- Email: [email protected]
Attendance for injury at accident and emergency departments in London: a cross-sectional study
Public Health (2008) 122, 838e844
www.elsevierhealth.com/journals/pubh
Original Research
Attendance for injury at accident and emergency departments in London: a cross-sectional study Konrad Jamrozika, Edgar Samarasunderab,*, Rebekah Miraclec, Mitch Blaird, Dinesh Sethie, Sonia Saxenab, Simon Bowenf a
School of Population Health, University of Queensland, Herston Road, Herston, Queensland 4006, Australia b Department of Primary Care and Social Medicine, Imperial College London, Reynolds Building, St Dunstan’s Road, London W6 8RP, UK c Institute of Archaeology, University College London, 31-34 Gordon Square, London WC1H 0PY, UK d Department of Paediatrics, River Island Paediatric and Child Health Academic Centre, Imperial College London, Northwick Park Hospital, Watford Road, Harrow HA1 3UJ, UK e Health Policy Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK f Brent Teaching Primary Care Trust, 116 Chaplin Road, Wembley HA0 4UZ, UK Received 20 March 2007; received in revised form 28 September 2007; accepted 26 October 2007 Available online 4 March 2008
KEYWORDS Epidemiology; Injury; Emergency departments; Surveillance
Summary Objective: In order to set the foundation for the possible development of injury surveillance initiatives in north-west London, data on all presentations during 2002 at the nine accident and emergency departments (AEDs) in the relevant strategic health authority were examined. Study design: Descriptive, cross-sectional study. Methods: A search algorithm was devised to extract records pertaining to injury presentations. The results were validated against a manually checked sample. Descriptive, quantitative analyses were performed. Results: Only four of the nine hospitals in the study area routinely recorded data in a form useful for research on injury. In these four hospitals, presentations with injury accounted for 29.7% of total attendances at the AED, which is markedly lower than the national average. Conclusions: Certain characteristics of London regarding provision of primary care may explain why attendances for injury are proportionately low. However, the unusual pattern also underlines the importance of improving the quality of AED
* Corresponding author. Tel.: þ44 (0) 20 7594 0838; fax þ44 (0) 20 7594 0854. E-mail address: [email protected] (E. Samarasundera). 0033-3506/$ - see front matter ª 2007 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.puhe.2007.10.011
Injury in a global city
839 data in order to support adequate local surveillance of injury as the basis of efforts to prevent such incidents and to plan services to deal with injuries. ª 2007 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved.
Introduction Data collected routinely by accident and emergency departments (AEDs) for their own operational reasons potentially provide a rich resource for health services research and planning, and for informing health interventions.1e3 The All Wales Injury Surveillance System used data from AEDs to identify that concrete surfaces in playgrounds contributed to the severity of fall injuries in children. This led to the adoption of bark and rubber surfaces in Wales as safer alternatives.4,5 Sharing anonymized AED data with community organizations contributed directly to the development of local violence prevention strategies in Cardiff.6 AED data also have significant potential in the planning of emergency services.7 However, the mixed urbanerural settings of currently published AED-based injury surveillance programmes differ in some respects from the purely urban context of major metropolitan areas, with their typically densely populated, ethnically diverse and mobile populations, and well-developed public transport infrastructures. This could mean that some of the findings of other population-based injury studies may not necessarily be applicable to the context of major metropolitan areas. Studies covering a range of health issues, including injury, have demonstrated the importance of geographical context; previous research has already highlighted that injury has been relatively understudied in London.8e11 This paper examines patterns in attendances for injury at AEDs in London as a precursor to evaluating the case for developing an injury surveillance programme in the study area. The research presented here describes: the methods used to identify episodes of injury from routinely compiled data on presentations to AEDs; the pattern of injury seen at AEDs serving a geographically defined population covered by one strategic health authority in London; the quality of data available in the study area; and how the findings may influence policy.
Methods Data collection Routinely compiled electronic data were collected for all presentations to eight of the nine AEDs in
the North-west London Strategic Health Authority during 2002. The ninth AED had no electronic record system in place during that year and was therefore excluded from this study on the basis that the data were not routinely available.
