socio-economic determinants of drugged driving – a register-based study

Socio-economic determinants of drugged driving – aregister-based studyadd_3422 1448..1459

Karoliina Karjalainen1, Tomi Lintonen2,3, Antti Impinen1, Pirjo Lillsunde1, Pia Mäkelä1,Ossi Rahkonen4, Jari Haukka1,4 & Aini Ostamo1,3

National Institute for Health and Welfare, Helsinki, Finland,1 The Finnish Foundation for Alcohol Studies, Helsinki, Finland,2 School of Health Sciences, Universityof Tampere, Finland3 and University of Helsinki, Department of Public Health, Helsinki, Finland4

ABSTRACT

Aims To examine the associations between socio-economic characteristics and driving under the influence of drugs(DUID), and to elaborate determinants of drugged driving. Design A register-based case–control study. Setting Fin-land. Participants Cases (n = 5859) apprehended by the police and suspected of DUID during 1993–2006 andcontrols (n = 74 809) drawn from the general Finnish population. Measurements The effects of parents’ and owneducation, urbanization of municipality, socio-economic position (SEP), main activity, income, marital status andliving arrangements on DUID were estimated using logistic regression analysis. The analyses were conducted sepa-rately for men and women, age groups of under 45 years and aged 45 or over, and for substance groups of benzodi-azepines only, benzodiazepines with alcohol, amphetamines and cannabinoids. Findings Low education,unemployment, disability pension, being divorced and living alone were the strongest individual predictors of DUID inall substance groups. Illicit drug users were more disadvantaged compared to those in the benzodiazepines groups.Contrary to other substance and age groups, higher educational level and higher SEP were associated with DUIDamong benzodiazepine users aged 45 or over. Conclusions A disadvantaged social background is a significant pre-dictor of driving while under the influence of drugs for all substance use groups in Finland. The gradient is greater foramphetamines and cannabinoids than benzodiazepines.

Keywords Amphetamines, benzodiazepines, cannabinoids, driving under the influence of drugs, register-basedstudy, socio-economic determinants.

Correspondence to: Karoliina Karjalainen, National Institute for Health and Welfare, PO Box 30, FI-00271 Helsinki, Finland.E-mail: [email protected] 31 May 2010; initial review completed 5 August 2010; final version accepted 25 February 2011

INTRODUCTION

Driving under the influence of drugs (DUID) poses a con-siderable threat to traffic safety [1–3]. In addition torisking other people’s health, drugged drivers can alsoharm themselves: their risk of premature death has beenshown to be high [4–6].

Socio-economic inequalities in health are widely rec-ognized [7]. There are socio-economic differences, forexample, in mortality and morbidity [8], and differenceshave been found similarly with regard to drug use. Theassociation between social background and illicit druguse has been studied among samples from general popu-lations [9–11], drug treatment programmes [12–14] andthose outside treatment [15]. These studies have shown

that a disadvantaged social background (e.g. lowereducation, lower income, unemployment) and socialmarginalization (e.g. homelessness or incarceration) areassociated with illicit drug use.

Similarly, disadvantaged social background is associ-ated with the use of legal prescription drugs, for examplebenzodiazepines. Population-based studies have shownthat, for example, lower education, lower income,unemployment, being divorced or widowed or being on adisability pension predict current or long-term benzodi-azepine use [16–20]. Some of these associations,however, attenuate or disappear after controlling forhealth status [16,17].

Social factors exposing to DUID have barelybeen studied, but some evidence exists. For example, a

RESEARCH REPORT doi:10.1111/j.1360-0443.2011.03422.x

© 2011 The Authors, Addiction © 2011 Society for the Study of Addiction Addiction, 106, 1448–1459

Canadian population-based study found that lower edu-cation and never being married predicted driving underthe influence of cannabis [21]. A Swedish register-basedstudy followed-up a national cohort of young drivers, andfound that impaired drivers with lower education orlower class of origin had an excess risk for severe trafficaccidents [22].

Because of the illegality and commonly hidden natureof drug abuse, drug users are not easily reached forresearch purposes. Surveys conducted among generalpopulations are unlikely to reach all problem drug users,and they may be biased due to under-reporting [23]. Con-versely, studies conducted among problem drug users, forexample clinical samples, are highly selected. Thus, noneof these results can be generalized to the entire popula-tion of drug users. Although drugged drivers do notcomprise a representative sample of all drug users, theyprovide a complementary picture of the characteristics ofdrug users in addition to studies on other drug-usingpopulations.

Difficulties in locating and following-up drug users isone reason for the scarcity of data. These common diffi-culties can partly be overcome in Finland, where differenthealth and social welfare registers have been maintainedsince the 1950s, and several of them have been shown tohave good coverage and validity [24]. All registers arebased on unique identity numbers for each individual,which enables the linking of different registers, and thusprovide an exceptional opportunity to use these registersfor research purposes.

This case–control study was based on extensiveregister-based material constructed by linking the regis-ter of drugged drivers with other registers in Finland. Theaim of this study was, first, to examine the associationsbetween socio-economic characteristics and druggeddriving, and secondly to elaborate the determinants ofdrugged driving. We examined how parents’ and owneducation, level of urbanization, socio-economic position(SEP), main activity, income, marital status and livingarrangements were associated with driving under theinfluence of drugs.

MATERIALS AND METHODS

The main reasons for detecting drunken/drugged driversin Finland include impaired or dangerous driving, trafficaccidents, information from a bystander and randomstop-checks. Finnish police are authorized by law tosubmit drivers to a preliminary breath alcohol screeningtest or oral fluid on-site drug test (launched in 2003). InFinnish society equity is highly valued so that, forexample, older and cheaper cars would not generally betargeted more than newer and more expensive cars.

The punishable thresholds of blood alcohol concen-tration (BAC) by Finnish drunken driving legislation [25;p. 58] are 0.5‰ (g/kg) for drunken driving and 1.2‰for aggravated drunken driving (1.5‰ until September1994). A zero tolerance law for illicit drugs and drivingwas introduced in Finland in February 2003 [25; p. 58].

