Front. Public HealthFrontiers in Public HealthFront. Public Health2296-2565Frontiers Media S.A.10.3389/fpubh.2025.1630811Original ResearchGlobal, regional, and national burden of ischemic heart disease attributable to environmental risk factors from 1990 to 2021: a systematic analysis based on the 2021 global burden of disease studyHuangHongtao1†Writing – original draftSunQingpiao2†SoftwareWriting – original draftLvWenqing3Writing – review & editingZhaoHanjun3Writing – review & editingHuangYu3*ResourcesWriting – review & editingWriting – original draftMethodologySupervisionDepartment of Cardiology, Gongli Hospital of Shanghai Pudong New Area, Shanghai, ChinaDepartment of Cardiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, ChinaDepartment of Cardiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaCorrespondence: Yu Huang, hy204308@163.com
These authors have contributed equally to this work and share first authorship
Ischemic heart disease (IHD) attributable to environmental factors is a global public health challenge. This study assesses the global burden of IHD due to environmental factors from 1990 to 2021 using Global Burden of Disease (GBD) 2021 data and projects trends to 2051.
Methods
Data on the mortality and disability-adjusted life years (DALYs) rates of IHD attributable to environmental factors were extracted from GBD 2021. The age-period-cohort (APC) model was employed to assess the independent effects of age, period, and cohort on the burden of IHD related to environmental factors. Additionally, the Bayesian age-period-cohort (BAPC) model was utilized to project future trends in the disease burden of IHD through 2051.
Results
In 2021, age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR) for IHD due to environmental factors were 39.70 (95% UI: 30.74, 47.81) and 827.52 (95% UI: 648.13, 987.15) per 100,000 population, respectively. Socio-demographic Index (SDI) was negatively correlated with ASMR and ASDR. APC analysis showed declining trends, while BAPC predicts ASMR and ASDR will decline by 53.67 (95% UI: 11.48, 95.86) and 986.76 (95% UI: 291.27, 1682.25) per 100,000 population by 2051.
Conclusion
The number of mortality and DALYs associated with IHD due to environmental factors has exhibited an increasing trend globally. However, projections suggest a general decline in the ASMR and ASDR of IHD burden attributable to environmental factors by 2050. Ambient particulate matter and household air pollution are the predominant contributors to the global IHD burden.
ischemic heart diseasesocio-demographic indexhealth inequalityglobal burden of disease 2021Bayesian age-period-cohort modelThe author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by The Key Discipline Construction Program of Shanghai Municipal Health System (2024ZDXK0024) and New Quality Clinical Specialty Program of High-end Medical Disciplinary Construction in Shanghai Pudong New Area (2024-PWXZ-03).section-at-acceptanceEnvironmental Health and ExposomeIntroduction
Ischemic Heart Disease (IHD) is a chronic condition characterized by reduced blood supply to the coronary arteries, leading to myocardial damage (1, 2). As one of the leading causes of mortality among non-communicable diseases (NCDs) worldwide, IHD poses a significant public health challenge, contributing to a substantial global medical and economic burden (3, 4). In response to the growing impact of NCDs, the United Nations has set a global target to reduce premature mortality from NCDs by one-third among individuals aged 30 to 70 years by 2030 (5). In 2019, IHD accounted for nearly 200 million years of life lost (YLLs) due to premature mortality, underscoring the urgent need for effective prevention and control strategies to mitigate this global health burden (6). Furthermore, IHD ranked as the second leading contributor to disability-adjusted life years (DALYs) across all age groups, accounting for 7.2% of total DALYs worldwide. Its burden was particularly pronounced among individuals aged 50, 74 years and those aged 75 years and older, making it the leading cause of mortality across all regions (1). The substantial healthcare costs and service demands associated with IHD further highlight the necessity of early interventions to prevent complications and reduce disease burden (7). According to projections from the Global Burden of Disease (GBD) 2019 study, both the prevalence and mortality of IHD were expected to rise significantly in the coming decades (1, 8).
The etiology of IHD is multifactorial, involving genetic, environmental, and behavioral determinants (6). Among these, environmental factors, particularly air pollution, have been increasingly recognized as significant contributors to IHD risk (9). Recent evidence confirms that both ambient and household air pollution act as major environmental drivers of IHD mortality, with their combined impact being particularly severe in low- and middle-income countries (10). Air pollution is the fourth leading global risk factor for morbidity and mortality, with more than 50% of air pollution-related mortality attributable to IHD and stroke (11, 12). Notably, PM2.5 pollution has been shown to disproportionately exacerbate the burden of environmentally induced IHD in low- and middle-income countries, particularly in regions such as Asia, Oceania, and sub-Saharan Africa (13). Critically, the burden of environmentally driven IHD is profoundly shaped by socio-economic development levels, which influence both exposure to pollutants and capacity for healthcare response (14).
However, existing studies examining the association between environmental risk factors and IHD often suffer from regional limitations or focus on individual risk factors, failing to comprehensively elucidate the long-term impact of environmental exposures on disease burden. Furthermore, a critical gap persists in understanding how socio-economic development (period effects), population aging (age effects), and generational exposure histories (cohort effects) interact to shape the temporal trends of environmentally attributable IHD. Consequently, robust, model-based long-term projections of this specific burden, which are crucial for proactive public health planning, remain scarce. Therefore, a multidimensional analysis of the relationship between environmental risk factors and health outcomes is essential to inform effective prevention and intervention strategies.
