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

重大飓风过后优化恢复精神和社会功能的努力

基本信息

批准号：
10364641
负责人：
SANDRO MD, MPH, DRPH GALEA
金额：
$ 57.7万
依托单位：
BOSTON UNIVERSITY MEDICAL CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-10 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10364641
关键词：
Address Affect Area Capital Caring Case Management Characteristics Communities Data Disasters Economics Effectiveness Event Future Geography Goals Government Health Healthcare Systems Housing Hurricane Income Individual Inequality Interruption Intervention Job loss Joints Light Louisiana Low income Measures Mental Depression Mental Health Mental Health Services Methodology Methods Modeling Natural Disasters Neighborhoods Oceans Outcome Population Post-Traumatic Stress Disorders Public Health Randomized Controlled Trials Recording of previous events Recovery Research Risk Sampling Seasons Shapes Social Functioning Social Work Structure Surveys Testing Texas Time Transportation Treatment Effectiveness United States Validation Work archive data archived data base built environment care systems climate change community-level factor comparative effectiveness comparative treatment cost effective cost effectiveness experience experimental study falls improved in silico individualized medicine metropolitan models and simulation multilevel analysis novel response simulation social social capital stressor treatment optimization treatment planning

项目摘要

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.

项目总结/文摘