Evaluation of low-and middle-income countries according to cardiovascular disease risk factors by using pythagorean fuzzy AHP and TOPSIS methods

Abstract

Background

Cardiovascular disease risk factors play a crucial role in determining individuals’ future health status and significantly affect health. This paper aimed to address cardiovascular disease risk factors in low- and middle-income countries using multi-criteria decision-making methods.

Methods

In line with this objective, 22 evaluation criteria were identified. Due to the unequal importance levels of the criteria, the interval-valued Pythagorean Fuzzy AHP (PF-AHP) method was employed for weighting. The TOPSIS method was utilized to rank the countries.

Results

The application of interval-valued PF-AHP revealed that metabolic, behavioral, and economic factors are more important in contributing to disease risk. Among adults, tobacco use prevalence was identified as the most significant risk factor. According to the TOPSIS method, Lebanon, Jordan, Solomon Islands, Serbia, and Bulgaria ranked highest, while Timor Leste, Benin, Ghana, Niger, and Ethiopia ranked lowest.

Conclusions

Identifying disease risk factors and preventing or reducing risks are crucial in combating cardiovascular diseases. Therefore, it is recommended that countries ranking higher take remedial actions to reduce disease risk.

Introduction

It is often assumed that all individuals understand the concept of illness similarly. Nonetheless, the definition of illness exhibits variability across societies and temporal contexts. In this context, illness can be conceptualized as the absence of dynamic well-being that aligns with the multifaceted demands of life, encompassing physical, mental, and social dimensions by age, culture, and individual responsibility. Diseases are a cumulative result of all protective and harmful events that affect people’s health throughout their lives [1]. Chronic or Noncommunicable Diseases (NCDs) mainly include cardiovascular diseases, cancers, and diabetes [2]. NCDs are caused by behavioral, metabolic, and environmental risk factors and usually occur due to societal conditions and lifestyle habits such as poor nutrition, tobacco use, excessive alcohol consumption, and physical inactivity. Degenerative, genetic, hereditary, and environmental factors are also crucial in the formation of diseases. The development of illness is a complex process involving the contribution of multiple factors [3]. 74% (74%) of all deaths worldwide are attributed to NCDs. 86% (86%) of premature deaths from NCDs occur in low- and middle-income countries. Tobacco use, physical inactivity, alcohol consumption, and unhealthy eating are the four main risk factors contributing to NCDs [4]. These risk factors are fundamental behavioral risks and pose widespread threats created by economic transitions, rapid urbanization, and lifestyles [3]. Physical, social, environmental, and lifestyle factors influence NCDs. It is emphasized that progress in city health depends on the strength of healthcare systems and shaping urban environments. Urban settlements, as a social determinant of health, bring along socio-economic, environmental, and occupational influences that exacerbate the impact of these risk factors. NCDs threaten human health and have significant economic implications for cities [5]. Factors such as global improvements in education and income levels, changes in dietary habits, and the control of infectious diseases increase life expectancy. While an increase in life expectancy is desirable, it also leads to an increase in NCDs [6]. NCDs are a significant public health problem that leads to increasing inequalities between countries and within populations [7], and they are influenced by factors such as rapid unplanned urbanization, the globalization of unhealthy lifestyles, and population aging [8]. Air quality generally deteriorates in many low- and middle-income countries parallel to large-scale urbanization and economic development [9, 10].

Furthermore, due to population aging and lifestyle changes, the global prevalence of NCDs has rapidly increased, making them a leading cause of death and disability worldwide [11]. NCDs are considered one of the most significant health challenges of the 21st century [12]. It is crucial to control the risk factors that contribute to the development of NCDs in order to reduce deaths caused by NCDs [4]. In the next 20 years, NCDs are projected to become the leading cause of death in low- and middle-income countries. The increasing prevalence of NCDs in these countries, driven by extreme poverty, nutritional changes, and lifestyle modifications, poses a significant threat to public health [3]. NCDs encompass various diseases that affect cardiovascular, neurological, respiratory, and other organ systems [11]. Cardiovascular diseases (CVDs), cancers, diabetes, and chronic respiratory diseases are the main NCDs [3].

In 2016, deaths related to heart and vascular diseases accounted for 31% of total deaths [12]. In 2019, CVDs were responsible for 38% of the 17 million premature deaths (under the age of 70) caused by NCDs. CVDs encompass a group of disorders affecting the heart and blood vessels, including coronary heart disease, cerebrovascular disease, peripheral artery disease, rheumatic heart disease, congenital heart disease, and all heart diseases related to deep vein thrombosis and pulmonary embolism [13]. In other words, CVDs refer to diseases affecting the heart and blood vessels [3]. Tobacco use, insufficient physical activity, alcohol consumption, unhealthy eating, hypertension, diabetes, high blood cholesterol, age, globalization, urbanization, and income are risk factors for CVDs [12, 14,15,16]. CVDs risk factors are crucial in determining individuals’ future health conditions. Risk factors for CVDs include the main risk factors for NCDs and social determinants such as aging, income, urbanization, and physiological factors like high blood pressure (hypertension), high blood cholesterol, and high blood sugar [15]. Addressing multiple risk factors rather than focusing on one risk factor to prevent diseases [17]. Most premature deaths resulting from NCDs can be prevented by improving health systems to effectively and equitably meet the healthcare needs of patients. Additionally, it is stated that significant prevention can be achieved by developing public policies targeting non-health sectors that address common risk factors such as tobacco use, unhealthy diet, physical inactivity, and harmful alcohol consumption [18].

Identifying countries with high CVDs risk potential is important for preventing and controlling the disease. There are many criteria to be considered for the evaluation of these countries. Such problems with many conflicting criteria can be considered as a multi-criteria decision making (MCDM) problem. MCDM methods enable decision makers to make decisions more confidently in the face of uncertainty, complexity and conflicting objectives. The AHP method is one of the most frequently used MCDM methods for determining criteria weights [19]. However, the input data for AHP analysis is based on human judgment; therefore, the data may always be imprecise and uncertain to some extent [20]. The MCDM methods can be extended with fuzzy sets to represent real-life uncertainty [21]. Fuzzy sets proposed by Zadeh [22], have been successfully integrated with AHP to minimize uncertainty. After the introduction of fuzzy sets, fuzzy sets have been extended in various ways by many researchers [23], such as intuitionistic fuzzy sets (IFs) [24], neutrosophic sets [25], hesitant fuzzy sets [26], Pythagorean fuzzy sets (PFSs) [27], picture fuzzy sets [28], orthopair fuzzy sets [29], spherical fuzzy sets [30], fermatean fuzzy sets [31]. PFSs are an extension of intuitionistic fuzzy sets used to express experts’ judgments about uncertainty and ambiguity in decision-making problems [32]. Therefore, this paper used AHP augmented by interval-valued Pythagorean fuzzy numbers (PFNs) to weight the risk factors.

In this paper, CVDs risk factors were determined first, and the weights of these factors were calculated using the interval-valued PF-AHP, one of the MCDM methods. Then, using these obtained weights, low- and middle-income countries were ranked by the TOPSIS method according to their CVDs risk potentials. This paper is significant because it examines the CVDs risk factors, which account for 32% of all deaths within NCDs [13], specifically in low- and middle-income countries. By evaluating multiple risk factors from the perspective of low- and middle-income countries, the research is expected to contribute to CVDs prevention and control efforts by identifying countries with the highest disease risk and the greatest need for preventive interventions.

