a data-matching study of the role of fatigue in work-related crashes

Transportation Research Part F 10 (2007) 242–253

www.elsevier.com/locate/trf

A data-matching study of the role of fatiguein work-related crashes

Ann Williamson *, Soufiane Boufous

NSW Injury Risk Management Research Centre, University of New South Wales, Building G2, Western Campus,

UNSW Sydney 2052, Australia

Received 21 March 2006; received in revised form 16 October 2006; accepted 20 October 2006

Abstract

This study investigated fatigue involvement in work and nonwork-related road traffic casualty crashes using a datasetformed by linking the New South Wales workers compensation dataset with the New South Wales road traffic crash data-base. In many crash databases work-relatedness cannot be identified. Other databases, such as workers compensation dataprovide information on work-related road traffic injury but little on the circumstances of the crash. Probabilistic linkageovercame these problems by matching cases from the workers compensation data to the crash data to produce a new data-set of work-related road traffic casualties. The patterns of fatigue-involvement in these casualty crashes showed similaritiesbetween work-related crashes and nonwork-related crashes. Fatigue-involved crashes were more likely to result in fatality,involved higher costs, were more likely to involve heavy and light trucks and to involve illegal alcohol or speeding nomatter whether they were work-related or not. Time of crash was the only characteristic that differed between workand nonwork-related crashes. Work-related fatigue-involved crashes tended to occur around dawn whereas work-relatednon-fatigue crashes occurred in peak hour traffic. Timing of work-related crashes involving fatigue varied little across the24-h period whereas those not involving fatigue showed an afternoon peak only. While fatigue-related crashes occur insimilar ways regardless of work status, strategies for work-related driver fatigue should not be left to be addressed onlyby general road safety strategies. As there is more control in the workplace over some of the fundamental causes of driverfatigue work-related fatigue management strategies are much more likely to be successful.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Fatigue; Driving; Crashes; Work; Data linkage

1. Introduction

Road traffic crashes represent a significant proportion of workplace deaths either while on duty or whilecommuting between home and work. For example, a national study of work-related fatalities in Australiabetween 1989 and 1992 showed that 49% of work-related fatalities involved road traffic crashes and around

1369-8478/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.trf.2006.10.002

* Corresponding author. Tel.: +61 2 9385 4599; fax: +61 2 9385 6040.E-mail address: [email protected] (A. Williamson).

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A. Williamson, S. Boufous / Transportation Research Part F 10 (2007) 242–253 243

30% of deaths occurred while on duty (Driscoll et al., 2001). These figures are similar to those reported inother developed countries. For example, in the US, road accidents accounted for around 25% of deaths whileworking (Pratt, 2003; Toscano & Windau, 1994) and 30% in Canada (Rossignol & Pineault, 1993), althoughsome countries show even higher proportions. In France nearly 40% of fatal work accidents occurred on theroad, increasing to 60% if commuting is taken into account (Charbotel, Chiron, Martin, & Bergeret, 2001).

Despite these compelling statistics, there have been comparatively few studies of the characteristics of work-related road traffic accidents. Available evidence shows that certain occupations and industries are at higherrisk of work-related traffic accidents. Professional drivers, especially truck drivers, and the transport industryhave been shown to have the highest risk of work-related road traffic accident in a number of studies (Bunn &Struttmann, 2003; Herbert & Landrigan, 2000; Rossignol & Pineault, 1993) but there is little evidence onwhether the nature or causes of work-related and nonwork-related crashes differ. This question is importantbecause we need to know whether work-related road safety should be treated differently to road safety in gen-eral and whether we need specific prevention strategies for work-related crashes. Few studies have looked atthe characteristics of work-related crashes, however because population data on road traffic crashes usuallydoes not contain information on work-relatedness. Furthermore, databases of occupational injury which iden-tify road traffic crashes do not contain much information about the causes of crashes. These problems can beovercome by linking databases of occupational injury and road traffic crashes so that crashes that are work-related can be identified. Linked datasets also have the advantage of combining the variables from the sourcedatasets and therefore providing a much richer picture of the circumstances in which the crash occurred.

The current study used this approach to look in some depth at the circumstances of work-related crashes inNSW during the period 1998–2002 by linking the New South Wales (NSW) Roads and Traffic Authority’sTraffic Accident Database system with the NSW Workcover compensation claims database. A paper describ-ing the general characteristics of work-related crashes from this analysis has been published elsewhere(Boufous & Williamson, 2006). The results showed that the majority of work-related crashes occurred duringcommuting, and on-duty crashes were more likely to involve males and professional transport workers. Speed-ing and fatigue were common contributors to work-related crashes.