Extraction of records of injuries The initial aim was to identify presentations for injury from the field for the primary clinical diagnosis. However, only six of the AEDs routinely recorded the medical diagnosis (MD) electronically, one of which had to be excluded from analyses because the classification system used did not permit the consistent identification of injury presentations, whilst another did not have electronic records covering the entire calendar year (see Table 1). The remaining two hospitals only recorded the presenting complaint, as recorded by the receptionist in the waiting area, and not the MD. Preliminary investigations were undertaken into the validity of using the presenting complaint as a proxy for the MD, making use of the two AED datasets that contained both fields. However, as analyses of sensitivity and positive predictive value suggested important variation between hospitals in the age-specific relationships of presenting complaint to MD, the two AEDS that did not record the MD were excluded from the study. Of the four AEDs with complete MD data, only one used a formal coding system that could be interrogated to identify cases of injury. Hence, the authors were obliged to develop a data-extraction routine to identify relevant records from free-text fields. An extraction routine was developed and validated using a training dataset comprised of an age-stratified random sample of 8000 records pooled from the four hospitals that routinely recorded MD in a form suitable for analysis. The algorithm was written in a way that avoided identification of the same record twice. Each record in this sample was manually checked against the relevant free text as referring to an ‘injury’, ‘non-injury’ or a person who ‘did not wait’: 2280 (28.5%) records had an injury as the MD. These 2280 records were used to refine the search terms (consisting of keywords and keyword stems) in the extraction routine until at least 98% sensitivity, specificity and positive predictive value were
840 Table 1
K. Jamrozik et al. Presentations to accident and emergency departments of eight hospitals in north-west London in 2002.
Hospital
Total records
Comments on data
Injuries (n)
Injuries (%)
A B and C combineda D E F G H Totals
79,615 77,405 53,798 74,890 71,657 64,617 32,490 454,472
MD; PC MD; PC MD; injuries not identifiable PC MD PC MD; ApreOct only 228,677 usable records
25,992 24,476 Unknown Unknown 17,445 Unknown Unknown 67,913
32.7 31.6 Unknown Unknown 24.4 Unknown Unknown 29.7
MD, medical diagnosis; PC, presenting complaint. a Data for Hospitals B and C have been combined as they were received in a single file from the relevant National Health Service Trust.
achieved in each of the four age categories (0e14, 15e44, 45e64, 65þ years) for records of injury as judged against the manually validated file. The finalized extraction routine was then run on an integrated dataset comprising all presentations to Hospitals A, B, C and F.
Statistical methods The aim was to obtain a picture of the overall frequency of injury resulting in presentation to an AED, and how this varied by person (age, sex) and time (season, day of the week, time of day). Thus, descriptive analyses of rates of presentation for injury were undertaken during 2002 using age- and sex-based estimates of mid-2002 populations for north-west London sourced from the Office for National Statistics as denominators.12 The estimate of the total denominator population for the four hospitals combined was calculated by summing the populations of the catchment areas of each. Each catchment area was delineated, using geographic information systems (GIS), to include all wards within a zone containing 90% of the total attendances at the given AED, giving a study denominator population of 404,100. The data from some AEDs also permitted examination of the context of injuries and the disposition of injured patients after their initial assessment. Beyond the current paper, the authors are conducting geographic analyses of attendances at the AEDs discussed here. The initial results of the ongoing research demonstrate that although the catchment areas of the AEDs in question are well defined, there is significant overlap between them (particularly between Hospitals B, C, D and F), suggesting that the populations served are broadly similar in demographic terms. At present, the relationships between attendance rates and neighbourhood-level contextual variables (distance
from AED, indicators of underlying community health, primary care access and material deprivation) are being explored. It is envisaged that the results of these small-area analyses will enable assessment of any potential variability within and between AED catchment populations in north-west London, which may yield further insight into the generalizability of the findings presented in the current paper.
Results Overview The eight AEDs that had electronic data systems in place in 2002 had 454,472 records of attendance for the year. Only four hospitals held data that were potentially of use in this type of research, yielding a figure of 228,677 (see Table 1). The data from Hospitals B and C were combined as they were received as a single consolidated file from the relevant National Health Service (NHS) hospital trust. Application of the validated extraction algorithm yielded 67,913 (29.7%) records in which the principal problem appeared to be an injury.
Demographic patterns Fig. 1 shows age- and sex-specific rates of attendance for injury from the four comparable AEDs listed in Table 1. Rates are lower in females than males until old age. Both sexes show a peak in the first 5 years of life, another in teenagers and persons in their early 20s, and then sharply accelerating rates after 70 years of age. The differences between the sexes are most marked at 20e24 years of age, when rates in men are double those in women. The demographic structure of injury
Injury in a global city
841
160
Attendance rates per 1000
140 120 100 Females
80
Males
60 40 20
85+
80–84
75–79
70–74
65–69
60–64
55–59
50–54
45–49
40–44
35–39
30–34
25–29
20–24
15–19
10–14
5–9
0–4
0
Five-year age bands
Figure 1 Age- and sex-specific attendance rates per 1000 for injury at accident and emergency departments in north-west London.
from the AEDs is typical of that for the UK as a whole. Using ‘raw’ counts and standardizing age-specific figures for each sex using the direct method, quinquennia of age, and the ‘European’ population as the external reference13 gave overall rates of 83 and 54 per 1000 person-years for males and females, respectively, and a figure of 68 for both sexes combined.
and F. There is a clear seasonality to injury amongst children, with the highest incidence coinciding with the summer holiday period. The same seasonal pattern is seen to a lesser extent in persons aged 16e44 years, and is least obvious amongst those aged 65 years and over. The two youngest strata also show isolated peaks corresponding to the bank holiday weekends at the beginning and the end of May.
