Greenspace visitation and risk of major adverse cardiovascular events: evidence from the Houston methodist cardiovascular learning health system registry.

IF 10 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Weichuan Dong, Sarayu Chandra Mouli, Izza Shahid, Rakesh Gullapelli, Budhaditya Bose, Tong Zhang, Jad Ardakani, Juan C Nicolas, Zulqarnain Javed, Kai Zhang, Zhuo Chen, Sanjay Rajagopalan, Jay Maddock, Khurram Nasir, Sadeer Al-Kindi
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引用次数: 0

Abstract

Aims: To evaluate whether mobility-derived greenspace visitation, as a behavioural measure of environmental exposure, is associated with major adverse cardiovascular events (MACE) independent of individual and neighbourhood factors.

Methods and results: We conducted a retrospective cohort study of adults in the Houston Methodist Cardiovascular Disease Learning Health System Registry (2016-2023). Greenspace visitation was defined at the census-tract level using mobility-derived foot-traffic data and modelled as annual visits per resident device. The primary outcome was incident MACE (myocardial infarction, stroke, coronary revascularization, and all-cause mortality). The Cox models, restricted cubic splines, and Kaplan-Meier curves assessed associations with sequential adjustment for demographics, comorbidities, smoking, and neighbourhood social vulnerability. Among 1 088 866 adults without baseline atherosclerotic cardiovascular disease, median (SD) age was 49 (17.6) years, and 59.9% were female. Greenspace visitation varied across neighbourhoods and by demographic and socioeconomic characteristics. Each additional 10 annual visits was associated with lower MACE risk in unadjusted models [hazard ratio (HR) 0.82; 95% confidence interval (CI): 0.81-0.83] and after adjustment for demographics, comorbidities, and smoking (HR 0.89; 95% CI: 0.88-0.91). A non-linear relationship showed risk reductions at low-to-moderate visitation, plateauing at 10-15 visits annually. After adjusting for social vulnerability, associations were attenuated and no longer significant. The Kaplan-Meier curves showed higher event-free survival in high-visitation areas, consistent across MACE components.

Conclusion: Mobility-derived greenspace visitation is associated with lower cardiovascular risk. These findings highlight greenspace engagement as a potentially modifiable behavioural exposure and support strategies to enhance access and utilization of greenspaces for cardiovascular prevention.

绿地访问和主要不良心血管事件的风险:来自休斯顿卫理公会心血管学习健康系统注册的证据。
目的:评估出行衍生的绿色空间访问,作为环境暴露的行为测量,是否与独立于个人和社区因素的主要不良心血管事件(MACE)相关。方法和结果:我们对休斯顿卫理公会心血管疾病学习健康系统登记处(2016-2023)的成年人进行了回顾性队列研究。绿色空间访问在人口普查区级别定义,使用流动性衍生的步行交通数据,并以每个居民设备的年访问次数为模型。主要结局是MACE事件(心肌梗死、卒中、冠状动脉血运重建术和全因死亡率)。Cox模型、受限三次样条曲线和Kaplan-Meier曲线评估了人口统计学、合并症、吸烟和邻里社会脆弱性与序列调整的关系。在无基线动脉粥样硬化性心血管疾病的1088866名成年人中,中位(SD)年龄为49岁(17.6)岁,其中59.9%为女性。不同社区、人口和社会经济特征对绿地的访问有所不同。在未调整的模型中,每增加10次年度就诊与较低的MACE风险相关[风险比(HR) 0.82;95%可信区间(CI): 0.81-0.83],在调整了人口统计学、合并症和吸烟因素后(HR 0.89; 95% CI: 0.88-0.91)。非线性关系表明,低至中等访问的风险降低,在每年10-15次访问时达到稳定。在调整了社会脆弱性后,关联减弱,不再显著。Kaplan-Meier曲线显示,在高出诊区域,无事件生存率更高,这与MACE组成部分一致。结论:出行绿色空间与较低的心血管风险相关。这些发现强调了绿色空间参与是一种潜在的可改变的行为暴露和支持策略,以加强绿色空间的获取和利用,以预防心血管疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European journal of preventive cardiology
European journal of preventive cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
12.50
自引率
12.00%
发文量
601
审稿时长
3-8 weeks
期刊介绍: European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.
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