Fatigue is a recognised risk factor for road traffic crashes (Connor et al., 2002). Since the driving experi-ences likely to increase fatigue, such as long hours of driving and driving in the period just before dawnare common characteristics of work-related driving, fatigue is likely to be a common feature of work-relateddriving. Available evidence supports this hypothesis. One of the few studies to investigate the causes of work-related road traffic crashes demonstrated that occupational fatalities were distinguished by involving driverdistraction, inattention or falling asleep, whereas nonoccupational fatal crashes were more likely to involvespeeding and use of alcohol (Bunn & Struttmann, 2003). Studies of professional drivers also show high levelsof self-reported fatigue due to long work hours, long hours in a monotonous task and irregular hours of work(Brown, 1994; Williamson, Feyer, Friswell, & Finlay-Brown, 2002). Developing a better understanding of thenature and circumstances of fatigue crashes while working and not working and any differences between themis important as it has implications for the development of prevention strategies in this area. The aims of thispaper are to use the police crash and workers compensation linked data to examine the role of fatigue incrashes while working and to identify whether fatigue involvement differs between working and nonworkingcrashes.

2. Methods

2.1. Data bases

The road traffic crash data used in this study included 83,974 records of controllers in casualty crashesreported in New South Wales (NSW) between 1998 and 2002. Controllers were defined as the drivers of vehi-cles. These cases were from the NSW Road Traffic Authority’s (RTA) Traffic Accident Database System(TADS) which contains information on all crashes occurring on NSW roads that are reported to the NSWPolice. The dataset selected from TADS for this study contained all motor vehicle controller casualties fromcrashes occurring between 1 January 1998 and 31 December 2002 inclusive. Thus the file contained a separaterecord for each driver or motorcycle rider who was killed or injured in a crash over this specified time period.

244 A. Williamson, S. Boufous / Transportation Research Part F 10 (2007) 242–253

The dataset contains considerable information regarding the circumstances of traffic crashes including thedate, time and location of the crash as well as characteristics of persons involved such as age and gender.The role of behavioural factors in the crash such as speeding, alcohol and fatigue were also included. Wherepossible, police recordings of vehicle speed and fatigue were used, but where there was no direct evidence ofspeeding or fatigue but evidence suggesting that they might have been involved, other criteria were used tomake the judgment (Roads & Traffic Authority, 2004). Judgments of involvement of speeding were madewhere either one or both of the following occurred:

1. The vehicle driver or rider was charged with a speeding offence or was described by police as traveling atexcessive speed or the stated speed of the vehicle was in excess of the speed limit, the vehicle.

2. The vehicle was performing a manoeuvre characteristic of excessive speed such as losing control on a curveor running off the road on a bend or corner when no other factors were responsible such as distraction,drowsiness, sudden illness, swerving to avoid an obstacle or equipment failure.

The definition of fatigue involvement considers fatigue to be a contributing factor to a road traffic accidentif that accident involved at least one fatigued motor vehicle controller. A motor vehicle controller is assessedas having been fatigued if the following conditions are met together or separately:

1. The vehicle’s controller was described by police as being asleep, drowsy or fatigued.2. The vehicle performed a manoeuvre which suggested loss of concentration of the controller due to fatigue,

that is:(i) The vehicle traveled onto the incorrect side of a straight road and was involved in a head-on collision

(and was not overtaking another vehicle and no other relevant factor was identified); or(ii) The vehicle ran off a straight road or off the road to the outside of a curve and the vehicle was not

directly identified as traveling at excessive speed and there was no other relevant factor identified forthe manoeuvre.

