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Quantile regression forests to identify determinants of stroke: implications for neighborhoods with high prevalence


 Background : Stroke exerts a massive burden on the U.S. health and economy. Place-based evidence is increasingly recognized as a critical part of stroke management but identifying the key determinants of stroke and the underlying effect mechanisms at the neighborhood level is a topic that has been treated sparingly in the literature. We aim to fill in the research gaps. We develop and apply analytical approaches to address two challenges. First, domain expertise on drivers of neighborhood-level stroke outcomes is limited. Second, commonly used linear regression methods may provide incomplete and biased conclusions.Methods: We created a new neighborhood health data set at census tract level by pooling information from multiple sources. We developed and applied a machine learning based quantile regression method to uncover crucial neighborhood characteristics for neighborhood stroke outcomes among vulnerable neighborhoods burdened with high prevalence of stroke. Results: Neighborhoods with a larger share of non-Hispanic blacks, older adults or people with insufficient sleep tended to have a higher prevalence of stroke, whereas neighborhoods with a higher socio-economic status in terms of income and education had a lower prevalence of stroke. The effects of five major determinants varied geographically and were significantly stronger among neighborhoods with high prevalence of stroke. Conclusions: Highly flexible machine learning identifies true drivers of neighborhood cardiovascular health outcomes from wide-ranging information in an agnostic and reproducible way. The identified major determinants and the effect mechanisms can provide important avenues for prioritizing and allocating resources to develop optimal community-level interventions for stroke prevention.