Variation by time Context of injuries Figs. 2 and 3 illustrate seasonal variation in sexspecific rates of attendance for injury by week of the year based on data from Hospitals A, B, C
Data on the context of injury incidents were only routinely available for Hospitals B and C (combined
2.5
Rate per 1000
2
1.5
Females 0–15 Females 16–44 Females 45–64 Females 65+
1
0.5
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 2
Weekly variation in attendance rates (females).
842
K. Jamrozik et al. 3.5 3
Rate per 1000
2.5 Males 0–15
2
Males 16–44 Males 45–64
1.5
Males 65+ 1 0.5 0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 3
Weekly variation in attendance rates (males).
n ¼ 24,476). The home was the most common location in which injuries occurred (42% of presentations with injury), especially for those aged 75 years and over (60% of presentations). The percentages for injuries associated with road incidents, sport, educational institutions and work were 3.5%, 3.6%, 0.8% and 7.4%, respectively. There is uncertainty over the figures for the latter four contexts since certain incidents recorded as ‘public place’ (13% of total injuries) may include some of these cases. The context of injury was not recorded for 20% of records pertaining to injury.
Disposition of patients with injuries Data from Hospitals B and C included information on destinations of patients after assessment of an injury in the AEDs. In this dataset, 8.5% of patients attending the AEDs with injuries were recorded as having been subsequently admitted as inpatients, while over two-thirds of patients were discharged back to the community, and four injured patients per 10,000 died in the AEDs. Hospitals D and F also included information on patient destinations, although the other AEDs in the study area did not. As the data from Hospital D did not permit the consistent identification of injuries from the recording of MD, patient disposition could not be analysed with respect to injury. The information on destinations from Hospital F showed some agreement with the results from Hospitals B and C, with 6.4% of attendees being subsequently admitted as inpatients and 55.2% being returned back to the community; no information on fatalities could be extracted for Hospital F, possibly suggesting some omissions from the disposition data.
Discussion Main finding of the study Injury accounted for 30% of all attendances at the four comparable AEDs in north-west London, which is low in comparison with the national average.14 This may be due, in part, to good access to general practitioners and walk-in services in London, resulting in many cases of injury not being treated at AEDs, or to proportionately greater ‘inappropriate’ use of AED services by overseas or unregistered parts of the population for non-injury-related, basic medical care. Such interpretations must be tentative, however, as the literature reveals inconsistent relationships between the level and sophistication of primary care services on the one hand and workloads in local AEDs on the other; the presence of two minor treatment centres in the study area may also impact significantly on the burden of injury presented to AEDs.15e18 In line with the present findings, research into inpatient admissions and mortality statistics has found that injury represents a smaller proportion of population ill health in London compared with other major European cities and other parts of the UK.8,14,19
Other findings There is obvious variation in rates of attendance between the sexes, across age groups, by season and by time of the week. Possible explanatory factors for the high rate of injury among adolescent and young adult males include alcohol, assault and sporting activity. The high rates amongst elderly females may reflect both incidents at home and
Injury in a global city falls in public places. In agreement with studies of paediatric injuries in Glasgow and road traffic deaths in the USA, summer was found to be the peak season for presentations with injury; a pattern marked in children and younger adults.20,21 Holidays, especially when the hours of daylight are longer, mean more travel and more time outdoors, probably in a wider variety of environments, some of which are not designed for safety, and possibly under reduced supervision. The resulting patterns of presentation with injury reflect exposure to risk. However, very few studies of injury account for period at risk in any systematic way.22 Although the demographic and temporal patterns found in the present work are in agreement with other studies, there was a difference in the seasonality of injury amongst the elderly. A report from Norway, for example, indicated that fractures amongst the elderly were most frequent from October to March, with ice and snow being aetiological factors in falls.23 In contrast, the present study found higher rates for elderly females in the autumn (see Fig. 2) and lower rates from January to March. The autumn rates are potentially consistent with falls on wet pavements covered with damp and slippery leaves, and with trips relating to fading light at home later in the year; older people may become relatively housebound with the lights on during the winter. This analysis of outcomes of attendance at AEDs provides a picture of part of the injury pyramid for London. The base of this pyramid will be underestimated due to the omission of cases of injury that do not present to an AED but instead are seen solely by general practitioners, treated at minor injury units or are self-managed. Likewise, the exclusion of fatalities that are transported direct to the mortuary, or the possibility of some serious traumatic cases being taken directly to intensive care units and not being recorded in AED databases, will lead to underestimation of the top of the pyramid. Nevertheless, it is clear that over two-thirds of presentations with injury to Hospitals B and C are followed by return of the patient to the community. Such minor-to-moderate cases add to the workloads of AEDs where waiting times are already a matter of political significance.7 It has been acknowledged elsewhere that there has been insufficient research into the burden of minor injuries.24 If the current restricted experiment in minor treatment centres became general policy for medium-to-large urban areas in the UK, at least 10% of the current workload of AEDs might conceivably be diverted to such alternative services.