Blood alcohol measurements taken by police were also recorded in the traffic accident database.The workers compensation data used in this study was from the NSW Workers Compensation Claims data-

base. Workers compensation data, is also known in other countries as workers insurance, workers compensa-tion systems and workers mutual data. This database includes all accepted claims in NSW for work-relatedinjury or disease which resulted in death, permanent incapacity or a temporary incapacity for which paymentswere made. Cases selected for this analysis included 61,328 records of compensation claimants injured as aresult of a transport crash between 1 January 1998 and 31 December 2002 inclusive. The selection criteriafor compensation claims were broader than used to select the road traffic crash cases (i.e.: controller involvedin traffic crashes) because it was not possible to determine the nature of transport crashes (traffic or non-traf-fic) from the workers compensation data or whether the victim was a driver. This means that it was not pos-sible to determine the percentage of the ‘expected’ matches that actually linked after completion of the recordlinkage process. The compensation dataset contains information regarding the characteristics of the claimantincluding their industry and occupation, personal characteristics (age and gender) and the nature and out-comes of the injury in terms of disability levels and death.

2.2. Linkage process

Probabilistic record linkage techniques were used to link the two datasets. The linkage was carried outusing LinkageWiz record linkage software (LinkageWiz, 2002). Record linkage is the joining of informationfrom two or more records that are considered to relate to a common entity whether that entity is an individual,family, event, business, or address (Newcombe, 1998). When joined, such records are said to be linked. Themanner in which record linkage is carried out varies according to the resources available, the type and amountof personal data held in each collection and the level of linkage accuracy which is deemed acceptable.

Probabilistic record linkage attempts to mimic the steps a human would go through mentally when decid-ing whether two records from two separate datasets belong to the same person. Steps such as allowing for


incomplete and/or error data; evaluating how common a particular name is in the sets of data being com-pared; how likely it is that a particular pair would match at random; and how likely it is that full or partialagreement on values in that field is indicative of agreement on the whole record (Clark, 2004).

Prior to the linkage, missing data and duplicate records were identified and removed for both datasets andstandard formats were applied particularly to variables common to both datasets that would be used in linkingdatasets. These variables (the matching variables) include surname, initials, date of birth, gender, postcode,day, month and year of crash (RTA data) and injury day, month and year (WorkCover data). Some of thedata cleaning tasks were carried out automatically by the LinkageWiz software, including removing non-alphanumeric characters from fields, removing hyphens from family names and phonetic encoding of familynames to take into account spelling errors when linking records. In addition, 130 duplicate records were iden-tified in the WorkCover dataset.

Variables used to link datasets (matching variables) were then assigned a linkage weight according to their‘‘reliability’’ and ‘‘discriminative power’’ (Roos, Wajda, & Nicol, 1986) in much the same process as would beused in a manual linking of records from two datasets. For example, agreement on date of birth is more sug-gestive of a match than is agreement on sex. Matches on date of birth are given a greater weight than sex.Agreements on rare values of a given matching variable (e.g. surname: McAlarey) are more suggestive thanagreements on common values (e.g. Smith).

Discriminatory Power (u probabilities) refers to the probability of a given matching variable agreeingpurely by chance for a comparison pair of two records not belonging to the same individual (i.e. a nonmatch).Reliability (m probabilities), on the other hand, refers to the probability of a given matching variable agreeingamong records accepted as links. The m probability was estimated during the specification of the record link-age strategy based upon prior information, by initial manual review of data and at a later stage on the pro-portion of agreements among the comparison pairs accepted as links.

The formulae used to calculate the weight for a given variable, according to Newcombe (1998) is as follows:

Global Frequency RatioðGFRÞ ¼ Frequency of agreement in LINKED pairs ðmÞFrequency of agreement in UNLINKED pairs ðuÞ

Weight ¼ LOG2ðGFRÞ

The estimation of u and m probabilities and the corresponding weights was repeated for all matching vari-ables. The Likelihood or probability weights were estimated given all observed agreements and disagreementson all data variables used for linking records together. The total weight for a given comparison pair is simplythe sum of the agreement/disagreement weights for each matching variable. Probabilistic linkage software,LinkageWiz, initially assigns agreement and disagreement weights for each variable based on the formulae/rules described above, but also allows the operator to modify the weights in later stages of the linkage.

Records in each data source were divided into groups (blocks) of records based on surname, date of birth,date of crash/injury to minimise the number of comparisons that must be undertaken. The probabilistic link-age process involved three linkage passes based on each blocking variable. That is, we compared records forthose with similar names, then those who had similar date of birth and finally those with similar date of crash/injury according to a set of matching variables. Multiple passes were used to ensure that any linkages missedby one pass would be picked up by another.

