a total of 204 countries and territories, categorized into five socio-demographic index (SDI) regions (high, high-middle, middle, low-middle, and low) (1).
The average annual percent change (AAPC) was determined using Joinpoint regression analysis to evaluate trends in ASDR (2). A decomposition method was utilized to assess the variations in DALYs due to population growth, aging, and changes in age-specific DALY rates (3). Both the absolute and relative contributions of these three factors to the alterations in DALYs were computed, using 1990 as a baseline. In countries where DALYs increases were linked to population aging, ratios of DALYs ascribed to changes in DALY rates (R1) and to population growth (R2) were separately calculated in comparison to those due to population aging. Comprehensive methodologies for data correction are provided in the
Supplementary Material . All processes of data handling, analytical procedures, and the production of graphical content were conducted using R (version 4.1.3, Development Core Team, Vienna, Austria).In 2021, the global burden of CVD amounted to approximately 428.3 million DALYs, with a 95% uncertainty interval (UI) of 403.7 to 453.7 million. The ASDR was 5,055.9 per 100,000 individuals (95% UI: 4,759.5, 5,359.2). From 1990 to 2021, there was a 1.3% decrease in the global ASDR for CVD [95% confidence interval (CI): ‒1.4%, ‒1.2%], despite a 44.0% increase in the global DALYs attributed to CVD. Among various SDI quintiles, DALYs consistently increased in all but the high SDI quintile. Notably, ASDR declined across all SDI categories, with the most pronounced decrease occurring in the high SDI quintile.(Figure 1 and Table 1). Rheumatic heart disease experienced the sharpest decrease in ASDR, with an AAPC of ‒2.5% (95% CI: ‒2.5%, ‒2.4%) (Table 1).
Figure 1.Global DALYs and ASDR of CVD from 1990 to 2021. (A) DALYs across various SDI regions. (B) ASDR across different SDI regions. (C) AAPC in ASDR.
Abbreviation: DALYs=disability-adjusted life years; ASDR=age-standardized DALY rate; CVD=cardiovascular disease; SDI=socio-demographic index; AAPC=average annual percent change.Characteristics 1990 2021 1990–2021 DALYs cases
No. ×104 (95% UI)ASDR per 100,000
No. (95% UI)DALYs cases
No. ×104 (95% UI)ASDR per 100,000
No. (95% UI)AAPC
No. (95% CI)Global 29,750.7
(28,460.1, 30,934.6)7,550.2
(7,181.5, 7,862.0)42,832.7
(40,368.4, 45,371.2)5,055.9
(4,759.5, 5,359.2)−1.3
(−1.4, −1.2)SDI High 5,984.0
(5,651.8, 6,203.0)5,494.2
(5,185.5, 5,700.0)5,338.5
(4,835.2, 5,708.6)2,588.8
(2,388.0, 2,762.8)−2.4
(−2.5, −2.4)High-middle 7,955.2
(7,549.6, 8,296.2)8,451.9
(8,001.6, 8,819.2)9,787.9
(8,980.2, 10,573.9)5,105.1
(4,685.7, 5,507.5)−1.6
(−1.8, −1.3)Middle 8,173.6
(7,673.6, 8,708.0)7,843.6
(7,348.3, 8,338.5)14,251.9
(13,208.1, 15,296.2)5,505.1
(5,088.9, 5,901.3)−1.2
(−1.3, −1.0)Low-middle 5,500.8
(5,143.9, 5,804.8)8,149.9
(7,607.7, 8,594.9)9,878.8
(9,203.1, 10,563.6)6,744.4
(6,289.1, 7,198.7)−0.6
(−0.7, −0.5)Low 2,096.8
(1,907.1, 2,295.2)8,236.3
(7,566.5, 8,920.5)3,533.5
(3,184.7, 3,911.8)6,474.7
(5,883.2, 7,124.5)−0.8
(−0.9, −0.7)Type of CVD Aortic aneurysm 188.4
(178.4, 200.7)48.8
(6.0, 51.8)310.8
(285.7, 335.4)36.5
(33.5, 39.5)−0.9
(−1.0, −0.8)Atrial fibrillation and flutter 335.9
(271.5, 414.2)100.8
(82.8, 122.6)835.9
(697.1, 1,013.3)101.4
(84.9, 122.4)0.0
