Well being, work environment and work accidents

Well being, work environment and work accidents

Alan Kirschenbaum*, Ludmilla Oigenblick, Albert I. Goldberg

Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Haifa 32000, Israel

Abstract

We examine factors that in¯uence accident proneness among employees. We agree that the determinants of

accident proneness include organizational, emotional and personal factors. Using logistic regression we estimatedthree models, and their predictability for accident proneness among sample of 200 injured workers interviewed uponentering hospital emergency wards in Israel. Work injuries were not contingent on age, religion, nor education. The

e�ects of gender were strong but non-signi®cant. Subcontracted and higher-paid workers are more likely to getrepeat injuries. Prior injury experience sensitized employees to stronger perceptions of risk associated with unsafepractices. Large family households, ameliorates stress feelings and lessens the likelihood of accident proneness while

poor housing conditions have the opposite e�ect. The full model demonstrates considerable prediction of injurieswhen focusing on type of employment, personal income level, being involved in dangerous jobs, emotional distressand a poor housing environment. The model contains most of the signi®cant results of interest and provides a high

level of predictability for work injuries. # 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Accident proneness; Injured workers; Work±family environment; Emotional well being; Israel

Introduction

The total number of work-related accidents each

year has grown to an estimated 125 million world-wide. Recently, the International Labour O�ceConstitution clearly committed the Organization's 173member States to the protection of workers against

sickness, disease and injury arising out of their employ-ment no matter what the economic stresses or competi-tive pressures in the global economy, (International

Labour O�ce, 1996). Despite e�orts to improve safetypractices on the job, accidents in the workplace con-tinue to pose a signi®cant problem for workers and

management alike (Holcom et al., 1993). Research onthe costs of injury to US employers estimated that oc-cupational injuries cost employers around US$155 bil-

lion, three-fourths of the total for all injuries, and overUS$1400 per injury (Miller, 1997). This same patternappears in Israel which, like most urban industrializednations, is experiencing increasing costs due to occu-

pational injuries. The annual surveys 1994±1995 and1995±1996 of the National Insurance Institute in Israelshowed an increasing rate of injury allowances for the

last years. During this period of time, numbers of reci-pients of injury allowances went up from 59,100 to92,000, an increase of 56% (Achdut, 1995; Yaniv,

1996). These increasing expensive trends in bothhuman su�ering and economic costs have led to inno-vate European Union measures mandating the re-duction and study of job-related accidents. This

mandate requires the analysis of the entire work en-

Social Science & Medicine 50 (2000) 631±639

0277-9536/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.

PII: S0277-9536(99 )00309-3

www.elsevier.com/locate/socscimed

* Corresponding author. Fax: +972-4-823-5194.

E-mail address: [email protected] (A.

Kirschenbaum).

vironment following an accident and a follow-up pre-ventive safety program (Maggi, 1996). Part of the di�-

culty in addressing and combating these issues, asidefrom getting accurate estimates of accident frequenciesand cost (which can be problematic in their own right)

is isolating and identifying mechanisms underlyingemployee accidents (Holcom et al., 1993). Concernsare about the concepts involved in work place accident

prevention, and there is an accelerating call to developinnovative measures mandating the study and re-duction of job-related accidents.

Occupational approach

Di�erent approaches to the attribution of cause of

industrial accidents exist. The most prevalent is builtaround an organizational perspective. This approachfocuses on organizational and/or institutional level

analysis in which the explanation of workplace acci-dents is based on the formal work organization anddependence on bureaucratic rationality (Wright andNichols, 1986; Korllos, 1993). The main locus is on in-

vestigating the process of socially constructing riskobjects and scrutinizing safety through organizationaldevelopments in creating, assessing and responding to

risk (Short and Clarke, 1992; Clarke and Short, 1993).Risky systems, from this perspective, have structuralfeatures that discourage safe operations, independent

of the inevitability of normal accidents. Financial®rm's resources and organizational structure are foundto be the primary predictors of accident and sickness