Data

The groups of cases and controls formed the study popu-lation (n = 80 668). Cases were drawn from the registerof DUI suspects. The cases (n = 5859) comprised 50% ofall people driving a motored vehicle in road traffic inFinland during 1993–2006 who were suspected of DUIby the police, and whose blood sample had one or morepositive findings for illicit and/or medicinal drugs poten-tially impairing driving skills. All drug/alcohol analyseswere carried out centrally at the National Institute forHealth and Welfare (THL) in Finland, and registered in adatabase. DUID suspects were aged 14–84 years. Sus-pected drivers driving under the influence of alcohol onlywere excluded from the present study.

A control group (n = 74 809) was drawn from thegeneral Finnish population not suspected of DUI. Theycomprised age- (�1 year) and gender-matched controlsfor people suspected of driving under the influence ofalcohol and/or drugs.

Socio-economic factors were obtained from theemployment register (an annual, individual-level registerof economic activity and employment status of perma-nent residents in Finland [26]) by Statistics Finland. Theregister was linked individually to the study population,and the data were available to anonymized researchers.To ensure privacy, some information was coarsened.

The outcome measure was the apprehension for sus-pected DUID. Cases were categorized by substances foundduring the first apprehension as ‘benzodiazepines only’(no other substances, n = 774), ‘benzodiazepines withalcohol’ (no other substances, n = 1234), ‘amphetaminesplus other impairing drugs’ (a finding for at least amphet-amines, possibly also other substances, n = 2110) and‘cannabinoids plus other impairing drugs’ (a finding forat least cannabinoids, possibly also other substances, butnot amphetamines, n = 485). These substance groupswere included because they were the largest and also themost interesting groups based on earlier studies [27–29].Also, the groups of ‘amphetamines only’ (n = 697) and‘cannabinoids only’ (n = 133) were analysed separately,but the emphasis of reporting is on the first four groups.

In the benzodiazepines with alcohol group, mean BACwas 1.3‰ (median 1.2‰). BAC exceeded 0.5‰ in 84%of cases and 1.2‰ in 49%. In the amphetamines group,33% of cases had a finding for amphetamines only, andthe rest also had a finding for benzodiazepines (83%),

Socio-economic determinants of DUID 1449


opioids (17%), cannabinoids (12%) or other drugsimpairing driving skills (10%). In the cannabinoidsgroup, 27% had a finding for cannabinoids only, and therest also had a finding for benzodiazepines (65%), opioids(14%) or other drugs impairing driving skills (10%).

Socio-economic characteristics

Cases and controls were classified according to gender andage (aged under 45 years or 45 years or over). The datawere divided into two age groups because we assumed,and the data confirmed, that there were significant differ-ences between socio-economic characteristics of the morecommon younger cases and less common older cases. Forthe analysis, age was classified further into 10-year agegroups. The level of education (both parents’ and own)was classified into three categories: high, intermediate andbasic or unknown. The level of urbanization of themunicipality of residence was urban, semi-urban or rural.SEP was based on the subject’s occupation and wasclassified as upper white-collar, lower white-collar, blue-collar, entrepreneur (including farmers) and ‘other’ orunknown. The employment register provided annual dataof SEP for years 2004–2006 only—before this, the SEPinformation was available every 5 years during 1970–2000. Because occupational data were not available foreconomically inactive people (unemployed, pensioner,unknown or missing), their SEP was defined retrospec-tively. Main activity included categories of employedlabour force, long-term unemployed (unemployed formore than 10 months in a given year), short-term unem-ployed, student, disability pensioner, unemployment pen-sioner, retirement pensioner, conscript and ‘other’ orunknown. Any income subject to state taxation wasdivided into quartiles, separately for men and women, dueto their different income distributions. Marital status wasclassified as married, single, divorced or widowed, andliving arrangement described as whether a person wasliving alone or with family (e.g. had a spouse and/or chil-dren or lived with his/her parents).

Information about age and found substances weretaken at the time of first apprehension. In order to ensurethat the socio-economic variables represented statusbefore the first apprehension, data from the precedingyear was used. For controls, information about socio-economic background was taken from the preceding yearof the corresponding DUI suspect’s apprehension. Whenthe preceding year’s data were not available (n = 3456,0.2% of the cases and 4.6% of the controls), all data weretaken from the year of apprehension.

Statistical analysis

A logistic regression model was used, with results pre-sented as odds ratios (OR) with 95% confidence intervals

(CI). Models were constructed with DUID as a dependentvariable. All the models, except univariate, included ageand geographical region (western, southern, eastern andnorthern Finland and Åland), and socio-economic vari-ables were added to the model in a rough temporal order.The modelling reveals to what extent an original associa-tion, e.g. that between SEP and DUID, is explained byconfounders (variables preceding SEP), and to whatextent it is mediated by variables located in between thefactor in question (e.g. SEP) and DUID. The extent of‘explanation’ and ‘mediation’ can be seen in the changeof ORs when variables are introduced into the model.

The modelling was conducted separately for men andwomen, for age groups under 45 years and aged 45 orover, and for substance groups of benzodiazepines only,benzodiazepines with alcohol, amphetamines and can-nabinoids. Parents’ education was excluded from theanalysis of the older age groups because of the largeamount of unknown information. Otherwise, the samplesize did not change markedly due to missing datathroughout modelling. PASW Statistics 18 (SPSS) wasused to analyse the data.

Research ethics

The study protocol was approved by the InstitutionalReview Board of the National Institute for Health andWelfare. Informed consent was not required because thedata were anonymous and coarsened register data, andindividuals were not contacted.

RESULTS

Socio-economic characteristics of cases and controls areshown in each table (Tables 1–5; see also Supportinginformation, Tables S1–S8). As most of the cases wereunder 45-year-old men, the emphasis is upon this groupin reporting the results.

Benzodiazepines only

Among under 45-year-old men, DUI of ‘benzodiazepinesonly’ was predicted by the lower level of mother’s, father’sand own education, urban environment, lower white-collar, blue-collar and ‘other’/unknown SEP, long- orshort-term unemployment, disability pension, ‘other’/unknown main activity, lower income, being single,divorced or widowed and living alone (Table 1, univariatemodel).

