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

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的药物使用情况。我们主要在新泽西(新泽西州)工作 由于我们与新泽西州帕特森和东奥兰治的密切联系，这两个城市的药物使用量都是最高的 该州最大的种族少数群体(如黑人和西班牙裔)的精神障碍。我的 Lab有意在社区层面上工作，因为我们发现，一刀切的方法适合所有结束 青少年吸毒流行病不会奏效，特别是在历史上 被边缘化了。我们最近的工作发现，针对个人层面的行为来促进行为 改变可能不足以结束青少年滥用药物的流行病，事实上，理解 社区特色可能是一种更合理的策略。在我们的工作中，我们已经表明 主要是城市社区，如新泽西州的帕特森和新泽西州的东奥兰治，有些是最低的 社区资源与青年的健康发展相关，因此可以有助于 吸食毒品并上瘾。此外，运用复杂的统计方法进行研究 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，在以城市社区为主的社区内，结构性风险和 与青少年物质使用相关的基于资产的邻里特征。与使用社交媒体相一致 长期以来，健康方法的决定因素、环境因素和基于地点的因素一直被等同于健康 结果，如青少年的呼吸系统疾病(如哮喘)。然而，确定准确的 社区内导致物质使用障碍的资源尚未被发现。这个 上瘾领域不知道邻里之间的确切特征 城市社区内物质使用障碍的保护性因素或危险因素。 在这份响应RFA-DA-23-026的先锋提案中， ，我们将结合创新的方法和多种形式的数据进行调查 通过使用参与式方法共同创建机器学习系统来预测邻里水平因素 并与社区成员一起预防药物使用障碍。我们打算通过这个项目来促进共同 社区成员和研究人员之间的学习可以为 社区。拟议的工作将阐明吸毒成瘾的重要性，并努力实现 通过将社区成员纳入所有阶段来消除数据集和预测算法中的种族偏见 模型的开发过程。这项研究的发现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 和社区成员在一起。这项工作可以大大有助于实现黑人和妇女的健康公平 城市社区中的西班牙裔青年。

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.