Injury Prevention through Data Linkage

Injury Prevention through Data Linkage Phase 3: The linkage of hospital inpatient data to police road traffic accident reports in East and West Sussex by Colin Cryer Sylvia Westrup Adam Cook CHSS at Tunbridge Wells May 2000

Summary Injury Prevention through Data Linkage Phase 3 CONTENTS Key Findings..iii Summary... v 1. Introduction... 1 1.1 Background... 1 1.2 The value of the project... 3 1.3 Aims and methods... 6 2 Linkage Performance... 9 2.1 Linkage study results... 9 2.2 Possible reasons why links were not made... 13 2.3 Characteristics of the unlinked cases... 15 2.4 Discussion in relation to other work... 18 3 Accuracy of the data... 21 3.1 Linkage study results... 21 3.2 Others work... 27 3.3 Implications... 30 4. Bias and the linked database... 31 4.1 Estimated proportion of cases in the linked database... 31 4.2 Investigation of bias... 32 4.3 Bias and linked serious and non-fatal injury cases... 36 5. Epidemiology... 47 5.1 All injury all road users... 48 5.2 Serious injury, all road users... 59 5.3 Occupants of motor vehicles... 70 5.4 Motorcyclists... 80 5.5 Pedal cyclists... 88 5.6 Pedestrians... 96 5.7 Child casualties... 104 5.8 Older road users... 114 5.9 Young drivers aged 17-24... 124 6. Principal Findings and Conclusions... 133 References... 141 i Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 ii Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Key Findings Serious injury resulting from road traffic crashes is an important health problem. Information derived solely from police road traffic accident reports (STATS19) can misinform accident prevention work Information on serious injuries, derived from hospital admission cases linked to police road traffic accident reports, is more robust than that from STATS19 alone. There were an estimated 1,340 non-fatal serious injuries that occurred on the roads in East and West Sussex during the period 1 April 1995 to 31 March 1998. The pattern of occurrence of non-fatal serious injury showed the following: All road users- There were high rates for people aged 15-24 and aged 75 and over. 37% were occupants of motor vehicles, 32% pedestrians, 21% motorcyclists, and 10% pedal cyclists. Half occurred on A-roads, most frequently on the A259, A23, and A27. Occupants of motor vehicles- Approximately equal proportions of men and women were seriously injured. 60% occurred on A-roads, most frequently on the A259 and A27. Motorcyclists- A high proportion occurred at junctions; he/she was making no manoeuvre in a high proportion of junction accidents. A high proportion of serious crash injuries involved side impacts to the motorcycle or the other vehicle involved. A high proportion of serious injuries were to the leg or foot (60%), and a low proportion to the head (7%); relatively few had short stays in hospital Pedal cyclists- He/she was making no manoeuvre in a high proportion of junction accidents. A high proportion resulted in serious head injuries (30%). Pedestrians- Highest rates were to those aged 75 and over. 25% of serious pedestrian injury occurred in Brighton. On average, there was at least one pedestrian injury in Sussex per month resulting in over 28 days stay in hospital. Child casualties- The majority seriously injured were pedestrians or cyclists. The average length of stay in hospital was less than for other user groups. Older casualties- Half were pedestrians, and a quarter were seriously injured when driving. The average length of stay in hospital was higher than for other road users. On average, there was at least one injury to an older person in Sussex per month resulting in over 28 days stay in hospital. Young drivers- Two thirds were to men; and two thirds occurred in West Sussex. A higher proportion occurred on A-roads, most often the A27, A29, and A259. The pattern of injury suggests a greater proportion of high velocity crashes. iii Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 iv Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Injury Prevention through Data Linkage Phase 3: The linkage of hospital inpatient data to police road traffic accident reports in East and West Sussex Summary The scope of the report This executive summary gives an overview of the road crash data generated by linking hospital inpatient data to police road traffic accident reports in the combined study area of East Sussex, Brighton & Hove, and West Sussex. (Following this, separate tables and figures will be produced for (1) East Sussex (excluding Brighton and Hove), (2) East Sussex Brighton & Hove, and (3) West Sussex.) The size of the problem Accidental injury is an important health problem, and road traffic crashes are one of the most important causes of accidental injury. In the period 1 April 1995 to 31 March 1998, there were 3,166 casualties recorded as Serious or Fatal on the police s road traffic accident database (STATS19) and there were 2,666 admissions to hospital recorded as road traffic crashes in the combined study area. The need for data linkage v Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 These data sources individually have limitations for the investigation of cause and the identification of potential methods of prevention. It was hypothesised that the linkage of hospital inpatient data records to STATS19 would provide a richer source of data for these purposes. STATS19 data is inaccurate As a result of data linkage, problems were identified with the accuracy of the STATS19 data. The magnitude and nature of these problems could result in injury prevention planning being misinformed. The linked database is incomplete. The linked database is also potentially biased; it is estimated that only 50% of RTAs admitted to hospital are included in the linked database. The most complete linkage was achieved for pedestrians, and car drivers, the least complete for pedal cyclists. but the evidence suggests that the linked database is superior to STATS19 for describing non-fatal serious injury The linked database is probably more suitable than STATS19 for investigating cause and prevention of RTAs resulting in non-fatal serious injuries since it appears to be less biased than STATS19 data. Reassuringly, the patterns of occurrence of non-fatal serious injuries from the linked database were similar to those for the hospital inpatient data, when tabulated by age, sex, and road user group. The patterns of occurrence for the STATS19 data was different to both of these. Serious injuries resulting from RTAs There were 1072 non-fatal serious injury cases identified from the admissions data. These were defined as road traffic crashes with a length of stay in hospital of 4 days or more, or for which a transfer to another hospital occurred, and which took place in the study area over this 3 year period. Of these, 668 were vi Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 included on the linked database and 64% were males. The linkage rate for these non-fatal serious injury cases was 62% over all road user groups including pedal cyclists. What follows within this executive summary are a number of figures and tables, that could only be produced from the linked database, which show the pattern of occurrence of non-fatal serious injury cases. For more detailed analysis, please see the main body of the report. These analyses have been repeated for the following subgroups: occupants of motor vehicles, motorcyclists, pedal cyclists, pedestrians, child casualties, older road users, and young drivers. Details of this work can be found in sections 5.3 to 5.9. Please note that in the tables and figures that follow, the rates and numbers that have been generated from the linked database are likely to be approximately one half those of the true rates and numbers of casualties with serious non-fatal injuries defined as casualties with a length of stay in hospital of 4 or more days or as transfers to another hospital. vii Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Age-specific Rates of Serious Injury as Estimated from the Admissions and Linked Databases 1995/6-1997/8 Rate per 100,000 130 120 110 100 90 80 70 60 50 40 30 20 10 0 00-14 15-24 25-34 35-64 65-74 75+ Age Band Admissions Linked - There were high rates for people aged 15-24 and aged 75 and over. - For pedestrians, the highest rates were for people aged 75 and over. - Across all road users, approximately two thirds of the serious casualties were male. - Approximately equal proportions of men and women occupants of motor vehicles were seriously injured. - A greater rate of serious injury was apparent in young men than young women drivers. - The vast majority of serious injuries cases amongst motorcyclists were men. - 70% of serious pedal cycle casualties were men or boys. viii Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Crashes resulting in Serious Injury by Road Type - 1995/6-1997/8 Unclassified 16% C Road 17% A Road 51% B Road 16% - Half of the non-fatal serious injuries occurred on A-roads, most frequently on the A259, A23, and A27. - For occupants of motor vehicles, 60% occurred on A-roads. - The highest proportion that occurred on A-roads was for young drivers aged 17-24, most often on the A27, A29, and A259. - For pedal cyclists, a lower proportion occurred on A-roads (36%), and a higher proportion on unclassified roads (30%). - For child casualties, there was a lower proportion of serious injuries on major roads than for other road users. ix Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Crashes Resulting in Serious Injury by District - 1995/6-1997/8 Brighton Eastbourne Hastings Hove Lew es Rother Wealden Adur Arun Chichester Craw ley Horsham Mid-Sussex Worthing 0 10 20 30 40 50 60 70 80 90 100 Number - 25% of serious pedestrian injury occurred in Brighton - Two thirds of non-fatal serious injuries to young drivers (aged 17-24) occurred in West Sussex. x Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Crashes Resulting in Serious Injury by Road User - 1995/6-1997/8 Pedestrian 0-4 5-9 10-15 16-29 30-44 45-59 60-74 75+ Pedal Cyclist 0-15 16+ Motorcycle Riders Passengers Car/Taxi Driver U17 Driver 17-24 Driver 25-34 Driver 35-44 Driver 45-59 Driver 60-74 Driver 75+ Passenger 1 3 9 14 18 19 18 24 26 29 28 33 34 32 40 39 55 57 83 106 0 10 20 30 40 50 60 70 80 90 100 110 Number - 37% of non-fatal serious injuries on Sussex roads were occupants of motor vehicles, 32% pedestrians, 21% motorcyclists, and 10% pedal cyclists. - There were a large number of non-fatal serious injuries to motorcyclists relative to their road use. - The majority of seriously injured child casualties were pedestrians or pedal cyclists. - Half of older casualties of RTAs were seriously injured as pedestrians, and a quarter whilst driving. xi Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Crashes Resulting in Serious Injury by Manoeuvre 1995/6-1997/8 Ahead (no manoeuvre) 324 Ahead - right hand bend 62 Ahead - left hand bend Overtaking stationary - offside 39 75 Overtaking moving - offside Turning right 31 50 Turning Left Starting Stopping Reversing Other 19 19 11 12 26 0 50 100 150 200 250 300 350 Number - In many cases, no manoeuvre was involved; but where it was, it often involved overtaking, turning right or turning left. - A high proportion of motorcycle and pedal cycle accidents resulting in serious injury occurred at junctions; he/she was making no manoeuvre in a high proportion of these accidents. - A high proportion of serious crash injuries to motorcyclists involved side impacts to the motorcycle or the other vehicle involved. - A high proportion of serious injuries to young drivers, compared with other occupants of motor vehicles, occurred on right turns. xii Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Crashes Resulting in Serious Injury by Junction Type - 1995/6-1997/8 No junction 315 Roundabout 28 Mini roundabout 1 T or staggered junction 212 Y junction 15 Slip road 7 Crossroads 38 Multiple junction 12 Private drive 35 Other 5 0 50 100 150 200 250 300 350 Number - Approximately half of road users seriously injured were injured at junctions, and in the majority of cases these were T-junctions. - A high proportion of motorcycle and pedal cycle accidents resulting in serious injury occurred at junctions. - Seventy percent of non-fatal serious RTA injuries to older people occurred at junctions. xiii Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Serious Injuries to Pedestrians by Type of Movement - 1995/6-1997/8 Crossing - driver's nearside 91 Crossing - driver's nearside masked 19 Crossing - driver's offside 48 Crossing - driver's offside masked 14 In carriageway - stationary 5 In carriageway - stationary masked 1 Walking in carriageway - facing traffic 6 Walking in carriageway - back to traffic 8 Unknown/Other 13 0 10 20 30 40 50 60 70 80 90 100 Number - Most pedestrians were injured whilst crossing the road. xiv Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Serious Injury to All Road Users by Nature of Injury 1995/6-1998/8 No injury code recorded Head Neck Thorax Abdomen, lower back, lumbar spine & pelvis Shoulder & upper arm Elbow & forearm Wrist & hand Hip & thigh Knee & lower leg Ankle & foot Multiple body regions Unspecified body part Poisonings Complications of trauma 1 1 3 7 14 13 12 20 31 42 59 60 89 128 188 0 20 40 60 80 100 120 140 160 180 200 Number - Across all road users, the body site of injury was the lower leg (28%), head (19%), hip and thigh (13%), chest (9%), and abdomen, back or pelvis (9%). - A high proportion of serious injuries to motorcyclists were to the leg or foot (60%) - A relatively high proportion of serious pedal cycle injuries were head injuries (30%). - A large proportion of serious injuries to pedestrians were to the legs and feet (almost 60%). - The pattern of injuries for young drivers suggests a greater proportion of high velocity crashes. xv Prepared by CHSS at Tunbridge Wells

Summary Injury Prevention through Data Linkage Phase 3 Non-fatal Injuries (All Severities) to All Road Users by Length of Stay in Hospital - 1995/6-1997/8 0 178 1 417 2 200 3-4 201 5-7 173 8-14 196 15-28 124 >28 80 0 50 100 150 200 250 300 350 400 450 Number - For all road users combined, discharge from hospital was the same or the following day in almost 40% of cases (all severities), was over a week in 26%, over a fortnight in 13%, and over a month in 5%. - Relatively few seriously injured motorcyclists had a short length of stay. - On average, there was at least one pedestrian injury and at least one injury to an older person in Sussex per month that resulted in over 28 days stay in hospital. - The average length of stay in hospital for child casualties was less, and for older casualties was greater, than for other road user groups. xvi Prepared by CHSS at Tunbridge Wells

Part 1: Introduction Injury Prevention through Data Linkage Phase 3 Injury Prevention through Data Linkage Phase 3: Linkage of Hospital Inpatient Data to Police Road Traffic Accident Reports in East and West Sussex Part 1. Introduction 1.1 Background Accidental injury is an important health problem. The prevention of accidental injury is a national priority, and it is one of four key areas in the Government s health strategy Saving Lives: Our Healthier Nation. Reducing the burden of injury is essential since: accidental injuries are responsible for 10,000 deaths a year across England accidents are the greatest single threat to life for children and young people and are responsible for more admissions to hospital amongst children than any other cause treating injuries costs the NHS in the region of 1.6 billion each year (Secretary of State for Health 1999). Injury has been reported to be the fifth leading cause of death, the third leading cause of potential years of life lost, and the fourth most common cause of hospital bed utilisation (Cryer et al. 1993) Road traffic accidents are one of the most important causes of accidental injury. There were almost a third of a million road accident casualties in 1998 in England of whom more than 3,500 died, half of these on rural roads (Secretary of State for Health 1999). Motor vehicle traffic accidents are the leading cause of injury death and the second leading cause of hospital bed utilisation for injury (Cryer et al 1993, Marchant et al 1997). Information has an important role to play in the prevention of accidents and injury: to accurately describe 1 Prepared by CHSS at Tunbridge Wells

Part 1: Introduction Injury Prevention through Data Linkage Phase 3 the distribution of accidents and injury across the population; for monitoring trends; to examine the longterm effects of accidents and injuries; to influence the setting of priorities; to influence the allocation of resources; to give clues to the causes of injury, and the events that lead to them; and to suggest methods of prevention. Readily accessible and relevant data systems available within the NHS are the hospital inpatient data and the OPCS registrations of deaths. These data have good information on the consequences of accidents, but contain limited data on the personal characteristics of the injured person, where the event occurred, and how it occurred. Data sources from other agencies also have their strengths and weaknesses. For example the data collected by the police on road traffic accidents have a wealth of information on the circumstances of the accident, but have limited information on its consequences. Given the limitations of any one source to provide comprehensive information relevant to accident prevention and injury control, it was postulated that if an accident database could be linked with the hospital inpatient data, the resultant database would provide a much more powerful tool for injury control. Phase 1: The assessment of data sources for data linkage Phase 1 of this project (Cryer and Aspinall, 1994) was a review of accident data sources in order to identify and describe the candidate accident data sources for linkage and these included: road traffic (STATS19); home and leisure (HASS and LASS); work (HSE RIDDOR system); school; and fire statistics. Phase 1 focussed on data sources available in East Sussex. Other sources of data related to injury prevention were also identified and described briefly but these were not amenable for data linkage within East Sussex. The information collected suggested that any one of three sources could be considered for linkage: STATS19, RIDDOR, or fire statistics. However, the report concluded that there was a greater probability of success (fewer difficulties) and the likelihood of greater benefits of linkage between STATS19 and inpatient data. Phase 2: Data linkage pilot: 2 Prepared by CHSS at Tunbridge Wells

Part 1: Introduction Injury Prevention through Data Linkage Phase 3 The linkage of police road traffic accident reports to hospital admissions to Eastbourne DGH and Conquest Hospital, Hastings. This work (Cryer et al. 1995) investigated the feasibility and utility of linking the police road traffic accident reports to hospital inpatient data within East Sussex. It concluded the following. - Within East Sussex, the linkage of STATS19 and hospital admission data can only be successfully achieved through the use of names and addresses of the casualties, and that as the data are currently organised, a successful matching procedure involves manually searching police registers and records. Linkage was particularly useful in highlighting inaccuracies in the Police RTA database which is currently used by the County Council for planning road safety and accident prevention initiatives. - The data linkage process provides a more reliable group of 'severe' motor-vehicle traffic accident cases than STATS19. For those cases that could be linked, there is more comprehensive data on the circumstances of the accident than would be available otherwise. This can provide insight into what is causing accidents that result in hospital admission, and so guide prevention. To make real progress in using linked data for accident prevention and injury control, a greater number of cases are required than were available in the pilot study. Consequently, it was recommended that several years data be linked in order to provide a suitable database to describe the epidemiology of injury resulting from road traffic accidents. 1.2 The value of the project Reported here are the results of the linkage of hospital admissions data to police road traffic accident reports for the whole of East and West Sussex and for the years: 1995/6, 1996/7 and 1997/8. The use of a linked data source by the road safety teams within the County Council Departments of Highways and Transport could highlight priorities for their work. In some instances, their current plans are influenced by the accident severity gradings provided by the police. The police use a crude system to code the severity of the casualty (ie. Fatal, Serious, Slight ) which provides only limited information of questionable accuracy on the seriousness of the injury. Through linkage to hospital data, more detail of the injury, 3 Prepared by CHSS at Tunbridge Wells

Part 1: Introduction Injury Prevention through Data Linkage Phase 3 length of hospital stay and discharge destination is available, and so the most seriously injured (non-fatal) casualties can be identified. In the pilot it was found that these crude STATS19 severity codes are quite inaccurate, and so it was anticipated that analysis of more reliable data resulting from data linkage would result in some changed priorities. The Health Authorities envisage that the more detailed cause of accident information from the police road traffic accident reports linked with the hospital information, which includes diagnosis, length of stay, and operative procedure, would provide valuable information for the development of health promotion and injury prevention programmes. Work commissioned by the Department of Health and carried out by the Public Health Information Strategy Group (1993) identified a number of data items required for an accident database (shown in Table 1). It can be seen from this table that STATS19 provides information on the accident characteristics, some personal characteristics, but very little on the consequences of the accident. On the other hand, the hospital inpatient data has much less on the accident characteristics, much more on the personal characteristics, as well as some data items relating to the consequences. Within a linked database, therefore, the majority of the recommended data items for an accident database would be available. Consequently, the expectation was that such a linked database would give a much more powerful base for injury control. Furthermore, it was felt that linkage would: - increase information available for injury prevention; - identify crashes that are not ascertained within the STATS19 database but which result in hospital admission; - identify errors in the databases: ie. where there is a mismatch in the information between STATS19 data and hospital in-patient data; - identify sources and magnitude of bias resulting from inaccurate data and missing cases. This knowledge would help to improve the data on both systems, and hence improve their usefulness. 4 Prepared by CHSS at Tunbridge Wells

Part 1: Introduction Injury Prevention through Data Linkage Phase 3 Table 1: Accident information requirements Structure Items STATS19 Hospital Inpatient Data Accident Characteristics Place of occurrence Geographic identifier of location Y Y Type Y Y Circumstances Y Personal Characteristics Age & sex Y Y Area of residence Y Socio-economic?Y Ethnic Group Y Activity Y Predisposing factors? Consequences Nature of injury Y Severity of injury??y Health service impact Y Outcome?? Key: Y = Recorded on the database?y = Indicator can be generated from the database? = Only limited information available <blank> = No information available 5 Prepared by CHSS at Tunbridge Wells

Part 2: Linkage performance Injury Prevention through Data Linkage Phase 3 1.3 Aims and Methods The aims of the project are: - to describe the epidemiology of non-fatal serious injury resulting from road traffic accidents using a database created by linking hospital inpatient data to police road traffic accident reports; - to identify crashes that are not ascertained within the STATS19 database but which result in hospital admission; - to identify errors in the databases. Population The population of interest are those who had a road traffic accident in East and West Sussex during the period 1 April 1995 to 31 March 1998. Selection of STATS19 cases In theory, all cases admitted to hospital should be classified as 'Serious' on the STATS19 database. A large proportion of admitted cases, however, are classified as 'Slight' - and so all casualties on the STATS19 database that fell within the catchment area were considered. Selection of hospital cases Cases, admitted to hospitals as a result of motor-vehicle traffic accidents sited in Brighton & Hove, East and West Sussex, during the period 1 April 1995 to 31 March 1998, were provided to CHSS at Tunbridge Wells by the Information Managers from the following acute hospital Trusts: Brighton, Eastbourne, Hastings & Rother, Mid Sussex (Haywards Health), Queen Victoria (East Grinstead), Royal West Sussex (Chichester), Crawley Horsham, and Worthing & Southland. 6 Prepared by CHSS at Tunbridge Wells

