Innovative methods to reduce racial and ethnic disparities in suicide risk prediction

自1999年以来，美国的自杀死亡率上升了35%。2018年，有超过48，000名 自杀死亡，估计有140万成年人企图自杀。作为回应，卫生系统 采用自杀风险预测模型，指导自杀预防干预措施的实施。 根据卫生保健记录估计的自杀预测模型可能会使目前的卫生保健差距永久化 获取、质量和成果。自杀预测模型可能无法准确识别所有高危患者 种族和民族群体。自杀率因种族和民族而异，最高和最低的自杀率都是 在传统上服务不足的人群中。美国印第安人和阿拉斯加人的自杀率最高 本地人（每10万人中22.1人），亚洲和太平洋岛民、黑人和西班牙裔人口中最低 （每10万人中有7.0-7.4人），而非西班牙裔白色人每10万人中有18.0人。 自杀风险预测模型在种族和民族亚组中的表现差异有三个方面 可能的来源首先，自杀风险的预测因子可能会有误差，而且误差可能不同 种族和民族亚组。第二，自杀企图和死亡可能被错误分类， 错误分类率可能因种族和族裔而异。第三，预测因素与 结果可能因种族和民族而异，即，风险修正 用于估计预测模型的现有方法并不旨在解决种族和民族差异， 性能评估程序侧重于优化整个群体的性能，而不是在 在不太普遍的亚组中，性能对总体准确性的影响很小。虽然机器 学习方法，如随机森林，探索预测因子与种族或民族，自杀之间的相互作用， 未遂和死亡是罕见事件，这限制了确定种族和族裔特异性 危险因素对于预测研究的样本量计算也没有足够的指导。 我们将为随机森林模型开发新的统计方法，以减少种族和民族差异， 通过解决当前方法中的差距来提高自杀预测模型的性能。Aim 1将开发新的 用于在种族和民族内最大化预测性能的预测模型估计的程序 子组，而不是最大化整个人群的平均表现。Aim 2将整合 在预测模型估计和评估中调整差异结果错误分类的方法。目的 3将设计样本量计算，以确定研究是否能够准确预测结果， 种族和民族亚组。我们将使用现有的自杀风险因素和结果的数据， 门诊心理健康，1000万初级保健，以及来自NIMH的200万急诊科就诊， 资助的心理健康研究网络，以实施我们的方法，并估计自杀预测模型， 每个设置都能准确识别所有种族和民族中自杀风险最高的患者。

Suicide death rates in the United States have increased 35% since 1999. In 2018, there were over 48,000 suicide deaths, and an estimated 1.4 million adults attempted suicide. In response, health systems are adopting suicide risk prediction models to guide delivery of suicide prevention interventions. Suicide prediction models estimated from health care records may perpetuate current disparities in health care access, quality, and outcomes. Suicide prediction models may not accurately identify high-risk patients from all racial and ethnic groups. Suicide rates vary by race and ethnicity, and both the highest and lowest rates are seen in traditionally underserved populations. Suicide rates are highest among American Indians and Alaskan Natives (22.1 per 100,000 people) and lowest in Asian and Pacific Islander, Black, and Hispanic populations (7.0-7.4 per 100,000 people) compared to 18.0 per 100,000 people for White non-Hispanics. Differences in performance of suicide risk prediction models across racial and ethnic subgroups have three possible sources. First, predictors of suicide risk may be measured with error, and this error may be different for racial and ethnic subgroups. Second, suicide attempts and deaths may be misclassified, and misclassification rates may differ by race and ethnicity. Third, the association between predictors and outcomes may vary by race and ethnicity, i.e., risk modification. Existing methods for estimating prediction models are not designed to address racial and ethnic disparities in performance. Estimation procedures focus on optimizing performance across the entire population, not within subgroups, and performance in less prevalent subgroups has little impact on overall accuracy. While machine learning methods, like random forest, explore interactions between predictors and race or ethnicity, suicide attempt and death are rare events, which limits the information available to identify race- and ethnicity-specific risk factors. There is also insufficient guidance on sample size calculations for prediction studies. We will develop novel statistical methods for random forest models that reduce racial and ethnic disparities in performance of suicide prediction models by addressing gaps in current methods. Aim 1 will develop new procedures for prediction model estimation that maximize predictive performance within racial and ethnic subgroups, rather than maximizing average performance across the entire population. Aim 2 will integrate methods to adjust for differential outcome misclassification in prediction model estimation and evaluation. Aim 3 will design sample size calculations to determine if a study is able to accurately predict outcomes within racial and ethnic subgroups. We will use existing data on suicide risk factors and outcomes for 15 million outpatient mental health, 10 million primary care, and 2 million emergency department visits from the NIMH- funded Mental Health Research Network to implement our methods and estimate suicide prediction models for each setting that accurately identify patients at highest risk of suicide across all races and ethnicities.