To address these gaps, we conducted a comprehensive assessment of the burden of IHD attributable to environmental factors from 1990 to 2021, leveraging the most recent data from the GBD 2021 study. Our study had three primary objectives: (1) to quantify the contribution of environmental factors to the global burden of IHD, (2) to analyze the independent effects of age, period, and cohort on the burden of environmentally induced IHD using the age-period-cohort (APC) model, and (3) to project trends in IHD burden attributable to environmental factors over the next 30 years. The findings of this study aim to provide a scientific basis for policymakers and public health practitioners to develop targeted prevention strategies and implement evidence-based interventions to mitigate the future burden of IHD.
MethodsData source
This study utilized data from the GBD 2021, published by the Institute for Health Metrics and Evaluation (IHME).1 The GBD database is the most comprehensive and detailed source of information on global diseases, injuries, and risk factors. The classification of world regions and locations followed the standard geographic hierarchy used in the GBD 2021 study, which includes 204 countries and territories aggregated into 21 regional groups. The complete list of locations and their regional assignments is available in the primary GBD 2021 publications (15). All data used in this study are publicly available and do not require additional ethical approval (16).
Case definition and data standards
Cases of IHD were defined according to the International Classification of Diseases, Tenth Revision (ICD-10) codes I20-I25 (17). Mortality and prevalence data in the GBD 2021 study were mapped to these codes. The Cause of Death Ensemble model (CODEm), a robust modeling framework developed by IHME, was used as the standard for calculating mortality estimates. This framework systematically incorporates and weights data from vital registration systems, verbal autopsy, and other sources to produce cause-specific mortality estimates (18).
Data collection
The GBD study employs DisMod-MR 2.1, a Bayesian meta-regression modeling framework, to estimate disease burden by integrating multiple data sources. This model accounts for measurement differences in mortality rates, case definitions, and data collection methodologies across different studies, allowing for standardized estimates of IHD mortality attributable to environmental factors (19).
Disability-adjusted life years (DALYs) rate is an indicator for measuring the disease burden, which combines the years of life lost (YLLs) due to premature death and the years of life lost due to disability (YLDs) caused by diseases. This indicator enables a comprehensive assessment of the impact of diseases and medical interventions, which is particularly important for disease prevention and control. The computation of DALYs follows the standard GBD methodology, where DALYs are the sum of YLLs and YLDs, providing a comprehensive metric of overall health loss (20). Using the relevant data provided by GBD 2021, we calculated the mortality and DALYs rates for diseases attributed to environmental factors and further analyzed their trends (21).
Socio-demographic index
The Socio-demographic index (SDI) is a composite measure that reflects the social and economic conditions influencing health outcomes. It is calculated based on educational attainment, per capita income, and total fertility rate, ranging from 0 to 1, where higher values indicate greater socio-economic development. Based on SDI values, countries and regions are classified into five categories: low SDI [0–0.4658), low-middle SDI [0.4658–0.6188), middle SDI [0.6188–0.7120), high-middle SDI [0.7120–0.8103), and high SDI [0.8103–1.0000] (22). This study analyzed the association between the burden of IHD attributable to environmental factors and socio-economic development levels across different SDI regions (23).
APC model
To assess the independent effects of age, period, and birth cohort on IHD burden, we applied the APC model, which provides a robust framework for disentangling the contributions of biological aging, historical period effects, and generational influences on disease trends. This model is particularly suited for identifying the underlying drivers of long-term trends in disease burden, as demonstrated in prior GBD-based studies (24). This approach goes beyond traditional epidemiological methods by incorporating the effects of social and technological changes over time. In our study, it allows us to determine whether trends in environmentally attributable IHD are driven by population aging, period-specific interventions, or the cumulative exposures of specific birth cohorts. The input data for the APC model included mortality and DALYs rates estimates for IHD attributable to environmental factors, as well as population data for each country and region from GBD 2021. The APC model requires equal intervals for both age groups and periods, ensuring consistency in the analysis (i.e., 5-year age groups aligned with 5-year periods) (25).
Health inequality analysis
To evaluate inequalities in the burden of attributable to environmental factors across socio-economic groups, we utilized two standard epidemiological indicators. The slope index of inequality (SII), a measure of absolute inequality, was calculated by regressing national mortality and DALYs rates on a ranked SDI scale, capturing the gradient of disparities in disease burden across SDI levels. The concentration index (CI), a measure of relative inequality, was derived from the Lorenz curve, quantifying the deviation of actual health outcomes from perfect equality. CI was computed by integrating cumulative mortality and DALYs rates against the cumulative proportion of the population ranked by SDI, with higher CI values indicating a more unequal distribution of disease burden (26).
Risk factors
In addition to analyzing overall IHD burden, this study also examined the impact of specific environmental risk factors on IHD mortality and DALYs rates. According to GBD 2021, key environmental risk factors include: household air pollution, ambient particulate matter pollution (PM2.5 exposure), low temperature, high temperature and lead exposure. The estimation of risk-attributable burden was based on the exposure distributions, theoretical minimum risk exposure levels (TMRELs), and effect sizes defined within the GBD 2021 comparative risk assessment framework (27). We assessed the relative contribution of each risk factor to the burden of IHD, providing insights into potential intervention strategies.
