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

 描述(由申请人提供)：社区已被证明是塑造个人实现最佳健康的机会的关键基础。随着癌症幸存者的数量持续增加，目前美国有超过1300万人，了解提高癌症诊断后与健康相关的生活质量(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许多途径影响健康结果--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现有三项癌症生存研究的社会人口学、临床、社会和行为因素的个人水平的数据，并将它们与邻里数据合并。具体地说，我们的目标是(1)检验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、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，(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、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，利用加州三项基于人口的研究(n=2,563，46%少数族裔)--癌症护理体验评估(APECC)、非霍奇金淋巴瘤幸存者的护理体验和健康结局(Echos-NHL)以及幸存者的后续护理使用(焦点)--的统一数据，我们将在采访中对地址进行地理编码，并将个人水平的数据与我们的社会(例如，社会经济、种族/民族构成、移民)和构建(例如，人口密度、街道连通性、通勤、便利设施、食品环境)环境数据进行合并。主要的HRQOL结果将包括身体和精神部分的总分。线性回归模型将被用来估计社区特征和两个HRQOL结果之间的未调整和已调整的关联。此外，我们将记录HRQOL中的种族/民族差异，并评估社区特征是否解释了这些差异。这项高效的研究将被用来检测重要的社区 对所有癌症部位以及5个特定部位--乳腺癌、结直肠、妇科、非霍奇金淋巴瘤和前列腺癌--的影响。我们的发现将为建筑和社会环境与HRQOL之间的关联提供初步证据，并有助于我们理解社区特征对HRQOL的影响。我们与Shanti项目的合作将确保我们的研究将为多水平干预措施的设计提供有意义的信息，这些干预措施不仅考虑到对HRQOL有贡献的个人因素，而且还考虑这些个人因素形成的背景。

 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.