(Perrow, 1984; MacLean, 1989). At the same time, evi-dence also points to a relationship between thebusiness cycle and industrial injury rate, ®nding it tobe pro-cyclical. Thus, certain segments of the manufac-

turing labor force are more vulnerable, and have suf-fered more accidents (Wright and Nichols, 1986;Nichols, 1989; Brun, 1995). The recent studies on work

safety add support to this growing recognition amongsafety experts that organizational factors can, and do,in¯uence safety performance (Hofmann and Stetzer,

1996).One implication of the organizational perspective is

the assumption that work features vary and result indi�erential vulnerability to injury. Simply, hazardous

conditions are more threatening to safety than otherconditions, and ought to have a greater impact onaccident proneness. This also applies to subcontracting

which entails a trade-o� between increased jobdemands and decreased reliability of individual com-ponents. Also involved are practices violating formal

training which are claimed to be widely used at theworkplace and result in increasing vulnerability toinjury (Hunt, 1995).

Human error theory

The second approach is based on human error the-ory. Here, individual failure is explained in terms ofmoral attitudes towards safety. The error leading to

injury occurs when an actor is designated as respon-sible because of a loss of control over work proceduresor systems due to insu�cient/inadequate training or

stress/fatigue (Keyser, 1989; Tombs, 1991; Jackson,1995). Injury di�erences are also seen as due to anindividual's background. For example, there are higher

rates of accidents among men who apparently takemore risks (Gee and Veevers, 1983; Carter, 1990). Ageand formal education, however, have no signi®cante�ect on the risk of nonfatal injury (Shepherd and

Brickley, 1996; Kelly and Miles, 1997). There is how-ever, evidence that concentration of immigrants andethnic minorities (usually in dangerous jobs) are re-

lated to industrial accidents (Lee and Wrench, 1980).Likewise, it was found that marital status and childrenare found to be strong predictors of accident prone-

ness. In a cross-sectional study by Balcazar et al.(1995), factors associated with work-related accidentsamong workers were risk perception and being a

household head.Along these same lines, there is a discernible e�ect

of perceived stress generated by environmental factors(stresses) and emotional well being on the propensity

to be injured (Caplan, 1983; Sauter and Murphy,1995). Most empirical studies support a positive re-lationship between job stress and accidents. Johnson

and Hall (1988) showed that extreme workload andtime pressure, and low social support are related tohigh levels of stress. From the perspective of person±

environment ®t theory, job stress signi®es a poor ®tbetween the demands of the work environment andwhat the individual is equipped to handle. This canlead to a higher rate of work accidents as evidenced by

higher liability and workers compensation claims, ill-ness and as a result, higher costs to the workplace(Burke and Greenglass, 1987; Cooper and Payne, 1988;

Ja�e, 1995).

Accident proneness

From these two basic perspectives, occupational

injuries appear not as accidental but as (a) embeddedin the social relations of production, or as error-proneprocedures, and (may) be treated as dysfunction of an

actor himself because of personality characteristics. Weargue that the organizational and human errorapproaches are complementary rather than competing,

and suggest that the di�erence between these perspec-tives lies in the roles of components investigated andattribution of cause they identify for accidents. These

A. Kirschenbaum et al. / Social Science & Medicine 50 (2000) 631±639632

theories are two sides of the same coin. The commonthread binding these approaches can be articulated in

the concept of accident proneness. Its basis is sup-ported by the argument that a large proportion ofaccidents can be attributed to human error. Add to

this the ®nding that similarly large proportions of acci-dents are experienced by a relatively small percentageof the work force. This leads to the conclusion that the

focus of research on accidents should be to identifythose personal characteristics which predispose someindividuals, rather than others, to be injured at work.

This type of disposition is called accident proneness(Iverson and Erwin, 1997). Unfortunately, we actuallyknow very little about how both individual and organ-izational factors are associated with accident involve-

ment. Accident proneness, as a proxy for thepredisposition of certain workers to be involved inaccidents, is measured by the frequency of injuries ex-

perienced by a person at work; be it for the ®rst-timeor as a repeat episode. This picture of an error-proneperson may be enhanced by organizationally based

dangerous job characteristics and/or individual levelstressed emotional well being. We tend to explainworker's accident proneness by interaction of these fac-

tors, and to identify di�erences between those withrepeat injuries and those with only a ®rst injury forfurther accident prevention and avoidance. Given thegrowing number of injured individuals in the work

place and the cost involved, this question has boththeoretical and practical importance.

