Abstract
Background Measuring the potential socioeconomic factors of cardiac mortality is fundamental to identifying treatments, setting priorities, and effectively allocating resources to minimize disease burden. The study sought to present a methodology that explores the connections between urbanization, population growth, human development index (HDI), access to energy, unemployment, and cardiovascular disease (CVD) mortality within the South Asian Association for Regional Cooperation (SAARC) nations to mitigate the cardiac disease burden. Methods This investigation uses multiple-criteria decision-making methodologies to analyze data between 2001 and 2017 commencing with a mathematical grey incidence analysis (GIA) methodology to estimate weights and rank nations based on CVD mortality. Then, utilizing the conservative min-max model approach, we sought to determine which country contributes the most to CVD mortality among all South Asian nations. The grey preference by similarity to ideal solution (G-TOPSIS) method is adopted for further optimization by prioritizing the selected factors that have the greatest influence on CVD mortality. Results The estimated statistic highlights that, among SAARC nations, Pakistan has a significant proportion of the disease burden attributable to cardiac events. In addition, HDI showed a significant contribution in the reduction of CVD mortality, whereas unemployment showed a significant contribution in the rise of CVD mortality among all selected variables. Conclusions This investigation may facilitate researchers with a multiple-criteria decision-making roadmap to help them enhance the quality of their studies and their understanding of how to use multiple-criteria decision-making techniques to evaluate and prioritize the influencing factors of disease mortality in healthcare research. Further, the study outcomes provide additional practical knowledge for appropriate policy solutions.
Highlights
Cardiovascular diseases (CVDs) are the world’s leading cause of death and disability [1]
CVD-related deaths have declined in several high-income countries (HICs) during the last several decades but have grown in low- and middleincome countries (LMICs), with about 80% of the burden falling on these nations [2, 3]. e population of South Asian nations has become 1.8 billion in 2020, accounting for 23% of the global population [4]
Results and Discussion e present investigation is carried out using grey incidence approaches to quantify the strength of association between socioeconomic variables and CVD mortality among South Asian Association for Regional Cooperation (SAARC) countries over the period 2001–2017
Summary
Measuring the potential socioeconomic factors of cardiac mortality is fundamental to identifying treatments, setting priorities, and effectively allocating resources to minimize disease burden. e study sought to present a methodology that explores the connections between urbanization, population growth, human development index (HDI), access to energy, unemployment, and cardiovascular disease (CVD) mortality within the South Asian Association for Regional Cooperation (SAARC) nations to mitigate the cardiac disease burden. E study sought to present a methodology that explores the connections between urbanization, population growth, human development index (HDI), access to energy, unemployment, and cardiovascular disease (CVD) mortality within the South Asian Association for Regional Cooperation (SAARC) nations to mitigate the cardiac disease burden. E grey preference by similarity to ideal solution (G-TOPSIS) method is adopted for further optimization by prioritizing the selected factors that have the greatest influence on CVD mortality. E estimated statistic highlights that, among SAARC nations, Pakistan has a significant proportion of the disease burden attributable to cardiac events. Is investigation may facilitate researchers with a multiple-criteria decision-making roadmap to help them enhance the quality of their studies and their understanding of how to use multiple-criteria decision-making techniques to evaluate and prioritize the influencing factors of disease mortality in healthcare research. The study outcomes provide additional practical knowledge for appropriate policy solutions
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