(0.0, 0.1)Cardiomyopathy and myocarditis 857.4
(765.6, 942.4)195.0
(175.9, 211.1)1,165.4
(1,070.8, 1,262.6)142.2
(130.6, 154.1)−1.0
(−1.4, −0.6)Endocarditis 133.4
(105.6, 150.9)28.3
(23.3, 31.6)207.6
(182.7, 230.9)25.6
(22.3, 28.4)−0.3
(−0.4, −0.2)Hypertensive heart disease 1,547.4
(1,231.1, 1,731.2)406.5
(328.9, 452.2)2,546.2
(2,149.3, 2,804.8)301.6
(255.1, 332.1)−1.0
(−1.0, −0.9)Ischemic heart disease 11,916.3
(11,454.8, 12,345.5)3,107.6
(2,966.5, 3,222.7)18,836.1
(17,703.7, 19,815.4)2,212.2
(2,075.5, 2,327.6)−1.1
(−1.2, −0.9)Non-rheumatic valvular heart disease 179.2
(164.6, 196.7)49.3
(45.3, 54.2)323.8
(293.4, 359.4)39.7
(35.8, 44.1)−0.7
(−0.8, −0.6)Other cardiovascular and circulatory diseases 664.2
(564.7, 766.9)147.5
(128.0, 169.5)998.6
(838.6, 1,210.7)121.5
(102.6, 145.8)−0.6
(−0.7, −0.5)Lower extremity peripheral arterial disease 91.3
(75.5, 118.0)26.6
(22.3, 33.8)155.8
(126.7, 204.6)18.6
(15.2, 24.2)−1.2
(−1.5, −0.9)Rheumatic heart disease 1,628.0
(1,370.7, 1,917.7)347.5
(292.5, 409.6)1,342.6
(1,151.7, 1,578.0)162.1
(139.1, 190.5)−2.5
(−2.5, −2.4)Stroke 12,140.5
(11,472.2, 12,762.5)3,079.0
(2,893.6, 3,237.3)16,045.7
(14,778.1, 17,164.3)1,886.2
(1,739.0, 2,017.9)−1.6
(−1.7, −1.5)Abbreviation: CVD=cardiovascular disease; SDI=socio-demographic index; DALYs=disability-adjusted life years; ASDR=age standardized DALY rate; UI=uncertainty interval; CI=confidence interval; AAPC=average annual percent change. Table 1. DALYs cases and the ASDR for CVD from 1990 to 2021.
Since 1990, global DALYs attributable to population aging have gradually increased, reaching 138.2 million by 2021. Over the same timeframe, population growth accounted for an additional 144.2 million DALYs, while changes in DALY rates led to a reduction of 151.7 million DALYs (Figure 2A and Table 2). The impact of these three determinants varied significantly across different regions and countries (Table 2 and
Supplementary Table S1 ). From 1991 to 2021, the percentage of DALYs linked to population aging rose globally and across most SDI quintiles, with figures ranging from 2.9% in the lowest SDI countries to 79.0% in middle SDI countries (Figure 2B). The extent of total DALYs changes due to population aging varied widely from ‒77.0% in Afghanistan to 148.9% in the United Arab Emirates, with 174 countries or territories recording an increase in DALYs attributed to aging (Supplementary Table S1 ). IHD and stroke were the conditions most affected by aging, with 42 countries noting more than a 25% increase in IHD DALYs and 36 countries observing a similar increase for stroke DALYs. Notably, the rise in DALYs related to aging between 1990 and 2021 was less than 5.0% for most diseases (Table 3).
Figure 2.Changes in DALYs attributed to population aging, population growth, and DALY rate changes. (A) Global DALYs changes associated with population aging, population growth, and DALY rate changes from 1990 to 2021. (B) Proportion of DALYs associated with population aging globally and by SDI, 1990–2021.
Note: A decomposition analysis was conducted using the number of DALYs in 1990 as the reference year. The attributable proportion of DALYs was calculated as the number of DALYs due to population aging divided by the total DALYs in 1990, then multiplied by 100%.
Abbreviation: DALYs=disability-adjusted life years; SDI=socio-demographic index.