Methodology

The paper used interval-valued PF-AHP for weighting criteria, and the TOPSIS method was used for ranking alternatives. The theoretical information about the methods used in the sub-sections of this section is provided.

Basic concepts of interval-valued pythagorean fuzzy sets

Classic MCDM methods generally assume that all criteria’ weights and importance levels are expressed with precise values so that alternatives can be ranked without any problems [33]. However, most decisions in real-world situations are made in an uncertain or uncertain environment [34]. The fuzzy logic theory introduced by Zadeh [22] is suitable for subjective reasoning and qualitative evaluation in the evaluation processes of decision-making problems [35]. After the introduction of fuzzy sets, fuzzy sets have been extended in various ways by many researchers, such as IFs [24], neutrosophic fuzzy sets [25] and hesitant fuzzy sets hesitant fuzzy sets [26].

PPFs, proposed by Yager [27], are a generalization of IFs. PFs are also defined with both membership and non-membership functions, just like. Unlike ifs, the sum of the two degrees of function can be more than 1, but for PFs the sum of the squares of the degrees cannot be more than 1. Therefore, if the problem involves more fuzziness and uncertainty, PFs are more powerful than IFs in resolving uncertainty [21]. Experts can use interval numbers instead of crisp numbers to reflect uncertainty better when evaluating criteria and alternatives in decision-making processes [35]. The preliminary information about interval-valued Pythagorean fuzzy sets (IVPFSs) used in this paper is provided below [34].

Definition 1

An IVPFS in is defined as

$$\:A=\left\{{\langle}x,\left[{\mu\:}_{A}^{L}\left(x\right),{\mu\:}_{A}^{U}\left(x\right)\right],\left[{v}_{A}^{L}\left(x\right),{v}_{A}^{U}\left(x\right)\right]\:\rangle|\:x\in\:X\right\}$$

(1)

$$\:\text{w}\text{h}\text{e}\text{r}\text{e}\:0\le\:{\mu\:}_{A}^{L}\left(x\right)\le\:{\mu\:}_{A}^{u}\le\:1,\:0\le\:{v}_{A}^{l}\left(x\right)\le\:{v}_{A}^{u}\le\:1\:\text{a}\text{n}\text{d}\:({{\mu\:}_{A}^{U}\left(x\right))}^{2}+{{(v}_{A}^{U}\left(x\right))}^{2}\le\:1.$$

Similar to PFSs, for each element $\:x\in\:X$, its hesitation interval relative to $\:A$ is given as

$$\:{\pi\:}_{A}\left(x\right)=\left[{\pi\:}_{A}^{L}\left(x\right),{\pi\:}_{A}^{U}\left(x\right)\right]=\sqrt{1-{\left({\mu\:}_{A}^{U}\left(x\right)\right)}^{2}-{\left({v}_{A}^{U}\left(x\right)\right)}^{2}},\sqrt{1-{\left({\mu\:}_{A}^{L}\left(x\right)\right)}^{2}-{\left({v}_{A}^{L}\left(x\right)\right)}^{2}}$$

(2)

For an IVPFS $\:A$, the pair $\langle\left[{\mu\:}_{A}^{L}\left(x\right),{\mu\:}_{A}^{U}\left(x\right)\right],\left[{v}_{A}^{L}\left(x\right),{v}_{A}^{U}\left(x\right)\right]\rangle$ is called an interval-valued interval-valued Pythagorean fuzzy number (IVPFN). For convenience, is often denoted by $\langle\left[a,b\right],\left[c,d\right]\rangle$ where

$$\:\left[a,b\right]\subset\:\left[\text{0,1}\right],\left[c,d\right]\subset\:\left[\text{0,1}\right]\:\text{a}\text{n}\text{d}\:{b}^{2}+{d}^{2}\le\:1$$

(3)

Obviously, $\:{\alpha\:}^{+}=\langle\left[\text{1,1}\right],\left[\text{0,0}\right]\rangle$ is the largest IVPFN, and $\:{\alpha\:}^{-}=\langle\left[\text{1,1}\right],\left[\text{0,0}\right]\rangle$ is the smallest IVPFN.

Definition 2

Let, and be IVPFNs then

$$\:{\alpha\:}_{1}\wedge\:{\alpha\:}_{2}=\langle\left[min\left\{{a}_{1},{a}_{2}\right\},min\left\{{b}_{1},{b}_{2}\right\}\right],\left[max\left\{{c}_{1},{c}_{2}\right\},max\left\{{d}_{1},{d}_{2}\right\}\right]\rangle$$

(4)

$$\:{\alpha\:}_{1}\vee\:{\alpha\:}_{2}=\langle\left[max\left\{{a}_{1},{a}_{2}\right\},max\left\{{b}_{1},{b}_{2}\right\}\right],\left[min\left\{{c}_{1},{c}_{2}\right\},min\left\{{d}_{1},{d}_{2}\right\}\right]\rangle$$

(5)

$$\:{\alpha\:}_{1}\oplus\:{\alpha\:}_{2}=\langle\left[\sqrt{{a}_{1}^{2}+{a}_{2}^{2}-{a}_{1}^{2}{a}_{2}^{2}},\sqrt{{b}_{1}^{2}+{b}_{2}^{2}-{b}_{1}^{2}{b}_{2}^{2}}\right],\left[{c}_{1}{c}_{2},{d}_{1}{d}_{2}\right]\rangle$$

(6)

$$\:{\alpha\:}_{1}\otimes\:{\alpha\:}_{2}=\langle\left[{a}_{1}{a}_{2},{b}_{1}{b}_{2}\right],\left[\sqrt{{c}_{1}^{2}+{c}_{2}^{2}-{c}_{1}^{2}{c}_{2}^{2}},\sqrt{{d}_{1}^{2}+{d}_{2}^{2}-{d}_{1}^{2}{d}_{2}^{2}}\right]\rangle$$

(7)

$$\:\lambda\:.\alpha\:=\langle\left[\sqrt{1-{\left(1-{a}^{2}\right)}^{\lambda\:}},\sqrt{1-{\left(1-{b}^{2}\right)}^{\lambda\:}}\right],\left[{c}^{\lambda\:},{d}^{\lambda\:}\right]\rangle,\:\lambda\:>0$$

(8)

$$\:{\alpha\:}^{\lambda\:}=\langle\left[{a}^{\lambda\:},{b}^{\lambda\:}\right],\left[\sqrt{1-{\left(1-{c}^{2}\right)}^{\lambda\:}},\sqrt{1-{\left(1-{d}^{2}\right)}^{\lambda\:}}\right]\rangle,\:\lambda\:>0$$

(9)

Definition 3

Let be IVPFNs, the score function of is defined as follows:

$$\:s\left(\alpha\:\right)=\frac{1}{2}\left[{a}^{2}+{b}^{2}-{c}^{2}-{d}^{2}\right],\:s\left(\alpha\:\right)\in\:\left[-1,\:1\right]$$

(10)

Definition 4

Let be IVPFNs, the accuracy function of is defined as follows:

$$\:a\left(\alpha\:\right)=\frac{1}{2}\left[{a}^{2}+{b}^{2}+{c}^{2}+{d}^{2}\right],\:a\left(\alpha\:\right)\in\:\left[0,\:1\right]$$

(11)

For any two IVPFNs, $\:{\alpha\:}_{1}$, $\:{\alpha\:}_{2}$, the comparison rule is defined as follows:

• 1. if $\:s\left({\alpha\:}_{1}\right)>s\left({\alpha\:}_{2}\right)$, then $\:{\alpha\:}_{1}>{\alpha\:}_{2}$;

• 2. if $\:s\left({\alpha\:}_{1}\right)=s\left({\alpha\:}_{2}\right)$, then:

• (a) if $\:a\left({\alpha\:}_{1}\right)>a\left({\alpha\:}_{2}\right)$, then $\:{\alpha\:}_{1}>{\alpha\:}_{2}$;

• (b) if $\:a\left({\alpha\:}_{1}\right)>a\left({\alpha\:}_{2}\right)$, then $\:{\alpha\:}_{1}={\alpha\:}_{2}$.