Predictions of the burden
To predict future trends in the burden of IHD attributable to environmental factors, we applied the Bayesian age-period-cohort (BAPC) model, a statistical approach that leverages historical trends to estimate future disease burden. The BAPC model is well-established for generating stable and interpretable long-term forecasts of disease burden, as evidenced by its application in forecasting other non-communicable diseases (28). The Integrated Nested Laplace Approximation (INLA) method was used to improve computational efficiency and ensure the accuracy of long-term predictions. This approach is critical for projecting the future course of IHD burden under current trends, thereby informing long-term public health planning. This model was used to forecast mortality and DALYs rates of IHD attributable to environmental factors up to 2051 (27).
Statistical analysis
To assess temporal trends in the age-standardized rates (ASR) of IHD attributable to environmental factors, we estimated the annual percentage change (EAPC). The ASR per 100,000 population was calculated using the following formula (29):
ASR=∑i=1Aaiwiwi×100000
where ai represents the age-specific population proportion, and Wi: the number of people in the corresponding ith age group in the standard population, A: the number of age groups.
The EAPC is calculated based on a log-linear regression model, where time is considered an independent variable. By fitting the natural logarithm of ASR to a straight line, the trend over time can be quantified using the slope of this regression line. The EAPC is computed using the following formula (30):
y=α+βx+εEAPC=100×[exp(β)−1]
where x: year, y: the natural logarithm of the rate, α: intercept, β: slope, ε: random error. The 95% uncertainty interval (UI) of EAPC also comes from this fitted model. Data analysis was conducted using R software (version 4.4.1). To ensure reproducibility, the R code used for analysis has been deposited in a permanent repository. The code is available via Zenodo at the following Digital Object Identifier (DOI): [10.6084/m9.figshare.30549986]. Figures were refined using Adobe Illustrator (version 2024).
ResultsGlobal trends of IHD
Between 1990 and 2021, the ASMR for IHD attributable to environmental factors declined from 57.64 per 100,000 population (95% UI: 45.79, 68.89) to 39.70 per 100,000 population (95% UI: 30.74, 47.81), with an EAPC of −1.27 (95% UI: −1.34, −1.19; Table 1). Similarly, the ASDR decreased from 1179.60 per 100,000 population (95% UI: 942.76, 1410.91) in 1990 to 827.52 per 100,000 population (95% UI: 648.13, 987.15) in 2021, with an EAPC of −1.23 (95% UI: −1.31, −1.16; Table 1).
Mortality and ASMR for IHD due to environmental factors in 1990 and 2021, with EAPCs from 1990 to 2021.
Location
Number in 1990 (95%CI)
Age-standardized mortality rate per 100,000 population in 1990 (95%UI)
Number in 2021 (95%CI)
Age-standardized mortality rate per 100,000 population in 2021 (95%UI)
1990–2021 EAPC (95%CI)
Global
1,994,917 (1,595,529,2,389,340)
57.64 (45.79,68.89)
3,302,951 (2,562,529,3,977,698)
39.7 (30.74,47.81)
−1.27 (−1.34, −1.19)
High SDI
448,498 (328,846,572,395)
40.74 (29.82,51.97)
283,197 (207,585,355,256)
12.06 (8.84,15.01)
−4.24 (−4.32, −4.15)
High-middle SDI
570,481 (441,037,704,233)
68.51 (52.16,84.43)
751,299 (576,094,924,686)
39.08 (29.91,48.03)
−2.08 (−2.32, −1.84)
Middle SDI
469,053 (380,961,556,424)
57.17 (45.91,67.78)
1,133,319 (865,376,1,381,260)
48 (36.18,58.25)
−0.44 (−0.58, −0.29)
Low-middle SDI
381,876 (310,676,451,884)
71.42 (58.02,84.41)
872,505 (694,815,1,031,237)
67.64 (53.54,80.26)
−0.03 (−0.12,0.06)
Low SDI
121,809 (96,643,145,923)
63.93 (51.08,76.53)
259,745 (207,978,309,125)
61.93 (49.91,73.6)