Most of these associations attenuated, but remainedafter adjusting for other socio-economic variables(Table 1, final model). Thus, father’s basic education,urban environment, own basic education, low SEP,long- and short-term unemployment, disability pension,‘other’/unknown main activity, marital status other than

1450 Karoliina Karjalainen et al.


Tabl

e1

Th

eef

fect

sof

soci

o-ec

onom

icch

arac

teri

stic

son

driv

ing

un

der

the

influ

ence

of‘b

enzo

diaz

epin

eson

ly’a

mon

gu

nde

r4

5-y

ear-

old

men

.

OR

Cas

e%

n=

51

2C

ontr

ol%

n=

44

90

3U

niva

riat

em

odel

(mod

el1

)M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6Fi

nalm

odel

(mod

el7

)(9

5%

CI)

a

Mot

her

’sed

uca

tion

Hig

h1

1.1

20

.01

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Inte

rmed

iate

28

.12

8.5

1.7

81

.52

1.4

91

.45

1.3

41

.33

1.3

4(0

.97

–1.8

6)

Bas

icor

un

know

n6

0.7

51

.52

.12

1.7

31

.53

1.5

01

.13

1.1

41

.13

(0.8

1–1

.56

)Fa

ther

’sed

uca

tion

Hig

h9

.41

9.9

1.0

01

.00

1.0

01

.00

1.0

01

.00

1.0

0In

term

edia

te1

8.8

23

.21

.72

1.5

51

.47

1.4

11

.30

1.2

91

.29

(0.9

0–1

.86

)B

asic

oru

nkn

own

71

.95

6.9

2.6

82

.67

2.3

92

.31

1.8

61

.85

1.8

3(1

.31

–2.5

8)

Leve

lof

urb

aniz

atio

nR

ura

l1

1.2

18

.51

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Sem

i-u

rban

15

.31

6.1

1.5

71

.57

1.6

21

.59

1.6

31

.64

1.6

1(1

.13

–2.2

9)

Urb

an7

3.6

65

.41

.87

1.8

91

.95

1.9

51

.83

1.8

41

.57

(1.1

6–2

.11

)Ed

uca

tion

Hig

h5

.51

5.8

1.0

01

.00

1.0

01

.00

1.0

01

.00

Inte

rmed

iate

32

.04

3.0

2.1

62

.19

1.6

31

.31

1.3

21

.22

(0.7

8–1

.89

)B

asic

oru

nkn

own

62

.54

1.1

4.4

04

.40

3.4

42

.68

2.8

42

.63

(1.6

9–4

.09

)So

cio-

econ

omic

posi

tion

Upp

erw

hit

e-co

llar

2.7

8.7

1.0

01

.00

1.0

01

.00

1.0

0Lo

wer

wh

ite-

colla

r8

.21

2.0

2.1

71

.55

1.4

31

.46

1.3

8(0

.74

–2.5

7)

Blu

e-co

llar

34

.42

6.4

4.1

42

.29

1.9

92

.01

1.8

3(1

.01

–3.2

9)

Entr

epre

neu

r3

.15

.61

.77

1.2

21

.36

1.4

11

.47

(0.6

9–3

.12

)O

ther

oru

nkn

own

51

.64

7.2

3.4

71

.64

1.2

31

.32

1.2

0(0

.66

–2.1

6)

Mai

nac

tivit

yEm

ploy

ed2

7.4

51

.51

.00

1.0

01

.00

1.0

0Lo

ng-

term

un

empl

oyed

14

.72

.51

0.9

89

.18

9.3

48

.03

(5.6

2–1

1.4

7)

Shor

t-te

rmu

nem

ploy

ed1

8.8

7.6

4.6

74

.05

4.4

84

.40

(3.2

5–5

.95

)St

ude

nt

15

.92

9.0

1.0

30

.69

0.8

30

.88

(0.6

1–1

.28

)D

isab

ility

pen

sion

er6

.81

.87

.29

6.3

76

.39

5.2

3(3

.38

–8.0

9)

Con

scri

pt1

.22

.80

.80

0.7

90

.94

0.9

5(0

.41

–2.2

4)

Oth

eror

un

know

n1

5.3

4.9

5.8

93

.85

4.7

24

.17

(2.8

8–6

.05

)In

com

esu

bjec

tto

stat

eta

xati

onH

igh

est

quar

tile

8.4

18

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1.0

01

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Th

ird

quar

tile

17

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3.9

1.6

10

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0.8

2(0

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0)

Seco

nd

quar

tile

37

.92

2.8

3.6

30

.87

0.7

5(0

.50

–1.1

4)

Low

est

quar

tile

36

.13

4.9

2.2

60

.64

0.6

2(0

.39

–1.0

0)

Mar

ital

stat

us

Mar

ried

9.4

26

.11

.00

1.0

0Si

ngl

e8

2.4

70

.73

.24

1.9

0(1

.36

–2.6

6)

Div

orce

d7

.83

.16

.92

3.1

6(2

.01

–4.9

7)

Wid

owed

0.4

0.1

12

.16

6.3

5(1

.41

–28

.54

)Li

vin

gar

ran

gem

ents

Livi

ng

wit

hfa

mily

48

.67

9.2

1.0

01

.00

Livi

ng

alon

e5

1.4

20

.84

.03

2.6

7(2

.19

–3.2

5)

Odd

sra

tio

(OR

)sh

own

inbo

ldty

pe,P

<0

.05

.Un

ivar

iate

mod

el(m

odel

1)i

ncl

ude

son

lyon

eva

riab

leat

ati

me.

Mod

els

2–7

:all

mod

els

incl

ude

the

vari

able

ssh

own

inth

eco

lum

n,a

nd

are

adju

sted

for

age

and

regi

on.a 9

5%

con

fiden

cein

terv

als

for

OR

sin

the

final

mod

el(m

odel

7).



Tabl

e2

Th

eef

fect

sof

soci

o-ec

onom

icch

arac

teri

stic

son

driv

ing

un

der

the

influ

ence

of‘b

enzo

diaz

epin

eson

ly’a

mon

gm

enag

ed4

5or

over

.

