Relationship between quality of life and child traffic fatalities

Relationship between quality of life and child traffic fatalities

Accident Analysis and Prevention 39 (2007) 826–832 Relationship between quality of life and child traffic fatalities Murat Darcin a,∗ , E. Selcen Dar...

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Accident Analysis and Prevention 39 (2007) 826–832

Relationship between quality of life and child traffic fatalities Murat Darcin a,∗ , E. Selcen Darcin b a

b

Ministry of Interior Affairs, Ankara, Turkey Institute of Education, Gazi University, Ankara, Turkey

Received 19 June 2006; received in revised form 31 October 2006; accepted 7 December 2006

Abstract Traffic injuries are the leading cause of morbidity and mortality of children around the world. Economic development and quality of life are main components of public health. Using canonical correlation analysis, this study examined the relationship between quality of life and traffic fatality among children. Data were collected from 19 countries by using World Health Organization and OECD statistics. The results indicated that there is a strong relationship between quality of life and children traffic fatality. Growing quality of life has become protective against child traffic accident mortality. Increasing in gross national income per capita, life expectancy at birth number of years, road motor vehicles per thousand population, and share of persons of working age (15–64 years) in employment also increases children traffic safety. © 2006 Elsevier Ltd. All rights reserved. Keywords: Canonical correlation; Child traffic fatality; Quality of life

1. Introduction Road traffic injuries which are one of the main public health problems facing modern society (Parada et al., 2001) are major and growing but neglected global public health issue. One of the leading causes of morbidity and mortality in both developed and developing countries are traffic accidents. Every day around the world, more than 3000 people die from road traffic injury and worldwide, an estimated 1.2 million people are killed in road crashes each year and as many as 50 million are injured. 2.1% of all fatalities and 25% of accident fatalities worldwide take place by reason of traffic accidents (WHO, 2004). Road traffic injuries are currently ranked ninth globally among the leading causes of disability adjusted life years lost, and the ranking is projected to rise to third by 2020 (Murray and Lopez, 1996). In 1998, developing countries accounted for more than 85% of all deaths due to road traffic crashes globally and for 96% of all children killed in traffic crashes worldwide (WHO, 1999). Moreover, about 90% of the disability adjusted life years lost worldwide due to road traffic injuries occur in developing countries (Murray and Lopez, 1996). The problem is increasing at a fast rate in developing countries due to rapid motorisation and other factors (Jacobs et al., 2000).



Corresponding author. E-mail address: [email protected] (M. Darcin).

0001-4575/$ – see front matter © 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2006.12.003

Traffic injuries are the leading cause of morbidity and mortality for children around the world. Traffic accidents resulting in 40% of fatalities of children under the age of 15 are accepted as one of the biggest risk of the leading cause of death and injury worldwide. Depending on the report of WHO, traffic accidents are one of the main reasons of 5–14 years old children death among the other all death reasons (WHO, 2004). The number of children killed and seriously injured on the roads has increased steadily for many years. By developing of techonogy, vehicles and roads present societies and also children have been started to travel much more. Children are very different from adults but they have not been considered to design of roads and vehicles. This is a big problem for children traffic safety issue. Children rarely cause traffic accidents, but take a part in traffic accidents more often as a pedestrian, a bicyclist or a passenger. The number of vehicles per unit is more in developed countries than developing countries but children fatalities in traffic accidents are much more in developing countries (UNICEF, 2001). In fact most of the deaths can be prevented by using basic safety rules. The use of safety belt, child safety seat, protective helmet and other safety devices can prevent many of the fatalities. Economic development and quality of life are the main components of public health. There is strong negative association between prosperity and the number of traffic deaths per motor vehicle (S¨oderlund and Zwi, 1995) and per vehicle/km. For

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instance, it is observed that nine people in Denmark, 11 people in Germany and 73 people in Turkey have been died in each 1,000,000 vehicle/km (IRTAD, 2004). In this study, the relationship between children traffic fatality and quality of life in some countries was examined by canonical correlation analysis method using NCSS (number cruncher statistical system) packaged-software.

