Accessing the impact of youth suicide prevention program in small communities

项目概要/摘要 在美国，自杀是导致死亡的主要原因，特别是在10-24岁的年轻人中。青年 更小的农村社区，特别是美洲印第安人/美洲土著青年面临更大的风险。 制定和提供有效的自杀预防计划需要严格评估 他们的影响进行自杀预防计划的影响评估面临许多共同的挑战 其他领域，如在估计反事实时，随机化是不可行的困难。这些 由于难以获得可靠的自杀率估计数， 由于社区规模小，风险较高的社区和人口部分。 在小区域估计技术的基础上，开发了贝叶斯疾病映射模型，以改善 通过借用邻近地区的信息来估计小地区的疾病或健康结果率。 尽管它们的潜力，这些模型还没有被应用到程序的影响评估。本研究 三个目标：（1）研究一种方法，以评估自杀率高的小地区的自杀预防效果 风险;（2）确定特定条件下，我们提出的方法可以优于替代方法 进行影响评估;以及（3）扩大早先对加勒特李史密斯（GLS）影响的评估 由物质滥用和精神卫生管理的纪念青年自杀预防计划 服务管理局，重点是小的，高风险的社区，如部落社区， 在以前的研究样本中代表性不足。 这项研究将使用模拟和真实的数据来经验性地评估所提出的方法的准确性 维斯三种估计影响的替代方法维斯：综合控制，弹性网络和矩阵完成。 模拟数据将使我们能够衡量不同水平的空间和时间依赖性，以及 其他相关参数的变化影响方法的相对性能。最后，我们将使用 2006年至2018年期间暴露于GLS计划的小都市和非核心社区样本， 估计GLS计划对青少年自杀和自残住院率的影响。 总之，通过将贝叶斯时空模型应用于自杀预防影响评估，我们期望 这两项研究都将该方法的实用性扩展到疾病地图之外，并提高了理解自杀的能力。 预防方案规划对一些高危人群的影响。

Project Summary/Abstract Suicide is a leading cause of death in the United States, particularly among youth aged 10–24 years. Youth in smaller, more rural communities, particularly American Indian/Native American youth, face heightened risk. Developing and delivering effective suicide prevention programs requires the means to rigorously evaluate their impact. Conducting impact evaluations of suicide prevention programs faces many challenges common to other fields, such as the difficulty in estimating the counterfactual when randomization is not feasible. These challenges are further compounded by the difficulty of obtaining reliable estimates of suicide rates in communities and segments of the population with heightened risk due to the small size of such communities. Building on small area estimation techniques, Bayesian disease-mapping models were developed to improve estimation of disease or health outcome rates in small areas by borrowing information from neighboring areas. Despite their potential, these models have not been applied to program impact evaluation. This study has three aims: (1) examine a method to assess suicide prevention impact in small areas with heightened suicide risk; (2) identify particular conditions under which our proposed method can outperform alternative methods for impact evaluation; and (3) extend earlier assessments of the impact of the Garrett Lee Smith (GLS) Memorial youth suicide prevention program administered by the Substance Abuse and Mental Health Services Administration by focusing on small, high-risk communities, such as tribal communities, that were underrepresented in previous study samples. This study will use both simulated and real data to empirically assess the accuracy of the proposed approach vis-à-vis three alternative methods to estimate impact: synthetic control, elastic net, and matrix completion. The simulated data will allow us to gauge how different levels of spatial and temporal dependence, as well as variations in other relevant parameters, affect the relative performance of approaches. Finally, we will use a sample of micropolitan and noncore communities exposed to the GLS program between 2006 and 2018 to estimate the impact of the GLS program on both suicide and self-harm hospitalization rates among youth. In sum, by applying Bayesian spatiotemporal models to suicide prevention impact evaluation, we expect to both extend the utility of the approach beyond disease mapping and advance the ability to understand suicide prevention programming impact in some of the highest-risk populations.