142nd APHA Annual Meeting and Exposition

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302376
Investigating connectivity of interventions for poor birth outcomes through network interpolation

142nd APHA Annual Meeting and Exposition (November 15 - November 19, 2014): http://www.apha.org/events-and-meetings/annual
Monday, November 17, 2014

Rajib Paul, PhD , Department of Statistics/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Kathleen Baker, PhD , Department of Geography/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Virginia Vanderveen, BS , Department of Geography/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Alberta Griffin-Stover, MA , Interdisciplinary Health Sciences PhD Program/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Amy B. Curtis, PhD, MPH , Interdisciplinary Health Sciences PhD Program/Health Data Research Analysis and Mapping (HDReAM) Center, Western Michigan University, Kalamazoo, MI
Common spatial statistical methods that quantify patterns in individual outcomes often rely on point or kernel density measures, while patients needing and accessing resources depend almost entirely upon infrastructure that is not considered by the analysis.   We proposed and are developing network interpolation methods using ArcGIS and R software that examine the connectivity of address matched individuals with respect to community structure. As a pilot study for these methods in targeting local public health interventions, poor birth outcomes from vital statistics were examined at the county scale for Kalamazoo and Calhoun in Michigan.  Records of poor birth outcomes were initially examined as densities within areas of overall high births, via point density and kernel density algorithms, and as aggregations by census units, controlling for demographic factors.  When compared with census demographic data and low income housing locations, these results were used by community leaders who were assessing locations for maternal and child health interventions. Preliminary analyses found high rate clusters, some of which crossed census tract lines. In addition to those established geospatial analysis methods, we further examined community neighborhood connectivity and network linkages to standard health services via network interpolation algorithms. We found that because counties have very different infrastructure patterns standard density measures can produce misleading results when connectivity, and therefore access, is not consistent directionally across urban or urban-suburban-rural interface. Targeted intervention could benefit when the unique connectivity and neighborhood structure that is specific to each county is considered during analysis of individual level data.

Learning Areas:

Biostatistics, economics
Planning of health education strategies, interventions, and programs
Public health or related research

Learning Objectives:
Compare use of regional (e.g. census tracts) versus network interpolation methods to identify clusters of poor birth outcomes. Explain how geographical and statistical techniques using ArcGIS and R were used to determine targeted sites for local public health interventions in counties with high rates of infant morbidity and mortality.

Keyword(s): Network Analysis, Geographic Information Systems (GIS)

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have a PhD in statistics and have expertise in bayesian and spatial statistics, with applications to health and climate. I also serve as the statistical lead on the research included in this abstract.
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.