Part 2: Linkage performance Injury Prevention through Data Linkage Phase 3 Within hospitals, the principal diagnosis at admission is placed in the first diagnosis field (DIAG1) on the computer record. If this was an injury, it should take a code between S00-S99, or T00-T98 within the International Classification of Diseases 10th revision (ICD10) codes (WHO 1992). Each injury diagnosis should be accompanied by an external cause code (E-code), usually in the next available diagnosis field (DIAG2), but occasionally will be held in the field DIAG3 to DIAG7. Transport accidents were selected for which DIAG1 had a code between S00-T98 inclusive, and where the first E-code following this principal diagnosis was in the range V01-V89 (ICD10). Matching procedure The schema shown in Figure 1 indicates how these cases were linked to the STATS19 data. The STATS19 records do not include names and addresses. Using names and addresses from the hospital data, the police s accident reference number for the casualty was found from the police paper records: their accident registers and accident reports. This police accident reference number was added to the inpatient record and so provided the link between the hospital records and the STATS19 computer record, which includes the accident reference number. Hospital Inpatient Records Link to STATS19 STATS19 Transcribe Accident Reference Number to Hospital Inpatient Record Match Police Accident Register: Manual search using name and date of accident Figure 1: A schema of the manual data linkage procedure In more detail, the following procedure was followed. The inpatient data was obtained from these hospitals and was loaded on to the secure computer at CHSS at Tunbridge Wells. Road traffic accidents were selected from these hospital records, and were ordered by name, in a list that included date of admission, and hospital where admitted. Research staff (SW) visited each police station in the two counties and manually matched these details to those held in the police's road accident register (held in books). A manual search of the police accident registers was carried out for names and dates of accidents 7 Prepared by CHSS at Tunbridge Wells

Part 2: Linkage performance Injury Prevention through Data Linkage Phase 3 that matched, approximately, to the names and dates of admission on each of the hospital records. The details from the inpatient record were kept confidential from the police during this procedure. Once a match was found, the police accident reference was written against the details from the hospital record, and then these were transcribed on to the inpatient data held at CHSS for the next phase of the matching. These records were then linked to the relevant STATS19 records using the accident reference number as the linking variable. All the links, and any multiple matches, were checked by comparing the information on age, sex, type of road user, and date of accident / admission between the two electronic databases. Where a mismatch on these variables existed, these cases were investigated through a manual search of the more extensive police accident (paper) files. A summary of the number of cases considered and linked is shown in Figure 2. Figure 2: Hospital inpatients linked to STATS19 casualties 5313 Transport accidents admitted to hospital 2666 Road traffic accident casualties admitted to hospital 1625 Cases linked to STATS19 casualties 1041 Cases unlinked 1567 Non-fatal injury cases admitted to hospital and linked to STATS19 8 Prepared by CHSS at Tunbridge Wells

Part 2: Linkage performance Injury Prevention through Data Linkage Phase 3 Part 2: Linkage Performance 2.1 Linkage study results A breakdown of the linkage performance is shown in Table 2 and Figure 3. Of the original 2,666 admissions coded as road traffic accidents, 1625 of these cases (61%) were linked to the registers and paper records in police offices and subsequently to the STATS19 record. The linkage rates were higher for admissions to Brighton (65%), Eastbourne (66%), Hastings (66%), and Worthing (65%). It was less for Mid-Sussex (54%), and Crawley Horsham (48%). Queen Victoria at East Grinstead is a tertiary referral hospital, ie. other hospitals (many of which are outside Sussex) refer their cases to this hospital for super-specialist care. This is likely to be a major contributor to the very low number of cases (n=33) and the very low linkage rate for these cases (6%). Table 2: Linkage performance by hospital and police station STATS19 Records Hospital Brighton Haywards Heath Crawley Eastbourne Hastings Worthing Chichester Unlinked Grand Total Brighton 328 51% 11 2% 81 13% 224 35% 644 Eastbourne 2 1% 207 63% 7 2% 113 34% 329 Hastings & 3 1% 242 65% 126 34% 371 Rother Mid-Sussex 156 50% 10 3% 144 46% 310 Queen 2 6% 31 94% 33 Victoria St.Richards 22 4% 320 57% 219 39% 561 Crawley & 109 48% 116 52% 225 Horsham Worthing 30 16% 73 38% 22 11% 68 35% 193 Grand Total 330 12 % 169 6% 139 5% 301 11 % 249 9% 95 4% 342 13% 1041 39% 2666 9 Prepared by CHSS at Tunbridge Wells

Part 2: Linkage performance Injury Prevention through Data Linkage Phase 3 Figure 3: Percentage of linked cases by hospital 100% 90% 80% 70% Percentage 60% 50% 40% 30% 20% 10% 0% Brighton Eastbourne Hastings & Rother Mid-Sussex Queen Victoria St.Richards Crawley & Horsham Worthing Linked Unlinked 10 Prepared by CHSS at Tunbridge Wells

Part 2: Linkage performance Injury Prevention through Data Linkage Phase 3 There were different linkage rates between the types of road user and between hospitals within road user (Table 3). This table shows similar linkage rates for car occupants (67%), motorcyclists (69%) and pedestrians (72%), but with a linkage rate of 31% for pedal cyclists. For most hospitals whose catchment is solely within the two counties, linkage rates are close to or exceed 70% for all groups except pedal cyclists. The linkage rate for pedal cyclists is consistently low across hospitals. Table 3: Linkage rates by type of road user and hospital Car Occupant Motor Cyclist Pedal Cyclist Pedestrian Total Hospital Linked Total % Linked Total % Linked Total % Linked Total % Linked Total % Brighton 121 163 74 65 80 81 61 152 40 173 249 69 420 644 65 Eastbourne 110 149 74 33 46 72 20 67 30 53 67 79 216 329 66 Hastings & 128 198 65 41 57 72 17 41 41 59 75 79 245 371 66 Rother Mid-Sussex 108 173 62 21 44 48 8 54 15 29 39 74 166 310 54 Queen 1 4 25 1 6 17 0 23 0 0 0 0 2 33 6 Victoria St.Richards 196 281 70 51 75 68 40 124 32 55 81 68 342 561 61 Crawley & 39 86 45 22 34 65 19 56 34 29 49 59 109 225 48 Horsham Worthing 62 83 75 20 24 83 13 51 25 30 35 86 125 193 65 Grand Total 765 1137 67 254 366 69 178 568 31 428 595 72 1625 2666 61 11 Prepared by CHSS at Tunbridge Wells