OR

Cas

e%

n=

14

3C

ontr

ol%

n=

20

27

0U

niva

riat

em

odel

(mod

el1

)M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6Fi

nalm

odel

(mod

el7

)(9

5%

CI)

a

Leve

lof

urb

aniz

atio

nR

ura

l1

6.1

22

.91

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Sem

i-u

rban

23

.11

7.9

1.8

41

.87

1.8

61

.87

1.9

31

.95

1.8

9(1

.09

–3.2

8)

Urb

an6

0.8

59

.31

.46

1.4

91

.47

1.5

21

.58

1.6

01

.44

(0.8

8–2

.35

)Ed

uca

tion

Hig

h2

6.6

25

.51

.00

1.0

01

.00

1.0

01

.00

1.0

0In

term

edia

te3

5.0

32

.51

.03

1.0

70

.78

0.6

60

.63

0.6

3(0

.37

–1.0

8)

Bas

icor

un

know

n3

8.5

41

.90

.88

0.8

90

.64

0.4

70

.44

0.4

5(0

.26

–0.7

8)

Soci

o-ec

onom

icpo

siti

onU

pper

wh

ite-

colla

r1

5.4

17

.01

.00

1.0

01

.00

1.0

01

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Low

erw

hit

e-co

llar

14

.01

7.6

0.8

80

.99

0.9

20

.86

0.8

8(0

.46

–1.6

8)

Blu

e-co

llar

50

.34

1.9

1.3

31

.78

1.5

01

.34

1.2

8(0

.68

–2.4

3)

Entr

epre

neu

r1

8.2

18

.41

.09

1.4

61

.46

1.3

31

.36

(0.6

9–2

.67

)O

ther

oru

nkn

own

2.1

5.0

0.5

60

.74

0.3

90

.40

0.3

9(0

.11

–1.4

2)

Mai

nac

tivit

yEm

ploy

ed3

1.5

62

.91

.00

1.0

01

.00

1.0

0Lo

ng-

term

un

empl

oyed

7.0

4.9

2.8

23

.53

2.9

62

.35

(1.0

8–5

.09

)Sh

ort-

term

un

empl

oyed

4.9

4.8

2.0

52

.51

2.2

82

.17

(0.9

5–5

.00

)St

ude

nt

b1

.1b

bb

b

Dis

abili

type

nsi

oner

40

.61

2.5

6.5

19

.58

8.2

76

.88

(4.2

4–1

1.1

7)

Un

empl

oym

ent

pen

sion

er0

.71

.60

.86

1.4

31

.22

1.1

1(0

.14

–8.5

9)

Ret

irem

ent

pen

sion

er1

1.2

8.8

2.5

53

.73

3.2

62

.93

(0.9

4–9

.16

)O

ther

oru

nkn

own

4.2

3.5

2.8

34

.07

5.7

94

.80

(1.7

3–1

3.3

3)

Inco

me

subj

ect

tost

ate

taxa

tion

Hig

hes

tqu

arti

le2

3.1

39

.11

.00

1.0

01

.00

Th

ird

quar

tile

39

.23

4.2

1.9

41

.38

1.3

6(0

.10

–1.6

9)

Seco

nd

quar

tile

35

.72

1.6

2.7

91

.45

1.2

5(0

.68

–2.2

7)

Low

est

quar

tile

2.1

5.1

0.7

00

.46

0.4

1(0

.82

–2.2

6)

Mar

ital

stat

us

Mar

ried

44

.16

8.6

1.0

01

.00

Sin

gle

18

.21

6.0

1.7

70

.57

(0.3

0–1

.06

)D

ivor

ced

34

.31

3.6

3.9

31

.42

(0.8

2–2

.46

)W

idow

ed3

.51

.83

.00

1.0

9(0

.39

–3.0

5)

Livi

ng

arra

nge

men

tsLi

vin

gw

ith

fam

ily4

8.3

79

.01

.00

1.0

0Li

vin

gal

one

51

.72

1.0

4.0

33

.41

(2.0

5–5

.74

)

Odd

sra

tio

(OR

)sh

own

inbo

ldty

pe,P

<0

.05

.Un

ivar

iate

mod

el(m

odel

1)i

ncl

ude

son

lyon

eva

riab

leat

ati

me.

Mod

els

2–7

:all

mod

els

incl

ude

the

vari

able

ssh

own

inth

eco

lum

n,a

nd

are

adju

sted

for

age

and

regi

on.a 9

5%

con

fiden

cein

terv

als

for

OR

sin

the

final

mod

el(m

odel

7).

b No

case

s.



Tabl

e3

Th

eef

fect

sof

soci

o-ec

onom

icch

arac

teri

stic

son

driv

ing

un

der

the

influ

ence

ofbe

nzo

diaz

epin

esan

dal

coh

olam

ong

un

der

45

-yea

r-ol

dm

en.

OR

Cas

e%

n=

88

8C

ontr

ol%

n=

44

90

3U

niva

riat

em

odel

(mod

el1

)M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6Fi

nalm

odel

(mod

el7

)(9

5%

CI)

a

Mot

her

’sed

uca

tion

Hig

h8

.82

0.0

1.0

01

.00

1.0

01

.00

1.0

01

.00

1.0

0In

term

edia

te2

6.0

28

.52

.08

1.6

91

.63

1.5

91

.44

1.4

71

.45

(1.1

0–1

.92

)B

asic

oru

nkn

own

65

.25

1.5

2.8

82

.11

1.7

51

.70

1.1

71

.21

1.1

8(0

.89

–1.5

6)

Fath

er’s

edu

cati

onH

igh

8.0

19

.91

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Inte

rmed

iate

19

.82

3.2

2.1

31

.79

1.6

31

.58

1.4

11

.42

1.4

1(1

.05

–1.8

9)

Bas

icor

un

know

n7

2.2

56

.93

.16

2.4

82

.03

1.9

61

.47

1.4

71

.44

(1.0

9–1

.92

)Le

velo

fu

rban

izat

ion

Ru

ral

14

.21

8.5

1.0

01

.00

1.0

01

.00

1.0

01

.00

1.0

0Se

mi-

urb

an1

4.9

16

.11

.21

1.2

61

.31

1.2

81

.34

1.3

71

.32

(1.0

1–1

.71

)U

rban

70

.96

5.4

1.4

11

.52

1.5

71

.54

1.4

01

.43

1.1

6(0

.94

–1.4

4)

Edu

cati

onH

igh

4.6

15

.81

.00

1.0

01

.00

1.0

01

.00

1.0

0In

term

edia

te2

6.6

43

.02

.12

2.4

21

.71

1.3

41

.26

1.1

2(0

.78

–1.6

1)

