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

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

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 CommunityInjuryAcute CareRehab

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 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 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. Background

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

Question 3: Can I just Google this? Background

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

Question 4: Is there any hope? Background

SAS Nav Systems

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

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 Introduction to APIs Graphic User Interface Standard API output (XML)

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. Introduction to APIs V6B 6P6 V6B Full Postal Code: Forward Sortation Area (FSA):

Introduction to the dataset IDp1p2GeoDescTOI TOA_VG H 1 V0N V5Z1M9North of Whisler1:0022:00 2 V8B V5Z1M9Squamish12:3015:00 3 V8B V5Z1M9Squamish12:3015:00 4V6BV5Z1M9Downtown9:009:45 5 V5K V5Z1M9PNE16:3018:30 6 V7R V5Z1M9Grouse Grind6:008:00 7 V2S V5Z1M9Abbotsford4:007:30 8 V0K V5Z1M9Lillooet7:0017:00 9 V7A V5Z1M9 George Massey Tunnel11:0012:00 10 V0K V5Z1M9Lillooet13:0023:30 11 V6X V5Z1M9Richmond8:009:00 12 V6J V5Z1M9Kits16:0016:30 Dataset: ACT_Raw ID Unique ID of participant P1 Forward Sortation Area of injury P2 Postal Code of Vancouver General Hospital GeoDesc Geographical description TOI Time of injury TOA_VGH Time of admission to Vancouver General Hospital Data Dictionary

Introduction to the dataset PC_ID V0N V5Z1M9 V8B V5Z1M9 V6B V5Z1M9 V5K V5Z1M9 V7R V5Z1M9 V2S V5Z1M9 V0K V5Z1M9 V7A V5Z1M9 V0K V5Z1M9 V6X V5Z1M9 V6J V5Z1M9 IDp1p2GeoDescriptTOI TOA_VG H 1 V0N V5Z1M9North of Whisler1:0022:00 2 V8B V5Z1M9Squamish12:3015:00 3 V8B V5Z1M9Squamish12:3015:00 4V6BV5Z1M9Downtown9:009:45 5 V5K V5Z1M9PNE16:3018:30 6 V7R V5Z1M9Grouse Grind6:008:00 7 V2S V5Z1M9Abbotsford4:007:30 8 V0K V5Z1M9Lillooet7:0017:00 9 V7A V5Z1M9 George Massey Tunnel11:0012:00 10 V0K V5Z1M9Lillooet13:0023:30 11 V6X V5Z1M9Richmond8:009:00 12 V6J V5Z1M9Kits16:0016:30 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.

Data Manipulations p1p2PC_ID V0KV5Z1M9V0K V5Z1M9 V0KV5Z1M9V0K V5Z1M9 V0NV5Z1M9V0N V5Z1M9 V2SV5Z1M9V2S V5Z1M9 V5KV5Z1M9V5K V5Z1M9 V6BV5Z1M9V6B V5Z1M9 V6JV5Z1M9V6J V5Z1M9 V6XV5Z1M9V6X V5Z1M9 V7AV5Z1M9V7A V5Z1M9 V7RV5Z1M9V7R V5Z1M9 V8BV5Z1M9V8B V5Z1M9 V8BV5Z1M9V8B V5Z1M9 IDp1p2NotesTOITOA_VGHPC_ID 1V0NV5Z1M9North1:00:0022:00:00V0N V5Z1M9 2V8BV5Z1M9Squam12:30:0015:00:00V8B V5Z1M9 3V8BV5Z1M9Squam12:30:0015:00:00V8B V5Z1M9 4V6BV5Z1M9Downt9:00:009:45:00V6B V5Z1M9 5V5KV5Z1M9PNE16:30:0018:30:00V5K V5Z1M9 6V7RV5Z1M9Grous6:00:008:00:00V7R V5Z1M9 7V2SV5Z1M9Abbos4:00:007:30:00V2S V5Z1M9 8V0KV5Z1M9Lilloo7:00:0017:00:00V0K V5Z1M9 9V7AV5Z1M9Georg11:00:0012:00:00V7A V5Z1M9 10V0KV5Z1M9Lilloo13:00:0023:30:00V0K V5Z1M9 11V6XV5Z1M9Richm8:00:009:00:00V6X V5Z1M9 12V6JV5Z1M9Kits16:00:0016:30:00V6J V5Z1M9 Dataset: ACT_Dataset (n=12) Dataset: PC (n=10) 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