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• X8 : quality of life – society – income inequality distribution of household disposable income among individuals, incomes from the mid 1980s to years around 2000: Gini coefficients, mid 1980s to years around 2000. • X9 : quality of life – passenger transport – road motor vehicles and road fatalities; road fatalities; per million vehicles. 2.2. Statistical method

2. Material and methods 2.1. Data Data of this study was collected from 19 countries (Austria, Belgium, Czech Republic, Denmark, United States, Finland, France, Germany, Hungary, Ireland, Italy, Turkey, Norway, Poland, Portugal, Spain, Sweden, Netherlands, and United Kingdom) by using World Health Organization (WHO) statistics, OECD statistics and for Turkey also Turkish Statistical Institute statistics. World Health Organization (WHO) statistics, OECD statistics and Turkish Statistical Institute statistics were used as the main source of children traffic fatality data set (Y variables set) and OECD Factbook, 2006: economic, environmental and social statistics were used as the main source of quality of life data set (X variables set). Y and X variables sets are defined as follows: • Y1 : death proportion of children in traffic accidents per 100 thousands children population. • Y2 : death proportion of pedestrian children in traffic accidents per 100 thousands children population. • Y3 : death proportion of bicyclist children in traffic accidents per 100 thousands children population. • Y4 : death proportion of passenger children in traffic accidents per 100 thousands children population. • Y5 : death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents. • X1 : macroeconomic trends – gross domestic product (GDP) – national income per capita; gross national income per capita; US dollars, current prices and PPPs. • X2 : quality of life – health – infant mortality; infant mortality; deaths per 1000 live births. • X3 : quality of life – health – life expectancy; life expectancy at birth, total; number of years. • X4 : quality of life – passenger transport – road motor vehicles and road fatalities; road motor vehicles per thousand population. • X5 : labour market – employment – employment rates by gender; employment rates, total; share of persons of working age (15–64 years) in employment. • X6 : quality of life – society – social exclusion; youths aged between 15 and 19 who are neither in education nor in employment; as percentage of persons in that age group males. • X7 : quality of life – society – social exclusion; youths aged between 15 and 19 who are neither in education nor in employment; as percentage of persons in that age group females.

Canonical correlation analysis which is a statistical analysis method to explain the correlation structure between two sets of variables is one of the most general of the multivariate techniques. It is used to investigate the relationship between a linear combination of the set of X variables with a linear combination of a set of Y variables. Consider two groups of variables (X and Y) such that one has p variables (X1 , X2 , . . . ,Xp ), and the other has q variables (Y1 , Y2 , . . . ,Yq ). Linear combinations of the original variables can be defined as canonical variates (Wm and Vm ) as follows: Wm = am1 X1 + am2 X2 + . . . + amp Xp

(1)

Vm = bm1 Y1 + bm2 Y2 + . . . + bmq Yq

(2)

The correlation between Wm and Vm can be called canonical correlation (Cm ). Squared canonical correlation (canonical roots or eigenvalues) represents the amount of variance in one canonical variate accounted for by the other canonical variate (Hair et al., 1998). The linear combination of the components of X and the components of Y would be W = a X and V = b Y, respectively. Variances and (co)variances of canonical variates as follows:  Var(W) = a Cov(X)a = a a (3) 11

Var(V ) = b Cov(Y )b = b



b

(4)

22

Cov(W, V ) = a Cov(X, Y )b = a



b

(5)

12

Then the correlation coefficient between W and V canonical variates is:  b a r(V, W) =     12 1/2 (6) a 11 a b 22 b The null hypotheses is that: H0 : r1 = r2 = . . . = rm = 0

(7)

and alternative hypotheses is that: H1 : r1 = r2 = . . . = rm = 0

(8)

For testing the above hypothesis, the most widely used test statistic Wilks’ lambda is defined as follows:  Λ= (1 − ri2 ) (9) i=1

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M. Darcin, E.S. Darcin / Accident Analysis and Prevention 39 (2007) 826–832

Bartlett (1941) used Wilks’ lambda statistic to develop an approximate χ2 -test with pq degrees of freedom: X2 = −[n − 0.5(p + q + 1)] ln Λ

 Redundancy (Y ) =

k 2 i=1 C Wi Yi



q

× λ1

(16)

(10)