Part 2: Linkage performance Injury Prevention through Data Linkage Phase 3 Looking at road user groups in more detail (Table 4), The linkage rates are particularly poor for child pedal cyclists (19%), and although somewhat better for cyclists aged 16 and over, are still low (40%). Linkage rates are higher for drivers than passengers for both cars and motorcycles, where the rate for passengers is 58% and 56% respectively. Linkage rates for pedestrians are reasonably high across all age groups, but are lower for children aged 10-15 (65%) and people aged 75 and over (69%). Table 4: Linkage rates by road user group Road User Linked % Total Pedestrian 0-4 19 79% 24 5-9 70 82% 85 10-15 151 65% 231 16-59 51 80% 64 60-74 58 75% 77 75+ 79 69% 114 Pedal Cyclist 0-15 45 19% 235 16+ 133 40% 333 Motorcycle Rider 195 75% 260 Passenger 59 56% 106 Car Driver U17 4 100% 4 Driver 17-24 99 77% 129 Driver 25-59 246 71% 345 Driver 60-74 68 79% 86 Driver 75+ 59 79% 75 Passenger 289 58% 498 Total 1625 61% 2666 12 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 There was a small decline in linkage rates over the financial years 1995/6 to 1997/8 (Table 5). The difference in rates between the first and last year is unlikely to be due to chance alone (p=0.05). Table 5: Linkage rates by year Financial Year Linked % Total 1995/96 472 63% 753 1996/97 628 62% 1008 1997/98 525 58% 905 Total 1625 61% 2666 2.2 Possible reasons why links were not made. 2.2.1 Definitional reasons Only hospital data coded to traffic accidents were selected for data linkage. The definition of a traffic accident used by hospitals is as follows: A traffic accident is any vehicle accidents occurring on a public highway [ie., originating on, terminating on, or involving a vehicle partially on the highways]...". A public highway is defined in the hospital data as "the entire width between property lines of land open to the public as a matter of right or custom for the purposes of moving persons or property from one place to another (WHO, 1992). The definitions of cases recorded on STATS19 are not totally consistent with the above definitions. As quoted in the STATS20 instructions for the completion of road accident reports: All road accidents involving human death or personal injury occurring on the Highway and notified to the police within 30 days of occurrence, and in which one or more vehicles are involved, are to be reported. This is a wider definition of road accidents than that used in the Road Traffic Acts (DETR, 1998). The STATS20 manual then goes on to note: The Road Traffic Act 1988 (section 170), as amended by Section 72 of the 1991 Act, stipulates that all fatal or injury accidents on public roads involving at least one mechanically propelled vehicle should be reported by the public to the police unless insurance documents, name and 13 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 address, and evidence of vehicle ownership and registration are exchanged between drivers. The interpretation of mechanically propelled vehicle varies widely between local forces, particularly about whether pedal cycle accidents, not involving a motor vehicle, should be reported. The STATS19 requirement is clear that all accidents involving non-motor vehicles such as pedal cycles and ridden horses on public roads should be reported, regardless of motor vehicle or pedestrian involvement. The Sussex STATS19 database will not necessarily, therefore, include all of the hospital cases where the accident occurred in Sussex. 2.2.2 Reporting behaviour A number of authors have found that reporting rates vary between road users. Most authors have found that reporting rates are the highest where the injured person is the occupant of a motor vehicle, and are lowest for motorcyclists and pedal cyclists (see section 2.4 for further discussion). This could partially explain the lower rates for pedal cyclists. 2.2.3 Catchment areas The hospital catchment areas are another potential reason for the differential linkage rates. For example, the catchment for Eastbourne Hospital is contained completely within Sussex, whereas that for the Crawley Horsham Hospital Trust includes parts of Surrey. When officers from the Surrey police attend an accident, that data will be lost to this study since only the STATS19 records for Sussex were obtained and only police offices in Sussex visited. This may account for at least some of the inferior linkage performance for casualties who attend Crawley Horsham Trust hospital (48%) compared with Eastbourne Hospital (66%). 2.2.4 Police records 14 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 The registers in each of the police records offices were not organised in a uniform way for the 1995/6 to 1997/8 years. In most offices, the police register is sectioned alphabetically by the first letter of the surname of the casualty and by date of the accident within first letter of the surname. In Hastings and Crawley, however, the register is listed by the first letter of the surname of the driver, rather than the surname of the casualty. In Haywards Heath, the register is organised by date only, but included all casualties. These differences could explain the different linkage performance observed for casualties recorded at these police offices, compared with other areas. 2.2.5 Failure to identify motor-vehicle traffic accidents on hospital systems A casualty of a road traffic accident admitted to hospital may not be identified on hospital systems. If a person is admitted to hospital, it will result in a record being generated on the in-patient data system. However, the area that is critical for linkage, is that those hospital cases can be identified as injury admissions caused by a vehicle accident on the road. It is the clinical coding fields that permits the identification of such cases. Clinical coding is not 100% accurate or complete. For injury admissions, which includes unintentional injury (ie. accidents), intentional injuries, and those of undetermined intent, the level of external cause coding (from which traffic accidents are identified) within Sussex fluctuated between 80% and 82% for those admissions whose principal diagnosis is injury in the study years 1995/6 to 1997/8. If these missing codes are distributed evenly across all causes of injury, then this would result in 18-20% of road traffic accident cases being missed due to incomplete external cause coding, in which case the opportunity to make a link with STAT19 for these cases would be denied. 2.3 Characteristics of the unlinked cases The percentage of unlinked cases has been found to be dependent on a number of factors: the hospital to which the case was admitted, the age, and the type of road user. 2.3.1 Admitting hospital 15 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 The percentage of unlinked cases admitted to the many of the study hospitals in 1995/6 to 1997/8 was disappointingly large for all the hospitals, but was particularly so for Mid-Sussex (Haywards Health) and Crawley Horsham - see Figure 3 in section 2.1. Reasons for this have already been discussed. 2.3.2 Sex Across the hospitals there was no difference in proportions of males and females in the unlinked cases compared with the linked cases. Consistent with this, Austin (1992) found little difference in reporting rates between males and females. 2.3.3 Age There were observed differences in the percentage of cases matched in the age groups considered (Table 6) due principally to the low linkage rate for children aged 0-15. Low linkage rates amongst pedal cyclists will influence this due to the greater use of cycles by children compared with adults. Table 6 - Linkage rates by age Age Linked % Total 0-15 231 47% 487 16-24 336 60% 557 25-34 279 64% 436 35-64 471 65% 729 64-74 123 69% 179 75+ 185 67% 278 Total 1625 61% 2666 2.3.4 Type of road user 16 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 There were differences in the percentage of cases matched for the different types of road user due to the low rate for pedal cyclists. This may indicate a lower likelihood of reporting of accidents to the police by pedal cyclists, and a consequent bias against pedal cyclists in STATS19 data. 2.3.6 Type of injury Surprisingly, the percentage of unlinked fracture cases, was similar to that for other injuries (Table 7). A number of these unlinked fracture cases had injuries which were potentially serious, including fractures to the skull, to the spine and trunk, as well as to the hip and thigh. We expected that the majority of the accidents that resulted in these serious injuries would have been reported to the police. This substantial number of serious injury cases that could not be linked indicates either a failure of our linkage method, or important missing cases from police records. Other potentially serious injuries (eg. intracranial injuries) may not have been perceived at the time of the accident as worthy of reporting to the police, but which nevertheless resulted in subsequent hospital admission. Table 7 - Linkage rates by nature of injury Linked Total Nature Fracture % Other % Total % Fracture Other Total No Injury Code 37 47 37 47 78 78 S00-S09 Inj. to head 53 49 488 63 541 61 109 772 881 S10-S19 Inj. to neck 19 59 60 69 79 66 32 87 119 S20-S29 Inj. to thorax 113 68 57 72 170 69 166 79 245 S30-S39 Inj. to abdomen, back, spine & pelvis 63 64 50 53 113 59 99 94 193 S40-S49 Inj. to shoulder & upper arm 92 66 9 47 101 64 139 19 158 S50-S59 Inj. to elbow & forearm 77 38 7 41 84 38 202 17 219 S60-S69 Inj. to wrist & hand 40 68 11 46 51 61 59 24 83 S70-S79 Inj. to hip & thigh 89 62 17 59 106 61 144 29 173 S80-S89 Inj. to knee & lower leg 229 70 41 53 270 67 326 78 404 S90-S99 Inj. to ankle & foot 21 64 11 55 32 60 33 20 53 T00-T07 Inj. involving multiple body regions 22 63 22 63 35 35 T08-T14 Inj. to unspecified body part 12 80 12 80 15 15 T15-T19 Foreign body entering through natural orifice 2 67 2 67 3 3 T36-T50 Poisonings 2 100 2 100 2 2 T79 Certain early complications of trauma 3 60 3 60 5 5 Total 796 61 829 61 1625 61 1309 1357 2666 2.4 Discussion in relation to others work 17 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 One of the earliest studies to link police records and hospital information was that undertaken by Hobbs and colleagues (1979) The study, based on over 3,600 casualties seen at one large hospital in Berkshire, found nearly 30% of casualties were not reported to the police even though they had attended hospital. This is similar to the linkage rates that we found for those hospitals whose catchments are entirely within Sussex and whose local police office registers record information on the casualty in date order within the first letter of the surname of the casualty. Shortly afterwards, work was commissioned by the TRRL to enhance the information on severity of injury and to provide details of the nature of injury or anatomical location of injury. This study (Nicholl 1980) attempted to match police STATS19 records and the Hospital In-Patient Enquiry (HIPE) for England and Wales (a 10% sample). An automatic (computer-based) method of matching was used using the following fields: date of accident / admission, sex, age (with tolerance), and distance between where the accident occurred and the hospital. It was estimated that about 50% of the hospital records could be readily matched and that less than 10% of the matches were incorrect. This was followed by a comprehensive matching of police accident records and 100% sample of hospital inpatient records for Scotland for the year 1980 (Stone 1984). A total of 6093 unique matches were obtained, representing 70% of health records. For the matches, the casualty records in the national road accident data files were enhanced by length of stay in hospital together with clinical details of injury coded to ICD and the severity of injury scale: AIS (Abbreviated Injury Scale). The matching algorithm used identifiers common to both data sets to achieve the matching: geographical location and time of accident, age and sex of casualty and class of road user. As seen above, our results are similar for selected hospitals. Austin (1992) developed an improved technique to link the police casualty data with hospital casualty data by using the name and address of the victim (matching on surname, forename, first line of address, casualty age, gender, and accident date). For hospital inpatients, the study reported linkage rates of 89.9% when manually matching the two sets of records, and the computer algorithm successfully matched 96.5% of these. The manual matching rate is comparable to the results of manual matching of the Eastbourne Hospital records in the pilot study. In the current study, we have been unable to replicate this high rate. Some of the above authors, as well as others, have commented on linkage performance in relation to a number of variables relating to the age, type of road user and injury severity. 18 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Age Unlike our results, Austin (1992) found that the young and the old were most likely to report, and those aged 20-64 least likely to report their accidents to the police. As indicated already, reporting rates with age are confounded by the type of road user, since there are different patterns of road use with age. Work reviewed by Haigney (1995) has also suggested that whether a case appears in STATS19 is related to age. For example she reports the work by Mills (1989) who found that amongst cyclists, older children were more likely than younger children to appear on the database, and that both groups were less likely to appear than adult cyclists. Our results are more consistent with this than with Austin (1992). Type of road user Several authors (Bull and Roberts (1973), Hobbs et al. (1979), Tundridge et al. (1988) and Harris (1990)) all found the highest level of reporting for vehicle occupants and the lowest level for cyclists and motorcyclists. This was confirmed in a review by James (1991). Austin (1992), on the contrary, found the highest reporting rates for the vulnerable road users, that is, cyclists, motorcyclists, and pedestrians, and the lowest reporting rates for the occupants of motor vehicles. The presumed low rates of reporting for cyclists found in our study are similar to all these authors except Austin (1992). Injury severity James' (1991) review reported that several studies in the UK found that reporting rates declined with injury severity, independently of type of road user. The information she found is summarised in Table 8. Factors affecting whether an accident was reported to the police are interrelated. For example, for motorcycle accidents, characteristics such as severity of injury, and whether or not anyone else was involved were some of the most important factors found to affect the likelihood of an accident being reported. Table 8: Linkage rates by STATS19 severity and type of road user (from James, 1991) 19 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Fatal Serious Slight Vehicle occupant 100% 89% 77% Pedestrian 100% 85% 67% Motorcyclist 100% 70% 51% Pedal cyclist 100% 33% 21% Total 100% 76% 62% 20 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Part 3: Accuracy of the data 3.1 Linkage study results The accuracy of the linked data was investigated by comparing the data for those variables collected on both systems: namely age, sex, date of accident / admission, and type of road user. Furthermore, the accuracy of the severity of the casualty classification used on STATS19 could be checked since, by convention, all admissions to hospital should have a severity classification of either 'Serious' or 'Fatal' on STATS19. Type of road user The correspondence of the information on type of road user is shown in Table 9. They corresponded in the vast majority of case (92%). Table 9: Type of road user - STATS19 by inpatient STATS19 Data Hospital Car Motor Pedal Pedestrian Other Total Inpatient Data Occupant Cyclist Cyclist Car Occupant 722 94% 4 1% 5 1% 5 1% 29 4% 765 Motor Cyclist 14 6% 227 89% 8 3% 2 1% 3 1% 254 Pedal Cyclist 16 9% 4 2% 146 82% 9 5% 3 2% 178 Pedestrian 16 4% 4 1% 9 2% 396 93% 4 1% 428 Total 767 47% 239 15% 168 10% 412 25% 39 2% 1625 Sex The sex of the casualty corresponded in 92% of cases. This is similar to the percentage found in the pilot. In the pilot, the name of the casualty was consistent with their gender classification on the hospital inpatient system. This discrepancy appears, therefore, to be due to incorrect coding 21 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 of sex on STATS19 since the accuracy of linkage between the two sources has been checked in Age the majority of cases. The age of the casualty was exactly the same on the inpatient system compared with STATS19 in 57% of cases. The ages were within 5 years of each other in 86% of cases and within 10 years of each other in 91% of cases - see Figure 4. This again is almost the same as the pilot results. For the 154 cases whose differences in ages on the two systems exceeded 10 years, the date of admission was the same day as the date of accident, or the day after in 116 (75%). Although these cases were matched on name, the recorded sex was different in 97 (63%) of these 154 cases. These discrepancies, particularly for those showing a difference in recorded ages of greater than 10 years, may be due to inaccurate linkage between data sources. However, for the majority of discrepant cases, the accuracy of the linkages were checked by hand searching the police files. For all cases that were checked on the linked database, the accuracy of the link was confirmed. As will be seen later, authors of other linkage studies have also found gross differences in age recorded on the two systems. 22 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Figure 4 - Differences between recorded age on the 2 systems 60% Figure 4 : Difference between recorded age in 2 systems 57% 50% 40% Percentage 30% 20% 20% 10% 9% 4% 2% 3% 5% 0% < -10-6 to -10-1 to -5 0 1 to 5 6 to 10 >10 Age difference Date of Accident / Admission The date of admission was either the same day or the day following the accident in a total of 1512 (93%) of cases. In 18 linked cases, the casualty had a recorded date of admission which was before the recorded date of the accident. The ages of the 112 cases not admitted on the same or the day after the accident were identical in 38 (34%) cases, within 5 years in 65 (58%) cases, and within 10 years in 73 (65%) of cases. The match by age for these 112 cases is worse than for the total linked database, so suggests that for a proportion of these cases the links may be in error. Their sexes matched in 87 (78%) of the cases. Again, this is lower than for the database as a whole indicating that there may be some incorrect links. Nevertheless, for a substantial portion of the database, where discrepancies exist, the records were hand searched to check for accuracy of linkage. For all cases that were checked on the linked database, the accuracy of the link was confirmed. Severity 23 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Among the 1625 linked hospital cases, there were 628 (39%) admissions that had an accident classification on STATS19 of 'Slight' (Table 10). All cases admitted to hospital should be classified by the police as 'Serious' (DETR 1998). The greatest proportion of cases classified to Slight occurred for occupants of motor vehicles (45%) and for pedal cyclists (40%). Table 10 - Type of road user by STATS19 severity Road User Fatal Serious Slight Total Car Occupant 34 4% 387 51% 344 45% 765 Motor Cyclist 3 1% 185 73% 66 26% 254 Pedal Cyclist 2 1% 104 58% 72 40% 178 Pedestrian 9 2% 273 64% 146 34% 428 Total 48 3% 949 58% 628 39% 1625 The STATS19 severity classification tabulated against nature of injury and length of stay in hospital is shown in Tables 11a and 11b. For those linked cases classified on STATS19 to Slight, there was a much greater proportion of short (0 and 1 days) lengths of stay in hospital indicating less severe cases. Nevertheless, there were still 290 (46%) which had a length of stay of 2 or more days, and so indicate that there were a number of casualties with a severe injury who had been classified as Slight on STATS19. These include 19 casualties with fractures of the neck or spine, 70 casualties with a head injury which resulted in 2 or more days stay in hospital, and 67 casualties with a serious long bone fracture that required some operative procedure to be carried out. Table 11a STATS19 severity by nature of injury 24 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 STATS19 Severity Fatal Serious Slight Total Nature No. % No. % No. % No. No Injury Code 8 22 29 78 37 S00-S09 Inj. to head 22 4 286 53 233 43 541 S10-S19 Inj. to neck 2 3 28 35 49 62 79 S20-S29 Inj. to thorax 3 2 91 54 76 45 170 S30-S39 Inj. to abdomen, back, spine & pelvis 2 2 63 56 48 42 113 S40-S49 Inj. to shoulder & upper arm 70 69 31 32 101 S50-S59 Inj. to elbow & forearm 3 4 56 67 25 30 84 S60-S69 Inj. to wrist & hand 28 55 23 45 51 S70-S79 Inj. to hip & thigh 4 4 81 76 21 20 106 S80-S89 Inj. to knee & lower leg 9 3 206 76 55 20 270 S90-S99 Inj. to ankle & foot 17 53 15 47 32 T00-T07 Inj. involving multiple body regions 1 5 8 36 13 59 22 T08-T14 Inj. to unspecified body part 1 8 4 33 7 58 12 T15-T19 Foreign body entering through natural 1 50 1 50 2 orifice T36-T50 Poisonings 1 50 1 50 2 T79 Certain early complications of trauma 2 67 1 33 3 Total 48 3 949 58 628 39 1625 Table 11b STATS19 severity by length of stay in hospital STATS19 Severity Fatal Serious Slight Total Nature No. % No. % No. % No. 0 days 11 6 78 41 103 54 192 1 day 12 3 182 42 235 55 429 2 days 3 1 107 53 93 46 203 3-4 days 6 3 137 66 64 31 207 5-7 days 1 1 121 70 52 30 174 8-14 days 8 4 149 73 48 23 205 15-28 days 6 5 101 77 24 18 131 > 28 days 1 1 74 88 9 11 84 Total 48 3 949 58 628 39 1625 Twenty one people died in hospital, of which 15 were recorded on STATS19 as dead, three cases as Serious, and three as Slight (Table 12). Three of these 6 cases died 37 days, 48 days and 102 days following the accident, and so a classification of non-fatal would be consistent with the 25 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 definitions used in the STATS19 (DETR, 1998). A further 33 cases were classified on STATS19 as dead. These had a recorded discharge destination on the hospital system of 'Usual residence' (n=20), other hospital or NHS establishment (n=6), or Temporary place of residence (n=1), and 6 had uncoded discharge destination. One of these patients was in hospital for 58 days. In that case, assuming an accurate link, the classification on STATS19 as Fatal is inconsistent with the fatal case definition for STATS19. For the remainder, it is possible they died subsequent to discharge but within 30 days of the accident and so may have been legitimately classified as 'Fatal' on STATS19. Table 12 - Severity of injury of casualty by discharge destination Fatal Serious Slight Total Discharge Destination No. % No. % No. % No. Usual place of residence 20 1 788 57 585 42 1393 Temporary place of residence 1 3 21 72 7 24 29 Penal establishment 4 80 1 20 5 Special hospital 1 7 11 79 2 14 14 NHS hospital general ward 4 6 54 77 12 17 70 NHS hospital mental ward 1 40 1 50 2 NHS nursing home 2 40 3 60 5 Other NHS establishment 1 3 31 89 3 9 35 Local authority care 1 100 1 Died 15 71 3 14 3 14 21 Non NHS care establishment 12 63 7 37 19 Not coded 6 19 21 68 4 13 31 Total 48 3 949 58 628 39 1625 As a result of data linkage, problems have been identified with the accuracy of the STATS19 data; the magnitude and nature of these problems could result in injury prevention planning being misinformed. Severity coding appears to be particularly inaccurate within STATS19 with 39% of admissions classified as Slight. All admissions to hospital should be coded to Serious or Fatal (DETR 1998). Other authors have also found major inaccuracies in the coding of severity of STATS19. There were discrepancies between the recorded age of the casualties found on STATS19 and on the hospitals admission data. The weight of evidence suggests that this is principally due to inaccuracies in STATS19. 26 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 3.2 Others work 3.2.1 Accuracy and usefulness of STATS19 The process of data collection and validation of the STATS19 data generally involves several stages. The police officer who attends the accident will record the details in his/her report book. This information results in an accident report and an entry in the accident register at the police station. The data on the accident report is coded and entered on to the computer and subsequently validated according to the requirements set out by the Department of Transport. Subsequently, about 80% of Highways Authorities consult the police when checking apparent errors in accident data (Ibrahim and Silcock, 1992). In her review of STATS19, Haigney (1995) states that STATS19 is not "a definitive or unimpeachable source of data on road accident statistics in Great Britain". She makes the point that there is evidence of cases being omitted from the database that could seriously impair its representativeness, and that there exists inaccuracies in the database which are likely to cause bias. For every five casualty records recorded by the police, four had errors in their socio-demographic data. Amongst the accident variables, Shikar and colleagues (1983) found that accuracy was greatest for accident location and time. Accuracy decreased for collision type, light conditions, weather conditions and accident severity. These authors emphasised that these inaccuracies mean that safety programmes evaluated on the basis of whether or not they result in a reduction of accidents reported to the police are of questionable scientific validity. 3.2.2 Accuracy of hospital data Evidence of accuracy of STATS19 is often based on matching to hospital data. Previous work tends to assume that the hospital data are accurate. Haigney (1995) believes that this is a supportable assumption. This view is also supported by work, which originated from the previous South East Thames Regional Health Authority (SETRHA, 1993), that found no major data quality problems with date of admission, date of discharge, discharge destination, age, date of birth, and district of residence. 27 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 3.2.3 Inaccuracies in specific variables Age In a review by James (1991), she reported that age was likely to be accurate in hospital data because it was derived from date of birth, but that it might be estimated in STATS19. Austin (undated) reported that age was omitted by the police in 3.6% of records. He found that age differed between police and hospital casualty (A&E) records by 1 year or less in 60% of cases, and within 5 years in 83% of cases. He also found casualties that differed by as much as 35 years. This is similar to our findings. Sex Austin (undated) found only 3 cases (0.3%) where the gender differed. In two out of the 3 cases the police classification of sex was inconsistent with the forename. The discrepancy is much greater than this in the current Sussex study. Place of Occurrence The inaccuracy of the place of occurrence of the accident as indicated by the grid reference or the plain language description have been found to be the two most frequent problems with STATS19 data (Ibrahim and Silcock, 1993). Many errors have been found, such as displaced figures, faulty translation of the 100 kilometre square letters to digits, and transposition of the grid reference easting and northing. The work by Austin (1993) concurs with this. In his review of Highways Authorities, 85% stated that accident location included the greatest number of errors. Injury Severity STATS20 (Department of Transport, 1991) lays down guidelines for recording the severity of casualties' injuries. A crude method for measuring the severity of the accident/casualty is used, based on only 3 codes: Fatal ; Serious ; and Slight. The recording of Fatal injuries by the police appears to be accurate, the criterion being explicit and unambiguous (ie. A fatal injury comprises only those cases where death occurs in less than 30 days as a result of the accident, but does not include death from natural causes or suicide.) 28 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 However, the differentiation between Serious and Slight injuries is frequently unsuccessful. The instructions for distinguishing between the two have generally been interpreted as implying that any casualty who is detained in hospital should be classified as seriously injured. However, STATS20 indicates that a casualty should be judged seriously injured even if he/she is not detained in hospital, but is judged to have one or more of the following injuries: fracture, internal injury, severe cuts and lacerations, crushing injury, and contusion. Nicholl (1980) maintains that 'appreciable numbers' of casualties detained in hospital are incorrectly recorded on the STATS19 form as 'Slight' rather than 'Serious' injuries. Stone (1984) also suggests some miscoding of severity on STATS19. Bull and Roberts (1973) reported that, because of misclassification of the severity of the casualty, the number of seriously injured cases should be increased by 13% above the figure recorded by the police. Amongst non-fatal injured casualties who were admitted to hospital, Austin (undated) reported that on the police files 190 were classified as Serious, and 65 as Slight. That is, 25% of inpatients were misclassified as 'Slight'. The level of misclassification estimated by the current study is even larger than this (39% classified to Slight ). Over all casualties, Austin (undated) found that 12 % had severity incorrectly coded (ie. Slight to Serious, or Serious to Slight ) on STATS19 based on data from Humberside. He also estimated the net effect of miscoding was that the number of seriously injured casualties should be increased by 35%. 3.3 Implications Care must be taken when analysing the STATS19 data. Both this and previous studies have identified likely levels of under-reporting of accidents to the police, as well as inaccuracies in the data. Fields that have been found to be less accurate include the severity classification, the age of the casualty, and the place of occurrence. The concern would be that analysis of STATS19 data on their own could produce misleading results and inaccurate conclusions. 29 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Part 4: Bias and the Linked Database 4.1 Estimated proportion of cases in the linked database The linked database is potentially biased; it is estimated that approximately 50% of RTAs admitted to hospital are included in the linked database. This is derived as follows. It is estimated that 20% of road traffic accidents (RTAs) admitted to hospital cannot be identified from hospital data in East and West Sussex due to incomplete external cause coding. Additionally, only 61% of admissions that could be identified as RTAs could be linked to the STATS19 data. 61% of 80% gives approximately 50% of RTAs in the linked database. There are better linkage rates for some road user groups; the most complete linkage was achieved for pedestrians, and car drivers, the least complete for pedal cyclists. Figure 5 shows the linkage rate by road user group. From these, it is estimated that 56% of RTAs admitted to hospital involving pedestrians are included in the linked database, but that for some age groups (5-9) it could be as high as 66%. For car occupants, on average it is estimated to be 54%, but for some user groups (eg. drivers aged 60-74 and 75+) it could be as high as 63%. For pedal cyclists of all ages it is estimated to be 25%, and for cyclists age 0-15 it is estimated to be as low as 15%. Figure 5 - Linkage rates by road user group 100% 90% 80% 70% Percentage 60% 50% 40% 30% 20% 10% 0% Pedestrian 0-4 5-9 10-15 16-59 60-74 75+ Pedal Cyclist 0-15 16+ Motorcycle Rider Passenger Car/Taxi Driver U17 Driver 17-24 Driver 25-59 Driver 60-74 Driver 75+ Passenger Total Road Users 30 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 4.2 Investigation of bias For the remainder of this report, the analysis has been carried out solely on non-fatal injuries for the following reasons: 1. The accuracy of STATS19 in identifying fatal injuries has been found to be good, and so the patterns and circumstances of road traffic crashes resulting in death can be investigated using STATS19 alone. 2. Hospital admissions, and hence the linked data, only include a minority of RTA deaths. 3. Because of the known deficiencies of STATS19 for the analysis of casualties with non-fatal injuries, then research work on non-fatal injury takes precedence at this stage. If each of these three databases (STATS19, hospital admissions, and linked) are not substantially biased for prevention work, then the patterns of occurrence of RTAs based on STATS19 Serious injuries, hospital admissions data, and the linked data should be fundamentally the same. For planning, the patterns of occurrence of RTAs are of interest. If the patterns of occurrence by age, sex, severity of injury, place of occurrence, etc. in the linked database reflect those of all road traffic accident admissions to hospital, then conclusions in regard to prevention priorities based on studying these patterns of occurrence in the linked database will not be compromised. Furthermore, if the patterns of occurrence of casualties coded to Serious on the STATS19 database reflect those of admissions to hospital, again the conclusions reached based on studying the STATS19 patterns of occurrence will not be misleading. For the non-fatal injury cases, when STATS19 Serious injuries, hospital admissions, and the linked data were compared on the variables of age, sex and road user group, the patterns of occurrence were similar for the hospital and the linked data, but STATS19 differed from both of these. The proportion of people classified to male in each of the databases differed little (Table 13 and Figure 6). The linked database and the hospital admissions data showed similar distributions across age groups; however STATS19 differed quite markedly, particularly in those aged less than 35 (Table 14 and Figure 7). Table 13 - Distribution of non-fatal injury cases by sex 31 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Male Female Total Sex No. % No. % No. STATS19 Serious 1947 67 961 33 2908 Admissions 1680 64 927 36 2607 Linked Data 995 63 572 37 1567 Figure 6 - Distribution of non- fatal injury cases by sex 80% 70% 60% 50% Percentage 40% 30% Stats19 Serious Admissions Linked Data 20% 10% 0% Male Sex Female 32 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Table 14 Distribution of non-fatal injury cases by age group 00-15 16-24 25-34 35-64 65-74 75+ Total Age Group No. % No. % No. % No. % No. % No. % No. STATS19 Serious 233 8 717 25 641 22 891 31 176 6 250 9 2908 Admissions 2480 18 543 21 431 17 714 27 173 7 266 10 2607 Linked Data 223 14 322 21 273 17 456 29 117 7 176 11 1567 Figure 7 - Distribution of non- fatal injury cases by age group 35% 30% 25% Percentage 20% 15% Stats19 Serious Admissions Linked Data 10% 5% 0% 00-15 16-24 25-34 35-64 65-74 75+ Age Group Excluding pedal cyclists, for which we know the linked data is under-represented, the distributions by road user group were similar for the linked database and the hospital admissions data; however, STATS19 again showed some larger differences in these distributions (Table 15 & Figure 8). 33 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Table 15 Distribution of non-fatal injury cases by road user group Pedestrian Pedal Cyclist Motor Cyclist Car Driver Car Total Road User Group 0-4 5-9 10-15 16-59 60-74 75+ 0-15 16+ Rider Passenger U17 17-24 25-59 60-74 75+ Passenger STATS19 Serious 21 61 123 306 83 121 90 281 559 23 3 274 485 107 77 288 2902 Admissions 24 64 82 228 75 108 234 331 258 105 4 125 335 82 74 478 2607 Linked Data 19 50 67 148 55 74 44 131 193 58 4 95 235 65 58 271 1567 STATS19 Serious 1% 2% 4% 11% 3% 4% 3% 10% 19% 1% 0% 9% 17% 4% 3% 10% 100% Admissions 1% 2% 3% 9% 3% 4% 9% 13% 10% 4% 0% 5% 13% 3% 3% 18% 100% Linked Data 1% 3% 4% 8% 4% 5% 3% 8% 12% 4% 0% 6% 15% 4% 4% 17% 100% Figure 8 - D istribution of non-fatal injury cases by road user group 2 0 % 1 8 % 1 6 % 1 4 % Percentage 1 2 % 1 0 % 8 % Stats19 Serious A dmissions Linked D ata 6 % 4 % 2 % 0 % 0-4 Pedestrian 5-9 10-15 16-59 60-74 75+ 0-15 Cyclist 16+ Rider Motor Cycle Passenger U17 Car Driver 17-24 25-59 60-74 75+ Passenger Car R oad user group 34 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 4.3 Bias and linked non-fatal serious injury cases STATS19 Serious injuries, hospital admissions, and the linked data each include cases with a wide range of injury severity. Each of these data sources include cases ranging from admissions to hospital for overnight observation of what turns out to be minor head injury, to major crush injuries that include major lacerations, fractures and damage to internal organs (eg. to the brain and spinal cord). Based solely on these routine data sources, a more homogeneous group of severe injuries can only be identified using data collected by the hospitals. It has already been shown that the STATS19 severity field includes a substantial amount of data that is inaccurate. No other field on STATS19 permits a more accurate method of identifying a group of serious injuries. Within the hospital admissions data, data from a number of fields can indicate cases of serious injury, namely: length of stay in hospital, injury diagnosis, the surgical procedures that were carried out, whether the casualty was treated in an ITU (intensive treatment unit), and the discharge destination. The indicator of serious non-fatal injury that is basis of the target within the Government s health strategy (Saving Lives: Our Healthier Nation), is admission to hospital with a length of stay of 4 or more days. Work by the Transport Research Laboratory (Hobbs et al. 1979) showed that a substantial majority of casualties who had a hospital inpatient stay of 4 or more days were serious injuries when classified by an objective injury severity code, namely the Abbreviated Injury Scale (AIS). The Department of Health have also investigated three types of injury, the majority of which are serious as classified by AIS: serious long bone fractures, head injuries admitted to hospital for greater than 1 day, and injuries to the neck and spine (Cryer 1999). Based on the above, the two methods of identifying non-fatal serious injury that we have considered here are as follows: (1) RTA cases who were transferred to another hospital, or who were admitted and stayed in hospital for 4 or more days (excluding deaths). 35 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 (2) RTA cases who were transferred to another hospital, or were admitted to hospital with one of the following injuries: serious long bone fractures, head injuries admitted to hospital for greater than 1 day, and injuries to the neck and spine (excluding deaths). Neither of these methods is exact, however. Use of any criteria based solely on electronic admissions data will result in some misclassification of the severity of injury, since measures derived from these data are simply indicators of severity rather than direct measures. When serious hospital admissions and the linked data were compared, using the definitions above, for the variables of age, sex, and road user group, the patterns of occurrence were fundamentally the same (Tables 16-21 and Figures 9-14). The results for serious injury defined using length of stay (Tables 16-18 and Figures 9-11) were slightly better than when defined by type of injury (Tables 19-21 and Figures 12-14). The patterns of occurrence of non-fatal serious injury, based on a length of stay definition, by road user group were similar for the hospital admission data compared with the linked data, even when pedal cyclists were included in the comparison (Table 18 and Figure 11). The distribution of occurrence of serious injury, defined by type of injury, by road user group were also similar for hospital admissions and the linked data; however, the discrepancies for pedal cyclists were greater (Table 21 and Figure 14). In Part 5, serious injury as defined by length of stay in hospital have been presented. 36 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Table 16 Distribution of non-fatal injury cases (length of stay based definition) by age group 00-15 16-24 25-34 35-64 65-74 75+ Total Age Group No. % No. % No. % No. % No. % No. % No. Admissions 109 10 199 19 182 17 322 30 96 9 164 15 1072 Linked Data 62 9 130 19 112 17 191 29 65 10 108 16 668 Figure 9 - Distribution of non- fatal serious injury (length of stay based definition) by age 30% 25% 20% Percentage 15% Admissions Linked 10% 5% 0% 00-16 16-24 25-34 35-64 65-74 75+ Age Group Table 17 Distribution of non-fatal serious injury (length of stay based definition) by sex 37 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Male Female Total Sex No. % No. % No. Admissions 682 64 390 36 1072 Linked Data 422 63 246 37 668 Figure 10 - Distribution of non- fatal serious injury (length of stay based definition) by sex 70% 60% 50% Percentage 40% 30% Admissions Linked 20% 10% 0% Male Sex Female 38 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Table 18 Distribution of non-fatal serious injury (length of stay based definition) by road user group Pedestrian Pedal Cyclist Motor Cyclist Car Driver Car Total Road User Group 0-4 5-9 10-15 16-59 60-74 75+ 0-15 16+ Rider Passenger U17 17-24 25-59 60-74 75+ Passenger Admissions 3 30 21 116 46 78 34 130 144 56 1 49 125 34 40 165 1072 Linked Data 3 14 26 77 39 57 9 55 106 33 1 34 79 24 28 83 668 Admissions 0% 3% 2% 11% 4% 7% 3% 12% 13% 5% 0% 5% 12% 3% 4% 15% 100% Linked Data 0% 2% 4% 12% 6% 9% 1% 8% 16% 5% 0% 5% 12% 4% 4% 12% 100% Figure 11 - D istribution of non- fatal serious injury (length of stay based definition) by road user group 20% 18% 16% 14% Percentage 12% 10% 8% A d m issions Linked 6% 4% 2% 0% 0-4 Pedestrian 5-9 10-15 16-59 60-74 75+ 0-15 Cyclist 16+ Rider Motor Cycle Passenger U17 Car Driver 17-24 25-59 60-74 75+ Passenger Car Road user 39 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Table 19 Distribution of non-fatal injury cases (nature of injury based definition) by age group 00-15 16-24 25-34 35-64 65-74 75+ Total Age Group No. % No. % No. % No. % No. % No. % No. Admissions 204 19 205 19 176 16 308 29 74 7 112 10 1079 Linked Data 93 15 127 20 111 18 181 29 47 7 70 11 629 Figure 12 - Distribution of non- fatal serious injury (nature of injury based definition) by age 30% 25% 20% Percentage 15% Admissions Linked 10% 5% 0% 00-16 16-24 25-34 35-64 65-74 75+ Age Group Table 20 Distribution of non-fatal serious injury (nature of injury based definition) by sex 40 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Male Female Total Sex No. % No. % No. Admissions 706 65 373 35 1079 Linked Data 411 65 218 35 629 Figure 13 - Distribution of non- fatal serious injury (nature of injury based definition) by sex 70% 60% 50% 40% 30% Admissions Linked 20% 10% 0% Male Female Sex 41 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 Table 21 Distribution of non-fatal serious injury (nature of injury based definition) by road user group Pedestrian Pedal Cyclist Motor Cyclist Car Driver Car Total Road User Group 0-4 5-9 10-15 16-59 60-74 75+ 0-15 16+ Rider Passenger U17 17-24 25-59 60-74 75+ Passenger Admissions 9 24 40 106 43 56 108 154 134 46 2 48 117 24 24 144 1079 Linked Data 7 15 37 72 37 42 19 55 99 27 2 32 72 15 17 81 629 Admissions 1% 2% 4% 10% 4% 5% 10% 14% 12% 4% 0% 4% 11% 2% 2% 13% 100% Linked Data 1% 2% 6% 11% 6% 7% 3% 9% 16% 4% 0% 5% 11% 2% 3% 13% 100% Figure 14 - Distribution of non- fatal serious injury (nature of inju ry based definition) by road user group 20% 18% 16% 14% Percentage 12% 10% 8% Admissions Linked 6% 4% 2% 0% 0-4 Pedestrian 5-9 10-15 16-59 60-74 75+ 0-15 Cyclist 16+ Rider Motor Cycle Passenger U17 Car Driver 17-24 25-59 60-74 75+ Passenger Car Road user 43 Prepared by CHSS at Tunbridge Wells

