Allen, Slavin, Bunn - Long Workhour Impact - JOEM Feb 2007

Do Long Workhours Impact Health, Safety,and Productivity at a Heavy Manufacturer?

Harris M. Allen, Jr., PhDThomas Slavin, MS, MBAWilliam B. Bunn III, MD, JD, MPH

Objectives: To test the health, safety, and productivity effects of longworkhours. Methods: Secondary analyses of a longitudinal employeepanel (n 2746). Average hours worked during spring 2001 wereassessed relative to health, safety, and productivity outcomes spanningsummer 2001 through spring 2002. Results: Employees workingovertime were no more likely to incur adverse physical or mental health,presenteeism, or disability outcomes. Those working 60 hours weremore likely to report new injuries and diagnoses, but these effects wereoverwhelmed by prior health, demographics, and compensation type.Conclusions: Much previous work has suggested that long workhoursgenerate a wide range of adverse outcomes across the employee contin-uum. This study found no evidence for pervasive workhour effects.Rather, long workhoursespecially weekly schedules at the 60 hour orabove markcan lead to problems in certain areas of health and safety.More research is needed that tests group differences across segmentedcharacteristics (eg, poor versus good health) but keeps workhour impactin perspective. (J Occup Environ Med. 2007;49:148171)

I ncreasing workloads, job insecurity,and pressures to perform have com-bined with trends in the marketplace(eg, increasing competition fromglobalization; drops in the numbersof workers as baby boomers age) tolengthen the average workweek inmuch of the industrialized West.Since the 1970s, overtime has in-creased steadily and is now commonin the United States.1 More recently,the shorter workweeks and longervacations of workers in Europe, longa source of pride and distinctionrelative to the US, have begun todissipate.2 Despite ratifying the 1993European Working Time Direc-tiveissued by the European Unionto limit normal working hours to nomore than 48 per week (averagedacross a four month period) severalmember countries have also passedprovisions for employees to volun-tarily opt out of these requirementsor to otherwise exercise flexibility intheir implementation. As a result,recent estimates of the average work-week in the United Kingdom andelsewhere have edged upward as theirprivate sectors grapple with laggingcompetitiveness in world markets andpersisting high unemployment.

Aware that long workhours couldbe problematic among its employees,International Truck and Engine Cor-poration (ITEC) recently examinedthe impact of workhours on its work-force with the assistance of work-force health and safety experts fromthe United Auto Workers and theNational Institute for OccupationalSafety and Health. A focus on im-proving such indicators as claimscosts and absenteeism had led ITEC

From the Harris Allen Group, LLC (Dr Allen), Brookline MA; the Department of Epidemiology andPublic Health, School of Medicine, Yale University (Dr Allen), New Haven, CT; the InternationalTruck & Engine Corporation (Mr. Slavin and Dr Bunn), Warrenville, IL; and Northwestern UniversitySchool of Medicine (Dr Bunn), Chicago, IL.

The views expressed in this article are those of the authors and do not represent the official opinionor policy of any organization with which authors, participants, and/or reviewers are affiliated.

Address correspondence to: Harris Allen, PhD, Harris Allen Group, LLC, 31 Stearns Road #3,Brookline, MA 02446; Email: [email protected]

Copyright 2007 by American College of Occupational and Environmental Medicine

DOI: 10.1097/JOM.0b013e31802f09ee

148 Long Workhours Impact at a Heavy Manufacturer Allen et al

to mount an extensive disease man-agement effort in 2001 targeting theburden of disease on employeehealth, productivity, and safety.36The integrated database developedfor that studyalthough not origi-nally conceived to examine the ef-fects of workhours specificallyfeatured a large longitudinal panelwith a wide array of survey andadministrative data points spanning atwo-year period. Among the mea-sures were self-reports of workhours.The addition of workhours fromITECs payroll database and otherenhancements made this integrateddatabase a promising platform foraddressing this second issue.

The project team took up severalquestions key to the health, produc-tivity, and safety mission at ITEC.Did the number of hours workedexhibit an association with adversehealth and productivity outcomes inITECs workforce? If so, what wasthe nature of this association and didit differ across different levels ofworkhours? Did prior health or workinjury history have any effect on theimpact of workhours on health andproductivity? What implications didthe answers to these questions havefor company policies toward longhours? These questions were both ofinterest to the project team and ger-mane to the literature on workhourimpact. This paper reports the resultsof tests guided by a reading of thisliterature and adapted to the studycontext at ITEC.

Work at ITECSeveral aspects of the work envi-

ronment at ITEC helped shape theapproach. The compensation profileof the companys workforce is oneexample. As depicted by the data-base, at the time of this study, nearlytwo-thirds (63%) of ITEC employeeswere compensated on an hourly ba-sis, while the balance (37%) werecompensated on a salaried basis. Al-most half (49%) of the salaried groupwere paid the same amount on asemi-monthly or monthly basis re-gardless of the number of hours they

worked; that is, their positions wereexempt from overtime pay. By defi-nition, all hourly employees heldnon-exempt positions, although lessthan half of one percent shifted eitherto or from a salaried/exempt positionduring the study period and werecoded as exempt in the database.ITECs payroll database incorpo-rated these distinctions by assigningthe equivalent of 40 hours per weekfor all salaried/exempt employeesacross all workweeks regardless ofthe numbers of hours actually worked,whereas hourly and salary/non-exemptemployees were assigned actual hoursworked. As described below, thesecoding rules led a hybrid approach tothe measurement of workhours for thisstudy.

Another aspect was that the pay-roll database contained some compa-ny-specific rules for the coding ofworkhours. For hourly employees,the database treated all hours workedduring the normal weekday shifts(eg, MondayFriday, 8 AM5 PM or 6AM3 PM) as regular hours and allhours worked outside the normalshifts (ie, weekday evening andweekend) as overtime hours. Thiswas the case regardless of the num-ber of hours worked during the week-day shifts. It was thus not uncommonto find non-zero overtime entries cou-pled with regular hour entries of lessthan 40 hours for the same time period.Furthermore, the payroll database didnot specify the time frame of itsworkhour entries (eg, whether thehours accumulated during the week-day or weekend). As such, character-istics like the length and timing ofeach shift were not retrievable.Moreover, some two thirds of theworkforce (roughly 68%) was union-represented. For the 96% affiliatedwith the United Auto Workers, theregular entry field actually capturedall regular and overtime hours, notjust regular hours only. Known asstraight time, this field minus thecorresponding overtime entry equaledactual regular hours accumulated. As aresult of these rules, total (regular overtime) hours became the sole focus

of the analysis. Other aspects of work-hour schedules (eg, extended shifts)were not examined.

Finally, although at the time of thisstudy the decision to work longhours was purely voluntary on thepart of employees, it was commonpracticeindeed the normfor thecompany to ask employees to workbeyond 40 hours per week. Companyoperations were in large part predi-cated on the assumption that many, ifnot most, employees would workbeyond 40 hours if asked, an expec-tation reinforced by organizationalculture. The number of hours actu-ally worked, estimated using themethod developed by a previousstudy to render the best estimatesavailable (Allen H. Using companypayroll data to test the validity ofemployee self-reports of workhours.2005, unpublished manuscript.Brookline, MA: The Harris AllenGroup), reflected this influence: em-ployees averaged 43.79 (SD 8.23)and 44.39 (SD 8.34) hours perworkweek respectively, during thespring and fall of 2001. As shall beseen below, this confluence requireda revisiting of what it means to worklong hours.

Long Workhour Effect: TheExisting Case

The empirical literature pertinentto a strategy conditioned to thesesituational parameters has grownconsiderably in recent years. Onereview of peer-reviewed publicationsfound 21 pertinent articles in the1960 to 1996 period.7 A second re-view,8 using essentially the samenarrowly defined search rules, iden-tified almost as many papers (19)over the next 5 years while a thirdreview,9 utilizing similar but some-what broader rules for 1995 to 2002,found 52 papers.

In virtually all cases, the focus ofthe investigators has been the work-force-impact of long workhoursacross the full continuum of employ-ees under study. Scant attention hasbeen paid to the possibility that this

JOEM Volume 49, Number 2, February 2007 149

impact might vary by health status,compensation type, job-type, orother potential differentiating char-acteristics. Moreover, there are onlya couple of instances in which mea-sures focusing on productivitythatis, work performancehave beentargeted. Yet the consistent focusthroughout, health, is fully at leasthalf of what needs to be prioritizedfrom the employer perspective. And,while the correlation between healthand productivity is not 1.0, the twoare invariably strongly (positively)related. As such, the lessons learnedto date in this research inform thetask at hand.