Bas

icor

un

know

n6

8.8

41

.15

.74

7.5

45

.35

3.8

33

.61

3.2

8(2

.30

–4.6

8)

Soci

o-ec

onom

icpo

siti

onU

pper

wh

ite-

colla

r2

.68

.71

.00

1.0

01

.00

1.0

01

.00

Low

erw

hit

e-co

llar

6.3

12

.01

.76

1.2

81

.16

1.1

01

.05

(0.6

2–1

.76

)B

lue-

colla

r3

6.4

26

.44

.62

2.3

91

.98

1.9

11

.75

(1.0

9–2

.81

)En

trep

ren

eur

2.8

5.6

1.6

80

.91

1.0

50

.93

1.0

2(0

.55

–1.8

8)

Oth

eror

un

know

n5

1.9

47

.23

.69

2.0

41

.27

1.1

81

.10

(0.6

9–1

.77

)M

ain

activ

ity

Empl

oyed

19

.75

1.5

1.0

01

.00

1.0

01

.00

Lon

g-te

rmu

nem

ploy

ed1

8.2

2.5

18

.95

13

.97

8.0

26

.71

(5.0

9–8

.83

)Sh

ort-

term

un

empl

oyed

24

.17

.68

.34

7.3

65

.86

5.7

3(4

.51

–7.2

7)

Stu

den

t1

2.6

29

.01

.14

0.8

50

.76

0.8

4(0

.62

–1.1

4)

Dis

abili

type

nsi

oner

4.9

1.8

7.2

55

.08

3.0

82

.48

(1.6

9–3

.63

)C

onsc

ript

0.5

2.8

0.4

30

.59

0.5

00

.52

(0.1

9–1

.43

)O

ther

oru

nkn

own

20

.04

.91

0.7

67

.43

6.2

95

.48

(4.1

4–7

.25

)In

com

esu

bjec

tto

stat

eta

xati

onH

igh

est

quar

tile

5.1

18

.31

.00

1.0

01

.00

Th

ird

quar

tile

10

.22

3.9

3.8

30

.86

0.8

1(0

.56

–1.1

8)

Seco

nd

quar

tile

47

.72

2.8

7.5

72

.30

1.9

2(1

.33

–2.7

6)

Low

est

quar

tile

36

.93

4.9

3.8

31

.36

1.2

5(0

.83

–1.8

8)

Mar

ital

stat

us

Mar

ried

10

.82

6.1

1.0

01

.00

Sin

gle

75

.97

0.7

2.5

91

.55

(1.2

1–1

.99

)D

ivor

ced

13

.03

.19

.97

4.2

0(3

.08

–5.7

4)

Wid

owed

0.3

0.1

9.2

14

.57

(1.2

5–1

6.6

9)

Livi

ng

arra

nge

men

tsLi

vin

gw

ith

fam

ily4

2.0

79

.21

.00

1.0

0Li

vin

gal

one

58

.02

0.8

5.2

73

.17

(2.7

1–3

.71

)

Odd

sra

tio

(OR

)sh

own

inbo

ldty

pe,P

<0

.05

.Un

ivar

iate

mod

el(m

odel

1)i

ncl

ude

son

lyon

eva

riab

leat

ati

me.

Mod

els

2–7

:all

mod

els

incl

ude

the

vari

able

ssh

own

inth

eco

lum

n,a

nd

are

adju

sted

for

age

and

regi

on.a 9

5%

con

fiden

cein

terv

als

for

OR

sin

the

final

mod

el(m

odel

7).



Tabl

e4

Th

eef

fect

sof

soci

o-ec

onom

icch

arac

teri

stic

son

driv

ing

un

der

the

influ

ence

ofam

phet

amin

espl

us

oth

erim

pair

ing

dru

gsam

ong

un

der

45

-yea

r-ol

dm

en.

OR

Cas

e%

n=

17

43

Con

trol

%n

=4

49

03

Uni

vari

ate

mod

el(m

odel

1)

Mod

el2

Mod

el3

Mod

el4

Mod

el5

Mod

el6

Fina

lmod

el(m

odel

7)

(95

%C

I)a

Mot

her

’sed

uca

tion

Hig

h1

1.5

20

.01

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Inte

rmed

iate

31

.82

8.5

1.9

41

.80

1.6

91

.62

1.4

61

.50

1.4

9(1

.24

–1.8

0)

Bas

icor

un

know

n5

6.7

51

.51

.91

1.6

01

.26

1.2

30

.83

0.8

50

.86

(0.7

1–1

.05

)Fa

ther

’sed

uca

tion

Hig

h1

0.6

19

.91

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Inte

rmed

iate

24

.42

3.2

1.9

81

.97

1.7

51

.66

1.4

61

.48

1.4

5(1

.19

–1.7

7)

Bas

icor

un

know

n6

5.0

56

.92

.14

2.1

61

.69

1.6

31

.15

1.1

61

.17

(0.9

7–1

.42

)Le

velo

fu

rban

izat

ion

Ru

ral

4.7

18

.51

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Sem

i-u

rban

9.0

16

.12

.19

1.9

82

.08

2.0

82

.19

2.2

42

.14

(1.6

1–2

.84

)U

rban

86

.26

5.4

5.1

54

.18

4.3

24

.53

4.0

44

.11

3.2

2(2

.53

–4.0

9)

Edu

cati

onH

igh

1.4

15

.81

.00

1.0

01

.00

1.0

01

.00

1.0

0In

term

edia

te2

9.3

43

.07

.84

9.7

97

.06

5.1

54

.69

4.3

4(2

.83

–6.6

4)

Bas

icor

un

know

n6

9.3

41

.11

9.3

92

7.1

02

0.6

31

3.8

11

2.4

01

1.6

9(7

.64

–17

.87

)So

cio-

econ

omic

posi

tion

Upp

erw

hit

e-co

llar

1.9

8.7

1.0

01

.00

1.0

01

.00

1.0

0Lo

wer

wh

ite-

colla

r7

.91

2.0

3.0

21

.64

1.6

41

.52

1.4

7(0

.96

–2.2

4)

Blu

e-co

llar

36

.62

6.4

6.3

52

.53

2.5

22

.39

2.1

3(1

.44

–3.1

6)