Part 4:Bias and the linked database Injury Prevention through Data Linkage Phase 3 44 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology Injury Prevention through Data Linkage Phase 3 Part 5: Epidemiology The analysis reported below is based on the 1567 linked cases, and 668 non-fatal serious injury cases on the linked database, out of the 2607 non-fatal injury admissions for road traffic accidents to the study hospitals during 1995/6 to 1997/8. As already mentioned, it is estimated that only around 50% of RTAs admitted to hospital, and slightly over 50% non-fatal serious injury cases, are included in the linked database. Nevertheless, the previous section has shown that for analyses whose aim is to identify cause and to influence prevention of these crashes, the use of the data on non-fatal serious injury from the linked database may be more robust than STATS19. The format used to label the tables has changed for this part of the report. This is most easily explained by the following example. The tables relevant to the text in subsection 5.1.1 Who were injured have been organised under the same heading namely: 5.1.1 Who were injured. 45 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology all injury severities, all road users Injury Prevention through Data Linkage Phase 3 5.1 All injury severities, all road users 5.1.1 Who were injured? Consistent with many other studies, the highest rates of non-fatal injury identified from the linked database were for people aged 15-24 (Figure 15). Approximately two thirds of the cases on the linked database were males. Many of the casualties were occupants of motor vehicles or pedestrians, although relative to their road usage there were also many motorcycle-related injuries (Figure 16). 5.1.2 Where were they injured? The distribution by district in which the crash occurred, the road class, and the road number on which the crash occurred are shown. Approximately half of the casualties in the linked database were injured on A- roads. A third of these occurred on either the A259 or the A27. Others with a significant number of casualties who were admitted to hospital were (in order of magnitude): A23 (Brighton and Mid-Sussex), A272 (Chichester and Mid-Sussex), A22 (Wealden), A270 (Brighton and Hove), A286 (Chichester), A21 (Rother), and A29 (Arun). The districts shown in brackets are those where the majority of accidents on these roads occurred. 5.1.3 Circumstances of injury In two thirds of cases the road surface was dry, and most of the remainder it was wet or damp. In only 1% of casualties was the road condition snowy or icy. Over 80% of the injuries occurred in fine weather. Around 13% occurred in rain, and 2% of cases this was exacerbated by high winds. In approximately half of the cases, the event occurred at a junction. Many of these were at a T-junction, with a significant number also occurring at crossroads, at the entrance to a private drive, or at a roundabout. In many cases, no manoeuvre was involved. Where it was, the main manoeuvres were turning right, and overtaking moving or stationary vehicles. Just over 40% were single vehicle accidents, almost 50% involved 2 vehicles and almost 10% involved 3. In almost 70% of cases, the point of impact was the front of the casualty s vehicle, and in 65% of cases involving more than one vehicle, the front was the first point of impact of the other vehicle involved. 46 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology all injury severities, all road users Injury Prevention through Data Linkage Phase 3 5.1.4 Nature of injury Injuries to the head accounted for a third of all injuries, and to the knee and lower leg accounted for one sixth. The other main sites of occurrence of injuries were as follows (in order of magnitude): chest, abdomen / back / pelvis, hip / thigh, shoulder/ upper arm, elbow / forearm, and neck. In total these account for 91% of all injuries. 5.1.5 Health service impact and severity of injury Almost 40% of cases were discharged from hospital the same or the following day. Over a quarter, however, stayed over a week in hospital, 13% stayed over 2 weeks, and 5% were detained in hospital for over a month. 47 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 5.1 All injury severities, all road users Figure 15 - Age-specific rate - all injury severities, all road users 225 200 175 150 125 100 75 50 25 0 0-14 15-24 25-34 35-64 65-74 75+ Age band Figure 16 - Four main road user types - all injury severities, all road users Pedestrian 26% Car Occupant 47% Pedal Cyclist 11% Motor Cyclist 16% 48 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 5.1.1 Who were injured Age Band No. % 00-15 223 14% 16-24 322 21% 25-34 273 17% 35-64 456 29% 65-74 117 7% 75+ 176 11% Total 1567 100% Sex No. % Male 995 63% Female 572 37% Total 1567 100% Road User Group No. % 49 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Pedestrian 0-4 19 1% Pedestrian 5-9 50 3% Pedestrian 10-15 67 4% Pedestrian 16-29 75 5% Pedestrian 30-44 44 3% Pedestrian 45-59 29 2% Pedestrian 60-74 55 4% Pedestrian 75+ 74 5% All Pedestrians 413 26% Cyclist 0-15 44 3% Cyclist 16+ 131 8% All Pedal Cyclists 175 11% Motorcycle Rider 193 12% Motorcycle Passenger 58 4% All Motorcyclists 251 16% Car/Taxi Driver Under 17 4 0% Car/Taxi Driver 17-24 95 6% Car/Taxi Driver 25-34 89 6% Car/Taxi Driver 35-44 56 4% Car/Taxi Driver 45-59 90 6% Car/Taxi Driver 60-74 65 4% Car/Taxi Driver 75+ 58 4% Car/Taxi Passenger 271 17% All Car Occupants 728 46% Total 1567 100% 5.1.2 Where were they injured 50 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 District Council No. % Brighton 213 14% Eastbourne 95 6% Hastings 94 6% Hove 110 7% Lewes 100 6% Rother 138 9% Wealden 141 9% Adur 17 1% Arun 137 9% Chichester 221 14% Crawley 51 3% Horsham 87 6% Mid-Sussex 119 8% Worthing 44 3% Total 1567 100% Road Classification No. % Motorway 1 0% A road 773 49% B Road 241 15% C road 271 17% Unclassified 281 18% Total 1567 100% Road Number No. % M23 1 0% A21 33 2% 51 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 A22 37 2% A23 62 4% A24 27 2% A26 12 1% A27 78 5% A28 9 1% A29 32 2% A259 181 12% A264 6 0% A265 8 1% A267 9 1% A268 5 0% A269 11 1% A270 36 2% A271 13 1% A272 38 2% A273 7 0% A275 9 1% A280 3 0% A281 11 1% A283 27 2% A284 3 0% A285 14 1% A286 35 2% A293 3 0% A295 5 0% A2004 1 0% A2010 7 0% A2011 1 0% A2021 8 1% A2023 2 0% A2025 2 0% A2029 2 0% A2031 8 1% A2032 1 0% A2038 4 0% A2073 2 0% A2100 6 0% A2101 5 0% A2102 4 0% A2219 2 0% A2220 4 0% B Roads 241 15% C Roads 271 17% Unclassified 281 18% Total 1567 100% 5.1.3 Circumstances of injury Road Surface No. % Dry 1050 67% 52 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Wet/Damp 490 31% Snow 6 0% Frost/Ice 18 1% Flood 3 0% Total 1567 100% Weather No. % Fine without high winds 1307 83% Raining without high winds 167 11% Snowing without high winds 10 1% Fine with high winds 24 2% Raining with high winds 29 2% Snowing with high winds 3 0% Fog or mist - if hazard 12 1% Other 10 1% Unknown 5 0% Total 1567 100% Junction Detail No. % Not at junction 753 48% Roundabout 61 4% Mini roundabout 3 0% T or staggered junction 466 30% Y junction 47 3% Slip road 16 1% Crossroads 115 7% Multiple junction 21 1% Private drive/entrance 73 5% Other junction 12 1% Total 1567 100% Manoeuvres No. % Reversing 18 1% parked 7 0% Waiting to go ahead 20 1% 53 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Stopping 26 2% Starting 36 2% U Turn 6 0% Turning left 33 2% Waiting to turn left 4 0% Turning right 116 7% Waiting to turn right 7 0% Changing lane to left 3 0% Changing lane to right 7 0% Overtaking moving vehicle - offside 78 5% Overtaking stationary vehicle - offside 90 6% Overtaking - nearside 8 1% Ahead - left hand bend 169 11% Ahead - right hand bend 165 11% Ahead - other 774 49% Total 1567 100% Number of vehicles No. % 1 651 42% 2 740 47% 3 136 9% 4 29 2% 5 9 1% 6 2 0% Total 1567 100% First point of impact casualty vehicle No. % No Impact 5 0% Front 797 69% Back 80 7% Offside 156 14% Nearside 116 10% Total 1154 100% First point of impact other vehicle No. % Front 563 65% Back 97 11% Offside 129 15% 54 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Nearside 82 9% Total 871 100% Pedestrian Movement No. % No Pedestrian 1169 75% Crossing - driver's nearside 168 11% Crossing - driver's nearside masked 58 4% Crossing - driver's offside 84 5% Crossing - driver's offside masked 29 2% In carriageway - not crossing 10 1% In carriageway - not crossing masked 2 0% Walking in carriageway - facing traffic 11 1% Walking in carriageway - back to traffic 13 1% Unknown /Other 23 1% Total 1567 100% 5.1.4 Nature of injury Nature of injury No. % No injury code recorded 37 2% S00-S09 Inj. to head 516 33% S10-S19 Inj. to neck 77 5% S20-S29 Inj. to thorax 166 11% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 110 7% S40-S49 Inj. to shoulder & upper arm 99 6% S50-S59 Inj. to elbow & forearm 81 5% S60-S69 Inj. to wrist & hand 51 3% S70-S79 Inj. to hip & thigh 102 7% S80-S89 Inj. to knee & lower leg 259 17% S90-S99 Inj. to ankle & foot 31 2% T00-T07 Inj. involving multiple body regions 21 1% T08-T14 Inj. to unspecified body part 11 1% T15-T19 Foriegn body entering through natural orifice 1 0% T36-T50 Poisonings 2 0% T79 Certain early complications of trauma 3 0% Total 1567 100% 5.1.5 Health Service Impact Length of Stay No. % 0 178 11% 55 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 1 417 27% 2 199 13% 3-4 200 13% 5-7 173 11% 8-14 196 13% 15-28 124 8% >28 80 5% Total 1567 100% 56 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 5.2 Serious injury, all road users 5.2.1 Who were injured? The highest rates of non-fatal serious injury identified from the linked database were for people aged 15-24 (Figure 17). In contrast to injuries of all severities, for non-fatal serious injury outcomes, there was a lower proportion for children under 15 and a higher proportion for older people, particularly for those aged 75 and over, who tend to be more frail. Approximately two thirds of the serious injury cases on the linked database were males. Many of the casualties were occupants of motor vehicles or pedestrians, although relative to their road usage there were also many motorcycle related injuries (Figure 18) 5.2.2 Where were they injured? The distribution by district in which the crash occurred, the road class, and the road number on which the crash occurred are shown. Approximately half of the casualties in the linked database were injured on A- roads. Just less than a quarter of these occurred on the A259 (all south coast districts), a further 10% on the A23 (Brighton) and 8% on the A27 (Lewes, Arun and Chichester). Others with at least 1 non-fatal serious injury casualty each quarter were as follows: A21 (Rother), A24 (Horsham), A29 (Arun), A270 (Brighton and Hove), and A272. The districts shown in brackets are those where the majority of accidents on these roads occurred. In total, these are the places of occurrence for almost a third of the non-fatal serious injury casualties. 5.2.3 Circumstances of injury In approximately 70% of cases the road surface was dry, and most of the remainder it was wet or damp. In only 1% of casualties was the road condition snowy or icy. 85% of the injuries occurred in fine weather, 13% occurred in rain, and for 1% of cases this was exacerbated by high winds. In approximately half of the cases, the event occurred at a junction. The majority of these occurred at a T- junction, and many of the remainder occurred at crossroads, at the entrance to a private drive, or at a roundabout. In many cases, no manoeuvre was involved. Where it was, the main manoeuvres were overtaking moving or stationary vehicles, or turning right. 45% were single vehicle accidents, 45% involved 2 vehicles and 7% involved 3. In approximately two 57 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 thirds of cases, the point of impact was the front of the casualty s vehicle, and in 64% of cases involving more than one vehicle, the front was the first point of impact of the other vehicle involved. 5.2.4 Nature of injury Injuries to the head account for approximately 20% of all injuries, which is lower than for all injury severities. Injuries to the knee and lower leg accounted for over a quarter of injuries, significantly greater than for all severities. The other main sites of occurrence of injuries were as follows (in order of magnitude): hip / thigh, abdomen / back / pelvis, chest, shoulder/ upper arm, and elbow / forearm. In total, these account for 90% of non-fatal serious injuries. Figure 17 - Age-specific rate - serious injury, all road users Figure 18 - Four main road user types - serious injuries, all road users 100 80 Rate per 100,000 60 40 Car Occupant 37% 5.2 Serious injury, all road users Pedestrian 32% 20 Pedal Cyclist 58 Prepared by CHSS at Tunbridge Wells 10% Motor Cyclist 21% 0 0-14 15-24 25-34 35-64 65-74 75+ Age band

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 5.2 Serious injury, all road users 5.2.1 Who were injured Age Band No. % 00-15 62 9% 16-24 130 19% 25-34 112 17% 35-64 191 29% 65-74 65 10% 75+ 108 16% Total 668 100% Sex No. % Male 422 63% Female 246 37% Total 668 100% 59 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Road User Group No. % Pedestrian 0-4 3 0% Pedestrian 5-9 14 2% Pedestrian 10-15 26 4% Pedestrian 16-29 40 6% Pedestrian 30-44 18 3% Pedestrian 45-59 19 3% Pedestrian 60-74 39 6% Pedestrian 75+ 57 9% Pedestrian 216 32% Cyclist 0-15 9 1% Cyclist 16+ 55 8% Pedal Cyclist 64 10% Motorcycle Rider 106 16% Motorcycle Passenger 33 5% Motorcyclist 139 21% Car/Taxi Driver Under 17 1 0% Car/Taxi Driver 17-24 34 5% Car/Taxi Driver 25-34 32 5% Car/Taxi Driver 35-44 18 3% Car/Taxi Driver 45-59 29 4% Car/Taxi Driver 60-74 24 4% Car/Taxi Driver 75+ 28 4% Car/Taxi Passenger 83 12% Car Occupant 249 37% Total 668 100% 60 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 5.2.2 Where were they injured District Council No. % Brighton 96 14% Eastbourne 49 7% Hastings 32 5% Hove 45 7% Lewes 43 6% Rother 62 9% Wealden 53 8% Adur 6 1% Arun 57 9% Chichester 79 12% Crawley 24 4% Horsham 42 6% Mid-Sussex 55 8% Worthing 25 4% Total 668 100% Road Classification No. % Motorway 0 0% A road 338 51% B Road 107 16% C road 114 17% Unclassified 109 16% Total 668 100% Classification No. % 61 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 M23 0 0% A21 17 3% A22 14 2% A23 33 5% A24 15 2% A26 8 1% A27 28 4% A28 4 1% A29 15 2% A259 75 11% A264 2 0% A265 5 1% A267 3 0% A268 1 0% A269 7 1% A270 16 2% A271 4 1% A272 15 2% A273 4 1% A275 6 1% A280 2 0% A281 7 1% A283 7 1% A284 2 0% A285 5 1% A286 13 2% A293 1 0% A295 2 0% A2004 1 0% A2010 2 0% A2011 1 0% A2021 5 1% A2023 1 0% A2025 1 0% A2029 0 0% A2031 4 1% A2032 0 0% A2038 1 0% A2073 1 0% A2100 3 0% A2101 0 0% A2102 3 0% A2219 2 0% A2220 2 0% B Roads 107 16% C Roads 114 17% Unclassified 109 16% Total 668 100% 5.2.3 Circumstances of injury 62 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Road Surface No. % Dry 459 69% Wet/Damp 198 30% Snow 2 0% Frost/Ice 8 1% Flood 1 0% Total 668 100% Weather No. % Fine without high winds 556 83% Raining without high winds 77 12% Snowing without high winds 4 1% Fine with high winds 11 2% Raining with high winds 8 1% Snowing with high winds 3 0% Fog or mist - if hazard 4 1% Other 3 0% Unknown 2 0% Total 668 100% Junction Detail No. % Not at junction 315 47% Roundabout 28 4% Mini roundabout 1 0% T or staggered junction 212 32% Y junction 15 2% Slip road 7 1% Crossroads 38 6% Multiple junction 12 2% Private drive/entrance 35 5% Other junction 5 1% Total 668 100% Manoeuvres No. % Reversing 12 2% 63 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 parked 4 1% Waiting to go ahead 7 1% Stopping 11 2% Starting 19 3% U Turn 4 1% Turning left 19 3% Waiting to turn left 1 0% Turning right 50 7% Waiting to turn right 2 0% Changing lane to left 2 0% Changing lane to right 3 0% Overtaking moving vehicle - offside 31 5% Overtaking stationary vehicle - offside 39 6% Overtaking - nearside 3 0% Ahead - left hand bend 75 11% Ahead - right hand bend 62 9% Ahead - other 324 49% Total 668 100% Number of vehicles No. % 1 299 45% 2 302 45% 3 50 7% 4 14 2% 5 3 0% 6 0 0% Total 668 100% First point of impact casualty vehicle No. % No Impact 3 1% Front 303 67% Back 24 5% Offside 74 16% Nearside 48 11% Total 452 100% First point of impact other vehicle No. % Front 225 64% 64 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Back 30 9% Offside 51 15% Nearside 43 12% Total 349 100% Pedestrian Movement No. % No Pedestrian 463 69% Crossing - driver's nearside 91 14% Crossing - driver's nearside masked 19 3% Crossing - driver's offside 48 7% Crossing - driver's offside masked 14 2% In carriageway - not crossing 5 1% In carriageway - not crossing masked 1 0% Walking in carriageway - facing traffic 6 1% Walking in carriageway - back to traffic 8 1% Unknown /Other 13 2% Total 668 100% 5.2.4 Nature of injury Nature of injury No. % No injury code recorded 7 1% S00-S09 Inj. to head 128 19% S10-S19 Inj. to neck 14 2% S20-S29 Inj. to thorax 59 9% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 60 9% S40-S49 Inj. to shoulder & upper arm 42 6% S50-S59 Inj. to elbow & forearm 31 5% S60-S69 Inj. to wrist & hand 13 2% S70-S79 Inj. to hip & thigh 89 13% S80-S89 Inj. to knee & lower leg 188 28% S90-S99 Inj. to ankle & foot 20 3% T00-T07 Inj. involving multiple body regions 12 2% T08-T14 Inj. to unspecified body part 3 0% T15-T19 Foriegn body entering through natural orifice 0 0% T36-T50 Poisonings 1 0% T79 Certain early complications of trauma 1 0% Total 668 100% 5.2.5 Health service impact 65 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology serious injury, all road users Injury Prevention through Data Linkage Phase 3 Length of Stay No. % 0 0 0% 1 2 0% 2 2 0% 3-4 91 14% 5-7 173 26% 8-14 196 29% 15-28 124 19% >28 80 12% Total 668 100% 66 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 5.3 Occupants of motor vehicles An estimated 37% of non-fatal serious injuries on Sussex roads were to occupants of motor vehicles. 5.3.1 Who were injured? For non-fatal serious injury outcomes, age-specific rates were highest for those aged 15-24 and 75 and over. (Figure 19). In contrast to the pattern for all road users, just over half of the casualties on the linked database were males, for all non-fatal hospitalised injuries as well as for non-fatal serious injuries. 5.3.2 Where were they injured? The distribution by district in which the crash occurred, the road class, and the road number on which the crash occurred are shown. Just over half of the casualties with non-fatal serious injury in the whole of the linked database were injured on A-roads; however for occupants of motor vehicles, 60% of those injuries occurred on A-roads. Just less than a sixth of these non-fatal serious injuries which occurred on A-roads occurred on the A259 (most south coast districts but particularly Arun), and just over 10% on the A27 (Lewes and Arun). Others with an average of at least 2 non-fatal serious injury casualties each year were as follows: A21 (Rother), A22 (Wealden), A23 (Mid-Sussex), A24 (Horsham), A29 (Arun), A272 (Chichester), A275 (Wealden), and A286 (Chichester). The districts shown in brackets are those where the majority of accidents on these roads occurred. In total, these are the places of occurrence for over 40% of the non-fatal serious injury casualties amongst occupants of motor vehicles. 5.3.3 Circumstances of injury Amongst the casualties with non-fatal serious injury, in approximately 60% of cases the road surface was dry, and most of the remainder it was wet, or damp. For 3% of casualties the road condition was snowy or icy. Approximately 80% of the non-fatal serious injuries occurred in fine weather. Around 15% occurred in rain, and 2% of cases this was exacerbated by high winds. In total, 4% occurred in high winds. In less than 40% of the serious injury cases, the event occurred at a junction. Approximately half of these occurred at a T-junction, and many of the remainder occurred at crossroads or at the entrance to a private drive. In two thirds of cases, no manoeuvre was involved. Where it was, the main manoeuvres were 67 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 overtaking moving or stationary vehicles, or turning right. For injury to occupants of motor vehicles (for all severity, as well as for non-fatal serious injury), a much lower proportion (approximately a quarter) were single vehicle accidents than for all road users. Almost 60% involved 2 vehicles and 12% involved 3. In over three quarters, the point of impact was the front of the casualty s vehicle, and in incidents involving 2 or more vehicles in over three quarters of cases the front was the first point of impact of the other vehicle involved. 5.3.4 Nature of injury Injuries to the head accounted for over 20% of all serious injuries. Injuries to the chest and to the knee / lower leg each accounted for approximately 15% of serious injuries. The other main site of occurrence of injuries were to the abdomen / back / pelvis, and to the hip / thigh. In total, these account for almost 80% of the non-fatal serious injuries. 5.3.5 Health service impact and severity of injury 45% of injury cases of any severity were discharged from hospital the same or the following day. In contrast to all road users, less than one fifth stayed over a week in hospital, and 9% stayed over 2 weeks. Nevertheless, there were still 4% of casualties that were detained in hospital for over a month. On average, this is approximately 1 casualty per month, who was an occupant of a motor vehicle, who had injuries of such severity that they remained in hospital for over 28 days. 5.3 Occupants of motor vehicles all and serious Figure 19 - Age-specific rate - serious injury, occupants of motor vehicles 40 68 Prepared by CHSS at Tunbridge Wells 35 30