A number of studies have reportedassociations between long work-hours and adverse health. Caruso etal found that overtime was associ-ated with poorer general health, in-creased injury rates, more illnesses,or increased mortality in 16 of 22studies.9 The meta-analysis by Sparkset al calculated a mean correlation of0.13 between weekly workhours andoverall ill health (0.06 for physiologi-cal health and 0.15 for psychologicalill health).7 Van der Hulst concludedthat, on balance, the evidence supportsthe contention that long workhoursmay have detrimental consequencesfor health, including cardiovasculardisease, diabetes, illnesses leading todisability retirement, subjectively re-ported physical ill health, and subjec-tive fatigue. She further noted researchthat has laid the groundwork for twocausal pathways by which long work-hours may affect health via insufficienttime for physiological recovery, andvia associations with changes in life-style behaviors (eg, obesity, smoking)that promote ill health and disease.8

The fields progress toward clari-fying the impact of long workhourshas been anything but smooth, how-ever. As exemplified by more than25% of the studies reviewed by Ca-ruso et al, the frequency of instancesin which either no associations orassociations in the unexpected direc-tion were observed has proven toolarge to ignore. The bias that typi-cally steers publication venues away

from papers with not significant resultsmay have led to an under-reportingof the null result. Moreover, as re-ported by Sparks et al, the extent ofthe positive association between longworkhours and the rate of adversehealth consequences, where docu-mented, has typically been modest.In many cases the findings have notbeen reflective of a strong effect inthis regard. On a related note, whilethere have been examples of studiesdocumenting greater injury riskswith extended hours (ie, 60 hourweeks; 12 hour days),1013 muchof the pertinent work has under-appreciated the possibility that theadverse health impact of workhoursdiffers across levels of overtime. Ex-plicit comparisons of the rates ofoccupational accidents and illnessesat, for example, 50-hour weeks ver-sus 60 hour weeks have beenrare.14

Furthermore, two aspects of theresearch have raised some troublingquestions. The first aspect has beenthe widespread omission of variablesreflecting factors that may moderatethe impact of workhours. Two basicoptions are available for analytictreatment in this regard: either asstatistical controls (wherein eachsuch measure is added as an indepen-dent variable in regression-basedanalysis) or as segmenting variables(wherein each such variable serves toidentify two or more groups, with theanalysis being run separately foreach group and compared). Whilethe first option might seem espe-cially relevant given the literaturespreoccupation with workforce-wideovertime effects, both options haveby and large been ignored in the vastmajority of the empirical work re-ported to date.

This oversight can cut two ways.In those instances where workhoureffects have been cited, these attribu-tions are vulnerable to competingexplanations that arise from charac-teristics (eg, demographics, workcharacteristics) that have little to dowith number of hours actuallyworked. Conversely, in those in-

stances where the null result hasbeen reported, the lack of appropriatestatistical control may in fact haveled to a Type II error. Recent workby Tucker and Rutherford provide anillustration.15 Their survey of traindrivers found no evidence of a direct(uncontrolled) relationship betweenworkhour impact and health. How-ever, respondents who lacked bothschedule autonomy and social sup-port showed positive associations be-tween workhours and the frequencyof physical health symptoms, whilethose who reported low schedule au-tonomy and high social supportshowed negative associations.

The other questionable aspect of theresearch to date has been the method-ological rigor applied, which has fre-quently fallen short of providing abasis for credible causal inference.Most studies have been cross-sec-tional, stratifying employees into dis-tinct groupings by the number of hoursthey work and looking at differences inhealth at the same point in time. Thisapproach is vulnerable to the familiarchicken versus egg dilemma, be-cause it is unable to establish causaldirection: did working long hours pre-cipitate an adverse health outcome ordid poor prior health determine thenumber of hours employees ended upworking?

Dembe et al addressed both ofthese questions in a recent studyfocusing on the impact of workhourexposure on the incidence of occu-pational diseases and illnesses.16They examined 110,235 job recordslinked with 10,793 working adults inthe United States who participated inthe National Longitudinal Survey ofYouth. Their cohortdrawn from57,000 household screening inter-views and weighted to produce anationally representative samplewas tracked across a 13-year period(19872000). Work schedules gener-ating overtime pay were linked to a61% increase in the injury rate, whileworking at least 60 hours per weekwas linked with a 23% increase. Inaddition, the correspondence be-tween hours and injuries was statis-


tically significant: the more hoursworked per week (above 40), thegreater the rate of injury.

This correspondence spoke to theheretofore largely unattended linkagebetween different levels of overtimeand adverse events by documenting aconsistently linear relationship be-tween them. The effects attributed tolong workhours were large in theirown right, thereby offering a counter-point to the previous findings of onlymodest effects. Moreover, the longi-tudinal approach and use of statisti-cal controls (eg, age, gender, type ofinjury) boosted confidence that itwas the exposure brought about bylong workhours that was specificallytested as the cause of the adverseoutcomes, not some confoundedcombination of factors unrelated toworkhours per se. Similarly, use ofstandardized 100 worker yearcomparisons further zeroed in on thecontribution of long hours by con-trolling for the expectation that em-ployees with longer hours will incurmore injuries than those workingshorter hours, even if the underlyingrisks are the same, because theformer spend more time at risk forinjury.Implications for This Study

In these respects, the Dembe et alstudy provides perhaps the mostcompelling evidence to date of anadverse effect stemming from longworkhours that holds (ie, is undiffer-entiated) across the employee con-tinuum. But, Dembe et al still leftunaddressed several issues germaneto the current study. First, their anal-ysis did not differentiate voluntaryfrom mandatory (compulsory,forced, involuntary) overtime.Mandatory overtime is overtimework required by employers, andnon-compliance can often subjectworkers to penalties including losingtheir jobs. It has been linked to ele-vated adverse consequences includ-ing stress, impaired performance,and the likelihood of accidents.17 Allsuch consequences may be less ap-plicable here given the voluntary na-

ture of overtime at ITEC during thisperiod.

Second, Dembe et al did not controlfor prior health status or medical con-ditions, or prior health risks or workinjuries. These controls would seemvital to any attempt to document longworkhour effects, particularly thosethat are purportedly operative acrossthe employee continuum of theworkforce under study. Pre-existinghealth factors deriving from genetics,family history, non-work environmentetc. (eg, functional status, well-being,disease, and health risk behaviors)could have been integrally involvedin the self-selection (sorting) of work-ers into different levels of workhours.Similarly, prior work injuriesal-though themselves perhaps the resultof the level of hours worked in thepastcould have been involved inthis sorting. Either or both of theseantecedents, in turn, could have com-prised the major doorway throughwhich workhours affected outcomes.Rather than directly generating out-comes on their own, workhours couldhave mediated the effects of theseself-selection processes. For example,for employees working longer hours,poor prior health or injuries could havebypassed workhours altogether in di-rectly affecting outcomes, overshad-owing any impact that actual hoursworked made in the process. At theother extreme, employees, with priorhealth issues such as disease or priorwork injuries, who as a result wereworking fewer (ie, less than 40) hoursper week may have colored the asso-ciation observed between workhoursand adverse outcomes. Without con-trols for prior health or work injuries,there was no way to test for thesepossibilities.

Third, Dembe et al made certainassumptions about the functionalform of the relationship betweenworkhours and adverse outcomesthat merit further study. Their use ofthe 100 worker year denominatorserved to define the null hypothesiswith the function [y/hrs a (b hrs)] or [y (a hrs) (b hrs)2].These tests assumed that 1) the effect

of workhours on outcomes increasesconsistently in some proportion tohours worked, and 2) the slope re-flecting the impact of workhours onhealth, however defined, goes throughzero when workhours equal zero. Bothassumptions can be questioned. Totake the just described selection pro-cess for example, in the presence ofprior health characteristics (eg, dis-ease) the rate of adverse outcomes maybe well above zero even when work-hours are zero.

Finally, following another firmlyestablished precedent in the litera-ture, the Dembe et al study made noeffort to explore the relative contri-bution of workhours versus otherfactors known to lead to adverseoutcomes. How important are work-hours as a predictor of adverse out-comes when considered in a largercontext that includes prior healthcharacteristics, health risks etc.?Companies like ITEC need answersto these questions before undertakinga review of corporate policies in thisarea.

The approach reported here ad-dressed these issues with tests thatwere designed to examine company-wide effects of long workhours; thatis, effects which embodied the liter-atures focus on the transcendent im-pacts of overtime. The first step wasto assess for any association betweenhours worked and various health,safety, and productivity outcomes.Tests were conducted to characterizethe magnitude, direction, and func-tional form of these relationships andto determine the impact on the con-clusions reached when the exposureto workhour impact was lengthened.We then endeavored to develop amore detailed look at both the pre-diction of workhours by a variety ofantecedent characteristics (eg, demo-graphic characteristics, as well asprior health and prior work injuries),as well as the impact of workhourson health and productivity outcomes.The intent was two-fold: 1) to probethe extent to which workhours ex-erted direct effects (eg, A3C) onthese outcomes and/or mediated the


indirect effects (eg, A3B3C) ofthe antecedent characteristics onthese same outcomes, and 2) to com-pare these direct and indirect effectsinvolving workhours to effects thatbypassed workhours altogether inlinking the antecedent characteristicsdirectly to outcomes. With these con-trols for selection, the comparisonsserved to sort out the effects of theantecedents and thereby clarify thecausal role of workhours.