The sum of comparison weights for each record pair was then calculated and if this value was below adefined ‘cut-off’ value, the record pair was rejected. If a total weight was above a much higher ‘threshold’,the record pair was defined as a ‘definite’ link. That is, record pairs with a high probability of referring tothe same individual. Records with values between the ‘cut-off’ and the ‘threshold’ were said to be ‘possible’links. These ’possible’ links were entered into the next pass and the process repeated, until the final remainingpool of ‘possible’ linkages was checked manually and obvious mismatches discarded. Refinements of weightsand thresholds were made at the end of each phase/pass in order to achieve a fine-tuned and data-sensitiverecord linkage.

After the three linkage passes, 12,674 RTA records were categorised as definitely linked to WorkCoverrecords and 1565 were classified as possible links and were checked manually. The records were reviewed inde-pendently by the two authors. The outcomes of this process were compared and a consensus was reached


based on a set of agreed criteria. As a result of the manual checking, a further 450 were added to the pool ofdefinite links, raising their final number to 13,124. The resulting ‘‘definite’’ links were assembled into groups oflinked records to form a one-to-one linkage result. Following linkage, personal identifying features wereremoved and replaced with unique numerical identifiers.

2.3. Study design

The study design was a descriptive study of the role of fatigue as a risk factor in work-related crashes bycomparing the characteristics of fatigue-involved and non-fatigue involved crashes within the linked dataset.The design also involved a comparison of fatigue-involved and non-fatigue involved nonwork-related crashesfrom the cases that did not match in the linkage process. It should be noted that the unmatched dataset is not adataset of only nonwork-related crashes as it almost certainly contained some work-related crashes. Not allwork-related road traffic crashes would have resulted in a compensation claim and so would not be pickedup in the linkage process. Work-related crashes involving self-employed persons would not be capturedas the self-employed are not covered by workers compensation. In addition, not all eligible crashes resultin claims for compensation, for example, crashes resulting in less severe injury and those involvingyoung workers (Boufous & Williamson, 2003). Nevertheless, the majority of unmatched crashes would nothave been work-related. The contribution of work-related crashes remaining in the unmatched datasetwould be very small. For this reason, the group of unmatched crashes used in this analysis was callednonwork-related.

For the analysis of fatigue and non-fatigue involved casualty crashes, the RTA definition of fatigue involve-ment was used as described above.

2.4. Analysis

The analysis compared fatigue and non-fatigued crashes for cases who were working and those who werenot working at the time of the crash. The variables examined were the outcome of the casualty crash (fatalityor casualty), the characteristics of the crash, causes of the crash, employment characteristics of the per-son involved (for work-related cases only) and the characteristics of the person involved. Chi-square andt-tests were computed where relevant to compare fatigue and non-fatigued crashes for various outcomes. Oddsratios were also provided to examine the magnitude of the observed differences between the two types ofcrashes.

3. Results

As a result of the linkage process 13,124 drivers were identified who were injured or died as a result of awork-related traffic crash in New South Wales between 1998 and 2002. This represented 15.18% of RTArecords and left a total of 70,850 unmatched RTA records that were classified as nonwork-related. Fatiguecrashes accounted for 6.3% of work-related or matched cases and 8.8% of nonwork-related.

3.1. Severity of fatigue and non-fatigue involved casualty crashes

There was a clear effect of fatigue involvement on the degree of casualty for both work-related and non-work-related datasets (X 2

ð1Þ ¼ 50:3, p < 0.0001 for work-related cases; X 2ð1Þ ¼ 308:4, p < 0.0001 for nonwork-

related cases). Casualty crashes judged to involve fatigue were around three times more likely to be a fatalityfor both work-related (5.0% and 1.6% for fatigue and non-fatigue crashes, respectively, OR = 3.3) and non-work-related cases (4.8% and 1.6% for fatigue and non-fatigue crashes, respectively, OR = 3.1). Consistentwith the overall higher severity of fatigue work-related cases, the compensation claim costs for fatigue-involved working crashes were also statistically significantly higher than for non-fatigue work-related caseswith average claim costs for fatigue crashes being $A62,785 (s.d. = 183,323) compared to only $A38,429(s.d. = 126,881) for non-fatigue crashes (t(876.6) = 3.8, p < 0.0001).


3.2. Characteristics of fatigue and non-fatigue involved casualty crashes

As shown in Fig. 1, fatigue and non-fatigue involved crashes were distinguished by vehicle type for bothwork-related crashes ðX 2

ð7Þ ¼ 269:5; p < 0:0001Þ and nonwork-related crashes ðX 2ð7Þ ¼ 480:4; p < 0:0001Þ.

