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

描述(由申请人提供)：项目摘要/摘要公共住房是为低收入家庭提供稳定的住房而开发的。自那以后，它就成了集中贫困的代名词。减贫计划的重点是拆除公共住房，取而代之的是混合收入设计的替代方案。研究表明，居住在公共住房的低收入居民会产生有害的健康后果。然而，目前尚不清楚，与采用替代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。将住房与社区获得资源和个人行为结合在一起的机械模型定义和理解得很差。然而，以协同的方式检查个人和邻里层面的数据可以为现有的卫生运动提供重要贡献，以解决低收入人口中的心血管疾病问题。目前的提案使用一种新的地理编码方法来分析与住房和来自不同来源的社区级别数据相关的初级保健电子临床记录数据，以便跟踪健康结果。这种分析方法侧重于患者住房和邻里环境背景下的个人水平数据。这一K01奖项旨在为钱伯斯博士发展职业轨迹提供概念和技术基础，该职业轨迹将健康的社会和环境决定因素方面的先前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。长期目标是发展临床和公共卫生伙伴关系，将使用 多源数据，包括住房和邻里环境，以制定针对弱势患者群体的干预措施。(摘要结束)

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)