What Factors Predict Subsequent Motor Vehicle Injuries? Analysis of the longitudinal Canadian National Population Health Survey

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1 T2007 Seattle, Washington What Factors Predict Subsequent Motor Vehicle Injuries? Analysis of the longitudinal Canadian National Population Health Survey Evelyn Vingilis PhD C.Psych. Population and Community Health Unit, Schulich School of Medicine and Dentistry, Piotr Wilk PhD School of Nursing, Faculty of Health Sciences, The University of Western Ontario, London, Ontario, Canada N6A 5C1 Abstract Objective: The purpose of this study was to examine predictors of subsequent motor vehicle collision injuries (MVCs), with a particular focus on health-related variables, using the longitudinal dataset from the Canadian National Population Health Survey (NPHS) for the years Methods: Path analysis technique was used to determine the relations between MVC injury and four risk factors: binge drinking; health status; distress; and medication use. The three demographic variables, age at baseline, sex, and immigration status, were added into the model as control variables. Three age groups were examined: young = ; middle-aged = and old = years of age. The total sample size was Results: A higher percentage of males, younger and Canadian born persons reported a MVC injury. Binge drinkers, respondents with poor health, respondents with distress and who reported using pain relievers, tranquillizers, antidepressants, codeine, Demerol, morphine, sleeping pills, and two or more medications reported a higher percentage of subsequent injuries. Path analysis found that among younger individuals, the variable binge drinking, was the only significant predictor of subsequent injuries. In contrast, among middle-aged individuals, the variable medication use, was the only statistically significant contributor to subsequent injuries. No variables were significant predictors of injuries for older individuals. Conclusions: Among a nationally representative sample of Canadians, various demographic and risk factors correlate with injuries. Reported binge drinking among young individuals and medication use among middle-aged individuals predict subsequent MVC injury. Given that this number represents a considerable economic burden, this study underscores the need for continued research and countermeasures on alcohol, drugs and driving. INTRODUCTION Most recent Canadian data indicate that in 2004, 2434 people died and people were injured in MVCs (Transport Canada, 2006). Thus, injuries outnumber fatalities by about 90 times, making injuries a major economic burden. Despite the high number of injuries due to MVCs, information on what psychosocial, health and lifestyle factors predict MVC injuries among large representative samples is limited. Most research on persons injured in MVCs is based on samples of persons who presented at emergency rooms and/or have been hospitalized (Cherpitel, 1988; Longo et al., 2000; Meropol

2 et al., 1995; Stoduto et al., 1993; Tavris et al., 2001) or on police collision reports (Abdel-Aty & Abdelwahab, 2000; Valent et al., 2002; Zhang et al., 2000). Research finds that data from official statistics, such as hospital or police data, tend to under-represent MVC injuries (Begg et al., 1999; Caroll, 1971; Ozanne-Smith & Haworth, 1993; Vingilis, 1983), and reflect biased samples of injuries as not all injured persons are reported to police or present at hospitals and hospitals vary in the type of patients they see. Some population-based studies, using survey data have been conducted, although they have often focussed on sub-populations, such as adolescent or elderly drivers or active duty army personnel (Begg et al., 1999; Bell et al., 2000; Hu et al., 1998). Moreover, most studies have been conducted using cross-sectional surveys. Thus, it is impossible to assess whether the factors that correlated with the outcome measures are predictors or sequelae. The purpose of this study was to examine predictors of subsequent MVC injuries, with a particular focus on health-related variables, using data from Canadian National Population Health Survey (NPHS). For the purposes of this research, we adopted Laflamme and Diderichsen s (2000) conceptual framework to integrate downstream explanations for injuries. At the individual level, the two downstream mechanisms through which certain factors can cause injuries are: 1) differential exposure and 2) differential susceptibility (Laflamme & Diderichsen, 2000). The predictor variables that were used in this study were based on the literature of what variables could increase exposure (e.g., gender) and/or susceptibility (e.g., binge drinking) to MVC injury and on the availability of the measures within the survey. The strength of using a longitudinal, national survey database is the large sample size, the representativeness and generalizability of the sample, and prospective nature of the survey. The limitation is that not all variables that could predict subsequent MVC injuries are available in the survey instrument. However, a major value of the survey is the availability of health status and medication use variables that have typically not been examined in studies of predictors of MVC injuries. Relevant variables that were available in the survey included control variables of age, sex, immigration status and four risk factors of binge drinking, health status, distress and psychotropic medication use. These variables could affect exposure, such as the case with sex whereby males drive more than females and therefore are at greater risk of being involved in a MVC (Chipman et al., 1992; 1993). Moreover, binge drinking, certain health conditions, distress and certain psychotropic medications can cause impairment and/or increase susceptibility for MVC injuries. For example, binge or heavy drinkers are more likely to drink and drive, to ride with drinking drivers and to not wear seatbelts (Chipman, 1995; McKinnon et al., 2004; van Beurden et al., 2005). Finally, some factors, such as alcohol and certain health conditions, can possibly potentiate injury and thus also increase susceptibility for MVC injuries (Li et al., 1997). The National Population Health Survey The NPHS is an on-going, longitudinal, biennial study of Canadians, focusing on health and injury status, use of health services, lifestyles, psychosocial factors, and socio-demographic information (Statistics Canada, 2004). People living on Native reserves, military bases, institutions and some remote areas in Ontario and Quebec were excluded from the surveys. To date, five waves of data collected over an eight-year period (measured in (Cycle 1); (Cycle 2); (Cycle 3); (Cycle 4); and (Cycle 5)) are available for analyses (for complete information about the NPHS, see Statistics Canada (2005)).