Materials and MethodsThe secondary analyses for this

undertaking required two stages. Thefirst used bivariate and multivariatedirect effect techniques to charac-terize the direction, magnitude, andfunctional form of the workhour/outcome relationships and to assessthese relationships when the expo-sure period was lengthened. Thisfirst stage was exploratory in natureand treated each outcome measureseparately. The second stage usedstructural equation techniques withthe capacity to assess both directand indirect effects in the contextof causal models that featured work-hour measures as both dependent andindependent variables in the middleof a hypothesized three-step se-quence. These techniques generatedconfirmatory tests that simultaneouslyexamined the relationships between allvariables in each model, whether pre-dictors or outcomes or the workhourmeasures themselves. In keeping withthe literatures focus on the transcen-dent impacts of overtime, all tests inboth stages were conducted at the levelof the aggregate sample.

Integrated DatabaseWhile focusing on disease, the

project that developed the databaseused for this study deployed a gen-eral population approach for datacollection. All 10,000 employeesat six selected sites were eligible forthis process, regardless of clinicalstatus. The project featured an edu-cational intervention mounted duringsummer 2001 in the context of aquasi-experimental/control, pre/post

design, with surveys administered inMay and September of 2001. Thesurvey results were supplementedwith administrative data on absentee-ism, group health, paid prescription,workers compensation and disabilityspanning the May 2000 to April2002 period.

The survey was roughly 100 itemsin length and covered a wide varietyof topics, including health status,chronic disease, presenteeism, absen-teeism, accidents, as well as health riskbehaviors and use of health services.Its administration in the May and Sep-tember timeframes was conducted bothelectronically by the web for somegroups and manually in on-sitekiosks for others. Both delivery ap-proaches garnered overall responserates in excess of 50%.

The measures of adverse eventswere captured from administrativedatabases already utilized by ITEC.The following sources were tapped:ITECs workers compensation (WC)and short-term disability (STD) da-tabase; the companys group health(GH) claims and paid prescriptiondatabases; and its eligibility and ab-senteeism databases. The first threedatabases in this list were each pop-ulated and maintained by a separateoutside organization under contractwith ITEC.

Of these five databases, the fol-lowing were accessed for the admin-istrative measures used in this study:WC, STD, and eligibility. The WCand STD database covered any andall claims filed by all eligible em-ployees. The measures drawn fromthe eligibility database (age and gen-der) similarly covered all employees.The absenteeism and GH databases,in contrast, covered only some 70%and 85% of ITEC active employees,respectively, during the time frameof this study. Since statistical tech-niques were used that required list-wise deletion, whereby only thoseemployees with non-missing data forall variables were eligible, the deci-sion was made to forego use ofmeasures from these latter two data-bases. The one exception was the

comparison of responders and non-responders reported next, where useof measures whose coverage fellshort of the entire employee popula-tion did not detract from the analyticrequirements at hand.

Study SampleA total of 2746 employees com-

pleted both the May and the Septem-ber surveys and could be linked tomeasures from each of the adminis-trative databases tapped for this ef-fort. This longitudinal panel servedas the base analytic sample. Asshown in Table 1, it constituted28.6% of those who were eligible forboth surveys and could be linked tothe administrative measures.

With an average age of 46.3 yearsand a gender split of 79.8% male,this samplelike the larger sampleof May responders (those who com-pleted the first survey but not neces-sarily the second)was comprisedof employees who were slightlyyounger and more likely to be femalethan those eligible employees whodid not respond (the non-respond-ers). Such modest demographic dif-ferences between those who elect tocomplete questionnaires versus thosewho do not are not uncommon insurvey research.

More germane to this study, how-ever, were the comparisons on ad-ministrative measures of health andsafety available on both respondersand non-responders. Respondersproved no more or less likely thannon-responders to have either a STDor WC episode in the 12 monthsprior to the first survey (Table 1).This pattern of no statistically signif-icant differences, in fact, held on avariety of other administrative mea-sures as well. In tests not detailedhere, longitudinal responders wereno more or less likely than non-responders to have incurred control-lable (ie, time away attributable toindividual health) absentee hoursduring the previous year (May 1,2000April 30, 2001). Longitudinalresponders were no more or lesslikely to incur outpatient office visits


(t 0.53; P 0.60), emergencyroom visits (t 1.58; P 0.11), orhospital visits (t 0.01; P 0.99).Nor were they more or less likely touse prescription medications (t 0.60; P 0.55). Similarly, theclaims dollars paid for longitudinalresponders were statistically similarto those paid for non-responders, notonly for workers compensation (t 1.38; P 0.17) and short-term dis-ability (t 0.06; P 0.96) but alsofor group health (t 1.46; P 0.14)and prescription medicines (t 0.60;P 0.55). In short, for purposes ofthis study our longitudinal sample pro-vided a reasonable representation of thestudy population during this period.

Study MeasuresRationale. With the two-year span

of the database and the timing of thetwo surveys in mind, the followingtime distinctions were made. Theworkhour measures focused on Aprilto June 2001 (herein referred to asspring 2001). The antecedent mea-sures focused on the preceding yearwhereas the outcome measures fo-cused on the following year. Workingwith the data available, all antecedentsreferenced either characteristics thathad been in place or events that hadtranspired prior to spring 2001, or ata minimum were contiguous time-wise with the workhour measures.With the exception of up to a maxi-mum of a modest (ie, three-month)overlap in two cases, the outcomemeasures all followed the spring2001 workhour measures in time.

This sequence invoked the ele-ment of time to set the stage for thetests of causality. Analyses of therelationships between the anteced-ents and spring 2001 workhourswere interpretable in terms of theantecedents as causal influences ofworkhours. Similarly, analyses of therelationships between the anteced-ents and spring 2001 workhours, onone hand, and the outcome measureson the other, were interpretable interms of the former as causal influ-ences of the outcome measures.

As with most working environ-ments, however, workhours were nota constant at ITEC during the two-year study period. The correlation ofthe studys measures of workhoursfor the April to June and August toOctober 2001 periods (describednext) was 0.68, a result indicative ofconsiderable flux at the employeelevel. The companys changing pro-duction requirements and its policiestoward overtime and part-time workschedules had the net effect of gen-erating notable variations in totalhours from week to week for sub-stantial numbers of individual em-ployees. While the database did nothave a complete measurement spec-ification for assessing the effects ofthis variation, it did have a second setof workhour measures referencingAugust to October 2001 (herein re-ferred to as fall 2001). This secondmeasure set was available to com-pare with spring 2001 workhours,thereby facilitating a look at whetherchanges in workhour exposure up to

halfway through the twelve-monthobservation period affected the con-clusions reached when workhourswere assessed at the beginning of theobservation period.

Workhours. The variables used forthe ensuing analyses (Table 2) in-cluded two sets of blended measuresthat assessed workhours, one ofwhich referenced April to June 2001and the other of which referencedAugust to October 2001. These mea-sures drew on two sources: 1) thecompanys payroll database and 2)self-reports in response to the ques-tion How many hours do you workin a typical workweek? that wasincluded in employee surveys thatwere fielded in May 2001 and againin September 2001. The blendedscoring for each was based on therecommendation of a prior validationstudy,18 which had determined thatthe following combinations providedthe best measures of workhoursavailable in this database:

for spring 2001: the weekly aver-age of total (regular plus overtime)payroll hours for AprilJune 2001for all hourly and non-exempt em-ployees, and self-reports on theMay survey for salaried/exemptemployees.

for fall 2001: the weekly averageof total (regular plus overtime)payroll hours for August to Octo-ber 2001 for all hourly and non-exempt employees, and self-re-ports on the September survey forsalaried/exempt employees.

TABLE 1Sample Characteristics: Responder Versus Non-Responder Comparisons

May 2001 SurveyLongitudinal Sample (May 2001 &

September 2001 Surveys)

Responder(n 4504)

Non-Responder(n 5656) P

Responder(n 2746)

Non-Responder(n 6865) P

Age, yrs 46.7 47.2 0.01 46.3 47.1 0.00Male 80.0% 84.9% 0.00 79.8% 84.1% 0.00Prob of 1 DISABILITY EPISODES,May 2000April 2001

11.6% 10.7% 0.14 11.2% 10.4% 0.26

Prob of 1 WORKERS COMPEPISODES, May 2000April 2001

9.9% 9.1% 0.14 9.5% 9.0% 0.42

Prob probability.


In each case, the prior studyshowed that the three-month timeframe was most congruent with whatrespondents had in mind when an-

swering the self-report survey itemon workhours.

