Optimizing Efforts to Restore Psychiatric and Social Function After a Major Hurricane

项目总结/摘要 2017年秋季大西洋飓风季节，包括飓风哈维，是有记录以来最极端的， 而且，鉴于全球气候变化，这可能是未来季节到来的预兆。日益关键 了解飓风前、飓风后和飓风后潜在的可改变因素如何塑造长期的心理状态， 健康，以便我们可以优化干预措施， 风暴飓风期间和之后的个人经历--如流离失所和失业--形成 飓风后的心理健康，但更好地了解这些个人层面的事件如何与 社区层面的因素产生的结果可以帮助我们进一步调整治疗方法， 个人和社区在灾害环境中的作用。重要的是，人们对飓风的影响知之甚少 救济工作-包括住房和收入援助-对长期成果的影响。我们将解决这些问题 利用飓风前、飓风中和飓风后的框架来组织暴露特征的影响 和心理健康结果的排序。在我们的第一个目标，我们将描述如何相互作用之间的前， 飓风能力（例如，社会资本）和脆弱性（例如，住房质量差，以及 飓风压力源（例如，断电）和保护器（例如，有效的政府应对措施）， 个人和社区一级-形成飓风后抑郁症和创伤后应激障碍。在我们 第二个目标是，我们将确定并检验假设的干预措施对飓风后心理健康的影响 通过离散随机模拟，在飓风前、飓风中和飓风后容量的不同剖面下， 源自目标1的脆弱性、压力源和保护者。本项目的主要目标是 建立并验证先前的仿真分析，以创建一套一流的仿真模型， 自然灾害后精神卫生服务的最佳方法，并预测其公共卫生 冲击为了实现这些目标，我们将对生活在飓风中的个人进行地理抽样和调查。 德克萨斯州受哈维影响的地区讲述他们的经历，纳入召回验证子样本， 之前收集的飓风数据。我们还将利用档案数据， 社区层面，如收入不平等措施，建筑环境质量和飓风风险 指标，在不同的地理水平进行多级分析。最后，我们将利用来自 我们的从头调查，以创建具有不同的事件前，事件后和事件后组合的合成人群， 因素，并使用正在进行的飓风后随机对照试验的数据来校准和验证 仿真模型。这种计算机模拟实验将揭示候选干预措施的有效性 并帮助我们了解其潜在的好处，包括比较治疗和成本效益。 这个项目将汇集一个经验丰富的团队，使用新颖的方法来解决一个重要的和及时的问题。 公共卫生问题，其影响也可能扩大到其他类型的灾害和情况。

PROJECT SUMMARY/ABSTRACT The 2017 fall Atlantic hurricane season, including Hurricane Harvey, was the most extreme in recorded history, and, in light of global climate change, a possible harbinger of future seasons to come. It is increasingly critical to understand how potentially modifiable pre-, peri-, and post-hurricane factors shape the long-term mental health of affected populations, so that we may optimize interventions to limit the ultimate impact of such storms. Individual-level experiences during and after hurricanes – such as displacement and job loss – shape post-hurricane mental health, but a better understanding of how these individual-level events interact with community-level factors to produce outcomes could help us to further tailor treatment approaches for individuals and communities in disaster settings. Critically, very little is known about the effects of hurricane relief efforts – including housing and income assistance – on longer-term outcomes. We will address these gaps using a pre-, peri-, and post-hurricane framework to organize the influences of exposure characteristics and sequencing on mental health outcomes. In our first aim, we will characterize how interactions among pre- hurricane capacities (e.g., social capital) and vulnerabilities (e.g., poor housing quality) as well as peri- hurricane stressors (e.g., power outages) and protectors (e.g., efficient government responses) – at both individual and community levels – shape post-hurricane depression and posttraumatic stress disorder. In our second aim, we will identify and test the effects of hypothetical interventions on post-hurricane mental health through discrete stochastic simulations, under varying profiles of pre-, peri-, and post-hurricane capacities, vulnerabilities, stressors, and protectors derived from aim 1. The primary goal of this proposed project is to build on and validate prior simulation analyses to create a set of first-in-class simulation models to identify optimal approaches to mental health services following natural disasters, and to project their public health impact. To achieve these aims, we will geographically sample and survey individuals who lived in Hurricane Harvey-affected areas of Texas about their experiences, incorporating a recall validation subsample with previously collected pre-hurricane data. We will also capitalize on archival data by collecting variables at the community level such as income inequality measures, quality of built environment, and hurricane exposure indicators, to perform multilevel analyses across varying geographic levels. Finally, we will leverage data from our de-novo survey to create synthetic populations with varying combinations of pre-, peri-, and post-event factors, and use data from an ongoing post-hurricane randomized control trial to calibrate and validate simulation models. Such in-silico experiments will shed light on the effectiveness of candidate interventions and help us to understand their potential benefits, including comparative treatment- and cost-effectiveness. This project will bring together an experienced team using novel methods to tackle an essential and timely public health problem, the implications of which may also be extended to other types of disasters and contexts.