3 In 1994 (Time 1), households were selected from across Canada, using a twostaged, stratified, random sampling procedure based on the Canadian Labour Force survey. In the first wave of the NPHS ( ), the sample was created by first selecting households and then within each household randomly choosing one member 12 years of age or older to be the longitudinal respondent. The NPHS longitudinal sample includes all longitudinal respondents who have completed at least the general component of the questionnaire in Time 1. The NPHS longitudinal follow-up sample includes persons from all ages who were to be interviewed every two years. Dependent Variable The dependent variable was derived from the outcomes of two questions: In the past 12 months, did you have any injuries that were serious enough to limit your normal activities? Participants who answered yes to this question in the first three waves were then asked, What happened? Participants who were injured were asked to identify the source of the injury. Participants who cited a transportation accident as the cause of their injuries were coded as 1 for the analysis (indicating an MVC) and all others were coded as a zero, i.e. 1 = injured ; 0 = not injured. The dependent variable was included for the years (Cycle 2); (Cycle 3); (Cycle 4); and (Cycle 5). If there were repeated events (more than one MVC injury), only the first event was taken into account. Independent Variables Sociodemographic variables: included age, sex and immigrant status. Sex was coded as a categorical variable with 1 = male ; 0 = female. Age was coded continuously from 12 to 85 years of age. Immigrant status was coded as 1 = immigrant ; and 0 = born in Canada. Binge drinking: was assessed using a derived frequency measure of consumption of five or more alcoholic beverages in one sitting in the past 12 months to assess frequency of bingeing behaviour, with 1 = yes and 0 = no. Health status: was assessed by a single question that asks: in general, would you say your health is excellent, very good, good, fair or poor? The question was coded as 1 = poor ; 2 = fair ; 3 = good ; 4 = very good ; 5 = excellent. This question has been found to be one of the best predictors of health care utilization, morbidity and mortality (Vingilis et al., 2002; in press). Psychological distress: is derived from six items included in the NPHS that are a subset of a generalized distress scale (Composite International Diagnostic Interview (CIDI)) developed at the University of Michigan (Robins et al., 1988). The items asked respondents to indicate the frequency of times in the previous month when the respondent felt nervous, restless, sad, hopeless, worthless, or that everything in life was an effort. The index ranged from 0 to 24 with higher scores indicating greater distress. Previous research has indicated an internal consistency of this six-item subscale to be α = 0.79 (Wade & Cairney, 1997). The original variables were recoded into a binary variable; score 7 and above on a 24 point scale indicates that a participant is distressed where 1 = yes and 0 = no. Medication use: included pain relievers; tranquillizers; antidepressants; codeine, Demerol, morphine; and sleeping pills in the past month. The original variables were added together to form a single score which was coded as 0 = no medication ; 1 = one medication ; 2 = two medications and 3 = three medications or more. The four risk factors were measured at the baseline (a wave previous to the cycle when the MVC injury was recorded); that is, there is two-year lag between the time of the report of MVC

