Accessing the impact of youth suicide prevention program in small communities

项目摘要/摘要 自杀是美国的主要死亡原因，特别是在10-24岁的年轻人中。青春年华 更小、更农村的社区，特别是美国印第安人/美国原住民青年，面临着更高的风险。 开发和实施有效的自杀预防计划需要严格评估手段 他们的影响。开展自杀预防项目的影响评估面临许多共同挑战 对于其他领域，例如当随机化不可行时，很难估计反事实。这些 由于很难获得可靠的自杀率估计数字，挑战进一步加剧。 由于这类社区的规模较小，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，以改进 通过借用邻近地区的信息来估计小地区的疾病或健康结局比率。 尽管这些模型具有潜力，但它们尚未被应用于项目影响评估。这项研究有 三个目标：(1)研究一种评估自杀高发地区自杀预防影响的方法 风险；(2)确定我们建议的方法优于替代方法的特定条件 用于影响评估；以及(3)延长对加勒特·李·史密斯(GLS)影响的早期评估 由药物滥用和心理健康管理的青少年自杀纪念预防计划 通过将重点放在小型、高风险社区，如部落社区，为政府提供服务 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数据来经验地评估所提出的方法的准确性 对估计影响的三种替代方法：综合控制法、弹性网法和矩阵完备法进行了比较。 模拟数据将使我们能够衡量不同水平的空间和时间依赖，以及 其他相关参数的变化会影响方法的相对性能。最后，我们将使用 2006至2018年间接受GLS计划的微型和非核心社区样本 评估GLS计划对年轻人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 两者都将这一方法的用途扩展到疾病图谱之外，并提高了理解自杀的能力 预防规划对一些高危人群产生影响。

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.