Accident Analysis and Prevention 83 (2015) 18–25
Contents lists available at ScienceDirect
Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
Estimating under-reporting of road crash injuries to police using multiple linked data collections Angela Watson* , Barry Watson, Kirsten Vallmuur CARRS-Q, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 4 December 2014 Received in revised form 29 April 2015 Accepted 29 June 2015 Available online xxx
The reliance on police data for the counting of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police which under-estimates the overall burden of road crash injuries. The aim of this study was to use multiple linked data sources to estimate the extent of under-reporting of road crash injuries to police in the Australian state of Queensland. Data from the Queensland Road Crash Database (QRCD), the Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and the Queensland Injury Surveillance Unit (QISU) for the year 2009 were linked. The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. In addition, the potential bias of this discordance (under-reporting) was assessed based on gender, age, road user group, and regional location. Results showed that the level of under-reporting varied depending on the data set with which the police data was compared. When all hospital data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around twothirds not linking to any record in the police data. The results also showed that the under-reporting was more likely for motorcyclists, cyclists, males, young people, and injuries occurring in Remote and Inner Regional areas. These results have important implications for road safety research and policy in terms of: prioritising funding and resources; targeting road safety interventions into areas of higher risk; and estimating the burden of road crash injuries. ã 2015 Elsevier Ltd. All rights reserved.
Keywords: Road crash under-reporting Data linkage Traffic injuries Injury surveillance
1. Introduction Police reported crash data are the primary source of crash information in most jurisdictions (International Traffic Safety Data and Analysis Group (IRTAD), 2011). However, the reliance on police data for the counting and analysis of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police (Alsop and Langley, 2001; Amoros et al., 2006; Boufous et al., 2008; Langley et al., 2003a). This under-reporting can have impacts not only on the overall measure of the impact of road crash injuries, but could potentially introduce biases relating to particular groups of road users. In the area of road crash incidents and injuries, a variety of data linkage projects have been conducted (Abay, 2015; Alsop and Langley, 2001; Amoros et al., 2006; Aptel et al., 1999; Boufous et al., 2008; Cercarelli et al., 1996; Elvik and Mysen, 1999; Hauer and Hakkert, 1988; Langley et al., 2003a). Abay (2015) merged
* Corresponding author. E-mail addresses: [email protected] (A. Watson), [email protected] (B. Watson), [email protected] (K. Vallmuur). http://dx.doi.org/10.1016/j.aap.2015.06.011 0001-4575/ ã 2015 Elsevier Ltd. All rights reserved.
emergency room data with police-reported crash data in a region in Denmark. It was found that there was significant bias in reporting to police including a severity bias (i.e., higher severity injuries are more likely to be reported). Alsop and Langley (2001) used linkage of police and hospital records in New Zealand. They found that less than two-thirds of all hospitalised road crash casualties were recorded in the police data. They also found that this varied based on the number of vehicles involved, the geographical location, age and injury severity. Amoros et al. (2006) conducted a similar study looking at the under-reporting of road crash casualties in France. They used probabilistic methods to link police crash data with the road trauma registry in Rhone County. The results showed a police reporting rate of around 38%. However, this rate varied according to injury severity, the road user type, and the location of the crash (i.e., metropolitan vs. rural). Another French study conducted by Aptel et al. (1999) found that after linking police and hospital data, only 37% of non-fatal road crash injuries were recorded by police. Similar to other studies, they found that rate of reporting varied depending place of crash, the type of vehicle involved, and the injury severity. They also determined that police-reports tended to over-estimate the
A. Watson et al. / Accident Analysis and Prevention 83 (2015) 18–25
severity of the injury sustained. Langley et al. (2003a,b) conducted linkage between hospital records and police records to specifically examine the potential under-reporting of cyclist injuries in New Zealand. The results showed that only 22% of cyclists that crashed on a public road could be linked to the police records. Of the crashes that involved a motor vehicle, 54% were recorded by police. They also found that age, ethnicity, and injury severity predicted whether a hospitalised cycle crash was more likely to be recorded in the police data. Within Australia, Cercarelli et al. (1996) linked police reports, hospital admissions and accident and emergency (A&E) department data from a single hospital in Western Australia. The researchers found that around 50% of attendances at the A&E were recorded by police, and that around 50% of cases recorded by police as being admitted to hospital were actually admitted. The researchers argue that while the discrepancy between the data sets does represent an under-reporting of cases, it suggests that differences in coding systems may also lead to cases not being linked. Another Australian study conducted in NSW by Boufous et al. (2008) linked hospital admissions data (Inpatient Statistics Collection [ISC]) with the Traffic Accident Data System (TADS). The researchers matched 56.2% of hospitalisations as a result of road crash with a record in TADS. The researchers also found that the linkage rate varied according to age (i.e., lower linkage rate for younger age groups), road user type (e.g., lower linkage rate for cyclists), severity (i.e., higher linkage rates with increased severity) and geographical location. While these studies highlight the issues of under-reporting and bias within police data systems, many of these studies tended to limit data linkage to only two data sets (e.g., hospital and police data) rather than exploring the methods, issues and findings from the linkage of several data sets at a state-wide level to obtain a more comprehensive understanding of road crash injury. The aim of this study was to use multiple linked data sources to better estimate the extent of under-reporting of road crash injuries to police in Queensland. There has been no research of this nature conducted in the jurisdiction of Queensland, Australia and it is important to assess whether the under-reporting that has been found elsewhere applies here. In addition, there may be some unique features of this jurisdiction (i.e., a large and decentralised state with a significant rural and remote population) that may influence the level of under-reporting and/or the patterns of this under-reporting. Also, this jurisdiction is part of the larger picture of the road trauma problem and the under-reporting needs to be quantified to inform the reporting of road crash injuries in Australia. 2. Methods Ethics approval was obtained from the Queensland Health Human Research Ethics Committee (#HREC/12/QHC/45). 2.1. Data collections Data were provided from the Queensland Road Crash Database (QRCD), Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and Queensland Injury Surveillance Unit (QISU) by each relevant custodian for the specified cases for 2009. The year 2009 was used as it was the most recently available data for all collections at the time the linkage was commenced. There are often significant delays with data being available to researchers. Also, gaining the necessary custodian approvals and the data linkage (conducted by the Queensland Health Record Linkage Group) took twenty months to complete. The QRCD includes all road crash injuries reported to police in Queensland in 2009. This includes information about all persons
