Patterns among factors associated with myocardial infarction: chi-squared automatic interaction detection tree and binary logit model

Abstract

Background

Although mortality from myocardial infarction (MI) has declined worldwide due to advancements in emergency medical care and evidence-based pharmacological treatments, MI remains a significant contributor to global cardiovascular morbidity. This study aims to examine the risk factors associated with individuals who have experienced an MI in Türkiye.

Methods

Microdata obtained from the Türkiye Health Survey conducted by Turkish Statistical Institute in 2019 were used in this study. Binary logistic regression, Chi-Square, and CHAID analyses were conducted to identify the risk factors affecting MI.

Results

The analysis identified several factors associated with an increased likelihood of MI, including hyperlipidemia, hypertension, diabetes, chronic disease status, male gender, older age, single marital status, lower education level, and unemployment. Marginal effects revealed that elevated hyperlipidemia levels increased the probability of MI by 4.6%, while the presence of hypertension, diabetes, or depression further heightened this risk. Additionally, individuals with chronic diseases lasting longer than six months were found to have a higher risk of MI. In contrast, factors such as being female, having higher education, being married, being employed, engaging in moderate physical activity, and moderate alcohol consumption were associated with a reduced risk of MI.

Conclusion

To prevent MI, emphasis should be placed on enhancing general education and health literacy. There should be a focus on increasing preventive public health education and practices to improve variables related to healthy lifestyle behaviours, such as diabetes, hypertension, and hyperlipidemia.

Introduction

Cardiovascular diseases (CVD) are among the leading causes of death at a global level [1]. As reported in the World Health Organization data, approximately 17.9 million people died due to cardiovascular diseases in 2019. Myocardial infarction (MI) and stroke are responsible for 85% of these deaths [2]. Examining deaths in Türkiye, circulatory system diseases rank first, constituting approx—36% of the total. Deaths related to acute myocardial infarction (AMI), among circulatory system diseases, have been increasing every year, reaching 39.38/100,000 in 2021 [3]. Coronary heart diseases in Türkiye have an annual mortality rate of 11.4/1,000 in the population aged 20 years and older [4].

MI is a major cause of death and disability worldwide and may be the first sign of coronary artery disease or occur repeatedly in patients with the disease [5]. AMI is the most common type of coronary heart disease (CHD). Risk factors include age, gender, diabetes, obesity, blood pressure, glycemic control, lipid profile, ischemic heart disease, and smoking [6,7,8,9,10,11,12]. Systemic hypertension is the most common cardiovascular risk factor, resulting in premature death [12]. Globally, more than 50% of MI, stroke, and heart failure cases are attributed to systemic hypertension [12]. The incidence of AMI is higher in men than in women, with a higher fatality rate in men [13].

Individuals with diabetes were reported to have a higher probability of dying due to AMI [14]. Diabetes continues to be a significant independent risk factor for atherosclerotic heart disease [10]. The prevalence of diabetes in Türkiye is above the world average [3]. The importance of diabetes as a risk factor was highlighted in a study comparing seven years of MI incidence in the Finnish population, with 1,373 non-diabetic individuals and 1,059 individuals with type 2 diabetes [15].

Depression and anxiety are associated with cardiovascular diseases and are considered risk factors for MI [16]. Smoking negatively affects the heart [17], increasing bleeding in the infarcted area and adverse cardiac outcomes due to heightened acute inflammation [18]. Other tobacco products, such as hookah, also pose risks for MI [19]. Although obesity is recognized as an MI risk factor, many MI patients exhibit rising abdominal obesity, which may promote recurring atherosclerotic diseases [20, 21]. Unhealthy metabolism accompanying obesity further escalates MI risk [22]. Comorbidities greatly influence MI prognosis: for instance, asthma may worsen outcomes in hypertensive patients [23], while inadequate nutrition accompanying obesity can have a negative impact [24].

