1. Mapping Access: Evaluating Access to Emergency Care Using Geospatial Analysis & Population Characteristics
Erin Simon DO, FACEP
Emergency Medicine Research Director Cleveland Clinic Akron General
Associate Professor Northeast Ohio Medical University

2. Disclosures
Physician advisory board for Tandem Hospital Partners

3. Introduction
Clinical emergencies including acute ischemic stroke, trauma and acute myocardial infarction require time-sensitive interventions, often initiated in Emergency Departments (ED).1
Increased time or distance to clinical care adversely affect mortality rates, supporting the need for access to emergency services.2,3

4. Objective
Examine the distribution and geographical access to Ohio EDs using geospatial analysis.

5. Methods Map of ED locations in Ohio
Addresses of Ohio EDs (n=199) were obtained from publically accessible state and federal databases and confirmed by staff at each location.
These were plotted onto a road network ArcGIS analysis.
Psychiatric, veterans’ affairs and pediatric EDs were excluded.
We wanted to identify potential gaps in geographical access to emergency care. The ACS survey was over a 5 year period from

6. Methods
Census block group (CBG) population-level data was obtained from the American Community Survey
Population-weighted CBG centroids (n=9,230) were used to approximate patient location.
Estimated travel time were calculated for each CBG-centroid using the closest ED in Network Analyst.
Time classified as <10 minute drive, >10-30 min drive and >30min
Multinomial regression was used to examine the association between travel time and population characteristics. Variables that were significant at the univariate level were included in the multivariate model.
Times were chosen based on literature linking increased mortality to these cut-points.4
CBGs located in Lake Erie, an island, or with zero population were excluded (n=14).

7. Results
74% (n=6774) of CBGs had a <10 minute centroid-ED travel time.
The average driving time from CBG-centroid to the nearest ED was 8.3 minutes (median 6.2)
25% (n=2315) had a minute travel time, and 1.5% (n=141) had a >30 minute travel time.

8. Figure 3: Categorized Centroid-ED Time

9. Characteristic
10-30 vs. < 10 minutes
>30 vs. < 10 minutes
AOR
CI (95%)
Median age
0.946
0.967
Population density
0.999
0.998
Percent Hispanic
0.974
0.782
Percent Non-Hispanic, Black
0.968
0.911
At least a college degree
0.975
0.925
Percent owner-occupied homes
1.02
1.027
Income:Poverty Ratio <1.0
0.993
1.026
Unemployment rate (%)
0.987
0.945
Vacant Houses (%)
1.007
1.064
Household vehicle access (%)
1.019
0.985

10. Table 2: Adjusted odds ratios of significant characteristics
10-30 vs. < 10 minutes
>30 vs. < 10 minutes
AOR
CI (95%)
Median age
0.946
0.967
Population density
0.999
0.998
Percent Hispanic
0.974
0.782
Percent Non-Hispanic, Black
0.968
0.911
At least a college degree
0.975
0.925
Percent owner-occupied homes
1.02
1.027
Income:Poverty Ratio <1.0
0.993
1.026
Unemployment rate (%)
0.987
0.945
Vacant Houses (%)
1.007
1.064
Household vehicle access (%)
1.019
0.985
CBGs with a higher proportion of Hispanics and Non-Hispanic Blacks were less likely to have an increased travel time to the closest ED.
CBGs with higher rates of college educated residents were less likely to have an increased travel time to the closest ED.
CBGs with higher income to poverty ratios had higher odds of being more than 30 minutes away from the closest ED.
CBGs with higher rates of vacant housing had a higher odds of being more than 30 minutes away from the closest ED.
Need to interpret results for audience. RYAN to help with this
Ten characteristics showed significant Adjusted Odds Ratios with one or both analysis categories. (Table 2)
The odds of CBGs with increased low-income populations and vacant housing had an increased odds of being >30 minutes from the closest ED by 2.6% and 6.4%, respectively.
Sex, insurance status, percent non-Hispanic (other), households classified as group quarters, HS diploma/GED/some college/associate’s degree did not have a statistically significant relationship with centroid-ED travel time.
Increase in a CBG’s median age, population density, percent Hispanic, percent non-Hispanic Black, percent with a college degree, and percent owner-occupied houses did not have an increased odds of having an increased drive time to an ED.
As percent of a CBG’s population fitting these characteristics increased, odds of being farther away decreased.

11. Discussion
The majority of Ohio CBG centroids have a <10 minute travel time to an ED, and there appears to be minimal gaps in access among the population characteristics.

12. Discussion
Use of GIS and fine-scale geographic units such as CBGs is an important methodology in evaluation of access to care and characteristics of patients impacted by new or closing EDs.
While Ohio’s ED access appears to be generally robust, more careful analysis into facilities should be conducted.

13. Discussion
Because a drive time over 30 minutes correlates with adverse patient outcomes, consideration of these CBGs when evaluating ED access is warranted.
In areas with reduced access to an ED (>30 min) alternative approaches to care and efficiency of EMS systems is crucial.
Discuss on slide before
Ryan to add text for college degree and race

14. References
ACCF/AHA Guidelines; 2013, 2014,
Crandall M., Sharp D, Wei X, et al. Effects of Closure of an urban level I trauma centre on adjacent hospitals and local injury mortality: a retrospective, observational study. BMJ Open. 2016;6e doi: /bmjopen
Nicholl J, West J, Goodacre S, Turner J. The relationship between distance to hospital and patient mortality in emergencies: an observational study. Emerg Med J. 2007;24(9): doi: /emj
Shen YC, Hsia R. Does Decreased Access to Emergency Departments Affect Patient Outcomes? Analysis of Acute Myocardial Infarction Population Health Services Research

15. Thank You Co-authors: Kate Joyce BS Thomas Veldman MS
Michelle Beeson BS
Ryan Burke MS
Nick Jouriles MD

16. Questions
Erin Simon