Examining Predictors of Myocardial Infarction

Cardiovascular diseases are the leading cause of death in the United States. This study analyzed predictors of myocardial infarction (MI) for those aged 35 and older based on demographic, socioeconomic, geographic, behavioral, and risk factors, as well as access to healthcare variables using the Cen...

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Autores principales:	Diane Dolezel, Alexander McLeod, Larry Fulton
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	myocardial infarction prediction cardiovascular Medicine R
Acceso en línea:	https://doaj.org/article/6da485ed1fcd4d5d8e1b8d7a04b49bd7
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spelling	oai:doaj.org-article:6da485ed1fcd4d5d8e1b8d7a04b49bd72021-11-11T16:26:32ZExamining Predictors of Myocardial Infarction10.3390/ijerph1821112841660-46011661-7827https://doaj.org/article/6da485ed1fcd4d5d8e1b8d7a04b49bd72021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11284https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601Cardiovascular diseases are the leading cause of death in the United States. This study analyzed predictors of myocardial infarction (MI) for those aged 35 and older based on demographic, socioeconomic, geographic, behavioral, and risk factors, as well as access to healthcare variables using the Center for Disease (CDC) Control Behavioral Risk Factor Surveillance System (BRFSS) survey for the year 2019. Multiple quasibinomial models were generated on an 80% training set hierarchically and then used to forecast the 20% test set. The final training model proved somewhat capable of prediction with a weighted F1-Score = 0.898. A complete model based on statistically significant variables using the entirety of the dataset was compared to the same model built on the training set. Models demonstrated coefficient stability. Similar to previous studies, age, gender, marital status, veteran status, income, home ownership, employment status, and education level were important demographic and socioeconomic predictors. The only geographic variable that remained in the model was associated with the West North Central Census Division (in-creased risk). Statistically important behavioral and risk factors as well as comorbidities included health status, smoking, alcohol consumption frequency, cholesterol, blood pressure, diabetes, stroke, chronic obstructive pulmonary disorder (COPD), kidney disease, and arthritis. Three access to healthcare variables proved statistically significant: lack of a primary care provider (Odds Ratio, OR = 0.853, <i>p</i> < 0.001), cost considerations prevented some care (OR = 1.232, <i>p</i> < 0.001), and lack of an annual checkup (OR = 0.807, <i>p</i> < 0.001). The directionality of these odds ratios is congruent with a marginal effects model and implies that those without MI are more likely not to have a primary provider or annual checkup, but those with MI are more likely to have missed care due to the cost of that care. Cost of healthcare for MI patients is associated with not receiving care after accounting for all other variables.Diane DolezelAlexander McLeodLarry FultonMDPI AGarticlemyocardial infarctionpredictioncardiovascularMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11284, p 11284 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	myocardial infarction prediction cardiovascular Medicine R
spellingShingle	myocardial infarction prediction cardiovascular Medicine R Diane Dolezel Alexander McLeod Larry Fulton Examining Predictors of Myocardial Infarction
description	Cardiovascular diseases are the leading cause of death in the United States. This study analyzed predictors of myocardial infarction (MI) for those aged 35 and older based on demographic, socioeconomic, geographic, behavioral, and risk factors, as well as access to healthcare variables using the Center for Disease (CDC) Control Behavioral Risk Factor Surveillance System (BRFSS) survey for the year 2019. Multiple quasibinomial models were generated on an 80% training set hierarchically and then used to forecast the 20% test set. The final training model proved somewhat capable of prediction with a weighted F1-Score = 0.898. A complete model based on statistically significant variables using the entirety of the dataset was compared to the same model built on the training set. Models demonstrated coefficient stability. Similar to previous studies, age, gender, marital status, veteran status, income, home ownership, employment status, and education level were important demographic and socioeconomic predictors. The only geographic variable that remained in the model was associated with the West North Central Census Division (in-creased risk). Statistically important behavioral and risk factors as well as comorbidities included health status, smoking, alcohol consumption frequency, cholesterol, blood pressure, diabetes, stroke, chronic obstructive pulmonary disorder (COPD), kidney disease, and arthritis. Three access to healthcare variables proved statistically significant: lack of a primary care provider (Odds Ratio, OR = 0.853, <i>p</i> < 0.001), cost considerations prevented some care (OR = 1.232, <i>p</i> < 0.001), and lack of an annual checkup (OR = 0.807, <i>p</i> < 0.001). The directionality of these odds ratios is congruent with a marginal effects model and implies that those without MI are more likely not to have a primary provider or annual checkup, but those with MI are more likely to have missed care due to the cost of that care. Cost of healthcare for MI patients is associated with not receiving care after accounting for all other variables.
format	article
author	Diane Dolezel Alexander McLeod Larry Fulton
author_facet	Diane Dolezel Alexander McLeod Larry Fulton
author_sort	Diane Dolezel
title	Examining Predictors of Myocardial Infarction
title_short	Examining Predictors of Myocardial Infarction
title_full	Examining Predictors of Myocardial Infarction
title_fullStr	Examining Predictors of Myocardial Infarction
title_full_unstemmed	Examining Predictors of Myocardial Infarction
title_sort	examining predictors of myocardial infarction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6da485ed1fcd4d5d8e1b8d7a04b49bd7
work_keys_str_mv	AT dianedolezel examiningpredictorsofmyocardialinfarction AT alexandermcleod examiningpredictorsofmyocardialinfarction AT larryfulton examiningpredictorsofmyocardialinfarction
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Examining Predictors of Myocardial Infarction

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