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

减少自杀风险预测中种族和民族差异的创新方法

基本信息

批准号：
10363191
负责人：
Rebecca Yates Coley
金额：
$ 41.94万
依托单位：
KAISER FOUNDATION RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10363191
关键词：
Address Adopted Adult Age Alaska Native Algorithms American Indians Asian Pacific Islander Black Populations Black race Cessation of life Clinical Clinical Data Complex Data Death Rate Development Differential Diagnosis Emergency department visit Ensure Ethnic Origin Ethnic group Evaluation Event Funding Gender Identity Health system Healthcare Healthcare Systems Hispanic Populations Individual Label Lead Measurement Measures Mental Health Methods Modeling Modification National Institute of Mental Health Not Hispanic or Latino Outcome Outpatients Paper Patient Care Patients Pattern Performance Persons Population Primary Health Care Procedures Psychiatry Race Records Research Research Personnel Risk Risk Factors Sample Size Sampling Self-Injurious Behavior Severity of illness Source Statistical Methods Stratification Subgroup Suicide Suicide attempt Suicide prevention Underserved Population United States Visit Vulnerable Populations Weight Work design health care availability health care service utilization health care settings health disparity high risk improved innovation interest machine learning method machine learning model novel outcome prediction predictive modeling prevent preventive intervention racial and ethnic racial and ethnic disparities random forest recidivism response risk prediction risk prediction model sexual identity simulation social health determinants suicidal behavior suicidal morbidity suicidal risk suicide rate trend

项目摘要

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.

自1999年以来，美国的自杀死亡率增长了35％。2018年，超过48,000 自杀死亡，估计有140万成年人自杀。作为回应，卫生系统是采用自杀风险预测模型来指导预防自杀的干预措施。从医疗保健记录估算的自杀预测模型可能会使医疗保健中当前差异永久存在访问，质量和结果。自杀预测模型可能无法准确地识别出所有人的高风险患者种族和种族。自杀率因种族和种族而异，最高和最低率的自杀率是在传统服务不足的人群中可见。美国印第安人和阿拉斯加自杀率最高亚洲和太平洋岛民，黑人和西班牙裔人口中的当地人（每10万人22.1人），最低（每100,000人7.0-7.4），而白人非西班牙裔人每10万人为18.0人。种族和种族亚组的自杀风险预测模型的表现差异有三个可能的来源。首先，自杀风险的预测因素可能会通过错误来衡量，并且此错误可能不同用于种族和种族亚组。其次，自杀企图和死亡可能被错误分类，并且错误分类率可能因种族和种族而有所不同。第三，预测变量与结果可能因种族和种族而异，即风险改变。估计预测模型的现有方法并非旨在解决种族和种族差异表现。估计程序的重点是优化整个人群的绩效，而不是在内亚组和较少普遍的亚组的性能对总体准确性几乎没有影响。机器学习方法，例如随机森林，探索预测因素与种族或种族之间的互动，自杀尝试和死亡是罕见的事件，它限制了可用的信息来识别特定于种族和种族的信息风险因素。对于预测研究的样本量计算也没有足够的指导。我们将开发新的统计方法，以减少种族和种族差异的随机森林模型通过解决当前方法中的差距来表现自杀预测模型。 AIM 1将发展新的预测模型估算的程序，以最大程度地提高种族和种族的预测绩效亚组，而不是最大化整个人群的平均表现。 AIM 2将集成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种族和种族亚组。我们将使用1500万的自杀危险因素和结果的现有数据门诊精神健康，1000万个初级保健和200万个急诊室就诊资助的心理健康研究网络以实施我们的方法并估算自杀预测模型每个种族和种族中准确识别出自杀风险最高的患者的环境。