ArticleBMC public health2025
Patterns among factors associated with myocardial infarction: chi-squared automatic interaction detection tree and binary logit model.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A next-generation prediction risk model for acute myocardial infarction: Derivation and validation in a multi-centre cohort.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Enhancing classification accuracy in medical datasets using a hybrid distance and cluster refinement-based K-means clustering method.Scientific reports · 2026Article
- Clinical profile and short-term outcomes of acute myocardial infarction patients.Bioinformation · 2026Article
- Emotional violence within intimate partner violence against Turkish women in rural and urban areas.BMC public health · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 Türkiye.
methodsMicrodata 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.
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.
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