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injured on public roads, including drivers, passengers, motorcycle riders, cyclists, and pedestrians. It should be noted that the following major exclusions apply: The incident occurs in an area outside the road or road related area. There is no moving vehicle involved. The incident is not attributable to vehicle movement. It should be noted that the definition of what constitutes a road crash injury in this study is based on this QRCD definition. QHAPDC contains data on all patients discharged, statistically separated, died, or transferred from a Queensland hospital permitted to admit patients (including public hospitals, licensed private hospitals, and day surgery units). External cause of injury information is captured in three data fields (i.e., external cause, place, and activity) using International Classification of Diseases, 10th Edition, Australian Modification (ICD-10-AM) (National Centre for Classification in Health, 2004). The Emergency Department Information System (EDIS) includes all emergency department presentations in twenty-nine hospitals across Queensland (approximately 75% of Queensland emergency departments). This collection does not code cause of injury information and requires the use of the ‘presenting problem’ text description to identify transport-related cases. The Queensland Injury Surveillance Unit collects data on injuries presenting at seventeen Queensland emergency departments (nine of the QISU hospital emergency departments are not included in EDIS). This collection captures cause of injury information in several data fields both coded and text-based, including: mechanism, external cause, major injury factor, place, activity, and an ‘injury description’ text field. The following was determined by the researcher as the selection criteria for each collection for the data linkage: RCD: all injury cases QHAPDC: all admitted patients cases coded as transport-related (ICD-10-AM External Cause Codes from V00-V99) EDIS: all emergency department presentations coded as an injury (discharge diagnosis S00–S99 and T00–T98) QISU: all emergency department injury presentations cases coded as transport-related (external definition of ‘motor vehicle—driver’; ‘motor vehicle—passenger’; ‘motorcycle—driver’; ‘motorcycle—passenger’; ‘pedal cyclist or pedal cyclist passenger’; and ‘pedestrian’) It should be noted that for the data linkage component (conducted by the Queensland Health Record Linkage Group), all injury cases in EDIS and all transport injury cases in QISU and QHAPDC were processed for linkage. This was done as there was some question over the accuracy of the coding of traffic (roadrelated) injuries in QHAPDC and QISU and the selection of transport injuries in EDIS (as this data collection only contains cause of injury information in an unstructured text field as described above). So, in order to capture those cases that may still link to the QRCD (police-reported data) despite not being coded or identified as a road crash in the three hospital data sets, a broader approach to the linkage was used. The researchers then applied the refinements described in Section 2.4 to identify relevant road crash cases for analysis. 2.2. Data linkage process The Population Health Research Network (PHRN) was established by the Australian Government’s National Collaborative Research Infrastructure Strategy with the aim to oversee the
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conduct of health data linkage across Australian States and Territories. The QHRLG is the Queensland node of this network. The process for linking the selected data collections was based on the ‘separation principle’. This is the current practice for all jurisdictions’ data linkage centres within Australia as guided by the PHRN. Each data custodian provided personal information to the QHRLG. No content (clinical) information was required to be sent to the record linkage group. The QHRLG then used these personal data to create links between the data collections. Each of the data collections was internally linked to remove duplicate records of the same person and same episode. Person details and demographic data (i.e, full name, date of birth, sex, and injured person’s address) were linked by the Queensland Health Record Linkage Group (QHRLG) using linkage software ‘LinkageWiz’ (LinkageWiz, 2002). Both deterministic and probabilistic methodologies were applied, as well as manual clerical reviews were required. Two linkage passes were conducted for the two blocking variables (surname and date of birth). Linkage weights were determined by the software with higher weights given to variables with higher specificity (e.g, family name) and lower weights for those with lower specificity (e.g, sex). The sum of the weights across all the personal information variables was used to calculate a score. This total score is used to identify record pairs that are considered to be links and non-links. Non-links were considered as any pair with a total score less than 12 (as determined by the LinkageWiz software). All links were then manually reviewed. For every link that was confirmed by the manual review, a linkage key was applied in the form of a common ‘person ID’. This common person ID was assigned to each common case in the link. A unique person ID was also assigned to those cases that did not link to any other data collection. All person IDs (for links and nonlinks) were then sent back to the data custodians. The data custodians then attached the person IDs to the content (clinical) data and sent this de-identified data to the researcher.
2.3. Data merging process As QHAPDC, QISU, and EDIS were episode based, there were some duplicate cases in the data. The first case for an individual was considered the index case. For cases where the admission/ presentation date of a duplicate case was within one day of the discharge date of the index case, the duplicate was removed. When there was more than one duplicate case, if each subsequent case had an admission/presentation date within one day of the discharge date of the previous case it was also considered part of the same injury series. If the duplicate was after this date, it was counted as a new injury case (i.e., the person was injured in a separate event). Each of the hospital data collections were merged based on the person id with the QRCD. The first set of merges was the one-toone linkages (e.g., QHAPDC and QRCD). If the person ID of a hospital data set case matched the person ID of a case in QRCD, then the case was considered to be a link and was coded as such. Non-links, for the purposes of calculating discordance rates and analysing discordance bias, were all cases in the other data collection that did not have a person ID in common with QRCD. The hospital (i.e., presented at hospital) data collections (i.e., QHAPDC, EDIS, and QISU) were combined to form a hospital population data set. This data set included all cases from each collection that linked to each other as well as the unique (nonlinked) cases from each data collection. These combined data sets were then used as the basis for the population estimates for merging with QRCD. 2.4. Coding of variables The selection of cases for discordance rates and the coding of variables included in the analyses are described in Table 1.