Definition 5

Let, be a collection of IVPFN, then the function interval-valued Pythagorean fuzzy weighted averaging operator ( and

$$\:{IVPFWA}_{w}\left({\alpha\:}_{j},\:{\alpha\:}_{j},\:\dots\:,\:{\alpha\:}_{j}\right)=\langle\left[\sqrt{1-\prod\:_{j=1}^{n}{(1-{a}_{j}^{2})}^{{w}_{j}}},\sqrt{1-\prod\:_{j=1}^{n}{(1-{b}_{j}^{2})}^{{w}_{j}}},\right],\left[\prod\:_{j=1}^{n}{c}_{j}^{{w}_{j}},\prod\:_{j=1}^{n}{d}_{j}^{{w}_{j}}\right]\rangle$$

(12)

where $\:{w}_{j}$ is the weight of $\:{\alpha\:}_{j}$ $\:(j=\text{1,2},\dots\:,n)$, $\:{w}_{j}\in\:\left[0,\:1\right]$ and $\:\sum\:_{j=1}^{n}{w}_{j}=1.$

Interval-valued pythagorean fuzzy AHP

In recent years, the interval-valued PF-AHP method has been used in the literature to solve various problems. Among these, it is mostly used in risk assessment problems. For example; Ilbahar et al. [36], weighting probability, severity and frequency parameters used in risk assessment in the field of occupational health and safety; Ak and Gul [37], weighting risk parameters in information security; Yucesan and Kahraman [38], weighting risk parameters for hydroelectric power plants; Ayyildiz and Taskin Gumus [21], weighting critical risk factors for hazardous material transportation operations. Shete et al. [20] used it to evaluate the factors that enable innovation in the supply chain. Yucesan and Gul [19] used Fuzzy TOPSIS methods together to evaluate hospital service quality. Çalık [32] integrated Pythagorean Fuzzy TOPSIS method and used it in green supplier selection problem. Lahane and Kant [39] used the performance results of the circular supply chain together with the Pythagorean fuzzy CoCoSo method to calculate the weights of the enablers in the ranking problem. Boyacı and Şişman [40] used it for weighting the criteria in the pandemic hospital location selection problem. The steps of interval-valued PF-AHP are as follows [36]:

Step 1

Construct the compromised pairwise comparison matrix $\:R={\left({r}_{ik}\right)}_{mxm}$ with linguistic evaluations of experts’ opinions based on Table 1.

Table 1 Linguistic scale for interval-valued PF-AHP

Step 2

Calculate the differences matrix $\:D={\left({d}_{ik}\right)}_{mxm}$ between the lower and upper values of the membership and non-membership functions by using Eqs. (13–14).

$$\:{d}_{{ik}_{L}}={\mu\:}_{{ik}_{L}}^{2}-{v}_{{ik}_{U}}^{2}$$

(13)

$$\:{d}_{{ik}_{U}}={\mu\:}_{{ik}_{U}}^{2}-{v}_{{ik}_{L}}^{2}$$

(14)

Step 3

Calculate the interval multiplicative matrix 

$\:S={\left({s}_{ik}\right)}_{mxm}$ by using Eqs. (15–16).

$$\:{S}_{{ik}_{L}}=\sqrt{{1000}^{{d}_{L}}}$$

(15)

$$\:{S}_{{ik}_{U}}=\sqrt{{1000}^{{d}_{U}}}$$

(16)

Step 4

Calculate the determinancy value $\:\tau\:={\left({\tau\:}_{ik}\right)}_{mxm}$ of the $\:{r}_{ik}$ using by Eq. (17).

$$\:{\tau\:}_{ik}=1-\left({{\mu\:}_{{ik}_{U}}}^{2}-{{\mu\:}_{{ik}_{L}}}^{2}\right)-\left({{v}_{{ik}_{U}}}^{2}-{{v}_{{ik}_{L}}}^{2}\right)$$

(17)

Step 5

Multiply the determinancy degrees with 

$\:S={\left({s}_{ik}\right)}_{mxm}$ matrix for obtaining the matrix of weights, $\:T={\left({t}_{ik}\right)}_{mxm}$, before normalization by using Eq. (18).

$$\:{t}_{ik}=\left(\frac{{S}_{{ik}_{L}}+{S}_{{ik}_{U}}}{2}\right){\tau\:}_{ik}$$

(18)

Step 6

Calculate the normalized priority weights $\:{w}_{i}$ by using Eq. (19).

$$\:{w}_{i}=\frac{\sum\:_{k=1}^{m}{t}_{ik}}{\sum\:_{i=1}^{m}\sum\:_{i=1}^{m}{t}_{ik}}$$

(19)

TOPSIS

Hwang and Yoon [41] have proposed TOPSIS method for ranking alternatives. Due to its simple application and comprehensibility, the TOPSIS is widely utilized. In this method, the optimal solution is determined as the alternative that is closest to the ideal solution while being farthest from the negative-ideal solution. Several studies have applied TOPSIS to resolve MCDM issues in multiple fields, such as supply chain management, business and marketing administration, production systems, chemical engineering, human resources management, and energy management [42]. The steps of the TOPSIS method are stated below [43]:

Step 1

Identifying objectives and defining evaluation criteria.