Characteristics Population aging
No. ×104Population growth
No. ×104DALY rate change
No. ×104R1 R2 Total 13,824.4 14,424.3 −15,166.7 −1.1 1.0 Types of CVD Aortic aneurysm 74.3 122.2 −74.2 −1.0 1.6 Atrial fibrillation and flutter 148.1 349.4 2.6 0.0 2.4 Cardiomyopathy and myocarditis 221.9 399.8 −313.8 −1.4 1.8 Endocarditis 28.9 65.3 −19.9 −0.7 2.3 Hypertensive heart disease 659.7 1,001.0 −661.9 −1.0 1.5 Ischemic heart disease 4,645.6 7,587.6 −5,313.5 −1.1 1.6 Non-rheumatic valvular heart disease 86.4 120.9 −62.4 −0.7 1.4 Other cardiovascular and circulatory diseases 177.3 321.0 −163.9 −0.9 1.8 Lower extremity peripheral arterial disease 20.0 90.1 −45.6 −2.3 4.5 Rheumatic heart disease 283.8 623.0 −1,192.1 −4.2 2.2 Stroke 5,523.8 5,676.4 −7,295.0 −1.3 1.0 SDI High 2,648.1 1,324.1 −4,617.8 −1.7 0.5 High-middle 4,674.5 1,877.7 −4,719.5 −1.0 0.4 Middle 6,459.7 3,959.1 −4,340.6 −0.7 0.6 Low-middle 2,348.9 3,776.3 −1,747.2 −0.7 1.6 Low 60.3 2,260.6 −884.2 −14.7 37.5 Region East Asia 6,540.8 1,671.1 −4,384.5 −0.7 0.3 Southeast Asia 1,562.5 1,294.5 −682.1 −0.4 0.8 Oceania 9.4 41.6 −10.3 −1.1 4.4 Central Asia 126.3 189.0 −140.8 −1.1 1.5 Central Europe 732.9 −115.0 −925.2 −1.3 −0.2 Eastern Europe 1,020.3 −269.1 −730.7 −0.7 −0.3 High-income Asia Pacific 742.2 58.5 −788.5 −1.1 0.1 Australasia 55.4 53.1 −128.2 −2.3 1.0 Western Europe 1,093.5 350.5 −2,408.9 −2.2 0.3 Southern Latin America 98.1 91.7 −231.3 −2.4 0.9 High-income North America 657.7 539.0 −1,124.7 −1.7 0.8 Caribbean 95.3 71.6 −81.8 −0.9 0.8 Andean Latin America 55.9 73.7 −76.4 −1.4 1.3 Central Latin America 384.1 270.0 −213.4 −0.6 0.7 Tropical Latin America 508.3 349.3 −627.9 −1.2 0.7 North Africa and Middle East 913.9 1,675.2 −1,425.4 −1.6 1.8 South Asia 2,552.3 3,599.6 −1,353.4 −0.5 1.4 Central Sub−Saharan Africa −2.0 293.5 −93.9 − − Eastern Sub−Saharan Africa 27.6 685.0 −313.4 −11.4 24.8 Southern Sub−Saharan Africa 70.6 102.1 −2.6 0.0 1.4 Western Sub−Saharan Africa −104.4 982.1 −322.7 − − Note: In countries where population aging corresponded with a rise in DALYs from 1990 to 2021, we calculated R1 and R2. R1 was determined by the equation “DALYs attributed to changes in DALY rate / DALYs attributed to population aging,” while R2 was computed as “DALYs attributed to population growth / DALYs attributed to population aging.”
Abbreviation: CVD=cardiovascular disease; SDI=socio-demographic index; DALYs=disability-adjusted life years.Table 2. Comparative contributions of change in DALY rates and population growth versus population aging to the change in the number of CVD DALYs between 1990 and 2021.
Cause of cardiovascular diseases DALYs Increase in attributed proportion of DALYs (number of countries/territories) 1.0%–5.0% 5.1%–10.0% 10.1–15.0% 15.1–20.0% 20.1–25.0% ≥25.1% Ischemic heart disease 16 25 23 35 22 42 Stroke 14 29 33 34 30 36 Hypertensive heart disease 97 20 2 0 0 0 Atrial fibrillation and flutter 43 0 0 0 0 0 Cardiomyopathy and myocarditis 62 1 0 0 0 0 Other cardiovascular and circulatory diseases 60 2 0 0 0 0 Rheumatic heart disease 30 1 0 0 0 0 Aortic aneurysm 14 0 0 0 0 0 Non-rheumatic valvular heart disease 18 0 0 0 0 0 Lower extremity peripheral arterial disease 0 0 0 0 0 0 Endocarditis 1 0 0 0 0 0 Note: The attributed proportion was calculated as the number of DALYs attributed to population aging for each cause of DALYs between 1990 and 2021 divided by total DALYs in 1990 ×100%. Countries and territories with an attribution rate of <1.0% were ignored.
Abbreviation: DALYs=disability-adjusted life years.Table 3. Number of countries and territories with different increases in cause-specific proportions of DALYs associated with population aging between 1990 and 2021.
