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

• 批准号: 10589163
• 负责人: Li Luo
• 金额: $ 16.25万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 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/10589163
• 关键词: Air Pollutants; American; Asthma; Bayesian Method; Behavior; Behavioral; Biological; Cause of Death; Chemical Exposure; Cheyenne; Chronic; Chronic Disease; Communities; Community Health; Complex; Crows; Data; Data Set; Databases; Development; Diabetes Mellitus; Diet; Dimensions; Disease; Disparity; Ensure; Environmental Exposure; Environmental Hazards; Environmental Health; Environmental Pollutants; Exposure disparity; Exposure to; Frequencies; Future; General Population; Genetic Predisposition to Disease; Health; Health Disparities Research; 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; Policy Making; Population; Preparation; Prevalence; Psychosocial Stress; Public Health; Reduce health disparities; Renal carcinoma; Reporting; Research; Research Project Grants; Resource Allocation; Resources; Risk; Risk Factors; Role; Sioux Indians; Socioeconomic Status; Solid Waste Disposals; Source; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Techniques; Uranium; War; Water; behavioral economics; cancer type; causal model; cold temperature; cost effective; data management; data quality; data reuse; dietary; disparity reduction; exposure pathway; frontier; health determinants; 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; transmission process; tribal community; tribal lands; wasting

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.

摘要 RP3分层统计建模和因果推断方法以阐明暴露 健康差异的潜在途径 美洲原住民人口和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因素之间的复杂相互作用。 越来越多的人认识到环境污染物在解释健康方面起着重要作用 由于它们与其他因素的组合或相互作用而造成的差异。印第安人的近在咫尺 从社区到废弃的铀矿(AUM)一直是一个特别令人关注的健康问题。这些慢性疾病 暴露于与AUM废物有关的金属混合物会增加罹患慢性及致命疾病的风险。 包括高血压、糖尿病、肾脏疾病和美洲原住民的癌症类型 与美国人口相比。这个项目的假设是三个美洲土著部落 这项研究中包括的社区(纳瓦霍民族、乌鸦和夏延河苏人)面临着极大的风险 暴露于环境危害(与矿山废物有关的金属混合物暴露、不受管制的水 资源、非法倾倒等)。这些危险的暴露以及社会经济地位， 心理社会压力、行为/生活方式因素影响产生健康的多种生物学途径 美洲原住民社区中的差异。包括膳食营养素在内的一组复杂的暴露变量， 个人和社区的体力活动、感染源、空气污染物和金属暴露 水平被认为是造成健康差距的因素，然而，他们对 潜在的原因还没有得到很好的研究。该项目的目标是采用数据驱动和 建模方法，以了解不同环境、行为和 原住民和美国国民之间健康差距的社会经济决定因素 人口。我们将使用创新的建模方法，如分解分析和结构分析 评估个人和社区层面风险因素对健康影响的因果模型 差距。在目标1中，我们将收集数据并总结主要慢性和致命疾病的频率分布 美洲原住民社区的疾病。在目标2中，我们将使用新的分层建模 估计不同风险因素在个人和社区层面的相对贡献的方法 健康水平的差距。在目标3中，我们将实施前沿因果路径分析，以说明 解释健康差距的中间机制。目标4是检查复数相关结构 在多维暴露、中间生物学反应和使用前沿的健康终点之间 统计学方法。我们预计这个项目将确定主要的贡献因素，以解释 健康差距的比例，此外还阐明了影响的中间原因路径 传播到健康差距终结点。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，以最大限度地缩小差距和改善社区健康。