Heavy trucks were markedly more likely in fatigue crashes whether work-related or not (OR = 4.3 forwork-related; OR = 2.9 for nonwork-related). Light trucks were also more likely in fatigue crashes in bothworking and nonworking crashes, but the effect was not as strong as for heavy trucks (OR = 1.4 for work-related; OR = 1.6 for nonwork-related). For buses, emergency service vehicles, taxis and motorcycles, crasheswere less likely to involve fatigue for working and nonworking crashes.

Analysis of the location of fatigue-involved crashes in work-related and nonwork-related datasets showedsignificant effects of road type (X 2

ð3Þ29:5, p < 0.0001 for work-related; X 2ð3Þ ¼ 165:7, p < 0.0001 for nonwork-

related), level of urbanisation (X(5) = 397.4, p < 0.0001 for work-related; X 2ð5Þ ¼ 1615:0, p < 0.0001 for

nonwork-related) and the speed limit at the location (X 2ð10Þ ¼ 347:3, p < 0.0001 for work-related;

X 2ð10Þ ¼ 1647:0, p < 0.0001 for nonwork-related). Fatigue crashes were more likely on freeway/motorway

and state highways than non-fatigue-involved crashes, for both datasets, although the effect was again morepronounced for work-related cases (OR = 1.5 for freeways, OR = 1.4 for state highways for work-related;OR = 1.2 for freeways, OR = 1.3 for state highways for nonwork-related).

For both datasets fatigue involved casualty crashes were markedly more common on country non-urbanroads (OR = 3.9 for work-related, OR = 2.9 for nonwork-related) and markedly less common in the Sydneyregion (OR = 0.3 for work-related, OR = 0.5 for nonwork-related). In addition, both datasets showed two tothree times higher proportions of fatigue-involved crashes in high speed zones of 100 to 110 kph (OR = 3.5 for100 kph, OR = 2.3 for 110 kph for work-related; OR = 2.6 for 100 kph and OR = 2.8 for 110 kph for non-work-related, see Fig. 2).

3.3. Causes of the fatigue and non-fatigue involved casualty crashes

Examination of some of the potential causal characteristics of fatigue and non-fatigue crashes showed thatfor both work-related and nonwork-related datasets, fatigue-involved crashes were approximately five times

0 10 20 30 40 50 60 70 80

Car type

Light truck

Heavy truck

Bus

Emergencyservice

Taxi

Motorcycle

Other

Work-related Fatigue Work-related Non-fatigue

Nonwork-related Fatigue Nonwork-related Not fatigued

Fig. 1. Type of vehicle in fatigue and non-fatigue casualty crashes from work-related and nonwork-related datasets.

0

10

20

30

40

50

60

70

80

90

50 kph >50-80 kph 90 kph 100 kph 110 kph

Speed limit

Per

cen

t



≤

Fig. 2. Speed limit of location of fatigue and non-fatigue involved casualty crashes in work-related and nonwork-related datasets.


more likely to also involve illegal levels of alcohol compared to crashes that were not judged to involve fatigue(OR = 5.9 for work-related, X 2

ð2Þ ¼ 167:8, p < 0.0001; OR = 5.0 for nonwork-related, X 2ð2Þ ¼ 2715:7,

p < 0.0001 see Fig. 3). Similarly, fatigue casualty crashes were more likely to involve speeding over the postedlimit, again for cases from both datasets, however the effect was not nearly as strong as for illegal alcohol(OR = 2.1 for work-related, X 2

ð1Þ ¼ 75:8, p < 0.0001; OR = 1.6 for nonwork-related, X 2ð1Þ ¼ 214:9,

p < 0.0001). Comparison of the time of the crash for fatigue and non-fatigue involved casualty crashes showedsignificant effects for each dataset (X 2

ð23Þ ¼ 366:2, p < 0.0001 for work-related; X 2ð23Þ ¼ 3546:9, p < 0.0001 for

nonwork-related, see Fig. 4). Fatigue-involved crashes were more likely in the midnight to dawn period forboth datasets, but for the work-related cases this effect was especially pronounced in the period around dawn(between 0500 and 0700 h). In addition, the fatigue-involved crashes in the nonwork-related dataset were fairlyevenly distributed across the 24 h period, whereas the fatigue-involved casualty crashes in the work-relateddataset still showed a very clear peak hour effect in the morning and late afternoon. In addition, the

0

5

10

15

20

25

Illegalalcohol Speed over postedlimit

Per

cen

t



Fig. 3. Percentage of fatigue and non-fatigue involved casualty crashes in work-related and nonwork-related datasets that also involvedillegal alcohol and/or speeding over the posted limit.

