Department of Social Policy and Intervention, University of Oxford, Oxford OX1 2ER, UK.
Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine; Stanford, CA 94305, USA.
Int J Environ Res Public Health. 2019 May 20;16(10):1786. doi: 10.3390/ijerph16101786.
Individual well-being is a complex concept that varies among and between individuals and is impacted by individual, interpersonal, community, organizational, policy and environmental factors. This research explored associations between select environmental characteristics measured at the ZIP code level and individual well-being. Participants ( = 3288, mean age = 41.4 years, 71.0% female, 57.9% white) were drawn from a registry of individuals who completed the Stanford WELL for Life Scale (SWLS), a 76-question online survey that asks about 10 domains of well-being: social connectedness, lifestyle and daily practices, physical health, stress and resilience, emotional and mental health, purpose and meaning, sense of self, financial security and satisfaction, spirituality and religiosity, and exploration and creativity. Based on a nationally-representative 2018 study of associations between an independent well-being measure and county-level characteristics, we selected twelve identical or analogous neighborhood (ZIP-code level) indicators to test against the SWLS measure and its ten constituent domains. Data were collected from secondary sources to describe socio-economic (median household income, percent unemployment, percent child poverty), demographic (race/ethnicity), and physical environment (commute by bicycle and public transit), and healthcare (number of healthcare facilities, percent mammogram screenings, percent preventable hospital stays). All continuous neighborhood factors were re-classified into quantile groups. Linear mixed models were fit to assess relationships between each neighborhood measure and each of the ten domains of well-being, as well as the overall SWLS well-being measure, and were adjusted for spatial autocorrelation and individual-level covariates. In models exploring associations between the overall SWLS score and neighborhood characteristics, six of the twelve neighborhood factors exhibited significant differences between quantile groups ( < 0.05). All of the ten SWLS domains had at least one instance of significant ( < 0.05) variation across quantile groups for a neighborhood factor; stress and resilience, emotional and mental health, and financial security had the greatest number of significant associations (6/12 factors), followed by physical health (5/12 factors) and social connectedness (4/12 factors). All but one of the neighborhood factors (number of Federally Qualified Health Centers) showed at least one significant association with a well-being domain. Among the neighborhood factors with the most associations with well-being domains were rate of preventable hospital stays (7/10 domains), percent holding bachelor's degrees (6/10 domains), and median income and percent with less than high school completion (5/10 domains). These observational insights suggest that neighborhood factors are associated with individuals' overall self-rated well-being, though variation exists among its constituent domains. Further research that employs such multi-dimensional measures of well-being is needed to determine targets for intervention at the neighborhood level that may improve well-being at both the individual and, ultimately, neighborhood levels.
个体幸福感是一个复杂的概念,在个体之间存在差异,并受到个体、人际、社区、组织、政策和环境因素的影响。本研究探讨了在邮政编码层面测量的特定环境特征与个体幸福感之间的关联。参与者(=3288,平均年龄=41.4 岁,71.0%为女性,57.9%为白人)来自斯坦福 WELL for Life 量表(SWLS)的参与者登记处,这是一项 76 个问题的在线调查,询问了幸福感的 10 个领域:社会联系、生活方式和日常实践、身体健康、压力和弹性、情绪和心理健康、目标和意义、自我意识、财务安全和满意度、精神信仰和宗教信仰以及探索和创造力。基于对 2018 年一项关于独立幸福感衡量标准与县级特征之间关联的全国性研究,我们选择了 12 个相同或类似的邻里(邮政编码层面)指标,以测试与 SWLS 衡量标准及其 10 个组成部分之间的关系。数据从二手资料中收集,用于描述社会经济(家庭中位数收入、失业率、儿童贫困率)、人口统计学(种族/族裔)和物理环境(骑自行车和公共交通通勤)以及医疗保健(医疗设施数量、乳房 X 光筛查百分比、可预防住院百分比)。所有连续的邻里因素都被重新分类为分位数组。线性混合模型用于评估每个邻里措施与幸福感的十个领域以及 SWLS 整体幸福感衡量标准之间的关系,并针对空间自相关和个体水平协变量进行了调整。在探索 SWLS 总分与邻里特征之间关联的模型中,有六个邻里因素在分位数组之间存在显著差异(<0.05)。在邻里因素中,有十个 SWLS 领域中的每一个都至少有一个在分位数组之间存在显著差异(<0.05);压力和弹性、情绪和心理健康以及财务安全领域的关联最多(12 个因素中的 6 个),其次是身体健康(12 个因素中的 5 个)和社会联系(12 个因素中的 4 个)。除了一个邻里因素(合格的联邦医疗中心数量)外,所有因素都与幸福感领域有至少一个显著关联。在与幸福感领域关联最多的邻里因素中,有可预防的住院率(10 个领域中的 7 个)、拥有学士学位的百分比(10 个领域中的 6 个)以及中位数收入和高中以下学历完成率(10 个领域中的 5 个)。这些观察结果表明,邻里因素与个体的整体自我报告幸福感相关,尽管其组成领域存在差异。需要进一步研究采用多维幸福感衡量标准,以确定邻里层面的干预目标,这可能会提高个体和最终邻里层面的幸福感。