The blended nature of these mea-sures gave rise to the possibility of

method effects; ie, the injection ofartifactual variation due to differ-ences in the psychometric perfor-mance (eg, reliability) of the two

TABLE 2Study Variables: Longitudinal Sample (n 2746)

Variable Label Time of Measure Range Mean SDMeaning ofHigh Score

WorkhoursWorkhours per week: spring WORKHOURS: S 46/01 199 43.8 8.2 More hra) 40 wkhrs per wk 40 WKHRS: S 46/01 01 27.2 (%) 44.5 Averages 40 hrsb) 40 wkhrs per wk 40 WKHRS: S 46/01 01 14.7 (%) 35.5 Averages 40 hrsc) 40.0148 wkhrs per wk 40.0148 WKHRS: S 46/01 01 36.1 (%) 48.9 Averages 40.0148 hrsd) 48.0159.99 wkhrs per wk 48.0159.99 WKHRS: S 46/01 01 17.5 (%) 35.9 Averages 48.0159.99

hrse) 60 wkhrs per wk 60 WKHRS: S 46/01 01 4.5 (%) 20.8 Averages 60 hrs

Workhours per week: fall WORKHRS: F 810/01 999 44.4 8.3 More hrWorkhours per week: fall-spring WORKHRS: FS (810/01)(46/01) 59 to 59 0.58 6.7 Increase in hr from

spring to fallDemographicsAge AGE 4/01 2174 46.3 10.9 OlderMale MALE 4/01 01 79.8 (%) 40.2 Employee is male

Job characteristicsHourly (vs salaried) HOURLY 4/01 01 0.64 0.48 HourlyExempt (from overtime) EXEMPT 4/01 01 0.17 0.37 ExemptSkilled job type SKILLED JOB 4/01 01 0.12 0.33 Has skilled jobProduction job type PRODUCTION JOB 4/01 01 0.52 0.50 Has production job

Prior work injuries/illnessesPrior workers compensation

episodesPRIOR WCOMPEPISODES(S)

5/01/004/30/01 05 0.12 0.42 More episodes

a) Prior acute/trauma injuries PRIOR ACUTE INJUR(IES) 5/01/004/30/01 03 0.05 0.24 More injuriesb) Prior musculoskeletal

injuriesPRIOR MUSCULOINJUR(IES)

5/01/004/30/01 03 0.04 0.22 More injuries

c) Prior other injuries PRIOR OTHER INJUR(IES) 5/01/004/30/01 03 0.04 0.22 More injuriesPrior short-term disability

episodesPRIOR DISABILITYEPISODE(S)

5/01/004/30/01 03 0.13 0.40 More episodes

Prior healthPrior physical health PRIOR PHYSICAL HEALTH 5/01 14.369.9 51.4 8.5 Better healthPrior mental health PRIOR MENTAL HEALTH 5/01 6.566.8 50.4 8.7 Better healthPre-existing diseases PRIOR DISEASE COUNT 5/01 018 1.8 2.0 More diseases

Health risk behaviorsHealth risk: being overweight OVERWEIGHT 5/01 01 30.3 (%) 45.9 At riskHealth risk: smoking SMOKING 5/01 01 18.9 (%) 39.2 At risk

Health and productivity outcomesPhysical health outcomes PHYSICAL HEALTH

OUTCOMES9/01 8.570.5 51.5 8.4 Better health

Mental health outcomes MENTAL HEALTHOUTCOMES

9/01 9.968.9 50.1 8.8 Better health

Disease count outcomes DISEASE COUNTOUTCOMES

9/01 014 1.7 2.0 More diseases

Acute/trauma injuries ACUTE INJURY OUTCOMES 5/01/014/30/02 02 0.06 0.26 More injuriesMusculoskeletal injuries MUSCULO INJURY

OUTCOMES5/01/014/30/02 03 0.05 0.24 More injuries

Other injuries OTHER INJURY OUTCOMES 5/01/014/30/02 02 0.01 0.12 More injuriesShort-term disability episodes DISABILITY EPISODE

OUTCOMES5/01/014/30/02 04 0.142 0.206 More episodes

Presenteeism/non-physicalOverall effectiveness atwork

OVERALL WORKEFFECTIVENESS

9/01 0100 14.2 20.6 More limitation

Time/mental work demands TIME/MENTAL DEMANDS 9/01 0100 17.2 26.0 More limitationOutput work demands OUTPUT DEMANDS 9/01 0100 18.1 26.0 More limitation

Presenteeism/physicalAbility to bend and twist PRESENTEEISM/PHYSICAL

OUTCOMES9/01 0100 31.1 34.1 More limitation


sources. To control for this possibil-ity, initial testing included a dummyvariable reflecting the data sourcetapped to measure workhours (0 survey; 1 payroll). The final mod-els at each stage, however, droppedthis dummy variable because of itsmulti-collinear correlation with theexempt from overtime measure (ap-proximately 0.80).

Antecedents. Five categoriescomprised the predictor/controls:demographics (age and gender),job characteristics (compensationand job type), prior work-relatedillnesses and injuries, prior health(health status and disease), andhealth risks (smoking and over-weight). The demographic and jobcharacteristic data were drawn fromITECs payroll database. Separatecounts of work-related acute, muscu-loskeletal, and other injuries as wellas of disability episodes incurredduring the prior year were developedfrom the companys workers comp/disability database. The health andhealth risk behavior data came fromthe May survey. The health measuresincluded a count of pre-existing dis-eases reported in response to a 21-condition checklist adapted from theMedical Outcomes Study19 andscales of prior physical health andmental health formed from surveyresponses to four items taken fromthe SF-36 Health Status survey.20The physical and mental health sta-tus measures referenced the fourweeks prior to the May survey.

Outcomes. This set included 11health, safety and productivity mea-sures. Three health measures camefrom the 9/01 survey: two assessedphysical health and mental healthduring the prior four weeks and werebased on a repeat assessment of thesame four health status items appear-ing in the May survey, while theother was a repeat count of the samelist of chronic conditions. To obtainthe measures of physical and mentalhealth at the two time points, a modi-fied regression estimate technique withan errors-in-variables correction wasused.21 The imputations utilized as

norms for the calculations a series ofmeasures taken from an in-house ac-tive employee database developed inprevious survey work with a group ofemployers (ie, the Xerox, DigitalEquipment and GTE Corporations22).

Four safety measures includedcounts of any short-term disabilityincidents or any acute, musculoskel-etal, or other work injuries incurredduring the period, May 1, 2001 toApril 30, 2002. In addition, six self-reports from the second (September2001) survey were scored to capturehealth-related limitations experi-enced during the four weeks prior tothe survey and herein classified asmeasures of presenteeism (ie, im-paired on-the-job work performance).The non-physical measures in this cat-egory included four items drawn fromthe Work Limitations Questionnaire tocapture health impact on the time,mental/physical and output demandsof work23 and a fifth drawn from theHealthy People Survey24 to rate healthimpact on overall effectiveness atwork. The physical measure in thiscategory included a fifth measuredrawn from the WLQ to capturehealth-related difficulties with bendingand twisting at work.

While measures of absenteeismwere available, they tracked time lostonly for hourly employees. Sincethey omitted those on salary, theywere excluded from the analysis.

Stage 1The first stage of analysis began

with ordinary least squares regres-sion models to probe the relation-ships between workhours and healthand productivity outcomes at the ag-gregate level. These models featuredthe nine outcome measures as thedependent variables and the springmeasure of workhours in its contin-uous form as the independent vari-able. One set of models examined thespring workhour measures and in-cluded terms reflecting the change inworkhours from spring to fall. Theseterms tested whether lengthening theexposure period altered the conclu-sions reached when the spring work-

hour measures were consideredalone. Another set of models in-cluded the polynomial square ofworkhours to test for non-linearity.

The next step used spline regressiontechniques to check for changes in theslope of the outcome/workhours rela-tionship at different levels of work-hours.25 This procedure includedterms operationalizing knots (ie, pre-dicted flexpoints for changes in slope)in the workhour distribution at 40, 48,and 60 hours. Each of these knotsreflected a meaningful shift vis-a`-viswork or the workhour literature. Forexample, the shift from less than 40hours to 40 hours signified the transi-tion from less than full-time to full-time, whereas the shift from less than60 hours to 60 hours or more signifiedthe transition from moderate overtimeto extended hours.

A first set of spline regressionscompared models positing linearpiecewise terms only versus modelsthat added higher order (eg, squared)terms. These tests focused on the fitof the linear piecewise model and theextent to which the higher orderterms improved this fit. Then, selectedmodels were re-run with controls fordemographics, job characteristics,prior health, and prior work-relatedinjuries/illnesses to assess the robust-ness of the workhour effect. All stage1 analyses were run using regressionprocedures in the STATA statisticalprogram.26

Stage 2Treatment of Workhours. Pivotal

to the indirect and direct tests of thesecond stage was the use of a dummyvariable scoring procedure to opera-tionalize workhours. This procedurerequired the identification of two-level (ie, 0,1) variables to stratify thebase sample into different workhourgroupings. It also required the iden-tification of a holdout group againstwhich these groupings were to becompared.

Informing our decision in this re-gard was the above-mentioned com-pany norm that in effect made 48hours the top end of the range of the


typical workweek for many employ-ees. This norm suggested that the con-ventional full-time norm of 40 hoursper week needed to be expanded inthis context. This distinction was sup-ported by the not significant result(F(1,2419) .85; P 0.36) obtainedwhen two terms, working 40 hours(0,1) and working 40.01 to 48 hours(0,1), were compared in the joint pre-diction of the studys outcome mea-sures controlling for demographics,prior health, prior injuries/illnesses, jobtype and compensation type. Accord-ingly, the stage 2 comparisons featuredthree dummy variables designed tostratify the sample by workhours, spe-cifically those working less than 40hours, 48.01 to 59.99 hours and 60 orgreater hours, respectively. These vari-ables specified those employees work-ing 40 to 48 hours as the holdoutgroup.