Table 1 Data selection criteria and coding for each data collection. General
5 year age groups (with the exception 85+). Recoded to Gender 1 = Female; 2 = Male 1 = Female; 2 = Male Severity 1. The Abbreviated Injury Scale (AIS) (1 = minor; 1 and 2. Principal 2 = moderate; 3 = serious; 4 = severe; diagnosis ICD-10-AM 5 = critical; and 6 = maximum). Mapped to codes mapped to the principal diagnosis ICD-10 codes in the data AIS and a SRR (Association for the Advancement of Automotive Medicine, 2008). 2. Survival Risk Ratios (SRR)—estimate of the probability of death from 0 (no chance of survival) to 1 (100% chance of survival). SRRs were mapped to principal diagnosis ICD codes (Stephenson et al., 2003). 1 = Major Cities; Accessibility/Remoteness Index of Australia ARIA+ (ARIA+)—an index of the accessibility of places 2 = Inner Regional; based on their distance from the five nearest 3 = Outer Regional; major population centres (National Centre for 4 = Remote; 5 = Very Social Applications of GIS, 2009). (1 = Major Remote Cities; 2 = Inner Regional; 3 = Outer Regional; 4 = Remote; 5 = Very Remote). Road 1 = Driver, 2 = Motorcyclist, 3 = Cyclist, Second and fourth 4 = Pedestrian; 5 = Car passenger characters of the ICDuser 10-AM external cause code. Age
5 year age groups (with the exception 85+).
QHAPDC
QISU
EDIS
Provided in single years re-coded into 5 year age groups
Provided in single years re-coded into 5 year age groups
1 = Female; 2 = Male
1 = Female; 2 = Male
1 and 2. Principal diagnosis ICD-10-AM codes mapped to the AIS and a SRR
1 and 2. Principal diagnosis ICD-10-AM codes mapped to the AIS and a SRR
Postcode mapped to ARIA+ using data from Postcode mapped to ARIA+ using data the Australian Bureau of Statistics (2013). from the Australian Bureau of Statistics (2013).
External code (motor vehicle driver = driver; motorcycle driver and motorcycle passenger = motorcyclist; pedal cyclist or pedal cyclist passenger = cyclist; pedestrian = pedestrian; motor vehicle passenger = passenger)
Presenting problem text search (e.g., driver = driver; motorcycle, MCA, MBA = motorcyclist; bicycle, PBS, PBA = cyclist; passenger = passenger; none of the keywords = unspecified)
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In cases where more than one health data collection was combined with QRCD, there was a hierarchy for selection of which data collection would provide the data in the variables outlined in Table 1. For example, if the linkage between QRCD, QHAPDC, QISU, and EDIS was being examined, if the case has a specified ICD-10AM principal diagnosis code in QHAPDC, this was the code that was used. The ICD-10-AM coding in QISU was used when QHAPDC was not available and the ICD-10-AM code for EDIS was used in cases where neither QHAPDC nor QISU code is available. This hierarchy was based on the assumption that QHAPDC coding of injury is superior to QISU and EDIS, as it is completed by trained clinical coders with access to the full medical records of the patients. QISU would be considered next best, as it has coded information for most variables, and EDIS last, as many of the variables rely on being created from text searching.
needed to be used in combination to determine the population of comparison. Due to the scope of each of the data collections covering the spectrum of care from emergency presentation through to admitted patient care, the combination of data sets provided a better representation of the population than the single data collection, and therefore made a better reference for comparison with QRCD. So as part of the assessment of discordance, a population estimate was calculated based on the combination of the hospital data collections (after accounting for overlap/linkage of cases within these collections). A data set was created from this population estimate to represent the largest possible ‘population’ of road crash injuries. Then the number of cases in QRCD not linking with this combined data collection was considered the initial estimate of under-reporting or underrepresentation in QRCD.
2.5. Selection of cases for analysis
2.7. Assessment of discordance bias
For the purposes of analyses, road crash injury only cases needed to be selected. The selection criteria were as follows:
Linked and non-linked cases were compared on a number of characteristics that may influence the discordance rates. Specifically, linked and non-linked cases were compared on: age, gender, road user, severity (AIS and SRR), and the Accessibility and Remoteness Index of Australia (ARIA+). The description and coding of these variables are presented in Table 1. Comparisons were made using Chi-square tests of independence. Due to the large sample size, a more stringent alpha of .001 was adopted. Also, Cramer’s V (fc) was calculated in order to provide an estimate of effect size to give a clearer idea of the meaningfulness of any statistical significance found. As suggested by Aron and Aron (1991), a Cramer’s V of less than .10 was considered to be a small effect size, between .10 and .30 moderate, and more than .30 a large effect size. Post-hoc analyses were also undertaken using an adjusted standardised residual statistic. This statistic can be used to identify those cells with observed frequencies significantly higher or lower than expected. With an alpha level set at .001, adjusted standard residuals outside 3.10 and +3.10 were considered significant (Haberman, 1973). Multivariate analysis in the form of a logistic regression was also performed. It should be noted that age needed to be re-categorised into four groups (0–16; 17–24; 25–59; 60+) due to violations of linearity in the relationship to the outcome when treated as ordinal (5 year intervals). Referent categories for the predictors in logistic regression were chosen based on either the absence of a condition (e.g., non-serious) or the group with the largest proportion of injuries (e.g., Major Cities, drivers, 25–59 age group).
QRCD: all casualties were included as all these injuries are road crash injuries by definition (n = 19,041). QHAPDC: all transport injuries (ICD-10-AM external cause codes from V00 to V99) with a fourth character ICD-10-AM external cause code of ‘traffic’ (n = 7278) EDIS: all transport cases identified using keyword search on the variable presenting problem (e.g., car, motorbike, pedestrian) and without exclusion keywords (e.g., trail, off-road, path, quad bike) (n = 23,624) QISU: transport injuries selected using the external definition field coded as: motor vehicle—driver; motor vehicle—passenger; motorcycle—driver; motorcycle—passenger; pedal cyclist or pedal cyclist passenger; pedestrian and type of place coded as ‘street/highway’ (n = 2478)
2.6. Assessment of discordance (under-reporting) This discordance between the hospital data collections and QRCD provided an indication of the underestimation of the population in QRCD, either due to under-reporting or misclassification. Specifically, the reference for discordance was the cases in the hospital data collections and the discordance rate was determined using the following formula: C 100 Discordance% ¼ 1 B where C is the number of cases linked and B is the number of cases in the hospital data collections. The discordance rate for each hospital data collection was determined for those whose coding was consistent with the definition of a road crash. As none of the other data collections are the ‘gold standard’ for the population (none of the data collections represent all road crash injuries), using this one to one discordance does not provide a very accurate assessment of the under-representation of QRCD. In order for a more accurate assessment to be made, the hospital data collections
3. Results 3.1. Discordance rates Of the coded traffic injury cases in QHAPDC, 3320 did not link to QRCD. These non-linked cases represent possible under-reporting to police and highlight the number of additional cases that linking QRCD with QHAPDC could provide. It should be noted that 329 cases in QHAPDC that were coded as non-traffic did actually link to QRCD (representing 8% of all linked cases).