Entr

epre

neu

r4

.55

.63

.66

2.3

23

.06

2.7

72

.83

(1.7

9–4

.49

)O

ther

oru

nkn

own

49

.14

7.2

4.7

71

.71

1.3

21

.21

1.1

3(0

.76

–1.6

6)

Mai

nac

tivit

yEm

ploy

ed1

9.4

51

.51

.00

1.0

01

.00

1.0

0Lo

ng-

term

un

empl

oyed

20

.12

.52

1.2

81

8.1

61

1.6

49

.59

(7.7

9–1

1.8

0)

Shor

t-te

rmu

nem

ploy

ed2

4.2

7.6

8.5

18

.48

6.8

46

.67

(5.5

8–7

.97

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ude

nt

9.9

29

.00

.91

0.6

80

.59

0.6

6(0

.52

–0.8

3)

Dis

abili

type

nsi

oner

2.5

1.8

3.7

43

.37

2.2

81

.67

(1.1

7–2

.38

)C

onsc

ript

0.9

2.8

0.8

31

.08

0.9

10

.92

(0.5

4–1

.59

)O

ther

oru

nkn

own

23

.04

.91

2.5

48

.68

7.2

66

.20

(5.0

4–7

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com

esu

bjec

tto

stat

eta

xati

onH

igh

est

quar

tile

3.1

18

.31

.00

1.0

01

.00

Th

ird

quar

tile

12

.32

3.9

3.0

41

.56

1.4

4(1

.05

–1.9

7)

Seco

nd

quar

tile

44

.52

2.8

11

.55

2.9

02

.47

(1.8

1–3

.38

)Lo

wes

tqu

arti

le4

0.1

34

.96

.80

2.0

61

.99

(1.4

2–2

.79

)M

arit

alst

atu

sM

arri

ed7

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6.1

1.0

01

.00

Sin

gle

84

.17

0.7

4.3

32

.39

(1.9

4–2

.96

)D

ivor

ced

8.7

3.1

10

.11

3.7

6(2

.83

–5.0

1)

Wid

owed

b0

.1b

b

Livi

ng

arra

nge

men

tsLi

vin

gw

ith

fam

ily3

6.4

79

.21

.00

1.0

0Li

vin

gal

one

63

.62

0.8

6.6

63

.53

(3.1

4–3

.98

)

Odd

sra

tio

(OR

)sh

own

inbo

ldty

pe,P

<0

.05

.Un

ivar

iate

mod

el(m

odel

1)i

ncl

ude

son

lyon

eva

riab

leat

ati

me.

Mod

els

2–7

:all

mod

els

incl

ude

the

vari

able

ssh

own

inth

eco

lum

n,a

nd

are

adju

sted

for

age

and

regi

on.a 9

5%

con

fiden

cein

terv

als

for

OR

sin

the

final

mod

el(m

odel

7).

b No

case

s.



Tabl

e5

Th

eef

fect

sof

soci

o-ec

onom

icch

arac

teri

stic

son

driv

ing

un

der

the

influ

ence

ofca

nn

abin

oids

plu

sot

her

impa

irin

gdr

ugs

(not

amph

etam

ines

)am

ong

un

der

45

-yea

r-ol

dm

en.

OR

Cas

e%

n=

45

2C

ontr

ol%

n=

44

90

3U

niva

riat

em

odel

(mod

el1

)M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6Fi

nalm

odel

(mod

el7

)(9

5%

CI)

a

Mot

her

’sed

uca

tion

Hig

h1

9.7

20

.01

.00

1.0

01

.00

1.0

01

.00

1.0

01

.00

Inte

rmed

iate

31

.22

8.5

1.1

11

.04

1.0

10

.99

0.9

20

.92

0.9

3(0

.70

–.1

24

)B

asic

oru

nkn

own

49

.15

1.5

0.9

71

.07

0.9

20

.90

0.6

40

.66

0.6

7(0

.49

–0.9

0)

Fath

er’s

edu

cati

onH

igh

13

.91

9.9

1.0

01

.00

1.0

01

.00

1.0

01

.00

1.0

0In

term

edia

te2

9.6

23

.21

.83

1.9

81

.85

1.8

11

.64

1.6

41

.61

(1.1

7–2

.23

)B

asic

oru

nkn

own

56

.45

6.9

1.4

21

.99

1.7

31

.69

1.2

91

.28

1.2

8(0

.93

–1.7

6)

Leve

lof

urb

aniz

atio

nR

ura

l6

.91

8.5

1.0

01

.00

1.0

01

.00

1.0

01

.00

1.0

0Se

mi-

urb

an1

4.1

16

.12

.34

2.3

02

.36

2.3

42

.41

2.4

52

.41

(1.5

5–3

.74

)U

rban

79

.06

5.4

3.2

23

.07

3.1

73

.17

2.9

02

.93

2.5

0(1

.70

–3.6

8)

Edu

cati

onH

igh

1.5

15

.81

.00

1.0

01

.00

1.0

01

.00

1.0

0In

term

edia

te2

8.5

43

.06

.78

6.2

85

.12

3.8

53

.67

3.3

5(1

.52

–7.3

5)

Bas

icor

un

know

n6

9.9

41

.11

7.3

91

5.0

11

2.6

39

.90

10

.03

9.4

4(4

.30

–20

.71

)So

cio-

econ

omic

posi

tion

Upp

erw

hit

e-co

llar

2.4

8.7

1.0

01

.00

1.0

01

.00

1.0

0Lo

wer

wh

ite-

colla

r6

.21

2.0

1.8

41

.01

1.0

40

.98

0.9

3(0

.45

–1.9

1)

Blu

e-co

llar

28

.62

6.4

3.8

61

.60

1.6

31

.57

1.4

1(0

.74

–2.7

0)

Entr

epre

neu

r2

.25

.61

.41

0.9

51

.15

1.1

21

.20

(0.4

9–2

.90

)O

ther

oru

nkn

own

60

.54

7.2

4.5

71

.20

1.0

61

.05

0.9

7(0

.52

–1.8

4)

Mai

nac

tivit

yEm

ploy

ed2

2.1

51

.51

.00

1.0

01

.00

1.0

0Lo

ng-

term

un

empl

oyed

11

.02

.51

0.1

48

.81

6.8

45

.74

(3.8

0–8

.65

)Sh

ort-

term

un

empl

oyed

21

.07

.66

.46

5.5

95

.53

5.4

9(3

.98

–7.5

7)

Stu

den

t2

1.0

29

.01

.69

0.7

60

.85

0.8

9(0

.62

–1.2

9)

Dis

abili

type

nsi

oner

4.0

1.8

5.3

04

.44

3.4

92

.70

(1.5

5–4

.72

)C

onsc

ript

1.8

2.8

1.5

01

.17

1.2

91

.30

(0.6

1–2

.78

)O

ther

oru

nkn

own

19

.04

.99

.08

5.1

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married and living alone were associated independentlywith DUI of benzodiazepines only.

