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Contributory Factors example

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==Contributory Factors example==
==Contributory Factors example==
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In this example, you want to look at alcohol as a factor (i.e. CF501 Driver Impaired by Alcohol and CF806 Pedestrian Impaired by Alcohol.
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In this example, you want to look at alcohol as a factor (i.e. CF501 Driver Impaired by Alcohol and CF806 Pedestrian Impaired by Alcohol).
It is conceivable that either of these factors could be attributed to a vehicle passenger or uninjured pedestrian and also that they were related to the crash and the identity of the impaired party was not recorded. This offers a range of approaches to analysing alcohol related collisions and their participants.
It is conceivable that either of these factors could be attributed to a vehicle passenger or uninjured pedestrian and also that they were related to the crash and the identity of the impaired party was not recorded. This offers a range of approaches to analysing alcohol related collisions and their participants.

Revision as of 08:36, 8 October 2013

Contributory Factors example

In this example, you want to look at alcohol as a factor (i.e. CF501 Driver Impaired by Alcohol and CF806 Pedestrian Impaired by Alcohol).

It is conceivable that either of these factors could be attributed to a vehicle passenger or uninjured pedestrian and also that they were related to the crash and the identity of the impaired party was not recorded. This offers a range of approaches to analysing alcohol related collisions and their participants.

For STATS20 definitions of Contributory Factors and the names of the dimensions in MAST, see the Contributory factors definitions list

Approach

  • Crashes – You could simply filter by ‘Crash Involved CF501 DriverIntoxDrink’ and just tick yes to get all crashes where CF501 was recorded.

In order to ensure that it was only attributed to a driver or rider then drill down under ‘Yes’ and filter on driver, which would exclude intoxicated passengers or pedestrians.

If you wanted to look at all crashes with any form of intoxication, including pedestrian intoxication, then you need to add both ‘Crash Involved CF501 DriverIntoxDrink’ and 'Crash Involved CF806 PedIntoxDrink’ to either the rows or the columns tabs and drill down to remove cases where both dimension equalled ‘No’. Putting one in the rows and the other in the columns, you would create a truth table to show the four combinations of CFs being attributed.

  • Vehicles – You could simply filter by ‘Vehicle Attributed CF501 DriverIntoxDrink’ and just tick yes to get only drivers/riders where this CF was attributed. If you add this dimension to the rows or columns you can drill down to see if the driver/rider was injured. This is where you would do your socio-demographic profiling of intoxicated drivers/riders.
  • Casualties – There are two approaches: do you want to look at intoxicated pedestrian and/or passenger casualties or total casualties in drink-driver collisions?

In order to do approach the first, you would add ‘Casualty Attributed CF501 IntoxDrink’ and/or 'Casualty Attributed CF806 PedIntoxDrink’ in the same manner as detailed above for the Crashes tab. With either of those dimensions, you could drill down to look at the particular casualty class you are interested in, either passengers or pedestrians.

Alternatively, if you are interested in all casualties injured in collisions which involved drink driving then you would use ‘Crash Involved Driver CF501 DriverIntoxDrink’ and filtering by that would give you all casualties in crashes involving at least one drunk driver, including the drunk drivers themselves, injured non drunk drivers, passengers in all vehicles and injured pedestrians regardless of whether they were struck by a drunk driver.

By definition, this would NOT include drunk drivers who were not themselves injured or uninjured intoxicated pedestrians.

So you would be able to state the number of pedestrians injured in collisions INVOLVING a drunk driver but you would not be able to say the number of pedestrians HIT by a drunk driver.

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