0.01 (−0.11,0.13)
Andean Latin America
8,152 (6,461,9,902)
44.7 (35.26,54.22)
10,696 (7,485,14,433)
18.92 (13.23,25.51)
−3.2 (−3.49, −2.91)
Australasia
6,990 (3,514,11,079)
30.76 (15.42,48.75)
5,304 (3,278,7,235)
8.59 (5.36,11.67)
−4.55 (−4.74, −4.37)
Caribbean
13,823 (8,870,19,296)
57.97 (37.04,81.19)
19,180 (12,226,26,468)
35.22 (22.52,48.62)
−1.58 (−1.77, −1.38)
Central Asia
48,062 (34,916,62,842)
117.14 (84.99,153.26)
63,357 (48,386,76,915)
96.01 (73.21,116.52)
−1.1 (−1.41, −0.79)
Central Europe
133,350 (92,488,171,385)
99.85 (68.89,128.32)
88,799 (67,619,109,407)
37.47 (28.57,46.13)
−3.55 (−3.73, −3.38)
Central Latin America
34,422 (24,983,43,673)
49.01 (35.55,62.23)
67,628 (44,626,91,092)
28.5 (18.77,38.43)
−1.92 (−2.07, −1.77)
Central Sub-Saharan Africa
11,409 (8,363,15,223)
66.07 (49.75,85.88)
22,767 (16,285,30,273)
54.88 (39.13,72.96)
−0.8 (−0.87, −0.73)
East Asia
295,511 (233,070,358,054)
48.67 (38.8,58.36)
894,463 (669,150,1,129,102)
48.42 (36.07,60.91)
0.44 (0.05,0.84)
Eastern Europe
251,788 (162,574,335,861)
103.55 (66.6,138.4)
189,276 (136,310,247,483)
52.92 (38.12,69.22)
−2.9 (−3.4, −2.38)
Eastern Sub-Saharan Africa
22,768 (18,147,27,759)
36.36 (28.83,44.09)
50,766 (39,128,61,291)
36.86 (28.51,44.69)
−0.14 (−0.23, −0.05)
High-income Asia Pacific
27,630 (15,976,42,388)
15.67 (9.03,24.07)
35,912 (24,578,47,050)
6.01 (4.22,7.76)
−3.11 (−3.31, −2.91)
High-income North America
144,974 (90,721,206,936)
39.95 (24.99,57.06)
76,918 (49,741,104,284)
10.86 (7.11,14.65)
−4.69 (−4.93, −4.46)
North Africa and Middle East
169,398 (133,888,203,091)
120.49 (94.5,144.48)
330,659 (260,073,405,105)
86.56 (67.35,105.68)
−1.1 (−1.13, −1.08)
Oceania
1927 (1,435,2,561)
77.05 (58.29,101.72)
4,423 (3,186,5,786)
67.34 (49.04,87)
−0.39 (−0.46, −0.33)
South Asia
373,446 (301,568,448,915)
72.42 (58.4,86.69)
986,089 (778,522,1,171,577)
74.11 (58.71,88.22)
0.24 (0.14,0.35)
Southeast Asia
113,560 (89,415,139,254)
51.85 (40.91,63.79)
215,368 (158,594,270,525)
37.7 (27.76,47.09)
−1.11 (−1.28, −0.93)
Southern Latin America
18,733 (12,666,25,326)
44.52 (29.98,60.24)
11,943 (8,401,15,764)
13.23 (9.31,17.44)
−3.69 (−3.88, −3.49)
Southern Sub-Saharan Africa
7,140 (5,511,8,813)
30.3 (23.12,37.58)
13,712 (10,436,16,961)
28.48 (21.65,35.15)
−0.24 (−0.68,0.2)
Tropical Latin America
33,243 (22,074,45,587)
42.29 (27.92,57.85)
34,882 (21,394,47,427)
13.89 (8.51,18.93)
−3.57 (−3.65, −3.48)
Western Europe
240,865 (158,124,335,087)
40.58 (26.52,56.41)
102,627 (72,752,132,254)
8.87 (6.4,11.36)
−5.22 (−5.37, −5.06)
Western Sub-Saharan Africa
37,726 (29,037,48,313)
52.95 (41.13,67.8)
78,182 (59,816,95,998)
51.54 (39.15,63.13)
−0.08 (−0.22,0.06)
Among SDI regions, the low-middle SDI group exhibited the highest ASMR (67.64 per 100,000 population, 95% UI: 53.54–80.26) and ASDR (1482.02 per 100,000 population, 95% UI: 1181.93–1753.75), with EAPCs of −0.03 (95% UI: −0.12. 0.06) and −0.19 (95% UI: −0.27, −0.11), respectively. In contrast, the high SDI group recorded the lowest ASMR (12.06 per 100,000 population, 95% UI: 8.84–15.01) and ASDR (240.11 per 100,000 population, 95% UI: 182.12–296.64), with EAPCs of −4.24 (95% UI: −4.32, −4.15) and −4.00 (95% UI: −4.09, −3.92), respectively (Table 1).
Regionally, Central Asia had the highest ASMR (96.01 per 100,000 population, 95% UI: 73.21–116.52) and ASDR (1749.56 per 100,000 population, 95% UI: 1328.65–2124.50), with EAPCs of −1.10 (95% UI: −1.41, −0.79) and −1.38 (95% UI: −1.74, −1.02), respectively. Conversely, the High-income Asia Pacific region exhibited the lowest ASMR (6.01 per 100,000 population, 95% UI: 4.22–7.76) and ASDR (113.84 per 100,000 population, 95% UI: 82.47–145.82), with EAPCs of −3.11 (95% UI: −3.31, −2.91) and −2.90 (95% UI: −3.03, −2.77), respectively (Table 1).