Controlling the risk factors is particularly important in the young patient group because their long-term prognosis is unfavourable [25]. It is argued that the rate of mortality associated with cardiovascular diseases can be reduced through a better understanding of the disease and risk factors, as well as the development of more effective treatments and preventive measures [26]. Therefore, this study aims to examine the risk factors of individuals who have experienced an MI in Türkiye. The increasing incidence of MI in an aging population, coupled with the rising prevalence of comorbid conditions such as hypertension, diabetes, and dyslipidemia, underscores the need for a closer examination of the biomedical and sociodemographic factors contributing to MI risk. Unlike previous studies that often focused on a single risk factor or a limited set of variables, this research aims to provide a comprehensive, data-driven understanding of how a broad range of factors—including gender, age, education, marital status, chronic illnesses, and lifestyle habits—shape the risk of MI.

The primary aim of this study is to determine and clarify the relative effects and interactions of sociodemographic factors (e.g., age, gender, marital status, education, employment), comorbid conditions (e.g., hypertension, diabetes, hyperlipidemia, chronic diseases), and lifestyle behaviors (e.g., tobacco exposure, alcohol consumption, physical activity levels) on the likelihood of experiencing MI in Türkiye.

This study stands out from existing research as the first comprehensive analysis utilizing microdata from health surveys of individuals in Türkiye. It evaluates specific risk factors using both logistic regression and CHAID analysis. Binary logistic regression was employed to identify the factors influencing MI, while the CHAID method provided detailed insights into how sociodemographic characteristics affect MI. This approach allowed for the identification of hierarchical interactions and subgroup-based risk models. By incorporating both classical risk determinants and socioeconomic, demographic, and psychosocial variables, this study adds a new dimension to the understanding of MI risk in Türkiye.

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Research Question 1: What is the effect of age, gender, marital status, and employment status on the likelihood of MI among individuals in Türkiye?

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Research Question 2: What is the effect of comorbid conditions such as diabetes, hyperlipidemia, hypertension, and depression on the likelihood of MI among individuals in Türkiye?

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Research Question 3: Could the daily activity status, exposure to tobacco smoke, and alcohol use have affected the likelihood of MI among individuals in Türkiye?

Method

This section will explain the details of the data used in the study, the dependent and independent variables, and the research method.

Sampling and study design

The microdata set from the 2019 Türkiye Health Survey, published by the Turkish Statistical Institute (TurkStat), was utilized in this study. Conducted between October 7 and December 31, 2019, the survey included 17,084 participants, making it a significant nationwide study. This survey is particularly valuable as it facilitates both national and international comparisons. It provides various health-related indicators, including the health status of infants, children, and adults, as well as information on individuals’ utilization of health services [27]. In line with the demands of national and international researchers, TurkStat provides access to and use of Türkiye Health Survey microdata within the legal framework.

Data from individuals aged 45 years and older (n = 7,889) were utilized in this study, as limited data are available on the frequency of MI among younger individuals. In the Framingham Heart Study, the incidence of MI during a 10-year follow-up was reported to be 12.9/1000 in males aged 30 – 34 years and 5.2/1000 in females aged 35 – 44 years [28]. The incidence of MI was reported to be eight to nine times higher in individuals aged 55 – 64 years. Some studies indicated that 4% to 10% of MI patients were ≤ 40 or 45 years old [29]. Being 45 years and older in males and 55 years and older in females are considered risk factors for coronary heart disease [30]. In Türkiye, it is emphasized that individuals aged 40 years and older should undergo cardiovascular risk assessment at least once, even if they do not have any cardiovascular diseases [31].

Dependent variable

The dependent variable in the present study is the occurrence of MI, measured by the question, “Have you experienced myocardial infarction or its chronic consequences in the last 12 months? (yes, no).” In the model constructed here, the dependent variable categories are assigned as 1 if the individual has had an MI and 0 if they haven’t.

Independent variables

The independent variables in the present study are the variables available in the Türkiye Health Survey. Risk factors that could influence MI were selected based on the literature [32].

The independent variables of the study include gender (female, male), age (45–54 years, 55–64 years, 65 years and older), education (illiterate/unfinished primary school, primary school, secondary school, high school, university), marital status (single, married), employment status (unemployed, employed), any health problem (no, yes), hypertension status (no, yes), diabetes diagnosis (no, yes), the experience of depression (no, yes), hyperlipidemia level status (no, yes), daily activity level (mostly sedentary or stationary, mostly walking or moderately physically demanding work, mostly heavy work or physically demanding work), exposure to tobacco smoke (none/almost none, yes), and alcohol consumption status (no, yes).

