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减少计划，特别是那些解决高危人群中ASEP健康差异的计划 美国印第安人（American Indian）。这些计划并没有考虑到广泛的 与ASEP有关的一系列因素，特别是亲密伴侣暴力的作用， 与其他两个ASEP指标（酒精和物质使用以及计划外 怀孕）。系统动力学方法是理解ASEP和这一过程的有效策略。 流行病是嵌套在一个更广泛的系统中的人际，个人内部和制度因素。这 这种方法对于解决人工智能中目前与ASEP相关的健康差异特别有益 社区.基于社区的系统动态模型使从业者和决策者能够确定 最好的系统领域，以执行将产生最大变化的政策和计划。的 目前的提案使用基于社区的方法，在两个月内为人工智能妇女开发ASEP系统模型。 社区：一个小地铁和一个邻近的保留地。这些模型允许研究人员社区 伙伴关系，以发现重要的系统杠杆点，为ASEP干预（减少ASEP内 孕妇）和ASEP预防（重点是IPV和酒精之间的周期关系， 物质使用）。这个项目的目标是建立和模拟系统动态模型， ASEP系统与我们高度合作的社区研究团队合作。我们将校准 并利用各种社区数据源验证这些模型，然后区分最有效的 减少ASEP的目标领域和ASEP预测因子，a）在社区和物质中推广 合法性，以及B）可能在特定社区或特定物质中唯一有效。这项工作将 通过个人层面的分析来补充，这些分析可以估计高优先级杠杆点的强度 个体ASEP和ASEP风险。拟议的研究是重要的，因为它占了经常被忽视的 维持ASEP和ASEP健康差异的社区水平的贡献者的基本矩阵，以及 提供了明确的建议，高影响力的方法，以减少有需要的社区内的ASEP。这 项目是创新的，由于系统模拟和社区为基础的方法来解决集成 复杂性，这一问题，并整合描述性和预测性分析，以提供不同的 在翻译框架内解决这些问题的基于语义的解决方案。坚强 跨学科的研究人员团队具有独特的，但互补的专业知识和密切的工作领域 研究人员和感兴趣的社区之间的伙伴关系是一个强大的合作， 促进项目成功并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健康差距的高效方法的重要信息，以及 为人工智能社区内的系统性变革制定战略蓝图的明确机制。

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.