Calculating Drive Time Between Injury & Hospital in

1. Calculating Drive Time Between Injury & Hospital in Spinal Cord Injury Research Using Online Navigation Tools Jayson H. Shurgold

2. Background ‘A world without paralysis after spinal cord injury’ • Access to Care and Timing (ACT) • Define the continuum of care experienced by individuals that suffer a traumatic spinal cord injury • Data is collected from 15 Canadian care centres that specialize in the acute care, treatment, and/or rehabilitation of patients with spinal cord injuries • The outcome is a mathematical model designed to predict the effect of specific scenarios, and the subsequent affect on patient flow and overall outcome Injury Acute Care Rehab Community

3. Background • One hypotheses is that spinal cord injuries sustained closer to a specialized treatment centre results in more timely access to specialized care and overall better outcomes for the patient. • How do we define proximity? • From our data: • Time of injury • Time of admission to specialized hospital • First 3 digits of the postal code where the • injury occurred • Address of closest specialized hospital

4. Background Question 1: Can we estimate proximity using T admission - T injury Injury at 2:00 PM Admission at 6:00 PM Proximity = 4 hours

5. Background Question 1: Can we estimate proximity using T admission - T injury Injury at 2:00 PM Admission at 6:00 PM Proximity = 4 hours Answer: Sometimes, but no. In reality, ~34% of the observations are considered ‘indirect’. This means patients are admitted to a non-specialized centre prior to admission to a specialized centre.

6. Background Question 2: Can we estimate proximity using built in functions? - Geodist(latitude-1, longitude-1, latitude-2, longitude-2) - Haverstine Formula - SASHELP.zipcode

7. Background Question 2: Can we estimate proximity using built in functions? - Geodist(latitude-1, longitude-1, latitude-2, longitude-2) - Haverstine Formula - SASHELP.zipcode Answer: Not ideal. If you happen to have the latitude and longitude of the incident and target in degrees, you can use the GeoDist function to calculate straight line distance. Ignores roads and natural barriers

8. Background Question 3: Can I just Google this?

9. Background Question 3: Can I just Google this? Answer: You can, but it’s not recommended. With large databases, doing this by hand takes a long time and is prone to error.

10. Background Question 4: Is there any hope?

11. SAS Nav Systems

12. Ash Roy & Yingbo Na Canadian Institute for Health Information Mike Zdeb University of Albany School of Public Health

13. Introduction to APIs • What is an API? • Application Programming Interface • ‘In most procedural languages, an API specifies a set of functions or routines that accomplish a specific task or are allowed to interact with a specific software component’ Graphic User Interface Standard API output (XML)

14. Introduction to APIs Searching non-specific addresses: Asking most navigation software to calculate directions between two FSAs or Postal Codes results in directions from ‘centroid to centroid’. For example, the calculated distance between ‘V6B’ and ‘V6B 6P6’ is 0.4Km, centroid to centroid. Forward Sortation Area (FSA): V6B Full Postal Code: V6B 6P6

15. Introduction to the dataset Dataset: ACT_Raw Data Dictionary

16. Introduction to the dataset Dataset: ACT_Raw Concatenate the source and target location information into a single variable: This is the start of determining the total number of unique queries.

17. Data Manipulations Dataset: PC (n=10) Dataset: ACT_Dataset (n=12) Remove duplicate postal code combinations: There is no need to look up the same postal code twice. This will save time. This step reduced the number of actual queries from 2101 to 994

18. API Macro Dataset: PC (n=10) Dataset: Dist_Time (n=10) Distance_Val (Kilometres) Time_Val (Seconds)

19. API Macro Dataset: PC (n=10) Dataset: Dist_Time (n=10) Distance_Val (Kilometres) Time_Val (Seconds) Errors output as -2

20. API Errors Dataset: Dist_Time (n=10) Dataset: Dist_Time (n=10) Actual manual data entry for the ACT project is 18 / 994.

21. Final Data Dataset: Dist_Time (n=10) Dataset: ACT_DriveTime (n=12) Now we have an accurate, timely, and reproducible method to define proximity based on two geographical locations*. *until the technology changes

22. Final Analysis What if… All patients were transported directly to a specialized health care centre, and how does this compare to the observed mean time to admission? Assumptions 12 minute average response time 10 minute load delay Restricted by speed limit No traffic delay

23. Final Analysis What if… All patients were transported directly to a specialized health care centre, and how does this compare to the observed mean time to admission? Dataset: ACT_Simple Dataset: ACT_Simple_Analysis

24. Thank you for listening • Acknowledgements: • Mike Zdeb • University of Albany School of Public Health • http://www.sascommunity.org/wiki/Driving_Distances_and_Drive_Times_using_SAS_and_Google_Maps • Ash Roy & Yingbo Na • Canadian Institute fore Health Information • http://support.sas.com/resources/papers/proceedings12/091-2012.pdf • Special Thanks: • Suzanne Humphreys • Rick Hansen Institute • Argelio Santos • Rick Hansen Institute