Statistical analysis

Survey statistics in Stata 15 (Stata Corporation) were utilized to account for the complex sampling design and weights. A weighted analysis was performed to determine the frequencies and percentages of MI among participants and their independent variables. Binary logistic regression and CHAID analysis were applied to identify the factors influencing MI incidents for individuals aged 45 years and older.

Decision trees are non-parametric techniques suitable for both regression and classification problems. These methods do not rely on specific functional forms or require prior probabilistic information but are computationally intensive [33,34,35]. In this study, the CHAID algorithm, a type of decision tree, was utilized due to its ability to quickly generate extensive trees and its reliance on substantial data for reliability [36,37,38]. CHAID decision trees present results in a logical, hierarchical, and descriptive structure, making the output easily interpretable. This method effectively captures complex, non-linear relationships between predictor and outcome variables, visually represents them, and is particularly advantageous for categorical data [39,40,41].

In social sciences, especially in socio-economic research, some of the variables examined are measured on a sensitive scale, while others consist of dichotomous data such as positive–negative, successful-unsuccessful, and yes–no. Dichotomous data are the most commonly used form of categorical data. When the dependent variable is dichotomous categorical data, logistic regression analysis is used to examine the cause-and-effect relationship between the dependent variable and the independent variable(s) [42].

Logistic regression estimates the probability of the dependent variable taking one of its possible values. This method does not require the dependent variable to follow a normal distribution. Binary logistic regression is applied when the dependent variable has two outcomes, while ordinal or multinomial logistic regression is used for cases with more than two outcomes, depending on the nature of the problem [43].

Machine learning, a branch of artificial intelligence, enables algorithms to learn from data and perform specific tasks. Depending on the study’s purpose, various methods, such as decision trees, artificial neural networks, k-nearest neighbors, naïve Bayes, random forest, and support vector machines, can be utilized [44,45,46,47,48,49,50,51].

Results

Table 1 displays the factors influencing MI in individuals aged 45 and older, along with Chi-Squared Automatic Interaction Detection (CHAID) test statistics. Given the results presented in Table 1, the occurrence of MI varies based on gender, age, education, marital status, employment status, comorbidity, hypertension, diabetes, depression, elevated hyperlipidemia levels, daily activity level, and alcohol consumption.

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In the study, multicollinearity between the independent variables included in the binary logistic regression model was tested [52, 53]. The variance inflation factor (VIF) indicates a moderate degree of multicollinearity with values of five and above, and a high degree with values of 10 and above [54,55,56]. In this study, no variable that contributed to the multicollinearity problem between the independent variables.

The predicted results of the binary logistic regression model are presented in Table 2. Examining the results presented in Table 2, it was determined that gender, age, education, marital status, employment status, the presence of any health problems, hypertension, diabetes, depression, and elevated hyperlipidemia levels, as well as daily activity status, exposure to tobacco smoke, and alcohol consumption, have an effect in MI.

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Examining the marginal effects shown in Table 2, it was determined that the probability of MI is 5% lower for females. Individuals aged 55–64 years have a 1.8% higher probability of experiencing an MI when compared to those aged 45–54 years, whereas individuals aged 65 years and older have a 1.4% higher probability than those aged 45–54 years. The probabilities of MI for individuals with elementary, secondary, high, and university education are 1.8%, 2.6%, 3.4%, and 3.2%, respectively, lower than those who are illiterate or have not completed any schooling. Given the analysis results, the probability of MI for married individuals is 1.2% lower than for single individuals and 1.3% lower for employed individuals than for unemployed individuals. The probability of MI is 4.8% higher for those with any health problems, 3.5% higher for individuals with hypertension, 1.4% higher for those with diabetes, and 1.8% higher for individuals with depression. Elevated hyperlipidemia levels increase the probability of MI by 4.6%.

Notably, individuals engaging in mostly walking or moderately physically demanding occupations have a 1.2% lower probability of experiencing a MI when compared to those who predominantly sit or stand. Exposure to tobacco smoke was associated with a 1.6% increase in the probability of MI, whereas alcohol consumption was associated with a 1.9% lower probability.