Step 2

Decision matrices with evaluation criteria are created in the alternatives columns in the rows. $\:{a}_{ij}$ in A decision matrix shows the actual value of alternative $\:i\:$in $\:A$ matrix according to the criterion $\:j$.

$$\:A=\:\left[\begin{array}{cccc}{a}_{11}&\:{a}_{12}&\:\cdots\:&\:{a}_{1n}\\\:{a}_{21}&\:{a}_{22}&\:\cdots\:&\:{a}_{2n}\\\:\cdots\:&\:\cdots\:&\:\cdots\:&\:\cdots\:\\\:\cdots\:&\:\cdots\:&\:\cdots\:&\:\cdots\:\\\:{a}_{m1}&\:{a}_{m2}&\:\cdots\:&\:{a}_{mn}\end{array}\right]$$

Step 3

After creating the decision matrix, the normalized decision matrix (R) is obtained using Eq. (20).

$$\:{r}_{ij}=\frac{{a}_{ij}}{\sqrt{\sum\:_{i=1}^{m}{a}_{ij}^{2}}}\:i=\text{1,2},\dots\:m;\:\:j=\text{1,2},\dots\:,n$$

(20)

$$\:R=\:\left[\begin{array}{cccc}{r}_{11}&\:{r}_{12}&\:\cdots\:&\:{r}_{1n}\\\:{r}_{21}&\:{r}_{22}&\:\cdots\:&\:{r}_{2n}\\\:\cdots\:&\:\cdots\:&\:\cdots\:&\:\cdots\:\\\:\cdots\:&\:\cdots\:&\:\cdots\:&\:\cdots\:\\\:{r}_{m1}&\:{r}_{m2}&\:\cdots\:&\:{r}_{mn}\end{array}\right]$$

Step 4

First, the evaluation criteria’ relative weight values ($\:{w}_{j}$: i: 1, 2,., n) are determined according to the purpose. Then, the weighted normalized decision matrix ($\:V$) is created by multiplying elements in each column of the $\:R$ matrix with the corresponding $\:{w}_{j}$ value.

$$\:V=\:\left[\begin{array}{cccc}{{w}_{1}r}_{11}&\:{w}_{2}{r}_{12}&\:\cdots\:&\:{{w}_{n}r}_{1n}\\\:{w}_{1}{r}_{21}&\:{w}_{2}{r}_{22}&\:\cdots\:&\:{{w}_{n}r}_{2n}\\\:\cdots\:&\:\cdots\:&\:\cdots\:&\:\cdots\:\\\:\cdots\:&\:\cdots\:&\:\cdots\:&\:\cdots\:\\\:{{w}_{1}r}_{m1}&\:{{w}_{2}r}_{m2}&\:\cdots\:&\:{{w}_{n}r}_{mn}\end{array}\right]$$

Step 5

The weighted evaluation factors in the $\:V$ matrix, which are the largest of the column values (the smallest if the corresponding evaluation factor is minimized) are selected to create the ideal solution set. The ideal solution and the negative ideal solution can be calculated using Eqs. (21) and (22) respectively. In both formulations, $\:J\:$represents the benefit (maximization), and $\:{J}^{{\prime\:}}\:$represents the cost (minimization) value.

$$\:{A}^{\text{*}}=\left\{\left(\underset{i}{\text{max}}{v}_{ij}|j\in\:J\right),\left(\underset{i}{\text{min}}{v}_{ij}|j\in\:{J}^{{\prime\:}}\right)\right\}$$

(21)

$$\:{A}^{-}=\left\{\left(\underset{i}{\text{min}}{v}_{ij}|j\in\:J\right),\left(\underset{i}{\text{max}}{v}_{ij}|j\in\:{J}^{{\prime\:}}\right)\right\}$$

(22)

Values obtained from Eq. (21) can be shown as $\:{A}^{\text{*}}=\left\{{v}_{1}^{\text{*}},{v}_{2}^{\text{*}},\dots\:,{v}_{n}^{\text{*}}\right\}\:$ and values obtained from Eq. (22) can be shown as $\:{A}^{-}=\left\{{v}_{1}^{-},{v}_{2}^{-},\dots\:,{v}_{n}^{-}\right\}$.

Step 6

In the TOPSIS method, the Euclidean distance approach is used to find the deviation of the evaluation factor value for each decision point from the ideal and negative ideal solution set. The distance of alternative $\:{J}_{i}\:$ to the ideal solution ($\:{S}_{i}^{\text{*}}$) and the distance from the negative ideal solution ($\:{S}_{i}^{-}$) are calculated using Eqs. (23) and (24) respectively.

$$\:{S}_{i}^{\text{*}}=\sqrt{\sum\:_{j=1}^{n}{({v}_{ij}-{v}_{j}^{\text{*}})}^{2}}$$

(23)

$$\:{S}_{i}^{-}=\sqrt{\sum\:_{j=1}^{n}{({v}_{ij}-{v}_{j}^{-})}^{2}}$$

(24)

Step 7

The relative proximity to the ideal solution ($\:{C}_{i}^{\text{*}}$) is calculated by using the Eq. (25).

$$\:{C}_{i}^{\text{*}}=\frac{{S}_{i}^{-}}{{S}_{i}^{-}+{S}_{i}^{\text{*}}}\:0\le\:{C}_{i}^{\text{*}}\le\:1$$

(25)

Step 8

Alternatives are ranked according to the relative proximity to the ideal solution ($\:{C}_{i}^{\text{*}}$).

Case Study

This paper ranks low- and middle-income countries based on cardiovascular disease risks. For this purpose, the flowchart of the applied methodology is shown in Fig. 1.

Fig. 1

Methodology for ranking countries

Definition of Criteria

A comprehensive literature review was conducted prior to selecting the criteria. The criteria were selected and grouped based on the World Health Organization (WHO) sources, primarily the “Noncommunicable Diseases Country Profiles 2018” report. In addition, consultations were held with four cardiology experts to finalize the grouping of criteria. A total of 22 factors were included for evaluation under seven main factor categories: socio-demographic, economic, behavioral, metabolic, health system and national capacity, and others. The criteria considered in the research were compiled from the WHO and The World Bank databases. The most recent data available for low- and middle-income countries were considered. The desired value was minimum for criteria with a minimization direction, while the desired value was maximum for criteria with a maximization direction. The research aimed to obtain a ranking from the country with the highest cardiovascular disease risk to the country with the lowest risk. Therefore, factors that increase disease risk were encoded with a maximization direction, while factors that reduce disease risk were encoded with a minimization direction and included in the evaluation. The main and sub-criteria, along with their direction and code, are provided in Table 2, and additional information about the criteria is presented below the table.

Table 2 Risk factors

The rapid increase in urban population and the expansion of cities bring along environmental issues such as wastewater, noise, and air pollution [44, 45]. People living in cities are exposed to NCDs and injuries. Additionally, alcohol and substance addiction rates are higher in urban areas [45]. Unregulated globalization and unplanned urbanization also increase the likelihood of exposure to CVDs risk factors. Moreover, unplanned urbanization restricts opportunities for physical activity and increases exposure to environmental pollution [14].

The prevalence of chronic NCDs increases with aging. Furthermore, multiple diseases can coexist in the same individual during old age. Chronic diseases negatively impact the clinical picture of the elderly and reduce treatment effectiveness. Therefore, as the elderly population increases, the number of individuals at risk of developing chronic diseases also rises [46]. The increase in the proportion of the elderly population leads to a shift of health issues towards NCDs observed in the elderly population [6]. It is noted that NCDs and related deaths increase with age worldwide. Accordingly, it can be stated that the prevalence of NCDs will rise with the increase in the elderly population [8]. The aging population is associated with increased age-related diseases, particularly dementia, cardiovascular diseases, and cerebrovascular diseases [47].

It can be said that the risk factors for NCDs are closely associated with income inequality. Income distribution represents a measure of social class differences in society and is related to health outcomes. NCDs risk factors can be indirectly linked to the economic status of countries, not just the population’s health status. In this context, the GINI coefficient, an economic inequality indicator, has been included as a risk factor in the study. The GINI coefficient is the literature’s most commonly used measure of inequality [48]. The GINI coefficient provides a numerical measure that gives an idea about income distribution. The coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality [49]. Poverty, unemployment, unhealthy housing, hazardous environments, and lack of access to medical services and healthy food are all associated with obesity, smoking, and alcohol addiction and form the basis of social inequalities in NCDs morbidity and mortality [3].