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 5.3.1 Who were injured Age Band No. % No. % 00-15 40 5% 7 3% 16-24 173 24% 56 22% 25-34 119 16% 41 16% 35-64 240 33% 73 29% 65-74 64 9% 26 10% 75+ 92 13% 46 18% Total 728 100% 249 100% Sex No. % No. % Male 401 55% 134 54% Female 327 45% 115 46% Total 728 100% 249 100% All Serious 69 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 Road User Group No. % No. % Car/Taxi Driver Under 17 4 1% 1 0% Car/Taxi Driver 17-24 95 13% 34 14% Car/Taxi Driver 25-34 89 12% 32 13% Car/Taxi Driver 35-44 56 8% 18 7% Car/Taxi Driver 45-59 90 12% 29 12% Car/Taxi Driver 60-74 65 9% 24 10% Car/Taxi Driver 75+ 58 8% 28 11% Car/Taxi Passenger 271 37% 83 33% Total 728 100% 249 100% 5.2.3 Where were they injured? District Council No. % No. % Brighton 51 7% 18 7% Eastbourne 33 5% 18 7% Hastings 38 5% 8 3% Hove 26 4% 10 4% Lewes 43 6% 20 8% Rother 82 11% 28 11% Wealden 90 12% 30 12% Adur 6 1% 0% Arun 67 9% 28 11% Chichester 133 18% 38 15% Crawley 13 2% 3 1% Horsham 48 7% 17 7% Mid-Sussex 78 11% 24 10% Worthing 20 3% 7 3% Total 728 100% 249 100% Road Classification No. % No. % Motorway 1 0% 0% 70 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 A road 399 55% 149 60% B Road 113 16% 35 14% C road 134 18% 39 16% Unclassified 81 11% 26 10% Total 728 100% 249 100% Road Number No. % No. % M23 1 0% 0% A21 20 3% 9 4% 71 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 A22 23 3% 8 3% A23 20 3% 9 4% A24 15 2% 7 3% A26 9 1% 5 2% A27 48 7% 16 6% A28 5 1% 2 1% A29 18 2% 10 4% A259 71 10% 22 9% A264 1 0% 0% A265 5 1% 2 1% A267 6 1% 2 1% A268 4 1% 1 0% A269 5 1% 3 1% A270 7 1% 1 0% A271 10 1% 3 1% A272 30 4% 11 4% A273 5 1% 2 1% A275 7 1% 6 2% A280 3 0% 2 1% A281 7 1% 3 1% A283 24 3% 5 2% A284 2 0% 1 0% A285 8 1% 3 1% A286 23 3% 6 2% A293 2 0% 1 0% A295 1 0% 0% A2021 3 0% 3 1% A2031 4 1% 1 0% A2038 2 0% 1 0% A2100 5 1% 2 1% A2101 3 0% 0% A2102 1 0% 0% A2219 1 0% 1 0% A2220 1 0% 1 0% B roads 113 16% 35 14% C roads 134 18% 39 16% Unclassified 81 11% 26 10% Total 728 100% 249 100% 5.3.3 Circumstances of injury Road Surface No. % No. % Dry 425 58% 152 61% 72 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 Wet/Damp 282 39% 88 35% Snow 3 0% 0% Frost/Ice 15 2% 8 3% Flood 3 0% 1 0% Total 728 100% 249 100% Weather No. % No. % Fine without high winds 585 80% 200 80% Raining without high winds 90 12% 32 13% Snowing without high winds 7 1% 3 1% Fine with high winds 9 1% 3 1% Raining with high winds 18 2% 4 2% Snowing with high winds 2 0% 2 1% Fog or mist - if hazard 8 1% 3 1% Other 5 1% 1 0% Unknown 4 1% 1 0% Total 728 100% 249 100% All Serious Junction Detail No. % No. % Not at junction 431 59% 152 61% Roundabout 21 3% 6 2% Mini roundabout 1 0% 1 0% T or staggered junction 156 21% 51 20% Y junction 20 3% 9 4% Slip road 7 1% 2 1% Crossroads 51 7% 13 5% Multiple junction 4 1% 1 0% Private drive/entrance 33 5% 13 5% Other junction 4 1% 1 0% Total 728 100% 249 100% Manoeuvres No. % No. % Reversing 2 0% 0% parked 2 0% 0% Waiting to go ahead 15 2% 4 2% 73 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 Stopping 13 2% 3 1% Starting 15 2% 8 3% U Turn 5 1% 3 1% Turning left 14 2% 7 3% Waiting to turn left 3 0% 0% Turning right 66 9% 24 10% Waiting to turn right 6 1% 1 0% Changing lane to left 1 0% 1 0% Changing lane to right 2 0% 0% Overtaking moving vehicle - offside 57 8% 23 9% Overtaking stationary vehicle - offside 20 3% 8 3% Overtaking - nearside 1 0% 0% Ahead - left hand bend 112 15% 36 14% Ahead - right hand bend 106 15% 36 14% Ahead - other 288 40% 95 38% Total 728 100% 249 100% Number of vehicles No. % No. % 1 197 27% 64 26% 2 406 56% 144 58% 3 96 13% 30 12% 4 19 3% 9 4% 5 8 1% 2 1% 6 2 0% 0% Total 728 100% 249 100% First point of impact - casualty vehicle No. % No. % No Impact 2 0% 1 0% Front 550 76% 189 76% Back 44 6% 7 3% 74 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 Offside 77 11% 36 14% Nearside 55 8% 16 6% Total 728 100% 249 100% First point of impact - other vehicle No. % No. % Front 387 75% 137 77% Back 53 10% 11 6% Offside 46 9% 18 10% Nearside 29 6% 13 7% Total 515 100% 179 100% 5.3.4 Nature of injury Nature of injury No. % No. % No injury code recorded 25 3% 4 2% S00-S09 Inj. to head 247 34% 54 22% S10-S19 Inj. to neck 64 9% 11 4% S20-S29 Inj. to thorax 145 20% 45 18% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 62 9% 29 12% S40-S49 Inj. to shoulder & upper arm 33 5% 15 6% S50-S59 Inj. to elbow & forearm 24 3% 11 4% S60-S69 Inj. to wrist & hand 17 2% 3 1% S70-S79 Inj. to hip & thigh 31 4% 28 11% S80-S89 Inj. to knee & lower leg 55 8% 37 15% S90-S99 Inj. to ankle & foot 6 1% 3 1% T00-T07 Inj. involving multiple body regions 9 1% 6 2% T08-T14 Inj. to unspecified body part 6 1% 2 1% T15-T19 Foriegn body entering through natural orifice 1 0% 0% T36-T50 Poisonings 2 0% 1 0% T79 Certain early complications of trauma 1 0% 0% Total 728 100% 249 100% 5.3.5 Health service impact Length of Stay No. % No. % 0 104 14% 0% 75 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology occupants of motor vehicles Injury Prevention through Data Linkage Phase 3 1 223 31% 0% 2 103 14% 1 0% 3-4 92 13% 42 17% 5-7 66 9% 66 27% 8-14 73 10% 73 29% 15-28 37 5% 37 15% >28 30 4% 30 12% Total 728 100% 249 100% 76 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 5.4 Motorcyclists An estimated 21% of non-fatal serious injuries on Sussex roads were to motorcyclists. 5.4.1 Who were injured? The vast majority of motorcycle casualties resulting in serious injury were men. Very few children and older people were involved. The highest rates were for those aged 15-24 and 25-34 (Figure 20). 5.4.2 Where were they injured? The distribution by district in which the crash occurred, the road class, and the road number on which the crash occurred are shown. Just over half of the casualties resulting in non-fatal serious injuries in the whole of the linked database were injured on A-roads; however for motorcyclists, 60% of those injuries occurred on A-roads. Almost a quarter of these non-fatal serious injuries on A-roads occurred on the A259 (Brighton, Eastbourne, Hastings and Rother), and approximately 10% on the A27 (Chichester, Arun and Lewes). Other roads with 5 or more serious injuries to motorcyclists over the 3 years: A21 (Rother), A23 (Brighton and Crawley), and A270 (Brighton and Hove). In total, these are the places of occurrence for over 40% of the non-fatal serious injury casualties amongst motorcycle riders. 5.4.3 Circumstances of injury In contrast to many other road users, 80% of motorcycle casualties occurred when the road surface was dry, and for all of the remainder it was wet, or damp. Approximately 90% of the non-fatal serious injuries occurred in fine weather, and most of the remainder occurred whilst it was raining. A small number (n=4, 3%) occurred in high winds. In contrast to occupants of vehicles, two thirds of the casualties resulted from accidents at junctions. The majority occurred at T-junctions, but many of the remainder occurred at roundabouts, crossroads or at the entrance to a private drive. In three quarters of cases, no manoeuvre was involved. These are consistent with the motorcyclist being struck by another vehicle which was making a manoeuvre. For motorcyclists, approximately a quarter were single vehicle accidents, approximately 65% involved 2 77 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 vehicles and around 10% involved 3. Although in many instances the point of impact was the front (60%), there was a much higher proportion of side impact accidents than for other road users (over 35%). Unlike other road users, in incidents involving 2 or more vehicles, the first point of impact was the front of the other vehicle in less than 50% of cases, and was the side in 37%. 5.4.4 Nature of injury For serious injury cases, the main body site was the knee and lower leg (almost 40%) and the hip and thigh (14%). It is estimated that around 60% of non-fatal serious injury include injuries to the foot and leg. Injuries to the hand and arm were present in 20% of non-fatal serious motorcycle casualties. Nonfatal serious injuries to the head were substantially less than other road users at 7%, which is probably attributable to the use of helmets by motorcyclists. 5.4.5 Health service impact and severity of injury Unlike occupants of motor vehicles, only a quarter of motorcycle rider injury cases of any severity were discharged from hospital the same or the following day. Over 30% stayed over a week in hospital, and around 15% stayed over 2 weeks. 4% of casualties that were detained in hospital for over a month. 5.4 Motorcyclists all and serious injury Figure 20 - Age-specific rate - serious injury, motor cyclists 25 78 Prepared by CHSS at Tunbridge Wells 20