The results of CHAID analysis indicate that factors such as elevated hyperlipidemia, chronic hypertension, chronic diabetes, chronic health conditions, gender, age, marital status, education level, and employment status have an impact on individuals experiencing an MI. The decision tree resulting from the CHAID modelling is illustrated in Figs. 1, 2, and 3. In the clear view of the tree, the number of individuals experiencing an MI at the root node is 393, accounting for a percentage of 4.98 among the total individuals.

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The initial variable splitting the decision tree reveals that elevated hyperlipidemia are the most influential factor affecting an individual’s likelihood of experiencing an MI. In the node where individuals have elevated hyperlipidemia, the percentage of patients experiencing an MI is 5.87%, whereas in the node where individuals do not have elevated hyperlipidemia, the percentage of patients experiencing an MI is observed to be 3.19%. According to this finding, elevated hyperlipidemia is identified as a factor that increases an individual’s probability of experiencing a MI.

The decision tree structure under the node containing individuals with hyperlipidemia levels is illustrated in Fig. 2. It can be seen that the variable distinguishing individuals with elevated hyperlipidemia levels in this node is chronic hypertension. In the node consisting of individuals with elevated hyperlipidemia levels and chronic hypertension, the rate of individuals experiencing an MI is 16.27%, whereas this rate is 5.63% in the node consisting of individuals without chronic hypertension. The presence of chronic hypertension in individuals with hyperlipidemia levels increases the likelihood of experiencing an MI.

In the node consisting of individuals without chronic hypertension and those with hyperlipidemia, it was observed that age is a distinguishing variable. In the node consisting of individuals aged 45–54 years, the rate of individuals experiencing an MI was 3.10%. In contrast, this rate was 7.27% in the node consisting of individuals aged 55 years and older. It was observed that an increase in age in the node consisting of individuals with hyperlipidemia and no chronic hypertension increases the likelihood of experiencing an MI. In the node consisting of individuals aged 55 years and older, gender was identified as a distinguishing variable. The rate of individuals experiencing an MI in the node consisting of males was 10.65%, whereas it was 4.78% in the node consisting of females. The probability of experiencing an MI for males aged 55 years and older without chronic hypertension and with hyperlipidemia is higher than that of females. The rate of patients experiencing an MI is 2.80% in the node consisting of females with hyperlipidemia, without chronic hypertension, and aged 55–64 years. In comparison, it was 8.05% in the node consisting of individuals aged 65 years and older. Therefore, it can be stated that an increase in age in females without chronic hypertension but with hyperlipidemia increases the risk of experiencing an MI.

Gender was identified as a distinguishing variable in the node consisting of individuals with hyperlipidemia and chronic hypertension. The rate of individuals experiencing an MI was 24.15% in the node consisting of males, while it was 12.78% in the node consisting of males.

Education level was identified as a distinguishing factor in the node consisting of males. The probability of experiencing an MI was 29.44% in the node consisting of males with hyperlipidemia, chronic hypertension, and elementary school education or no formal education. In contrast, this rate was 13.40% in the node consisting of individuals with at least primary education. Therefore, the probability of experiencing an MI decreases as the education level increases. The node consisting of males with low education or no education and high cholesterol accompanied by high blood pressure was identified as the node with the highest probability of experiencing an MI. The presence of high blood pressure accompanying high cholesterol in poorly educated or uneducated male individuals was observed to be a factor that increases the probability of experiencing an MI when compared to educated male individuals. Marital status is identified as a distinguishing factor in the node where females are present. The rate of individuals experiencing an MI was 17.24% in the node consisting of females with hyperlipidemia, chronic hypertension, and single. In contrast, this rate was 9.90% in the node of married individuals.

