Injuries Resulting from Motor Vehicle Collisions Seen at Kingston General Hospital, 1999 to 2008
February 2010
| Authors & Citation information | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Authors:Matthew Hodgetts, Research Assistant, Research and Education Program, KFL&A Public Health Suzanne Sinclair, Epidemiologist, Research and Education Program, KFL&A Public Health Kathleen O'Connor, Director, Research and Education Program, KFL&A Public Health Recommended Citation:Research and Education Program of Kingston, Frontenac and Lennox & Addington Public Health. Injuries resulting from motor vehicle collisions seen at Kingston General Hospital from 1999 to 2008. Kingston, ON: Author; 2010 February. |
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| Introduction | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| The Ontario Public Health Standards (OPHS) outline the responsibilities of boards of health across the province in the provision of mandatory public health programs and services. One of two chronic diseases and injuries program standards is, in part, "to reduce the frequency, severity, and impact of preventable injury."1 One of the requirements of the foundational standard of the OPHS is that "local populations," whenever possible, be the basis for, and target of, programs and services.1 In order to achieve this requirement, "priority populations" should be identified "to the extent possible."1 In presenting local data on injuries resulting from motor vehicle collisions, this report effectively contributes to the Kingston, Frontenac and Lennox & Addington (KFL&A) Board of Health's mandate by both providing local data and by identifying a priority population.
A common source of injury is motor vehicle collisions. The data for this report came from a summary of all hospital admissions to the major regional trauma center (Kingston General Hospital) of injuries sustained in motor vehicle collisions for the years 1999 through 2008. |
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| Background | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Motor vehicle collisions were the most common accidental cause of death in Canada from 2000 through 2004.2 Further, motor vehicle collisions in Canada resulted in 8.9 fatalities and 604.0 injuries per 100,000 people and 13.0 fatalities and 884.5 injuries per 100,000 licensed drivers in 2006.3
The literature on motor vehicle collisions reveals some consistent information about risk factors. In several studies looking at similar sources of collision data, the youngest age groups (16/17-24) represented the highest rates of collisions resulting in injury, by a wide margin.4,5 A study looking at fatality statistics in Hamilton in a similar time period as this report (1999-2004), found the highest rate of fatalities to be in the 20-<30 age group.6 Data from the National Highway Traffic Safety Administration indicates that, in 2004, drivers aged 16-20 made up only 6.3% of licensed drivers, but accounted for 13.3% of crash fatalities and 17.9% of all those injured.8 Results concerning gender are not as consistent. One study from the U.S. found women to be 26% more likely to be involved in a injury-causing collision than men,5 while an Australian study found drivers in crashes significantly more likely to be male.4 A study looking at trauma centre data in Toronto found that men represented 77.9% of admissions,7 and the Hamilton fatality study found that 71.7% of victims were men.6 Similarly, a study using U.S. data found that males were significantly more likely than females to be involved in fatal crashes, with young males being the most likely of all age groups stratified by gender.9 |
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| Methods | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
This is an analysis of local collision data from Kingston General Hospital Trauma Registry and patient records.
Data CollectionThe source of data for this analysis is an aggregated summary of the records of all patients admitted to Kingston General Hospital involved in a motor vehicle collision. Pedestrians and cyclists injured in collisions were excluded. The data were abstracted from mid-1999 through mid-2008 annually by a data analyst attached to the Trauma Service. The abstracted data were then aggregated and compiled into a spreadsheet. The results were reviewed for accuracy, and errors were corrected. The resulting tables included the numbers, by year, of all patients involved in a motor vehicle collision, of all patients grouped by gender, and of all patients grouped by age group. Numbers were reported separately of the subgroup admitted by the Trauma Team. These individuals exhibited more severe injuries. Blood alcohol was measured in patients admitted by the Trauma Team if the patient was ten years of age or older and seen at the lead trauma hospital within 12 hours of injury. Blood alcohol level was measured by ethanol serum levels in mmol/L. Because the legal limit is 17 mmol/L, ethanol levels, where detected, were recorded as less than 17.0 mmol/L or above 16.9 mmol/L. Patients admitted by the Trauma Team were further stratified by gender, by age, and whether the collision involved one or more than one vehicle. Data AnalysisThe data were analysed using SPSS (Statistical Package for the Social Sciences) 15, as well as online analysis tools compiled by Statpages.net (Interactive Statistical Calculation Pages). Graphs were produced and analysed using both SPSS and Microsoft Excel 2003. Where appropriate, significance was determined using a GraphPad two-tailed Fisher's test, or a two-tailed chi-square test.10, 11 Linear regression was performed using SPSS 15.12 LimitationsThere were several critical limitations to this dataset. The first is that only aggregate data were provided, which means that several cross-tabulations of interest (gender by age, level of intoxication by gender) could not be done. Secondly, many variables of interest were missing from the dataset, such as status of the individual as driver or passenger, responsibility for the collision, time and season of the collision, and number of people injured. As a result, passengers could not be removed from analysis to allow a standardized risk of injury among drivers of different age groups to be calculated and compared. Standardization of rates is done by dividing the number of injured drivers for an age group by the number of licensed drivers in the age group. The larger the group, the more collisions there may be simply because there are more individuals driving. Standardization eliminates the size of the group as a contributing factor. |