843
Limitations of the study The current variations in the extent and quality of data between hospitals prevented the authors from obtaining a full picture of the pattern of injury as presented to all AEDs in the study area. Perhaps most significant was the lack of information on the context of injury incidents, with its resultant constraints on identifying aetiological processes. The same deficiencies mean that proper planning of health services for injuries and the systematic development of injury prevention initiatives for London are not feasible at present. Nevertheless, all injury surveillance and prevention initiatives must begin from some starting point, and a motive for improving AED data in a region can be the start of a surveillance programme, although studies of data quality in existing AED-based surveillance programmes have demonstrated widely varying levels of sensitivity between AEDs and widespread systematic biases.25,26 These findings suggest that the variable quality of AED data in north-west London is typical of the wider situation. A key question for public health specialists to consider is whether founding large-scale surveillance initiatives is an optimal use of resources, and it has been proposed that only injuries above a given severity threshold should be recorded for surveillance purposes and used in conjunction with health surveys.27,28 The situation regarding data quality in north-west London has progressed somewhat since 2002, with improved data collection at Hospital E and the implementation of an electronic recording system at the excluded ninth hospital. Nevertheless, further progress needs to be made if any form of AED-based surveillance is to be attempted in the study area. Another limitation of this study relates to the problems of defining denominators in populationbased studies; a difficulty made more complex in the context of London due to the mobility of the populace of the city and its hinterland. The authors’ ongoing GIS-based research in the study area may answer some of these currently unaddressed questions.
Summary Prior to this study, there was a noticeable lack of literature on population-based, whole-area studies of injury in large, exclusively metropolitan contexts. In addition, the research presented here highlights the limited availability of routine data on AED activity in London. Despite its other unique features, London is likely to be typical of the wider
844 UK situation regarding quality of data on injuries from AEDs as there have been few injury surveillance initiatives based on AEDs in the UK. Good models have, however, been established in Wales and the West Midlands.1,2 Notwithstanding the many other pressures on NHS trusts, now would be an appropriate time for AEDs to adopt a standardized minimum dataset for injury surveillance drawing on these models, as the Connecting for Health initiative (the NHS information technology programme) is still in its formative stages.29 The needs of health surveillance should be incorporated into this programme, and the drive towards this goal may need to come from the national level. The use of severity thresholds may provide a way forward in balancing different resource needs.
Acknowledgements
K. Jamrozik et al.
7.
8.
9.
10.
11.
12. 13.
The authors thank the directors of the AEDs in north-west London for providing anonymized copies of their electronic data for 2002.
Ethical approval Riverside LREC: Ref. 05/Q0401/57.
14.
15.
16. 17.
Funding West London Research Network: Project Code 02306.
18.
19.
Competing interests None declared.
References 1. Lyons RA, Jones S, Kemp A, Sibert J, Shepherd J, Richmond P, et al. Development of a population based injury surveillance system: the All Wales Injury Surveillance System (AWISS). Inj Prev 2002;8:83e6. 2. Downing A, Wilson R. Regional surveillance of accident and emergency department attendances: experiences from the West Midlands. J Public Health 2005;27:82e4. 3. National Data Standards for Injury Surveillance (NDSIS) Version 2.1 http://www.nisu.flinders.edu.au/pubs/manuals/ ndsis/ [accessed 01.03.07]. 4. Mott A, Rolfe K, James R, Evans R, Kemp A, Dunstan F, et al. Safety of surfaces and equipment for children in playgrounds. Lancet 1997;349:1874e6. 5. Sibert JR, Mott A, Rolfe K, James R, Evans R, Kemp A, et al. Preventing injuries in public playgrounds through partnership between health services and local authority: community intervention study. BMJ 1999;318:1595. 6. Warburton JL, Shepherd JP. Development, utilisation, and importance of accident and emergency department derived