Theoretical model

The proposed complementary model suggested hererepresents means of estimating accident probability. Itdraws on the two main reasons in explaining occu-pational injuries; namely as the result of being (a)

embedded in organizational systems, and caused bydangerous work procedures and (b) as dysfunction ofthe worker due to individual characteristics and errors

generated by stress. The speci®c research question weare aiming for concerns discerning what is/may be therelationship between the sets of independent variables,

which act to predict a worker's accident proneness?The statistical model of the process of becoming a

repeat-injured worker, which we construct, investigates

a theoretical framework, based on three sets of inde-pendent explanators of accident proneness in terms ofpersonal, organizational and emotional factors. Theseinclude the (1) socio-demographic characteristics of

those injured, (2) their work conditions and (3)emotional well being. From these basic categoriesevolved three additive and sequential models. Model 1

shows whether or not accident proneness is associatedwith socio-demographic characteristics of the

employee. Model 2 adds the e�ects of work conditionsincluding danger at work, unsafe procedures, and lack

of social support, resulting in increasing vulnerabilityto injury. This was done on the assumption that theimmediate types of work conditions will overpower

other types as the primary cause of work injuries.Model 3 adds the e�ect of emotional well being onpropensity to be injured. This ®nal additive model con-

tains the complete set of predictors. The whole scale ofperceived stress contains items that measure controlover life events and ability to cope with problems, feel-

ings and states that are characteristic of disorder andstress.

Methods

Questionnaire survey

A structured questionnaire including open-endedquestions was used to collect ®eld data relevant to

accident proneness. The questionnaire was divided intofour sections, including (1) socio-demographic infor-mation, (2) employment characteristics, (3) work con-

ditions and (4) perceived stress construct. Speci®canswer categories were provided for each question.The study questionnaire was administered to all work-injured employees who arrived at the Emergency

Departments at two major medical centers in thenorthern region of Israel. We chose the emergencydepartment in order to obtain real-time or at the very

least recent information about job characteristics andperceived emotional well being of the respondent onthe eve of the injury. Within a 3-month period during

normal hours in 1998, we interviewed 200 respondentsadmitted to the emergency departments and registeredas work injured. To discern the respondents' predispo-sition to injuries, we ask the questions ``Have you been

injured at work recently?'' Among them, 123 employ-ees reported multiple accidents: of them 74 workershad one prior accident, 41 Ð two accidents and 8 Ð

three or more accidents.

The sample

The sample itself was obtained through the use of apurposeful non-random sampling technique which is

widely used in social science surveys. It yields useful in-formation when it is di�cult to identify members ofthe research population and helps establish the exist-ence of a problem area (Henry, 1990; Wicks and

Baldwin, 1997). In our case, we sought work-injuredindividuals at the Emergency Departments where theywould appear, and the interpreting and extrapolating

the ®ndings based on this type of non-probabilitysampling must be taken with caution.

A. Kirschenbaum et al. / Social Science & Medicine 50 (2000) 631±639 633

The sampling is largely representative of the overallethnic composition of the Israeli population. The

respondents were overwhelmingly Jewish (60%) withthe remainder Arab (27%), Christians (9%) or`òther''. Over 80% were men. On average the injured

workers were 37.5 years old (S.D.=11.7), and com-pleted 11 years of schooling (S.D.=3.2), therebyattaining a secondary education. Three-quarters of the

respondents were married and had children (68%).The majority (64%) of injured workers was employedin blue-collar occupations with a mean monthly wage

of 3000±6000 NIS. On average, the respondentsworked 48 h per week with 20% working night shifts.This picture of injured workers re¯ects their concen-tration in blue-collar production-oriented organiz-

ations. It is therefore not representative of the generaloccupational distribution in Israel but does ®t the pat-tern of work injuries found in most industrialized

nations. Thus, the proportion in blue-collar productionjobs in Israel is 61%, similar to that of the UnitedKingdom (63%), Finland (64%), Denmark (61%),

Belgium (69%), France (70%) and Sweden (56%)(International Labour Organization, 1997).

