Identifying MAIS 3+ injury severity collisions in UK police collision records. (21st March 2018)

• Record Type: Journal Article
• Authors: Nunn, James; Barnes, Jo; Morris, Andrew; Petherick, Emily; Mackenzie, Roderick; Staton, Matt
• Is Part Of: Traffic injury prevention. Volume 19(2018)Supplement 2
• Page Start: S142
• Page End: S144
• Publication Date: 2018-03-21
• Subjects: MAIS 3+ -- data -- linking -- STATS19 -- Trauma Audit and Research Network (TARN) -- collision; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2018.1532205
• Languages: English
• ISSNs: 1538-9588

Abstract

ABSTRACT: Objective: This study represents the first stage of a project to identify serious injury, at the level of Maximum Abbreviated Injury Scale (MAIS) 3 + (excluding fatal collisions) from within the police collision data. The resulting data will then be used to identify the vehicle drivers concerned and in later studies these will be culpability scored and profiled to allow targeting of interventions. Method: UK police collision data known as STATS19 for the county of Cambridgeshire were linked using Stata with Trauma Audit and Research Network (TARN) hospital trauma patient data for the same geographical area for the period April 2012 to March 2017. Linking was 2-stage: A deterministic process followed by a probabilistic process. Results: The linked records represent an individual trauma patient from TARN data linked to an individual trauma casualty from STATS19 data. Full collision data for the incident resulting in the trauma casualty were extracted. The resulting subset of collisions has the MAIS 3+ injury criteria applied. From the 10, 498 recorded collisions, the deterministic linking process was successful in linking 257 MAIS 3+ trauma patients to collision injury subjects from 232 separate collisions with the probabilistic process linking a further 22 MAIS 3+ subjects from 21 collision events. The combined collision data for the 253 collisions involved 434 motor vehicle drivers. Conclusions: We produced viable results from the available data to identify MAIS 3+ collisions from the overall collision data.