Integrating Community Based Participatory Research and Machine Learning Methods to Predict Youth Substance Use Disorders for Urban Cities in New Jersey

整合基于社区的参与性研究和机器学习方法来预测新泽西州城市的青少年药物使用障碍

• 批准号: 10740570
• 负责人: Ijeoma Opara
• 金额: $ 117.25万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10740570
• 关键词: Asthma; Award; Behavior; Black race; Characteristics; Cities; Communities; Complex; Data; Data Set; Development; Epidemic; Equation; Equity; Health; Hispanic; Individual; Learning; Machine Learning; Methods; National Institute of Drug Abuse; Neighborhoods; New Jersey; Oranges; Outcome; Prevention; Prevention Research; Process; Racial Equity; Reduce health disparities; Research Design; Research Personnel; Resources; Risk; Risk Factors; Role; Scientist; Statistical Methods; Statistical Study; Substance Use Disorder; System; Urban Community; Work; Youth; addiction; adolescent substance use; behavior change; children of color; community based participatory research; ethnic minority; health equity; innovation; machine learning method; marginalization; member; model development; novel; prediction algorithm; protective factors; racial bias; racial minority population; respiratory; social health determinants; substance misuse prevention; substance use; substance use prevention

Project Summary My lab uses a community-based participatory research approach to reduce health disparities in substance use among Black and Hispanic youth in urban communities. We primarily work in New Jersey (NJ) due to our close ties with Paterson and East Orange, NJ which both have the highest number of substance use disorders in the State and the largest group of racial-ethnic minorities (e.g. Black and Hispanic) in the state. My lab intentionally works on the community level as we have found that one-size-fits all approaches to ending the youth substance use epidemic will not work, particularly in communities that have been historically marginalized. Our recent work has discovered that targeting individual level behaviors to promote behavior change may not be enough to end the youth substance use epidemic and in fact, understanding the role of neighborhood characteristics may be a more plausible strategy. In our work, we have shown that predominantly urban communities such as Paterson, NJ and East Orange, NJ have some of the lowest neighborhood resources associated with healthy youth development and therefore can contribute to likelihood of using substances and becoming addicted. In addition, the use of complex statistical methods and study designs, may contribute to lack of mistrust of researchers, participation in studies and of the data by community members. We hypothesize that within predominantly urban communities, there is variability in structural risk and asset-based neighborhood characteristics associated with youth substance use. In line with using a social determinants of health approach, environmental and place-based factors have long been equated with health outcomes such as respiratory conditions (e.g. asthma) among youth. However, determining the exact resources within the community that contributes to substance use disorders have not been discovered. The field of addiction does not know the exact characteristics within a neighborhood that can serve as either protective or risk factors to substance use disorders within an urban community. In this Pioneer proposal which is responding to the RFA-DA-23-026, “NIDA Racial Equity Visionary Award DP1 mechanism”, we will combine innovative approaches and multiple forms of data to investigate neighborhood level factors by using participatory methods to co-create machine learning systems to predict and prevent substance use disorders with community members. We intend for this project to promote co- learning between community members and researchers that can lead to sustainable solutions for the community. The proposed work will shed light on the importance of place in addiction and also work towards eliminating racial bias in data sets and predictive algorithms by incorporating community members in all stages of the model development process. Findings from this study have the potential to change the way we as researchers conduct substance use and misuse prevention research and the way in which we formally engage with community members. This work can contribute significantly to achieving health equity for Black and Hispanic youth in urban communities.

项目摘要 我的实验室使用基于社区的参与式研究方法来减少健康差距， 城市社区黑人和西班牙裔青年药物使用情况。我们主要在新泽西（新泽西州）工作 由于我们与帕特森和新泽西州东橙子的密切联系，这两个地方的物质使用量最高 在该州，最大的种族-少数民族群体（如黑人和西班牙裔）。我 实验室有意在社区层面开展工作，因为我们发现， 青年药物使用流行病不会起作用，特别是在历史上 被边缘化了我们最近的研究发现，针对个人层面的行为，以促进行为， 改变可能不足以结束青年药物使用流行病，事实上， 邻域特征可能是更合理的策略。在我们的工作中，我们已经表明， 主要是城市社区，如帕特森，新泽西州和东橙子，新泽西州有一些最低的 社区资源与健康的青年发展，因此可以有助于可能性 吸毒成瘾的风险此外，使用复杂的统计方法和研究 设计，可能会导致缺乏对研究人员的不信任，参与研究和数据， 社区成员。 我们假设，在以城市为主的社区内，结构性风险存在变异性， 与青少年物质使用相关的基于资产的社区特征。与使用社交 健康方法的决定因素、环境和地点因素长期以来一直等同于健康 青年人的呼吸系统疾病（如哮喘）等后果。然而，确定确切的 尚未发现社区内导致药物使用障碍的资源。的 成瘾领域不知道一个社区内的确切特征， 城市社区内物质使用障碍的保护性或风险因素。 在这份回应RFA-DA-23-026的先锋提案中， 奖DP 1机制”，我们将联合收割机结合创新方法和多种形式的数据进行调查 邻里水平的因素，通过使用参与式方法共同创建机器学习系统来预测 并与社区成员一起预防药物使用障碍。我们希望这个项目能促进合作， 社区成员和研究人员之间的学习，可以为 社区拟议中的工作将阐明成瘾的重要性，并致力于 通过在所有阶段纳入社区成员，消除数据集和预测算法中的种族偏见 模型开发过程。这项研究的结果有可能改变我们作为 研究人员进行物质使用和滥用预防研究，以及我们正式参与的方式 与社区成员。这项工作可以大大有助于实现黑人的健康公平， 城市社区的西班牙裔青年。