The air we breathe contains emissions from motor vehicles, industries, heating, commercial sources, tobacco smoke, and emissions from household fuels. Air pollution harms individuals’ health, particularly those vulnerable due to age or existing health conditions. Air pollution is a significant cause of deaths, hospital admissions, and exacerbation of symptoms, primarily related to heart and respiratory diseases [50]. The disease burden attributable to air pollution is estimated at the same level as other key global health risks, such as unhealthy diet and tobacco use. It is stated that exposure to air pollution leads to millions of deaths and loss of healthy life years every year. The World Health Assembly recognized air pollution as a risk factor for NCDs in 2015 [51].

Particulate matter (PM) is a complex mixture of various chemical and physical components in urban and non-urban environments. PM 2.5 refers to particles with an aerodynamic diameter equal to or smaller than 2.5 μm [11]. Exposure to PM has been associated with various cardiovascular diseases. Consistent evidence from epidemiological and experimental studies demonstrates that short and long-term exposure to PM, particularly to the finest particles, is associated with cardiovascular morbidity and mortality [52]. Air pollution becomes an independent risk factor for cardiovascular morbidity and mortality. Studies have shown a strong association between PM in air pollution and increased cardiovascular diseases such as myocardial infarction, cardiac arrhythmias, ischemic stroke, vascular dysfunction, hypertension, and atherosclerosis [52, 53]. It is also noted that PM 2.5 is identified as the primary cause of the adverse cardiovascular effects of air pollution on human health [54, 55]. In addition to increased morbidity and mortality, the disease burden resulting from air pollution also imposes a significant economic burden. Consequently, ways to improve air quality worldwide and reduce the public health burden and associated costs of air pollution are being sought [11].

Diabetes occurs as a result of insufficient production or release of insulin in the pancreas, leading to decreased or lost effectiveness of insulin due to genetic or environmental factors [3]. Diabetes is a lifelong disease that restricts dietary choices, increases the risk of kidney and eye diseases in the long term, and requires individuals with diabetes to make lifestyle changes [14]. In other words, diabetes is a chronic disease that requires continuous medical care and self-management education to prevent acute complications and reduce the risk of long-term complications [56]. Additionally, diabetes also leads to elevated blood sugar levels. Diabetes is both a standalone disease and a significant risk factor for CVDs [3]. CVDs are the most common cause of morbidity and mortality in individuals with type 1 or 2 diabetes. Generally, individuals with diabetes also have other comorbidities, such as obesity, hypertension, and dyslipidemia, contributing to increased CVDs risk [56, 57].

Tobacco, primarily in cigarette consumption, is a significant health risk. Cigarette smoking is the most prevalent tobacco use [3]. There is a significant contribution of cigarette smoking to the development of mortality and morbidity related to CVDs. Identifying the relationship between smoking and heart disease dates back to the 1940s. Since then, various studies have demonstrated that smoking increases the risk of CVDs, stroke, sudden death, heart attack, peripheral vascular diseases, and aortic aneurysms. Smoking remains one of the most common preventable causes of mortality worldwide [16]. Every year, more than 8 million people die from tobacco use. Most tobacco-related deaths occur in low- and middle-income countries, often targeted by intense tobacco industry intervention and marketing efforts [58]. The health risks of smoking arise from direct tobacco use and exposure to secondhand smoke [14]. Some individuals are exposed to secondhand smoke despite not being smokers themselves. Exposure to secondhand smoke leads to adverse health outcomes and causes 1.2 million deaths annually. Approximately half of children breathe air contaminated with tobacco smoke, and each year, 65,000 children die from diseases attributed to passive smoking [58]. Nutritional habits are critical factors in the causes of NCDs [3]. It can be said that obesity is one of the most significant risk factors, along with smoking. Obesity refers to increased body fat compared to lean body mass due to an imbalance between energy intake and expenditure [59]. Both overweight and obesity have reached epidemic levels globally in high-income and low-income countries [3]. The rise of obesity and overweight can be attributed to the increased consumption of high-fat and high-sugar foods, the growing sedentary nature of many forms of work, changing modes of transportation, and increased urbanization leading to decreased physical activity [60]. The most commonly used and well-known method for assessing obesity is Body Mass Index (BMI) [59]. BMI is a simple height-to-weight ratio widely used to classify overweight and obesity in adults. It is calculated by dividing a person’s weight in kilograms by the square of their height in meters (kg/m2). For adults, overweight is defined as a BMI equal to or greater than 25, and obesity is defined as a BMI equal to or greater than 30 [60]. With an increase in BMI, undesired metabolic effects such as blood pressure, cholesterol, triglycerides, and insulin resistance are affected, leading to an increased risk of coronary heart disease, ischemic stroke, and type 2 diabetes [3].

Excessive calorie intake, high cholesterol, salt consumption, and low physical activity levels are fundamental factors contributing to the high prevalence of CVDs worldwide [3]. Obesity can lead to various structural and functional changes in the heart. Due to the structural alterations, it induces on the heart, obesity alone increases the risk of CVDs. The coexistence of obesity and hypertension intensifies the impact on the structure and function of the heart. Various studies suggest that obesity is an independent risk factor for all-cause mortality in individuals with coronary heart disease. In addition to being an independent risk factor for CVDs, there is increasing evidence that obesity contributes to other risk factors, such as hypertension [59].

Blood pressure is the force exerted by the circulating blood on the walls of the body’s major blood vessels, known as arteries. Hypertension refers to a condition when blood pressure is excessively high [61]. High blood pressure is the leading cause of death and disability, particularly cardiovascular diseases, in adults. The risk of cardiovascular diseases progressively increases with elevated blood pressure [62, 63]. Obesity is one of the most significant risk factors for hypertension. Other risk factors include alcohol consumption, sodium intake through diet, and a sedentary lifestyle [59]. Hypertension is a fundamental risk factor for all cardiovascular diseases, including coronary heart disease, heart failure, ischemic stroke, and peripheral vascular disease [3]. Hypertension is often called the “silent killer” because most people with hypertension are unaware of the problem. This is because hypertension may not have any warning signs or symptoms. Therefore, regular blood pressure measurements are necessary [61]. Hypertension is a significant risk factor for coronary heart disease, chronic kidney disease, and ischemic/hemorrhagic stroke. Although the exact cause of hypertension is unknown in most cases, high salt intake, overweight/obesity, excessive alcohol consumption, physical inactivity, stress, air pollution, and smoking increase the likelihood of hypertension [14].

