Hierarchical statistical modeling and causal inference approaches to elucidate exposure pathways underlying health disparities

分层统计模型和因果推理方法阐明健康差异背后的暴露途径

基本信息

批准号：
10062404
负责人：
Li Luo
金额：
$ 15.96万
依托单位：
UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10062404
关键词：
Air Pollutants American Asthma Bayesian Method Behavior Behavioral Biological Cause of Death Chemical Exposure Chronic Chronic Disease Communities Community Health Complex Crows Data Data Set Databases Development Diabetes Mellitus Diet Disease Ensure Environmental Exposure Environmental Hazards Environmental Health Environmental Pollution Exposure to Frequencies Future General Population Genetic Predisposition to Disease Geology Health Hypertension Incidence Individual Infectious Agent Infrastructure Intervention Intervention Studies Kidney Diseases Knowledge Life Style Machine Learning Malignant Neoplasms Malignant neoplasm of liver Manuscripts Mediation Metal exposure Metals Mining Modeling National Cancer Institute National Health and Nutrition Examination Survey Native Americans Navajo Not Hispanic or Latino Nutrient Obesity Outcome Pathway Analysis Pathway interactions Physical activity Play Population Preparation Prevalence Psychosocial Stress Public Health Renal carcinoma Reporting Research Research Project Grants Resource Allocation Resources Risk Risk Factors Rivers Role Sioux Indians Socioeconomic Status Solid Waste Disposals Source Statistical Data Interpretation Statistical Methods Statistical Models Structure Techniques Uranium War Water cancer type causal model cold temperature cost effective data management data quality data reuse disparity reduction exposure pathway frontier health disparity health equity high risk improved innovation large datasets lifestyle factors malignant stomach neoplasm member modifiable risk novel response social social determinants sociodemographics socioeconomics statistics stressor tribal community tribal lands wasting

项目摘要

Summary RP3 Hierarchical statistical modeling and causal inference approaches to elucidate exposure pathways underlying health disparities The health disparity between the Native American population and the US general population arises from the complex interplay between multiple socio-demographic, behavior, lifestyle and genetic susceptibility factors. Environmental contaminants are increasingly acknowledged to play an important part in explaining health disparity through their combined or interaction effects with other factors. Proximities of Native American communities to abandoned uranium mines (AUM) have been of particular health concern. These chronic exposures to AUM waste related metal mixtures pose higher risk for developing chronic and fatal diseases including hypertension, diabetes, kidney disease, and types of cancer in Native American populations compared to the US population. The hypothesis of this project is that the three Native American tribal communities included in this study (Navajo Nation, Crow, and Cheyenne River Sioux) encounter great risk of exposures to environmental hazards (mine waste related metal mixture exposures, unregulated water resources, and illegal dumping, etc.). These hazardous exposures along with socioeconomic status, psychosocial stress, behavior/lifestyle factors influence multiple biological pathways to produce health disparities in Native American communities. The complex set of exposure variables including dietary nutrients, physical activity, infectious agents, air pollutants and metal exposures at both the individual and community levels are acknowledged as contributors to health disparities, however, their relative contributions of the potential causal factors have not been well studied. The objective of this project is to employ data-driven and modeling approaches to understand the relative contribution of different environmental, behavior, and socioeconomic determinants of the health disparities between the native population and the US national population. We will use innovative modeling approaches such as decomposition analyses and structural causal models to estimate the effects of risk factors at the individual and community level on the health disparities. In Aim 1, we will collect data and summarize the frequency distributions for major chronic and fatal diseases in the Native American communities. In Aim 2, we will employ novel hierarchical modeling approaches to estimate the relative contribution of different risk factors at the individual level and community level to the health disparities. In Aim 3, we will implement frontier causal pathway analyses to illustrate the intermediate mechanisms explaining the health disparity. Aim 4 is to examine the complex correlation structure among multi-dimensional exposures, intermediate biological responses, and health endpoints using frontier statistical approaches. We expect this project will identify major contributing factors that explain a large proportion of the health disparity, and in addition elucidate the intermediate causal pathway that the effects are transmitted to the health disparity endpoints. These findings have the potential to inform policymaking on the cost-effective resource allocation to maximally reduce disparity and improve community health.

概括 RP3 阐明暴露情况的分层统计模型和因果推理方法健康差异的潜在途径美洲原住民人口与美国普通人口之间的健康差距源于多种社会人口、行为、生活方式和遗传易感性因素之间复杂的相互作用。人们越来越认识到环境污染物在解释健康方面发挥着重要作用差异是通过它们与其他因素的组合或相互作用而产生的。接近美洲原住民废弃铀矿 (AUM) 社区的健康尤其令人担忧。这些慢性接触 AUM 废物相关金属混合物会增加患慢性和致命疾病的风险包括美洲原住民人群中的高血压、糖尿病、肾病和癌症类型与美国人口相比。该项目的假设是三个美洲原住民部落本研究中包含的社区（纳瓦霍族、克罗族和夏延河苏族）面临着巨大的风险暴露于环境危害（与矿山废物相关的金属混合物暴露、不受管制的水资源、非法倾倒等）。这些危险暴露以及社会经济地位，社会心理压力、行为/生活方式因素影响产生健康的多种生物途径美洲原住民社区的差异。一系列复杂的暴露变量，包括膳食营养素、个人和社区的体力活动、传染源、空气污染物和金属暴露公认的水平是造成健康差异的因素，但是，它们对健康差异的相对贡献潜在的致病因素尚未得到充分研究。该项目的目标是采用数据驱动和建模方法来了解不同环境、行为和行为的相对贡献本地人口与美国国民之间健康差异的社会经济决定因素人口。我们将使用创新的建模方法，例如分解分析和结构分析因果模型估计个人和社区层面的风险因素对健康的影响差异。在目标 1 中，我们将收集数据并总结主要慢性病和致命病的频率分布美洲原住民社区的疾病。在目标 2 中，我们将采用新颖的分层建模估计不同风险因素在个人和社区层面的相对贡献的方法水平的健康差异。在目标 3 中，我们将实施前沿因果路径分析来说明解释健康差异的中间机制。目标 4 是检查复杂的相关结构使用前沿在多维暴露、中间生物反应和健康终点之间进行评估统计方法。我们预计该项目将确定解释重大问题的主要影响因素健康差异的比例，此外还阐明了影响的中间因果路径传输到健康差异端点。这些发现有可能为政策制定提供信息具有成本效益的资源分配，以最大限度地减少差距并改善社区健康。