Globally, the reduction in total CVD DALYs due to decreased DALY rates (‒151.7 million) surpassed the increase caused by population aging (138.2 million) from 1990 to 2021, with an R1 value of ‒1.1. R1 values varied, with ‒1.7 in high SDI regions and ‒14.7 in low SDI regions (Figure 2 and Table 2). Out of 174 countries that saw an increase in DALYs due to population aging, 103 recorded R1 values of ≤‒1, 64 reported values between ‒1 and 0, and 7 had values greater than 0 (Supplementary Table S1). On a global scale, the increase in DALYs due to population growth was greater than that due to aging (Figure 2). Among the countries studied, 1 had R2 values of ≤‒1, 33 ranged between ‒1 and 0, and 140 had R2 values greater than 0. R2 values ranged from ‒1.0 in Georgia to 353.6 in Democratic Republic of the Congo (
Supplementary Table S1 ).Characteristics DALYs in
1990
No.×104Population aging
No. ×104Population growth
No. ×104DALY rate change
No. ×104Proportion of DALYs
attributed to population
aging (%)R1 R2 Afghanistan 123.8 −95.3 176.0 −59.2 −77.0 − − Albania 15.6 20.7 −4.6 −7.9 132.4 −0.4 −0.2 Algeria 120.2 88.0 93.2 −92.4 73.2 −1.1 1.1 American Samoa 0.2 0.2 0.0 0.0 82.2 0.0 0.0 Andorra 0.2 0.1 0.1 −0.2 84.2 −2.0 1.0 Angola 44.0 −1.7 79.6 −29.1 −3.9 − − Antigua and Barbuda 0.4 0.1 0.2 −0.2 27.1 −2.0 2.0 Argentina 221.4 53.0 67.8 −162.5 23.9 −3.1 1.3 Armenia 23.6 16.3 −3.4 −10.8 69.0 −0.7 −0.2 Australia 98.7 47.0 43.6 −106.6 47.7 −2.3 0.9 Austria 70.3 21.3 9.0 −54.0 30.3 −2.5 0.4 Azerbaijan 55.3 23.3 24.7 −22.7 42.2 −1.0 1.1 Bahamas 1.2 0.8 0.7 −0.6 69.0 −0.8 0.9 Bahrain 2.1 1.7 3.7 −3.6 83.4 −2.1 2.2 Bangladesh 515.9 400.6 292.0 −310.3 77.7 −0.8 0.7 Barbados 1.6 0.6 0.3 −0.8 39.6 −1.3 0.5 Belarus 123.9 44.9 −15.4 −9.0 36.3 −0.2 −0.3 Belgium 71.6 20.6 8.6 −57.2 28.8 −2.8 0.4 Belize 0.5 0.2 0.7 −0.3 45.9 −1.5 3.5 Benin 16.0 −1.7 24.6 −6.6 −10.6 − − Bermuda 0.5 0.3 0.0 −0.4 76.9 −1.3 0.0 Bhutan 1.9 1.6 0.5 −0.9 82.2 −0.6 0.3 Bolivia 25.1 11.1 19.2 −20.7 44.2 −1.9 1.7 Bosnia and Herzegovina 31.8 27.4 −10.8 −14.8 86.2 −0.5 −0.4 Botswana 4.7 2.3 3.7 −3.0 49.3 −1.3 1.6 Brazil 688.7 502.8 337.8 −622.4 73.0 −1.2 0.7 Brunei Darussalam 0.9 0.6 0.7 −0.8 73.4 −1.3 1.2 Bulgaria 149.1 73.1 −36.2 −52.0 49.0 −0.7 −0.5 Burkina Faso 31.4 −3.6 39.6 −5.9 −11.4 − − Burundi 28.1 −2.9 29.1 −20.3 −10.2 − − Cabo Verde 1.3 0.3 0.8 0.0 25.3 0.0 2.7 Cambodia 45.3 30.1 32.2 −24.6 66.6 −0.8 1.1 Cameroon 35.8 −3.2 68.6 −5.8 −8.9 − − Canada 147.4 77.9 50.6 −132.2 52.8 −1.7 0.6 Central African Republic 14.9 −0.3 13.4 −4.2 −2.2 − − Chad 