259777 New Visual and Geospatial Tools for the Analysis of Hazard and Health Outcome Data on the Maryland Environmental Public Health Tracking Network

Monday, October 29, 2012

Min Qi Wang, PhD, MS , Department of Behavioral and Community Health, University of Maryland School of Public Health, College Park, MD
John T. Braggio, PhD, MPH , EPHT/ Environmental Health and Food Protection/IDEHA, Maryland Department of Health and Mental Hygiene, Baltimore, MD
Jed L. Miller, MD, MPH , Maryland Department of the Environment, Baltimore, MD
Rashid Malik , Maryland Department of Health and Mental Hygiene, Baltimore, MD
Valerie Agwale , Maryland Department of Health and Mental Hygiene, Baltimore, MD
Clifford S. Mitchell, MS, MD, MPH , Environmental Health and Food Protection/IDEHA, Maryland Dept of Health and Mental Hygiene, Baltimore, MD
Background: As more complex environmental and health data become available online, a major challenge becomes providing simple tools to allow a wide variety of users to analyze the data for a variety of needs. The Maryland Environmental Public Health Tracking Network developed a series of tools to dynamically characterize and compare data that take advantage of geospatial analytic freeware.

Objective: This presentation will explore how these visual and geospatial tools can be used to display and evaluate environmental hazard and health outcome data.

Methods: We developed a combination of C# and server-based R-statistics to allow users to compute statistics including age-adjusted rates, 95% confidence intervals (CI), and various statistical tests including autocorrelations and general linear models for any user-defined areas.

Results: Users can now analyze environmental health data with thematic GIS mapping that visually identifies areas with higher disease prevalence; use geospatial tools to select, aggregate, and describe areas using age-adjusted rates and 95% CIs; and compare disease prevalence between geographic areas by using statistics from R modules. By using the buffering tool to select an area with a 10-mile radius from the centroid of the cluster's periphery, adjacent polygons can be selected, and then computed age-adjusted rates and 95% CIs for the two combined areas can be displayed, along with the inclusion of census measures for the two areas. The R statistical procedures (multilevel models, spatial significance using Gi*(d) statistic, etc.) can then compare the selected areas for a given statistical test.

Conclusions: These spatial tools enhance available Maryland EPHTN data analysis resources by allowing health officials to quickly and efficiently describe and compare the health characteristics of selected areas. While the analyses may be limited by geographic resolution of the underlying data, these analytical tools represent an important resource for public health.

Learning Areas:
Assessment of individual and community needs for health education
Biostatistics, economics
Communication and informatics
Environmental health sciences
Epidemiology

Learning Objectives:
Demonstrate geospatial and statistical information in map displays

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been the principal investigator or co-investigator of multiple federally funded grants
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

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