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

项目摘要 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 ASEP减少方案，特别是那些解决高危人群中ASEP健康差异的方案， 社区，如美国印第安人（AI）妇女。这些计划并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， 与其他两个ASEP指标（酒精和物质使用和计划外怀孕）相关。系统 动力学方法是理解ASEP和这种流行病如何嵌套在 一个更广泛的人际、个人内部和制度因素的系统。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。以社区为基础的系统 动态模型使实践者和决策者能够确定实施的最佳系统领域 这些政策和计划将在ASEP中产生最大的变化。目前的提案使用社区- 为两个社区内的AI妇女开发ASEP系统模型的方法：一个小地铁和一个 邻近的保留地。这些模型允许研究人员与社区合作， ASEP干预（减少孕妇中的ASEP）和ASEP预防的系统杠杆点 （重点是IPV与酒精和物质使用之间的循环关系）。该项目的目标是 建立和模拟系统动态模型，代表ASEP系统与我们的高度合作， 协作社区研究团队。我们将利用各种方法校准和验证这些模型， 社区数据源，然后区分减少ASEP和ASEP的最有效目标区域 预测因素，a）在社区和物质合法性之间进行概括，以及B）可能在 特定的社区或特定的物质。这项工作将辅之以个人层面的分析 它可以估计高优先级杠杆点对个人ASEP和ASEP风险的强度。拟议 研究是重要的，因为它解释了经常被忽视的贡献者的基本矩阵， 社区层面的ASEP和ASEP健康差距，并提供明确的建议，高影响 在有需要的社区内减少ASEP的方法。本项目的创新之处在于系统的集成 模拟和社区为基础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，以提供不同的基于性能的解决方案， 翻译框架。强大的跨学科研究团队，具有独特但互补的 专业领域和研究人员与感兴趣的社区之间的密切合作伙伴关系， 共同努力，促进项目的成功，为社区健康做出有意义的贡献。 这项研究的结果提供了关于减少ASEP和ASEP的高效方法的关键信息 健康差距，以及制定一个明确的机制，为内部的系统变革制定战略蓝图， AI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.