23.0 −11.1 39.1 −0.5 −48.3 − − Chile 48.7 34.4 19.9 −47.3 70.6 −1.4 0.6 China 6,325.4 6,367.8 1,615.9 −4,288.3 100.7 −0.7 0.3 Colombia 105.0 97.2 57.6 −104.4 92.5 −1.1 0.6 Comoros 1.7 0.9 1.1 −1.0 48.8 −1.1 1.2 Congo 13.2 2.7 14.8 −7.3 20.1 −2.7 5.5 Cook Islands 0.1 0.1 0.0 −0.1 81.9 −1.0 0.0 Costa Rica 7.6 7.1 4.9 −6.0 92.9 −0.8 0.7 Cote d'Ivoire 38.3 10.7 49.3 −10.7 27.8 −1.0 4.6 Croatia 53.2 32.2 −7.1 −41.3 60.6 −1.3 −0.2 Cuba 64.3 45.0 2.9 −33.2 70.1 −0.7 0.1 Cyprus 5.1 3.8 3.4 −7.2 74.5 −1.9 0.9 Czechia 139.7 52.2 3.8 −114.4 37.3 −2.2 0.1 North Korea 126.5 83.9 45.5 −5.8 66.3 −0.1 0.5 Democratic Republic of the Congo 145.6 0.5 176.8 −50.7 0.4 −101.4 353.6 Denmark 48.1 9.9 4.9 −41.0 20.5 −4.1 0.5 Djibouti 1.1 0.9 2.5 −0.6 81.9 −0.7 2.8 Dominica 0.4 0.2 0.0 −0.1 40.3 −0.5 0.0 Dominican Republic 24.7 19.3 16.9 −2.9 78.0 −0.2 0.9 Ecuador 26.6 17.1 22.1 −18.3 64.2 −1.1 1.3 Egypt 530.0 90.4 417.3 −295.9 17.1 −3.3 4.6 El Salvador 14.9 8.4 3.5 −6.9 56.7 −0.8 0.4 Equatorial Guinea 2.3 −0.8 4.2 −2.2 −33.7 − − Eritrea 13.7 4.1 11.8 −7.8 30.0 −1.9 2.9 Estonia 22.0 9.1 −3.2 −16.5 41.6 −1.8 −0.4 Eswatini 2.4 0.9 1.2 0.0 38.5 0.0 1.3 Ethiopia 183.9 10.8 159.2 −156.4 5.9 −14.5 14.7 Fiji 5.4 3.0 1.3 −1.7 56.6 −0.6 0.4 Finland 48.0 22.2 4.5 −39.1 46.3 −1.8 0.2 France 321.5 142.9 43.7 −240.9 44.5 −1.7 0.3 Gabon 5.1 −0.1 3.8 −1.8 −1.1 − − Gambia 3.2 0.7 4.8 −0.1 22.1 −0.1 6.9 Georgia 71.1 24.2 −24.5 −28.4 34.1 −1.2 −1.0 Germany 785.9 279.1 45.5 −593.7 35.5 −2.1 0.2 Ghana 69.5 16.2 83.3 −33.6 23.4 −2.1 5.1 Greece 83.4 51.1 −1.8 −59.4 61.3 −1.2 0.0 Greenland 0.3 0.2 0.0 −0.3 63.8 −1.5 0.0 Grenada 0.6 0.2 0.1 −0.3 27.4 −1.5 0.5 Guam 0.6 0.7 0.1 −0.3 111.0 −0.4 0.1 Guatemala 18.9 12.8 17.0 −14.1 68.0 −1.1 1.3 Guinea 28.6 −8.8 30.4 −4.3 −30.6 − − Guinea−Bissau 5.7 −0.5 5.2 −1.9 −8.2 − − Guyana 5.6 2.9 −0.1 −2.9 51.9 −1.0 0.0 Haiti 51.3 6.3 46.3 −24.3 12.3 −3.9 7.3 Honduras 12.6 8.1 17.7 1.7 64.7 0.2 2.2 Hungary 154.1 51.9 −10.6 −92.9 33.7 −1.8 −0.2 Iceland 1.4 0.5 0.4 −1.2 38.8 −2.4 0.8 India 3,689.0 2,092.5 2,707.6 −1,041.9 56.7 −0.5 1.3 Indonesia 904.0 618.5 571.2 −62.7 68.4 −0.1 0.9 Iran 242.5 212.6 129.4 −211.7 87.6 −1.0 0.6 Iraq 104.6 39.5 126.6 −54.4 37.8 −1.4 3.2 Ireland 26.9 8.8 7.5 −28.7 32.8 −3.3 0.9 Israel 23.9 8.4 16.7 −29.7 35.0 −3.5 2.0 Italy 395.7 214.2 19.8 −312.6 54.1 −1.5 0.1 Jamaica 9.7 4.2 1.9 −3.0 42.9 −0.7 0.5 Japan 562.6 573.0 9.8 −545.7 101.8 −1.0 0.0 Jordan 13.2 11.0 27.3 −21.1 83.2 −1.9 2.5 Kazakhstan 129.6 26.1 20.1 −29.9 20.1 −1.1 0.8 Kenya 38.1 14.5 50.2 2.1 38.1 0.1 3.5 Kiribati 0.5 0.1 0.3 −0.1 23.4 −1.0 3.0 Kuwait 4.9 4.5 8.3 −5.8 92.3 −1.3 1.8 Kyrgyzstan 30.7 1.7 14.8 −10.3 5.7 −6.1 8.7 Laos 33.5 9.3 22.6 −22.7 27.8 −2.4 2.4 Latvia 39.4 16.1 −11.7 −18.1 40.8 −1.1 −0.7 Lebanon 20.1 10.2 14.2 −23.8 50.9 −2.3 1.4 Lesotho 5.0 0.2 1.4 3.0 3.9 15.0 7.0 Liberia 11.0 −2.2 11.3 −3.2 −19.5 − − Libya 16.5 8.5 12.4 0.7 51.2 0.1 1.5 Lithuania 41.9 22.5 −11.7 −19.5 53.6 −0.9 −0.5 Luxembourg 3.1 0.4 1.3 −2.7 14.2 −6.4 2.7 Madagascar 63.7 −3.4 80.0 −18.9 −5.3 − − Malawi 30.0 −0.5 27.9 −3.7 −1.6 − − Malaysia 78.5 59.2 70.7 −40.7 75.5 −0.7 1.2 Maldives 1.0 0.7 1.2 −1.6 72.1 −2.3 1.7 Mali 31.0 −7.1 44.7 −14.2 −22.9 − − Malta 2.5 1.9 0.5 −2.8 74.7 −1.5 0.3 Marshall Islands 0.3 0.2 0.1 0.0 71.4 0.0 0.5 Mauritania 9.9 0.3 9.4 −5.4 2.7 −18.0 31.3 Mauritius 7.8 6.5 1.3 −7.3 82.7 −1.1 0.2 Mexico 177.0 175.9 118.9 −52.2 99.3 −0.3 0.7 Micronesia 0.8 0.4 0.0 −0.2 45.6 −0.5 0.0 Monaco 0.3 0.0 0.1 −0.2 10.9 − − Mongolia 12.9 5.6 6.9 −7.3 43.0 −1.3 1.2 Montenegro 4.9 2.7 −0.1 0.2 54.4 0.1 0.0 Morocco 174.7 102.5 89.6 −65.5 58.7 −0.6 0.9 Mozambique 47.0 −13.7 59.5 6.0 −29.0 − − Myanmar 329.6 137.9 122.6 −211.2 41.8 −1.5 0.9 Namibia 5.4 1.6 4.0 −1.4 29.4 −0.9 2.5 Nauru 0.1 0.0 0.0 0.0 14.0 − − Nepal 81.6 42.4 51.1 −36.1 52.0 −0.9 1.2 Netherlands 96.9 38.6 12.6 −82.0 39.8 −2.1 0.3 New Zealand 22.5 8.4 9.4 −21.6 37.2 −2.6 1.1 Nicaragua 6.8 5.2 5.4 −3.8 76.0 −0.7 1.0 Niger 24.7 −1.1 43.9 −14.9 −4.3 − − Nigeria 369.6 −85.5 447.3 −199.8 −23.1 − − Niue 0.0 0.0 0.0 0.0 − − − North Macedonia 19.6 10.9 2.0 −7.6 55.3 −0.7 0.2 Northern Mariana Islands 0.2 0.2 0.0 0.0 113.1 0.0 0.0 Norway 37.8 4.3 7.3 −31.4 11.3 −7.3 1.7 Oman 9.2 2.6 10.4 −9.0 28.3 −3.5 4.0 Pakistan 448.9 29.6 527.5 42.5 6.6 1.4 17.8 Palau 0.1 0.1 0.0 0.0 78.2 0.0 0.0 Palestine 10.0 1.4 12.3 −8.2 13.7 −5.9 8.8 Panama 6.4 4.4 5.4 −4.3 68.7 −1.0 1.2 Papua New Guinea 21.7 3.9 32.8 −6.4 18.1 −1.6 8.4 Paraguay 12.7 6.7 10.3 −5.5 52.9 −0.8 1.5 Peru 51.0 27.3 33.4 −37.9 53.5 −1.4 1.2 Philippines 242.1 152.6 233.8 −9.8 63.0 −0.1 1.5 Poland 424.2 196.0 0.7 −325.4 46.2 −1.7 0.0 Portugal 85.3 52.1 3.6 −89.2 61.1 −1.7 0.1 Puerto Rico 17.0 14.8 −1.7 −15.0 87.2 −1.0 −0.1 Qatar 1.3 0.9 5.5 −3.8 67.5 −4.2 6.1 Republic of Korea 177.2 217.3 31.9 −275.4 122.6 −1.3 0.1 Moldova 44.3 27.3 −9.9 −18.3 61.5 −0.7 −0.4 Romania 284.5 167.5 −59.7 −139.8 58.9 −0.8 −0.4 Russian Federation 1,901.1 673.9 −80.5 −600.6 35.4 −0.9 −0.1 Rwanda 37.9 7.5 24.5 −35.6 19.8 −4.7 3.3 Saint Kitts and Nevis 0.4 0.1 0.1 −0.3 22.6 −3.0 1.0 Saint Lucia 0.7 0.7 0.2 −0.7 96.2 −1.0 0.3 Saint Vincent and the