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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：
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
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/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

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.

总结