Improving Suicide Risk Prediction with Social Determinants Data

利用社会决定因素数据改进自杀风险预测

基本信息

批准号：
10528534
负责人：
Robert B. Penfold
金额：
$ 46.84万
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 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10528534
关键词：
Area Bankruptcy Cessation of life Collection Data Data Element Data Set Diagnosis Divorce Enrollment Epidemiology Event Face Feeling Financial Hardship Foreclosure Future Goals Health Health Care Visit Health Professional Health system Healthcare Home Household Housing Impulsivity Individual Intervention Learning Life Link Loneliness Machine Learning Measures Mediation Medical Mental Depression Mental Health Modeling Motivation National Institute of Mental Health Performance Persons Predictive Value Prevention Prevention program Privacy Property Proxy ROC Curve Records Research Research Institute Research Priority Risk Risk Factors Severities Social isolation Specialist Suicide Suicide attempt Suicide prevention Symptoms Thinking Time United States Veterans Health Administration Visit Washington case control data streams improved machine learning algorithm member mortality outreach program payment predictive modeling reducing suicide residence risk prediction risk prediction model social determinants social factors stressor success suicidal morbidity suicidal risk suicide rate theories

项目摘要

ABSTRACT Suicide accounted for 47,511 deaths in the United States in 2019 and the suicide rate has increased by 39% since 1999. Suicide prevention is an NIMH research priority. Recent research in estimating machine learning algorithms to predict suicide risk has been tremendously successful. The models have been implemented as part of routine prevention programs in health systems such as Kaiser Permanente Washington, HealthPartners, and the Veterans Health Administration. Despite these successes, existing models have important shortcomings. A significant proportion of suicides followed healthcare visits where the predicted risk was low (and where an intervention might have taken place otherwise). The models do not currently include any information about social determinants of suicide (e.g., living alone, financial stress) or negative life events (NLE), such as divorce, bankruptcy, and criminal arrest. Adding social determinants data and NLE data to models may improve predictive accuracy. The specific aims of this study are: (1) expand and enhance the risk prediction dataset with over 1500 date-stamped variables describing social determinants of suicide risk and NLE; (2) construct and evaluate suicide risk prediction models using social determinants and NLE data alone; (3) construct and evaluate suicide risk prediction models using social determinants, NLE and healthcare data together and estimate interaction terms between social determinants, NLE, and healthcare predictors. An example would be “depression diagnosis” interacted with “divorce filing in last 30 days”. This will be the first large scale study to incorporate individual-level, date-stamped measures of social determinants and NLE into machine learning suicide risk prediction models. Upon successful completion of this study we expect to know how much incorporating these new data contributes to the accuracy of suicide risk prediction models. This will be an important next step towards implementing better suicide prevention programs and reducing overall suicide rates.

抽象的自1999年以来，2019年，自杀率为47,511人死亡，自杀率增加了39％。预防自杀是NIMH研究的重点。估计机器学习算法的最新研究以预测自杀风险取得了巨大的成功。这些模型已作为常规预防的一部分实施卫生系统的计划，例如Kaiser Permanente Washington，HealthPartners和退伍军人卫生行政。尽管取得了这些成功，但现有模型仍有重要的缺点。很大一部分自杀遵循预测风险较低的医疗访问（以及可能已经进行干预的情况下）否则）。这些模型目前不包含有关自杀社会决定者的任何信息（例如，独自生活，财务压力）或负面生活事件（NLE），例如离婚，破产和犯罪逮捕。增加社交确定数据和NLE数据对模型可能提高预测精度。这项研究的具体目的是：（1）扩展和增强风险预测数据集，其中1500多个描述社会决定者的日期戳变量自杀风险和NLE；（2）使用社会决定者和NLE数据构建和评估自杀风险预测模型独自的; （3）使用社会决定者，NLE和医疗保健数据构建和评估自杀风险预测模型共同估计社会决定者，NLE和医疗保健预测指标之间的相互作用术语。一个例子将是“抑郁诊断”与“过去30天内的离婚申请”相互作用。这将是第一个大规模研究合并社会决定者的个人级别，日期扫描措施，并将其纳入机器学习自杀风险预测模型。成功完成这项研究后，我们希望知道将这些新数据纳入多少有助于自杀风险预测模型的准确性。这将是实施的重要下一步更好的预防自杀计划并降低总体自杀率。