Using machine learning to accelerate our understanding of risks for early substance use among child-welfare and community youth

项目摘要/摘要 据估计，药物使用/滥用造成的经济损失每年超过7400亿美元 事故、医疗保健、无家可归、失业和犯罪活动。青春期是一个关键的时期 干预措施，因为90%符合成瘾标准的成年人在 青春期。然而，初级保健的最低限度物质使用筛查阻碍了预防工作。 医疗服务提供者以及青少年在医疗环境中的低揭发率。这些挑战使得有必要 发现关键风险因素和改进筛查方法的新方法。重要的是，青少年患有 虐待儿童的经历更容易受到早期药物使用的影响，并且更有可能进展 从实验到上瘾比没有受到虐待的年轻人。不断积累的证据和我们初步的 数据表明，早期使用药物的顶级预测因素与儿童福利(CW)青少年无关， 需要对这一弱势群体的相关风险因素进行新的研究。建议的研究内容包括 这些差距通过使用尖端的机器学习模型来提供关于风险的重要新证据 CW人群特有的因素以及CW和CW可能共同存在的早期使用药物的风险 和非CW青年。我们将使用两个唯一的数据源来实现这一点。我们的主要数据将来自 南加州凯撒永久(KPSC)成员电子健康记录(EHR)(估计 2007-2020年，样本规模为340万儿童)。我们将使用虐待的诊断代码来指示 CW样本，一个合理的转介儿童福利的假设。危险因素将从诊断中获得 儿童和家长以及县犯罪和健康记录中的代码和摘要进展说明 地理收入数据。第二，解决EHR的局限性，以捕捉更详细的心理社会 数据，我们将使用现有的纵向数据集，其中454名青年，303名来自儿童福利机构，151名在 对照组(YAP研究)。参与者的平均年龄分别为11、13、15和18岁， 种族/民族多元化。收集的数据包括孩子级别、父母级别、家庭级别和 社区危险因素和CW病例记录。这两个数据源将允许我们：1)生成关键的 关于CW与非CW青年早期使用药物的相关预测因素的新知识和2) 使用密集调查数据(YAP)确定目前未在EHR数据(KPSC)中收集的风险因素 这可能会为新的筛查问题的发展提供信息。最后，我们的预测模型具有平移 在儿科初级保健中改进青少年物质使用风险筛查方法的潜力 使用集成到临床决策支持工具中的风险评分。这些发现，如果在临床护理中实施 设置，将允许医疗提供者更准确地识别风险人群，并触发分层 预防药物滥用的不同治疗途径。

PROJECT SUMMARY/ABSTRACT The economic toll of substance use/abuse is estimated to be over $740 billion annually as a result of accidents, health care, homelessness, unemployment, and criminal activity. Adolescence is a critical time for intervention, as 90% of adults who meet the criteria for addiction initiate use of alcohol or drugs in adolescence. Yet, prevention efforts have been hindered by minimal substance use screening by primary care providers as well as low rates of disclosure by adolescents in medical settings. These challenges necessitate new approaches to detect key risk factors and enhance screening methods. Importantly, adolescents with experiences of child maltreatment are more susceptible to early substance use and more likely to progress from experimentation to addiction than non-maltreated youth. The accumulating evidence and our preliminary data suggest that the top predictors of early substance use are not relevant for child welfare (CW) youth, requiring new studies of the relevant risk factors for this vulnerable population. The proposed study addresses these gaps by using cutting-edge Machine Learning models to provide vital new evidence regarding risk factors specific to the CW population as well as risks for early substance use that may be common to both CW and non-CW youth. We will use two unique data sources to accomplish this. Our primary data will come from electronic health records (EHR) of Kaiser Permanente Southern California (KPSC) members (estimated sample size of 3.4 million children, 2007-2020). We will use diagnosis codes for maltreatment to indicate the CW sample, a reasonable assumption of referral to child welfare. Risk factors will be obtained from diagnosis codes and abstractable progress notes in the EHR of children and parents as well as county crime and geographic income data. Second, to address the limitations of EHR to capture more detailed psychosocial data, we will use an existing longitudinal dataset of 454 youth, 303 referred from child welfare and 151 in a comparison group (YAP study). Participants were seen at mean ages 11, 13, 15, and 18 years old and are racially/ethnically diverse. Collected data includes measures of child level, parent level, family level, and neighborhood risk factors and CW case records. These two data sources will allow us to: 1) produce critical new knowledge regarding the relevant predictors of early substance use for CW versus non-CW youth and 2) use intensive survey data (YAP) to determine risk factors that are not currently collected in EHR data (KPSC) that may inform the development of new screening questions. Lastly, our predictive model has translational potential to advance screening methods for adolescent substance use risk in pediatric primary care through the use of risk scores integrated into clinical decision support tools. These findings, if implemented in clinical care settings, would allow medical providers to more accurately identify those at risk and trigger stratification into different treatment pathways to prevent substance abuse.