Table 3 describes and comparesthe four workhour groups. As shown,relative to the benchmark (the 4048hour holdout group), the two over-time groups were older and morelikely to be male and exempt fromovertime. They reported better men-tal health and were less likely to

smoke but were also prone to reportmore diseases. Employees workingless than 40 hours, in contrast, dif-fered sharply on several of thesemeasures. They too were older thanthe benchmark but were also morelikely to be female and have hourly,non-exempt jobs. They also reportedpoorer prior health, more prior work-related injuries and illnesses and ahigher prevalence of smoking.

Testable Model. The three dummyworkhour variables were placed inthe center of a three-step causalstructure hypothesized to guide test-ing for the second stage. They werepredicted by, on the left, demograph-ic/job/health characteristics that wereclearly antecedent to or contiguouswith the workhour measures. Theypredicted, placed to the right, healthand productivity outcome measuresthat spanned timeframes clearly sub-sequent to the workhour measures.

This structure specified that work-hours could affect health and produc-tivity outcomes directly as well asmediate the indirect effects of ante-cedent demographic/job/health char-acteristics on health and productivityoutcomes. It also posited direct ef-

fects that bypassed workhours bylinking antecedent characteristics tothe subsequent outcome measures. Itfurthermore allowed all exogenousor independent variables (ie, vari-ables not predicted by other variablesin the model) to correlate. Likewise,it allowed the error terms associatedwith the workhour measures posi-tioned as dependent variables in themiddle step to correlate and, as well,the error terms for all outcome vari-ables positioned as ultimate depen-dent variables in the third step tocorrelate.

Statistical Program. The EQS soft-ware programnotable among thevarious structural equation programsavailable for the simplicity and userfriendly approach it brings to the an-alysis and reporting of inherentlycomplex modelswas used for theseanalyses.27,28 For each proposed struc-ture, EQS used multivariate analysisto estimate simultaneously the pa-rameters of a set of measurementequations (each specifying the predic-tion of a measured variable from othermeasured variables, latent variables,and an error variance) and a set ofconstruct equations (each specifying

TABLE 3Workhour Groups by Predictor Variables

Predictor

60WKHRS: S(n 124) F P

DemographicsAge 47.79 44.44 48.69 49.62 30.62 0.00Male 75.50 77.72 89.80 89.52 17.07 0.00

Job characteristicsHourly 97.05 48.57 58.87 50.81 209.00 0.00Exempt 1.07 18.05 30.90 42.74 96.30 0.00Skilled job 12.05 13.32 10.02 13.78 1.29 0.28Production job 53.41 52.8 50.52 47.58 0.71 0.55

Injuries/illnessesPrior disability episode(s) 22.22 10.53 8.35 8.06 18.19 0.00Prior acute injur(ies) 6.96 3.30 5.22 2.42 4.19 0.01Prior musculo injur(ies) 7.36 2.72 2.51 0.00 9.97 0.00Prior other injur(ies) 1.60 0.79 0.84 0.00 1.62 0.18

HealthPrior physical health 50.74 51.52 51.52 52.48 2.27 0.08Prior mental health 49.61 50.397 50.02 52.17 4.54 0.00Prior disease count 2.11 1.64 1.80 1.74 8.98 0.00

Health risk behaviorsOverweight 29.27 29.94 30.82 37.50 1.15 0.33Smoking 24.04 17.54 16.91 11.86 6.47 0.00


the prediction of a latent variable orfactor from measured variables, otherlatent variables and a residual variance).These parameter estimates generateda predicted pattern of inter-correla-tions among the measured variables,which was compared to the observedpattern of inter-correlations. Sum-mary test statistics were computed toallow comparative inferences as tohow faithfully each model repre-sented the sample data.

Analytic Sequence. The Appendixprovides the input correlation matrix,calculated on the aggregate sample,for the variables used for the modelbuilding sequence for this secondstage. The sequence began with across-validation procedure that ran-domly split the sample into two halfsamples. All steps for identificationof a fully specified model (M1) wereconducted on one of these samples.The resulting specification was thenreplicated on the other half sample.This procedure served to guardagainst capitalization on chance,since the test on the second samplewas free of any bias that may haveresulted from ad hoc modification onthe first sample.

The next steps focused on the en-tire sample. First, the null model ofcomplete independence specifyingno freed parameters was calculated.As the most restricted case, thismodel provided a maximum 2against which the gains in informa-

tion with subsequent less restrictedmodels could be evaluated. Then,starting with the measurement andconstruct equations for the proposedcausal structure and then improvinggoodness of fit by freeing and/orfixing parameters where lack of fitwas indicated, the full model wasidentified. Only improvements thatmaintained the integrity of the under-lying theoretical formulation wereallowed in a process that continueduntil the statistical standard of ac-ceptable fita not significant 2was attained. In contrast to the nullmodel, this full model provided aminimum 2 against which losses ininformation sustained by more re-strictive models could be assessed.

Then, various sets of parameterswere dropped and the model re-estimated. Each of these modifica-tions yielded a nested variant of thefull model. The nested sequence al-lowed 2 difference tests to be cal-culated to ascertain the contributionof the parameters to the full model.For these tests, the null hypothesiswas proposed, stating that the givenparameter(s) were not present in theproposed causal structure. A differ-ence 2 corresponding to a probabil-ity level of less than 0.05 was takenas sufficient reason to reject the nullhypothesis. The a priori hypothesesguiding these changes (as opposed topurely post hoc goodness of fit-driven considerations) precluded

bias from affecting the estimates ofthese 2 differences.

The Bentler-Bonett normed fit in-dex was utilized to assess thechanges in overall goodness of fitresulting from these modifications.29This index is an incremental fit sta-tistic varying from 0 to 1, which isfree from biases resulting from largesample size and number of variablesthat normally hinder the 2 good-ness-of-fit statistic. It enabled eachof the models of interest to be com-pared to the null model of completeindependence, thereby allowing infer-ences about extent of information gainto be made across all models, whethersignificant or not significant.

All steps for this second stageinvoked the pair-wise deletion fortreatment of missing values. Thismethod computed correlations basedon all available cases that had scoreson pairs of variables, ie, no caseswere eliminated and no score impu-tation was done.

Results

Stage 1Tests of Linearity. Table 4 exam-

ines the relationships between work-hours and outcomes when no priordemographic, job or health charac-teristics were controlled. As shown,these tests found that eight of the 11outcome measures produced maineffect terms for workhours that either

TABLE 4Health, Safety, and Productivity Outcomes as a Function of Workhours: Linear and Curvilinear Tests

Workhours Workhours Squared

b t P b t P

Physical health outcomes 0.0509 2.08 0.04 0.0012 1.46 0.15Mental health outcomes 0.1453 5.70 0.00 0.00170 2.07 0.04Disease count episodes 0.0041 0.71 0.48 0.0002 1.03 0.30Disability episode outcomes 0.0046 3.54 0.00 0.0001 1.69 0.09Acute injury outcomes 0.0005 0.60 0.55 0.0000 0.33 0.74Musculo injury outcomes 0.0016 2.30 0.02 0.0000 0.65 0.52Other injury outcomes 0.0007 1.83 0.07 0.0000 0.86 0.39Presenteeism/non-physicalOverall work effectiveness 0.2391 3.95 0.00 0.0049 1.98 0.05Time/mental demands 0.1781 2.30 0.02 0.0035 1.43 0.15Output demands 0.2184 2.84 0.00 0.0049 1.98 0.05

Presenteeism/physical bend & twist 0.1213 1.19 0.23 0.0003 0.10 0.92


exceeded or approximated the con-ventional significance criterion ofP 0.05: PHYSICAL and MENTALHEALTH; DISABILITY EPISODEOUTCOMES, MUSCULO INJURYand OTHER INJURY OUTCOMES,OVERALL WORK EFFECTIVE-NESS, and TIME/MENTAL andOUTPUT DEMANDS. Bearing inmind that better outcomes werescored high on the health measuresand low on the productivity mea-sures, the sign of the unstandardizedbeta coefficient expressing the maineffect term in all but one of thesecases (ie, OTHER INJURY OUT-COMES) linked greater hours withbetter outcomes. Thus, as hours in-creased, employees reported bettermental and physical health, fewerdisability episodes and musculoskel-etal injuries, and less limitation intheir capacity to work effectively andto meet time/mental and outputdemands.

However, for six of the outcomemeasures, the square of workhoursalso produced terms that either ex-ceeded or approximated significance.In each case, the effect of the squared

term was in the opposite direction.These results thus provided initialevidence of non-linearity in over halfof the workhour/outcome measurepairs.

Functional Form. Table 5 delvesdeeper into the non-linearity issue bycomparing the fit of three splinemodels on the outcome measures:linear piecewise, squared, and cubic(following testing convention, thehigher order spline models containedincluded all lower order effects: eg,the squared model also contained thelinear piecewise terms). The resultsshowed two general patterns. In one,the linear piecewise spline modelprovided the best (ie, most parsimo-nious) fit and could not be improvedupon by the other alternatives. Thisheld for five measures: MENTALHEALTH OUTCOMES, DISABIL-ITY EPISODE OUTCOMES, MUS-CULO INJURY OUTCOMES,OVERALL WORK EFFECTIVE-NESS, and TIME/MENTAL DE-MANDS. In virtually all of thesecases, while the F statistics stayedsignificant as the higher order termswere added, the corresponding R

squared terms indicated that in noinstance did the squared or cubicspline terms add appreciably to theamount of variance explained.

