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:
- 金额:$ 16.25万
- 依托单位:
- 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:
- 关键词:Air PollutantsAmericanAsthmaBayesian MethodBehaviorBehavioralBiologicalCause of DeathChemical ExposureCheyenneChronicChronic DiseaseCommunitiesCommunity HealthComplexCrowsDataData SetDatabasesDevelopmentDiabetes MellitusDietDimensionsDiseaseDisparityEnsureEnvironmental ExposureEnvironmental HazardsEnvironmental HealthEnvironmental PollutantsExposure disparityExposure toFrequenciesFutureGeneral PopulationGenetic Predisposition to DiseaseHealthHealth Disparities ResearchHypertensionIncidenceIndividualInfectious AgentInfrastructureInterventionIntervention StudiesKidney DiseasesKnowledgeLife StyleMachine LearningMalignant NeoplasmsMalignant neoplasm of liverManuscriptsMediationMetal exposureMetalsMiningModelingNational Cancer InstituteNational Health and Nutrition Examination SurveyNative AmericansNavajoNot Hispanic or LatinoNutrientObesityOutcomePathway AnalysisPathway interactionsPhysical activityPlayPolicy MakingPopulationPreparationPrevalencePsychosocial StressPublic HealthReduce health disparitiesRenal carcinomaReportingResearchResearch Project GrantsResource AllocationResourcesRiskRisk FactorsRoleSioux IndiansSocioeconomic StatusSolid Waste DisposalsSourceStatistical Data InterpretationStatistical MethodsStatistical ModelsStructureTechniquesUraniumWarWaterbehavioral economicscancer typecausal modelcold temperaturecost effectivedata managementdata qualitydata reusedietarydisparity reductionexposure pathwayfrontierhealth determinantshealth disparityhealth equityhigh riskimprovedinnovationlarge datasetslifestyle factorsmalignant stomach neoplasmmembermodifiable risknovelresponsesocialsocial determinantssociodemographicssocioeconomicsstatisticsstressortransmission processtribal communitytribal landswasting
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
项目成果
期刊论文数量(0)
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{{ truncateString('Li Luo', 18)}}的其他基金
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:
10372187 - 财政年份:2015
- 资助金额:
$ 16.25万 - 项目类别:
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:
10062404 - 财政年份:2015
- 资助金额:
$ 16.25万 - 项目类别:
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:
10218051 - 财政年份:2015
- 资助金额:
$ 16.25万 - 项目类别:
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