0

2

4

6

8

10

12

1 3 5 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time of day

Per

cen

t



2 4 6 8

Fig. 4. Time of day of fatigue and non-fatigue involved casualty crashes in work-related and nonwork-related datasets.


work-related dataset also showed a much longer morning peak crash period for fatigue cases than for non-fatigue crashes (05:00 to 9:00 for fatigue involved compared to 08:00 to 09:00 for non-fatigue involved).

3.4. Employment characteristics of casualties in fatigue and non-fatigue involved crashes

Analysis of the employment characteristics for cases in the work-related dataset showed that the majority offatigue and non-fatigue involved cases occurred while commuting but fatigue cases were slightly more likely tohave occurred on duty rather than commuting compared to non-fatigue cases (21.6% compared to 17.4%,OR = 1.3, X 2

ð2Þ ¼ 16:2, p < 0.0001). Similarly, most fatigue and non-fatigue involved cases were working fulltime at the time of the crash, but fatigue involved cases were more likely to have been working part time(11.9% compared to 8.5%, OR = 1.5, X 2

ð2Þ ¼ 11:5, p < 0.0001). Fatigue and non-fatigue involved cases differedin the distribution of occupation (X 2

ð7Þ ¼ 102:5, p < 0.0001), with plant and machine operators and drivers andtradespersons more likely to be among fatigue involved cases (OR = 1.8 and 1.6, respectively) and clerks,managers, professionals and paraprofessionals less likely to be fatigue involved (OR = 0.5, 0.6, and 0.6,respectively, see Fig. 5). There were also differences in the distribution of the type of industry of fatigueand non-fatigue involved cases (X 2

ð17Þ ¼ 132:1, p < 0.0001, see Fig. 6). Fatigue involved cases were more thantwice as likely to be from the agriculture, forestry and fishing industry (OR = 2.7). In addition, fatigue caseswere nearly twice as likely to be from the transport industry or accommodation, cafes and restaurant industrycompared to non-fatigue cases (OR = 1.9 and 1.7, respectively). Mining, construction and cultural andrecreational services were also moderately more likely to have been fatigue involved (OR = 1.5, 1.4 and1.4, respectively). In contrast, the education, wholesale trade and finance and insurance industries wereunder-represented amongst the fatigue involved cases (OR = 0.4, 0.5 and 0.5, respectively).

3.5. Characteristics of casualties in fatigue and non-fatigue involved crashes

Fatigue involved cases were considerably more likely to be male for both work-related and nonwork-related datasets (73.4% compared to 58.6%, OR = 1.95, X 2

ð2Þ ¼ 70:9, p < 0.0001 for work-related; 72.0%compared to 57.5%, OR = 1.9, X 2

ð2Þ ¼ 493:6, p < 0.0001 for nonwork-related). Fatigue cases also showed a sig-nificantly different age distribution compared to non-fatigue involved cases for both datasets (X 2

ð6Þ ¼ 49:0,p < 0.0001 for work-related, X 2

ð6Þ ¼ 132:1, p < 0.0001 for nonwork-related). This difference was due to a sig-nificantly higher representation of younger drivers in the 16 to 20 years age group amongst fatigue involved

0 10 15 20 25 30

Managers and administrators

Professionals

Para-professionals

Tradespersons

Clerks

Sales and personal service workers

Plant and machine operators and drivers

Labourers and related workers

PercentFatigued Non fatigued

5

Fig. 5. Occupation for fatigue and non-fatigue involved work-related casualty crashes.