4 injury and the measurement of the risk factors. Control variables, Sex and Immigration status, were measured at Cycle 1 ( ) whereas Age was measured at baseline. Path Analysis Path analysis technique was used to determine the relations between MVC injury and the four risk factors. Path analysis is a special case of structural equation modelling that uses only one measure per latent construct of interest. It is a very flexible statistical technique that permits testing complex hypotheses by allowing a variable to be a dependent variable in one relationship and an independent variable in another. Model. We created a path model based on previous research to assess the relative importance of the four risk factors, Binge drinking, Health status, Distress, and Medication use, on MVC injuries (Figure 1). In this model, Binge drinking, Health status, Distress, and Medication use, are hypothesized as predictors of MVC injury. The variables of MVC injury, Binge drinking, and Distress, are binary whereas variables of Health status and Medication use are assumed to be measured on a continuous scale. The three demographic variables, Age at baseline, Sex, and Immigration status, were added into the model as control variables. Direct and indirect effects were specified from the control variables of Sex and Immigration status, to MVC injury whereas the control variable Age at baseline had only indirect effect on MVC injury through the four risk factors. Multiple group analysis: Since age has been found to play an important role in explaining the relationship between the four risk factors and MVC injury (e.g. binge drinking more common among younger persons, medication use more common among older persons), we hypothesized that the nature of these relationships would differ across individuals of different ages. As testing the proposed model across different age groups can be informative, a multiple-group path analysis was conducted. Based on the baseline data and the injury rates from Transport Canada s traffic collision statistics (Transport Canada, 2006), three age groups were identified: 1) young individuals those between the ages of 12 and 29.9; 2) middle-aged individuals those between the ages of 30 and 59.9, and 3) older individuals those between the ages of 60 and 85. Analysis: Mixture modelling techniques, offered in Mplus 4 (Muthen & Muthen, 2006) were used to conduct the statistical analysis. In general, mixture modelling analysis refers to models with categorical latent variables that represent subpopulations where population membership is not known but is inferred from the data (Muthen & Muthen, 2006). Multiple-group path analysis can be perceived as a special case of a mixture model where class membership for the categorical latent variable representing a subpopulation is known. In our analysis, class membership corresponds to the three age groups; i.e., young individuals, middle-aged individuals, and older individuals. A maximum likelihood estimator with robust standard errors (MLR) employing a numerical integration algorithm (Monte Carlo integration) was used. The MLR standard errors are computed using a sandwich estimator. The total sample size (all individuals between the ages of 12 and 85 in the data set) was Listwise deletion for all cases with the missing data points would reduce the available sample by 2821 cases. We addressed the issue of missing data explicitly by proposing that missing data can be modelled as a function of observed covariates and observed outcomes (missing at random (MAR) assumption). In the end, we were able to retain most of the cases with missing values except for 134 cases that either had missing data points on the exogenous variables and/or on all the endogenous variables. Estimation was conducted using the sampling

5 weights that were calculated by post-stratifying the Cycle 1 stripped weight to the population estimates based on 1996 Census counts by age group and sex within each province. RESULTS The distribution of the sample by the three age groups, based on participant s age at baseline were: 12 to 29.9 years of age = 36.5%; 30 to 59.9 years of age = 47.3% and 60 to 85 years of age = 16.1%. Table 1 presents the weighted frequencies and percentages of respondents who did and did not report a MVC injury between 1996 and 2002 for the demographic and risk factor variables. As can be seen, a higher percentage of males, younger and Canadian born persons reported a subsequent MVC injury. Among risk factors, 2.3% of non-binge drinkers reported a subsequent MVC injury while 6.2% of respondents who reported binge drinking more than once a week reported a subsequent MVC injury. A higher percentage of respondents with poor health reported a subsequent MVC injury compared to those with good to excellent health. Similarly, a higher percentage of respondents who indicated distress had MVC injuries. Finally a higher percentage of respondents who reported using pain relievers, tranquillizers, antidepressants, codeine, Demerol, morphine and sleeping pills reported subsequent MVC injuries. Triple the percentage of respondents who indicated they were using two or more medications reported subsequent MVC injuries compared to respondents who reported no medication use. Table 2 presents the estimates of path coefficients, standard errors, and the corresponding odd ratios for the three age groups. Among younger individuals, the variable Binge drinking, was the only significant predictor of MVC injuries, with a path coefficient of A logit weight of suggests that individuals between the ages of 12 and 29.9 who engaged in binge drinking were times more likely to experience a MVC injury when compared to individuals who did not engage in such a behaviour. In contrast, among middle-aged individuals, the variable Medication use, was the only statistically significant contributor to the outcome variable MVC injuries (path coefficient = 0.509). A logit weight of suggests that, among individuals between the ages of 30 and 59.9, an increase in the number of medications used by one, increases the odds of experiencing MVC injury times. Among the older drivers, no variables were significant predictors of MVC injuries. DISCUSSION The results of this longitudinal study of a nationally representative sample of Canadians provide important insights on MVC injuries and predictors of subsequent injuries. In considering demographic factors, it is interesting to note that contrary to some studies, persons aged have the lowest percentage of reported MVC injuries. Indeed three times the percentage of years old reported an MVC injury compared to persons years old. These differences may reflect age differences in exposure and/or susceptibility. In examining exposure, Chipman et al. (1992) found that drivers younger than 20 years were less exposed (approximately 23% less in distance and time) than drivers years of age, although MVCs of younger drivers involve more passengers (Jelalian et al., 2000). Braver and Tempel (2004) found non-fatal injuries rate ratios for driver injuries to be 3.72 for year olds, 2.53 for year olds and 1.67 for year olds, while the non-fatal injuries rate ratios for passenger injuries to be 5.30 for passengers riding with drivers aged 16-19, 2.84 for passengers riding with drivers aged and 1.81 for passengers riding with drivers aged For all other age groups, except drivers over 75 years of age injuring themselves, drivers and passengers had rate ratios of 1. However, exposure may be a factor for older drivers as Chipman et al., (1992) found that drivers aged 60-