Grenadines 0.6 0.4 0.0 −0.3 71.4 −0.8 0.0 Samoa 0.9 0.3 0.3 −0.1 34.3 −0.3 1.0 San Marino 0.1 0.1 0.0 −0.1 57.4 −1.0 0.0 Sao Tome and Principe 0.4 0.0 0.3 0.0 −2.8 − − Saudi Arabia 67.1 42.9 98.5 −27.2 64.0 −0.6 2.3 Senegal 31.6 5.6 31.8 −12.0 17.6 −2.1 5.7 Serbia 115.4 78.1 −9.4 −70.0 67.7 −0.9 −0.1 Seychelles 0.5 0.2 0.2 −0.3 38.0 −1.5 1.0 Sierra Leone 21.0 −3.5 20.4 −5.4 −16.6 − − Singapore 13.8 12.1 10.9 −22.4 87.9 −1.9 0.9 Slovakia 60.4 25.5 1.6 −38.1 42.2 −1.5 0.1 Slovenia 15.5 8.6 0.7 −13.7 55.6 −1.6 0.1 Solomon Islands 2.1 0.8 2.3 −0.3 38.9 −0.4 2.9 Somalia 26.5 −1.4 38.3 −14.1 −5.1 − − South Africa 124.6 58.6 76.0 −15.1 47.0 −0.3 1.3 South Sudan 21.7 −1.6 12.3 −5.2 −7.6 − − Spain 227.4 119.3 36.5 −192.9 52.5 −1.6 0.3 Sri Lanka 78.1 63.6 26.6 −50.7 81.4 −0.8 0.4 Sudan 164.0 −3.2 150.1 −106.7 −1.9 − − Suriname 2.1 1.2 1.1 −1.2 59.6 −1.0 0.9 Sweden 81.0 13.5 12.9 −59.1 16.7 −4.4 1.0 Switzerland 44.9 13.7 10.6 −39.4 30.6 −2.9 0.8 Syrian Arab Republic 85.8 67.5 10.5 −38.9 78.7 −0.6 0.2 Taiwan (Province of China) 77.3 81.6 13.9 −87.0 105.5 −1.1 0.2 Tajikistan 29.4 3.2 23.3 −13.9 10.7 −4.3 −7.3 Thailand 180.6 223.3 42.8 −128.0 123.6 −0.6 0.2 Timor−Leste 2.7 2.0 2.6 −0.4 71.8 −0.2 1.3 Togo 12.0 5.1 16.4 −2.9 42.8 −0.6 3.2 Tokelau 0.0 0.0 0.0 0.0 − − − Tonga 0.4 0.1 0.0 0.0 32.9 0.0 0.0 Trinidad and Tobago 7.4 5.8 1.3 −5.5 79.0 −0.9 0.2 Tunisia 38.8 33.5 19.2 −19.7 86.5 −0.6 0.6 Turkey 315.7 219.4 143.6 −267.8 69.5 −1.2 0.7 Turkmenistan 24.3 13.6 11.4 −2.9 55.8 −0.2 0.8 Tuvalu 0.1 0.0 0.0 0.0 18.1 − − Uganda 48.6 −4.6 60.7 −23.4 −9.4 − − Ukraine 714.6 237.2 −150.3 −46.0 33.2 −0.2 −0.6 United Arab Emirates 5.5 8.2 21.7 −16.3 148.9 −2.0 2.6 United Kingdom 552.3 91.3 74.7 −440.4 16.5 −4.8 0.8 Tanzania 76.8 5.8 91.8 −18.7 7.5 −3.2 15.8 USA 1,700.1 576.0 486.8 −986.9 33.9 −1.7 0.8 United States Virgin Islands 0.6 0.6 −0.1 −0.5 105.3 −0.8 −0.2 Uruguay 24.4 6.4 1.8 −14.9 26.3 −2.3 0.3 Uzbekistan 117.4 49.5 82.9 −19.0 42.2 −0.4 1.7 Vanuatu 1.1 0.5 1.2 −0.2 43.7 −0.4 2.4 Venezuela 64.8 73.5 36.1 −28.2 113.5 −0.4 0.5 Viet Nam 285.5 209.8 162.8 −71.9 73.5 −0.3 0.8 Yemen 83.1 17.3 108.4 −50.2 20.8 −2.9 6.3 Zambia 24.6 0.6 34.4 −4.0 2.5 −6.7 57.3 Zimbabwe 23.2 3.8 15.8 16.9 16.3 4.4 4.2 Note: For countries where population aging was associated with increases in DALYs between 1990 and 2021, we calculated the R1 and R2. R1 was calculated as “DALYs attributed to DALY rate change / DALYs attributed to population aging”; R2 was calculated as “DALYs attributed to population growth / DALYs attributed to population aging”.
