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，特别是那些解决高危人群中ASEP健康差距的方案 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 与ASEP有关的一系列因素，特别是亲密伴侣暴力的作用，它形成了 与其他两个ASEP指标(酒精和药物使用和计划外指标)的共同关联 怀孕)。系统动力学方法是理解ASEP和这一点的有效策略 共同性嵌套在一个更广泛的系统中，包括人际、个人和制度因素。这 该方法特别有利于解决目前人工智能中与ASEP相关的健康差距 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 实施政策和计划的最佳系统领域，将在ASEP中产生最大的变化。这个 目前的提案使用基于社区的方法为人工智能女性在两个月内开发ASEP系统模型 社区：一座小地铁和一处邻近的保留地。这些模型允许研究人员-社区 合作发现用于ASEP干预的重要系统杠杆点(在 孕妇)和ASEP预防(重点关注IPV和酒精之间的循环关系 物质使用)。该项目的目标是构建和模拟系统动态模型，以表示 ASEP系统与我们高度协作的社区研究团队合作。我们会校准的 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和ASEP预测因素的目标领域，a)跨社区和实体推广 合法性，以及b)可能在特定社区内或对特定物质唯一有效。这项工作将 辅以个人层面的分析，可以估计高优先级杠杆点的强度 关于个人ASEP和ASEP风险。这项拟议的研究意义重大，因为它解释了经常被忽视的 维持ASEP和ASEP健康差距的社区水平的基本贡献者矩阵，以及 为在有需要的社区内减少ASEP的高影响方法提供明确的建议。这 该项目具有创新性，因为它集成了系统模拟和基于社区的方法来解决 这一问题的复杂性，以及描述性和预测性分析的整合，以提供不同的 以经验为基础的解决方案，在翻译框架内解决这些问题。强者 具有独特但互补的专业知识和密切合作的跨学科研究团队 研究人员和感兴趣的社区之间的伙伴关系是一种强大的协作 促进项目成功并对社区卫生作出有意义的贡献。这项研究的发现提供了 关于减少ASEP和ASEP健康差距的高效方法的关键信息，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.