6 69 drove about 33% less distance and 19% less time than middle-aged drivers, which may partially explain the lower percentages of MVC injuries found in this study. Among susceptibility factors, Petridou et al. (1997) found that young persons who had ridden with drunk drivers were more likely to drink, to drink and drive themselves and to not wear seat belts. Moreover, when younger drivers drove with peer passengers, they drove faster than other drivers, left less distance between their vehicles and the vehicles in front of them and engaged in other risky driving behaviours, such as speeding (Simons-Morton et al., 2005). Additionally, Williams and Shabanova (2002) found that adolescents, but not adults, decreased driver belt use with increasing number of passengers. Driver belt use was lowest when adolescent drivers were driving with passengers in their twenties, while it was highest when they were driving with passengers aged 30 years and older. Passenger belt use also decreased among adolescents as the number of passengers in the vehicle increased. Moreover, as this study and other studies show, young persons engage in higher levels of binge drinking (Jonah, 1990; Naimi et al., 2003; Williams & Shabanova, 2002). The sex differences found in this study were consistent with other studies (Broyles et al., 2003; Langley & McLoughlin, 1989; Powell et al., 2002; Singleton et al., 2004); a greater percentage of females reported MVC injuries than males. All risk factors showed significant differences in MVC injuries. As Table 1 shows, persons who reported binge drinking, and medication use were more likely to report a subsequent MVC injury. Users of antidepressants, codeine, Demerol and morphine reported sustaining a MVC injury at double the percentage of non-users. Certainly binge drinking and these medications can increase susceptibility to MVC injuries as alcohol and various medications have been found in experimental and other studies to cause impairment in driving skills (Vingilis & McDonald, 2002). Moreover, binge drinking passengers are more likely to ride with drinking drivers (Jelalian et al, 2000). Health status was also found to be associated with MVC injuries. A higher percentage of persons reporting poorer health and distress reported subsequent MVC injuries. However, when the data were subjected to path analyses with age, sex and immigration status as control variables, only binge drinking was a significant predictor for those aged years, while medication use was a significant predictor for those aged years. These findings do confirm the role of heavy drinking in MVC injuries, particularly for the under-30 age cohort and reinforce the need to engage in drinking driving interventions for this age cohort. Equally important is the link between medication use and MVC injury among the older cohort. In recent years, interest and concern over the impairing properties of medications has expanded. However, the epidemiological studies on medication use and MVC injuries have often been limited to one class of medications, such as benzodiazepines (Ellinwood & Heatherly, 1985), or cross-sectional studies (Chipman, 1995). This study provides evidence in support of the various initiatives in the European Union and other jurisdictions to educate health care professionals and the public about the impairing properties of various medications (Alverez et al., 2001; Jones, 2005; Walsh et al., 2005). Clearly, risk of subsequent MVC injury has been found among this nationally representative sample of middle-aged Canadians who use certain medications, although it is not possible to assess whether the medication use, per se, or the medical conditions for which the medications are being used, increases susceptibility of MVC injuries. However, continued examination of the role of medication use and MVC injuries through other studies is indicated.