Dataset: PC (n=10) API Macro p1p2PC_ID V0KV5Z1M9V0K V5Z1M9 V0NV5Z1M9V0N V5Z1M9 V2SV5Z1M9V2S V5Z1M9 V5KV5Z1M9V5K V5Z1M9 V6BV5Z1M9V6B V5Z1M9 V6JV5Z1M9V6J V5Z1M9 V6XV5Z1M9V6X V5Z1M9 V7AV5Z1M9V7A V5Z1M9 V7RV5Z1M9V7R V5Z1M9 V8BV5Z1M9V8B V5Z1M9 p1p2distance_valtime_val V0KV5Z1M9339.90815343 V0NV5Z1M9440.6427232 V2SV5Z1M975.4534093 V5KV5Z1M99.561069 V6BV5Z1M92.921327 V6JV5Z1M92.004240 V6XV5Z1M912.5561224 V7AV5Z1M918.9051530 V7RV5Z1M9-2 V8BV5Z1M966.8023958 Dataset: Dist_Time (n=10) Distance_Val (Kilometres) Time_Val (Seconds)

Dataset: PC (n=10) API Macro p1p2PC_ID V0KV5Z1M9V0K V5Z1M9 V0NV5Z1M9V0N V5Z1M9 V2SV5Z1M9V2S V5Z1M9 V5KV5Z1M9V5K V5Z1M9 V6BV5Z1M9V6B V5Z1M9 V6JV5Z1M9V6J V5Z1M9 V6XV5Z1M9V6X V5Z1M9 V7AV5Z1M9V7A V5Z1M9 V7RV5Z1M9V7R V5Z1M9 V8BV5Z1M9V8B V5Z1M9 p1p2distance_valtime_val V0KV5Z1M9339.90815343 V0NV5Z1M9440.6427232 V2SV5Z1M975.4534093 V5KV5Z1M99.561069 V6BV5Z1M92.921327 V6JV5Z1M92.004240 V6XV5Z1M912.5561224 V7AV5Z1M918.9051530 V7RV5Z1M9-2 V8BV5Z1M966.8023958 Dataset: Dist_Time (n=10) Distance_Val (Kilometres) Time_Val (Seconds) Errors output as -2

p1p2distance_v al time_val V0KV5Z1M9339.90815343 V0NV5Z1M9440.6427232 V2SV5Z1M975.4534093 V5KV5Z1M99.561069 V6BV5Z1M92.921327 V6JV5Z1M92.004240 V6XV5Z1M912.5561224 V7AV5Z1M918.9051530 V7RV5Z1M9-2 V8BV5Z1M966.8023958 Dataset: Dist_Time (n=10) p1p2distance_v al time_val V0KV5Z1M9339.90815343 V0NV5Z1M9440.6427232 V2SV5Z1M975.4534093 V5KV5Z1M99.561069 V6BV5Z1M92.921327 V6JV5Z1M92.004240 V6XV5Z1M912.5561224 V7AV5Z1M918.9051530 V7RV5Z1M915.71503 V8BV5Z1M966.8023958 Dataset: Dist_Time (n=10) API Errors Actual manual data entry for the ACT project is 18 / 994.

Now we have an accurate, timely, and reproducible method to define proximity based on two geographical locations*. Dataset: Dist_Time (n=10) Final Data p1p2distance_v al time_val V0KV5Z1M9339.90815343 V0NV5Z1M9440.6427232 V2SV5Z1M975.4534093 V5KV5Z1M99.561069 V6BV5Z1M92.921327 V6JV5Z1M92.004240 V6XV5Z1M912.5561224 V7AV5Z1M918.9051530 V7RV5Z1M915.71503 V8BV5Z1M966.8023958 Dataset: ACT_DriveTime (n=12) *until the technology changes IDp1p2TOITOA_VGHdistance_valtime_val 1V0NV5Z1M91:00:0022:00:00440.6427232 2V8BV5Z1M912:30:0015:00:0066.8023958 3V8BV5Z1M912:30:0015:00:0066.8023958 4V6BV5Z1M99:00:009:45:002.921327 5V5KV5Z1M916:30:0018:30:009.561069 6V7RV5Z1M96:00:008:00:0012.1391118 7V2SV5Z1M94:00:007:30:0075.4534093 8V0KV5Z1M97:00:0017:00:00339.90815343 9V7AV5Z1M911:00:0012:00:0018.9051530 10V0KV5Z1M913:00:0023:30:00339.90815343 11V6XV5Z1M98:00:009:00:0012.5561224 12V6JV5Z1M916:00:0016:30:002.004240

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

Final Analysis IDtime_val_MTTA_VGH_O 1453.86671200 265.96667150 365.96667150 45.4545 517.81667120 618.63333120 768.21667210 8255.7167600 925.560 10255.7167630 1120.460 12430 Dataset: ACT_Simple IDtime_val_MTTA_VGH_OTTA_VGH_E 1453.8671200475.867 265.96715087.967 365.96715087.967 45.454527.45 517.81712039.817 618.63312040.633 768.21721090.217 8255.717600277.717 925.56047.5 10255.717630277.717 1120.46042.4 1243026 Dataset: ACT_Simple_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?

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

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