Figure 1 reports on DISABILITYEPISODES to depict the generalfunction that emerged with this pat-tern when statistical controls weremade for all antecedent measures.The 95% confidence intervals aregiven for the averages within thefollowing hour categories: less than30, 30 to 40, 40 to 50, 50 to 60, 60 to70 and greater than 70. This functionlinked workhour impact with nega-tive outcomes at the low end ofworkhours. As work hours increased,the impact of workhours was linkedwith increasingly more positive out-comes up to the knot at 40 hours.From there to the highest levels ofworkhours, the impact of workhoursexerted little change. Statisticallyspeaking, there was little evidence thatoutcomes either notably worsened ornotably improved as hours increasedbeyond the 40-hour flexpoint.

The width of the confidence inter-val for the below 30 average meantthat it was possible that the no

TABLE 5Health, Safety, and Productivity Outcomes as a Function of Spring 2001 Workhours: Comparison of the Fit of AlternativeSpline Slope Models

Spline Workhour Model

Linear Piecewise Linear/QuadraticLinear/Quadratic/

Cubic

F R2 F R2 F R2

Physical health outcomes 1.82 0.003 1.37 0.004 1.41 0.005Mental health outcomes 9.90* 0.014 5.58* 0.014 4.28* 0.016Disease count outcomes 1.32 0.002 1.25 0.003 1.29 0.005Disability episode outcomes 6.00* 0.009 4.96* 0.013 3.50* 0.013Acute injury outcomes 1.80 0.003 1.63 0.004 2.28* 0.008Musculo injury outcomes 1.85 0.003 2.30* 0.006 1.70 0.006Other injury outcomes 1.88 0.003 1.48 0.004 1.27 0.005Presenteeism/non-physical outcomesOverall work effectiveness 6.58* 0.010 6.19* 0.016 4.78* 0.017Time/mental demands 3.46* 0.005 2.03* 0.005 1.93* 0.007Output demands 4.55* 0.007 3.11* 0.008 2.78* 0.010

Presenteeism/physical outcomesBend & twist 1.90 0.003 1.20 0.003 1.55 0.006

*P 0.05.Predictor model: Main effect; Spline:40 hrs; Spline:48 hrs; Spline:60 hrs.Predictor model: Main effect; Spline:40 hrs; Spline:48 hrs; Spline:60 hrs; Spline:40 hrs2; Spline:48 hrs2; Spline: 60 hrs2.Predictor model: Main effect; Spline:40 hrs; Spline:48 hrs; Spline:60 hrs; Spline:40 hrs2; Spline:48 hrs2; Spline:60 hrs2; Spline:40 hrs3;

Spline:48 hrs3; Spline:60 hrs3.


change in slope finding above 40hours could have extended down be-low 40 hours as well, essentiallymaking for a straight line in the slopeacross the workhour continuum. Thatsuch a function could only have oc-curred at the extreme low end of theconfidence interval, however, madeit a very low probability event.Moreover, either way, this patternsharply contrasted with what wouldhave been expected with the Dembeet al. approach. The rate of adverseoutcomes did not consistently in-crease as workhours increased, nordid the slope of the function gothrough zero when workhours werezero.

In the second pattern, the highestorder spline (ie, cubic) model nota-bly improved the fit of the lowerorder variants. Specifically, in thecontext of the cubic model forACUTE INJURY OUTCOMES, thesquared spline term at 60 hoursproved significant. The change inslope this term captured enabled thefit of the overall model to improvefrom non-significance for the lowerorder spline variants to statistical sig-nificance for the cubic model.

Figure 2 portrays the result. Incontrast to Fig. 1, here the mostnegative outcomes emerged towardthe upper end of the workhour con-tinuum. The critical knot occurred

at 60 hours; that is, below 60 hoursthe injury rate was negligible,whereas at the 60-hour mark, theinjury rate increased, with the rateitself steadily increasing (reflectingthe squared function) until cresting atabout 80 hours per week. Beyond 80hours, the rate turned downward andculminated in the vicinity of the ratesobserved at the lowest end of theworkhour continuum, a shift thatcould well have been due to outliers.The only employees reporting aver-age workweeks above 80 hours were15 salaried workers reporting 99hour weeks. Their position as poten-

tial outliers left open the possibilitythat the return to lower injury rateswas less a function of the number ofhours worked and more a function ofthe nature of work performed (eg,sedentary) by this subgroup.

This anomaly notwithstanding, thegeneral pattern shown in Fig. 2 wasmore in keeping with Dembe et al.The slope of the injury rate did in-deed start at roughly zero with zeroworkhours, and showed a markedincrease by 70 workhours. Althoughthis increase was not linear between0 and 60 hours as posited by Dembeet al. and although it was bracketedby wide confidence intervals that didnot rule out the possibility of a nochange in slope result, it did repli-cate their fundamental finding thatthe probability of an adverse eventincreases as workhours increase.

Lengthening the Exposure Period.Table 6 reports the impact of springworkhours when the change fromspring to fall workhours was addedas an additional control. As a conser-vative check, the baseline score foreach dependent variable was alsoadded as a control. As shown, theeffect of spring workhours was sig-nificant or virtually significant onMENTAL HEALTH OUTCOMES,DISABILITY EPISODES andACUTE and OTHER INJURYOUTCOMES. Of these (as above),

Fig. 1. Workhours and disability episode outcomes.

Fig. 2. Workhours and acute injury outcomes.


only the two injury effects linkedincreasing workhours to worseningoutcomes.

Similarly, the effect of spring/fallchange in workhours was significantor virtually significant on PHYSI-CAL HEALTH OUTCOMES,DISABILITY EPISODES, TIME/MENTAL DEMANDS, and theBEND & TWIST measure. In noneof these instances, however, did theeffect of change in workhours linkincreasing workhours to worseningoutcomes. Moreover, on none of theeleven outcome measures did theworkhour change effect prove signif-icantly different from the springworkhour effect. As such, the addi-tion of the workhour change mea-surewhile producing intriguingfindings that merit future research inits own rightdid not materiallyalter the results.

Stage 2Model Development. To shed fur-

ther light on these relationships, thethree-step structure used to guidemodel building in stage 2 specified15 antecedent variables, three work-week hour dummy variables, and ninehealth safety and productivity out-comes. Figure 3 identifies these vari-ables and depicts the hypothesized

structural parameters linking themwith single-headed arrows. It employsconventional modeling notation to des-ignate those measures treated as ob-served variables in rectangles andthose treated as latent factors in ovals.

As shown, the measurement portionof this structure treated all measures asseparate observed variables with oneexception. A latent factor, labeledPRESENTEEISM (Non-physical),was defined and confirmed to underlieOVERALL WORK EFFECTIVE-NESS, OUTPUT DEMANDS andTIME/MENTAL DEMANDS. Sepa-rate factor analyses confirmed that theremaining productivity self-report, thePRESENTEEISM (Physical) itemBEND & TWIST, was best treated asa separate observed variable.

The causal portion of this structurespecified a total of 27 (ie, 3 9)paths to test the direct effects of eachworkhour variable on each outcomevariable; a total of 12 paths to reflectthe effects of prior work injuries/illnesses on workhours and 36 pathsto reflect the impact of prior injurieson the outcomes; and a total of 33paths to test the impact of demo-graphic/job/health antecedents onworkhours and another 99 paths forthe direct impact of these anteced-ents on outcomes. Our tests exam-

ined these linkages by evaluatingresults at the level of individual ef-fects (conducted by comparing pa-rameter estimates) and at the level ofgroups of effects (conducted by com-paring nested models).

The steps taken to build the modelculminated in the specification forthe full workhour model (M1) thatwas successfully fit on the first oftwo random half-samples and cross-validated on the second (Table 7).Each half-sample recorded a not sig-nificant overall 2 goodness of fitstatistic and a near perfect (0.99)normed fit index. With this cross-validation in hand, the two half sam-ples were recombined to maximizethe subjects-to-(freed) parameter ra-tioover 7 to 1 in the aggregateanalytic sample.