0 4 8 12 14 16 18

Agriculture, forestry, fishing

Mining

Manufacturing

Electricity, gas and water supply

Construction

Wholesale trade

Retail trade

Accommodation, cafes and restaurants

Transport

Storage

Communication Services

Finance and Insurance

Property and Business services

Government Administration and Defence

Education

Health and Community Services

Cultural and Recreational Services

Personal and Other Services

PercentFatigue Not fatigued

2 6 10

Fig. 6. Industry of fatigue and non-fatigue involved work-related casualty crashes.



cases in both datasets, but especially in the work-related dataset (15.2% and 8.2% for fatigue and non-fatiguecrashes, OR = 2.0 for work-related, 17.5% and 14.6% for fatigue and non-fatigue crashes, OR = 1.6 for non-work-related). Fatigue and non-fatigue involved cases also differed in distribution of licence status in eachdataset (X 2

ð4Þ ¼ 517:9, p < 0.0001 for work-related, X 2ð4Þ ¼ 38:2, p < 0.0001 for nonwork-related). In both data-

sets, fatigue involved cases were more than twice as likely to have an expired licence (OR = 2.3 for work-related and OR = 2.5 for nonwork-related). For the work-related dataset, fatigue involved cases were alsotwice as likely to have a provisional licence as non-fatigue involved cases (OR = 2.0) whereas for the non-work-related dataset, fatigue involved cases were around 50% more likely to have a learners licence(OR = 1.5), whereas for the work-related dataset, fatigue-involved cases were less likely to be learners(OR = 0.7), although the number of cases with a learner licence in this dataset was very small as would beexpected for a working population.

4. Discussion

Using a data linkage approach it was possible to identify work-related crashes from two population-based datasets. While it was not possible to locate all controllers injured in work-related crashes in the roadtraffic crash database, the data subset of linked cases provided an opportunity to investigate the character-istics of controllers injured and the nature of the work-related crashes. The primary aim of this study wasto look at how fatigue and non-fatigue crashes differ for work-related and nonwork-related crashes. Theresulting patterns showed that fatigue involved crashes were very different from those where fatigue wasnot involved and that the patterns were very similar for work-related and nonwork-related casualty crashes.It seems that fatigue involvement plays a similar role in casualty crashes while working and while notworking. Fatigue involved crashes in both datasets were much more serious than those that did not involvefatigue, with fatalities around three times more likely and significantly higher costs for fatigue involvedcrashes. Clearly, fatigue involved crashes are a substantial road safety problem, regardless of work-relatedness.

Fatigue involved crashes were distinguished by the characteristics of the drivers. Drivers in fatigue involvedcrashes were more likely to be male, younger and working in driving-related occupations and the transportindustry. Overall, fatigue involved crashes were more likely to have occurred while the person was on dutyrather than while commuting, which is consistent with the higher representation of driving-related occupationsand industries in fatigue involved crashes.

The type of vehicles and location of the crash also distinguished crashes with fatigue involvement, again forboth working and not working crash datasets. Heavy trucks and to a lesser extent light trucks were more likelyin fatigue crashes but other working vehicles, like taxis, buses and emergency service vehicles were not. Fatiguecrashes were much more likely on country roads including freeways and state highways and especially in highspeed zones where they were around three times more common.

Fatigue involved casualty crashes were around five times more likely to have involved illegal levels of alco-hol and around twice as likely to involve speeding over the posted limit. Again, this effect was the same forboth work-related and nonwork-related datasets. This finding was unexpected especially as the work-relatedcases were overall much less likely than the nonwork-related cases to involve alcohol or speeding. Just overone percent of working casualty crashes involved illegal alcohol (1.4%) compared to around seven percentof nonwork-related crashes (7.4%) and 12.6% of working casualty crashes compared to 16.8% of nonwork-related casualty crashes involved speeding over the legal limit. It seems that drivers in fatigue involved casualtycrashes were more likely to be breaking road safety rules in general.

Previous analysis of road traffic crash data has also shown higher involvement of illegal alcohol and speed-ing in fatigue involved crashes (Roads & Traffic Authority, 2000). The reason for these relationships is notclear, although there are a number of possible explanations. The combination of high alcohol-use and fatigueis certainly possible due to the overall sedating effects of alcohol. A mechanism for the combined effects ofspeeding and fatigue is less obvious. Research on the fatigue experiences of professional drivers suggests thatfatigue has the effect of reducing speed rather than increasing it (Williamson et al., 2002). It may be that thelink between speeding and fatigue is due to higher risk-taking while driving so encouraging faster driving anddriving when tired. Certainly the high representation of very young drivers in fatigue-related crashes would


support this contention. The propensity to risky behaviour has been an often demonstrated characteristic ofyoung drivers (Arnett, 2002; Gregersen & Bjurulf, 1996).