Measures

Socio-demographic variables included measures ofsex, age religion, marital status, number of children

and immigration status. Organizational variables wererepresented by measures of economic branch, occu-pation, length of service, educational level, personal

monthly income, average work hours and type ofemployment.Work conditions identi®ed with level of risk in the

work environment included respondents' perceptionsof (1) danger; (2) safety and (3) supervisor and co-workers' support. A Likert type 5-point scale from`very much' (1) to `not at all' (5) measured each vari-

able. Ten indicator variables were used as a proxy ofeach risk environment and based on actual encounterswith risk situations. For example, we emphasized the

factor of safety violation, asking the following ques-tion, ``To what degree are you forced to break safetyregulations?''

Stress was aggregated from two sources of stressfeelings: stress as a mood and stressed non-work en-vironment. Stress as a mood refers to the extent to

which one responds to life with con®dence and opti-mism or has the feeling of life disorder. Stressed non-work environment refers to tensions derived from in-teractions with family and housing problems and feel-

ing of loneliness. To assess a state that places peopleat risk and measure the degree to which situations inone's life are appraised as stressful, we took advantage

of a 17-item construct using Cohen's Perceived StressScale (PSS) (Cohen et al., 1983). The response scale

was adjusted to a 5-point Likert scale. The respondentsindicated how frequently they felt stressed and per-

ceived life disorder in the last month. We obtained aninternal consistency Cronbach's a 0.89 for the per-ceived stress construct.

Data analysis

As the dependent variable àccident proneness' is adichotomous variable (®rst-time versus repeat injured),a logistic regression was employed in the analysis.

With a fairly large number of potentially importantvariables, we developed the relevant regressionequations: forward selection for strong interactivee�ects followed by backward selection for stable strong

e�ects. The decision which variables were to beincluded in the regression was based on correlationand likelihood-ratio statistics.

We used three models to describe the dependence ofaccident proneness on the three sets of potential expla-natory variables: (1) personal and employment charac-

teristics, (2) work conditions and (3) perceived stress.The primary base model includes the respondents per-sonal and employment characteristics that are

regressed in all subsequent models. The second modeladds the variables relating to work conditions to thebasic model. The ®nal model adds the variables repre-sentative of emotional well-being. Our analysis pro-

vided a ¯exible and robust method for examining thee�ects of three sets of predictors on the dependentvariable with minimal statistical bias and loss of infor-

mation.

Results

The results revealed background di�erences between®rst-time injuries (38%) and workers who experienced

repeated work accidents (62%). The ratio of malesamong those who have recurring injuries (88%) is sig-ni®cantly higher than among ®rst-time injured

( p< 0.01). This is consistent with studies, which haveattributed high accident involvement of men due totheir typically higher exposure to risky job activities

(Deguire and Messing, 1995). No signi®cant di�erenceswere found by age. The repeat injured were on average38.3 years old (S.D.=11.0), and ®rst-time injured 36.2

(S.D.=12.8). This is compatible with the recent studyby Iverson and Erwin (1997) who did not ®nd age tobe signi®cantly related with occupational injury. Beingmarried (76%) and having children (74%) were found

to be accountable for accident proneness ( p < 0.01).Here again, we ®nd support for our ®ndings inBalcazar et al. (1995) which reports that household

heads are more likely to be injured at work.Educational di�erences were not found to be signi®-


cant (11.6 vs. 10.9 years), also consistent with the ®nd-ings of Kelly and Miles (1997), Shepherd and Brickley

(1996) and Lee and Wrench (1980) (Table 1).In terms of personal variables, only sex and marital

status was found to predict injury. Most of the respon-dents who had experienced repeat injuries were blue-

collar workers (70%). Half the repeat injured workerswere employed in metal working industries and con-struction compared to 40% of ®rst-injured employees.

The most dangerous jobs were found in construction(69%) ( p< 0.01). A large proportion of both repeat

and ®rst-time injured workers were subcontractedlabor (74 vs. 64%) with di�erences in length of servicebetween the two groups negligible (7 vs. 8 years).

We found no signi®cant di�erences in the estimationof perceived stress by the two accident-prone groups.