In formula (10) n is the number of cases, ln states the natural logarithm function, p the number of variables in one set and q is the number of variables in the other set. The statistical significance of χ2 -test is compared with α = 0.05, 0.01, 0.001 critical value of χ2 statistic with pq degrees of freedom. Matrix scores on canonical variates of Vi and Wi are calculated by using values in original data. The sum of canonical scores for each variate is equal to zero. Correlation coefficients between canonical scores (Vi and Wi ) and observed values (Xi , Yi ) are called as canonical weights or canonical structure and calculated as follows: CVi Xi = corr(Vi , Xi )

(11)

CVi Yi = corr(Vi , Yi )

(12)

Canonical weights are used to determine which variables effect markedly to which one of the canonical variates. The canonical weights allow the user to understand how each variable in each set uniquely contributes to the respective weighted sum of canonical variate. Explained variance is the sum of the squared canonical weights divided by the number of variables in the set and defines how much variance each canonical variate explains.  C 2 Vi X i Explained variance (X) = i=1 (13) p k C 2 W i Yi (14) Explained variance (Y ) = i=1 q The high number of explained variance can explain that eigenvalue solution matrix of canonical correlation is adequate level or not to account for correlation between observed two sets. Canonical correlation maximizes the correlation between linear combinations of variables in X and Y variable sets. To determine how much of the variance in one set of variables is accounted for by the other set of variables, redundancy measure is calculated (Sharma, 1996). Redundancy determines the amount of variances accounted for in one set of variables by the other set of variables. For example, the redundancy Xj is the proportion of the total variance in the set of X variables that is explained by the jth canonical variate of the Y variables set. The redundancy measure can be calculated for each canonical correlation. To calculate redundancy measure, the sum of the squared canonical weights divided by the number of variables in the set and obtained value multiplies by the biggest root. 

k 2 i=1 C Vi Xi Redundancy (X) = (15) × λ1 p

In formulas (15) and formula (16), C2 Vi xi is the squared canonical weights of first set; C2 Wi Yi is the squared canonical weights of second set; p denotes the number of variables in the first (Y) set of variables, and q denotes the number of variables in the second (X) set of variables; λ1 is the respective squared canonical correlation. 3. Results Descriptive statistics (the mean values and standard deviation) of each variable considered in both sets are presented in Table 1. The Pearson’s correlations between variables of children traffic fatality and variables of quality of life are shown in Table 2. These correlations show that gross national income per capita (x1), life expectancy at birth number of years (x3), and share of persons of working age (15–64 years) in employment (x5) are negatively correlated to the variables of children traffic fatality, death proportion of children in traffic accidents per 100 thousands children population (y1); death proportion of pedestrian children in traffic accidents per 100 thousands children population (y2); death proportion of passenger children in traffic accidents per 100 thousands children population (y4); and death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents (xy); but (x1, x3, x5) are not negatively correlated to death proportion of bicyclist children in traffic accidents per 100 thousands children population (y3). Infant mortality; deaths per 1000 live births (x2), youths aged between 15 and 19 who are not in education or in employment; as percentage of persons in that age group males (x6) and females (x7) are positively correlated to death proportion of children in traffic accidents per 100 thousands children population (y1), death proportion of passenger children in traffic accidents per 100 thousands children population (y4); and traffic fatality of 0–14 years old children per total death in traffic accidents (y5).

Table 1 Descriptive statistics section Type

Variable

Mean

Standard deviation

X X X X X X X X X Y Y Y Y Y

x1 x2 x3 x4 x5 x6 x7 x8 x9 y1 y2 y3 y4 y5

25623.95 5.936842 77.41053 509.4211 65.09473 8.089474 8.505263 29.96316 217.2737 3.134211 1.013158 0.472105 1.326842 4.154211

8316.651 5.719383 2.901512 148.7269 8.075854 4.901802 9.115583 5.489708 139.2244 1.200608 0.554227 0.264587 0.813115 2.001703