Table 2 Number of population sample set cases linked with QRCD. Population set
Number in population
Discordance rate
QHAPDC EDIS QISU Hospital data set (QHAPDC, EDIS and QISU)
7278 23,624 2478 28,220
45.6% 70.2% 63.7% 68.6%
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A. Watson et al. / Accident Analysis and Prevention 83 (2015) 18–25
Of the coded road crash injury cases in EDIS, 16,580 did not link to QRCD. These non-linked cases represent possible underreporting to police and the number of additional cases that linking QRCD with EDIS could potentially provide. It should be noted that 2531 cases in EDIS that were coded as non-road crash did link to QRCD (representing 26% of all linked cases). Of the coded road crash injury cases in QISU, 1579 did not link to QRCD. Once again, these non-linked cases represent the possible under-reporting to police and the number of additional cases that linking QRCD with QISU could provide. It should be noted that 72 cases in QISU that were coded as non-road crash did link to QRCD (representing 8% of all linked cases). Table 2 includes the population numbers and discordance rates for each combination of links with QRCD. The one-to-one discordance rates (e.g., QRCD and QHAPDC) have already been reported above however they are included here for comparison purposes. For the entire hospital road crash injury population, as measured by the combination of all hospital data collections (QHAPDC, EDIS, and QISU), the discordance rate was 68.6%. Of the 19,359 road crash injury cases in the hospital data set that did not link with a police data, EDIS contributed 13,359 (69%) unique injury cases; QHAPDC contributed 1086 (5.6%) unique injury cases; and QISU contributed 474 (2.4%) unique injury cases. 3.2. Discordance bias The consistency of the discordance rate was examined for QRCD cases and the combined hospital data (i.e., QHAPDC, EDIS, and QISU). There was statistically significant difference in the discordance rate based on road user for road crash-coded hospital data cases [x2(4) = 5686.25, p < .001, fc = .52]. Specifically, motorcyclists and cyclists had a higher than expected discordance rate (see Table 3). There was also a statistically significant difference in discordance rate based on age for road crash-coded hospital data cases [x2(18) = 1800.32, p < .001, fc = .25]. Specifically, those aged 19 years and younger had a higher than expected discordance rate (see Fig. 1). Discordance rates statistically significantly differed on the gender of the injured person for road crash-coded hospital data cases [x2(1) = 725.02, p < .001, fc = .16]. Specifically, males had a higher than expected discordance rate (73.1% vs. 57.6% for females). There was a statistically significant, but small difference in discordance rates based on ARIA+ for road crash-coded hospital data cases [x2(4) = 117.63, p < .001, fc = .06], with Inner Regional and Remote areas having a higher than expected discordance rate (see Table 4). There were statistically significant differences in discordance rates based on serious SRR classification for road crash-coded hospital data cases [x2(1) = 259.14, p < .001, fc = .10] and AIS serious injury classification for road crash-coded hospital cases [x2(1) = 133.70, p < .001, fc = .07]. Specifically, serious cases had a higher than expected discordance rate (see Table 5). With all variables in the logistic regression, the model was statistically significant, x2(13) = 6334.93, p < .001 (Nagelkerke
Fig. 1. Discordance rates between QRCD and road crash coded hospital data cases for different age groups.
R2 = .39). With all variables in the model, gender was no longer significant. In contrast, age, road user, ARIA+, and serious injury remained statistically significant. Specifically, those aged 0–16 and 17–24 had higher odds (3.5 and 1.9 times, respectively) and those aged 60+ had 1.4 times lower odds of being discordant with QRCD compared to those aged 25–59. Motorcyclist and cyclist cases in hospital data had higher odds of discordance with QRCD. Remote cases had higher odds of discordance compared to Major Cities. Also, non-serious cases and in hospital data had higher odds (2.2 times) of discordance with QRCD (see Table 6). 4. Discussion The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. The level of discordance varied depending on the data set being compared. When all data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around twothirds not linking to any record in the police data. This discordance indicates the level of under-reporting of road crash injuries to police is somewhat similar to the level of discordance found in some other studies (Alsop and Langley, 2001; Amoros et al., 2006; Boufous et al., 2008). However, with the inclusion of linkage with more than just hospitalised data cases, the level of under-reporting found here is larger than that found in studies only linking with a hospitalised data collection. It should be noted that the discordance was lower (around 50%) when only linkage with QHAPDC (admitted to hospital) was considered. There may be a number of reasons for less discordance with these data collections. The lower discordance with QHAPDC could indicate that when a road crash injury is more severe (i.e., requiring hospital admission); the more likely it would be to come to the attention of police. It is also possible, that the discordance rates with EDIS and QISU may also be the result of misclassification of cases. This may particularly be the case with EDIS, where the identification of cases relies on text searching which may overestimate the population. Regardless of the differences in the discordance rates, the results show that there is a significant level of under-reporting to police. In addition, the results showed that each of the hospital data collections added
Table 3 Discordance rates between QRCD and road crash coded hospital data cases for different road users. Road user
Number of cases in hospital data
Number of non-linked cases
Discordance rate
Driver Motorcyclist Cyclist Pedestrian Passenger
4883 6169 6095 721 3034
1571 5010 5651 316 1496
32.2% 81.2% 92.7% 43.8% 49.3%
Note: Standardised residuals outside 3.10 are bolded.
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Table 4 Discordance rates between QRCD and road crash coded hospital data cases for different ARIA+. ARIA+
Number of cases in hospital data
Number of non-linked cases
Discordance rate
Major cities Inner Regional Outer Regional Remote Very Remote
14,434 8530 4738 680 203
9487 6055 3159 544 142
65.7% 71.0% 66.7% 80.0% 70.0%
Note: Standardised residuals outside 3.10 are bolded.
