Impact of social and built environments on health-related quality of life for cancer survivors

 描述（由申请人提供）：邻里关系已被证明是塑造个人实现最佳健康的机会的关键基础。随着癌症幸存者人数的持续增加，目前美国有超过1300万人，了解癌症诊断后改善健康相关生活质量（HRQOL）的因素至关重要。除非我们确定并解决相关的背景因素，否则只针对个别因素的干预措施可能效果有限。然而，只有三项研究考虑了社区因素，尽管社区已被证明通过多种途径影响健康结果-环境暴露，物质匮乏，心理社会机制，健康行为和获得资源。因此，我们建议利用和汇集来自三项现有癌症生存率研究的社会人口统计学，临床，社会和行为因素的个人水平数据，并将其与社区数据合并。具体来说，我们的目标是（1）检查一套全面的社会和建筑环境特征与HRQOL之间的关联，（a）调整重要的协变量（例如，临床、人口统计学、社会和行为因素）和（B）评估这些关联是否因种族/民族、年龄或性别而异，和（2）评估HRQOL中的种族/民族差异，并评估这些差异是否由临床、人口统计学、社会和行为因素、邻里因素或它们的组合来解释。为了实现这些目标，使用三项加州基于人群的研究的协调数据（n = 2，563，46%少数民族）-评估患者的癌症护理经验（APECC），非霍奇金淋巴瘤幸存者的护理经验和健康结局（ECHOS-NHL），和幸存者随访护理使用（FOCUS）-我们将在访谈时对地址进行地理编码，并将个人层面的数据与我们的社会数据（例如，社会经济、种族/民族构成、移民）和建造（例如，人口密度、街道连通性、通勤、便利设施、食品环境）环境数据。主要HRQOL结局将包括身体和精神成分总分。将使用线性回归模型估计邻域特征与两种HRQOL结局之间的未调整和调整相关性。此外，我们将记录HRQOL中的种族/民族差异，并评估邻里功能是否可以解释这些差异。这项有效的研究将被用来检测重要的社区 所有癌症部位以及5个特定部位-乳腺癌、结直肠癌、妇科癌、NHL和前列腺癌的疗效。我们的研究结果将提供初步的证据之间的关联建成和社会环境和HRQOL，并有助于我们了解哪些邻里功能影响HRQOL显着。我们与Shanti项目的合作将确保我们的研究将有意义地为多层次干预措施的设计提供信息，这些干预措施不仅考虑了影响HRQOL的个体因素，而且还考虑了这些个体因素形成的背景。

 DESCRIPTION (provided by applicant): Neighborhoods have been shown to be a critical foundation for shaping individuals' opportunities for achieving optimal health. As the number of cancer survivors continues to increase, with over 13 million in the U.S. currently, understanding factors that improve health-related quality of life (HRQOL) after cancer diagnosis is critical. Unless we identify and address the relevant contextual factors, interventions that only target individual factors are likely to have limited effectiveness. However, only three studies have considered neighborhood factors, despite that neighborhoods have been shown to influence health outcomes through a number of pathways--environmental exposures, material deprivation, psychosocial mechanisms, health behaviors, and access to resources. Therefore, we propose to leverage and pool individual-level data on sociodemographic, clinical, social, and behavioral factors from three existing cancer survivorship studies and merge them with neighborhood data. Specifically, we aim to (1) examine the associations between a comprehensive suite of social and built environment features and HRQOL, (a) adjusting for important covariates (e.g., clinical, demographic, social and behavioral factors) and (b) evaluating whether these associations vary by race/ethnicity, age, or gender, and (2) assess racial/ethnic disparities in HRQOL and evaluate whether these disparities are explained by clinical, demographic, social and behavioral factors, neighborhood factors, or a combination of them. To achieve these aims, with harmonized data across three California population-based studies (n=2,563, 46% minority)-Assessment of Patients' Experience of Cancer Care (APECC), Experiences of Care and Health Outcomes of survivors of Non-Hodgkin's Lymphoma (ECHOS-NHL), and Follow-up Care Use among Survivors (FOCUS)-we will geocode address at interview and merge the individual-level data with our social (e.g., socioeconomic, racial/ethnic composition, immigration) and built (e.g., population density, street connectivity, commuting, amenities, food environment) environment data. The main HRQOL outcomes will include physical and mental component summary scores. Linear regression models will be used to estimate unadjusted and adjusted associations between neighborhood features and the two HRQOL outcomes. Further, we will document racial/ethnic disparities in HRQOL and evaluate whether neighborhood features explain these disparities. This efficient study will be powered to detect significant neighborhoods effects across all of the cancer sites, as well as for 5 specific sites -breast, colorectal, gynecologic, NHL, and prostate. Our findings will provide the preliminary evidence for associations between the built and social environment and HRQOL and contribute significantly to our understanding of which neighborhood features influence HRQOL. Our collaboration with the Shanti Project will ensure that our study will meaningfully inform the design of multilevel interventions that not only account for individual factors that contribute to HRQOL, but also the context through which those individual factors are shaped.