Alcohol consumption is a modifiable risk factor for CVDs [64]. Heavy alcohol consumption is a significant cause of death and disability [65]. Excessive or lifelong high alcohol intake is harmful to most cardiovascular functions. High alcohol consumption increases morbidity and mortality by causing cardiovascular dysfunction and structural damage. It also contributes to developing hypertension, dyslipidemia, and diabetes mellitus [66]. There is a relationship between high-dose alcohol consumption and CVDs, certain cancers, and liver diseases. The relationship between alcohol consumption and CVDs is complex and closely related to the amount and consumption pattern [3]. The quantity, type, and pattern of alcohol consumption can have different associations with health outcomes [67]. Research suggests that moderate and high doses of alcohol consumption have adverse effects on CVDs, while low doses of alcohol (1–2 drinks per day) are associated with a lower risk of CVDs [64, 68, 69]. Additionally, many individuals do not follow a regular drinking pattern, and low-to-moderate consumption can pose a risk for CVDs when combined with heavy/episodic drinking periods [69]. High alcohol intake has been associated with increased mortality [67]. High alcohol consumption also increases the risk of stroke and peripheral artery disease [65]. The cardiovascular system is sensitive to the harmful effects of alcohol. Alcohol is an active toxin that widely spreads in the body, causing multiple simultaneous and synergistic effects, and both excessive and lifelong consumption and light doses are not recommended [66]. Physical activity is any bodily movement produced by skeletal muscles that requires energy expenditure. It can be performed in various forms, such as walking, cycling, dancing, and yoga, and can be integrated into daily tasks or household chores [70]. Physical inactivity is a proven risk factor for premature death and certain noncommunicable diseases [71] and harms mental health and quality of life [72]. Insufficiently physically active individuals have an estimated 20–30% higher risk of death for any reason than those who are active. On average, it is believed that 30 min of physical activity per day can reduce the risk of ischemic heart disease by 30% and the risk of diabetes by 27% [3]. However, with adequate duration and intensity, all physical activity can provide health benefits when performed regularly. Regular physical activity has been proven to help prevent and treat noncommunicable diseases such as cardiovascular disease, stroke, diabetes, and breast and colon cancer. It also assists in preventing hypertension, overweight, and obesity and improves mental health, quality of life, and well-being [70]. Promoting non-motorized modes of transportation such as walking and cycling is recommended to reduce physical inactivity, as well as encouraging active recreation and sports participation during leisure time and implementing national policies [72].

Salt is one of the major determinants of high blood pressure and increased cardiovascular risk worldwide [3, 73]. The impact of salt on health can be likened to other dietary and lifestyle changes, such as healthy eating, increasing physical activity, and reducing smoking [62]. In recent years, substantial evidence has shown a cause-and-effect relationship between salt intake and cardiovascular and renal damage. Increased salt intake is reported to raise arterial pressure, leading to adverse cardiovascular and renal outcomes [73, 74]. Excessive salt intake also adversely affects cardiovascular and renal morbidity and mortality [57]. The recommended average salt intake is < 5 g per day per individual to prevent CVDs. Reducing salt intake to the recommended levels significantly impacts the prevention of CVDs [3]. Consuming less than 5 g of salt daily for adults helps reduce blood pressure and the risk of cardiovascular diseases, stroke, and coronary heart disease. The primary benefit of reducing salt intake is the corresponding decrease in high blood pressure [75]. It is noted that reducing salt intake lowers blood pressure and decreases the risk of cardiovascular diseases [62]. If global salt consumption is reduced to the recommended levels, it is estimated that approximately 2.5 million deaths could be prevented each year [75].

High cholesterol levels increase the risk of heart disease and stroke [76, 77]. Globally, one-third of ischemic heart disease can be attributed to high cholesterol. Overall, it is estimated that high cholesterol contributes to 2.6 million deaths. Elevated total cholesterol is a significant contributing factor to the burden of disease related to ischemic heart disease and stroke [76]. High cholesterol has no signs or symptoms, so cholesterol control is the only way to understand it [77]. Cholesterol is measured in milligrams per deciliter (mg/dL). High cholesterol is a total cholesterol level above 200 mg/dL, known as hyperlipidemia. Certain health conditions, such as type 2 diabetes and obesity, can increase the risk of high cholesterol. Lifestyle factors such as consuming a diet high in saturated and trans fats and lack of physical activity also contribute to an increased risk of high cholesterol [77]. It is noted that every 10% increase in weight leads to a 10–15 mg/dL increase in cholesterol levels. Weight loss, on the other hand, helps lower LDL cholesterol and triglyceride levels while increasing HDL cholesterol levels. These changes improve the lipid profile and reduce cardiovascular risk [59].

Cardiovascular diseases pose a significant challenge for low- and middle-income countries, as these nations often lack sufficient access to early detection and treatment programs for individuals at risk and integrated primary healthcare services [78]. Considering that healthcare infrastructure and healthcare workforce affect healthcare service delivery and, consequently, health status, infrastructure factors such as the number of physicians, nurses, and hospital beds were taken into account in the scope of the research. A significant portion of the burden of diseases can be reduced by controlling the primary risk factor. It can be said that strategy action plans, standards, and protocols serve this purpose.

Definition of alternatives

All countries that are members of the United Nations (UN) can become members of the WHO. Other countries are accepted as members when a simple majority of the World Health Assembly approves their applications. WHO members are grouped according to regional distribution. There are a total of 194 member states, with 47 in the African Region, 21 in the Eastern Mediterranean Region, 53 in the European Region, 35 in the Region of the Americas, 11 in the South-East Asia Region, and 27 in the Western Pacific Region [79]. More than three-quarters of deaths from cardiovascular diseases occur in low- and middle-income countries [13]. Therefore, the assessment is limited to low- and middle-income countries, excluding high-income countries. Out of the 194 WHO member countries, 133 are low- and middle-income countries [80]. Complete data is available for 90 out of the 133 countries. Accordingly, the research is based on the alternatives provided by 90 low- and middle-income countries. The alternatives, their codes, and the regions they belong to are given in Table 3. In the following steps, the countries will be referred to by their codes.

Table 3 Alternatives and regions

Weighting of criteria

For the evaluation of the cardiovascular disease risk potential of low and middle-income countries, a classification has been made under 6 main criteria and a total of 22 sub-criteria as given in Table 2. The criteria given in Table 2 have different weights. In this paper, the interval-valued PF-AHP method was used to calculate criterion weights. An expert decision-making team has been determined to evaluate the criteria. The decision-makers consist of a total of 5 experts, including 3 from the field of Cardiovascular Surgery with more than 10 years of experience and academic titles, and 2 from the field of Cardiology. These experts are experienced and specialized physicians who have been working in the field of Cardiovascular Surgery or Cardiology at a university hospital for many years. Each decision-maker evaluated the main criteria and sub-criteria under each main criterion in accordance with the classification given in Table 2. Due to the large number of steps in the interval-valued PF-AHP method, the application steps of the method have been explained through the Socio-Demographic sub-criteria. All evaluations except for the Socio-Demographic sub-criteria are additionally provided in Tables A2, A3, A4, A5, A6 and A7.

In the calculation of the weights of socio-demographic sub-criteria using the interval-valued PF-AHP method, firstly, each decision maker (DM) compared the criteria pairwise using the linguistic scale given in Table 1 (Table 4). Then, linguistic comparisons were converted to IVPFNs using the scale given in Table 1 (Table 5). When there are multiple DMs, the compromised pairwise comparison matrix is calculated using the IVPFWA given in Eq. (10). The aggregated pairwise comparison matrix is given in Table 6. To calculate the difference between the lower and upper values of the membership and non-membership functions, the difference matrix $\:D={\left({d}_{ik}\right)}_{mxm}\:$can be obtained by applying Eqs. (11–12) (Table 7). The interval multiplicative matrix $\:S={\left({s}_{ik}\right)}_{mxm}$ was calculated by applying Eqs. (13–14) (Table 8). Then, the determinancy values were calculated using Eq. (15) (Table 9). The determinancy degrees and the $\:S={\left({s}_{ik}\right)}_{mxm}$ matrix were multiplied using Eq. (16) (Table 10). Finally, criterion weights were obtained by using the normalization method given in Eq. (17) (Table 11).