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assault data in violence management. Emerg Med J 2004;21: 473e7. Downing A, Wilson RC, Cooke MW. Which patients spend more than 4 hours in the accident and emergency department? J Public Health 2004;26:172e6. Lowdell C, Fitzpatrick J, Wallis R, Mindell J, Jacobson B. Too high a price: injuries and accidents in London. London: London Health Observatory Health of Londoners Programme; 2002. Cubbin C, LeClere FB, Smith GS. Socioeconomic status and injury mortality: individual and neighbourhood determinants. J Epidemiol Community Health 2000;54: 517e24. O’Campo P, Xue X, Wang M-C, Caughy M. Neighborhood risk factors for low birthweight in Baltimore: a multilevel analysis. Am J Public Health 1997;87:1113e8. Winkleby M, Cubbin C, Ahn D. Effect of cross-level interaction between individual and neighborhood socio-economic status on adult mortality rates. Am J Public Health 2006; 96:2145e53. Office for National Statistics, http://www.statistics.gov.uk/ [accessed 15.11.04]. Doll R, Cook P. Summarizing indices for comparison of cancer incidence data. Int J Cancer 1967;2:269e79. Gorman DR, Ramsay LJ, Wilson GS, Freeland P. Using routine accident and emergency department data to describe local injury epidemiology. Public Health 1999; 113:285e9. Joseph AE, Phillips DR. Accessibility and utilization: geographical perspectives on health care delivery. London: Harper and Row; 1984. Dale J, Dolan B. Do patients use minor injury units appropriately? J Public Health Med 1996;8:152e6. Prince M, Worth C. A study of ‘inappropriate’ attendances to a paediatric accident and emergency department. J Public Health Med 1992;14:177e82. Chalder M, Sharp D, Moore L, Salisbury C. Impact of NHS walk-in centres on the workload of other local healthcare providers: time series analysis. BMJ 2003;326:532e6. Bardsley M. Project Megapoles: health in Europe’s capitals. London: European Commission; 1999. Morrison A, Stone DH, Doraiswamy N, Ramsay L. Injury surveillance in an accident and emergency department: a year in the life of CHIRPP. Arch Dis Child 1999;80:533e6. Farmer CM, Williams AF. Temporal factors in vehicle crash deaths. Inj Prev 2005;11:18e23. Stevenson MR, Jamrozik K, Burton P. A case-control study of child pedestrian injuries in Perth, Western Australia. J Epidemiol Community Health 1996;50:280e7. Bulajic-Kopjar M. Seasonal variations in incidence of fractures amongst elderly people. Inj Prev 2000;6:6e19. Lyons R. Comments on Cryer’s paper. Int J Inj Control Safety Promotion 2005;12:225. MacArthur C, Pless IB. Evaluation of the quality of an injury surveillance system. Am J Epidemiol 1999;149: 586e92. MacArthur C, Pless IB. Sensitivity and representativeness of a childhood injury surveillance system. Inj Prev 1999;5: 214e6. Morrison A, Stone DH. Injury surveillance in accident and emergency departments: to sample or not to sample? Inj Prev 1998;4:50e3. Stone DH, Morrison A, Smith GS. Emergency department injury surveillance: the best use of limited resources? Inj Prev 1999;5:166e7. NHS Connecting for Health, http://www.connectingforhealth.nhs.uk/ [accessed 27.09.07].
www.elsevierhealth.com/journals/pubh
Original Research
Attendance for injury at accident and emergency departments in London: a cross-sectional study Konrad Jamrozika, Edgar Samarasunderab,*, Rebekah Miraclec, Mitch Blaird, Dinesh Sethie, Sonia Saxenab, Simon Bowenf a
School of Population Health, University of Queensland, Herston Road, Herston, Queensland 4006, Australia b Department of Primary Care and Social Medicine, Imperial College London, Reynolds Building, St Dunstan’s Road, London W6 8RP, UK c Institute of Archaeology, University College London, 31-34 Gordon Square, London WC1H 0PY, UK d Department of Paediatrics, River Island Paediatric and Child Health Academic Centre, Imperial College London, Northwick Park Hospital, Watford Road, Harrow HA1 3UJ, UK e Health Policy Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK f Brent Teaching Primary Care Trust, 116 Chaplin Road, Wembley HA0 4UZ, UK Received 20 March 2007; received in revised form 28 September 2007; accepted 26 October 2007 Available online 4 March 2008
KEYWORDS Epidemiology; Injury; Emergency departments; Surveillance
Summary Objective: In order to set the foundation for the possible development of injury surveillance initiatives in north-west London, data on all presentations during 2002 at the nine accident and emergency departments (AEDs) in the relevant strategic health authority were examined. Study design: Descriptive, cross-sectional study. Methods: A search algorithm was devised to extract records pertaining to injury presentations. The results were validated against a manually checked sample. Descriptive, quantitative analyses were performed. Results: Only four of the nine hospitals in the study area routinely recorded data in a form useful for research on injury. In these four hospitals, presentations with injury accounted for 29.7% of total attendances at the AED, which is markedly lower than the national average. Conclusions: Certain characteristics of London regarding provision of primary care may explain why attendances for injury are proportionately low. However, the unusual pattern also underlines the importance of improving the quality of AED
* Corresponding author. Tel.: þ44 (0) 20 7594 0838; fax þ44 (0) 20 7594 0854. E-mail address: [email protected] (E. Samarasundera). 0033-3506/$ - see front matter ª 2007 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.puhe.2007.10.011
Injury in a global city
839 data in order to support adequate local surveillance of injury as the basis of efforts to prevent such incidents and to plan services to deal with injuries. ª 2007 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved.