This led us to explore how stress was perceived andevaluated. A factor analysis of stress variables did

reveal distinct conceptual di�erences. The ®rst factoraccounted for 43% of the variance which we called`life disorder', and describes how injured employees

expressed their feelings towards life hardships. The sec-ond factor, ùnhappiness with family life', related to

losing control over important events and dissatisfac-tion with family life. This factor explained 8% of the

variance. The third factor, `loneliness', related to thefeelings of loneliness and perceived inadequacy ofhousing conditions, and explained 7% of the variance.

Together, all three factors explained 58.5% of the var-iance.

Signi®cant di�erences were also found in how eachaccident-prone group perceived risks in terms of their

work conditions. The repeatedly injured employeesmore frequently perceived their jobs as dangerous( p < 0.01), violated safety rules and were less familiar

with safety instructions ( p< 0.05). Comparing thetwo groups while controlling for violation of safety

rules, we found a meaningful di�erence in estimationof risks inherent in their work environment. Those

who were forced to break the safety rules (`violated')reported signi®cantly worse work conditions thanthose who were not (`non-violated') and had strongerperceptions of risk associated with unsafe practices.

This same group ranked being involved in dangerousjob highest among the work variables. They also indi-cated work overload, inappropriate equipment

( p< 0.01) and fast pace ( p < 0.05) as factors relatedto injuries.These results helped us understand the source of

injuries among those that either violated or kept thesafety rules in their work place. Apparently, those who`violated' safety rules more frequently felt angry about

lack of control, encountered unexpected events, feltthings going wrong, and were unable to overcome di�-culties ( p < 0.01). A lack of social support of col-leagues among those who `violated' safety rules

resulted in feelings of loneliness and lost control overpersonal time ( p< 0.05).These results provided the basis for a further investi-

gation into determining the relevance of prior injuryexperience and perceived lack of safety at the workplace on accident proneness.

Analysis of accident proneness

Socio-economic characteristicsModel 1 restricts the explanatory variables to deter-

mine whether or not personal and employment charac-teristics such as gender marital status, wage and typeof employment are associated with injury proneness

(see Table 3). Signi®cant regression coe�cients wereobtained for gender and marital status on accidentproneness ( p< 0.01) suggesting that both have a sub-

Table 1

Weighted means and standard deviations for variables used in the analysis

Independent variable First-injured Repeat-injured Total

Male (=1) 0.74 (0.44) 0.88 (0.32) 0.82 (0.38)

Age 36.25 (12.82) 38.31 (11.09) 37.52 (11.79)

Jewish (=1) 0.66 (0.47) 0.55 (0.49) 0.60 (0.49)

Years of education 11.67 (3.62) 10.96 (2.98) 11.24 (3.25)

Blue-collar workers (=1) 0.53 (0.50) 0.70 (0.46) 0.64 (0.48)

Years worked at place 6.88 (8.44) 8.05 (9.00) 7.60 (8.79)

Hours worked a week 46.70 (14.91) 48.08 (10.17) 47.55 (12.20)

Married (=1) 0.58 (0.49) 0.76 (0.42) 0.69 (0.46)

Children (=1) 0.58 (0.49) 0.74 (0.44) 0.68 (0.46)

Number of children 1.71 (1.79) 2.28 (2.24) 2.06 (2.10)

Monthly wage 1.57 (0.88) 1.81 (0.73) 1.72 (0.80)

Employed by sub-contracting (=1) 0.64 (0.48) 0.74 (0.44) 0.70 (0.45)

Number of respondents 77 123 200


stantive impact on the odds of being involved in mul-tiple work related injuries.

Personal income signi®cantly in¯uences the prob-ability to be injured ( p< 0.05). For example, the oddsof being injured for high-paid employees (wages more

than 6000 NIS) increase by a factor of 1.7 in contrastto low-paid employees. The signi®cant correlation ofabove average wages with such variables as work

hours ( p < 0.01), perceived ®nancial problems( p < 0.05), and low latitude of control over life events( p < 0.05), may contribute to understanding why

`wages' act as such a powerful predictor of work inju-ries.The propensity to be injured is rather strongly as-

sociated with type of employment ( p < 0.01), particu-

larly, subcontracting. Subcontracting includesprovisions for restricted time contracts, facilitating ter-mination, reducing social bene®ts and encouraging

unsafe working conditions. The dangerous conditionsare largely created by subcontracting with its practiceof speed and low attention to safety regulations. Each

of these factors may well contribute to work accidents(Bernstein, 1986). Those who are employed under sub-contracting are signi®cantly ( p < 0.05) more prone to

subsequent work injuries. Regression coe�cients onthis variable are stable in all three investigated models.Model 1 accounts for 138 respondents correctly classi-®ed of whom 104 were involved in additional accident

episodes. The model w 2 is 30.1 ( p< 0.01).