A further possible explanation is that there are problems with the classification of fatigue involvement incrashes and distinguishing its involvement from that of other causal factors such as speeding and alcohol.While the definitions of speeding and fatigue involvement, for example, specifically exclude each other, policemay have coded both factors as occurring together. Others have raised the difficulty of defining fatigueinvolvement in crashes with the classification methodology currently used by the RTA in NSW as well asin and other states (Cercarelli & Haworth, 2002; Dobbie, 2002). The definition of fatigue involvement incrashes and of distinguishing its role from that of other factors is clearly of great importance for understand-ing the causes of fatigue in crashes. Further work is needed to clarify the reliability and validity of the currentdefinition.

The distribution of crashes across time of day was the only characteristic of fatigue involved crashes thatdiffered between work-related and nonwork-related cases. Work-related fatigue crashes occurred much earlierthan the expected work-related morning peak, occurring around dawn (around 0500–0700), were less likelyduring the middle of the day and early evening and showed a much smaller afternoon peak at around1400–1600 h. This pattern corresponds to the expected circadian rhythm of higher fatigue in the early morningand again in the early to mid afternoon (Akerstedt, 1995) and indicates that the fatigue definition used in thisstudy is likely to be valid. Work-related crashes not involving fatigue showed the same morning and afternoonpeaks corresponding to the expected commuting periods, as discussed in the previous section. In contrast, thedistribution of fatigue-involved nonwork-related casualty crashes was roughly even across the 24 h period andshowed no expected circadian peaks and troughs. The nonwork-related crashes showed the same pattern infatigue and non-fatigue involved crashes with only a late afternoon peak.

Overall, fatigue involvement in crashes was clearly not different for work-related and nonwork-relatedcrashes. It seems that apart from the time of day effects, the factors that cause fatigue involvement incrashes are the same whether or not the driver is working. Factors that increase the likelihood of driving attimes when fatigue is more likely to occur, such as professional drivers and other 24 h-related occupationssuch as shiftworkers in trades or cafe and restaurant workers are also at higher risk of fatigue involved crashes.This is one of the few dimensions on which the impact of fatigue differs between work and nonwork-relatedcrashes.

In interpreting the findings of this analysis, it is important to note that the data linkage process is likely tohave underestimated the number of work-related crashes identified from the Traffic Accident Database. TheWorkers Compensation database is not a comprehensive or exhaustive collection of work-related injury as itdoes not cover self-employed workers plus not all injured workers make a compensation claim. The extent andnature of the representation bias in identifying work-related crashes in this analysis cannot be known. It isestimated that 15–20% of the workforce in NSW are self-employed (Macaskill & Driscoll, 1998) but no infor-mation is available on the range of industries or occupations they represent. Little guidance is available on theextent of bias due to failure to make workers compensation claims, although a comparison between work-related injury reported to hospitals and workers compensation claim data in NSW indicates that young work-ers are considerably less likely to make workers compensation claims (Boufous & Williamson, 2003).

A further potential limitation of the study is the interpretation of the results of significance tests. Due to thelarge sample size, interpretation of the significance levels shown needs to be cautious. The large sample sizes ineach group practically assure statistical significance. However, in most cases, the difference between to twogroups was large enough to be of practical importance.

Despite these limitations, comparisons within the dataset highlight fatigue as a significant contributor toroad traffic crashes, whether the driver was working or not. Fatigue crashes occurred for very similar reasonsregardless of working status. The implications of these findings for injury prevention are that general roadsafety strategic approaches to fatigue management should also address work-related driver fatigue. It shouldbe recognised, however, that general road safety strategies for fatigue management are limited to guidance andadvisory information about resting when tired as there are currently no non-subjective methods for detectingincreasing fatigue levels. On the other hand, workplace fatigue management strategies are likely to be moreeffective as there is greater control over aspects of work likely to increase fatigue, such as timing and durationof work and rest periods. An important opportunity to implement more effective fatigue management


strategies would be lost if work-related driver fatigue was to be only seen as a road safety issue. Driver fatigueneeds to be seen as a workplace issue and addressed as such.

Acknowledgements

This work was funded by the NSW Roads and Traffic Authority. The authors would like to thankTony Davies from WorkCover NSW and Robert Ramsey from NSW RTA for providing and assisting us withthe data analysed in this study. A version of this paper was presented at the International Conference onFatigue Management in Transportation Operations held Seattle, USA, September, 2005.

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