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

使用基于机器学习的新型数据集成程序来了解切罗基族社区人口健康状况

• 批准号: 10671754
• 负责人: Sixia Chen
• 金额: $ 7.95万
• 依托单位: CHEROKEE NATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10671754
• 关键词: Adult; Age; Agreement; American; American Indians; Area; Behavior; Behavioral; Behavioral Risk Factor Surveillance System; Calibration; Cherokee Indian; Clinic; Code; Communities; Community Health; Community Surveys; Complex; County; Cross-Sectional Studies; Data; Data Files; Data Set; Data Sources; Diabetes Mellitus; Disparity; Educational workshop; Environmental Risk Factor; Ethnic Population; Funding; Future; Geographic Factor; Geographic Information Systems; Geography; Goals; Health; Health Surveys; Health behavior; High Prevalence; Individual; Institutional Review Boards; Machine Learning; Methodology; Methods; Modeling; Native American Research Center for Health; Not Hispanic or Latino; Obesity; Oklahoma; Outcome; Performance; Population; Population Analysis; Prevalence; Probability Samples; Procedures; Public Health; Publications; Research; Research Personnel; Risk Factors; Sample Size; Sampling; Selection Bias; Shapes; Smoking; Source; Surveys; Testing; Tobacco; Tobacco use; Training; Tribes; United States Indian Health Service; Update; Work; Youth; behavioral study; cigarette smoking; data integration; data quality; data sharing; design; experience; improved; innovation; meetings; multidisciplinary; multilevel analysis; multiple data sources; novel; population based; population health; programs; racial population; symposium; therapy development; tool

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.

项目摘要 以往的研究表明，美国印第安人（AI）之间的健康和行为流行的差异， 人口和其他种族或族裔群体。大多数健康调查在研究人工智能方面都有一定的局限性， 人工智能群体的小样本量。切罗基族（CN）健康调查收集的数据提供了 是一个为认可机构进行研究的绝佳机会，因为调查的样本数目庞大，而调查内容包括 广泛的信息。然而，CN健康调查仅关注使用CN诊所的CN公民， 因此，样本可能会遭受抽样，覆盖和无响应错误，而没有进一步的适当 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群体的分析。 我们的一般假设是，数据整合通过结合信息从非概率和概率 样本可以减少原始非概率样本中的抽样、覆盖和无应答错误。的 该项目的目标是为人工智能人口分析开发一种准确和强大的数据集成方法 专门针对健康和行为研究，并向当地利益攸关方传播方法。 近年来，我们进行了以下研究：1）使用校准和参数建模方法进行数据集成; 2） 调查抽样和其他领域的机器学习和倾向评分建模方法;以及 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颖数据集成方法 以及通过真实的数据进行倾向评分建模。 我们将使用真实的数据来验证所提出的方法的准确性和鲁棒性的各种数据 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一个独特的数据， 来源和扩大印度卫生服务（IHS）资助的研究的影响。我们期待这种新颖的整合 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， 深入了解基于AI人群的健康和行为研究。 目标2。建立县级小面积估算模型，并研究小面积估算的关联性 估计与县级地理和健康相关的环境信息。 我们将比较基于SAE的估计值与目标1中获得的直接估计值。多层次模型将 旨在研究健康相关结果与县级地理和 环境因素 目标3.向当地和国家利益相关者传播我们的研究产品。 在CN IRB批准后，我们将传播我们提出的方法、我们数据文件的使用和计算 通过讲习班、培训， 各种会议。