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(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=R21ES033343&format=word)
| Principal Investigator: Hajat, Anjum | |
|---|---|
| Institute Receiving Award | University Of Washington |
| Location | Seattle, WA |
| Grant Number | R21ES033343 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 03 Aug 2021 to 30 Nov 2023 |
| DESCRIPTION (provided by applicant): | Abstract Contextual level factors like neighborhood socioeconomic status (NSES) and racial residential segregation (RRS) are potential confounders that may bias air pollution epidemiology studies. Few studies have systematically examined the magnitude of confounding by these important contextual factors. To our knowledge no studies have included RRS as a confounder in air pollution-cardiovascular disease (CVD) studies. We propose to conduct a systematic assessment of the confounding and synergist roles of SES (both at the neighborhood and individual levels) and RRS in a unique and robust data source made-up of eight well- characterized chronic disease cohort studies. Extensive covariate data, consistent and lengthy follow-up of participants, high quality air pollution exposures and standardized collection of CVD outcomes makes this an ideal data source within which to conduct this important work. An existing project is underway to harmonize data across these cohorts. Our aims are to: 1) assess the association between different indicators of SES and RRS and long-term ambient air pollution exposures cross-sectionally and over time, by developing state-of-the- art measures of SES and RRS 2) conduct quantitative bias analysis to evaluate the magnitude of confounding by SES and RRS in the association between long-term air pollution and CVD mortality and events and 3) examine the joint effects of long-term air pollution and SES and RRS on cardiovascular events and mortality. We will create a time-varying NSES index from principal components analysis for the time period 1990 - 2015 and measures of evenness (dissimilarity index) and isolation (isolation index) to evaluate RRS. We will use spatial regression approaches for aim 1, probabilistic quantitative bias analysis for aim 2 and survival analysis for aim 3. The public health impact of this proposal is three-fold. First, evaluating the magnitude of confounding by SES in air pollution epidemiology studies will strengthen the evidence used for national air pollution standards. Second, exploration of RRS could establish a new contextual confounder for air pollution epidemiology. And third, developing a rigorous and strongly theoretically grounded framework for assessing multiple spatially varying factors will help us better understand longstanding environmental health disparities and may help design future interventions and policy. |
| Science Code(s)/Area of Science(s) |
Primary: 59 - Social Environment/Built Environment Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | No publications associated with this grant |
| Program Officer | Lindsey Martin |