Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence.
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...
| Publicado en: | Journal of Urban Health Vol. 98; no. 2; pp. 259 - 271 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Apr2021
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=150023701&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 150023701 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10993460 GMF jtl: Journal of Urban Health issn: 10993460 maglogo: N pubinfo: dt: Apr2021 vid: 98 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 150023701 10.1007/s11524-020-00478-y ppf: 259 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence. aug: au: Hu, Liangyuan Ji, Jiayi Li, Yan Liu, Bian Zhang, Yiyi affil: Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, 10029, New York, NY, USA Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA Division of General Medicine, Columbia University, New York, NY, USA su: Neighborhoods Urban health Older people Quantile regression Machine learning sug: subj: Neighborhoods Urban health Older people Quantile regression Machine learning keyword: Cardiovascular health Neighborhood Prevention Cardiovascular health Neighborhood Prevention ab: 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. 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 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. 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|