Juntao Tan,1 Min Xue,2 Huanyin Li,3 Yang Liu,3 Yuxin He,4 Jing Liu,5 Jie Liu,1 Luojia Tang,6 Jixian Lin3 

1College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Department of Respiratory, Minhang Hospital, Fudan University, Shanghai, 201101, People’s Republic of China; 3Department of Neurology, Minhang Hospital, Fudan University, Shanghai, 201101, People’s Republic of China; 4Department of Medical Administration, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People’s Republic of China; 5Department of Nursing, Minhang Hospital, Fudan University, Shanghai, 201101, People’s Republic of China; 6Emergency Department of Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China

Correspondence: Jixian Lin, Department of Neurology, Minhang Hospital, Fudan University, Shanghai, 201101, People’s Republic of China, Email linjixian@fudan.edu.cn Luojia Tang, Emergency Department of Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China, Email tang.luojia@zs-hospital.sh.cn

Background: A systematic relational assessment of the global, regional, and national Ischemic heart disease (IHD) burden and its attributable risk factors is essential for developing more targeted prevention and intervention strategies.
Methods: The GBD 2021 comparative risk assessment framework was employed to evaluate stroke burden attributable to environmental, behavioral, metabolic, and dietary risk factors, and a total of 25 risk factors were included. Specifically, we used the joinpoint regression model, decomposition analysis, and systematic fixed-effects analysis to reveal the global, regional, and national burden of IHD attributable to these 25 risk factors and their exposure value across 204 countries and territories with different socio-demographic index (SDI) levels from different perspectives.
Results: Joinpoint regression revealed similar trends in summary exposure value (SEV) and attributable burdens for 25 IHD risk factors. From 1990 to 2021, SEV rankings increased for 12/25 risk factors, decreased for 10/25, and remained unchanged for 3/25. Decomposition analysis indicated that from 1990 to 2021, low SDI countries experienced the most significant increase in IHD burden attributable to 25 risk factors due to population growth, while upper-middle and high SDI countries were most affected by population aging, and high SDI countries demonstrated the greatest reduction in IHD burden attributed to epidemiological changes. Panel data analysis elucidated the impact of SEV, SDI, and quality-of-care index (QCI) on attributable IHD burden.
Conclusion: This study emphasizing the critical role of risk factor control. Tailored interventions and exploration of country-specific factors are crucial for effectively reducing the global IHD burden.

Keywords: ischemic heart disease, disease burden, risk factors, socio-demographic index, systematic fixed-effects analysis