Modeling Social Behavior for Healthcare Utilization in Depression

抑郁症患者医疗保健利用的社会行为建模

• 批准号: 9313941
• 负责人: Jyotishman Pathak
• 金额: $ 45.8万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9313941
• 关键词: Absenteeism; Active Learning; Address; Adherence; Adult; Affect; Algorithms; American; Asses; Behavior; Big Data; Bioethics; Censuses; Characteristics; Clinic; Code; Communities; Computer Simulation; Data; Data Set; Data Sources; Detection; Diagnosis; Early identification; Early treatment; Electronic Health Record; Epidemiology; Focus Groups; Foundations; Geography; Goals; Health; Health Professional; Health Services Research; Healthcare; Healthcare Systems; Individual; Information Resources Management; Information Retrieval; Insurance; Internet; Intervention Studies; Lead; Link; Longevity; Major Depressive Disorder; Managed Care; Mental Depression; Mental Health; Mental Health Services; Mental disorders; Methods; Mining; Modeling; Motivation; Neighborhoods; Outcome; Patients; Pattern; Population; Prevalence; Privatization; Procedures; Productivity; Provider; Psychiatrist; Public Health; Quality of life; Research; Risk; Services; Signal Transduction; Social Behavior; Social Functioning; Social Network; Source; Surveys; System; Techniques; Technology; Testing; Validation; Variant; Wood material; Workplace; World Health Organization; base; behavioral health; behavioral outcome; beneficiary; biomedical informatics; burden of illness; computer science; data integration; data mining; depressive behavior; depressive symptoms; design; disability; health care service utilization; improved; information seeking behavior; innovation; mental health center; multidisciplinary; public health relevance; service utilization; social; social integration; social media; social networking website; social stigma

 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.

 描述(申请人提供)：抑郁症非常普遍，在美国和世界各地都是如此。在美国成年人中，估计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，严重抑郁障碍(MDD)是全球第三大疾病负担原因，也是发达国家疾病负担最高的原因。然而，尽管抑郁症的患病率和负担很大，但在所有实践环境中，包括管理性护理，只有不到三分之一的抑郁症成年人获得适当的专业治疗，抑郁症仍然得不到充分的认识和治疗。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家，并将抑郁症视为个人软弱的标志。特别是，“自我污名”已被认为对接受精神服务、希望和生活质量产生负面影响，并成为社会融合的障碍。此外，由于自我耻辱可以在没有来自公众的实际耻辱的情况下存在，而且更隐蔽和更内在，它似乎是对抑郁症患者最糟糕的耻辱形式，并可能直接影响患者的整体福祉。研究表明，及早识别和治疗抑郁行为和症状可以改善社会功能，提高工作效率，减少工作场所的缺勤。然而，对抑郁症的认识，特别是在早期阶段，仍然具有挑战性。为了解决这个问题，在这项建议中，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，因为抑郁症受到社会决定因素和社会生态因素变化的显著影响。特别是，我们将利用Mayo诊所和私人保险(UnitedHealthcare/Optom Labs)强大和纵向的电子健康记录(EHR)系统报销和索赔数据，以及来自Twitter和PatientsLikeMe的在线社交媒体数据以及地理编码的社区和环境数据，开发一个用于识别在线社会行为因素和社区环境条件组合的“大数据”平台，以实现检测社区内抑郁行为的创新方法，并确定美国不同社区和地理地区对抑郁症的医疗利用模式和变化。