Modeling Social Behavior for Healthcare Utilization in Depression

 描述（由申请人提供）：抑郁症在美国和世界各地都非常普遍。在美国成年人中，估计的12个月和终生患病率分别为8.3%和19.2%。世界卫生组织认为重性抑郁症（MDD）是世界范围内第三大疾病负担原因，也是发达国家疾病负担的最高原因。然而，尽管抑郁症的患病率和负担，在所有实践环境中，包括管理式护理中，抑郁症仍然严重认识不足和治疗不足，其中不到三分之一的成年抑郁症患者获得适当的专业治疗。否认疾病和耻辱是正确识别和治疗抑郁症的两个主要障碍。许多抑郁症患者羞于寻求心理健康专业人士，并认为抑郁症是个人弱点的标志。特别是，“自我污名”已与对精神病服务的坚持、希望和生活质量产生负面影响有关，并成为社会融合的障碍。此外，由于自我污名可以在没有来自公众的实际污名的情况下存在，并且更加隐藏和内在，它似乎是对抑郁症患者最糟糕的污名形式，可以直接影响患者的整体福祉。研究表明，早期识别和治疗抑郁行为和症状可以改善社会功能，提高生产力，减少工作场所的缺勤。然而，识别抑郁症，特别是在早期阶段，仍然具有挑战性。为了解决这个问题，在这个提案中，我们计划开发有效的方法来检测抑郁行为，不仅在个人层面，而且在社区层面。后者是高度相关的，因为抑郁症是显着的社会决定因素和社会生态因素的变化的影响。特别是，我们将在马约诊所和私人保险公司利用强大的纵向电子健康记录（EHR）系统（UnitedHealthCare/Optum Labs）报销和索赔数据沿着来自Twitter和PatientsLikeMe的在线社交媒体数据以及地理编码的邻里和环境数据，以开发一个“大数据”平台，用于识别在线社交媒体和环境数据的组合。行为因素和邻里环境条件，以实现创新的方法来检测社区内的抑郁行为，并确定美国不同社区和地区的抑郁症医疗保健利用的模式和变化。

 DESCRIPTION (provided by applicant): Depression is highly prevalent, both in the US and worldwide. Among US adults, the estimated 12-month and lifetime prevalence rates are 8.3% and 19.2%, respectively. The World Health Organization considers major depressive disorder (MDD) as the third-highest cause of disease burden worldwide, and the highest cause of disease burden in the developed world. However, despite its prevalence and burden, depression remains significantly under-recognized and under-treated in all practice settings, including managed care where less than one third of adults with depression obtain appropriate professional treatment. Denial of illness and stigma are two primary barriers to proper identification and treatment of depression. Many individuals with depression are ashamed to seek out a mental health professional and consider depression a sign of personal weakness. In particular, "self-stigma" has been associated to affect adherence to psychiatric services, hope and quality of life negatively, and also poses as a barrier for social integration. Further, since self-stigma can exist without actual stigma from the public, and is more hidden and inside, it seems to be the worst form of stigma against people with depression and can directly affect the patients' over all well-being. Studies suggest that early recognition and treatment of depressive behavior and symptoms can improve social function, increase productivity, and decrease absenteeism in the workplace. However, recognition of depression, particularly in early stages, is still challenging. To address this problem, in this proposal we plan to develop effective methods for detection of depressive behavior, not only at an individual-level, but also at a community-level. The latter is highly pertinent because depression is significantly influenced by variations in social determinants and socio- ecological factors. In particular, we will leverage robust and longitudinal electronic health record (EHR) systems at Mayo Clinic and private insurance (UnitedHealthCare/Optum Labs) reimbursement and claims data along with online social media data from Twitter and PatientsLikeMe as well as geo-coded neighborhood and environmental data to develop a "big data" platform for identifying combinations of online socio-behavioral factors and neighborhood environmental conditions to enable innovative ways for detection of depressive behavior within communities and identify patterns and changes in health care utilization for depression across different communities and geographies within U.S.