1 Correlates of Motor Vehicle Injuries: Analyses of the National Population Health Survey

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1 1 Correlates of Motor Vehicle Injuries: Analyses of the National Population Health Survey JS Seeley 1 K Boksman ER Vingilis 1 Population & Community Health Unit, University of Western Ontario, Collip Circle, London, Ontario, Canada, N6G 4X8, Ph: (519) ext. 1, jsseeley@uwo.ca Background Every day about 16,000 people die of injuries throughout the world (1). Recent estimates indicate that injuries are a leading cause of morbidity and mortality, accounting for 16% of the global burden of disease. In Canada, injury ranks as the third highest burden of illness (2). A recent report by Ontario s Chief Medical Officer of Health indicates that across all age groups, unintentional injury is the fourth leading cause of death and hospitalization in Ontario (3). Motor vehicle collisions (MVCs) are a major cause of injuries and fatalities. Most recent Canadian data indicate that in 2001, 2,778 people died and 221,158 people were injured in MVCs (4). The World Health Organization projects that injuries due to road traffic collisions will move within the rank order of leading causes of disease burden from ninth place in 1990 to third place by 2020 (5). Despite the high prevalence of injuries due to road traffic collisions, information on correlates of motor vehicle injuries among large representative samples is limited. The National Population Health Survey allows analyses to be conducted on a nationally representative sample of households in Canada which is collected in a prospective manner. Methods Analyses were conducted using the National Population Health Survey (NPHS), in which 19,600 households were selected from across Canada, using a two-staged, stratified, random sampling procedure. People living in Native reserves, military bases, institutions, and some remote areas of Ontario or Québec were excluded. One person from each household was randomly selected to provide detailed personal information for the survey. Analyses were undertaken to determine which variables of those selected from the survey were significantly related to self-reported injury from MVCs. The independent variables investigated include demographic, lifestyle, socioeconomic, psychological, physiological, and pharmacological indicators. Those injured in MVCs responded affirmatively to both a question asking if, in the past 12 months, the respondent sustained any injuries that were serious enough to limit his or her physical activities, as well as a question categorizing the injury as a result of a MVC. Chi-square and correlational statistics were computed for categorical variables and continuous variables, respectively. Results Table I presents results of chi-square analyses of all categorical independent variables by MVC injury status. Table II displays the distribution of the categorical variables, by MVC injury status, plus odds ratios for those variables deemed significant in the chi-square analyses. Table III presents the distribution by MVC injury status and correlations for continuous variables.

2 2 Demographic variable results indicated significant relationships with MVC injury only for marital status and lower age. Sex, income adequacy, family income level, and whether one lived in an urban or rural setting were not significant. Retirement, the most common reason for not working, appears to be related to a lower incidence of MVC injury relative to other unemployed respondents. With the exception of this result, no other work-related variables were found to be significant correlates of MVC injury. Pain and discomfort and severity of pain were highly significant correlates. Reports of vision problems proved to be significantly related to MVC injury occurrence. Lower scores on the general health index were also correlated with MVC injuries. All other health-related variables analyzed were found to be non-significant. Many measures of stress were significantly associated with MVC injury. Self-perceived happiness, emotions, chronicity of distress, general chronic stress, recent life events, child and adult stressors, work stress, self-esteem, distress, self-rated health, and depression were all highly significant psychosocial correlates. Use of certain psychotropic medications was associated with MVC injuries: Specifically, antidepressants, allergy medications, codeine, Demerol or morphine, cough or cold remedies, heart and blood pressure medications, and pain relievers. Use of asthma medications, sleeping pills, steroids, and tranquilizers were not associated with a greater incidence of MVC injury. The number of different medications used concurrently did not correlate with MVC injury. Interestingly, binge drinking was significantly associated with MVC injuries, but drinking frequency was not. Discussion The results indicate that significant correlates of MVC injuries include demographic, drug- and alcohol-use, health-related, and psychological factors. Marital status, age, and gender are commonly occurring correlates of MVC injuries. Emerging concerns for crash involvement are health status and psychotropic medication use. With our national Canadian sample, this study confirms previous findings that age and marital status appear related to MVC injuries (6, 7). In contrast to extant trends, our results have indicated that sex is not significantly associated with MVC injury occurrence. Use of medicinal drugs and binge drinking were significant correlates, but drinking frequency was not associated with MVC injuries. Sleeping pills and tranquilizers were not significantly associated with MVC injury occurrence which is contrary to the literature (8). Health status measures were significantly related to MVC injuries. Among the most intriguing findings are the strengths of the relationships between the various psychological factors, such as stress and happiness, and MVC injuries. Although one cannot draw direct links to road rage incidents, psychosocial traits and states (e.g., negative or low emotions, chronic or transient stressors, difficult life events and poorer mental health) could serve as predictors of a MVC injury or as mediating variables leading to other behaviours (e.g., alcohol or drug use, poor sleeping habits) to increase the risk of a MVC injury occurrence. The significant psychosocial results in this study are noteworthy, as there is currently a paucity of research investigating the influence of cognitive, emotional, and mental health factors in driving behaviour and MVCs. These early analyses are cross-sectional in nature, hence it is not possible to determine whether these variables serve as predictors or sequelae of MVC injuries. It would be highly relevant to prevention, education and intervention efforts to understand whether or not the significant psychosocial correlates reported here are predictors or sequelae of MVC injuries. Further analyses, already underway, will examine and report longitudinal results across subsequent waves of the NPHS, and address the issue of the directionality of effects.