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 5.4.1 Who were injured Age Band No. % No. % 00-15 3 1% 3 2% 16-24 61 24% 34 24% 25-34 84 33% 42 30% 35-64 94 37% 53 38% 65-74 6 2% 6 4% 75+ 3 1% 1 1% Total 251 100% 139 100% Sex No. % No. % Male 230 92% 131 94% Female 21 8% 8 6% Total 251 100% 139 100% Road User Group No. % No. % Motorcycle Rider 193 77% 106 76% Motorcycle Passenger 58 23% 33 24% 79 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 Total 251 100% 139 100% 5.4.2 Where were they injured? District Council No. % No. % Brighton 27 11% 15 11% Eastbourne 16 6% 5 4% Hastings 11 4% 7 5% Hove 21 8% 14 10% Lewes 17 7% 8 6% Rother 27 11% 18 13% Wealden 25 10% 12 9% Adur 8 3% 3 2% Arun 20 8% 7 5% Chichester 33 13% 15 11% Crawley 12 5% 7 5% Horsham 13 5% 12 9% Mid-Sussex 15 6% 11 8% Worthing 6 2% 5 4% Total 251 100% 139 100% Road Classification No. % No. % A road 137 55% 84 60% B Road 38 15% 23 17% C road 42 17% 17 12% Unclassified 34 14% 15 11% Total 251 100% 139 100% Road Number No. % No. % A21 8 3% 6 4% A22 6 2% 1 1% 80 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 A23 8 3% 7 5% A24 3 1% 3 2% A26 2 1% 2 1% A27 16 6% 8 6% A28 2 1% 1 1% A29 8 3% 4 3% A259 36 14% 20 14% A264 2 1% 1 1% A265 1 0% 1 1% A267 3 1% 1 1% A268 1 0% 0% A269 1 0% 1 1% A270 9 4% 5 4% A271 2 1% 1 1% A272 4 2% 2 1% A273 1 0% 1 1% A281 2 1% 2 1% A283 2 1% 2 1% A284 1 0% 1 1% A285 2 1% 1 1% A286 3 1% 3 2% A295 1 0% 1 1% A2011 1 0% 1 1% A2021 1 0% 1 1% A2023 1 0% 1 1% A2025 2 1% 1 1% A2029 1 0% 0% A2031 1 0% 1 1% A2073 1 0% 1 1% A2100 1 0% 1 1% A2101 1 0% 0% A2102 1 0% 1 1% A2219 1 0% 1 1% A2220 1 0% 0% B 38 15% 23 17% C 42 17% 17 12% U 34 14% 15 11% Total 251 100% 139 100% 5.4.3 Circumstances of injury Road Surface No. % No. % 81 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 Dry 191 76% 111 80% Wet/Damp 57 23% 28 20% Snow 1 0% 0% Frost/Ice 2 1% 0% Total 251 100% 139 100% Weather No. % No. % Fine without high winds 217 86% 121 87% Raining without high winds 19 8% 12 9% Snowing without high winds 1 0% 0% Fine with high winds 3 1% 3 2% Raining with high winds 3 1% 0% Snowing with high winds 1 0% 1 1% Fog or mist - if hazard 4 2% 1 1% Other 3 1% 1 1% Total 251 100% 139 100% Junction Detail No. % No. % Not at junction 83 33% 45 32% Roundabout 12 5% 8 6% Mini roundabout 1 0% 0% T or staggered junction 94 37% 57 41% Y junction 10 4% 3 2% Slip road 3 1% 2 1% Crossroads 22 9% 8 6% Multiple junction 3 1% 3 2% Private drive/entrance 22 9% 12 9% Other junction 1 0% 1 1% Total 251 100% 139 100% Manoeuvres No. % No. % Waiting to go ahead 2 1% 2 1% 82 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 Stopping 6 2% 4 3% Starting 2 1% 1 1% U Turn 1 0% 1 1% Turning left 4 2% 3 2% Turning right 13 5% 5 4% Changing lane to left 2 1% 1 1% Changing lane to right 1 0% 0% Overtaking moving vehicle - offside 18 7% 8 6% Overtaking stationary vehicle - offside 9 4% 6 4% Overtaking - nearside 6 2% 2 1% Ahead - left hand bend 38 15% 29 21% Ahead - right hand bend 31 12% 14 10% Ahead - other 118 47% 63 45% Total 251 100% 139 100% Number of vehicles No. % No. % 1 62 25% 32 23% 2 161 64% 88 63% 3 21 8% 15 11% 4 6 2% 3 2% 5 1 0% 1 1% Total 251 100% 139 100% First point of impact casualty vehicle No. % No. % No Impact 1 0% 0% Front 150 60% 83 60% Back 12 5% 6 4% Offside 48 19% 26 19% Nearside 40 16% 24 17% Total 251 100% 139 100% First point of impact other vehicle No. % No. % Front 85 48% 51 49% 83 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology motorcyclists Injury Prevention through Data Linkage Phase 3 Back 23 13% 14 13% Offside 45 25% 23 22% Nearside 25 14% 16 15% Total 178 100% 104 100% 5.4.4 Nature of Injury Nature of injury No. % No. % No injury code recorded 3 1% 0% S00-S09 Inj. to head 34 14% 10 7% S10-S19 Inj. to neck 7 3% 2 1% S20-S29 Inj. to thorax 11 4% 7 5% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 12 5% 6 4% S40-S49 Inj. to shoulder & upper arm 27 11% 10 7% S50-S59 Inj. to elbow & forearm 29 12% 11 8% S60-S69 Inj. to wrist & hand 21 8% 7 5% S70-S79 Inj. to hip & thigh 26 10% 19 14% S80-S89 Inj. to knee & lower leg 64 25% 54 39% S90-S99 Inj. to ankle & foot 12 5% 9 6% T00-T07 Inj. involving multiple body regions 5 2% 4 3% Total 251 100% 139 100% 5.4.5 Health service impact Length of Stay No. % No. % 0 14 6% 0% 1 50 20% 0% 2 30 12% 0% 3-4 34 14% 16 12% 5-7 43 17% 43 31% 8-14 44 18% 44 32% 15-28 25 10% 25 18% >28 11 4% 11 8% Total 251 100% 139 100% 84 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 5.5 Pedal cyclists An estimated 10% of non-fatal serious injuries on Sussex roads were to pedal cyclists. Because of the bias associated with the results for all injury, only those for serious injury are reported. The analysis below is based on only 64 cases. 5.5.1 Who were injured? Serious injury to pedal cyclists occurs at all ages, with the highest rate for people aged 15-24 (Figure 21). Approximately 70% of casualties were men /boys. 5.5.2 Where were they injured? The distribution by district in which the crash occurred, the age-sex breakdown within districts (all nonfatal casualties), the road class, and the road number on which the crash occurred are shown. A much lower proportion of the serious injury casualties occurred on A-roads (approximately 35%) and a much higher proportion on unclassified roads (n=19, 30%) than occupants / riders of motor vehicles. Nevertheless, there were at least 8 (13%) non-fatal serious injuries that occurred on the A259, the majority of which were in Chichester, Arun and Eastbourne. 5.5.3 Circumstances of injury In two thirds of cases, the road was dry and in one third it was wet or damp. Approximately 90% of the non-fatal serious injuries occurred in fine weather, and most of the remainder occurred whilst it was raining. Almost 60% of the serious injury casualties resulted from accidents at junctions. These mainly occurred at T-junctions. In approximately 60% of cases, no manoeuvre was involved, which indicates that for some of these the cyclist was struck by another vehicle making a manoeuvre. Turning right and overtaking a stationary vehicle were the manoeuvres with the highest number of serious injury casualties amongst pedal cyclists. For pedal cyclists, approximately a quarter were single vehicle accidents and approximately 70% 85 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 involved 2 vehicles. Although in many instances the point of impact was the front (48%), there was a much higher proportion of side impact accidents than for occupants of motor vehicles (over 30%), and a much higher proportion of rear impacts than other road users. In incidents involving other vehicles, the first point of impact was the front of the other vehicle in almost 60% of cases, and was the side in around 40% of incidents. 5.5.4 Nature of injury For serious injury cases, the results show the vulnerability of the cyclists head and limbs, particularly the leg; approximately 30% of serious injury occurred to the head or neck and 40% to the leg and foot. Around 15% involved the hand and / or arm, and another 11% involved the abdomen / back/ pelvis. This is a very different pattern to that for motorcyclists, with a greater proportion with head injury and injury to the abdomen / back / pelvis, and a lower proportion with leg injury. This is probably influenced by crash dynamics and the low use of helmets amongst pedal cyclists. 5.5.5 Health service impact and severity of injury Around 40% of pedal cyclist injury cases of any severity were discharged from hospital the same or the following day. 35 casualties (20%) stayed over a week in hospital, however, 15 stayed at least 2 weeks and 3 of the casualties were detained in hospital for over a month. 5.5 Pedal cyclists all and serious 86 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 Figure 21 - Age-specific rate - serious injury, pedal cyclists 10 9 8 7 Rate per 100,000 6 5 4 3 2 1 0 0-14 15-24 25-34 35-64 65-74 75+ Age band 5.5.1 Who were injured Age Band No. % No. % 00-15 44 25% 9 14% 16-24 34 19% 14 22% 25-34 29 17% 6 9% 35-64 53 30% 26 41% 65-74 8 5% 5 8% 75+ 7 4% 4 6% Total 175 100% 64 100% Sex No. % No. % Male 139 79% 45 70% Female 36 21% 19 30% Total 175 100% 64 100% 5.5.2 Where were they injured? 87 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 District Council No. % No. % Brighton 27 15% 8 13% Eastbourne 11 6% 7 11% Hastings 10 6% 5 8% Hove 20 11% 5 8% Lewes 13 7% 3 5% Rother 7 4% 3 5% Wealden 9 5% 2 3% Arun 15 9% 8 13% Chichester 30 17% 10 16% Crawley 8 5% 2 3% Horsham 13 7% 4 6% Mid-Sussex 7 4% 4 6% Worthing 5 3% 3 5% Total 175 100% 64 100% Age within district Male Female 0-15 16+ Total 0-15 16+ Total Total Total 41 98 139 3 33 36 175 23% 56% 79% 2% 19% 21% 100% Brighton 3 16 19 8 8 27 Eastbourne 3 6 9 2 2 11 Hastings 5 4 9 1 1 10 Hove 5 11 16 4 4 20 Lewes 7 5 12 1 1 13 Rother 2 5 7 0 7 Wealden 3 4 7 2 2 9 Adur 0 0 0 Arun 2 9 11 1 3 4 15 Chichester 7 16 23 7 7 30 Crawley 2 4 6 2 2 8 Horsham 1 10 11 1 1 2 13 Mid-Sussex 6 6 1 1 7 Worthing 1 2 3 2 2 5 East Sussex, Brighton & Hove 28 51 79 1 17 18 97 29% 53% 81% 1% 18% 19% 100% East Sussex (No Brighton & Hove) 20 24 44 1 5 6 50 40% 48% 88% 2% 10% 12% 100% West Sussex 13 47 60 2 16 18 78 17% 60% 77% 3% 21% 23% 100% All Serious 88 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 Road Classification No. % No. % A road 83 47% 23 36% B Road 22 13% 7 11% C road 29 17% 15 23% Unclassified 41 23% 19 30% Total 175 100% 64 100% Road Number No. % No. % A21 3 2% 1 2% A22 2 1% 2 3% A23 10 6% 3 5% A24 5 3% 2 3% A27 9 5% 1 2% A28 1 1% 0% A29 1 1% 0% A259 20 11% 8 13% A264 2 1% 0% A269 1 1% 0% A270 8 5% 2 3% A272 2 1% 1 2% A283 1 1% 0% A285 2 1% 0% A286 5 3% 1 2% A293 1 1% 0% A295 2 1% 0% A2021 2 1% 1 2% A2023 1 1% 0% A2032 1 1% 0% A2073 1 1% 0% A2101 1 1% 0% A2102 1 1% 1 2% A2220 1 1% 0% B Roads 22 13% 7 11% C Roads 29 17% 15 23% Unclassified 41 23% 19 30% Total 175 100% 64 100% 5.5.3 Circumstances of injury 89 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 Road Surface No. % No. % Dry 131 75% 42 66% Wet/Damp 43 25% 21 33% Snow 1 1% 1 2% Total 175 100% 64 100% Weather No. % No. % Fine without high winds 149 85% 55 86% Raining without high winds 16 9% 6 9% Snowing without high winds 1 1% 0% Fine with high winds 5 3% 1 2% Raining with high winds 2 1% 1 2% Other 1 1% 0% Unknown 1 1% 1 2% Total 175 100% 64 100% Junction detail No. % No. % Not at junction 61 35% 27 42% Roundabout 17 10% 5 8% Mini roundabout 1 1% 0% T or staggered junction 61 35% 23 36% Y junction 3 2% 0% Slip road 3 2% 1 2% Crossroads 13 7% 3 5% Multiple junction 6 3% 3 5% Private drive/entrance 8 5% 2 3% Other junction 2 1% 0% Total 175 100% 64 100% All Serious 90 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 Manoeuvres No. % No. % parked 1 1% 1 2% Waiting to go ahead 2 1% 0% Stopping 1 1% 1 2% Starting 10 6% 3 5% Turning left 4 2% 2 3% Waiting to turn left 1 1% 1 2% Turning right 17 10% 9 14% Waiting to turn right 1 1% 1 2% Changing lane to right 2 1% 1 2% Overtaking moving vehicle - offside 3 2% 0% Overtaking stationary vehicle - offside 14 8% 6 9% Ahead - left hand bend 5 3% 3 5% Ahead - right hand bend 20 11% 7 11% Ahead - other 94 54% 29 45% Total 175 100% 64 100% Number of vehicles No. % No. % 1 28 16% 15 23% 2 134 77% 46 72% 3 11 6% 2 3% 4 2 1% 1 2% Total 175 100% 64 100% First point of impact casualty vehicle No. % No. % No Impact 2 1% 2 3% Front 97 55% 31 48% Back 24 14% 11 17% Offside 31 18% 12 19% Nearside 21 12% 8 13% Total 175 100% 64 100% All Serious 91 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedal cyclists Injury Prevention through Data Linkage Phase 3 First point of impact other vehicle No. % No. % Front 77 53% 28 58% Back 13 9% 1 2% Offside 30 21% 6 13% Nearside 24 17% 13 27% Total 144 100% 48 100% 5.5.4 Nature of injury Nature of injury No. % No. % No injury code recorded 3 2% 1 2% S00-S09 Inj. to head 76 43% 17 27% S10-S19 Inj. to neck 6 3% 1 2% S20-S29 Inj. to thorax 3 2% 2 3% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 9 5% 7 11% S40-S49 Inj. to shoulder & upper arm 18 10% 5 8% S50-S59 Inj. to elbow & forearm 13 7% 3 5% S60-S69 Inj. to wrist & hand 9 5% 2 3% S70-S79 Inj. to hip & thigh 11 6% 10 16% S80-S89 Inj. to knee & lower leg 19 11% 13 20% S90-S99 Inj. to ankle & foot 3 2% 2 3% T00-T07 Inj. involving multiple body regions 1 1% 0% T08-T14 Inj. to unspecified body part 4 2% 1 2% Total 175 100% 64 100% 5.5.5 Health service impact Length of Stay No. % No. % 0 22 13% 0% 1 45 26% 0% 2 26 15% 0% 3-4 30 17% 12 19% 5-7 17 10% 17 27% 8-14 20 11% 20 31% 15-28 12 7% 12 19% >28 3 2% 3 5% Total 175 100% 64 100% 92 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 5.6 Pedestrians An estimated 32% of non-fatal serious injuries on Sussex roads were to pedestrians. 5.6.1 Who were injured? Consistent with many other studies, the highest rates of non-fatal serious injury identified from the linked database were for older people aged 75 and over (Figure 22). Just over half of the casualties on the linked database were males, for all non-fatal hospitalised injuries as well as for non-fatal serious injuries. 5.6.2 Where were they injured? The distribution by district in which the crash occurred, by age within district (all non-fatal casualties only), the road class, and the road number on which the crash occurred are shown. A quarter of the pedestrian casualties occurred in Brighton. Almost 40% of the casualties in the linked database were injured on A-roads. Amongst the non-fatal serious injury casualties, almost a quarter of the pedestrians were injured on unclassified roads. Almost a third of these non-fatal serious injuries which occurred on A- roads occurred on the A259 (distributed across most of the southern districts), a sixth on the A23 (almost all in Brighton), and around 10% on the A270 (Brighton and Hove). 5.6.3 Circumstances of injury Amongst the casualties with non-fatal serious injury, in approximately 70% of cases the road surface was dry, and for almost all of the remainder it was wet, or damp. Approximately 85% of the non-fatal serious injuries occurred in fine weather and the remainder occurred when it was raining. In around 60% of the serious injury cases, the event occurred at a junction. The majority of these occurred at a T-junction, and many of the remainder occurred at crossroads, roundabouts or at the entrance to a private drive. In around 70% of cases, no manoeuvre was involved, although 5% occurred on bends. In 9% the vehicle involved was overtaking, 6% turning right, and 3% turning left, and in 6% the vehicle was reversing. For the great majority of the serious injuries, the pedestrian was crossing the road. 5.6.4 Nature of injury 93 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 For serious injury cases, the results show the vulnerability of the pedestrians head and limbs, particularly the leg; over a fifth of serious injury occurred to the head and almost 60% to the leg and foot. Almost 10% involved the hand or arm, and another 8% involved the abdomen / back/ pelvis. 5.6.5 Health service impact and severity of injury A third of injury cases of any severity were discharged from hospital the same or the following day. A third stayed in hospital over a week, and a fifth stayed over 2 weeks. Almost 10% of casualties were detained in hospital for over a month. This represents at least 1 per month across the two counties who had injuries sufficiently severe to result in over 28 days stay in hospital. 5.6 Pedestrians all and serious Figure 22 - Age-specific rate - serious injury, pedestrians 40 35 94 Prepared by CHSS at Tunbridge Wells 30