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Table 2 Correlation section

x1 x2 x3 x4 x5 x6 x7 x8 x9 y1 y2 y3 y4 y5

x1

x2

x3

x4

x5

x6

x7

x8

x9

y1

y2

y3

y4

y5

1 −0.59 0.78 0.64 0.8 −0.35 −0.52 −0.55 −0.85 −0.49 −0.58 0.17 −0.51 −0.42

1 −0.81 −0.64 −0.63 0.67 0.93 0.69 0.81 0.5 0.16 −0.26 0.8 0.93

1 0.74 0.65 −0.38 −0.66 −0.53 −0.93 −0.53 −0.41 0.08 −0.61 −0.71

1 0.5 −0.26 −0.47 −0.14 −0.76 −0.05 −0.04 0 −0.26 −0.56

1 −0.52 −0.61 −0.66 −0.82 −0.42 −0.34 0.24 −0.56 −0.46

1 0.86 0.48 0.44 0.34 −0.02 −0.33 0.67 0.6

1 0.66 0.68 0.5 0.1 −0.33 0.84 0.85

1 0.61 0.65 0.54 −0.45 0.69 0.64

1 0.53 0.41 −0.23 0.65 0.66

1 0.75 0.01 0.84 0.49

1 −0.01 0.42 0.15

1 −0.24 −0.15

1 0.73

1

Infant mortality is highly correlated to death of passenger children (0.80), and there is very strong relationship (0.93) between infant mortality and death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents. Young people aged between 15 and 19 who are not in education or in employment; as percentage of persons in that age group females is also highly correlated to death of passenger children (0.84) and death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents (0.85). Road motor vehicles per thousand population (x4) is negatively correlated to traffic fatality of 0–14 years old per total death in traffic accidents (y5). Income inequality: Gini coefficients (x8), and road fatalities per million vehicles (x9) are positively correlated to variables of children traffic fatality, death proportion of children in traffic accidents per 100 thousands children population (y1); death proportion of pedestrian children in traffic accidents per unit children population (y2); death proportion of passenger children in traffic accidents per 100 thousands children population (y4); and death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents (y5). Relationships between death proportion of bicyclist children in traffic accidents per 100 thousands children population and quality of life variables are interesting. Death of bicyclist children is positively correlated to the gross national income per capita, the life expectancy at birth number of years and the the share of persons of working age (15–64 years) in employment;

and negatively correlated to the infant mortality, young people aged between 15 and 19 who are not in education or in employment as percentage of persons in that age group males and females, income inequality: Gini coefficients, and road fatalities per million vehicles. Through canonical correlation analysis, a composite (also called as canonical function) of the quality of life accounts that correlate with a composite of the child traffic fatality accounts is derived. The canonical correlation analysis procedure provides as many pairs as there are accounts in the smaller set, which is five in this study. The test statistics for the canonical correlation analysis are presented in Table 3. The canonical correlations between the first (0.997) and the second pair (0.949) were found to be significant (p < 0.01) from the likelihood ratio test. The remaining canonical correlation is not statistically significant (p > 0.05). By construeing the first canonical variate it is possible to find relationship between quality of life and child traffic fatality as rate of 99.48%. The first canonical variate suggests that about 35.1% of the variation in X variables is explained by the Y variables and about 21.9% of the variation in Y variables is explained by the X variables (Table 4). These values indicate that quality of life and child traffic fatality interdependencies were strong. The second canonical variate suggests that about 12.7% of the variation in X variables is explained by the Y variables and about 27.6% of the variation in Y variables is explained by the X variables (Table 4).

Table 3 Canonical correlations section Variate number

Canonical correlation

R2

F-Value

Num DF

Den DF

Prob level

Wilks’ lambda

1 2 3 4 5

0.997407 0.948706 0.9001 0.846265 0.493305

0.994821 0.900043 0.81018 0.716165 0.24335

5.72 2.56 2.01 1.54 0.58

45 32 21 12 5

25 24 21 16 9

0.000007 0.010317 0.059414 0.205881 0.716186

0.000021 0.004075 0.040766 0.214764 0.75665

F-Value tests whether this canonical correlation and those following are zero.