3 3 Table I Chi-Square Analyses of Categorical Variables df χ 2 p Educational Level Sex Income Adequacy Family Income Level Marital Status < Rural/Urban Residence Employment Variables Main Activity Work Status Work Shift Work Pattern Student Status Reason for Not Working < Health-Related Variables Pain prevents activities < Free of Pain & Discomfort < Vision Cataracts Glaucoma Diabetes Self-Rated Health Self-Perceived Happiness Chronicity of Distress Cognition Ability to remember things Ability to think & solve Antidepressants Asthma Medications Allergy Medications < Codeine, Demerol, Morphine < Cough/Cold Remedies Medications for Heart Medications for Blood Pain Relievers < Sleeping Pills Steroids Tranquilizers Alcohol Variables Drinking Frequency

4 Table II Distribution by MVC involvement and Odds Ratios for Significant Categorical Variables N % MVC % Not MVC Odds Ratio Educational Level Elementary school Secondary school graduate Other post-secondary Marital Status Married/Common-Law/Living with Single Widowed/Divorced/Separated Employment Variables * Reason for Not Working Retired Health-Related Variables Pain prevents activities All else No pain or does not prevent activity Yes Free of Pain/ Discomfort Yes No Vision Problems No vision problems Vision problems * Cataracts Yes No Self-Rated Health Excellent Self-Perceived Happiness Very Good Good/Fair/Poor Happy and interested in life Somewhat happy to unhappy Chronicity of distress More or less than usual Usual * Antidepressants Yes No Allergy Medications Yes No * Blood Pressure Medications Yes * Codeine, Demerol, or Morphine No Yes No Cough/Cold Remedies Yes No * Heart Medications Yes No Pain Relievers Yes No * indicates one or more of the cells containing frequency counts had a value less than or equal to 30 and hence may not be reported. 4

5 Table III Correlational Analyses of Continuous Variables N Mean Mode Std. R p Dev. Age < Working Hours General Chronic Stress < Recent Life Events Score < Child and Adult Stressors < Work Stress Self-Esteem Mastery Sense of Coherence < General Health (Health Status) < Distress < Depression < Social Supports Number of drugs taken at one Alcohol Variables Binge Drinking References 1. Krug EG, Sharma GK, Lozano R. The global burden of injuries. Am J Public Health 2000; 90: Moore R, Mao Y, Zhang J, Clarke K. Economic Burden on Illness in Canada, Health Canada 1997; Available from: URL: 3. D Cunha C Chief Medical Office of Health Report. Injury: Predictable and Preventable. Available from: URL: Toronto: Queen s Printer for Ontario; Transport Canada Canadian Motor Vehicle Collision Statistics Accessed at 5. World Bank Group. Health, Nutrition and Population, Public Health. Why is road safety such an important public health issue? Road Safety at a Glance 2002; March Accessed at: /26705f1f0d0a5e b840072b49d/$FILE/ATTZC02P/Road%20Safety%20AAG.pdf. 6. Claret PL, Castillo JD, Moleón JJ, Cavanillas AB, Martín MG, Vargas RG. Age and sex differences in the risk of causing vehicle collisions in Spain, Accid Anal Prev 2003; 35: Li G, Braver ER, Chen L. Fragility versus excessive crash involvement as determinants of high death rates per vehicle-mile of travel among older drivers. Accid Anal Prev 2003; 35: Vingilis, E., Macdonald, S. Review: Drugs and Traffic Collisions. Traffic Inj Prev. 3(1), 1-11.