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| Results | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Overall CollisionsFigure 1 displays the total number of individuals presenting to Kingston General Hospital with injuries resulting from collisions, by year. There is a significant downward trend (p<.01) in the number of injuries from motor vehicle collisions by year. Figure 1: Total injuries resulting from motor vehicle collisions
Figure 2 shows the total number of injuries resulting from collisions by year as well as the number of injuries seen by the Trauma Team. Although the total number of collisions decreases over this time period, we do not see the same decrease in the number of injuries seen by the Trauma Team. This indicates that the number of serious injuries occurring as a result of a motor vehicle collision has remained roughly the same over the years, as the Trauma Team only treats the most serious of injuries. Figure 2: Total injuries and injuries seen by the trauma team by year
Analysis of Collisions by AgeFigure 3 graphs the frequency of individuals injured in motor vehicle collisions by age group, and by year. Note that these individuals include all occupants of the vehicle, not just drivers. The only age groups which can really be compared are those of equal and known intervals, 21 to 60* In the KFL&A area, the largest proportion of individuals are in the 41-50 age group, but the largest number of injuries are seen in the 21-30 age group. Ideally one would like to standardize the rates in Figure 3 using driver's license data. Unfortunately this could not be done accurately because the dataset does not differentiate between drivers and passengers. Furthermore, the collision data may include individuals from out of town or out of province. See the Appendix for further discussion. Figure 3: Total injuries resulting from motor vehicle collisions by age group and year
*The largest age group is 41-50 years. See "Fact and Figures": Demographics at www.kflapublichealth.ca for more information Figure 4 displays the average number, per year, of total injuries resulting from collisions for each age group (blue) and the average number of serious injuries resulting from collisions for each age group (red). Serious injuries follow a trend similar to total injuries. Figure 4: Average number of total injuries and serious injuries per year by age group
To further examine the contribution of the younger age groups to the frequency of serious injury, Figure 5 shows the proportion of trauma and non-trauma injuries involving individuals aged 30 or less. Individuals <30 are significantly more likely to be involved in trauma injuries than non- trauma injuries (p<.02). The same holds true for individuals under 21 (data not shown). Figure 5: Non-trauma and trauma injuries to individuals ≤ 30 as a percentage of all non- trauma and trauma injuries, by year
Analysis of Collisions by GenderFigure 6 shows the proportion of collisions, resulting in trauma and non-trauma injuries, in which the injured individual was male. Males represented 61% of individuals injured in collisions and 64% of individuals with trauma injuries resulting from collisions. In both cases this was significantly higher than the expected proportion (p<.0001). In addition, a higher proportion of injured males than injured females presented with severe (trauma) injuries (p<.02). Figure 6: Percentage of trauma and non-trauma injuries which involve males
AlcoholThe number of individuals seen by the Trauma Team, by year and blood alcohol level, is shown in Figure 7. The red bar indicates those who were legally intoxicated or impaired, with an ethanol level greater than 16.9 mmol/L in their system. The blue shows the number of people who had alcohol in their system, but less than 17.0 mmol/L. The green bar shows the number of people who tested negative for alcohol in their system, and the purple bar shows those who were not tested. The number of intoxicated individuals seriously injured in collisions remained unchanged over this time period. Figure 7. Number of injured individuals seen by the Trauma Team by blood alcohol level.
Figure 8 shows the proportion of single and multiple vehicle accidents in which the individual involved was impaired. Impaired individuals were significantly more likely to be involved in a single than in a multiple vehicle collision (p<.001). Figure 8: The proportion of single and multiple vehicle accidents in which the individual involved was impaired.
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| Implications for Public Health | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| While this report is limited by the factors discussed in the limitations section, the results nevertheless follow the same trends as the literature surveyed in the introduction. If further research confirms this, it will be possible to conclude that males, specifically young males, are at higher risk of being in a collision involving serious injury in KFL&A, just as they are elsewhere. However, regardless of any further research, we know from the data analysed in this report that males and young individuals make up a disproportionate number of hospital admissions following collisions. We also know that single vehicle collisions involve impaired individuals more frequently than multi-vehicle collisions. Also, while total injuries resulting from collisions have significantly declined over the period of study, the number of serious injuries has remained relatively steady. All of this information should help guide intervention and prevention programs in compliance with the OPHS mandate. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| References | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Appendix | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Table A1: Data for Figures 1 and 2
Figure A1 graphs the mean from Figure 3 (the blue line in Figure 4) with the standardized mean from Figure A2 below. Figure A2 is the standardization of Figure 3 by the number of driver's licenses in Ontario. It is presented with two caveats. The first is that the license data is by calendar year, while the dataset uses periods of 12 months straddling two years; the figure below uses the first of the two years for pairing with the license data (e.g. 1999/2000 is paired with 1999 license data. The second caveat is that the figure makes several untestable assumptions - that all individuals in the dataset are drivers (no two injuries from the same vehicle) and that they have Ontario driver's licenses. Figure A1: Unadjusted and adjusted means of total collisions across all years by age group
Figure A2: Total injuries resulting from motor vehicle collisions by age group and year, standardized by number of driver's licenses
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