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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)

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slide1
Calculating Drive Time Between Injury & Hospital in

Spinal Cord Injury Research Using Online Navigation Tools

Jayson H. Shurgold

slide2
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

slide3
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
slide4
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

slide5
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.

slide6
Background

Question 2:

Can we estimate proximity using built in functions?

- Geodist(latitude-1, longitude-1, latitude-2, longitude-2)

- Haverstine Formula

- SASHELP.zipcode

slide7
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

slide8
Background

Question 3:

Can I just Google this?

slide9
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.

slide10
Background

Question 4:

Is there any hope?

slide11
SAS

Nav Systems

slide12
Ash Roy & Yingbo Na

Canadian Institute for Health Information

Mike Zdeb

University of Albany School of Public Health

slide13
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)

slide14
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

slide15
Introduction to the dataset

Dataset: ACT_Raw

Data Dictionary

slide16
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.

slide17
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

slide18
API Macro

Dataset: PC (n=10)

Dataset: Dist_Time (n=10)

Distance_Val (Kilometres)

Time_Val (Seconds)

slide19
API Macro

Dataset: PC (n=10)

Dataset: Dist_Time (n=10)

Distance_Val (Kilometres)

Time_Val (Seconds)

Errors output as -2

slide20
API Errors

Dataset: Dist_Time (n=10)

Dataset: Dist_Time (n=10)

Actual manual data entry for the ACT project is 18 / 994.

slide21
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

slide22
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

slide23
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

slide24
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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