Table 5 Discordance rates between QRCD and road crash coded hospital cases for different severities. Severity
Number of cases in hospital
Number of non-linked cases
Discordance rate
AIS
Serious (>2) Non-serious (
Contents lists available at ScienceDirect
Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
Estimating under-reporting of road crash injuries to police using multiple linked data collections Angela Watson* , Barry Watson, Kirsten Vallmuur CARRS-Q, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 4 December 2014 Received in revised form 29 April 2015 Accepted 29 June 2015 Available online xxx
The reliance on police data for the counting of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police which under-estimates the overall burden of road crash injuries. The aim of this study was to use multiple linked data sources to estimate the extent of under-reporting of road crash injuries to police in the Australian state of Queensland. Data from the Queensland Road Crash Database (QRCD), the Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and the Queensland Injury Surveillance Unit (QISU) for the year 2009 were linked. The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. In addition, the potential bias of this discordance (under-reporting) was assessed based on gender, age, road user group, and regional location. Results showed that the level of under-reporting varied depending on the data set with which the police data was compared. When all hospital data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around twothirds not linking to any record in the police data. The results also showed that the under-reporting was more likely for motorcyclists, cyclists, males, young people, and injuries occurring in Remote and Inner Regional areas. These results have important implications for road safety research and policy in terms of: prioritising funding and resources; targeting road safety interventions into areas of higher risk; and estimating the burden of road crash injuries. ã 2015 Elsevier Ltd. All rights reserved.
Keywords: Road crash under-reporting Data linkage Traffic injuries Injury surveillance
1. Introduction Police reported crash data are the primary source of crash information in most jurisdictions (International Traffic Safety Data and Analysis Group (IRTAD), 2011). However, the reliance on police data for the counting and analysis of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police (Alsop and Langley, 2001; Amoros et al., 2006; Boufous et al., 2008; Langley et al., 2003a). This under-reporting can have impacts not only on the overall measure of the impact of road crash injuries, but could potentially introduce biases relating to particular groups of road users. In the area of road crash incidents and injuries, a variety of data linkage projects have been conducted (Abay, 2015; Alsop and Langley, 2001; Amoros et al., 2006; Aptel et al., 1999; Boufous et al., 2008; Cercarelli et al., 1996; Elvik and Mysen, 1999; Hauer and Hakkert, 1988; Langley et al., 2003a). Abay (2015) merged
* Corresponding author. E-mail addresses: [email protected] (A. Watson), [email protected] (B. Watson), [email protected] (K. Vallmuur). http://dx.doi.org/10.1016/j.aap.2015.06.011 0001-4575/ ã 2015 Elsevier Ltd. All rights reserved.
emergency room data with police-reported crash data in a region in Denmark. It was found that there was significant bias in reporting to police including a severity bias (i.e., higher severity injuries are more likely to be reported). Alsop and Langley (2001) used linkage of police and hospital records in New Zealand. They found that less than two-thirds of all hospitalised road crash casualties were recorded in the police data. They also found that this varied based on the number of vehicles involved, the geographical location, age and injury severity. Amoros et al. (2006) conducted a similar study looking at the under-reporting of road crash casualties in France. They used probabilistic methods to link police crash data with the road trauma registry in Rhone County. The results showed a police reporting rate of around 38%. However, this rate varied according to injury severity, the road user type, and the location of the crash (i.e., metropolitan vs. rural). Another French study conducted by Aptel et al. (1999) found that after linking police and hospital data, only 37% of non-fatal road crash injuries were recorded by police. Similar to other studies, they found that rate of reporting varied depending place of crash, the type of vehicle involved, and the injury severity. They also determined that police-reports tended to over-estimate the
A. Watson et al. / Accident Analysis and Prevention 83 (2015) 18–25
severity of the injury sustained. Langley et al. (2003a,b) conducted linkage between hospital records and police records to specifically examine the potential under-reporting of cyclist injuries in New Zealand. The results showed that only 22% of cyclists that crashed on a public road could be linked to the police records. Of the crashes that involved a motor vehicle, 54% were recorded by police. They also found that age, ethnicity, and injury severity predicted whether a hospitalised cycle crash was more likely to be recorded in the police data. Within Australia, Cercarelli et al. (1996) linked police reports, hospital admissions and accident and emergency (A&E) department data from a single hospital in Western Australia. The researchers found that around 50% of attendances at the A&E were recorded by police, and that around 50% of cases recorded by police as being admitted to hospital were actually admitted. The researchers argue that while the discrepancy between the data sets does represent an under-reporting of cases, it suggests that differences in coding systems may also lead to cases not being linked. Another Australian study conducted in NSW by Boufous et al. (2008) linked hospital admissions data (Inpatient Statistics Collection [ISC]) with the Traffic Accident Data System (TADS). The researchers matched 56.2% of hospitalisations as a result of road crash with a record in TADS. The researchers also found that the linkage rate varied according to age (i.e., lower linkage rate for younger age groups), road user type (e.g., lower linkage rate for cyclists), severity (i.e., higher linkage rates with increased severity) and geographical location. While these studies highlight the issues of under-reporting and bias within police data systems, many of these studies tended to limit data linkage to only two data sets (e.g., hospital and police data) rather than exploring the methods, issues and findings from the linkage of several data sets at a state-wide level to obtain a more comprehensive understanding of road crash injury. The aim of this study was to use multiple linked data sources to better estimate the extent of under-reporting of road crash injuries to police in Queensland. There has been no research of this nature conducted in the jurisdiction of Queensland, Australia and it is important to assess whether the under-reporting that has been found elsewhere applies here. In addition, there may be some unique features of this jurisdiction (i.e., a large and decentralised state with a significant rural and remote population) that may influence the level of under-reporting and/or the patterns of this under-reporting. Also, this jurisdiction is part of the larger picture of the road trauma problem and the under-reporting needs to be quantified to inform the reporting of road crash injuries in Australia. 2. Methods Ethics approval was obtained from the Queensland Health Human Research Ethics Committee (#HREC/12/QHC/45). 2.1. Data collections Data were provided from the Queensland Road Crash Database (QRCD), Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and Queensland Injury Surveillance Unit (QISU) by each relevant custodian for the specified cases for 2009. The year 2009 was used as it was the most recently available data for all collections at the time the linkage was commenced. There are often significant delays with data being available to researchers. Also, gaining the necessary custodian approvals and the data linkage (conducted by the Queensland Health Record Linkage Group) took twenty months to complete. The QRCD includes all road crash injuries reported to police in Queensland in 2009. This includes information about all persons
