Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, USA.
Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, USA.
Sci Rep. 2024 Nov 28;14(1):29563. doi: 10.1038/s41598-024-80627-4.
The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called "deaths in social proximity", to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100, 000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties. Our analysis provides a pathway for public health interventions informed by social network structures. The statistical robustness of our primary variable of interest, deaths in social proximity, supports the hypothesis of a social network effect on OODs. Using agent-based modeling (ABM) to simulate social networks can offer an effective method to design interventions that incorporate the dynamics of social networks for maximum impact.
美国阿片类药物过量危机是近几十年来的一个主要公共卫生关注点。人们越来越认识到，其病因部分源于调节物质使用和获取的社会环境；然而，文献中缺乏可靠的社会影响统计衡量标准。我们使用 Facebook 的社交关联指数（SCI）作为代理，来衡量不同空间区域的真实社交网络，以量化不同空间单元之间的社交连通性。这是衡量地点之间连接的相对概率的一种方法，为理解社交网络对健康结果的影响提供了独特的视角。我们使用 SCI 开发了一个变量，称为“社交临近死亡”，以衡量社交网络对美国县阿片类药物过量死亡（OOD）的影响。我们的研究结果表明，在控制空间接近度以及人口统计学和临床协变量的情况下，社交临近死亡对美国县 OOD 的影响具有统计学意义。在我们的聚类稳健线性回归模型中，标准化社交临近死亡的效应大小表明，在相邻美国县的自我县社交临近中，每增加一个标准差，即 11.70 人/每 10 万人，与自我县每 10 万人中增加 13 人死亡相关。为了进一步验证我们的发现，我们使用网络自相关模型、空间自相关模型和双向固定效应模型进行了一系列稳健性检查，以考虑社交网络效应、捕捉空间依赖性以及控制未观察到的空间和时间不变特征。这些检查一致提供了关于社交网络对美国县 OOD 有正向影响的具有统计学意义的证据。我们的分析为公共卫生干预措施提供了一个基于社交网络结构的途径。我们关注的主要变量“社交临近死亡”的统计稳健性支持了社交网络对 OOD 有影响的假设。使用基于代理的建模（ABM）模拟社交网络可以提供一种有效的方法来设计干预措施，这些措施将社交网络的动态纳入其中，以实现最大效果。