Own education, SEP, main activity and income werethe main mediating factors between different socio-economic variables and DUI of benzodiazepines, as wellas in other substance groups. This means that adding oneof these variables reduced the independent effect of othervariables. For example, the effect of mother’s educationon DUID was mediated fully by the subject’s own educa-tion, SEP and main activity (Table 1, model 5). Similarly,in a univariate model the OR for basic own education wasmore than four times as high when compared to a higheducation level, but the effect attenuated especially afteradjusting for SEP (model 4) and for main activity (model5). Conversely, urban environment was not mediated byother variables, as it maintained its effect on DUID, i.e. theORs remained at the same level throughout modelling.

Among under 45-year-old women as well as amongmen and women aged 45 or over, disability pension wasan important independent predictor (see final models inTable 2 and in Supporting information; Tables S2–S3).Furthermore, among younger women unemployment,divorce and living alone predicted DUID, among oldermen long-term unemployment, ‘other’/unknown mainactivity and living alone predicted DUID, and amongolder women being an entrepreneur, divorced or widowedhad the same effect. Surprisingly, high education wasassociated with DUI of ‘benzodiazepines only’ amongolder men (Table 2, final model) and women.

Benzodiazepines with alcohol

Among under 45-year-old men suspected of DUI of ben-zodiazepines with alcohol the effects of socio-economiccharacteristic and mediating mechanisms were verysimilar to those among under 45-year-old men with ben-zodiazepines only, with a couple of exceptions: parents’educational level had a stronger effect, and lower incomewas an independent predictor (Table 3, final model) ofDUI of benzodiazepines with alcohol.

Unemployment and disability pension were indepen-dent risk factors of DUI of benzodiazepines with alcoholamong under 45-year-old women and among men andwomen aged 45 or over (see Supporting information;Tables S4–S6). In addition, among younger women DUIDwas also associated independently with urban environ-ment, lower education, ‘other’/unknown main activity,not being married and living alone. Among oldermen, unemployment and retirement pension, ‘other’/unknown main activity, being divorced and living alonepredicted DUID. High education was a risk factor for DUIDamong older men and women, as in the ‘benzodiazepinesonly’ group. In addition, among older men an upperwhite-collar SEP predicted DUID.

Amphetamines plus other impairing drugs

Among under 45-year-old men the effect of urban envi-ronment on DUI of amphetamines was strong, as wasthe effect of lower education (Table 4). Compared to othersubstance groups, this was the only group where beingan entrepreneur predicted DUID. The independent effectof a disability pension was not strong and the risk of DUIDwas lower among students than in the reference group.

The results among under 45-year-old women andmen aged 45 or over were very similar compared toyounger men in this amphetamines group (see Support-ing information; Tables S7–S8). Similarly, the results inthe group of ‘amphetamines only’ were highly similarwhen compared to the group of ‘amphetamines plusother impairing drugs’ (data not shown). There were notenough cases to perform an analysis among older womenwith amphetamines findings.

Cannabinoids plus other impairing drugs(not amphetamines)

Among under 45-year-old men suspected of DUI ofcannabinoids (Table 5), the effects of socio-economicvariables were similar compared to amphetaminesusers: urban environment and especially low educationwere associated highly with DUID, and disability pensionwas not a strong predictor of DUID compared to thebenzodiazepines group. However, among the group of‘cannabinoids only’, fewer socio-economic factors wereassociated with DUID compared to the group of ‘cannab-inoids plus other impairing drugs’ (data not shown).

Analyses concerning cannabinoids could not be per-formed in other age and gender groups due to the smallnumber of cases (n = 0–17).

DISCUSSION

Main findings

The main findings of this study were as follows: regard-less of substance group, the strongest predictors of DUIDwere low education, unemployment, disability pensionand living alone (especially when being divorced); theseeffects prevailed even when the effects of other socio-economic factors were taken into account. Most differ-ences between substance, gender and age groups wereseen in the strength of the associations between differentsocio-economic factors and DUID. Amphetamines andcannabinoids, i.e. illicit drug users, seemed to be the mostdisadvantaged group. Surprisingly, among men aged 45or over, high education in both benzodiazepines groupsand a high SEP in benzodiazepines with alcohol groupwas associated with DUID when other factors were heldconstant. This was an opposite result compared to otherage and substance groups.



Our finding that the factors indicating lower socio-economic position were significant predictors of DUID isin accordance with results from other studies concerninglicit or illicit drug use. Finnish problem drug users(mainly opiate or stimulant users) are usually sociallymarginalized: drug treatment clients in 2008 weremainly single (62%), unemployed (64%) and had loweducation (70%) [30]. Numerous other studies haveshown that lower social status predicts benzodiazepineuse [16–20], as well as amphetamines [9,10] and otherillicit drug use [31].

The effect of many socio-economic characteristicspredicting DUID remained even when confounding vari-ables were held constant, but these associations were alsomediated by other socio-economic factors. The mostimportant mediating factors in all substance groupsexamined were education, SEP, main activity and income,the effects of which overlapped. For example, in univari-ate models the higher risk of DUID among lower-incomegroups was not caused by the amount of income per se,but lower income reflected lower socio-economic positionand lower employment status, which predicted DUID.Similarly, parents’ educational level had only a minorindependent effect on DUID; it was mediated almost fullyby characteristics indicating DUID suspects’ own socio-economic position. Thus, the effect of parents’ educationwas significant, but indirect; parents’ education influ-enced DUID suspects’ own education and socio-economicposition which, in turn, was a risk factor for DUID. Livingarrangements and marital status also overlapped. Maritalstatus was not as important a predictor of DUID if theperson had some kind of family (e.g. a spouse, children orparents) in the same household.

