Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implica
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Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence Liangyuan Hu Zhang
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Jiayi Ji & Yan Li & Bian Liu & Yiyi
# The New York Academy of Medicine 2020
Abstract Stroke 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. L. Hu (*) : J. Ji : Y. Li : B. Liu Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA e-mail: [email protected]
J. Ji e-mail: [email protected] Y. Li e-mail: [email protected] B. Liu e-mail: [email protected] L. Hu Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA Y. Li Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA Y. Zhang Division of General Medicine, Columbia University, New York, NY, USA
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 nonHispanic blacks, older adults, or people with insufficient sleep tended to have a higher prevalence of stroke, whereas neighborhoods with a higher socioeconomic 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 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.
Keywords Prevention . Cardiovascular health . Neighborhood . Machine learning . Quantile regression
L. Hu et al.
Background Stroke is the fifth leading cause of death in the USA and is a major cause of serious disability for adults [1]. The prevalence of stroke is approximately 3%, accounting for one of every 20 deaths. With an estimated $45.5 billion in direct an
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