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Text
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URL Address
<a href="http://doi.org/10.5811/westjem.2018.9.38957" target="_blank" rel="noreferrer noopener">http://doi.org/10.5811/westjem.2018.9.38957</a>
Pages
1043–1048
Issue
6
Volume
19
Dublin Core
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Title
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Use of Fine-scale Geospatial Units and Population Data to Evaluate Access to Emergency Care.
Publisher
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The western journal of emergency medicine
Date
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2018
2018-11
Subject
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Adult; Female; Humans; *Censuses; *Travel; Geographic Information Systems/*instrumentation; Health Services Accessibility/*statistics & numerical data; Male; Middle Aged; Ohio; Regression Analysis; Socioeconomic Factors; Spatial Analysis; Time Factors; Odds Ratio; Confidence Intervals; Human; Surveys; Blacks; Hispanics; Regression; Emergency Service; Hospital/*statistics & numerical data; Census; Geographic Information Systems; Housing; Population Density; Emergency Care – Utilization – Ohio; Emergency Service – Utilization – Ohio
Creator
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Joyce Katherine M; Burke Ryan C; Veldman Thomas J; Beeson Michelle M; Simon Erin L
Description
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Introduction: Time to facility is a crucial element in emergency medicine (EM). Fine-scale geospatial units such as census block groups (CBG) and publicly available population datasets offer a low-cost and accurate approach to modeling geographic access to and utilization of emergency departments (ED). These methods are relevant to the emergency physician in evaluating patient utilization patterns, emergency medical services protocols, and opportunities for improved patient outcomes and cost utilization. We describe the practical application of geographic information system (GIS) and fine-scale analysis for EM using Ohio ED access as a case study. Methods: Ohio ED locations (n=198), CBGs (n=9,238) and 2015 United States Census five-year American Community Survey (ACS) socioeconomic data were collected July-August 2016. We estimated drive time and distance between population-weighted CBGs and nearest ED using ArcGIS and 2010 CBG shapefiles. We examined drive times vs. ACS characteristics using multinomial regression and mapping. Results: We categorized CBGs by centroid-ED travel time in minutes: \textless10 (73.4%; n=6,774), 10-30 (25.1%; n=2,315), and \textgreater30 (1.5%; n=141). CBGs with increased median age, Hispanic and non-Hispanic Black population, and college graduation rates had significantly decreased travel time. CBGs with increased low-income populations (adjusted odds ratio [AOR] [1.03], 95% confidence interval [CI] [1.01-1.04]) and vacant housing (AOR [1.06], 95% CI [1.05-1.08]) had increased odds of \textgreater30 minute travel time. Conclusion: Use of fine-scale geographic analysis and population data can be used to evaluate geographic accessibility and utilization of EDs. Methods described offer guidance to approaching questions of geographic accessibility and have numerous ED and pre-hospital applications.
Identifier
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<a href="http://doi.org/10.5811/westjem.2018.9.38957" target="_blank" rel="noreferrer noopener">10.5811/westjem.2018.9.38957</a>
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Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
*Censuses
*Travel
2018
Adult
Beeson Michelle M
Blacks
Burke Ryan C
Census
Confidence Intervals
Department of Emergency Medicine
Emergency Care – Utilization – Ohio
Emergency Service
Emergency Service – Utilization – Ohio
Female
Geographic Information Systems
Geographic Information Systems/*instrumentation
Health Services Accessibility/*statistics & numerical data
Hispanics
Hospital/*statistics & numerical data
Housing
Human
Humans
Joyce Katherine M
Male
Middle Aged
NEOMED College of Medicine
Odds Ratio
Ohio
Population Density
Regression
Regression Analysis
Simon Erin L
Socioeconomic Factors
Spatial Analysis
Surveys
The western journal of emergency medicine
Time Factors
Veldman Thomas J