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 5.6.1 Who were injured Age band No. % No. % 0-4 19 5% 3 1% 5-9 50 12% 14 6% 10-15 67 16% 26 12% 16-29 75 18% 40 19% 30-44 44 11% 18 8% 45-59 29 7% 19 9% 60-74 55 13% 39 18% 75+ 74 18% 57 26% Total 413 100% 216 100% Sex No. % No. % Male 225 54% 112 52% Female 188 46% 104 48% Total 413 100% 216 100% 5.6.2 Where were they injured? District Council No. % No. % Brighton 108 26% 55 25% 95 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 Eastbourne 35 8% 19 9% Hastings 35 8% 12 6% Hove 43 10% 16 7% Lewes 27 7% 12 6% Rother 22 5% 13 6% Wealden 17 4% 9 4% Adur 3 1% 3 1% Arun 35 8% 14 6% Chichester 25 6% 16 7% Crawley 18 4% 12 6% Horsham 13 3% 9 4% Mid-Sussex 19 5% 16 7% Worthing 13 3% 10 5% Total 413 100% 216 100% Age within district 0-4 5-9 10-15 16-59 60-74 75+ Total Total 19 50 67 148 55 74 413 5% 12% 16% 36% 13% 18% 100% Brighton 5 13 10 46 12 22 108 Eastbourne 2 3 4 10 7 9 35 Hastings 1 9 9 10 4 2 35 Hove 2 9 3 12 6 11 43 Lewes 1 9 12 3 2 27 Rother 2 2 8 7 3 22 Wealden 1 1 3 6 3 3 17 Adur 1 1 1 3 Arun 2 5 6 12 2 8 35 Chichester 2 3 3 14 3 25 Crawley 1 3 6 4 3 1 18 Horsham 2 2 5 3 1 13 Mid-Sussex 7 6 2 4 19 Worthing 1 1 2 2 2 5 13 East Sussex, Brighton & Hove 11 38 40 104 42 52 287 4% 13% 14% 36% 15% 18% 100% East Sussex (No Brighton & Hove) 4 16 27 46 24 19 136 3% 12% 20% 34% 18% 14% 100% West Sussex 8 12 27 44 13 22 126 6% 10% 21% 35% 10% 17% 100% Road Classification No. % No. % A road 154 37% 82 38% 96 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 B Road 68 16% 42 19% C road 66 16% 43 20% Unclassified 125 30% 49 23% Total 413 100% 216 100% Road Number No. % No. % A21 2 0% 1 0% A22 6 1% 3 1% A23 24 6% 14 6% A24 4 1% 3 1% A26 1 0% 1 0% A27 5 1% 3 1% A28 1 0% 1 0% A29 5 1% 1 0% A259 54 13% 25 12% A264 1 0% 1 0% A265 2 0% 2 1% A269 4 1% 3 1% A270 12 3% 8 4% A271 1 0% 0% A272 2 0% 1 0% A273 1 0% 1 0% A275 2 0% 0% A281 2 0% 2 1% A285 2 0% 1 0% A286 4 1% 3 1% A295 1 0% 1 0% A2004 1 0% 1 0% A2010 7 2% 2 1% A2021 2 0% 0% A2029 1 0% 0% A2031 3 1% 2 1% A2038 2 0% 0% A2102 1 0% 1 0% A2220 1 0% 1 0% B Roads 68 16% 42 19% C Roads 66 16% 43 20% Unclassified 125 30% 49 23% Total 413 100% 216 100% 5.6.3 Circumstances of injury Road Surface No. % No. % 97 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 Dry 303 73% 154 71% Wet/Damp 108 26% 61 28% Snow 1 0% 1 0% Frost/Ice 1 0% 0% Total 413 100% 216 100% Weather No. % No. % Fine without high winds 356 86% 180 83% Raining without high winds 42 10% 27 13% Snowing without high winds 1 0% 1 0% Fine with high winds 7 2% 4 2% Raining with high winds 6 1% 3 1% Other 1 0% 1 0% Total 413 100% 216 100% Junction detail No. % No. % Not at junction 178 43% 91 42% Roundabout 11 3% 9 4% T or staggered junction 155 38% 81 38% Y junction 14 3% 3 1% Slip road 3 1% 2 1% Crossroads 29 7% 14 6% Multiple junction 8 2% 5 2% Private drive/entrance 10 2% 8 4% Other junction 5 1% 3 1% Total 413 100% 216 100% Manoeuvres No. % No. % Reversing 16 4% 12 6% Parked 4 1% 3 1% 98 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 Waiting to go ahead 1 0% 1 0% Stopping 6 1% 3 1% Starting 9 2% 7 3% Turning left 11 3% 7 3% Turning right 20 5% 12 6% Changing lane to right 2 0% 2 1% Overtaking stationary vehicle - offside 47 11% 19 9% Overtaking - nearside 1 0% 1 0% Ahead - left hand bend 14 3% 7 3% Ahead - right hand bend 8 2% 5 2% Ahead - other 274 66% 137 63% Total 413 100% 216 100% Pedestrian Movement No. % No. % No Pedestrian* 31 8% 17 8% Crossing - driver's nearside 159 38% 88 41% Crossing - driver's nearside masked 57 14% 19 9% Crossing - driver's offside 83 20% 47 22% Crossing - driver's offside masked 28 7% 14 6% In carriageway - not crossing 10 2% 5 2% In carriageway - not crossing masked 2 0% 1 0% Walking in carriageway - facing traffic 9 2% 5 2% Walking in carriageway - back to traffic 13 3% 8 4% Unknown /Other 21 5% 12 6% Total 413 100% 216 100% * Classified as pedestrian in hospital data but not on STATS19 5.6.4 Nature of injury Nature of injury No. % No. % 99 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology pedestrians Injury Prevention through Data Linkage Phase 3 No injury code recorded 6 1% 2 1% S00-S09 Inj. to head 159 38% 47 22% S20-S29 Inj. to thorax 7 2% 5 2% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 27 7% 18 8% S40-S49 Inj. to shoulder & upper arm 21 5% 12 6% S50-S59 Inj. to elbow & forearm 15 4% 6 3% S60-S69 Inj. to wrist & hand 4 1% 1 0% S70-S79 Inj. to hip & thigh 34 8% 32 15% S80-S89 Inj. to knee & lower leg 121 29% 84 39% S90-S99 Inj. to ankle & foot 10 2% 6 3% T00-T07 Inj. involving multiple body regions 6 1% 2 1% T08-T14 Inj. to unspecified body part 1 0% 0% T79 Certain early complications of trauma 2 0% 1 0% Total 413 100% 216 100% 5.6.5 Health service impact Length of Stay No. % No. % 0 38 9% 0% 1 99 24% 2 1% 2 40 10% 1 0% 3-4 44 11% 21 10% 5-7 47 11% 47 22% 8-14 59 14% 59 27% 15-28 50 12% 50 23% >28 36 9% 36 17% Total 413 100% 216 100% 100 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 5.7 Child casualties An estimated 9% of non-fatal serious injuries on Sussex roads were to children aged 0-15. There were relatively few serious child casualties resulting from RTAs (n=62) so care must be taken when interpreting these results. 5.7.1 Who were injured? Around 60% of the non-fatal serious injuries to children were boys. Children were mainly injured as pedestrians, particularly in the age groups 5-9 and 10-15, and as cyclists. 5.7.2 Where were they injured? The distribution by district in which the crash occurred, the age, sex and road user group within district (all non-fatal casualties only), the road class, and the road number on which the crash occurred are shown. A quarter of the serious child casualties occurred in Brighton and Hove. The main place of occurrence of serious child casualties was on unclassified roads. This is consistent with child casualties occurring predominantly in urban areas. Only 30% of the serious casualties in the linked database were injured on A-roads. Approximately 10% of the serious child casualties occurred on the A259 (mainly in Hastings and Rother). 5.7.3 Circumstances of injury Amongst the casualties with non-fatal serious injury, in over 80% of cases the road surface was dry, and for the remainder it was wet, or damp. Approximately 95% of the non-fatal serious injuries occurred in fine weather and the remainder occurred when it was raining. In around 55% of the serious injury cases, the event occurred at a junction. Over two thirds of these occurred at a T-junction. In around 70% of cases, the vehicle involved was making no manoeuvre, although 5% occurred on bends. In 11% the vehicle involved was overtaking a stationary vehicle, in 7% it was turning left or right. One vehicle was involved in two thirds, and 2 vehicles in 30% of incidents. For almost all of the non-fatal serious pedestrian injuries to children, the child was crossing the road. 101 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 Approximately half of the non-fatal serious injuries to pedestrians occurred where there was no junction, and half at T-junctions. The vehicle that struck the pedestrians was involved in no manoeuvre in 75% of cases, and for many of the remainder, the vehicle was overtaking a stationary vehicle. 5.7.4 Nature of injury Around a quarter of the serious injuries were to the head and over 60% to the leg. 5.7.5 Health service impact and severity of injury Over 45% of injury cases, of any severity, to children were discharged from hospital the same or the following day. Twelve percent stayed in hospital over a week, and 8% stayed over 2 weeks. There were ten cases (4%) on our linked database who were detained in hospital for over a month. Child casualties all and serious 5.7.1 Who were injured 102 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 Sex No. % No. % Male 142 64% 38 61% Female 81 36% 24 39% Total 223 100% 62 100% Road User Group No. % No. % Pedestrian 0-4 19 9% 3 5% Pedestrian 5-9 50 22% 14 23% Pedestrian 10-15 67 30% 26 42% Cyclist 0-15 44 20% 9 15% Motorcycle Rider 3 1% 3 5% Car/Taxi Driver Under 17 3 1% 1 2% Car/Taxi Passenger 37 17% 6 10% Total 223 100% 62 100% 5.7.1 Where were they injured? District Council No. % No. % Brighton 36 16% 10 16% Eastbourne 13 6% 7 11% Hastings 31 14% 6 10% Hove 23 10% 5 8% Lewes 18 8% 5 8% Rother 8 4% 3 5% Wealden 14 6% 5 8% Adur 1 0% 1 2% Arun 21 9% 1 2% Chichester 20 9% 2 3% Crawley 13 6% 5 8% Horsham 7 3% 3 5% Mid-Sussex 9 4% 5 8% Worthing 9 4% 4 6% Total 223 100% 62 100% Age and sex within Male Female Total district 0-4 5-9 10-14 15 Total 0-4 5-9 10-14 15 Total Total 17 36 74 15 142 13 29 32 7 81 223 103 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 8% 16% 33% 7% 64% 6% 13% 14% 3% 36% 100% Brighton 4 9 8 2 23 3 6 4 13 36 Eastbourne 2 2 3 1 8 2 3 5 13 Hastings 2 5 10 1 18 2 6 4 1 13 31 Hove 3 8 11 2 7 2 1 12 23 Lewes 10 2 12 1 4 1 6 18 Rother 2 3 1 6 2 2 8 Wealden 3 1 3 1 8 1 4 1 6 14 Adur 1 1 0 1 Arun 1 5 7 2 15 1 1 3 1 6 21 Chichester 1 5 8 1 15 1 2 2 5 20 Crawley 2 5 7 1 2 3 6 13 Horsham 1 2 1 4 1 1 1 3 7 Mid-Sussex 3 3 6 2 1 3 9 Worthing 3 2 3 8 1 1 9 East Sussex, 11 22 45 8 86 9 23 21 4 57 143 Brighton & Hove 8% 15% 31% 6% 60% 6% 16% 15% 3% 40% 100% East Sussex (No 7 10 29 6 52 4 10 15 3 32 84 Brighton & Hove) 8% 12% 35% 7% 62% 5% 12% 18% 4% 38% 100% West Sussex 6 14 29 7 56 4 6 11 3 24 80 8% 18% 36% 9% 70% 5% 8% 14% 4% 30% 100% Road user group Pedestrian Cyclist Motorcycle Car Total within district 0-4 5-9 10-15 0-15 Rider Driver Passenger Total 19 50 67 44 3 3 37 223 9% 22% 30% 20% 1% 1% 17% 100% 104 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 Brighton 5 13 10 3 5 36 Eastbourne 2 3 4 3 1 13 Hastings 1 9 9 6 6 31 Hove 2 9 3 5 1 2 1 23 Lewes 1 9 7 1 18 Rother 2 2 2 2 8 Wealden 1 1 3 3 1 5 14 Adur 1 1 Arun 2 5 6 3 1 4 21 Chichester 2 3 3 7 5 20 Crawley 1 3 6 2 1 13 Horsham 2 2 2 1 7 Mid-Sussex 7 2 9 Worthing 1 1 2 1 4 9 East Sussex, 11 38 40 29 3 2 20 143 Brighton & Hove 8% 27% 28% 20% 2% 1% 14% 100% East Sussex (No 4 16 27 21 2 0 14 84 Brighton & Hove) 5% 19% 32% 25% 2% 0% 17% 100% West Sussex 8 12 27 15 0 1 17 80 10% 15% 34% 19% 0% 1% 21% 100% Road Classification No. % No. % A road 67 30% 18 29% B Road 21 9% 5 8% C road 43 19% 15 24% Unclassified 92 41% 24 39% Total 223 100% 62 100% Road Number No. % No. % A21 3 1% 0% 105 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 A23 7 3% 2 3% A24 1 0% 0% A27 7 3% 2 3% A29 5 2% 0% A259 17 8% 6 10% A269 2 1% 1 2% A270 8 4% 2 3% A271 1 0% 0% A281 2 1% 1 2% A283 1 0% 1 2% A284 1 0% 0% A285 2 1% 0% A286 4 2% 0% A295 2 1% 0% A2004 1 0% 1 2% A2031 1 0% 1 2% A2032 1 0% 0% A2100 1 0% 1 2% B Roads 21 9% 5 8% C Roads 43 19% 15 24% Unclassified 92 41% 24 39% Total 223 100% 62 100% 106 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 5.7.3 Circumstances of injury Road Surface No. % No. % Dry 186 83% 50 81% Wet/Damp 36 16% 12 19% Frost/Ice 1 0% 0% Total 223 100% 62 100% Weather No. % No. % Fine without high winds 211 95% 59 95% Raining without high winds 6 3% 2 3% Fine with high winds 3 1% 0% Raining with high winds 2 1% 1 2% Other 1 0% 0% Total 223 100% 62 100% Junction Detail No. % No. % Not at junction 103 46% 28 45% Roundabout 9 4% 3 5% Mini roundabout 1 0% 1 2% T or staggered junction 80 36% 25 40% Y junction 6 3% 0% Slip road 1 0% 0% Crossroads 15 7% 3 5% Multiple junction 1 0% 1 2% Private drive/entrance 4 2% 1 2% Other junction 3 1% 0% Total 223 100% 62 100% 107 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 Manoeuvres No. % No. % Reversing 2 1% 0% parked 3 1% 2 3% Waiting to go ahead 4 2% 1 2% Stopping 5 2% 0% Starting 8 4% 3 5% Turning left 4 2% 1 2% Turning right 9 4% 3 5% Waiting to turn right 1 0% 0% Changing lane to right 1 0% 1 2% Overtaking moving vehicle - offside 2 1% 0% Overtaking stationary vehicle - offside 29 13% 7 11% Overtaking - nearside 1 0% 1 2% Ahead - left hand bend 12 5% 2 3% Ahead - right hand bend 7 3% 1 2% Ahead - other 135 61% 40 65% Total 223 100% 62 100% Number of vehicles No. % No. % 1 142 64% 41 66% 2 68 30% 18 29% 3 11 5% 3 5% 4 1 0% 0% 5 1 0% 0% Total 223 100% 62 100% First point of impact casualty vehicle No. % No. % No Impact 4 2% 3 5% Front 140 63% 40 65% Back 11 5% 2 3% Offside 34 15% 9 15% Nearside 34 15% 8 13% Total 223 100% 62 100% All Serious 108 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 First point of impact other vehicle No. % No. % Front 53 70% 12 67% Back 7 9% 0% Offside 10 13% 3 17% Nearside 6 8% 3 17% Total 76 100% 18 100% Pedestrian Movement No. % No. % No Pedestrian 90 40% 20 32% Crossing - driver's nearside 53 24% 21 34% Crossing - driver's nearside masked 35 16% 8 13% Crossing - driver's offside 18 8% 4 6% Crossing - driver's offside masked 18 8% 7 11% In carriageway - not crossing 3 1% 1 2% In carriageway - not crossing masked 1 0% 0% Walking in carriageway - facing traffic 1 0% 1 2% Unknown /Other 4 2% 0% Total 223 100% 62 100% 5.7.4 Nature of injury All Serious Nature of injury No. % No. % No injury code recorded 3 1% 1 2% S00-S09 Inj. to head 108 48% 15 24% S10-S19 Inj. to neck 3 1% 0% S20-S29 Inj. to thorax 4 2% 1 2% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 9 4% 2 3% S40-S49 Inj. to shoulder & upper arm 10 4% 1 2% S50-S59 Inj. to elbow & forearm 7 3% 1 2% S60-S69 Inj. to wrist & hand 4 2% 1 2% S70-S79 Inj. to hip & thigh 14 6% 12 19% S80-S89 Inj. to knee & lower leg 53 24% 26 42% S90-S99 Inj. to ankle & foot 3 1% 2 3% T00-T07 Inj. involving multiple body regions 2 1% 0% T08-T14 Inj. to unspecified body part 3 1% 0% Total 223 100% 62 100% 5.7.5 Health service impact 109 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology child casualties Injury Prevention through Data Linkage Phase 3 Length of Stay No. % No. % 0 12 5% 0% 1 92 41% 2 3% 2 39 17% 1 2% 3-4 35 16% 14 23% 5-7 18 8% 18 29% 8-14 8 4% 8 13% 15-28 9 4% 9 15% >28 10 4% 10 16% Total 223 100% 62 100% 110 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 5.8 Older people An estimated 26% of non-fatal serious injuries on Sussex roads were to older people aged 65 and over. 5.8.1 Who were injured? Just over half of the non-fatal serious injuries to older people were women. Half of these older people were seriously injured as pedestrians, a quarter as drivers, and a sixth as passengers. 5.8.2 Where were they injured? The distribution by district in which the crash occurred, by age, sex and road user group within district (all non-fatal casualties only), the road class, and the road number on which the crash occurred are shown. 43% of serious injuries occurred on A-roads, and a quarter on B-roads. Casualties on A-roads were distributed around the counties, although one in eleven of these serious injuries occurred on the A259. The majority of these occurred in Worthing, Brighton, Eastbourne, Hastings and Rother. 5.8.3 Circumstances of injury Amongst the casualties with non-fatal serious injury, over 70% of cases the road surface was dry, and for the remainder it was wet, or damp. Approximately 90% of the non-fatal serious injuries occurred in fine weather and the remainder occurred when it was raining, with the exception of one case which occurred when snowing. In around 60% of the serious injury cases, the event occurred at a junction, and over half of these occurred at a T-junction, with approximately 12% occurring at crossroads. In almost 60% of cases, the motor vehicle involved was making no manoeuvre, although over 10% occurred on bends. In 10% the vehicle involved was overtaking, 12% was turning right and 5% turning left. One vehicle was involved in just over half, and 2 vehicles in 40% of incidents. Seven percent of older casualties were seriously injured by a driver reversing. Single vehicle accidents were responsible for a lower percentage of serious casualties to older drivers and passengers (approximately 20%) than for those of younger age. For almost all of the serious pedestrian injuries to older people, the serious injury occurred whilst crossing 111 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 the road. 5.8.4 Nature of injury The pattern of body site of serious injury is different to other groups. Almost 40% occurred to the leg or foot, over 20% to the head, 17% to the chest and 10% to the arm. 5.8.5 Health service impact and severity of injury 27% of injury cases, of any severity, amongst older people were discharged from hospital the same or the following day. Over 40% stayed in hospital over a week, and a quarter over 2 weeks. Thirty five cases (12%) stayed in hospital over 28 days. There was an average of at least 1 older person injured on the roads in Sussex each month which resulted in over 28 days in hospital. 5.8 Older people all and serious 5.8.1 Who were injured Age Band No. % No. % 112 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 65-74 117 40% 65 38% 75+ 176 60% 108 62% Total 293 100% 173 100% Sex No. % No. % Male 139 47% 82 47% Female 154 53% 91 53% Total 293 100% 173 100% Road User Group No. % No. % Pedestrian 60-74 39 13% 28 16% Pedestrian 75+ 74 25% 57 33% Cyclist 16+ 15 5% 9 5% Motorcycle Rider 5 2% 4 2% Motorcycle Passenger 4 1% 3 2% Car/Taxi Driver 60-74 41 14% 17 10% Car/Taxi Driver 75+ 58 20% 28 16% Car/Taxi Passenger 57 19% 27 16% Total 293 100% 173 100% 5.8.2 Where were they injured? District Council No. % No. % Brighton 37 13% 27 16% 113 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 Eastbourne 26 9% 17 10% Hastings 16 5% 10 6% Hove 24 8% 13 8% Lewes 13 4% 7 4% Rother 27 9% 16 9% Wealden 25 9% 14 8% Adur 3 1% 0% Arun 28 10% 14 8% Chichester 46 16% 22 13% Crawley 5 2% 5 3% Horsham 12 4% 6 3% Mid-Sussex 20 7% 12 7% Worthing 11 4% 10 6% Total 293 100% 173 100% Age and sex within district Total Male Female Total 65-69 70-74 75-79 80-84 85+ Total 65-69 70-74 75-79 80-84 85+ Total 28 35 29 21 26 139 25 29 36 37 27 154 293 10% 12% 10% 7% 9% 47% 9% 10% 12% 13% 9% 53% 100% Brighton 4 4 4 7 19 3 4 7 4 18 37 Eastbourne 1 3 3 3 1 11 1 5 4 4 1 15 26 Hastings 2 2 1 1 6 2 3 2 1 2 10 16 Hove 1 4 1 5 11 3 2 3 3 2 13 24 Lewes 2 4 1 7 2 2 1 1 6 13 Rother 2 4 4 2 2 14 1 3 5 1 3 13 27 Wealden 4 1 1 1 1 8 2 4 4 4 3 17 25 Adur 0 2 1 3 3 Arun 2 2 4 4 3 15 2 1 5 2 3 13 28 Chichester 7 5 6 4 3 25 6 3 4 5 3 21 46 Crawley 1 1 1 3 1 1 2 5 Horsham 2 1 2 5 1 1 2 2 1 7 12 Mid-Sussex 1 4 1 4 1 11 2 1 2 3 1 9 20 Worthing 2 2 4 2 1 1 1 2 7 11 East Sussex, 16 22 15 6 17 76 11 22 22 21 16 92 168 Brighton & 10% 13% 9% 4% 10% 45% 7% 13% 13% 13% 10% 55% 100% Hove East Sussex (No 11 14 10 6 5 46 8 17 15 11 10 61 107 Brighton & 10% 13% 9% 6% 5% 43% 7% 16% 14% 10% 9% 57% 100% Hove) West Sussex 12 13 14 15 9 63 14 7 14 16 11 62 125 10% 10% 11% 12% 7% 50% 11% 6% 11% 13% 9% 50% 100% Road user group within Pedestrian Cyclist Motorcycle Car Driver Car Total district 65-74 75+ 65+ Rider Passenger 65-74 75+ Passenger Total 39 74 15 5 4 41 58 57 293 13% 25% 5% 2% 1% 14% 20% 19% 100% 114 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 Brighton 8 22 1 2 3 1 37 Eastbourne 6 9 1 5 5 26 Hastings 4 2 1 1 2 1 5 16 Hove 4 11 1 2 1 5 24 Lewes 3 2 6 2 13 Rother 4 3 1 2 7 10 27 Wealden 2 3 1 1 5 3 10 25 Adur 2 1 3 Arun 1 8 3 4 7 5 28 Chichester 3 7 2 2 10 17 5 46 Crawley 2 1 1 1 5 Horsham 3 1 1 5 2 12 Mid-Sussex 4 1 6 5 4 20 Worthing 2 5 1 1 2 11 East Sussex, Brighton 31 52 3 2 2 20 20 38 168 & Hove 18% 31% 2% 1% 1% 12% 12% 23% 100% East Sussex (No 19 19 2 1 2 16 16 32 107 Brighton & Hove) 18% 18% 2% 1% 2% 15% 15% 30% 100% West Sussex 8 22 12 3 2 21 38 19 125 6% 18% 10% 2% 2% 17% 30% 15% 100% Road Classification No. % No. % A road 135 46% 75 43% B Road 58 20% 43 25% C road 49 17% 29 17% Unclassified 51 17% 26 15% Total 293 100% 173 100% Road Number No. % No. % A21 4 1% 2 1% A22 9 3% 6 3% A23 8 3% 6 3% 115 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 A24 6 2% 4 2% A26 2 1% 2 1% A27 10 3% 3 2% A28 4 1% 2 1% A29 1 0% 1 1% A259 29 10% 16 9% A265 1 0% 1 1% A267 2 1% 1 1% A268 1 0% 1 1% A269 4 1% 2 1% A270 4 1% 1 1% A271 2 1% 2 1% A272 12 4% 5 3% A273 3 1% 3 2% A275 1 0% 1 1% A280 2 1% 1 1% A281 1 0% 1 1% A283 6 2% 0% A285 2 1% 1 1% A286 8 3% 4 2% A293 1 0% 1 1% A295 1 0% 1 1% A2011 2 1% 2 1% A2021 2 1% 1 1% A2031 2 1% 2 1% A2038 1 0% 0% A2101 2 1% 0% A2102 1 0% 1 1% A2219 1 0% 1 1% B Roads 58 20% 43 25% C Roads 49 17% 29 17% Unclassified 51 17% 26 15% Total 293 100% 173 100% 5.8.3 Circumstances of injury Surface No. % No. % Dry 200 68% 125 72% 116 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 Wet/Damp 91 31% 48 28% Snow 1 0% 0% Frost/Ice 1 0% 0% Total 293 100% 173 100% Weather No. % No. % Fine without high winds 249 85% 152 88% Raining without high winds 32 11% 16 9% Snowing without high winds 2 1% 1 1% Fine with high winds 4 1% 2 1% Raining with high winds 4 1% 2 1% Fog or mist - if hazard 2 1% 0% Total 293 100% 173 100% Junction detail No. % No. % Not at junction 119 41% 66 38% Roundabout 9 3% 8 5% T or staggered junction 93 32% 59 34% Y junction 9 3% 6 3% Slip road 2 1% 1 1% Crossroads 37 13% 20 12% Multiple junction 5 2% 3 2% Private drive/entrance 16 5% 9 5% Other junction 3 1% 1 1% Total 293 100% 173 100% Manoeuvres No. % No. % Reversing 15 5% 12 7% parked 2 1% 1 1% Waiting to go ahead 1 0% 1 1% 117 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 Stopping 5 2% 3 2% Starting 12 4% 9 5% U Turn 1 0% 1 1% Turning left 11 4% 8 5% Turning right 38 13% 21 12% Changing lane to left 1 0% 1 1% Overtaking moving vehicle - offside 10 3% 8 5% Overtaking stationary vehicle - offside 18 6% 9 5% Ahead - left hand bend 20 7% 11 6% Ahead - right hand bend 23 8% 11 6% Ahead - other 136 46% 77 45% Total 293 100% 173 100% Number of vehicles No. % No. % 1 136 46% 93 54% 2 131 45% 70 40% 3 17 6% 6 3% 4 8 3% 3 2% 5 1 0% 1 1% Total 293 100% 173 100% First point of impact casualty vehicle No. % No. % No Impact 2 1% 1 1% Front 220 75% 124 72% Back 21 7% 14 8% Offside 28 10% 18 10% Nearside 22 8% 16 9% Total 293 100% 173 100% First point of impact other vehicle No. % No. % Front 103 67% 57 73% Back 21 14% 7 9% 118 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 Offside 19 12% 10 13% Nearside 10 7% 4 5% Total 153 100% 78 100% Pedestrian Movement No. % No. % No Pedestrian 187 64% 94 54% Crossing - driver's nearside 53 18% 36 21% Crossing - driver's nearside masked 8 3% 7 4% Crossing - driver's offside 25 9% 22 13% Crossing - driver's offside masked 5 2% 3 2% Walking in carriageway - facing traffic 5 2% 4 2% Walking in carriageway - back to traffic 3 1% 1 1% Unknown /Other 7 2% 6 3% Total 293 100% 173 100% 5.8.4 Nature of injury Nature of injury No. % No. % No injury code recorded 8 3% 2 1% S00-S09 Inj. to head 83 28% 36 21% S10-S19 Inj. to neck 9 3% 3 2% S20-S29 Inj. to thorax 66 23% 30 17% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 13 4% 12 7% S40-S49 Inj. to shoulder & upper arm 18 6% 12 7% S50-S59 Inj. to elbow & forearm 8 3% 6 3% S60-S69 Inj. to wrist & hand 4 1% 2 1% S70-S79 Inj. to hip & thigh 26 9% 26 15% S80-S89 Inj. to knee & lower leg 48 16% 38 22% S90-S99 Inj. to ankle & foot 3 1% 2 1% T00-T07 Inj. involving multiple body regions 3 1% 3 2% T08-T14 Inj. to unspecified body part 2 1% 0% T79 Certain early complications of trauma 2 1% 1 1% Total 293 100% 173 100% 5.8.5 Health service impact Length of Stay No. % No. % 0 14 5% 0% 1 63 22% 0% 119 Prepared by CHSS at Tunbridge Wells

