Community Based System Dynamics Models of Alcohol and Substance Exposed Pregnancy in Northern Plains American Indian Women

项目总结 酒精和物质暴露妊娠(ASEP)的持续趋势表明对更高质量的需求很大 减少ASEP方案，特别是那些解决ASEP高危人群和 社区，如美国印第安人(AI)妇女。这些计划没有考虑到广泛的星座 与ASEP有关的因素，特别是亲密伴侣暴力(IPV)的作用，这构成了一种共性 与另外两个ASEP指标(酒精和药物使用以及计划外怀孕)有关。系统 动力学方法是理解ASEP和这种共性是如何嵌套在 更广泛的人际、个人和制度因素体系。这种方法特别有益。 解决目前人工智能社区内与ASEP相关的健康差距。基于社区的系统 动态模型使从业者和政策制定者能够确定实施的最佳系统领域 将在ASEP中产生最大变化的政策和计划。目前的提案使用社区- 为两个社区内的人工智能女性开发ASEP系统模型的基于方法：一个小型地铁和一个 邻近的保留地。这些模型允许研究人员和社区合作发现重要的 ASEP干预(减少孕妇中的ASEP)和预防ASEP的系统杠杆点 (重点是IPV与酒精和药物使用之间的循环关系)。这个项目的目标是 建立和模拟系统动态模型，代表ASEP系统与我们的高度合作 协作型社区研究团队。我们将使用各种工具来校准和验证这些模型 社区数据源，然后区分降低ASEP和ASEP的最有效的目标区域 A)跨社区推广和物质合法性，以及b)可能在以下方面唯一有效的预测因素 对于特定的社区或特定物质。这项工作将得到个人层面分析的补充 它可以估计高优先级杠杆点在单个ASEP和ASEP风险上的强度。建议数 研究意义重大，因为它解释了经常被忽视的潜在贡献者矩阵，这些贡献者保持 社区层面的Asep和Asep健康差距，并为高影响提供明确的建议 在有需要时减少社区内不良反应的方法。由于系统的集成，本项目具有创新性 模拟和基于社区的方法来解决这个问题的复杂性，以及 描述性和预测性分析，以提供独特的基于经验的解决方案来解决这些问题 一个翻译框架。强大的跨学科研究团队，拥有独特但互补的 研究人员和有关社区之间的专业领域和密切合作伙伴关系包括 共同努力，促进项目成功，为社区卫生做出有意义的贡献。 这项研究的发现为降低asp和asep的高效方法提供了关键信息。 健康差距，以及制定系统变革战略蓝图的明确机制 人工智能社区。

PROJECT SUMMARY Continued trends of alcohol and substance exposed pregnancy (ASEP) indicate a great need for higher quality ASEP-reduction programs, particularly those that address ASEP health disparities within at-risk populations and communities, such as American Indian (AI) women. These programs do not account for the broad constellation of factors pertinent to ASEP, in particular, the role of intimate partner violence (IPV), which forms a syndemic association with two other ASEP indicators (alcohol and substance use and unplanned pregnancy). System dynamics methods are effective strategies for understanding of how ASEP and this syndemic are nested within a broader system of interpersonal, intrapersonal, and institutional factors. This method is especially beneficial for addressing the current ASEP-related health disparities within AI communities. Community-based system dynamic models allow practitioners and policymakers to determine the best system areas for implementing policies and programs that will produce the biggest changes in ASEP. The current proposal uses community- based approaches to develop ASEP system models for AI women within two communities: a small metro and a neighboring reservation. These models allow for a researcher-community partnership to discover important system leverage points for ASEP intervention (reducing ASEP within pregnant women) and ASEP prevention (focusing on the cyclic relationship between IPV and alcohol and substance use). The goals of this project are to build and simulate system dynamic models that that represent the ASEP system in partnership with our highly collaborative community-researcher team. We will calibrate and validate these models utilizing a variety of community data sources, and then distinguish the most effective areas to target for reducing ASEP and ASEP predictors that a) generalize across communities and substance legality, and b) may be uniquely effective within specific communities or for specific substances. This work will be complemented by individual-level analyses which can estimate the strength of high-priority leverage points on individual ASEP and ASEP risk. The proposed research is significant as it accounts for the often-ignored underlying matrix of contributors which maintain community levels of ASEP and ASEP health disparities, and provides clear recommendations for high-impact methods to reduce ASEP within communities at need. This project is innovative due to the integration of system simulation and community-based approaches to address complexity, of this issue, and the integration of descriptive and predictive analyses to provide distinct empirically-based solutions to address these issues within a translational framework. The strong interdisciplinary team of researchers with unique, but complementary areas of expertise and the close working partnership between researchers and the communities of interest are together a powerful collaborative to facilitate project success and meaningful contributions to community health. Findings from this study provide critical information about highly effective ways of reducing ASEP and ASEP health disparities, as well as a clear mechanism for developing a strategic blueprint for systematic change within AI communities.