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

项目摘要/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，这可能预示着未来几个季节的到来。它变得越来越关键 为了了解飓风前、飓风周和飓风后潜在的可改变因素如何塑造长期心理 受影响人群的健康，以便我们可以优化干预措施，以限制这种情况的最终影响 暴风雨。个人在飓风期间和之后的经历--例如流离失所和失业--形成 飓风过后的心理健康，但更好地理解这些个人层面的事件如何与 社区层面产生结果的因素可以帮助我们进一步调整治疗方法 灾害环境中的个人和社区。关键的是，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 使用飓风前、飓风前后和飓风后框架来组织暴露特征的影响的差距 以及心理健康结果的排序。在我们的第一个目标中，我们将描述前两者之间的相互作用 飓风能力(例如，社会资本)和脆弱性(例如，糟糕的住房质量)以及 飓风应激源(例如，停电)和保护者(例如，有效的政府反应)-在两者 个人和社区层面--形成飓风后抑郁和创伤后应激障碍。在我们的 第二个目标，我们将确定和测试假设性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、飓风周和飓风后的不同轮廓下， 来自目标1的脆弱性、压力源和保护器。此提议项目的主要目标是 在先前模拟分析的基础上进行构建和验证，以创建一组一流的模拟模型以确定 自然灾害后精神卫生服务的最佳方法，并预测其公共卫生 冲击力。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 关于德克萨斯州受哈维影响的地区的经验，将召回验证子样本与 之前收集的飓风前数据。我们还将利用档案数据，在 社区层面，如收入不平等衡量标准、建筑环境质量和飓风风险暴露 指标，在不同的地理水平上执行多层次分析。最后，我们将利用来自 我们的从头调查旨在创建包含事前、事中和事后不同组合的合成人群 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 模拟模型。这样的电子实验将阐明候选干预措施的有效性 并帮助我们了解它们的潜在好处，包括比较治疗和成本效益。 这个项目将汇集一支经验丰富的团队，使用新的方法来处理必要的和及时的 公共卫生问题，其影响也可能扩大到其他类型的灾害和背景。

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.