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injured on public roads, including drivers, passengers, motorcycle riders, cyclists, and pedestrians. It should be noted that the following major exclusions apply: The incident occurs in an area outside the road or road related area. There is no moving vehicle involved. The incident is not attributable to vehicle movement. It should be noted that the definition of what constitutes a road crash injury in this study is based on this QRCD definition. QHAPDC contains data on all patients discharged, statistically separated, died, or transferred from a Queensland hospital permitted to admit patients (including public hospitals, licensed private hospitals, and day surgery units). External cause of injury information is captured in three data fields (i.e., external cause, place, and activity) using International Classification of Diseases, 10th Edition, Australian Modification (ICD-10-AM) (National Centre for Classification in Health, 2004). The Emergency Department Information System (EDIS) includes all emergency department presentations in twenty-nine hospitals across Queensland (approximately 75% of Queensland emergency departments). This collection does not code cause of injury information and requires the use of the ‘presenting problem’ text description to identify transport-related cases. The Queensland Injury Surveillance Unit collects data on injuries presenting at seventeen Queensland emergency departments (nine of the QISU hospital emergency departments are not included in EDIS). This collection captures cause of injury information in several data fields both coded and text-based, including: mechanism, external cause, major injury factor, place, activity, and an ‘injury description’ text field. The following was determined by the researcher as the selection criteria for each collection for the data linkage: RCD: all injury cases QHAPDC: all admitted patients cases coded as transport-related (ICD-10-AM External Cause Codes from V00-V99) EDIS: all emergency department presentations coded as an injury (discharge diagnosis S00–S99 and T00–T98) QISU: all emergency department injury presentations cases coded as transport-related (external definition of ‘motor vehicle—driver’; ‘motor vehicle—passenger’; ‘motorcycle—driver’; ‘motorcycle—passenger’; ‘pedal cyclist or pedal cyclist passenger’; and ‘pedestrian’) It should be noted that for the data linkage component (conducted by the Queensland Health Record Linkage Group), all injury cases in EDIS and all transport injury cases in QISU and QHAPDC were processed for linkage. This was done as there was some question over the accuracy of the coding of traffic (roadrelated) injuries in QHAPDC and QISU and the selection of transport injuries in EDIS (as this data collection only contains cause of injury information in an unstructured text field as described above). So, in order to capture those cases that may still link to the QRCD (police-reported data) despite not being coded or identified as a road crash in the three hospital data sets, a broader approach to the linkage was used. The researchers then applied the refinements described in Section 2.4 to identify relevant road crash cases for analysis. 2.2. Data linkage process The Population Health Research Network (PHRN) was established by the Australian Government’s National Collaborative Research Infrastructure Strategy with the aim to oversee the
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conduct of health data linkage across Australian States and Territories. The QHRLG is the Queensland node of this network. The process for linking the selected data collections was based on the ‘separation principle’. This is the current practice for all jurisdictions’ data linkage centres within Australia as guided by the PHRN. Each data custodian provided personal information to the QHRLG. No content (clinical) information was required to be sent to the record linkage group. The QHRLG then used these personal data to create links between the data collections. Each of the data collections was internally linked to remove duplicate records of the same person and same episode. Person details and demographic data (i.e, full name, date of birth, sex, and injured person’s address) were linked by the Queensland Health Record Linkage Group (QHRLG) using linkage software ‘LinkageWiz’ (LinkageWiz, 2002). Both deterministic and probabilistic methodologies were applied, as well as manual clerical reviews were required. Two linkage passes were conducted for the two blocking variables (surname and date of birth). Linkage weights were determined by the software with higher weights given to variables with higher specificity (e.g, family name) and lower weights for those with lower specificity (e.g, sex). The sum of the weights across all the personal information variables was used to calculate a score. This total score is used to identify record pairs that are considered to be links and non-links. Non-links were considered as any pair with a total score less than 12 (as determined by the LinkageWiz software). All links were then manually reviewed. For every link that was confirmed by the manual review, a linkage key was applied in the form of a common ‘person ID’. This common person ID was assigned to each common case in the link. A unique person ID was also assigned to those cases that did not link to any other data collection. All person IDs (for links and nonlinks) were then sent back to the data custodians. The data custodians then attached the person IDs to the content (clinical) data and sent this de-identified data to the researcher.
2.3. Data merging process As QHAPDC, QISU, and EDIS were episode based, there were some duplicate cases in the data. The first case for an individual was considered the index case. For cases where the admission/ presentation date of a duplicate case was within one day of the discharge date of the index case, the duplicate was removed. When there was more than one duplicate case, if each subsequent case had an admission/presentation date within one day of the discharge date of the previous case it was also considered part of the same injury series. If the duplicate was after this date, it was counted as a new injury case (i.e., the person was injured in a separate event). Each of the hospital data collections were merged based on the person id with the QRCD. The first set of merges was the one-toone linkages (e.g., QHAPDC and QRCD). If the person ID of a hospital data set case matched the person ID of a case in QRCD, then the case was considered to be a link and was coded as such. Non-links, for the purposes of calculating discordance rates and analysing discordance bias, were all cases in the other data collection that did not have a person ID in common with QRCD. The hospital (i.e., presented at hospital) data collections (i.e., QHAPDC, EDIS, and QISU) were combined to form a hospital population data set. This data set included all cases from each collection that linked to each other as well as the unique (nonlinked) cases from each data collection. These combined data sets were then used as the basis for the population estimates for merging with QRCD. 2.4. Coding of variables The selection of cases for discordance rates and the coding of variables included in the analyses are described in Table 1.