Part 5: Epidemiology older people Injury Prevention through Data Linkage Phase 3 2 28 10% 0% 3-4 31 11% 16 9% 5-7 36 12% 36 21% 8-14 46 16% 46 27% 15-28 40 14% 40 23% >28 35 12% 35 20% Total 293 100% 173 100% 120 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 5.9 Young drivers aged 17-24 An estimated 5% of non-fatal serious injuries were to young drivers aged 17-24. There were relatively small numbers of serious injury cases (n=34), and so care must be taken when interpreting the tables. 5.9.1 Who were injured? Two thirds of the young drivers on the linked database were males, for all non-fatal hospitalised injuries as well as for non-fatal serious injuries. 5.9.2 Where were they injured? The distribution by district in which the crash occurred, by age and sex within district (all non-fatal casualties only), the road class, and the road number on which the crash occurred are shown. In contrast to other road user groups, two thirds of the serious injuries occurred in West Sussex. Around two thirds of the serious injuries occurred on A-roads and a fifth on C-roads. Almost half of these non-fatal serious injuries on A-roads occurred on the A27, A29, and A259. 5.9.3 Circumstances of injury A smaller proportion (just over 50%) of non-fatal serious injuries to young drivers occurred when the road surface was dry. This might be due to lack of experience when driving in less than ideal conditions. Approximately three quarters of the non-fatal serious injuries occurred in fine weather, 20% of serious casualties occurred in rain, and in 3 cases in mist or fog. In less than half of the serious injury cases, the event occurred at a junction, half of which occurred at a T- junction. In almost three quarters of cases, no manoeuvre was involved, although it occurred on a bend in around a half of these cases. A right turn manoeuvre was involved in 15% of cases. A slightly higher proportion (approximately 30%) were single vehicle accidents than for occupants of all ages. Around 55% involved 2 vehicles and 12% involved 3. In almost three quarters, the point of impact was the front of the casualty s vehicle, and in a quarter it was the offside, consistent with right turn accidents. In incidents involving 2 or more vehicles the front was the first point of impact of the other 121 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 vehicle involved in 86% of cases. 5.9.4 Nature of injury The pattern of site of occurrence of injury was different for young drivers compared to that for occupants of all ages, perhaps consistent with higher velocity crashes. A quarter of serious injuries occurred to the hip and thigh, a quarter to the abdomen, back, and pelvis, and a quarter to the head or neck. 5.9.5 Health service impact and severity of injury Half of the injury cases of any severity to young drivers were discharged from hospital the same or the following day. In contrast to all road users, just over one fifth stayed over a week in hospital, and approximately one in 10 stayed over 2 weeks. There were at least four cases which stayed over 28 days in hospital. 5.9 Young drivers aged 17-24 all and serious 5.9.1 Who were injured 122 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 Sex No. % No. % Male 64 67% 22 65% Female 31 33% 12 35% Total 95 100% 34 100% 5.9.2 Where were they injured District Council No. % No. % Brighton 7 7% 4 12% Eastbourne 2 2% 1 3% Hastings 5 5% 0% Hove 2 2% 1 3% Lewes 6 6% 2 6% Rother 10 11% 1 3% Wealden 15 16% 3 9% Adur 1 1% 0% Arun 8 8% 6 18% Chichester 16 17% 6 18% Crawley 2 2% 2 6% Horsham 5 5% 2 6% Mid-Sussex 14 15% 4 12% Worthing 2 2% 2 6% Total 95 100% 34 100% Age and sex within district Male Female Total 17-19 20-24 Total 17-19 20-24 Total Total 23 41 64 11 20 31 95 123 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 24% 43% 67% 12% 21% 33% 100% Brighton 2 2 4 3 3 7 Eastbourne 0 2 2 2 Hastings 1 3 4 1 1 5 Hove 1 1 2 0 2 Lewes 1 4 5 1 1 6 Rother 2 6 8 1 1 2 10 Wealden 5 8 13 2 2 15 Adur 1 1 0 1 Arun 1 3 4 4 4 8 Chichester 6 5 11 1 4 5 16 Crawley 1 1 2 0 2 Horsham 2 2 1 2 3 5 Mid-Sussex 3 4 7 3 4 7 14 Worthing 1 1 1 1 2 East Sussex, Brighton & Hove 12 24 36 6 5 11 47 26% 51% 77% 13% 11% 23% 100% East Sussex (No Brighton & Hove) 9 21 30 6 2 8 38 24% 55% 79% 16% 5% 21% 100% West Sussex 11 17 28 5 15 20 48 23% 35% 58% 10% 31% 42% 100% Road Classification No. % No. % A road 58 61% 21 62% B Road 10 11% 3 9% C road 19 20% 7 21% Unclassified 8 8% 3 9% Total 95 100% 34 100% Road Number No. % No. % A21 3 3% 1 3% 124 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 A22 3 3% 0% A23 3 3% 1 3% A24 2 2% 1 3% A26 1 1% 0% A27 4 4% 3 9% A29 4 4% 3 9% A259 12 13% 4 12% A265 2 2% 0% A267 1 1% 0% A268 1 1% 0% A269 2 2% 1 3% A271 1 1% 0% A272 6 6% 2 6% A273 1 1% 0% A275 1 1% 1 3% A280 3 3% 0% A284 1 1% 1 3% A285 2 2% 0% A286 3 3% 2 6% A2100 1 1% 0% A2220 1 1% 1 3% B Roads 10 11% 3 9% C Roads 19 20% 7 21% Unclassified 8 8% 3 9% Total 95 100% 34 100% 5.9.3 Circumstances of injury Road Surface No. % No. % Dry 44 46% 19 56% Wet/Damp 47 49% 13 38% Snow 1 1% 0% Frost/Ice 2 2% 2 6% Flood 1 1% 0% Total 95 100% 34 100% Weather No. % No. % Fine without high winds 68 72% 24 71% 125 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 Raining without high winds 14 15% 6 18% Snowing without high winds 1 1% 0% Fine with high winds 2 2% 1 3% Raining with high winds 3 3% 0% Fog or mist - if hazard 4 4% 3 9% Unknown 3 3% 0% Total 95 100% 34 100% Junction detail No. % No. % Not at junction 61 64% 19 56% Roundabout 1 1% 1 3% T or staggered junction 19 20% 7 21% Y junction 3 3% 2 6% Crossroads 6 6% 2 6% Private drive/entrance 5 5% 3 9% Total 95 100% 34 100% Manoeuvres No. % No. % Stopping 1 1% 0% Starting 2 2% 0% U Turn 1 1% 1 3% Turning left 2 2% 1 3% Turning right 12 13% 5 15% Overtaking moving vehicle - offside 10 11% 2 6% Overtaking stationary vehicle - offside 1 1% 0% Ahead - left hand bend 18 19% 8 24% Ahead - right hand bend 14 15% 4 12% Ahead - other 34 36% 13 38% Total 95 100% 34 100% Number of vehicles No. % No. % 1 30 32% 10 29% 2 48 51% 19 56% 126 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 3 15 16% 4 12% 5 2 2% 1 3% Total 95 100% 34 100% First point of impact casualty vehicle No. % No. % Front 67 71% 25 74% Back 3 3% 0% Offside 16 17% 9 26% Nearside 9 9% 0% Total 95 100% 34 100% First point of impact No. % No. % Front 51 85% 19 86% Back 3 5% 1 5% Offside 4 7% 2 9% Nearside 2 3% 0% Total 60 100% 22 100% 5.9.4 Nature of injury Nature of injury No. % No. % No injury code recorded 2 2% 0% S00-S09 Inj. to head 41 43% 6 18% S10-S19 Inj. to neck 6 6% 2 6% S20-S29 Inj. to thorax 4 4% 1 3% S30-S39 Inj. to abdomen, lower back, lumbar spine & pelvis 11 12% 8 24% S40-S49 Inj. to shoulder & upper arm 4 4% 2 6% S50-S59 Inj. to elbow & forearm 4 4% 3 9% S60-S69 Inj. to wrist & hand 7 7% 0% S70-S79 Inj. to hip & thigh 8 8% 8 24% S80-S89 Inj. to knee & lower leg 6 6% 3 9% S90-S99 Inj. to ankle & foot 1 1% 0% T00-T07 Inj. involving multiple body regions 1 1% 1 3% Total 95 100% 34 100% 5.9.5 Health service impact All Serious 127 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 Length of Stay No. % No. % 0 16 17% 0% 1 30 32% 0% 2 10 11% 0% 3-4 7 7% 2 6% 5-7 11 12% 11 32% 8-14 11 12% 11 32% 15-28 6 6% 6 18% >28 4 4% 4 12% Total 95 100% 34 100% 128 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 Part 6: Principal Findings Accidental injury in general, and road traffic accidents in particular, are important contributors to ill health Accidental injury is an important health problem, and road traffic crashes are one of the most important causes of accidental injury. For the period 1 April 1995 to 31 March 1998, there were 3,166 casualties recorded as Serious or Fatal on the police s road traffic accident database (STATS19) and there were 2,666 admissions to hospital recorded as road traffic crashes which occurred in Sussex. Routinely collected data sources have limitations for the investigation of cause and the identification of potential methods of prevention. Relevant data sources for the investigation of cause and methods of prevention of RTAs in Sussex are: mortality data, hospital admissions data, and police road traffic accident reports (STATS19). Other potential data sources (eg. hospital A&E department data) have been too unreliable or incomplete for this purpose. Both the mortality and hospital admissions data are weaker on the cause of accidents, but stronger on the consequences of the accidents, whereas STATS19 is stronger on the causes and weaker on the consequences of the accidents. It was hypothesised that the linkage of hospital inpatient data records to STATS19 would provide a richer source of data to identify the causes and consequences of road traffic accidents resulting in non-fatal injury, than these two sources separately. For practical reasons, consideration was limited to these two data sources. A linked database of inpatient data to STATS19 data would provide, within one source, the majority of the data items relating to a casualty that have been recommended by the Department of Health for injury prevention work. Of the original 2,666 admissions during 1995/6 to 1997/8 to the 8 Sussex hospitals, 1625 of these 129 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 (61%) were linked to the STATS19 record. Linkage rates were seen to vary with hospital and type of road user: occupants of motor vehicles, motorcyclists, pedal cyclists, and pedestrians. Reasons for these differences in linkage performance include the nature of the hospital catchment areas, the organisation of the registers of accidents in the different police offices, and differential reporting rates of accidents by road users to the police. Other s work also suggests that there would be greater proportions of unlinked cases amongst those least seriously injured. As a result of data linkage, problems were identified with the accuracy of the STATS19 data; the magnitude and nature of these problems could result in injury prevention planning being misinformed. Severity coding appears to be particularly inaccurate within STATS19. 39% of RTA casualties admitted to hospital in Sussex were classified as Slight on STATS19. All admissions to hospital should be coded to Serious or Fatal. Other authors have found major inaccuracies in the severity coding on STATS19. There are discrepancies between the age of the casualties as recorded by STATS19 and as recorded on the hospital admission data. The weight of evidence suggests that this is principally due to inaccuracies within STATS19. The linked database is also potentially biased; it is estimated that only 50% of RTAs admitted to hospital are included in the linked database. It is estimated that 80% of road traffic accidents (RTAs) admitted to hospital can be identified from hospital data in East and West Sussex due to incomplete external cause of injury coding on the hospital inpatient data. Additionally, only 61% of cases that could be identified as RTAs from hospital data could be linked to the STATS19 data. 61% of 80% gives approximately 50% of RTAs in the linked database. If each of these databases are not substantially biased for prevention work, then the patterns of occurrence of RTAs based on STATS19 serious injuries, hospital admissions data (excluding fatal injuries), and the linked data (excluding fatal injuries) should be fundamentally the same. 130 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 For planning, it is the patterns of occurrence of RTAs that are of interest. If the patterns of occurrence by age, sex, severity of injury, place of occurrence, etc. based on non-fatal injuries in the linked database reflect those of all non-fatal RTA injury admissions to hospital, then conclusions, eg. in regard to prevention priorities, based on studying these patterns of occurrence in the linked database, will not be compromised. Furthermore, if the patterns of occurrence of casualties coded to Serious on the STATS19 database reflect those of the admissions to hospital and of the linked database for non-fatal injury, again the conclusions reached based on studying the STATS19 patterns of occurrence will not be misleading. When STATS19 Serious injuries, hospital admissions (non-fatals), and the linked data (non-fatals) were compared on the variables of age, sex and road user group, the patterns of occurrence were similar for the hospital and the linked data, but STATS19 differed from both of these. The proportion of people classified as males and females in each of the databases differed little. The linked database and the hospital admissions data showed similar distributions across age groups; however STATS19 differed quite markedly, particularly in those aged less than 35. Excluding pedal cyclists, for which we know the linked data is under-represented, the distributions by road user group were similar for the linked database and the hospital admissions data; however, STATS19 again showed some major differences in these distributions. STATS19 Serious injuries, hospital admissions (non-fatal RTA injuries only), and the linked data (non-fatal injuries only) each include cases with a wide range of injury severity. Based on routine data sources alone, a more homogeneous group of non-fatal serious injuries can only be achieved using data collected by the hospitals. It has already been shown that the STATS19 severity field includes a substantial amount of data that is inaccurate. No other field on STATS19 permits a more accurate method of identifying a 131 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 group of serious injuries. Within the hospital admissions data, data from a number of data fields can be used to indicate cases of serious injury, namely: length of stay in hospital, injury diagnosis, the surgical procedures that were carried out, whether the casualty was treated in an ITU (intensive treatment unit), and the discharge destination. The patterns of non-fatal serious injury occurrence (by age, sex and road user group) were fundamentally the same when either hospital admissions and the linked data were used, for two different severity definitions. The results for non-fatal serious injury using a definition based on length of stay were slightly better than when defined by type of injury. For serious injury identified as a hospital stay of 4 or more days or which results in a transfer to another hospital, the distributions of occurrence by age and sex were very similar for hospital data compared with the linked data. The patterns of occurrence of non-fatal serious injury by road user group were also similar, even when pedal cyclists were included in the comparison. 132 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 The principal epidemiological findings for non-fatal serious injury, based on the length of stay definition, are as follows: 1. All road users 1.1 The highest age-specific rates of non-fatal injury (all severity) was road users aged 15-24. When rates were considered for serious injury, this group was still important, but there were high rates for older people aged 75 and over, which is likely to be due to their increased likelihood of serious injury in a crash. 1.2 Approximately two thirds of serious injury cases were male. 1.3 37% were occupants of motor vehicles, 32% pedestrians, 21% motorcyclists, and 10% pedal cyclists. 1.4 Approximately half of the serious injuries occurred on A-roads; the roads on which most serious injuries occurred were the A259 coast road, the A23, and the A27. 1.5 Approximately half were injured at junctions, and in the majority of cases these were T-junctions. 1.6 In many cases, no manoeuvre was involved; but where it was it often involved overtaking, turning right or turning left. 1.7 On most occasions, unfavourable road or weather conditions did not appear to play a part. 1.8 In almost half, a single vehicle was involved 1.9 The body site of injury was the lower leg (28%), head (19%), hip and thigh (13%), chest (9%), and abdomen, back or pelvis (9%). 1.10 Discharge from hospital was the same or the following day in almost 40% of cases (all severities). The number of days the casualty stayed in hospital was over a week in 26% of cases, over a fortnight in 13%, and over a month in 5%. 2 Occupants of motor vehicles 2.1 In contrast to all road users, there were almost equal proportions of male and female serious injury casualties. 2.2 A greater proportion of serious injuries to occupants of motor vehicles occurred on A-roads (60%); the greatest proportion occurring on the A259 and the A27. 2.3 Single vehicle crashes were responsible for 26% of casualties, 58% involved 2-vehicles and 17% 3 or more. 3. Motorcyclists 133 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 3.1 The highest age-specific rates involved riders aged 15-24 and 25-34. 3.2 The vast majority of casualties were men. 3.3 The great majority of serious injuries occurred in favourable road and weather conditions; a greater proportion than for occupants of motor vehicles. 3.4 A much higher proportion occurred at junctions, and the majority of these were T-junctions. For three quarters, the motorcyclist was not carrying out a manoeuvre. 3.5 The pattern of impacts was different for motorcycle casualties compared with occupants of motor vehicles. There was a higher proportion (over 35%) of initial impacts that were on the side of the motorcycle, and higher proportion where the motorcycle struck the side of the second vehicle (37%). 3.6 The pattern of injury was different to occupants of motor vehicles. There were a high proportion of injuries to the leg or foot (60%), and less to the head (7%). This reflects the vulnerability of the motorcycle riders legs in a crash, and the protection offered by their helmet. 3.7 Relatively few motorcyclists admitted to hospital had short stays. 4. Pedal cyclists 4.1 70% of the casualties were men or boys. 4.2 A lower proportion occurred on A-roads (36%), and a higher proportion on unclassified roads (30%). Nevertheless, the A259 was still the place of occurrence of a relatively large number (8/64). 4.3 Like motorcyclists, a relatively high percentage (60%) occurred at junctions, which involved no manoeuvre by the cyclist. 4.4 The circumstances of the crash resulting in serious injury show similarities to those for motorcyclists (see 3.5 above). 4.5 The pattern of injury was dissimilar to that for motorcyclists, with higher proportions of head injury (27%), higher proportions of injury to the abdomen, back and pelvis (11%), and lower proportion of injuries to the foot or leg (39%). This is likely to reflect crash dynamics and the relatively low proportion of pedal cyclists who use helmets. 134 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 5. Pedestrians 5.1 The highest age-specific rates were amongst those aged 5-14 and aged 75 and over. 5.2 25% of non-fatal serious pedestrian injury occurred in Brighton. 5.3 The patterns of serious injury, by road type, and junction type, show similarities to pedal cyclists, another vulnerable group. The A-roads where most serious injury occurred were the A259 (12%), A23 (6%), and A270 (4%). 5.4 A large proportion of serious injuries to pedestrians were to the feet and legs (almost 60%). 5.5 There were a large proportion of pedestrian admissions with long lengths of stay. During this period, there were, on average, at least one pedestrian injury per month resulting in RTAs in Sussex that resulted in more than 28 days stay in hospital. 6. Child casualties 6.1 The majority of children were seriously injured on the roads as pedestrians or cyclists. 6.2 There was a lower proportion of serious injuries to children on major roads than for other road users; nevertheless, 10% occurred on the A259. 6.3 The circumstances of injury and the body site of injury had similarities to those described for both pedestrians and pedal cyclists. 6.4 The average length of stay in hospital for children admitted to hospital was less than most other road user groups. 7. Older people 7.1 Half of the serious injuries to older people were as pedestrians, a quarter as drivers, and a sixth as passengers. 7.2 Seventy percent of these serious injuries occurred at junctions. This is consistent with the higher proportion of pedestrian related casualties at junctions. 7.3 The average length of stay in hospital was higher than for other road users. 7.4 During this period, there was at least 1 older person injured on the roads in Sussex each month that resulted in over 28 days stay in hospital. 135 Prepared by CHSS at Tunbridge Wells

Part 6: Principal findings Injury Prevention through Data Linkage Phase 3 8. Young drivers (aged 17-24) 8.1 Consistent with others work, a greater rate of serious injury was apparent in young men compared with young women. 8.2 A higher proportion of serious injury occurred on A-roads than for any of the other road user groups. Two thirds of the serious injuries were to men; and two thirds occurred in West Sussex. 8.3 The roads implicated in almost a third of these serious casualties were the A27, the A29, and the A259 (caution: small numbers). 8.4 A greater proportion of accidents which resulted in serious injury to young drivers occurred on wet roads, and / or in the rain, or in fog or mist. 8.5 A high proportion compared to other occupants of motor vehicles, occurred on right turns. 8.6 The pattern of injury by body site suggests a greater proportion of high velocity crashes, with injuries to the thigh and hip (24%), to the abdomen, back and pelvis (24%) and to the head and neck (24%). The analysis that has been reported here is not comprehensive and those interested are invited to discuss further analysis of the data with Colin Cryer. 136 Prepared by CHSS at Tunbridge Wells

References Injury Prevention through Data Linkage Phase 3 References Austin KP. A Linked Police and Hospital Road Accident Database for Humberside. Working Paper 369, Institute for Transport Studies, University of Leeds, July 1992 (18pp.). Austin KP. A linked police and hospital database for Humberside. Traffic Engineering and Control, 1992; 33: 674-683. Austin K. The collection and use of additional sources of road safety data in highways authorities. Traffic Engineering and Control 1993; 34: 540-543. Austin K. The identification of mistakes in road accident records: casualty variables. Leeds: Institute of Transport Studies, undated. Bull JP, Roberts BJ. Road accident statistics - A comparison of police and hospital information. Accid. Anal. & Prev. 1973; 5: 45-53. Cryer C. Indicators of serious injury. A critical appraisal of four proposed indicators. Report to the Department of Health. Tunbridge Wells: South East Institute of Public Health 1999. Cryer C, Aspinall P. Injury prevention through data linkage. Phase 1: Assessment of data sources. South East Institute of Public Health, July 1994. Cryer C, Brunning D, Rahman M. Injury prevention through data linkage. Phase 2: Data linkage pilot. The linkage of police road traffic accidents reports to hospital admissions in East Sussex. Tunbridge Wells: South East Institute of Public Health, December 1995. Cryer C, Davidson L, Styles C. Injury epidemiology in the South East: Identifying priorities for action. Health of our Region Paper No 6, Bexhill: South East Thames Regional Health Authority, November 1993. Department of the Environment, Transport and the Regions. Instructions for the completion of Road Accident Reports. London: DETR, 1998. 137 Prepared by CHSS at Tunbridge Wells

References Injury Prevention through Data Linkage Phase 3 Department of Transport. Instructions for the completion of Road Accident Report form STATS19 (STATS20 manual), 6 th edition. London: Department of Transport, 1991. Haigney D. STATS19. Journal of the Institute of Road Safety Officers, October 1995: 11-17. Harris S. The real number of road traffic accident casualties in the Netherlands: A Year-Long Survey. Accid. Anal. & Prev. 1990; 22: 371-378. Hobbs CA, Grattan E, and Hobbs JA. Classification of injury severity by length of stay in hospital. TRRL Laboratory Report 871. Crowthorne: Transport and Road Research Laboratory, 1979. Ibrahim K, Silcock DT. The accuracy of accident data. Traffic Engineering and Control 1992; 33: 492-497. James HF. Under-reporting of road traffic accidents. Traffic Engineering and Control 1991; 32: 574-583. Marchant M, Moore K, Ditchburn J. Hospitalisations due to injury: South Thames (East) residents from 1989 to 1995. Tunbridge Wells: South East Institute of Public Health, February 1997. Mills. Pedal cycle accidents - a hospital-based study. TRRL Research Report RR 220. Crowthorne: Transport and Road Research Laboratory, 1989. Nicholl JP. The use of hospital in-patient data in the analysis of the injuries sustained by road accident casualties. TRRL Supplementary Report 628. Transport and Road Research Laboratory, 1980. Public Health Information Strategy Group. Improving information on accidents. Implementation Project 19. London: Department of Health, 1993. Secretary of State for Health. Saving Lives: Our Healthier Nation. London: The Stationery Office, 1999. SETRHA. Data quality comparison report. A report of the quality of data held on regional admitted 138 Prepared by CHSS at Tunbridge Wells

References Injury Prevention through Data Linkage Phase 3 patient care flat files. Bexhill: South East Thames Regional Health Authority, December 1993. Shikar, Treat, McDonald. The validity of police reported accident data. Accid Anal Prev 1983; 15: 175-191. Stone RD. Computer linkage of transport and health data. TRRL Laboratory Report 1130. Transport and Road Research Laboratory, 1984. Tundridge RJJ, Everest JT, Wild BR, Johnston RA. An in-depth study of road accident casualties and their injury patterns. TRRL Report RR136, Crowthorne: Transport and Road Research Laboratory, 1988. WHO. International Statistical Classification of Diseases and Related Health Problems. 10th revision, Volume 1. Geneva: World Health Organisation, 1992. 139 Prepared by CHSS at Tunbridge Wells