Urban public housing, neighborhood, and cardiovascular disease in primary care

项目概述/摘要公共住房是为低收入家庭提供稳定的住房而开发的。从那以后，它就成了集中贫困的代名词。减少贫困的项目侧重于拆除公共住房，如果有的话，用混合收入设计替代方案来取代。研究表明，居住在公共住房的低收入居民对健康有害。然而，目前尚不清楚，与其他低收入住房安排的居民相比，公共住房居民在一些心血管健康指标上的表现如何。将住房与社区资源获取和个人行为结合起来的机制模型定义和理解都很差。然而，以协同方式检查个人和社区水平的数据可以为现有的卫生运动提供重要贡献，以解决低收入人群的心血管疾病。目前的建议使用一种新的地理编码方法来分析与各种来源的住房和社区一级数据相关的初级保健电子临床记录数据，以跟踪健康结果。这种分析方法侧重于患者住房和社区环境背景下的个人水平数据。该k01奖旨在为钱伯斯博士提供概念和技术基础，以发展职业轨迹，将先前在健康的社会和建筑环境决定因素方面的经验与社会生态模型、图形科学、多层次统计建模和研究信息学方面的新知识相结合。长期目标是建立临床和公共卫生伙伴关系

DESCRIPTION (provided by applicant): Project Summary/ Abstract Public housing was developed to provide stable housing for low-income families. It has since become synonymous with concentrated poverty. Programs to reduce poverty have focused on dismantling public housing to be replaced, if at all, by mixed income design alternatives. Studies have shown that low-income residents living in public housing have deleterious health outcomes. It is not clear, however, how public housing residents fare on a number of cardiovascular health indicators compared to those with an alternate low-income housing arrangement. Mechanistic models that incorporate housing with neighborhood access to resources and individual behaviors are poorly defined and understood. However, examining individual- and neighborhood- level data in synergistic ways can provide an important contribution to existing health campaigns to address cardiovascular disease in low-income populations. The current proposal uses a novel geocoding approach to analyze primary care electronic clinical record data in relation to housing and neighborhood level data from various sources in order to track health outcomes. This analysis approach focuses on individual level data within the context of the patient's housing and neighborhood environment. This K 01 award is designed to provide the foundation both conceptual and technical for Dr. Chambers to develop a career trajectory that combines prior experience in social and built environmental determinants of health with new knowledge in socio-ecologic models, graphical science, multi-level statistical modeling, and research informatics. The long-term goal is to develop clinical and public health partnerships that will use multi- source data, including housing and neighborhood environment, to the develop interventions that target vulnerable patient populations. (End of Abstract)