Table 1 Data selection criteria and coding for each data collection. General
5 year age groups (with the exception 85+). Recoded to Gender 1 = Female; 2 = Male 1 = Female; 2 = Male Severity 1. The Abbreviated Injury Scale (AIS) (1 = minor; 1 and 2. Principal 2 = moderate; 3 = serious; 4 = severe; diagnosis ICD-10-AM 5 = critical; and 6 = maximum). Mapped to codes mapped to the principal diagnosis ICD-10 codes in the data AIS and a SRR (Association for the Advancement of Automotive Medicine, 2008). 2. Survival Risk Ratios (SRR)—estimate of the probability of death from 0 (no chance of survival) to 1 (100% chance of survival). SRRs were mapped to principal diagnosis ICD codes (Stephenson et al., 2003). 1 = Major Cities; Accessibility/Remoteness Index of Australia ARIA+ (ARIA+)—an index of the accessibility of places 2 = Inner Regional; based on their distance from the five nearest 3 = Outer Regional; major population centres (National Centre for 4 = Remote; 5 = Very Social Applications of GIS, 2009). (1 = Major Remote Cities; 2 = Inner Regional; 3 = Outer Regional; 4 = Remote; 5 = Very Remote). Road 1 = Driver, 2 = Motorcyclist, 3 = Cyclist, Second and fourth 4 = Pedestrian; 5 = Car passenger characters of the ICDuser 10-AM external cause code. Age
5 year age groups (with the exception 85+).
QHAPDC
QISU
EDIS
Provided in single years re-coded into 5 year age groups
Provided in single years re-coded into 5 year age groups
1 = Female; 2 = Male
1 = Female; 2 = Male
1 and 2. Principal diagnosis ICD-10-AM codes mapped to the AIS and a SRR
1 and 2. Principal diagnosis ICD-10-AM codes mapped to the AIS and a SRR
Postcode mapped to ARIA+ using data from Postcode mapped to ARIA+ using data the Australian Bureau of Statistics (2013). from the Australian Bureau of Statistics (2013).
External code (motor vehicle driver = driver; motorcycle driver and motorcycle passenger = motorcyclist; pedal cyclist or pedal cyclist passenger = cyclist; pedestrian = pedestrian; motor vehicle passenger = passenger)
Presenting problem text search (e.g., driver = driver; motorcycle, MCA, MBA = motorcyclist; bicycle, PBS, PBA = cyclist; passenger = passenger; none of the keywords = unspecified)
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In cases where more than one health data collection was combined with QRCD, there was a hierarchy for selection of which data collection would provide the data in the variables outlined in Table 1. For example, if the linkage between QRCD, QHAPDC, QISU, and EDIS was being examined, if the case has a specified ICD-10AM principal diagnosis code in QHAPDC, this was the code that was used. The ICD-10-AM coding in QISU was used when QHAPDC was not available and the ICD-10-AM code for EDIS was used in cases where neither QHAPDC nor QISU code is available. This hierarchy was based on the assumption that QHAPDC coding of injury is superior to QISU and EDIS, as it is completed by trained clinical coders with access to the full medical records of the patients. QISU would be considered next best, as it has coded information for most variables, and EDIS last, as many of the variables rely on being created from text searching.
needed to be used in combination to determine the population of comparison. Due to the scope of each of the data collections covering the spectrum of care from emergency presentation through to admitted patient care, the combination of data sets provided a better representation of the population than the single data collection, and therefore made a better reference for comparison with QRCD. So as part of the assessment of discordance, a population estimate was calculated based on the combination of the hospital data collections (after accounting for overlap/linkage of cases within these collections). A data set was created from this population estimate to represent the largest possible ‘population’ of road crash injuries. Then the number of cases in QRCD not linking with this combined data collection was considered the initial estimate of under-reporting or underrepresentation in QRCD.
2.5. Selection of cases for analysis
2.7. Assessment of discordance bias
For the purposes of analyses, road crash injury only cases needed to be selected. The selection criteria were as follows:
Linked and non-linked cases were compared on a number of characteristics that may influence the discordance rates. Specifically, linked and non-linked cases were compared on: age, gender, road user, severity (AIS and SRR), and the Accessibility and Remoteness Index of Australia (ARIA+). The description and coding of these variables are presented in Table 1. Comparisons were made using Chi-square tests of independence. Due to the large sample size, a more stringent alpha of .001 was adopted. Also, Cramer’s V (fc) was calculated in order to provide an estimate of effect size to give a clearer idea of the meaningfulness of any statistical significance found. As suggested by Aron and Aron (1991), a Cramer’s V of less than .10 was considered to be a small effect size, between .10 and .30 moderate, and more than .30 a large effect size. Post-hoc analyses were also undertaken using an adjusted standardised residual statistic. This statistic can be used to identify those cells with observed frequencies significantly higher or lower than expected. With an alpha level set at .001, adjusted standard residuals outside 3.10 and +3.10 were considered significant (Haberman, 1973). Multivariate analysis in the form of a logistic regression was also performed. It should be noted that age needed to be re-categorised into four groups (0–16; 17–24; 25–59; 60+) due to violations of linearity in the relationship to the outcome when treated as ordinal (5 year intervals). Referent categories for the predictors in logistic regression were chosen based on either the absence of a condition (e.g., non-serious) or the group with the largest proportion of injuries (e.g., Major Cities, drivers, 25–59 age group).
QRCD: all casualties were included as all these injuries are road crash injuries by definition (n = 19,041). QHAPDC: all transport injuries (ICD-10-AM external cause codes from V00 to V99) with a fourth character ICD-10-AM external cause code of ‘traffic’ (n = 7278) EDIS: all transport cases identified using keyword search on the variable presenting problem (e.g., car, motorbike, pedestrian) and without exclusion keywords (e.g., trail, off-road, path, quad bike) (n = 23,624) QISU: transport injuries selected using the external definition field coded as: motor vehicle—driver; motor vehicle—passenger; motorcycle—driver; motorcycle—passenger; pedal cyclist or pedal cyclist passenger; pedestrian and type of place coded as ‘street/highway’ (n = 2478)
2.6. Assessment of discordance (under-reporting) This discordance between the hospital data collections and QRCD provided an indication of the underestimation of the population in QRCD, either due to under-reporting or misclassification. Specifically, the reference for discordance was the cases in the hospital data collections and the discordance rate was determined using the following formula: C 100 Discordance% ¼ 1 B where C is the number of cases linked and B is the number of cases in the hospital data collections. The discordance rate for each hospital data collection was determined for those whose coding was consistent with the definition of a road crash. As none of the other data collections are the ‘gold standard’ for the population (none of the data collections represent all road crash injuries), using this one to one discordance does not provide a very accurate assessment of the under-representation of QRCD. In order for a more accurate assessment to be made, the hospital data collections
3. Results 3.1. Discordance rates Of the coded traffic injury cases in QHAPDC, 3320 did not link to QRCD. These non-linked cases represent possible under-reporting to police and highlight the number of additional cases that linking QRCD with QHAPDC could provide. It should be noted that 329 cases in QHAPDC that were coded as non-traffic did actually link to QRCD (representing 8% of all linked cases).