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Table 4 Variation explained section Canonical variate number

Variation in these variables

Explained by these variates

Individual percent explained

Cumulative percent explained

Canonical correlation squared

1 2 1 2

X X Y Y

Y Y X X

35.1 12.7 21.9 27.6

35.1 47.8 21.9 49.5

0.9948 0.9000 0.9948 0.9000

Table 5 Standardized canonical coefficients section Standardized Y canonical coefficients section

X1

x1

x2

x3

x4

x5

x6

x7

x8

x9

0.039224

−0.89532

−0.31448

0.000277

0.432081

0.335794

−1.00475

0.739729

0.388839

Standardized X canonical coefficients section

Y1

y1

y2

y3

y4

y5

2.048198

−0.37069

−0.49528

−2.22511

−0.15484

Table 6 Variable-variate correlations (canonical loadings) X variable set

X1

x1

x2

x3

x4

x5

x6

x7

x8

x9

0.342302

−0.81281

0.496777

0.570252

0.454716

−0.71542

−0.86902

−0.28798

−0.51807

Y variable set

Y1

y1

y2

y3

y4

y5

−0.18969

0.263167

0.121379

−0.64253

−0.75238

Standardized canonical coefficients for the first X, Y variate are given in Table 5. Standardized canonical coefficients shows variation (kind of standard deviation) in canonical variate in parallel with one standart deviation increase in orijinal variables. In other words these coefficients represent relative contributions of orijinal variables to the related variate. Equations of X1 and Y1 canonical variate are as follows:

Y1 = 2.04820y1 − 0.37068y2 − 0.49527y3 − 2.22510y4 − 0.15484y5 Since the canonical coefficients can be unstable due to small sample size or presence of multicolinearity in the data, the loadings were also considered to provide substantive meaning of each variable for the canonical variate (Akbas and Takma, 2005). To evaluate the important accounts of the significant canonical function, canonical loadings were used in this study. Canonical loadings greater than ±0.30 were considered to be important (Hair et al., 1998).

X1 = 0.03922 × 1 − 0.89531 × 2 − 0.31447 × 3 + 0.00027 × 4 + 0.43208 × 5 + 0.33579 × 6 − 1.0047 × 7 + 0.73973 × 8 + 0.38883 × 9 Table 7 Variable-variate correlations (canonical cross loadings) X variable set

Y1

x1

x2

x3

x4

x5

x6

x7

x8

x9

0.341414

−0.81071

0.495489

0.568774

0.453537

−0.71356

−0.86677

−0.28724

−0.51672

Y variable set

X1

y1

y2

y3

y4

y5

−0.1892

0.262485

0.121064

−0.64086

−0.75043

M. Darcin, E.S. Darcin / Accident Analysis and Prevention 39 (2007) 826–832

The variable-variate correlations (canonical loadings and canonical cross loadings) of the first canonical variate are presented in Tables 6 and 7. Youths aged between 15 and 19 who are not in education nor in employment, as percentage of persons in that age group females (x7) and males (x6); infant mortality; (x2); are the most influential variables in forming X1 . Death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents (y5) and death proportion of passenger children in traffic accidents per 100 thousands children population (y4) are the most influential variables in forming Y2 . Five of the variables (infant mortality; deaths per 1000 live births (x2); youths aged between 15 and 19 who are not in education or in employment, as percentage of persons in that age group males (x6); youth aged between 15 and 19 who are not in education or in employment, as percentage of persons in that age group females (x7); income inequality: Gini coefficients (x8), and road fatalities per million vehicles (x9)) have negative loadings and four of the variables (gross national income per capita (x1); life expectancy at birth number of years (x3); road motor vehicles per thousand population (x4); and share of persons of working age (15–64 years) in employment (x5)) have positive loadings on the quality of life set. Three of the variables (death proportion of children in traffic accidents per 100 thousands children population (y1); death proportion of passenger children in traffic accidents per 100 thousands children population (y4); death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents (y5)) have negative loadings and two of the variables (death proportion of pedestrian children in traffic accidents per 100 thousands children population (y2); death proportion of bicyclist children in traffic accidents per 100 thousands children population (y3)) have positive loadings on the child traffic fatality set. Canonical cross loadings of variables with variate are almost the same as canonical loadings. 4. Discussion Quality of life is one of the main components of decreasing of mortality and disease. Life of quality has become protective against child traffic accident mortality. There is a strong inverse reletionship between quality of life and children traffic fatalities. Previous studies have shown inverse association between economic development-prosperity levels and unintentional injury mortality among children. Plitponkarnpim et al. (1999) found the strongest negative association between economic development and unintentional child injury mortality. It seems as if prosperity acts on traffic accidents in the same way as on other major diseases (van Beeck et al., 2000). van Beeck et al. (2000) indicated that increasing prosperity becomes protective against traffic accident mortality because it is accompanied by a declining number of traffic deaths per motor vehicle or ‘fatal injury rate’. This could be an indirect effect of growing prosperity, which might facilitate several adaptations,