7 A unique finding, however, is the lack of predictors of MVC injuries among the older respondents. This may be due to the fact that only 16.1% of the entire sample comprised the older age group and statistical power may be an issue. Yet, the 60 and older age group also reported the lowest percentage of MVC injuries. Among this nationally representative sample of elderly, many may not be driving and among those who do, they are probably driving less. Thus, although among crashed drivers, elderly drivers may have a higher crash rate in regards to vehicle kilometer travelled, the elderly per capita, may not represent a high crash problem (Braver & Trempel, 2004). Interestingly, medication use and health status were not associated with significantly increased MVC injury risk, possibly because research has shown that elderly drivers tend to reduce or stop driving because of health and vision problems (Ball et al., 1998; Chipman et al., 1998; Hakamies-Blomqvist, & Wahlstrom, 1998; Rudman et al., 2006). Other studies have argued that elderly drivers may not be at increased risk of injuring passengers (Braver & Trempel, 2004) or that the higher elder crash risk found in various studies is a possible artifact of differences in types of exposure, that is, the different nature of driving undertaken by many elderly drivers (Keall & Frith, 2004). In summary, this study finds that 2.8% of a nationally representative sample of Canadians, between the ages of 12 and 85, reported experiencing a MVC injury in the previous year. Various demographic and risk factors were associated with MVC injuries; binge drinking among young persons and medication use among middle-aged persons were predictive of subsequent MVC injury, controlling for demographic variables. Given that this number represents a considerable economic burden, this study underscores the need for continued research and countermeasures on alcohol, drugs and driving. Acknowledgement: This research was supported by a grant from AUTO21, a member of the Networks of Centres of Excellence (NCE) program, which is administered and funded by the Natural Sciences and Engineering Research Council (NSERC), the Canadian Institutes of Health Research (CIHR) and the Social Sciences and Humanities Research Council (SSHRC), in partnership with Industry Canada. Access to the National Population Health Survey microdata files was granted through an application to the CISS-ACCESS to the Research Data Centre Program. While the research and analysis are based on data from Statistics Canada, the opinions expressed do not necessarily represent the views of Statistics Canada. REFERENCES Abdel-Aty MA, Abdelwahab HT. (2000) Exploring the relationship between alcohol and the driver characteristics in motor vehicle accidents. Accid. Anal. Prev. 32, Alvarez JF, de Gier JJ. (2001) Prescribing and dispensing guidelines for medicinal drugs affecting driving performance. Ball K, Owsley C, Stalvey B, Roenker DL, Sloane ME, Graves M. (1998) Driving avoidance and functional impairment in older drivers. Accid. Anal. Prev. 30(3), Bell NS, Amoroso PJ, Yore MM, Smith GS, Jones BH. (2000) Self reported risk-taking behaviours and hospitalization among active duty army personnel. Am. J Prev. Med.18(38), Begg. DJ, Langley, JD, Williams, SM. (1999) A longitudinal study of lifestyle factors as predictors of injuries and crashes among young adults. Accid. Anal. Prev. 31(1-2),1-11.

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11 Table 1. Weighted Frequencies and Percentages of Respondents' Reported Involvement in MVC Injuries by Demographic and Risk Factors from the Canadian National Population Health Survey Age Gender Motor Vehicle Collision Injury No Yes Number Percentage (%) Number Percentage (%) Total Female Male Immigration Born in Canada status Immigrant Frequency of having 5 or more drinks Self perceived health Distress Never < Once/month Once/month times/month Once/week > Once/week Poor Fair Good Very Good Excellent No Yes Use of pain No relievers Yes Use of No tranquillizers Yes Use of No antidepress ts Yes

12 Use of codeine, Demerol, and morphine No Yes Use of No sleeping pills Yes Use of medications

13 Table 2 Path Analysis Results for Predictors of MVC Injury by Age Group Variables Logit Weights S.E. Est./S.E. Odds Ratios years Male Immigration Alcohol bingeing* Health status Distress Medication use years Male Immigration Alcohol bingeing Health status Distress Medication use* years Male Immigration Alcohol bingeing Health status Distress Medication use * significant predictor

14 Motor Vehicle Collision Injury Path Model Sex Alcohol Bingeing Age at Baseline Health Status Distress MVC injury Immigration Status Medication Use Figure 1: Motor Vehicle Collision Injury Path Model