The decision tree structure under the node consisting of individuals without hyperlipidemia is depicted in Fig. 3. The disease status variable was determined to be a distinguishing factor in the node of individuals without hyperlipidemia. The rate of individuals experiencing an MI is 3.10% in the node consisting of individuals without hyperlipidemia but with disease duration longer than 6 months. In contrast, this rate was 0.13% in the other node. Accordingly, individuals with a prolonged duration of illness are more likely to experience an MI. The age variable was determined to be a distinguishing factor in the node consisting of individuals without hyperlipidemia and a disease duration exceeding 6 months. The probability of experiencing an MI was 1.82% in the node consisting of individuals aged 45–54 years. In contrast, this probability was observed to be 4.04% in the node of individuals aged 55. Therefore, it can be seen that individuals without hyperlipidemia but with a disease duration exceeding six months had an increased likelihood of experiencing an MI as age advances. Diabetes was identified as the distinguishing variable in the node with individuals aged 45–54 years. The rate of individuals experiencing an MI in the node consisting of individuals without hyperlipidemia, with a disease duration exceeding six months, aged 45–54 years, and having diabetes was 6.00%. In contrast, this rate was 1.23% in the node of individuals without diabetes. Gender was determined to be the distinguishing variable in the node consisting of individuals aged 55 years or older. Female individuals had an MI rate of 2.75% in the node consisting of individuals without hyperlipidemia, with a disease duration exceeding six months, aged 55 years and older. In comparison, this rate was 5.28% in the node consisting of males.

Employment status was the distinguishing variable in the node consisting of individuals without hyperlipidemia and a disease duration longer than six months. The rate of patients experiencing an MI was 0.28% in the node consisting of individuals without hyperlipidemia, without a disease duration longer than six months, and employed. Education level was the distinguishing variable in the node consisting of individuals without hyperlipidemia, without a disease duration longer than 6 months, and employed. The rate of individuals experiencing an MI was 0.54% in the node consisting of individuals without hyperlipidemia, without a disease duration longer than 6 months, employed, and having either elementary school education or no formal education.

Discussion

It was determined in this study that factors such as elevated hyperlipidemia, chronic hypertension, chronic diabetes, chronic disease status, gender, age, marital status, education level, and employment status significantly affect individuals’ likelihood of experiencing an MI. The results achieved in this study indicated that the most influential variable affecting the occurrence of an MI in the study group is elevated hyperlipidemia. The presence of chronic hypertension in individuals with elevated hyperlipidemia increases the probability of experiencing an MI. Age, gender, obesity, blood pressure, glycemic control, lipid profile, ischemic heart disease, and smoking status were also reported as conditions that increase the risk of MI in the literature [6,7,8, 11].

Given the results achieved in this study, the probability of women experiencing an MI was lower. In Türkiye, women are less susceptible to coronary heart diseases in comparison to men [4]. Previous studies also reported a higher probability of MI among men [13]. However, there are also studies expressing gender differences in prognosis and post-MI quality of life. In a study examining gender and age together, it was found that young women have higher MI mortality rates, whereas the likelihood of dying from MI is higher in men at older ages [57]. The incidence of coronary heart disease observed in men and particularly in women higher than expected requires consideration. Three significant risk factors—total (or LDL) cholesterol levels, high blood pressure, and obesity— were found to be higher in Turkish women when compared to men. Thus, the partial explanation for Turkish women being exposed to coronary heart disease at a rate close to men, except for smoking, may originate from having more non-smoking risk factors. However, a crucial deficiency in the functions of protective proteins (apo A-I, apo A-II, apoE, apo C-III, HDL, adiponectin, and SHBG) underlies this explanation [4].

Consistent with previous studies, advancing age was associated with an increased likelihood of experiencing an MI [13, 58,59,60]. MI tend to increase with age [13]. Advancing age is a significant risk factor for MI [58]. Therefore, advancing age is an effective variable in experiencing an MI. In addition to advancing age, having elevated hyperlipidemia and being male also increase the likelihood of experiencing an MI. Hospital mortality for patients aged 65 years and older was reported to be three times higher in comparison to young patients [61]. AMI is an age-related cardiovascular disease in which cell ageing and immune and inflammatory factors alter the course [62]. Risk factors such as obesity and hypertension, which play an effective role in experiencing an MI, also increase with advancing age [63]. Arterial hypertension, which is a leading risk factor in the development of atherosclerosis, increases with advancing age in Türkiye [4].

Education level decreases the likelihood of experiencing an MI. This finding is consistent with the existing literature [64,65,66]. Education is a significant variable in preventing cardiovascular diseases [67, 68]. Individuals with higher education levels have higher health literacy [69], which is important in supporting healthy lifestyle habits and preventing chronic diseases [70]. Low health literacy is associated with a wide range of unhealthy behaviours, including poor dietary habits, physical inactivity, and substance dependence [71, 72]. Nutrition and sleep are important variables for heart health. Individuals with higher education levels were reported to have better nutrition and sleep habits [73]. Moreover, an increase in education is associated with higher income levels. High education and health literacy are essential for developing positive health behaviours.

