Department of Biology and Environmental Science, Auburn University at Montgomery, Montgomery, AL, USA.
Int J Environ Health Res. 2019 Apr;29(2):140-153. doi: 10.1080/09603123.2018.1521915. Epub 2018 Sep 19.
This research explores geographic variability of factors on social inequality related to mental health in the United States using county-level data in 2014. First, we account for complex design factors in Behavioural Risk Factor Surveillance System (BRFSS) data such as clustering, stratification, and sample weight using Complex Samples General Linear Model (CSGLM). Then, three variables are used in the model as indicators of social inequality, low socioeconomic status (SES): unemployment, education status, and social association status. A geographically weighted regression analysis is applied to examine the spatial variations in the associations of mentally unhealthy days (MUDs) with the indicators of SES in the United States. The results demonstrate that unemployment and education level show global positive and negative influences respectively on MUDs. Social association status ranged from positive to negative across the United States, implying some geographic clustering. These findings suggest that social and health policies should be adjusted to address the different effects of indicators of social inequality on mental health across different social characteristics of communities to more effectively manage mental health problems.
本研究使用 2014 年的县级数据，探讨了美国心理健康相关社会不平等因素的地域差异。首先，我们使用行为风险因素监测系统（BRFSS）数据中的复杂设计因素，如聚类、分层和样本权重，采用复杂样本广义线性模型（CSGLM）进行分析。然后，该模型使用三个变量作为社会不平等的指标，即低社会经济地位（SES）：失业、教育状况和社会联系状况。应用地理加权回归分析来检验美国心理健康不良天数（MUDs）与 SES 指标之间关联的空间变化。结果表明，失业和教育水平对 MUDs 分别有全球性的正向和负向影响。社会联系状况在美国各地的范围从正到负不等，这意味着存在一些地理集聚。这些发现表明，社会和卫生政策应该进行调整，以解决不同社区社会特征下社会不平等指标对心理健康的不同影响，从而更有效地管理心理健康问题。
