Spatial Epidemiology Research Group
University of California, Irvine

Methods

Traditional methods for studying geographic patterns of diseases have relied on grouping people by census tract, zip code, state, or other arbitrary geographic boundaries that may not adequately capture underlying disease disparities. Moreover, disease risk factors are difficult to study using grouped data due to ecological fallacy. Instead, our research group uses modern statistical methods such as smoothing within generalized additive models to study associations between spatiotemporal locations, other individual-level risk factors, and health outcomes, avoiding arbitrary boundaries and the ecological fallacy. These methods are also useful for studying complex interactions among chemical and non-chemical stressors. We've developed free software to make these methods easier for others to use: MapGAM, a library for the R software environment.

Here are some key references for our methods:

Kelsall J, Diggle P. Spatial variation in risk of disease: a nonparametric binary regression approach. J Roy Stat Soc C-App 1998, 47:559-573.

Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D. Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data. Environmental Health 2005, 4:11.

Webster T, Vieira V, Weinberg J, Aschengrau A. Method for mapping population-based case-control studies using Generalized Additive Models. International Journal of Health Geographics 2006, 5:26.

Young RL, Weinberg J, Vieira V, Ozonoff A, Webster TF. A power comparison of generalized additive models and the spatial scan statistic in a case-control setting. International Journal of Health Geographics 2010, 9:37.

Current and Past Funding

R01MD009697
National Institute of Minority Health and Health Disparities (NIMHD)
Spatiotemporal Analysis of Disparities in Ovarian Cancer Treatment and Survival
Goal: The goal of the current proposal is to define the contribution of geographic location to racial and socioeconomic disparities in ovarian cancer treatment and survival. This research will contribute to our understanding of the relationship between distance to receiving care and ovarian cancer mortality and quality of care.

5P42 ES007381-23
National Institute of Environmental Health Sciences (NIEHS)
Assessing The Relation Of Chemical And Non-Chemical Stressors With Risk-Taking Behavior And Related Outcomes Among Adolescents Living Near The New Bedford Harbor Superfund Site. Project 2 of the Superfund Basic Research Program at Boston University (David Sherr, Project Director; Veronica Vieira, Co-investigator of Project 2).
Goal: The goals of Project 2 are: (1) to investigate the relation of prenatal exposure to chemical and non-chemical stressors with adolescents' risk-taking and related behaviors, and (2) to combine results from these epidemiologic analyses with contemporary exposure models to build health risk models for the community living near the New Bedford Harbor (NBH) Superfund Site.

P42 ES007381
National Institute of Environmental Health Sciences (NIEHS)
Detecting and Analyzing Patterns in Epidemiologic and Toxicologic Data. Project 2 of the Superfund Basic Research Program at Boston University (David Ozonoff, Project Director at Boston University; Verónica Vieira, Principal Investigator of Project 2)
Goal: Development and application of methods for spatial epidemiology. Application of these and related methods for analysis of chemical mixtures (analysis of chemical responses surfaces).

R01 ES019897
National Institute of Environmental Health Sciences (NIEHS)
Spatial and Temporal Modeling of PM2.5 and Infant Morbidity (Verónica Vieira, Principal Investigator; Scott Bartell, Co-Investigator)
Goal: Investigate the relationships between ambient PM2.5 concentrations and infant morbidity in a population-based cohort using novel spatial and temporal modeling methods including remote sensing and generalized additive models.

R21 ES023120
National Institute of Environmental Health Sciences (NIEHS)
Bayesian integration of biomarkers and spatial exposure data (Scott Bartell, Principal Investigator; Verónica Vieira; Co-Investigator)
Goal: Determine the sensitivity of previously reported C8 Health Project epidemiologic associations between PFCs and pregnancy-induced hypertension/preeclampsia to spatiotemporal exposure uncertainty, and to reanalyze those data using new Bayesian models that combine spatiotemporal exposure estimates with exposure biomarkers.

IIR13264020
Komen Foundation
Environmental exposures, early proliferative changes and breast cancer risk (Rulla Tamimi and Francine Laden, Principal Investigators at Harvard University; Verónica Vieira, Principal Investigator on UC Irvine subcontract)
Goal: Examine the spatial associations between breast cancer risk, environmental exposures, and contextual factors including community-level socioeconomic status and housing characteristics.


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