This study aimed to examine the relationships between lifestyle factors and stroke risk, with a special emphasis on whether health insurance status modifies these associations. The logistic model used on the BRFSS dataset allowed for the assessment of both direct effects and interactions related to stroke risk, providing insights into how these lifestyle factors influence stroke outcomes.

The findings demonstrated that several variables can significantly influence stroke risk. Contrary to most literature that identifies smoking as a major risk factor for stroke [10], smoking in this sample was unexpectedly found to be negatively associated with stroke. This anomaly may be a result of self-reported bias or limitations in the smoking measurement (i.e., ever smoked at least 100 cigarettes). Such bias can potentially result in an inaccurate capture of current smoking behaviors. Similarly, No significant association was found between alcohol consumption and stroke risk in this analysis and this is also contrary to literature that identifies alcohol consumption as a risk factor[13].

Both mental and physical health, as measured by the number of unwell days in the previous month, demonstrated minor but statistically significant protective effects against stroke. This implies that persons who have fewer days of poor mental or physical health are at a lower risk of stroke, underlining the importance of general well-being in stroke prevention. Sleeping hours also showed a protective impact, with longer sleep hours reducing stroke risk. The findings are consistent with existing research[14] demonstrating that proper sleep benefits cardiovascular health.

Notably, health insurance had a robust protective effect against stroke risk. Such an effect highlighted the critical role of healthcare access. People having insurance were considerably less likely to have a stroke. This could be attributed to improved access to preventive services such as regular health screenings and early detection of cardiovascular disease [15]. The interaction effect evaluation also revealed that health insurance can moderate the effects of mental health, physical health, and sleeping hours on stroke risk. Specifically, insured individuals with fewer poor mental or physical health days or longer sleep duration had an even lower stroke risk. This implies that access to healthcare magnifies the benefits of good health and enough rest.

However, the interaction effects of health insurance on smoking, alcohol consumption, BMI, and sex were not statistically significant. This suggests that healthcare access may have a more indirect influence on these behaviors and the influence is likely to be caused by broader social or personal factors beyond insurance coverage.

This study’s strengths include its large, representative sample and the exploration of interaction effects with a focus on the moderating role of health insurance. However, some limitations must be acknowledged. The reliance on self-reported data introduces potential biases, especially regarding sensitive behaviors like smoking and alcohol use. Additionally, the cross-sectional design of the BRFSS limits causal inferences about the relationships between lifestyle factors and stroke risk. Future longitudinal research could delve deep into these associations over time. Additionally, the logistic regression model can be applied to interpret the relationship between the dependent variable and the independent variable, but it is not capable of predicting the occurrence of stroke accurately based on the independent variables above due to the imbalanced data. Alternative prediction algorithms, such as random forest, as well as strategies for dealing with imbalanced data, such as the Synthetic Minority Over-sampling Technique (SMOTE), can be used to improve prediction[16].