Table 4 Pairwise linguistic comparisons of Socio-demographic sub-criteria

Table 5 Pairwise comparisons of Socio-demographic sub-criteria obtained by using PFNs

Table 6 Aggregated pairwise comparison matrix of Socio-demographic sub-criteria

Table 7 The difference matrix of Socio-demographic sub-criteria

Table 8 The interval multiplicative matrix of Socio-demographic sub-criteria

Table 9 The determinacy values of Socio-demographic sub-criteria

Table 10 Socio-demographic sub-criteria weights before normalization

Table 11 Socio-demographic sub-criteria weights

Similar steps were followed in calculating the weights of the main criteria and other sub-criteria. These calculated weights are the local weights of the criteria. The local weights of the main criteria and sub-criteria are given in columns 2 and 4 of Table 12, respectively. However, each sub-criterion has a level of importance based on the weight of the main criterion to which it is linked in the hierarchical structure. For this reason, global weights were obtained by multiplying all sub-criteria weights with the main criteria weights. The sum of the global weights of all sub-criteria is 1. Thus, the weights of all sub-criteria can be compared with each other. The global weights of all sub-criteria are given in Table 12.

Table 12 Main and sub-criteria weights

According to the results obtained through the interval-valued PF-AHP method for determining the level of importance, it can be observed that the C4, C3, and C2 main criteria have higher levels of importance compared to the other main criteria, while the C6, C5, and C1 criteria have lower levels of importance. Based on these results, it can be said that metabolic, behavioral, and economic factors are more important in the formation of disease risk. It is concluded that factors related to national capacity, the healthcare system, and socio-demographic factors have a lower level of importance.

The burden of NCDs is increasing rapidly in low- and middle-income countries. It is known that 77% of all deaths due to NCDs occur in low- and middle-income countries. Most of these deaths are also due to CVDs factors such as changing living conditions, alcohol and tobacco use, urbanization and air pollution, changes in eating habits, and lack of physical activity increase the risk of death [8]. In this context, it is considered essential to address CVDs risk factors from the point of view of low- and middle-income countries.

When looking at the sub-criteria, it is determined that the C3.1, C4.2, C4.1, C4.6, and C4.5 criteria have higher levels of importance than others. On the other hand, the C1.1, C5.4, C1.3, C5.2, and C1.2 criteria have a lower level of importance. The sub-criterion with the highest importance level is the prevalence of tobacco use in adults. This can be explained by the fact that tobacco use is a significant risk factor for cardiovascular and respiratory diseases and many types of cancer and other debilitating health conditions [58]. Additionally, tobacco use is one of the greatest public health threats, with more than 8 million tobacco-related deaths annually. Therefore, tobacco control remains a global health priority [81]. The second and third important sub-criteria are high blood sugar and blood pressure. The prevalence of hypertension and diabetes is also ranked high. Based on the results, it can be said that behavioral risk factors, especially metabolic ones, have a higher impact on the emergence of the disease compared to other criteria. High blood pressure, smoking, diabetes, and lipid abnormalities are major modifiable risk factors for CVDs [82]. Metabolic, behavioral, environmental, and social risk factors are major drivers of CVDs [83]. Another reason for these factors to be ranked high is that the level of importance of the main criteria is higher than that of other criteria. Urban population, number of hospital beds, air pollution rate, nurses and midwives, and population aged 65 and over are considered sub-criteria with the lowest level of importance. Although these criteria pose a risk for cardiovascular diseases, it is determined that they pose a lower risk level than other criteria. All socio-economic factors are ranked at lower levels. This result can be associated with the low level of importance of the main criterion.

Ranking of alternatives

The TOPSIS method was used to rank low- and middle-income countries. The importance levels of criteria calculated using the interval-valued PF-AHP method, as provided in Table 12, were utilized in applying the method. Data pertaining to countries were obtained from the WHO and The World Bank databases and the WHO’s “Noncommunicable Diseases Country Profiles 2018” report, per the established criteria. The decision matrix containing the data of 90 alternative countries is given in Table A1 as an annex to the paper. Alternatives were ranked by applying Eqs. (20–25) to the decision matrix. The relative closeness values ($\:{C}^{\text{*}}$) calculated for each alternative as a result of the application of the TOPSIS method and the ranking according to these values are given in Table 13.

Table 13 Relative closeness of alternatives and final rank

According to the TOPSIS results, Lebanon (A42), Jordan (A37), Solomon Islands (A73), Serbia (A71), and Bulgaria (A10) ranked at the top. The respective countries’ higher importance level and benefit-oriented values of criteria C3.1, C4.1, C4.2, C4.5, and C4.6 can interpret this result. Criterion C3.1, which is particularly significant compared to other criteria, has been decisive in the ranking results. Timor Leste (A79), Benin (A6), Ghana (A30), Niger (A60), and Ethiopia (A26) are the countries that rank at the bottom. The countries at the lower ranks can be interpreted by the low values of the maximization-oriented criterion, which have higher importance levels. In summary, countries with high values for maximization-oriented criteria and low values for minimization-oriented criteria ranked higher. Additionally, as with criterion C3.1, high-importance level criteria have impacted the ranking results.

It is stated that in Lebanon (A42), cardiovascular diseases are the leading cause of morbidity and mortality and also the primary cause of hospital admissions. It is stated that the burden of cardiovascular diseases is caused by the migration of nurses at a high rate, and the lack of workforce has a negative impact on patient outcomes [84]. With generally limited knowledge, attitude, and practice towards CVDs, the Lebanese population needs targeted national campaigns on CVDs to prevent and alleviate complications from CVDs [85]. In a paper conducted in Lebanon in 2017, it was concluded that the highest declared awareness of CVDs risk factors was related to smoking [86]. In our research, the most critical risk factor was found to be smoking. In this context, it can be stated that the relevant research supports the results of our paper.

It is stated that between 1990 and 2019, the burden of CVDs decreased in Jordan, but the prevalence of the disease and the number of deaths increased. CVDs remain the leading cause of death in Jordan [87]. Jordan ranked second in the research.

Ranking third is the Solomon Islands (A73) Pacific Region country. Other Pacific countries ranked differently in the research. A study on Pacific people revealed that the epidemiology of CVDs varies according to specific ethnic groups, place of birth, and country of residence [88]. The relevant research results are parallel to our research results.

In the ranking obtained from the TOPSIS method, it can be observed that the majority of the countries at the top are from the “European Region.” Following that, the “Eastern Mediterranean Region” is represented. The countries at the bottom are primarily from the “African Region”, followed by the “South East Asia Region”. It can be said that urbanization, an aging population, and increasing air pollution are parallel with the development level. The obtained result can be explained by the lower level of development in the African region compared to the countries in the European Region.

Scenario analysis

In this section, scenario analyses are conducted to analyze the impact of changes in criteria weights on the final rankings. For this purpose, five different scenarios were created and tested. The scenarios and their descriptions are summarized in Table 14. According to the criteria weights determined from the scenarios, final rankings were obtained by the TOPSIS method for each scenario. Table 15 shows the rankings obtained as a result of five different scenarios.