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including improvement in the traffic infrastructure and medical care for injury victims (van Beeck et al., 2000; van Beeck and Looman, 1998). The results of our study suggest that, in general, increasing quality of life value leads to decrease in the fatal children traffic accident rate. Our results indicate that increasing gross national income per capita gives rise to more vehicles per thousand population. These results support the study of Kopits and Cropper (2003, 2005) who suggested that motorization is strongly correlated with income. High-income countries tend to have more vehicles per capita than lower income countries. Road motor vehicles per thousand population is negatively correlated to death proportion of 0–14 years old children in traffic accidents per total death in traffic accidents. Smeed (1949) firstly demonstrated that fatalities per vehicle decreases as vehicles per person increases. Study of Kopits and Cropper (2003, 2005) attested to this fact. Gross national income per capita is negatively correlated to children traffic fatality except for the bicyclist children. Fatalities per vehicle, by contrast, appear to decline rapidly with income, at least after some low level of income, and then continue to decline at a slower rate at higher income levels (Kopits and Cropper, 2003, 2005). There are positive correlation between bicyclist children fatality and quality of life. Growing gross national income per capita means more bicyled children fatalities. For example death proportion of bicyclist children in traffic accidents per 100 thousands children population is 0.93 in Denmark, 0.42 in France, 0.54 in Germany, 1.09 in Netherlands, 0.18 in Turkey. Reason of this situation can be children use more bicycle per unit in developed countries than developing countries. We can say that bicyclist children fatality is a ‘disease of affluence’. There is a very strong positive relationship between infant mortality and children traffic fatality. This result show that children traffic fatality is a backwardness problem and an indicator of underdevelopment. The cross-product moment correlation gives information only on the relationship between two variables without considering simultaneously other variables that related with each other. Canonical correlation, however, gives us the chance to estimate the correlation between two sets of variables including more than one trait in each at the same time (Akbas and Takma, 2005). In this study it is possible to find relationship between quality of life and child traffic fatality as a rate of 99.48% by using the first canonical variate. Reduction in death proportion of 0–14 years old children in traffic accidents per total traffic fatality increases quality of life. Reduction in infant mortality, youths aged between 15 and 19 who are not in education or in employment, as percentage of persons in that age group males and females and road fatalities per million vehicles increases children traffic safety. Increasing in gross national income per capita, life expectancy at birth, road motor vehicles per thousand population, and share of persons of working age (15–64 years) in employment is also increased children traffic safety.

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5. Conclusions Traffic fatality is recognized as an important public health problem not only in high-income countries, but also in low- and middle-income countries. This study aimed to examine the cross-sectional association and cononical correlation between quality of life and traffic fatality among children. The results show that with the exception of the bicyclist fatalities, traffic fatalities among children tend to decline with increasing quality of life. Increasing prosperity level also decreases children traffic fatality. As a result, problem of traffic fatality among children is an underdevelopment matter. References Akbas, Y., Takma, C., 2005. Canonical correlation analysis for studying the relationship between egg production traits and body weight, egg weight and age at sexual maturity in layers. Czech J. Anim. Sci. 4, 163–168. Bartlett, M.S., 1941. The statis significance of canonical correlations. Biometrika 32 (1), 29–37. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1998. Multivariate Data Analysis, 5th ed. Prentice-Hall, Upper Saddle River, New Jersey. International Road Traffic and Accident Database (IRTAD), OECD, 2004. http://www.bast.de/htdocs/fachthemen/irtad/english/we2.html. Jacobs, G., Aaron Thomas, A., Astrop, A., 2000. Estimating Global Road Fatalities. (TRL Report 445). Transport Research Laboratory, London.

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