At the national level, Egypt (181.22 per 100,000, 95% UI: 139.82–227.91), Afghanistan (154.48 per 100,000, 95% UI: 113.30–201.29), and Vanuatu (141.56 per 100,000, 95% UI: 108.04–176.99) recorded the highest ASMRs in 2021. Conversely, the lowest ASMRs were observed in Puerto Rico (4.08 per 100,000, 95% UI: 1.34–6.83), San Marino (4.47 per 100,000, 95% UI: 2.72–6.59), and Norway (5.07 per 100,000, 95% UI: 3.26–6.86; Figure 1A). Similarly, the highest ASDRs were reported in Egypt (3538.05 per 100,000, 95% UI: 2708.97–4480.72), Afghanistan (3361.53 per 100,000, 95% UI: 2399.57–4478.56), and Vanuatu (3265.58 per 100,000, 95% UI: 2457.72–4084.33). In contrast, the lowest ASDRs were observed in San Marino (78.89 per 100,000, 95% UI: 48.91–116.05), Puerto Rico (82.50 per 100,000, 95% UI: 28.45–139.64), and Norway (83.19 per 100,000, 95% UI: 54.37–112.99; Figure 1B).
Burden of IHD attributable to environmental factors across 204 countries and regions in 2021. (A) ASMR of IHD due to environmental factors. (B) ASDR of IHD due to environmental factors.
Two world maps display ASMR and ASDR data for 2021. Map A shows ASMR with a color gradient from blue to red, indicating increasing values from 50 to 150. Map B illustrates ASDR using a similar gradient, with values ranging from 1000 to 3000. Regions with higher rates are highlighted in warmer colors.Trends of IHD due to environmental factors by age and sex
The mortality rate and number of mortality due to IHD increased with age, with higher mortality rates in males compared to females across all age groups. The highest mortality in males was observed in the 70–74 age group, while the highest female mortality was recorded in the 80–84 age group (Figure 2A). Similarly, DALYs and DALYs rates also increased with age, with males experiencing a higher DALYs burden than females in most age groups. The highest number of DALYs in males was observed in the 65–69 age group, whereas for females, the peak was in the 70–74 age group (Figure 2B).
Global burden of IHD due to environmental factors stratified by age and sex in 2021. (A) Global mortality and mortality rates by age and sex. (B) DALYs and DALYs rates by age and sex.
Chart A displays the number of deaths and rates per 100,000 by age and gender in 2021. Chart B depicts DALYs (Disability-Adjusted Life Years) and rates per 100,000 for the same demographics. Both charts show increasing trends with age; males generally have higher numbers and rates across categories, depicted with bars and lines.Trends in environmental factors leading to IHD by SDI classification
In 2021, both the ASMR and ASDR exhibited a wave-like relationship with the SDI. When SDI was below 0.46, both ASMR and ASDR increased rapidly. Between SDI 0.46 and 0.6, these rates gradually declined, followed by a slow increase in the SDI range of 0.6 to 0.7. However, when SDI exceeded 0.7, ASMR and ASDR sharply decreased (Figures 3A,C). While this trend was generally consistent across regions, Sub-Saharan Africa, Central Asia, Central Europe, and Eastern Europe exhibited higher overall levels of ASMR and ASDR (Figures 3A,C). Further analysis indicated that the burden of IHD attributable to environmental factors was most pronounced in middle SDI regions, with ASMR and ASDR peaking around an SDI of 0.6 before gradually declining (Figures 3B,D). Overall, SDI demonstrated a negative correlation with both ASMR and ASDR, suggesting that the burden of IHD was highest in middle SDI regions and relatively lower in high SDI countries.
Trends in environmental factors leading to IHD from 1990 to 2021 by SDI classification. (A) Relationship between IHD ASMR and SDI across 21 GBD regions. (B) Correlation analysis of IHD ASMR and SDI. (C) Relationship between IHD ASDR and SDI across 21 GBD regions. (D) Correlation analysis of IHD ASDR and SDI.
Four-panel image analyzing age-standardized mortality rates versus the socio-demographic index (SDI). Panels A and B show trends from 1990 to 2020 across global regions with various trajectories for ASMR and ASDR. Panels C and D depict a scatter plot for 2019 data, highlighting an inverse relationship between ASMR/ASDR and SDI, with countries color-coded by SDI quintiles. Regression lines indicate negative correlations, with significant p-values. A legend details locations and SDI categories.APC model
Using the APC model, this study assessed the burden of IHD attributable to environmental factors. The results indicated an increase in global mortality and DALYs rates of IHD with advancing age, reaching their highest levels in the 95 + age group, at 1153.58 (95% UI: 1126.51, 1181.31) per 100,000 population and 9485.96 (95% UI: 9156.76, 9827.01) per 100,000 population (Figures 4A,D), respectively. Period effects demonstrated a declining trend in both mortality and DALYs rates. Between 2017 and 2021, the risks ratios (RR) of mortality and DALYs were at their lowest, recorded at 0.8 (95% UI: 0.79, 0.81) and 0.802 (95% UI: 0.794, 0.809; Figures 4B,E), respectively. Cohort effects revealed a downward trend in mortality and DALYs rates among more recent birth cohorts. Specifically, the cohort born between 2002 and 2006 exhibited the lowest rates, with RRs of 0.588 (95% UI: 0.375, 0.92) for mortality and 0.589 (95% UI: 0.459, 0.755) for DALYs rates (Figures 4C,F).
Age-period-cohort analysis of the global mortality and DALYs rate for IHD due to Environmental Factors. (A) Age effect on mortality rate; (B) period effect on mortality rate; (C) cohort effect on mortality rate; (D) age effect on DALYs rate; (E) period effect on DALYs rate; (F) cohort effect on DALYs rate.