Introduction Data collected routinely by accident and emergency departments (AEDs) for their own operational reasons potentially provide a rich resource for health services research and planning, and for informing health interventions.1e3 The All Wales Injury Surveillance System used data from AEDs to identify that concrete surfaces in playgrounds contributed to the severity of fall injuries in children. This led to the adoption of bark and rubber surfaces in Wales as safer alternatives.4,5 Sharing anonymized AED data with community organizations contributed directly to the development of local violence prevention strategies in Cardiff.6 AED data also have significant potential in the planning of emergency services.7 However, the mixed urbanerural settings of currently published AED-based injury surveillance programmes differ in some respects from the purely urban context of major metropolitan areas, with their typically densely populated, ethnically diverse and mobile populations, and well-developed public transport infrastructures. This could mean that some of the findings of other population-based injury studies may not necessarily be applicable to the context of major metropolitan areas. Studies covering a range of health issues, including injury, have demonstrated the importance of geographical context; previous research has already highlighted that injury has been relatively understudied in London.8e11 This paper examines patterns in attendances for injury at AEDs in London as a precursor to evaluating the case for developing an injury surveillance programme in the study area. The research presented here describes: the methods used to identify episodes of injury from routinely compiled data on presentations to AEDs; the pattern of injury seen at AEDs serving a geographically defined population covered by one strategic health authority in London; the quality of data available in the study area; and how the findings may influence policy.
Methods Data collection Routinely compiled electronic data were collected for all presentations to eight of the nine AEDs in
the North-west London Strategic Health Authority during 2002. The ninth AED had no electronic record system in place during that year and was therefore excluded from this study on the basis that the data were not routinely available.
Extraction of records of injuries The initial aim was to identify presentations for injury from the field for the primary clinical diagnosis. However, only six of the AEDs routinely recorded the medical diagnosis (MD) electronically, one of which had to be excluded from analyses because the classification system used did not permit the consistent identification of injury presentations, whilst another did not have electronic records covering the entire calendar year (see Table 1). The remaining two hospitals only recorded the presenting complaint, as recorded by the receptionist in the waiting area, and not the MD. Preliminary investigations were undertaken into the validity of using the presenting complaint as a proxy for the MD, making use of the two AED datasets that contained both fields. However, as analyses of sensitivity and positive predictive value suggested important variation between hospitals in the age-specific relationships of presenting complaint to MD, the two AEDS that did not record the MD were excluded from the study. Of the four AEDs with complete MD data, only one used a formal coding system that could be interrogated to identify cases of injury. Hence, the authors were obliged to develop a data-extraction routine to identify relevant records from free-text fields. An extraction routine was developed and validated using a training dataset comprised of an age-stratified random sample of 8000 records pooled from the four hospitals that routinely recorded MD in a form suitable for analysis. The algorithm was written in a way that avoided identification of the same record twice. Each record in this sample was manually checked against the relevant free text as referring to an ‘injury’, ‘non-injury’ or a person who ‘did not wait’: 2280 (28.5%) records had an injury as the MD. These 2280 records were used to refine the search terms (consisting of keywords and keyword stems) in the extraction routine until at least 98% sensitivity, specificity and positive predictive value were
840 Table 1
K. Jamrozik et al. Presentations to accident and emergency departments of eight hospitals in north-west London in 2002.
Hospital
Total records
Comments on data
Injuries (n)
Injuries (%)
A B and C combineda D E F G H Totals
79,615 77,405 53,798 74,890 71,657 64,617 32,490 454,472
MD; PC MD; PC MD; injuries not identifiable PC MD PC MD; ApreOct only 228,677 usable records
25,992 24,476 Unknown Unknown 17,445 Unknown Unknown 67,913
32.7 31.6 Unknown Unknown 24.4 Unknown Unknown 29.7
MD, medical diagnosis; PC, presenting complaint. a Data for Hospitals B and C have been combined as they were received in a single file from the relevant National Health Service Trust.
achieved in each of the four age categories (0e14, 15e44, 45e64, 65þ years) for records of injury as judged against the manually validated file. The finalized extraction routine was then run on an integrated dataset comprising all presentations to Hospitals A, B, C and F.