Work conditions

Model 2 adds the e�ects of dangerous conditionsand work overload to model 1. The values of genderand income decline while the values of being married

and subcontracting remained highly signi®cant( p < 0.01). Performing a dangerous job is positivelyrelated to accident proneness ( p< 0.01) as well as notfollowing safety instructions at the work place

( p < 0.05). A possible explanation of these ®ndingsmight be found in how each accident-prone group per-ceived risk in their work environment. Comparing the

two groups while controlling for violation of safetyrules, we found that repeatedly injured employeesmore frequently perceived their jobs as dangerous

( p < 0.01). Overall, model 2 accounts for 134 respon-dents correctly classi®ed among whom 98 are repeatinjured. The model w 2 is 11.8 ( p < 0.01).

Emotional well beingModel 3 contains the accumulated set of predictors.

This full model demonstrates considerable prediction

ability of repeat injuries according to personal, organ-izational and emotional characteristics. In spite ofentering the additional set of the variables, four predic-

tors maintain their steadfast signi®cance for injury pro-neness: being married, personal income level, type of

employment and being involved in dangerous jobs.The 17 factor loadings derived from the factor analysis(Table 2) entered model 3 and were estimated using

backward elimination for automated model building,with the change in likelihood based on observed sig-ni®cance level used for removing variables from the

model.Four additional predictors strengthened our

observed result, namely, feeling that things are going

wrong ( p < 0.05), anger about losing control overthings ( p < 0.07), unhappiness with family ( p < 0.01)and housing ( p < 0.01). All four feelings make sense

within the framework of studies on stress-related beha-vior. For example, Frone et al. (1997) explained work-place injuries and illnesses by family±work spill over.Peres and Yuchtman-Yaar (1992) found that house-

hold size is negatively related to feelings of tolerance.Both support our ®ndings that external stressors cana�ect work injuries.

The data also reveal both positive and negative vari-ables of well being explaining injury proneness. Thisdichotomy needs to be explained in terms of the tech-

nical nature of the analysis. As the sample containsonly injured employees, that is, ®rst-injured (0) andrepeat injured (1) with the reference group ®rst-injured.This meant that the coe�cients for the variable ``things

are going wrong'' and `ùnhappy with family'' re¯ectchanges in log odds when repeat injured are comparedto employees with a ®rst injury. Therefore, we obtain

what seems at ®rst sight to be a strange ®nding thatthe more one is unhappy with family and the morethat things are perceived to be going wrong, the less

Table 2

Results of factor analysis on stress measuresa

Factor 1 Factor 2 Factor 3

Irritated 0.81 0.02 0.08

Things are going wrong 0.77 0.31 0.14

Nervous and stressed 0.72 0.40 0.11

Angry 0.70 0.18 0.20

Couldn't cope with things 0.70 0.13 0.32

Di�culties are piling up 0.66 0.12 0.31

Unable to control time 0.57 0.26 0.35

Upset 0.54 0.44 0.31

Worried about ®nancing 0.46 0.33 0.37

Unhappy with family life ÿ0.02 0.75 0.22

Unable to control things 0.43 0.68 0.09

Unable to handle problems 0.41 0.65 0.07

Couldn't cope with changes 0.51 0.57 0.07

No friends at work 0.25 ÿ0.09 0.74

Lonely 0.31 0.10 0.64

Unhappy with housing ÿ0.06 0.43 0.62

No sense that work completed 0.21 0.32 0.54

a Explained variance for factor 1: 43%; explained variance

for factor 2: 8%; explained variance for factor 3: 7%.


