'Over to you': using a STAMP control structure analysis to probe deeper into the control of UK road safety at a municipal level - the case of Cambridgeshire.

The UK has seen little progress in reducing road death over the last decade and as a result, the government has been criticised by industry stakeholders for a lack of leadership, including the removal of national targets in 2011 and the devolution of powers to a municipal level. The aim of this paper is to understand how decision-making at a municipal level takes place from a systems perspective, using the case study of Cambridgeshire. Actors involved were mapped using a STAMP control structure analysis and highlighted a key role for formal and informal partnerships between local and national government agencies and non-government organisations at the same level in the control structure. The changing international context of the model for the UK is also discussed in relation to the UK's withdrawal from the European Union and provides a useful tool for future analysis of its effect on policy and decision-making. Practitioner summary: This paper uses a STAMP control structure analysis to understand how decision-making at a municipal level takes place from a systems perspective, using the case study of Cambridgeshire. It highlights a key role for formal and informal partnerships between organisations at the same level in the control structure. Abbreviations: STAMP: System Theoretic Accident Model and Processes; STAMP-CAST: Systems-Theoretic Accident Model and Processes - Causal Analysis using Systems Theory; FRAM: Functional Resonance Analysis Method; HFACS: Human Factors Analysis and Classification Scheme; NGO: Non-Government organisation; iRAP: International Road Assessment Programme; EuroRAP: European Road Assessment Programme; NCAP: New Car Assessment Programme; CCG: Clinical Commissioning Group; GP: General Practitioner; PPE: Personal Protective Equipment; SD: standard deviation; Beds: Bedfordshire; Cambs: Cambridgeshire; Herts: Hertfordshire; Pboro: Peterborough.

Identifying MAIS 3+ injury severity collisions in UK police collision records.

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.