Parameter Interpretation. Table 8delivers all parameter estimates in-cluded in the prediction of eachworkhour measure in M1. Table 9delivers all parameter estimates in-cluded in the prediction of each ofM1s nine outcome measures (themodels ultimate dependent vari-ables). These estimates are providedin unstandardized form, along withstandard errors and significance testinformation. Figures 4 to 6 give thestandardized beta estimates for the

TABLE 6Health, Safety, and Productivity Outcomes as a Function of Change in Workhours: Testing the Effects When the ExposurePeriod is Lengthened

Workhours: Spring Workhours: FallSpringSlopes Spring vs(FallSpring)

Beta t P Beta t P F P

Physical health outcomes 0.0067* 0.35 0.73 0.0354 2.24 0.05 2.63 0.10Mental health outcomes 0.0500 2.56 0.01 0.0098 0.53 0.60 2.69 0.10Disease count episodes 0.0161 1.09 0.27 0.0132 0.95 0.34 3.43 0.06Disability episodes 0.0335 1.51 0.13 0.0386 1.83 0.07 0.27 0.60Acute injury outcomes 0.0525 2.28 0.02 0.0280 1.04 0.30 0.80 0.37Musculo injury outcomes 0.0026 0.12 0.91 0.0073 0.34 0.74 0.18 0.67Other injury outcomes 0.0604 2.60 0.01 0.0307 1.39 0.17 0.16 0.68Presenteeism/non-physicalOverall work effectiveness 0.0200 0.93 0.35 0.0062 0.31 0.76 0.24 0.62Time/mental demands 0.0359 1.61 0.11 0.0392 1.86 0.06 0.20 0.65Output demands 0.0052 0.24 0.81 0.0093 0.44 0.66 0.05 0.82

Presenteeism/physical bend & twist 0.0189 0.81 0.42 0.0498 2.26 0.02 2.38 0.13

*All numeric entries adjusted for the baseline score for the dependent variable in question as well as age, gender, job class (salaried, payroll),job type (skilled, production), exempt status, baseline health status (physical, mental, disease count), prior work-related disability episodes andinjuries (acute, musculoskeletal, other), and health risk (overweight, smoking).


key structural parameters from thesetwo tables. Figure 4 summarizes theresults for the direct and indirecteffect tests associated with the ante-cedent and workhour measures in-volved in the prediction of the threehealth outcomes, while Figs. 5 and 6give the parallel results for the foursafety and two productivity out-comes, respectively. The estimatesfor the three figures come from thestandardized solution; only those that

were significant (P 0.05) are in-cluded. Table 10 provides the resultsof the 2 difference tests that werecomputed to assess the significanceof the contributions made by the keysets of parameters in M1.

Of the two overtime measures,working 60 hours led to the onsetof one or more new diseases and to agreater likelihood of at least oneacute or other work injury. Working60 hours, in turn, was predicted by

age, compensation status and over-weight: older employees, with exemptstatus and who were overweight wereall more likely to be working ex-tended overtime (Table 9 and Figs. 4and 5). Moderate overtime (ie,48.0159.99 hours), on the otherhand, did not come close to register-ing a significant association with anyhealth, safety or productivity out-come. In contrast, working less than40 hours predicted poor mental

TABLE 7Cross-Validation of Workhour Causal Model

Description

First Half Second Half

2 df P BBN* 2 df P BBN*

Null model of independence 7783.3 406 7160.1 406 Full hypothesized model 62.7 47 0.06 0.992 58.7 47 0.12 0.993

*Bentler-Bonett Normed Index.

Fig. 3. The workhours causal model: full specification (M1).


TABLE 9Health, Safety, and Productivity Outcomes Predicted by Workhours and Prior Personal, Work, and HealthCharacteristics in M1

Predictor

Outcomes

Overall Physical Health Overall Mental Health Disease Count

b SE z b SE z b SE z

Workhours40 wkhrs 0.012 0.033 0.364 0.088 0.036 2.427* 0.093 0.063 1.47348.0159.99 wkrhrs 0.006 0.036 0.164 0.030 0.039 0.761 0.021 0.068 0.31160 wkrhrs 0.001 0.063 0.021 0.121 0.069 1.762 0.246 0.119 2.067*

DemographicsAge 0.044 0.013 3.306* 0.049 0.014 3.420* 0.137 0.025 5.561*Male 0.004 0.033 0.109 0.095 0.036 2.637* 0.122 0.062 1.958

Job characteristicsHourly 0.153 0.039 3.951* 0.119 0.042 2.853* 0.080 0.072 1.112Exempt (overtime) 0.026 0.044 0.597 0.017 0.047 0.368 0.063 0.082 0.775Skilled job 0.024 0.042 0.557 0.004 0.046 0.080 0.037 0.079 0.470Production job 0.036 0.027 1.330 0.008 0.030 0.266 0.027 0.052 0.520

Injuries/illnessesPrior acute injuries 0.026 0.053 0.486 0.068 0.057 1.181 0.177 0.100 1.778Prior muscul injuries 0.028 0.061 0.465 0.029 0.065 0.451 0.004 0.112 0.032Prior other injuries 0.034 0.128 0.263 0.055 0.139 0.397 0.156 0.241 0.647Prior disability episodes 0.035 0.034 1.031 0.051 0.036 1.406 0.141 0.063 2.244*

HealthPrior physical health 0.450 0.017 26.893* 0.121 0.018 6.704* 0.101 0.031 3.253*Prior mental health 0.124 0.016 7.981* 0.471 0.017 28.142* 0.088 0.029 3.052*Prior diseases 0.046 0.008 6.034* 0.045 0.008 5.494* 0.699 0.014 49.905*

Health risk behaviorsOverweight 0.105 0.029 3.646* 0.051 0.031 1.647 0.087 0.053 1.626Smoking 0.019 0.033 0.563 0.071 0.036 1.983* 0.024 0.062 0.394

(Continued)

TABLE 8Workhours Predicted by Prior Personal, Work, and Health Characteristics in Full Model (M1)

Predictor

60 Wkhrs

b SE z b SE z B SE z

DemographicsAge 0.014 0.008 1.760 0.032 0.007 4.370* 0.013 0.004 3.234*Male 0.124 0.020 6.321* 0.097 0.018 5.328* 0.019 0.010 1.835

Job characteristicsHourly 0.408 0.021 19.166* 0.030 0.020 1.506 0.008 0.011 0.777Exempt (overtime) 0.001 0.025 0.037 0.207 0.023 8.819* 0.091 0.013 6.999*Skilled job 0.008 0.025 0.318 0.052 0.023 2.248* 0.002 0.013 0.126Production job 0.009 0.017 0.542 0.023 0.015 1.478 0.006 0.008 0.758

Injuries/illnessesPrior acute injuries 0.008 0.032 0.249 0.051 0.030 1.731 0.004 0.016 0.225Prior muscul injuries 0.077 0.036 2.156* 0.012 0.033 0.363 0.019 0.018 1.025Prior other injuries 0.056 0.077 0.730 0.008 0.071 0.116 0.035 0.040 0.879Prior disability episodes 0.038 0.020 1.900 0.040 0.018 2.146* 0.003 0.010 0.308

HealthPrior physical health 0.013 0.010 1.311 0.002 0.009 0.178 0.007 0.005 1.388Prior mental health 0.016 0.009 1.777 0.000 0.009 0.037 0.006 0.005 1.369Prior diseases 0.008 0.004 1.904 0.001 0.004 0.307 0.000 0.002 0.143

Health risk behaviorsOverweight 0.037 0.017 2.207* 0.000 0.016 0.008 0.018 0.009 2.025*Smoking 0.014 0.020 0.713 0.007 0.018 0.406 0.010 0.010 1.001

*P 0.05.


TABLE 9Continued

Predictor

Outcomes

Acute Injuries Musculoskeletal Injuries Other Injuries


Workhours40 wkhrs 0.016 0.013 1.270 0.010 0.012 0.858 0.007 0.006 1.17748.0159.99 wkrhrs 0.003 0.014 0.187 0.006 0.013 0.438 0.009 0.007 1.33260 wkrhrs 0.074 0.024 3.037* 0.005 0.022 0.241 0.025 0.012 2.163*

DemographicsAge 0.001 0.005 0.291 0.019 0.005 4.121* 0.001 0.002 0.392Male 0.018 0.013 1.439 0.041 0.012 3.501* 0.014 0.006 2.328*

Job characteristicsHourly 0.078 0.015 5.332* 0.077 0.013 5.762* 0.015 0.007 2.108*Exempt (overtime) 0.011 0.017 0.685 0.007 0.015 0.444 0.007 0.008 0.939Skilled job 0.010 0.016 0.597 0.020 0.015 1.371 0.007 0.008 0.852Production job 0.011 0.011 1.019 0.001 0.010 0.105 0.010 0.005 2.015*

Injuries/illnessesPrior acute injuries 0.015 0.020 0.738 0.063 0.019 3.363* 0.025 0.010 2.548*Prior muscul injuries 0.039 0.023 1.689 0.072 0.021 3.447* 0.046 0.011 4.222*Prior other injuries 0.071 0.049 1.442 0.047 0.045 1.054 0.020 0.024 0.851Prior disability episodes 0.045 0.013 3.537* 0.028 0.012 2.364* 0.005 0.006 0.809

HealthPrior physical health 0.002 0.006 0.266 0.004 0.006 0.680 0.003 0.003 0.964Prior mental health 0.002 0.006 0.273 0.011 0.005 1.970* 0.004 0.003 1.493Prior diseases 0.007 0.003 2.340* 0.008 0.003 3.128* 0.001 0.001 1.011

Health risk behaviorsOverweight 0.009 0.011 0.876 0.003 0.010 0.319 0.004 0.005 0.687Smoking 0.007 0.013 0.578 0.010 0.012 0.844 0.000 0.006 0.014

Disability Episodes Presenteeism (Non-Physical) Presenteeism (Physical)


Workhours40 wkhrs 0.014 0.021 0.666 0.028 0.032 0.873 0.134 0.165 0.81248.0159.99 wkrhrs 0.027 0.023 1.165 0.007 0.034 0.195 0.156 0.179 0.87260 wkrhrs 0.045 0.040 1.124 0.097 0.060 1.616 0.114 0.313 0.364