Multiple graphs showing age-related data. Chart A and D are red longitudinal age curves showing rate increase with age from 20 to over 100 years. Chart B and E are blue period relative rates, decreasing from 1995 to 2020. Chart C and F are green cohort relative rates showing a decline from early 1900s to 2000s.Health inequality analysis
From 1990 to 2021, the health inequality outcomes of IHD due to environmental factors showed a downward trend in the SII for both mortality and DALYs rates, which decreased from 27.78 to 5.45 and from 433.81 to 49.25, respectively, indicating a reduction in the absolute disparity in mortality and DALYs rates (Figures 5A,C). However, the CI for mortality and DALYs rates increased, from 0.04 to 0.09 and from 0.11 to 0.15, respectively, suggesting a growing inequality in the distribution of these rates across different SDI levels (Figures 5B,D). Overall, while some indicators show a reduction in the absolute disparity of health inequalities, the overall unevenness in distribution has increased.
Absolute and relative cross-national inequalities in IHD mortality and DALYs rates from 1990 to 2021. (A) Health inequality regression curve for mortality rate. (B) Concentration curve for mortality rate. (C) Health inequality regression curve for DALYs rate. (D) Concentration curve for DALYs rate.
Four charts labeled A, B, C, and D compare health data from 1990 and 2021. Chart A shows crude DALYs rate versus relative rank by SDI with a positive inequality slope. Chart B displays cumulative DALYs against population fraction, indicating concentration index changes. Chart C presents crude mortality rate versus SDI rank with a smaller inequality slope. Chart D illustrates cumulative mortality against population fraction with index shifts. Orange represents 2021, and blue represents 1990. Circle sizes depict population, with larger circles indicating bigger populations.
From 1990 to 2021, health inequality outcomes related to IHD due to environmental factors showed a downward trend in the SII for both mortality and DALYs rates. The SII for mortality decreased from 27.78 to 5.45, while the SII for DALYs declined from 433.81 to 49.25, indicating a reduction in absolute disparities in these measures (Figures 5A,C). However, the CI for both mortality and DALYs increased from 0.04 to 0.09 and from 0.11 to 0.15, respectively, suggesting a growing inequality in the distribution of these rates across different SDI levels (Figures 5B,D). Taken together, these findings indicate that while absolute disparities in health inequalities have declined, relative inequalities in distribution have increased.
Risk factor analysis
Between 1990 and 2021, ambient particulate matter pollution was the primary environmental risk factor contributing to IHD mortality and DALYs rates in global, high SDI, and high-middle SDI regions, although its contribution declined annually. Mortality rates decreased from 25.55 per 100,000 (95% UI: 17.27, 34.24) to 20.85 per 100,000 (95% UI: 14.63, 27.57) globally, from 26.12 per 100,000 (95% UI: 15.90, 36.69) to 6.71 per 100,000 (95% UI: 4.54, 9.00) in high SDI regions, and from 40.02 per 100,000 (95% UI: 25.87, 55.56) to 25.78 per 100,000 (95% UI: 17.84, 33.84) in high-middle SDI regions, with respective estimated annual percentage changes (EAPCs) of −0.55 (95% UI: −0.72, −0.38), −4.64 (95% UI: −4.73, −4.55), and −1.42 (95% UI: −1.59, −1.25; Figures 6A–C). Similarly, DALYs rates declined in these regions (Figures 6D–F).
Risk factors for IHD caused by environmental factors in 1990 and 2021 globally and across 5 SDI regions. (A) ASMR in 1990, (B) ASMR in 2019, (C) EAPC of ASMR from 1990 to 2019, (D) ASDR in 1990, (E) Annual ASDR of IHD caused by environmental factors in 2019, (F) EAPC of ASDR from 1990 to 2019.
Six heat maps display age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life year rate (ASDR) in 1990 and 2021, plus the estimated annual percentage change (EAPC) from 1990 to 2021. Categories include household air pollution, ambient particulate matter pollution, lead exposure, low temperature, and high temperature. SDI refers to socio-demographic index levels: Middle, Low, Low-Middle, High, High-Middle, and Global. Colors range from light to dark, indicating low to high values.
In middle SDI, low-middle SDI, and low SDI regions, household air pollution from solid fuels was the leading environmental contributor to IHD mortality and DALYs rates, although its impact also exhibited a declining trend. Mortality rates decreased from 29.54 per 100,000 (95% UI: 21.71, 38.06) to 29.48 per 100,000 (95% UI: 19.55, 39.08) in middle SDI regions, from 45.37 per 100,000 (95% UI: 34.95, 56.20) to 28.85 per 100,000 (95% UI: 17.29, 42.01) in low-middle SDI regions, and from 45.78 per 100,000 (95% UI: 35.86, 56.90) to 40.63 per 100,000 (95% UI: 31.24, 50.09) in low SDI regions, with respective EAPC of 2.11 (95% UI: 1.9, 2.32), 2.31 (95% UI: 2.06, 2.55), and 1.32 (95% UI: 0.92, 1.72; Figures 6A–C). Similar trends were observed for DALYs rates (Figures 6D–F). These findings highlight that while the disease burden from ambient particulate matter pollution and household air pollution has decreased, they remain the primary environmental factors contributing to IHD.