The probability of MI is lower among married individuals. Although direct evidence on the relationship between marriage and heart attacks is lacking in the literature, studies suggest that marriage may have a protective effect against risk factors associated with MI [74,75,76,77]. Examining the lifestyles of married and unmarried people, it can be seen that there are some differences. Single individuals tend to smoke more cigarettes [78, 79]. Being married can bring spousal support since spouses can offer support against the psychological consequences of injustices individuals may face [80]. Furthermore, a happy marriage improves health and quality of life [81]. On the other hand, being unmarried is associated with poor dietary habits [82, 83]. Relationships were found between marital status, healthy eating, and lifestyle, but more substantial evidence may be required to articulate a more explicit association between MI and marriage.

The probability of experiencing an MI is lower among employed individuals when compared to non-employed individuals. There is no clear evidence in the literature on whether having a job reduces the likelihood of having an MI. As reported in the literature, a low socioeconomic level increases cardiovascular risk [84]. Employment is a variable that is related to income level and education. Higher-income patients tend to have easier access to treatment and care services and are more likely to accept them promptly, reducing cardiovascular risk [85]. Unemployment is also associated with significant psychological distress, which is considered a cardiovascular risk [86]. Economic problems and weak social support increase the likelihood of psychological distress [87].

In Türkiye, the most influential independent predictor of cardiovascular disease and mortality in both genders is high systolic blood pressure [4]. This finding is consistent with the existing literature [88,89,90]. Pre-existing conditions such as hypertension, diabetes, and hyperlipidemia were reported to increase the future atherothrombotic risk [91]. The presence of accompanying hypertension in individuals increases the likelihood of an MI [9, 12]. Diabetes was indicated as a significant risk factor for MI [10]. The STOP-HF follow-up program report showed a higher risk of MI among diabetes patients [92]. There is a correlation between the risk of MI and hyperlipidemia levels [93, 94]. In this study, individuals having hypertension, diabetes, and hyperlipidemia had a higher probability of experiencing an MI. This might be associated with the increased atherothrombotic risk related to conditions such as hypertension, diabetes, and hyperlipidemia.

Individuals with depression are more likely to experience an MI. The frequent comorbidity of MI and depression draws attention. Although the literature suggests that depression may be associated with MI outcomes [95, 96], evidence also indicates that depression may increase the risk of MI and exacerbate MI-related outcomes [97,98,99]. The relationship between them is explained by various mechanisms, including increased proinflammatory and prothrombotic factor activity in depression [100]. Depression is suggested to be related to a higher genetic predisposition for heart failure and small vessel stroke risks. Genetic predisposition to depression is partly associated with higher risks of coronary artery disease and MI mediated by type 2 diabetes and smoking [101]. Depression and anxiety are considered risk factors for an MI [16].

Exposure to tobacco smoke increases the likelihood of experiencing an MI. It was noted that nicotine in tobacco elevates serum nicotine levels and may lead to silent MI [102]. It is reported that the majority of individuals presenting with an MI have a history of tobacco use [103].

It was found in the present study that the probability of experiencing an MI is lower in those who consume alcohol. Even though there is a clear association between excessive alcohol consumption and cardiovascular disease (CVD), there is no consensus on the effects of moderate alcohol consumption on CVD [104]. With increased alcohol taxes in Türkiye, alcohol consumption decreased by 14% [4]. It was mentioned that consuming alcohol 2–3 times a week has positive effects on the heart [105]. Alcohol consumption was reported to increase HDL-cholesterol levels in both genders (independently of age and smoking) after 10 and 12 years, with a significant increase of 2 to 6 mg/dl in men [4]. However, the combination of advancing age, smoking, and alcohol consumption increases the risk of coronary heart disease [4]. Despite the positive effects of alcohol on HDL, it also has a harmful impact on heart health due to liver steatosis, increased total cholesterol, and thickening of the waist circumference. Severe smoking increases the risk of diabetes and CVD mortality in men [4].