Table 2 Number of population sample set cases linked with QRCD. Population set
Number in population
Discordance rate
QHAPDC EDIS QISU Hospital data set (QHAPDC, EDIS and QISU)
7278 23,624 2478 28,220
45.6% 70.2% 63.7% 68.6%
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Of the coded road crash injury cases in EDIS, 16,580 did not link to QRCD. These non-linked cases represent possible underreporting to police and the number of additional cases that linking QRCD with EDIS could potentially provide. It should be noted that 2531 cases in EDIS that were coded as non-road crash did link to QRCD (representing 26% of all linked cases). Of the coded road crash injury cases in QISU, 1579 did not link to QRCD. Once again, these non-linked cases represent the possible under-reporting to police and the number of additional cases that linking QRCD with QISU could provide. It should be noted that 72 cases in QISU that were coded as non-road crash did link to QRCD (representing 8% of all linked cases). Table 2 includes the population numbers and discordance rates for each combination of links with QRCD. The one-to-one discordance rates (e.g., QRCD and QHAPDC) have already been reported above however they are included here for comparison purposes. For the entire hospital road crash injury population, as measured by the combination of all hospital data collections (QHAPDC, EDIS, and QISU), the discordance rate was 68.6%. Of the 19,359 road crash injury cases in the hospital data set that did not link with a police data, EDIS contributed 13,359 (69%) unique injury cases; QHAPDC contributed 1086 (5.6%) unique injury cases; and QISU contributed 474 (2.4%) unique injury cases. 3.2. Discordance bias The consistency of the discordance rate was examined for QRCD cases and the combined hospital data (i.e., QHAPDC, EDIS, and QISU). There was statistically significant difference in the discordance rate based on road user for road crash-coded hospital data cases [x2(4) = 5686.25, p < .001, fc = .52]. Specifically, motorcyclists and cyclists had a higher than expected discordance rate (see Table 3). There was also a statistically significant difference in discordance rate based on age for road crash-coded hospital data cases [x2(18) = 1800.32, p < .001, fc = .25]. Specifically, those aged 19 years and younger had a higher than expected discordance rate (see Fig. 1). Discordance rates statistically significantly differed on the gender of the injured person for road crash-coded hospital data cases [x2(1) = 725.02, p < .001, fc = .16]. Specifically, males had a higher than expected discordance rate (73.1% vs. 57.6% for females). There was a statistically significant, but small difference in discordance rates based on ARIA+ for road crash-coded hospital data cases [x2(4) = 117.63, p < .001, fc = .06], with Inner Regional and Remote areas having a higher than expected discordance rate (see Table 4). There were statistically significant differences in discordance rates based on serious SRR classification for road crash-coded hospital data cases [x2(1) = 259.14, p < .001, fc = .10] and AIS serious injury classification for road crash-coded hospital cases [x2(1) = 133.70, p < .001, fc = .07]. Specifically, serious cases had a higher than expected discordance rate (see Table 5). With all variables in the logistic regression, the model was statistically significant, x2(13) = 6334.93, p < .001 (Nagelkerke
Fig. 1. Discordance rates between QRCD and road crash coded hospital data cases for different age groups.
R2 = .39). With all variables in the model, gender was no longer significant. In contrast, age, road user, ARIA+, and serious injury remained statistically significant. Specifically, those aged 0–16 and 17–24 had higher odds (3.5 and 1.9 times, respectively) and those aged 60+ had 1.4 times lower odds of being discordant with QRCD compared to those aged 25–59. Motorcyclist and cyclist cases in hospital data had higher odds of discordance with QRCD. Remote cases had higher odds of discordance compared to Major Cities. Also, non-serious cases and in hospital data had higher odds (2.2 times) of discordance with QRCD (see Table 6). 4. Discussion The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. The level of discordance varied depending on the data set being compared. When all data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around twothirds not linking to any record in the police data. This discordance indicates the level of under-reporting of road crash injuries to police is somewhat similar to the level of discordance found in some other studies (Alsop and Langley, 2001; Amoros et al., 2006; Boufous et al., 2008). However, with the inclusion of linkage with more than just hospitalised data cases, the level of under-reporting found here is larger than that found in studies only linking with a hospitalised data collection. It should be noted that the discordance was lower (around 50%) when only linkage with QHAPDC (admitted to hospital) was considered. There may be a number of reasons for less discordance with these data collections. The lower discordance with QHAPDC could indicate that when a road crash injury is more severe (i.e., requiring hospital admission); the more likely it would be to come to the attention of police. It is also possible, that the discordance rates with EDIS and QISU may also be the result of misclassification of cases. This may particularly be the case with EDIS, where the identification of cases relies on text searching which may overestimate the population. Regardless of the differences in the discordance rates, the results show that there is a significant level of under-reporting to police. In addition, the results showed that each of the hospital data collections added
Table 3 Discordance rates between QRCD and road crash coded hospital data cases for different road users. Road user
Number of cases in hospital data
Number of non-linked cases
Discordance rate
Driver Motorcyclist Cyclist Pedestrian Passenger
4883 6169 6095 721 3034
1571 5010 5651 316 1496
32.2% 81.2% 92.7% 43.8% 49.3%
Note: Standardised residuals outside 3.10 are bolded.
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Table 4 Discordance rates between QRCD and road crash coded hospital data cases for different ARIA+. ARIA+
Number of cases in hospital data
Number of non-linked cases
Discordance rate
Major cities Inner Regional Outer Regional Remote Very Remote
14,434 8530 4738 680 203
9487 6055 3159 544 142
65.7% 71.0% 66.7% 80.0% 70.0%
Note: Standardised residuals outside 3.10 are bolded.
Table 5 Discordance rates between QRCD and road crash coded hospital cases for different severities. Severity
Number of cases in hospital
Number of non-linked cases
Discordance rate
AIS
Serious (>2) Non-serious (