Predictions of the burden of IHD
Based on the BAPC model, the burden of IHD attributable to environmental factors is projected to continue declining in terms of ASMR and ASDR by 2051. By 2051, the ASMR is expected to be 53.67 (95% UI: 11.48, 95.86) per 100,000 for both sexes, 58.65 (95% UI: 16.92, 100.38) per 100,000 for males, and 38.21 (95% UI: 8.48, 67.93) per 100,000 for females. Similarly, the ASDR is projected to reach 986.76 (95% UI: 291.27, 1682.25) per 100,000 for both sexes, 1230.79 (95% UI: 391.05, 2070.53) per 100,000 for males, and 624.08 (95% UI: 184.53, 1063.63) per 100,000 for females (Figure 7).
Prediction of IHD trends due to environmental factors over the next 30 years. (A) ASMR for both sexes. (B) ASMR for males. (C) ASMR for females. (D) ASDR for both sexes. (E) ASDR for males. (F) ASDR for females.
Six line graphs depict age-standardized rates per 100,000 from 1990 to 2050, with projections beyond 2020. Graph A shows both sexes for ASMR, B shows males, and C shows females. Graph D shows both sexes for ASDR, E shows males, and F shows females. Each graph features a dotted line up to 2020, followed by a shaded prediction interval. ASMR generally decreases, while ASDR shows similar trends across graphs.Discussion
This study provides a comprehensive assessment of the global, regional, and national burden of IHD attributable to environmental factors from 1990 to 2021, with projections extending to 2051. The findings underscore the substantial and persistent impact of environmental risk factors on IHD, revealing notable disparities across socioeconomic and geographic contexts. These global patterns were reflected in substantial heterogeneities at finer geographical resolutions.
At the national level, countries with the highest burden included Egypt, Afghanistan, and Vanuatu. These nations share common challenges including limited healthcare infrastructure, high exposure to household and ambient air pollution, and ongoing environmental degradation. In middle SDI regions, rapid industrialization coupled with inadequate environmental regulations has created particularly severe exposure scenarios, explaining their disproportionately high ASMR and ASDR indicators. The primary environmental risk factors contributing to the global IHD burden in 2021 were ambient particulate matter pollution (PM2.5), household air pollution, and exposure to extreme temperatures. These factors are well-established contributors to cardiovascular diseases, including IHD, due to their adverse effects on both the respiratory and cardiovascular systems. Among these, ambient PM2.5 exposure emerged as the predominant contributor, particularly in low- and middle-income regions where elevated exposure levels result from reliance on biomass fuels and suboptimal air quality. This finding is consistent with previous studies identifying air pollution as a major environmental determinant of cardiovascular disease (2, 31, 32). While the direct contribution of air pollution to IHD is well-documented, emerging evidence suggests that other environmental factors, such as extreme temperatures, are increasingly significant contributors to the disease burden. Both extreme heat and cold have been associated with exacerbations of cardiovascular conditions, leading to elevated IHD mortality, particularly among older populations (33). Additionally, household air pollution, largely driven by solid fuel combustion, remains a critical public health concern in low-income countries where access to clean energy alternatives is limited. Recent studies highlight that household air pollution substantially contributes to the IHD burden in regions such as South Asia, emphasizing the urgent need for improved access to cleaner cooking technologies (34). These findings reinforce the necessity of mitigating environmental risk factors to reduce the global burden of IHD.
The divergent trends observed in the health inequality indices, a declining SII alongside a rising CI, reveal a nuanced picture of how the environmental IHD burden is distributed globally. This pattern suggests that while broad, absolute gains in reducing the IHD burden have been made (as reflected in the SII), these gains have not been equitably shared. The increasing CI indicates a widening relative disadvantage, meaning the burden is becoming progressively more concentrated among socio-economically disadvantaged populations over time. This could be driven by slower adoption of environmental regulations, persistent reliance on polluting fuels, and lagging healthcare infrastructure in lower-SDI regions, even as higher-SDI regions accelerate their improvements. Therefore, the overall decline in burden masks a critical public health challenge: the escalation of relative health inequity related to environmental risks.
The analysis revealed significant disparities in the IHD burden across different age groups, sexes, and socioeconomic levels. The burden attributable to environmental factors increases with age, particularly among individuals aged 50 and above, reflecting the cumulative impact of long-term exposure to environmental pollutants. This pattern aligns with well-established risk factors, such as hypertension, diabetes, and dyslipidemia, which become more prevalent with aging (35). Males exhibited higher mortality and DALYs rates than females, with peak mortality observed at ages 70–74 for males and 80–84 for females. These findings are consistent with existing literature suggesting that biological differences, lifestyle factors, and occupational exposures contribute to an increased IHD risk in males (6). Furthermore, an inverse correlation between IHD burden and SDI was observed, with middle-SDI regions experiencing the highest burden. This underscores the complex interplay between socioeconomic development and environmental health risks, wherein rapid industrialization and urbanization in middle-SDI regions exacerbate exposure to environmental pollutants (31, 32). Sex-based differences in environmental exposure also play a crucial role. In low- and middle-income countries, males are more likely to be exposed to outdoor air pollution and occupational hazards, which may explain their higher IHD burden (36). Conversely, females in these settings face greater exposure to household air pollution due to traditional cooking methods, making household air pollution a significant risk factor for them (37, 38). These gender-specific exposure patterns highlight the need for targeted interventions addressing distinct environmental health risks faced by each sex.