Abbreviation: DALYs=disability-adjusted life years.Table S1. Proportion of DALYs associated with population aging and comparative contributions from 1990 to 2021 across 204 countries and territories.
-
This study examined the burden of CVD and its trends from 1990 to 2021, concentrating on the changes in DALYs that resulted from population aging, using a decomposition method. During this period, the global ASDR decreased by 1.3%, whereas the global DALYs attributable to total CVD increased by 44.0%. Significant variations in DALY changes associated with population aging were observed across SDI categories, regions, countries, and types of CVD. Given the influence of population aging on the escalating burden of CVD, there was an urgent need for increased investments in healthcare infrastructure, enhancements in screening and early intervention programs for high-risk elderly populations, and the implementation of public health education campaigns to foster awareness and encourage healthy lifestyles.
The analysis highlighted global disparities in the burden and trends of CVD. ASDRs had declined in most countries, whereas DALYs saw an increase from 1990 to 2021. For example, China witnessed a 58.5% surge in CVD-related DALYs, escalating from 63.2 million in 1990 to 100.2 million in 2021. This rise was linked to rapid economic transformation, industrialization, urbanization, and globalization in developing nations over the past 32 years. These developments markedly altered lifestyles and diets, consequently contributing to the increase in CVD. Simultaneously, China had advanced the standardization of clinical pathways for major CVDs, which shortened hospital stays, improved the quality of care, enhanced treatment effectiveness, and significantly boosted patient survival rates (4). These advancements provide valuable experience for the development of CVD prevention and control strategies in China, underscoring the importance of comprehensive reforms in healthcare systems, social security, and risk factor management.
Population aging correlates with a consistent increase in DALYs, which is mainly due to higher DALY rates among the elderly and an expanding older population segment. Aging results in a gradual decline in physiological integrity, diminished function, and an elevated vulnerability to diseases and mortality in older age groups. Although social advancements and enhancements in healthcare services have extended life expectancy, the effects of population aging differ significantly by region and country. Regions with higher SDI benefit from superior education, healthcare systems, and policy priorities, facilitating more efficient management of disease burdens. Conversely, regions with lower SDI, which are hampered by inadequate healthcare infrastructure, find it challenging to effectively mitigate disease burdens. As low-income countries progress, they encounter challenges linked to population aging and should draw lessons from the experiences of high-income nations (e.g., equitable healthcare, drug availability, and fertility policies) to allocate resources towards proven health interventions that promote healthy aging (5).
Despite the challenges posed by an aging population, the increase in DALYs for total CVD and specific categories has been mitigated by decreasing DALY rates over time. This favorable outcome likely results from reduced risk-attribution rates that effectively counterbalance the DALYs increase due to aging. This reflects advancements in the prevention and control of diseases such as stroke and peripheral artery disease (6). However, progress has been uneven across different disease categories. To sustain these gains, significant investments are necessary to improve disease monitoring, early warning systems, and healthcare infrastructure, especially in regions where the effects of population aging surpass the reductions in DALY rates. It is crucial to implement continuous, cost-effective interventions and policies to meet the 2030 goal of a ≥30% reduction in premature noncommunicable disease mortality (7). Enhancing primary prevention through better control of risk factors, improving access to early screening and diagnosis for timely treatment, and strengthening healthcare capacity, particularly in primary healthcare services, are essential steps.
This study is subject to some limitations. First, the findings were contingent on the quality of DALYs and population estimates from the GBD 2021, which might be subject to bias stemming from variations in population-based studies and access to CVD diagnostics across different countries. Second, factors such as increasing life expectancy and declining fertility rates were not examined due to data constraints, although these are known to be associated with the rising burden of DALYs due to population aging. Lastly, our methodology only incorporated three variables and did not account for additional factors like lifestyle and healthcare accessibility.
In conclusion, the global burden of CVD has been profoundly influenced by demographic shifts. From 1990 to 2021, the global burden of CVD DALYs increased due to population aging, with variations observed across SDI levels, regions, countries, and types of CVD. Notably, significant reductions in DALY rates in certain regions largely mitigated the increases. Addressing the impact of aging populations requires collaborative efforts from stakeholders, policymakers, and researchers. Strategies may include promoting healthy lifestyles, adjusting fertility policies, enhancing healthcare access, and implementing interventions aimed at reducing CVD risk factors among the elderly.
HTML
| Citation: |
|