probability of being injured repeatedly. This resultmay be an artifact of the question itself. In depth inter-

views pointed out that this type of stress (family andthings going wrong) is short lived, the result of a tem-porary con¯ict which subsides very quickly (over chil-

dren, spouse or partner). Such type of stress mighttemporarily a�ect attention to task performance andresult in an accident. However, its not long lived and

therefore, as the data show, not lead to increasedrepeat injuries.In contrast, we found that `poor housing conditions'

and `being angry' are positively related to repeated

injuries. Here, we are looking at more permanent stressfactors. It is known that if the stressor is su�cientlysevere and prolonged and the stress continues una-

bated, symptoms reappear and accidents occur (Selye,1983). In general, poor housing conditions re¯ect de-pressed family economic circumstances and exasperate

the inability to cope with feelings of anger. In thiscase, the employees may work more hours in order toimprove their income (and consequently their housing

conditions), having less time for recreation. This couldwell explain their proneness for their later sequence ofaccidents. It is interesting that the relationship betweenthe number of children and emotional feelings of anger

( p < 0.01) and unhappiness with housing conditions issigni®cant ( p < 0.09) and indicates that those with fewchildren (one or two) are more frequently experiencing

stress.A total of 140 respondents were correctly predicted

in model 3. As a means of establishing the adequacy

of this model, we compared our predictions to theobserved outcomes. The results demonstrate that the

model is adequate in its ability to predict correct out-comes of accident proneness and pinpoint variables

involved in such predictions. For example, 42 workerswho experienced their ®rst work injury were correctlypredicted by the model not to have been involved in

further injuries. Similarly, 98 respondents with second-ary injury were correctly predicted to be involved infurther work accidents. Only 60 of the total of 200

cases were incorrectly classi®ed in the example. Thistranslates into an overall 70% success rate in correctlyclassifying accident proneness. The model w 2 is 15.9( p< 0.01).

Probability risk groups

The use of the logistic regression coe�cients allowedus to demonstrate probabilities of accident proneness.Based on the estimated coe�cients from Table 3, we

could predict that a new (future) accident is very likelyto happen to male workers who are married, highlypaid, are subcontracted, work under dangerous con-

ditions and express feelings of anger. The estimatedprobability of accident proneness for this group is0.894. By selecting the salaried employees who workunder dangerous conditions but do not feel angry, the

probability is reduced to 0.637. Almost the same prob-ability (0.649) exists for the employees with safe workconditions but who are angry about losing control

over life events. We can suggest that the dangerousconditions and feeling of anger in our case are equallyimportant for the propensity to be injured. The prob-

ability to be injured is drastically decreased if theemployee is not married and low paid (0.299).

Table 3

Coe�cients for models predicting accident proneness from emotional stresses, work conditions and background characteristics:

injured workers ages 15±65b

Predictor variables Model 1 Model 2 Model 3a

Male 1.201�� (0.451) 0.742 (0.487) 0.418 (0.514)

Married 1.258�� (0.337) 1.320�� (0.351) 1.491�� (0.378)Wage less 3000 NIS ÿ0.874 (0.471) ÿ0.699 (0.488) ÿ0.887 (0.519)

Wage more 6000 NIS 0.508� (0.262) 0.472 (0.270) 0.606� (0.285)Employed by subcontracting 0.934�� (0.392) 0.920� (0.402) 1.239�� (0.435)Employed at dangerous jobs ± 0.962�� (0.388) 1.100�� (0.412)Work without pursuing safety instructions 1.036� (0.557) 0.881 (0.613)

Things are going wrong ± ± ÿ1.657� (0.874)Angry ± ± 1.647 (0.909)

Unhappy with family life ± ± ÿ2.660�� (0.974)Unhappy with housing ± ± 3.396�� (1.284)Constant ÿ3.591�� ÿ3.574�� ÿ3.877��R 2 ± ± ±

Number of correctly classi®ed cases 138 134 140

� p< 0.05; ��p < 0.01.a Numbers in parentheses are standard errors.b Logistic regression model coe�cients are log odd ratios.