Individuals who engage in regular walking have a lower probability of experiencing an MI when compared to those with a sedentary lifestyle. The finding that regular walking may protect heart health is consistent with the existing literature [106,107,108]. A significant rise is expected in the future risks of hypertension, metabolic syndrome, and diabetes as sedentary behaviour increases in Türkiye [4]. In the literature, moderate sweating exercise was reported to reduce the risks of MI, stroke, hypertension, type 2 diabetes, and cancers of the stomach, lungs, liver, head, and neck [109]. Physical activity has a protective effect on heart diseases [110, 111].

There are some limitations in this study. The first one is that the data used in the study are secondary data obtained from the 2019 Türkiye Health Survey collected by the Turkish Statistical Institute. This prevented the researchers from creating their own variables or adding detailed information, such as additional laboratory data. The second limitation is that the study included individuals aged 45 and older. Younger populations and different demographic groups at risk for heart attack were excluded. The third limitation is the lack of laboratory data. Biological parameters such as hyperlipidemia profile, glucose levels or inflammatory markers were not included. This limited a deeper understanding of heart attack risk factors. In the fourth limitation, although some psychosocial variables such as depression and physical activity were included in the study, other factors such as stress, sleep patterns, environmental pollution and social support could not be examined due to the secondary nature of the study. The fifth limitation is that some of the data obtained from the research is based on personal declaration. Finally, there is a lack of information on medicines that reduce the risk of CVD.

Conclusions

MI is a physiopathological condition resulting from the occlusion of coronary vessels. Numerous physiological mechanisms contribute to the development of atherosclerosis, leading to coronary vessel occlusion. Factors such as age, body mass index, dietary habits, biochemical parameters, concomitant diseases, and stress can affect the occurrence of an MI.

This study revealed that in Türkiye, individuals with hyperlipidemia levels, chronic hypertension, chronic diabetes, and the presence of a chronic disease condition are at an increased risk of experiencing an MI. Moreover, the present study suggests that higher education levels, being female, being employed, and being married may play a significant role in reducing the likelihood of an MI. The probability of experiencing an MI further increases when variables such as hyperlipidemia levels, hypertension, being male, and having a lower level of education converge. Even in individuals without hyperlipidemia levels, the simultaneous presence of concomitant diseases and diabetes was found to increase the likelihood of an MI.

The analyses indicate complex interactions between various factors in MI, emphasizing the need to evaluate these factors together. When planning new research, especially experimental studies and preventive public health education for MI prevention, it is essential to consider the effective variables.

The present study suggests that the education level of individuals might be a significant risk factor for preserving heart health. Therefore, increasing general education and health literacy could make prevention and intervention against heart risks more accessible. Consequently, it is recommended that public education and health literacy be improved. Moreover, conditions such as diabetes, hypertension, depression, and hyperlipidemia were identified as potential risks. Given these findings, classes should be organised after conducting a cardiovascular risk screening in primary care, and multidisciplinary education should be provided to protect individuals from MI by establishing classes. Primary healthcare plays a vital role in preserving public health, and it is recommended to develop and train public health nurses working in primary healthcare institutions on cardiovascular risk indicators and management. Developing cardiovascular risk screening inventories, conducting community-wide education, and increasing advertisements are suggested to preserve public health.

The present study also has several limitations. The data in this study are secondary, relying on an existing dataset for statistical analysis. There are no different variables or laboratory findings.

Future studies aiming to prevent MI could focus on individuals with comorbid diseases, diabetes, hyperlipidemia, or depression to reduce their risky situations and help them manage existing conditions. Since men in Türkiye at a higher risk of experiencing an MI, future studies could examine men’s health. Moreover, studies comparing Türkiye to other countries could also be planned.

It is recommended to conduct prospective studies to better understand the causal relationships between variables, to compare heart attack risk factors in different age and gender groups by selecting a sample that includes younger individuals, to include biological data such as blood tests, genetic analysis and inflammatory markers, and to conduct more detailed risk analyses. In addition, the impact of factors such as stress, sleep patterns, social support and work-life balance should be taken into account in future studies. In addition, complex interactions of risk factors can be analyzed by using advanced multi-analytic methods.

Data availability

The data underlying this study is subject to third-party restrictions by the Turkish Statistical Institute. Data are available from the Turkish Statistical Institute ([email protected]) for researchers who meet the criteria for access to confidential data. The authors of the study did not receive any special privileges in accessing the data.

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