ArticleJournal of urban health : bulletin of the New York Academy of Medicine2021
Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence.
Article in Journal of urban health : bulletin of the New York Academy of Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 22 citations in OpenAlex.
- Neighborhood economic and demographic landscape as predictors of 90-day outcomes post-stroke hospitalization.Frontiers in stroke · 2026Article
- A social determinants-based county-level cardiovascular mortality index to identify high-risk counties in the USA.BMC medicine · 2025Article
- Neighborhood socio-demographic profile associated with adequate transitions of stroke care: The transitions of care stroke disparities study.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2025Observational
- Income, education, and the clustering of risk in cardiovascular disease in the US, 1999-2018: an observational study.Lancet regional health. Americas · 2025Article
- Empirical design of population health strategies accounting for the distribution of population health risks.SSM - population health · 2025Article
- Determinants of Economic Costs Following Road Traffic Injuries in Canada: A Quantile Regression Forests Machine Learning Approach.ClinicoEconomics and outcomes research : CEOR · 2025Article
- Reciprocal Neighborhood Dynamics in Gun Violence Exposure, Community Health, and Concentrated Disadvantage in One Hundred US Cities.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study.Frontiers in public health · 2023Article
- Determinants of Total End-of-Life Health Care Costs of Medicare Beneficiaries: A Quantile Regression Forests Analysis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.BMC medical research methodology · 2022Article
- Variable selection with missing data in both covariates and outcomes: Imputation and machine learning.Statistical methods in medical research · 2021Article
- Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach.BMC health services research · 2020Article
- Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.Journal of the American Heart Association · 2020Article
- Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach.BMC public health · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.