An exploratory decision tree analysis to predict cardiovascular disease risk in African American women

Objective: African American (AA) women are at greater risk for cardiovascular disease (CVD) compared to White women, which can be attributed to disparities in risk factors. The built environment may contribute to improving CVD risk factors by increasing physical activity (PA). This study used recursive partitioning, a multivariate decision tree risk classification approach, to determine which built environment characteristics contributed to the classification of AA women as having 4 or more CVD risk factors at optimal levels. Method: Recursive partitioning has the ability to detect interactions and does not have sample size limitations to detect effects. The Classification and Regression Trees (CR&T) growing method was used to group participants as having 4 or more versus 3 or fewer risk factors at optimal levels. Risk factors were smoking, body mass index (BMI), PA, healthy diet, cholesterol, glucose, and blood pressure. Built environment predictors were presence and quality of neighborhood PA resources (PARs), walkability, traffic safety, and crime. Results: Participants (N = 30, mean age of 54.1 ± 7.5) all had at least 1 risk factor at the optimal level, none had all 7, and 66.7% had 4 or more risk factors at optimal levels. The CR&T identified participants with few, low-quality neighborhood PARs and who were older than 55 as least likely to have 4 or more CVD risk factors at optimal levels. Conclusion: Being younger than 55 years old and having many, high-quality neighborhood PARs may predict lower risk for CVD in AA women. Results should be used in future studies with larger sample sizes to inform logistic regression models.