Statistical methods The aim was to obtain a picture of the overall frequency of injury resulting in presentation to an AED, and how this varied by person (age, sex) and time (season, day of the week, time of day). Thus, descriptive analyses of rates of presentation for injury were undertaken during 2002 using age- and sex-based estimates of mid-2002 populations for north-west London sourced from the Office for National Statistics as denominators.12 The estimate of the total denominator population for the four hospitals combined was calculated by summing the populations of the catchment areas of each. Each catchment area was delineated, using geographic information systems (GIS), to include all wards within a zone containing 90% of the total attendances at the given AED, giving a study denominator population of 404,100. The data from some AEDs also permitted examination of the context of injuries and the disposition of injured patients after their initial assessment. Beyond the current paper, the authors are conducting geographic analyses of attendances at the AEDs discussed here. The initial results of the ongoing research demonstrate that although the catchment areas of the AEDs in question are well defined, there is significant overlap between them (particularly between Hospitals B, C, D and F), suggesting that the populations served are broadly similar in demographic terms. At present, the relationships between attendance rates and neighbourhood-level contextual variables (distance
from AED, indicators of underlying community health, primary care access and material deprivation) are being explored. It is envisaged that the results of these small-area analyses will enable assessment of any potential variability within and between AED catchment populations in north-west London, which may yield further insight into the generalizability of the findings presented in the current paper.
Results Overview The eight AEDs that had electronic data systems in place in 2002 had 454,472 records of attendance for the year. Only four hospitals held data that were potentially of use in this type of research, yielding a figure of 228,677 (see Table 1). The data from Hospitals B and C were combined as they were received as a single consolidated file from the relevant National Health Service (NHS) hospital trust. Application of the validated extraction algorithm yielded 67,913 (29.7%) records in which the principal problem appeared to be an injury.
Demographic patterns Fig. 1 shows age- and sex-specific rates of attendance for injury from the four comparable AEDs listed in Table 1. Rates are lower in females than males until old age. Both sexes show a peak in the first 5 years of life, another in teenagers and persons in their early 20s, and then sharply accelerating rates after 70 years of age. The differences between the sexes are most marked at 20e24 years of age, when rates in men are double those in women. The demographic structure of injury
Injury in a global city
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Figure 1 Age- and sex-specific attendance rates per 1000 for injury at accident and emergency departments in north-west London.
from the AEDs is typical of that for the UK as a whole. Using ‘raw’ counts and standardizing age-specific figures for each sex using the direct method, quinquennia of age, and the ‘European’ population as the external reference13 gave overall rates of 83 and 54 per 1000 person-years for males and females, respectively, and a figure of 68 for both sexes combined.
and F. There is a clear seasonality to injury amongst children, with the highest incidence coinciding with the summer holiday period. The same seasonal pattern is seen to a lesser extent in persons aged 16e44 years, and is least obvious amongst those aged 65 years and over. The two youngest strata also show isolated peaks corresponding to the bank holiday weekends at the beginning and the end of May.
Variation by time Context of injuries Figs. 2 and 3 illustrate seasonal variation in sexspecific rates of attendance for injury by week of the year based on data from Hospitals A, B, C
Data on the context of injury incidents were only routinely available for Hospitals B and C (combined
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Figure 2
Weekly variation in attendance rates (females).
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Weekly variation in attendance rates (males).
n ¼ 24,476). The home was the most common location in which injuries occurred (42% of presentations with injury), especially for those aged 75 years and over (60% of presentations). The percentages for injuries associated with road incidents, sport, educational institutions and work were 3.5%, 3.6%, 0.8% and 7.4%, respectively. There is uncertainty over the figures for the latter four contexts since certain incidents recorded as ‘public place’ (13% of total injuries) may include some of these cases. The context of injury was not recorded for 20% of records pertaining to injury.
Disposition of patients with injuries Data from Hospitals B and C included information on destinations of patients after assessment of an injury in the AEDs. In this dataset, 8.5% of patients attending the AEDs with injuries were recorded as having been subsequently admitted as inpatients, while over two-thirds of patients were discharged back to the community, and four injured patients per 10,000 died in the AEDs. Hospitals D and F also included information on patient destinations, although the other AEDs in the study area did not. As the data from Hospital D did not permit the consistent identification of injuries from the recording of MD, patient disposition could not be analysed with respect to injury. The information on destinations from Hospital F showed some agreement with the results from Hospitals B and C, with 6.4% of attendees being subsequently admitted as inpatients and 55.2% being returned back to the community; no information on fatalities could be extracted for Hospital F, possibly suggesting some omissions from the disposition data.
