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

BackgroundAlthough 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 T & uuml;rkiye.MethodsMicrodata obtained from the T & uuml;rkiye 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.ResultsThe 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.ConclusionTo 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.

Journal

BMC PUBLIC HEALTH

Year

2025

Volume

25

Issue

1

eISSN

1471-2458

DOI

10.1186/s12889-025-21536-7

WoS UID

WOS:001403668700004