Dolezel Diane, McLeod Alexander, Fulton Larry
Health Information Management Department, Texas State University, San Marcos, TX 78666, USA.
Computer Information Systems & Quantitative Methods Department, Texas State University, San Marcos, TX 78666, USA.
Int J Environ Res Public Health. 2021 Oct 27;18(21):11284. doi: 10.3390/ijerph182111284.
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, < 0.001), cost considerations prevented some care (OR = 1.232, < 0.001), and lack of an annual checkup (OR = 0.807, < 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.
心血管疾病是美国的主要死因。本研究基于人口统计学、社会经济、地理、行为和风险因素，以及使用疾病控制中心（CDC）2019年行为风险因素监测系统（BRFSS）调查中的医疗保健变量获取情况，分析了35岁及以上人群心肌梗死（MI）的预测因素。在80%的训练集上分层生成多个拟二项式模型，然后用于预测20%的测试集。最终的训练模型在加权F1分数=0.898时显示出一定的预测能力。将基于整个数据集的具有统计学意义变量的完整模型与基于训练集构建的相同模型进行比较。模型显示出系数稳定性。与先前的研究类似，年龄、性别、婚姻状况、退伍军人身份、收入、房屋所有权、就业状况和教育水平是重要的人口统计学和社会经济预测因素。模型中保留的唯一地理变量与西中北部人口普查区相关（风险增加）。具有统计学意义的行为和风险因素以及合并症包括健康状况、吸烟、饮酒频率、胆固醇、血压、糖尿病、中风、慢性阻塞性肺疾病（COPD）、肾病和关节炎。三个医疗保健变量获取情况被证明具有统计学意义：缺乏初级保健提供者（优势比，OR = 0.853，<0.001）、费用考虑导致部分医疗无法进行（OR = 1.232，<0.001）以及缺乏年度体检（OR = 0.807，<0.001）。这些优势比的方向性与边际效应模型一致，意味着没有患心肌梗死的人更有可能没有初级保健提供者或年度体检，但患心肌梗死的人更有可能因医疗费用而错过治疗。在考虑所有其他变量后，心肌梗死患者的医疗费用与未接受治疗相关。