Table 14 Descriptions of scenarios

When the final rankings obtained as a result of the scenarios are analyzed, it is seen that Lebonanon (A42), which ranked first in the ranking obtained by evaluating all criteria (Table 13), also ranked first in the S5 scenario. This can be explained by the fact that the sum of the importance levels of the sub-criteria under the main criteria C3 and C4 is approximately 0.8. Similarly, Solomon Islands (A73) ranks third in Table 13 and second in the S5 scenario. However, Ethiopia (A26) ranks last in both the final ranking in Table 13 and in the S3 and S5 scenarios. This can be explained by the fact that Ethiopia (A26) has low values in most of the criteria with high importance levels. Apart from these cases, when the rankings obtained as a result of the other scenarios are analyzed, it is seen that there are significant differences. This is due to the fact that the importance levels of the main criteria C1, C2, C5, and C6 are considerably lower than the main criteria C3 and C4. When the scenarios are examined, it is seen that the S5 scenario gives the closest results to the research results. Studies in the literature confirm that behavioral and metabolic risk factors are more significant than other factors [12, 83, 89,90,91,92,93,94]. A42 ranks first in the research result and the S5 scenario. The S3 scenario gives similar results. In the S3 scenario, A42 ranked fourth. The rankings in other scenarios are different. The order of A37 is the same for scenarios S1 and S2. It is seen that the closest result is again in the S5 scenario. A73 ranks second in the S5 scenario. For A73, the rankings of S3 and S4 are very close. A73 ranks seventh in S3 and eighth in S4. A26 ranks last in the research. The country coded as A26 also ranked last in S3 and S5 scenarios.

Table 15 Scenario analysis

Conclusion

In the paper, 22 criteria were identified to assess the risk of cardiovascular disease in low and middle-income countries. Due to the varying importance levels of the criteria, the interval-valued PF-AHP method was used to calculate the importance levels of the criteria. Five expert physicians were consulted for the evaluation of the criteria. As a result of the assessments, the criteria with the highest importance levels were determined to be adult tobacco use prevalence (C3.1), Raised blood glucose among adults aged 18 years and older (C4.2), Raised blood pressure among adults aged 18 years and older (C4.1), Hypertension prevalence among adults aged 30–79 years (C4.6), and Diabetes prevalence among individuals aged 20–79 years (C4.5). On the other hand, the criteria with the lowest importance levels were found to be Urban population (C1.1), Hospital beds per 10,000 population (C5.4), PM2.5 air pollution, mean annual exposure (C1.3), Nurses and midwives per 10,000 population (C5.2), and Population aged 65 years and older (C1.2). According to the results of the interval-valued PF-AHP, the criterion with the highest importance level was identified as adult tobacco use prevalence. It is believed that measures such as restricting smoking or implementing smoke-free policies can help reduce the risk of disease. Therefore, policies aimed at reducing tobacco use can be adopted, such as increasing cigarette prices, restricting cigarette advertisements, and educating the public about the dangers of smoking.

Following the interval-valued PF-AHP, the determined importance levels of the criteria and compiled data for countries, the TOPSIS method was applied, and country rankings were obtained. The ranking results show that the countries with higher cardiovascular risk are Lebanon, Jordan, Solomon Islands, Serbia, and Bulgaria, in that order. On the other hand, the countries ranked at the bottom are Timor-Leste, Benin, Ghana, Niger, and Ethiopia, respectively.

Reducing the burden of cardiovascular disease or death from any cause for individuals and societies can be achieved by better understanding cardiovascular risk factors and region- and gender-specific factors in disease development [95]. Countries ranking high in TOPSIS results are those with the highest cardiovascular risk. In this context, it is recommended that these countries engage in various activities to reduce the risk of cardiovascular disease. Measures can be taken not only for CVDs but also for lifestyle and habits that play a role in the development of NCDs. It is advisable to ensure and maintain a safe environment, raise awareness among the community about environmental factors that pose a risk of disease, and assist with lifestyle changes that contribute to health risks. Individuals can be encouraged to adopt a healthy diet, limit salt consumption, engage in regular exercise, quit smoking, aim for and maintain a healthy body weight, and manage stress, all of which can help reduce cardiovascular disease risk at the individual level.

Primary and secondary preventive activities should be implemented to reduce the burden of CVDs, along with institutional support, public education, community-based risk reduction efforts, healthy work environments, information systems for monitoring morbidity, and well-informed healthcare personnel [3]. The approach to CVDs prevention should be multidisciplinary, focusing on reducing overall risk by considering multiple risk factors rather than individual ones. The goal of preventing heart and vascular diseases is to decrease fatal and non-fatal atherosclerotic events, complications, and the need for percutaneous or surgical revascularization while improving and extending quality of life [14]. To achieve this goal, a comprehensive assessment of total cardiovascular risk and a corresponding treatment strategy is necessary. Within this framework, remedial actions can be undertaken. In the fight against cardiovascular diseases, identifying cardiovascular risk factors and preventing or minimizing these risks should be targeted. Educating the community about the benefits of healthy eating and regular physical activity for a healthier life is recommended. Developing and implementing policies aimed at reducing CVDs risk would be beneficial. Countries with a high CVDs risk can support policies that promote healthy eating and physical activity. Activities such as improving the accessibility of healthy foods, limiting advertisements that encourage the consumption of unhealthy foods, and implementing healthy eating programs in schools and workplaces can be pursued. Strengthening healthcare services can enhance early diagnosis and treatment options. Information campaigns can be organized to raise awareness about CVDs screening and risk assessment. Increasing the number of expert physicians who assess and manage CVD risk is also considered beneficial.

As a result of the evaluations, the risk factors with the highest and lowest levels of importance were identified. Prioritization of CVDs risk factors can contribute to managing disease risk, providing education and counseling services, improving community health, and increasing community awareness. In addition, prioritization of CVDs risk factors can provide health professionals with education and counseling according to risk factors. Identifying high-risk factors may provide the opportunity to take the necessary measures for these factors and early intervention. In this way, health outcomes related to the disease can be improved, but the burden on health systems can also be reduced. Health professionals can decide on forming clinical guidelines according to more critical risk factors. At the same time, community health programs can be designed by considering more critical risk factors. Sorting risk factors according to their severity can help health professionals decide which risk factors should be addressed first. Prioritization of CVDs risk factors may also be helpful in the design of social health policies.

The approach proposed in this paper to rank low- and middle-income countries regarding cardiovascular disease risk factors has some limitations. Some of these problems are related to the MCDM method, while others are related to the data. The results obtained in the interval-valued PF-AHP method used to weight cardiovascular disease risk factors were calculated as a result of the personal judgments of the decision-making team. In future studies, the results can be discussed with the participation of more decision makers. PFSs and the linguistic scale used are not universal. Different fuzzy set extensions and scales can be used in different studies. It could not be included in this paper due to the limited number of countries sharing data for the “red meat consumption” criterion, which may be effective in the formation of cardiovascular disease. In addition, some low- and middle-income countries could not be evaluated due to lack of data on the criteria used. Providing this missing data allows countries to be re-ranked according to their risk potential.