Summary and conclusions

The focus of this research has been on explainingwork accident proneness. To do so, a set of personal,

organizational and emotional variables theoreticallylinked to work accidents was incorporated into anexplanatory regression model. The model combined

theoretical perspectives from organizational theory andthe theory of human error. The previous injury back-

ground of the respondents determined accident prone-ness. The main ®ndings of this cross-sectional analysisof 200 injured blue-collar workers residing in Israel

were that: (1) speci®c aspects of the work environmentdi�erentially a�ected employees proneness toward their®rst or repeated work injury, (2) accident proneness is

conditional on being occupied in a dangerous job andbeing employed in a subcontracting status, (3) the

magnitude of these factors was enhanced by feeling ofanger, (4) accident proneness was a�ected by the inter-action of longer hours of work and level of wages, (5)

poor housing conditions is positively related to acci-dent proneness, (6) accident proneness was not a�ectedby gender, age or education and (7) accident prone-

ness (®rst and repeat) was not a�ected by length of ser-vice.

We also discovered that risk perception (i.e., unsafework environment) was more developed among thoseexperienced multiple rather than ®rst time work acci-

dents. Those frequently injured emphasized the lack ofsafety conditions pointing toward unsafe technologies

and management practices for their injuries. Firstinjured employees emphasized work overload and puta major portion of the blame for their injury on them-

selves. Those involved in ®rst time accidents were notaware of the necessity to demand and follow the safetyinstructions at the workplace. In general, the perceived

lack of safety at the work place increased with injuryexperience. This is consistent with the results of

Kirschenbaum (1996) who demonstrated how fear of arecurring emergency event a�ected attitudes towardseeking an alternative behavior.

Our results also con®rm the in¯uence that subcon-tracting has on accident proneness. The correlation

between cheap labor and hazardous working con-ditions appears to be a potent combination in increas-ing chances of being involved in an accident. Adding

to these environmental conditions is the emotionalstate of the worker. The analysis clearly demonstrated

that respondents with high levels of negative emotionalstresses are most prone to work accidents. Of particu-lar interest was that stressed emotional well-being

a�ects accident proneness with the same magnitude asdoes the dangerous work environment.The regression model which was developed provided

evidence of the critical importance that speci®c aspectsof the work environment and emotional well-being

a�ected the probability to be injured at work. Thesetwo components represent an organizational and

human error explanatory perspectives of why workaccidents occur. There was virtually no evidence thatpersonal variables, except gender and marital status,

played a part in a�ecting accident proneness.These results compelled us to rethink some of our

assumptions about accident proneness. On the one

hand, working in a high risk work environment with-out adequate safety measures made sense. So too,being under speci®cally focused emotional stresses

could impair work behavior. Yet, there also appearedsome involvement of the family, vis-a-vis marital statusand the presence of children. It may very well be thataccident proneness may also be triggered by work±

family con¯ict, a situation which may generate pre-accident emotive stress and fragile health outcomes(Frone et al., 1997). A second problematic assumption

dealt with how attitudes toward risk were a�ected byperceived safety at the job, i.e., greater awareness oflack of safety increases with injury experience. Simply,

workers learned from their experience to apply safetyrules so as to avoid injury. Yet, in a supplementaryanalysis we found that those who knowingly broke the

safety regulations reported signi®cantly worse workconditions than non-violators. Why is it that accumu-lated experience involving work accidents, whichshould have increased awareness of risk perception

and safety rules, did not diminish the propensity ofinjury?These questions provoke additional issues for future

research. Firstly, there is the relationship betweenwork±family stress and occupational injury. Perhaps amore re®ned focus on the work±family environment

and emotional well-being will provide a more enligh-tened derivation of occupational injuries. Second, thereis the issue involving safety and what appears to be itsinconsistent link to work accidents. Finally, seeking

out the origin of emotional well-being and the circum-stances under which it plays the role of bu�er or, insome cases, augment the propensity to be involved in a

work injury. To test these propositions, a more sophis-ticated sampling strategy is needed which should dis-tinguish injured and non-injured employees.

Acknowledgements

This research was supported by a Technion grant.All opinions are those of the authors alone. We would

like to thank members of the medical center sta� fortheir cooperation and the helpful comments by the twoanonymous referees.


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Well being, work environment and work accidents