Discussion Main finding of the study Injury accounted for 30% of all attendances at the four comparable AEDs in north-west London, which is low in comparison with the national average.14 This may be due, in part, to good access to general practitioners and walk-in services in London, resulting in many cases of injury not being treated at AEDs, or to proportionately greater ‘inappropriate’ use of AED services by overseas or unregistered parts of the population for non-injury-related, basic medical care. Such interpretations must be tentative, however, as the literature reveals inconsistent relationships between the level and sophistication of primary care services on the one hand and workloads in local AEDs on the other; the presence of two minor treatment centres in the study area may also impact significantly on the burden of injury presented to AEDs.15e18 In line with the present findings, research into inpatient admissions and mortality statistics has found that injury represents a smaller proportion of population ill health in London compared with other major European cities and other parts of the UK.8,14,19
Other findings There is obvious variation in rates of attendance between the sexes, across age groups, by season and by time of the week. Possible explanatory factors for the high rate of injury among adolescent and young adult males include alcohol, assault and sporting activity. The high rates amongst elderly females may reflect both incidents at home and
Injury in a global city falls in public places. In agreement with studies of paediatric injuries in Glasgow and road traffic deaths in the USA, summer was found to be the peak season for presentations with injury; a pattern marked in children and younger adults.20,21 Holidays, especially when the hours of daylight are longer, mean more travel and more time outdoors, probably in a wider variety of environments, some of which are not designed for safety, and possibly under reduced supervision. The resulting patterns of presentation with injury reflect exposure to risk. However, very few studies of injury account for period at risk in any systematic way.22 Although the demographic and temporal patterns found in the present work are in agreement with other studies, there was a difference in the seasonality of injury amongst the elderly. A report from Norway, for example, indicated that fractures amongst the elderly were most frequent from October to March, with ice and snow being aetiological factors in falls.23 In contrast, the present study found higher rates for elderly females in the autumn (see Fig. 2) and lower rates from January to March. The autumn rates are potentially consistent with falls on wet pavements covered with damp and slippery leaves, and with trips relating to fading light at home later in the year; older people may become relatively housebound with the lights on during the winter. This analysis of outcomes of attendance at AEDs provides a picture of part of the injury pyramid for London. The base of this pyramid will be underestimated due to the omission of cases of injury that do not present to an AED but instead are seen solely by general practitioners, treated at minor injury units or are self-managed. Likewise, the exclusion of fatalities that are transported direct to the mortuary, or the possibility of some serious traumatic cases being taken directly to intensive care units and not being recorded in AED databases, will lead to underestimation of the top of the pyramid. Nevertheless, it is clear that over two-thirds of presentations with injury to Hospitals B and C are followed by return of the patient to the community. Such minor-to-moderate cases add to the workloads of AEDs where waiting times are already a matter of political significance.7 It has been acknowledged elsewhere that there has been insufficient research into the burden of minor injuries.24 If the current restricted experiment in minor treatment centres became general policy for medium-to-large urban areas in the UK, at least 10% of the current workload of AEDs might conceivably be diverted to such alternative services.
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Limitations of the study The current variations in the extent and quality of data between hospitals prevented the authors from obtaining a full picture of the pattern of injury as presented to all AEDs in the study area. Perhaps most significant was the lack of information on the context of injury incidents, with its resultant constraints on identifying aetiological processes. The same deficiencies mean that proper planning of health services for injuries and the systematic development of injury prevention initiatives for London are not feasible at present. Nevertheless, all injury surveillance and prevention initiatives must begin from some starting point, and a motive for improving AED data in a region can be the start of a surveillance programme, although studies of data quality in existing AED-based surveillance programmes have demonstrated widely varying levels of sensitivity between AEDs and widespread systematic biases.25,26 These findings suggest that the variable quality of AED data in north-west London is typical of the wider situation. A key question for public health specialists to consider is whether founding large-scale surveillance initiatives is an optimal use of resources, and it has been proposed that only injuries above a given severity threshold should be recorded for surveillance purposes and used in conjunction with health surveys.27,28 The situation regarding data quality in north-west London has progressed somewhat since 2002, with improved data collection at Hospital E and the implementation of an electronic recording system at the excluded ninth hospital. Nevertheless, further progress needs to be made if any form of AED-based surveillance is to be attempted in the study area. Another limitation of this study relates to the problems of defining denominators in populationbased studies; a difficulty made more complex in the context of London due to the mobility of the populace of the city and its hinterland. The authors’ ongoing GIS-based research in the study area may answer some of these currently unaddressed questions.
Summary Prior to this study, there was a noticeable lack of literature on population-based, whole-area studies of injury in large, exclusively metropolitan contexts. In addition, the research presented here highlights the limited availability of routine data on AED activity in London. Despite its other unique features, London is likely to be typical of the wider
844 UK situation regarding quality of data on injuries from AEDs as there have been few injury surveillance initiatives based on AEDs in the UK. Good models have, however, been established in Wales and the West Midlands.1,2 Notwithstanding the many other pressures on NHS trusts, now would be an appropriate time for AEDs to adopt a standardized minimum dataset for injury surveillance drawing on these models, as the Connecting for Health initiative (the NHS information technology programme) is still in its formative stages.29 The needs of health surveillance should be incorporated into this programme, and the drive towards this goal may need to come from the national level. The use of severity thresholds may provide a way forward in balancing different resource needs.
Acknowledgements
K. Jamrozik et al.
7.
8.
9.
10.
11.
12. 13.
The authors thank the directors of the AEDs in north-west London for providing anonymized copies of their electronic data for 2002.
Ethical approval Riverside LREC: Ref. 05/Q0401/57.
14.
15.
16. 17.
Funding West London Research Network: Project Code 02306.
18.
19.
Competing interests None declared.
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