Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence

AbstractStroke exerts a massive burden on the US health and economy. Place-based evidence is increasingly recognized as a critical part of stroke management, but identifying the key determinants of neighborhood stroke prevalence and the underlying effect mechanisms is a topic that has been treated sparingly in the literature. We aim to fill in the research gaps with a study focusing on urban health. 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. 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. Neighborhoods with a larger share of non-Hispanic blacks, older adults, or people with insufficient sleep tende d 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. Highly flexible machine learning identifies true drivers of neighborhood card...
Source: Journal of Urban Health - Category: Health Management Source Type: research