Although the socio-economic characteristics predict-ing DUID and the mediating mechanisms were verysimilar among all substance groups examined, some dif-ferences could be observed, especially between the benzo-diazepines (prescription drugs, in that sense licit) andamphetamines/cannabinoids (illicit drugs) groups. First,living in an urban or semi-urban municipality had astronger effect on DUI of illicit drugs than benzodiaz-epines. This was expected, as illicit drugs are more readilyavailable in urban than rural areas.

Secondly, although SEP, main activity and incomemediated the effect of education, as in the benzodiaz-epines groups, the association between lower educationand DUI of illicit drugs remained extremely high. Oneexplanation may be that drug experiments are initiated ata relatively young age [32], and experiments leading tosubstance abuse probably have an effect on school atten-dance and level of education. Indeed, experimenting withor use of any drugs (including amphetamines andcannabinoids) have been found to be more common invocational schools than in upper secondary schools [33].

In addition, we found that students had a lower risk forDUI of amphetamines. Most of the students in the datawere aged more than 18 years, which means that theyare primarily college/university students.

Thirdly, the effect of disability pension was muchstronger in the benzodiazepines groups. This finding is inaccordance with Norwegian longitudinal studies, whichhave shown that benzodiazepines are used extensivelyamong disability pensioners [34,35].

All in all, the users of illicit drugs were the most dis-advantaged, whereas the social status of ‘benzodiaz-epines only’ cases seemed to be better. This was expected,as benzodiazepines are a legal prescription drug, and thusa finding for ‘benzodiazepines only’ does not necessarilyindicate drug abuse.

Although lower socio-economic status was shown topredict benzodiazepine use among general populations,we found that a higher SEP and a higher educational levelwere risk factors for DUID among benzodiazepines usersaged 45 or over. This was an unexpected result. One expla-nation might be that a person who is more educated witha higher SEP and who ends up driving under the influencemay be more capable of seeking medical help for theirproblems. Inequalities in the usage of medical servicesexist in Finland—medical services are more likely to beused by groups with a higher socio-economic status [36].

We did not know whether benzodiazepines used byDUID suspects were used legally (according to a prescrip-tion) or illegally. However, having a prescription for ben-zodiazepines does not necessarily mean that they are notabused. Subjects driving under the influence of benzodi-azepines attracted police attention, which indicatesimpaired driving caused possibly by the abuse of benzo-diazepines. Finland has the highest use of benzodiaz-epines compared to other Nordic countries [37], and inFinland benzodiazepines are used widely both among thegeneral population [38] and among substance abusers[29,39]. Extensive use of benzodiazepines could also beseen in our results. These findings raise the questions ofwhether the prescription policy of benzodiazepines inFinland is too loose, and whether it should be controlledmore strictly, particularly when considering the problemscaused by benzodiazepine use, such as development oftolerance and dependence, and the symptoms of with-drawal caused by abrupt cessation [40].

In addition to drugged drivers, our results provideinformation about drug users in general. We found DUIDsuspects to be socially disadvantaged, even though somesubgroups of drug users are likely to be even more disad-vantaged with, for example, no access to cars. Hence, theresults of our study are in accordance with other studiesof drug users. Initiatives designed to reduce or eliminateinequalities in general among different socio-economicgroups would also benefit drug users and drugged drivers.



Because most drug use is initiated in adolescence andmost of the drugged drivers are young, preventive workand early interventions against intoxicant use amongyouth are crucial.

Limitations and strengths

The DUID cases studied became a part of this study’sdatabase because they were suspected and apprehended ofDUID by the police. Not all people driving under the influ-ence are caught; only fewer than 10% of the cases werefound in random control by the police, while the rest wereapprehended because of impaired driving or a trafficaccident. Hence, the most impaired drivers were over-represented. Therefore, the results need to be generalizedwith caution, and they do not represent overall druggeddriving in traffic flow. It should also be noted that the firstapprehension does not necessarily mean or prove that theDUID suspect was driving under the influence for the firsttime. Thus, although socio-economic characteristics wereexamined in the preceding year of the first apprehension,the direction of causality remains unclear in this study.

The data studied were based on two registers that werelinked by personal ID numbers. With register data a largesample size is relatively easy to gather, and thus sufficientpower can be reached. This type of linked register data isinternationally unique, and the good coverage and valid-ity of several Finnish registers [24] increases reliability.

CONCLUSION

In conclusion, a disadvantaged social background was asignificant predictor of DUID in all substance groups,although there was a gradient, so that amphetaminesand cannabinoids users were the most disadvantagedcompared to users of benzodiazepines.

Declarations of interest

Connection with tobacco, alcohol, pharmaceutical orgaming industry: Jari Haukka has been in research col-laborations with Janssen-Cilag and Eli Lilly, and has beena member of the expert advisory group for Astellas.

Acknowledgements

The authors wish to thank the Academy of Finland(grant no. 118563), Emil Aaltonen’s Foundation andDoctoral Programs in Public Health (DPPH) for theirfinancial support.

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Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Table S1. Socio-economic characteristics among casesand controls by age group and gender.

Table S2. The effects of socio-economic characteristics ondriving under the influence of ‘benzodiazepines only’among under 45-year-old women.

Table S3. The effects of socio-economic characteristics ondriving under the influence of ‘benzodiazepines only’among women aged 45 or over.

Table S4. The effects of socio-economic characteristics ondriving under the influence of benzodiazepines andalcohol among under 45-year-old women.

Table S5. The effects of socio-economic characteristics ondriving under the influence of benzodiazepines andalcohol among men aged 45 or over.

Table S6. The effects of socio-economic characteristics ondriving under the influence of benzodiazepines andalcohol among women aged 45 or over.

Table S7. The effects of socio-economic characteristics ondriving under the influence of amphetamines plus otherimpairing drugs among under 45-year-old women.

Table S8. The effects of socio-economic characteristics ondriving under the influence of amphetamines plus otherimpairing drugs among men aged 45 or over.

Please note: Wiley-Blackwell are not responsible for thecontent or functionality of any supporting materials sup-plied by the authors. Any queries (other than missingmaterial) should be directed to the corresponding authorfor the article.



socio-economic determinants of drugged driving – a register-based study

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