Using a Novel Machine Learning Based Data Integration Procedure to Understand the Cherokee Nation Community Population Health

项目总结 先前的研究表明，美国印第安人(AI)的健康和行为流行率存在差异 人口和其他种族或民族群体。大多数健康调查在研究认可机构方面都有一定的局限性。 人工智能种群的小样本量。切诺基国家(CN)健康调查收集的数据提供 这是为认可机构进行研究的绝佳机会，因为样本量很大，而调查包括 广泛的信息。然而，CN健康调查只关注使用CN诊所的CN公民，以及 因此，样本可能会受到采样、覆盖和无响应错误的影响，如果没有进一步的适当 调整。这些困难极大地阻碍了在健康和行为研究中对人工智能人群的分析。 我们的一般假设是，通过组合来自非概率和概率的信息来进行数据集成 样本可以减少原始非概率样本中的抽样、覆盖和无响应错误。这个 该项目的目标是为人工智能种群分析开发一种准确和健壮的数据集成方法 专门为健康和行为研究量身定做，并向当地利益攸关方传播该方法。 近年来，我们进行了以下几个方面的研究：1)利用定标和参数建模方法进行数据集成研究；2) 研究了调查抽样等领域中的机器学习和倾向得分建模方法； 3)组建了一支经验丰富的多学科专家团队。 在这个项目中，我们建议利用我们的专业知识，实现以下具体目标： 目标1.使用机器学习开发和评估我们提出的新数据集成方法 用真实数据进行倾向性得分建模。 我们将使用实际数据来验证所提出的方法的准确性和对各种数据的稳健性 类型。还将通过与现有数据集成方法的结果进行比较来评估性能 例如校准和参数建模方法。这项计划中的研究利用了独特的数据 获取并扩大印度卫生服务(IHS)资助的研究的影响。我们期待着这种新颖的整合 方法将通过促进基于非概率样本的分析来垂直推进该领域，这可以 提供关于人工智能基于人群的健康和行为研究的深入理解。 AIM2.开发县级小区域估计(SAE)模型并研究SAE的关联性 使用与县级地理和健康相关的环境信息进行估计。 我们将比较基于SAE的估计与在目标1中获得的直接估计。多水平模型将是 旨在检查与健康相关的结果与县级地理和 环境因素。 目标3.向地方和国家利益攸关方传播我们的研究产品。 在CN IRB批准后，我们将发布我们建议的方法、数据文件的使用和计算 通过研讨会、培训、 会议，和会议。

PROJECT SUMMARY Previous studies show discrepancies of health and behavior prevalence between American Indian (AI) populations and other racial or ethnic groups. Most health surveys have certain limitations for studying AIs due to the small sample sizes for AI populations. Data collected by Cherokee Nation (CN) Health Survey provides an excellent opportunity to conduct research for AIs since the sample size is large and the survey contains extensive information. However, the CN Health Survey focused only on CN citizens who used CN clinics, and thus the sample may suffer from sampling, coverage, and nonresponse errors without further proper adjustments. Such difficulties greatly hamper the analysis of AI populations in health and behavior research. Our general hypothesis is that data integration by combining information from non-probability and probability samples can reduce sampling, coverage, and nonresponse errors in the original non-probability sample. The Goal of this project is to develop an accurate and robust data integration methodology for AI population analysis specifically tailored to health and behavior research and disseminate the methodology to local stakeholders. In recent years, we have: 1) studied data integration using calibration and parametric modeling approaches; 2) investigated machine learning and propensity score modeling methods in survey sampling and other fields; and 3) assembled an experienced multi-disciplinary team of experts. In this project, we propose to capitalize on our expertise and fulfill the following Specific Aims: Aim 1. Develop and evaluate our proposed novel data integration approaches using machine learning and propensity score modeling by real data. We will use real data to validate the proposed methods in terms of accuracy and robustness to the various data types. The performance will also be assessed by comparing with results from existing data integration methods such as calibration and parametric modeling approaches. The planned study takes advantage of a unique data source and expands the impact of Indian Health Service (IHS)-funded research. We expect this novel integration method will vertically advance the field by facilitating the analysis based on non-probability samples, which can provide in-depth understanding regarding AI population-based health and behavior studies. Aim2. Develop county-level small area estimation (SAE) models and examine the association of SAE estimates with county-level geographic and health related environmental information. We will compare the estimates based on SAE with direct estimates obtained in Aim 1. Multi-level model will be built to examine the association between health-related outcomes with county-level geographic and environmental factors. Aim 3. Disseminate our research products to local and national stakeholders. After CN IRB approval, we will disseminate our proposed methods, usage of our data files, and Computational Codes (e.g. SAS macros and/or R packages) to local and national stakeholders through workshops, trainings, conferences, and meetings.