The study found that the burden of IHD attributable to environmental factors was highest in low-middle SDI regions and lowest in high SDI regions. This finding supports the notion that socioeconomic development is a key determinant in reducing the burden of environmentally induced diseases. High SDI countries generally implement stringent air quality regulations, provide better healthcare access, and allocate greater resources to mitigate environmental risk factors, leading to a lower IHD burden. In contrast, low- and middle-SDI regions, characterized by rapid industrialization and urbanization, experience higher air pollution exposure and possess limited healthcare infrastructure to address the growing IHD burden (31, 39, 40). The wave-like relationship between SDI and IHD burden, particularly the peak in ASMR and ASDR observed in regions with an SDI around 0.6, highlights a critical challenge for middle-income countries. These regions face a dual burden of environmental risk factors and an increasing prevalence of non-communicable diseases like IHD, yet their health systems remain insufficiently equipped to address prevention and treatment needs. In regions such as Central Asia and Sub-Saharan Africa, where environmental pollution and IHD prevalence are high, integrated public health policies that promote both environmental improvements and cardiovascular health interventions are urgently needed (9, 41).
The APC analysis revealed significant cohort effects, with successive birth cohorts exhibiting progressively lower IHD risk. The cohort born in 2002–2006 showed the lowest risk ratios, suggesting that younger generations benefit from cumulative improvements in environmental regulations, public health interventions, and living conditions over past decades (42). However, this protective cohort effect appears attenuated in regions with ongoing environmental degradation, emphasizing the need for sustained environmental policies.
Projections based on the BAPC model suggest a decline in both ASMR and ASDR for IHD attributable to environmental factors by 2051. However, significant regional variations are anticipated. These differential trajectories highlight the need for region-specific intervention strategies. This anticipated decline is likely driven by improvements in air quality, healthcare infrastructure, and the effectiveness of public health interventions targeting environmental risks. However, these projections are contingent upon sustained global efforts to combat air pollution, enhance environmental regulations, and implement targeted interventions in high-burden regions (43). Despite the projected decrease in the environmental burden of IHD, it will remain a major public health concern, particularly in low- and middle-income countries. Strengthening environmental policies, increasing investment in clean energy solutions, and raising public awareness about the health risks of environmental exposure will be essential (44–46). Furthermore, addressing the dual burden of infectious and non-communicable diseases in these regions will be critical for mitigating the future impact of IHD (41, 44, 47).
This study has several strengths, including comprehensive global coverage of 204 countries, use of advanced statistical models, integration of health inequality metrics, and provision of long-term projections to 2051. These features provide novel insights into the temporal evolution and equitable distribution of the environmental IHD burden.
However, the findings should be interpreted in the context of several limitations. Most importantly, the ecological nature of the GBD data limits causal inference at the individual level. Although the estimated associations are strong and biologically plausible, they cannot definitively establish causality. Other limitations include the potential for residual confounding from unmeasured variables (e.g., diet, physical activity) and the assumption in our projections that historical trends will continue, which may not account for future policy or technological changes. Furthermore, by focusing on environmental risk factors, our study did not fully account for other IHD determinants, such as genetic predisposition. Future research should explore the interplay between environmental and non-environmental risk factors to provide a more comprehensive understanding of the global IHD burden.
Conclusion
This study utilized data from the GBD 2021 to systematically assess the global, regional, and national burden of IHD attributable to environmental risk factors from 1990 to 2021. The findings indicate that while the absolute number of global deaths and DALYs due to IHD from environmental factors increased substantially by 65.57 and 54.3%, respectively, the age-standardized rates exhibited a declining trend, with ASMR and ASDR falling by 31.12 and 29.8%, respectively. This suggests that the rise in absolute numbers is likely driven by population growth and aging, while the decline in standardized rates reflects improvements in healthcare and environmental policies. The regional and SDI-level analysis showed that the highest ASMR and ASDR were observed in low-middle SDI regions and Central Asia. Analysis by age and sex revealed that the burden of IHD was more pronounced in males, with mortality peaking at ages 70–74 for men and 80–84 for women. Additionally, the inverse correlation between SDI and both ASMR and ASDR highlights a disproportionately higher burden in middle-SDI regions and a relatively lower burden in high SDI countries. Ambient particulate matter pollution and household air pollution were identified as the leading environmental contributors to the global IHD burden. To effectively mitigate this burden, countries must strengthen prevention strategies, enhance early screening efforts, and implement targeted interventions for IHD attributable to environmental risk factors. Addressing these factors is crucial for reducing future disease burdens and improving global public health outcomes.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
We would like to express our sincere thanks to all those who made contributions to the Global Burden of Disease Study 2021.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Edited by: Paweł Zagożdżon, Medical University of Gdansk, Poland
Reviewed by: Ricardas Radisauskas, Lithuanian University of Health Sciences, Lithuania
Alvin Ang, Ateneo de Manila University, Philippines