DemographicsAge 0.018 0.008 2.207* 0.027 0.013 2.050* 0.196 0.065 3.013*Male 0.047 0.021 2.236* 0.021 0.032 0.679 0.060 0.164 0.367

Job characteristicsHourly 0.146 0.024 6.043* 0.193 0.043 4.497* 0.636 0.190 3.349*Exempt (overtime) 0.004 0.028 0.157 0.123 0.048 2.569* 0.285 0.216 1.319Skilled job 0.028 0.027 1.040 0.059 0.040 1.479 0.106 0.208 0.510Production job 0.001 0.017 0.042 0.005 0.026 0.186 0.061 0.136 0.445

Injuries/illnessesPrior acute injuries 0.018 0.034 0.542 0.125 0.051 2.464* 0.208 0.262 0.793Prior muscul injuries 0.011 0.038 0.299 0.110 0.057 1.926 0.028 0.297 0.095Prior other injuries 0.005 0.081 0.064 0.040 0.121 0.331 0.533 0.634 0.841Prior disability episodes 0.170 0.021 8.033* 0.069 0.032 2.156* 0.167 0.166 1.008

HealthPrior physical health 0.034 0.010 3.293* 0.098 0.018 5.612* 0.519 0.082 6.318*Prior mental health 0.005 0.010 0.550 0.159 0.018 8.709* 0.178 0.076 2.334*Prior diseases 0.013 0.005 2.844* 0.013 0.007 1.823 0.117 0.037 3.160*

Health risk behaviorsOverweight 0.009 0.018 0.503 0.056 0.027 2.058* 0.247 0.141 1.750Smoking 0.005 0.021 0.254 0.005 0.031 0.149 0.066 0.163 0.404

*P 0.05.


health outcomes. Working less than40 hours, in turn, was predicted bygender, compensation type and mus-culoskeletal injuries: females whoheld hourly jobs and had a history ofat least one musculoskeletal injurywere all more likely to be workingless than a normal workweek (Table9 and Fig. 4).

Prior disability episodes registeredsignificant direct effects on DiseaseCount Outcomes, Disability EpisodeOutcomes, Acute and MusculoskeletalInjury Outcomes, and Presenteeism/Non-Physical Outcomes: Employeeswho incurred one or more disabilityepisodes in the previous 12 monthswere more likely to record a newdisease as well as a new disabilityepisode and/or an acute or musculo-skeletal injury in the post period (Table9 and Fig. 5). Having had 1 prior

acute and/or 1 musculoskeletal inju-ries, in turn, increased the likelihood ofat least one new musculoskeletal orother work injury in the post period. Inaddition, having at least one prioracute injury was associated with newpresenteeism/non-physical limitationoutcomes (Table 9 and Fig. 6).

Poor prior physical and/or mentalhealth was associated with poorphysical and mental health outcomes,the onset of at least one new disease,the incurral of a new disability episodeand/or new musculoskeletal injury, aswell as new presenteeism limitations(both physical and non-physical) (Ta-ble 9 and Figs. 46). Likewise havingone or more prior diseases led to poorhealth outcomes (physical and men-tal), one or more new diseases, one ormore disability, acute and/or musculo-skeletal injuries, and new presentee-

ism/physical limitations. Being at riskfor overweight, on the other hand,predicted poor physical health out-comes but fewer presenteeism/non-physical limitations.

Model Difference Tests. The model2 difference tests summarizing thesedirect and indirect effects documentedthe influence of workhours at threelevels (Table 10). First, the combinedcontribution of the direct effects asso-ciated with the three workhour mea-sures to the overall model (M1) wassignificant. This was primarily on thestrength of the three effects linking60 workhours to 1 acute and/orother injuries as well as the onset of1 new diseases (see M2BM1 in Ta-ble 10).

Second, the combined direct ef-fects associated with prior work in-juries and illnessesthemselves

Fig. 4. Predicting health outcomes in M1: significant structural equation parameter estimates.


conceivably at least in part the resultof hours worked in the pastonoutcomes were notable (M3M1). Asa set, the linkages tying the four priorwork injury/illness terms directly tooutcomes made a significant overallcontribution to the full model, on thestrength of effects tying prior disabil-ity episodes and prior acute and mus-culoskeletal injuries to one or morework injury/illness outcomes as wellas effects linking prior acute injuriesand disability episodes to poor pre-senteeism/non-physical outcomes.Third, the tests for indirect effectswith workhours serving as mediatorsof antecedent characteristics also ev-idenced the influence of workhours.The three workhour measures com-bined made a significant contributionto M1 as mediators of the indirecteffects of prior work injuries and

illnesses on outcomes (M3AM1).They made an even greater contribu-tion to M1 as mediators of the indi-rect effects of prior health, job anddemographic characteristics thatwere non-work related, adding overfour points on the Bentler-Bonettnormed fit index (M4M1).

However, all of the direct andindirect effects involving workhoursconsidered together were over-whelmed by the combined contribu-tion of the direct effects associatedwith the prior (non-work related)health, job and demographic charac-teristics alone. This was so even whenall effects involving work-related inju-ries/illnesses were ascribed to the im-pact of long workhours. As shown inTable 10, all (direct and indirect) ef-fects involving the workhour termscombined with all effects linking prior

work injuries/illnesses to outcomesproduced a difference on the Bentler-Bonett normed fit indexrelative toM1of 0.074. The direct effectcontribution of the other antecedentsalone contributed nearly four timesthat amount (BBndiff 0.286).

Although it was plausible to havePRIOR PHYSICAL and MENTALHEALTH among the predictors ofPHYSICAL HEALTH OUTCOMESand MENTAL HEALTH OUT-COMES in this model comparison,their inclusion raised the issue of apossible method confound. This con-found derived from the use of thesame items and computation steps toconstruct the before and after ver-sions of these measures which couldhave artificially inflated the intercor-relations. Accordingly, the modeltesting the contribution of the prior

Fig. 5. Predicting safety outcomes in M1: significant structural equation parameter estimates.


health/job/demographic characteris-tics was re-run with the three priorhealth3health outcome paths re-inserted. The resulting 2 test stillproduced a difference on the normedfit statistic vis-a`-vis M1 that was verysimilar to the corresponding statisticwhen all direct and indirect work-hour effects and work-related inju-ries and illnesses were consideredtogether ([M6AM1] versus [M5M1]or 0.062 versus 0.074). When themodel testing the contribution of thework-related injuries and illnesseswas similarly rerun with the priorinjury/illness3injury/illness out-comes paths re-inserted, this differ-ence became virtually equivalent([M6AM1] versus [M5AM1] or0.062 versus 0.069).

DiscussionInvestment in human capital im-

provement has become a core strat-

egy for gaining the competitive edge,and the field of health and produc-tivity is devoting much effort toidentifying and managing those mal-leable factors that limit or inhibitwell-being and functioning in theworkplace.30,31 While most employ-ers and researchers are currently pre-occupied with the burden of disease,long workhours could well become anew priority.

This study sought to break newground bridging the field of healthand productivity with the growingliterature on long workhours, whileat the same time generating anapproach that would advance theunderstanding of workhour impactat ITEC shared by the various par-ties participating in the study. Toremain consistent with the primarythrust of the literature, this ap-proach conducted all testing at theaggregate sample level. The depen-

dent variables included measuresthat encompassed health, safety, andproductivity concerns, not just healthalone. With a couple of minor excep-tions, these measures unambiguouslyfollowed the workhour measures intimethe workhour and dependentvariables were not contiguous in timeas has so often been the case in previ-ous research.

The results for Stage 1 recordedassociations between hours workedand health, safety and productivityoutcomes, but with several provisos.First, the effects detected were notespecially large in their own right,nor were they observed across all ofthe outcomes tested. Second, twofunctional forms characterized therelationship between workhours andoutcomes, depending on the type ofoutcome involved. One of theseforms was consistent with the longworkhour literature in documenting

Fig. 6. Predicting productivity outcomes in M1: significant structural equation parameter estimates.


an increase in adverse events asworkhours increased, although thiseffect was non-linear in slope. Thatis, these tests at the level of theaggregate sample indicated that work

schedules needed to climb wellabove the 40 to 48 hour benchmarkfor this effect to begin to registerwith a slope that itself changed ashours increased a pattern that stood

in contrast to the steadily increasingabove 40 hour relationship reportedin previous work.

In contrast, the other functionalformmore prominent by virtue of

TABLE 10Accounting for Selected Direct & Indirect Effects in the Full Workhour Model (M1)

Goodness of Fit

Model Model 2 df P BBNM0 Null Model of Complete Independence 15261.6 406 M1 Full Model 56.4 47 0.16 0.996M2 M1, w/o Wkhr3Outcome direct paths 109.0 74 0.01 0.993M2A M1, w/o 40 Wkhr3Outcome direct paths 70.9 56 0.09 0.995M2B M1, w/o 48.0159.99 & 60 Wkhr Outcome direct paths 87.3 65 0.03 0.994M3 M1, w/o Prior Wk Injury/Illness3Wkhr3Outcome paths 124.9 86 0.00 0.992M3A M1, w/o Prior Wk Injury/Illness3Wkhr3Outcome paths & Prior Wk

Injury/Illness3Outcome paths282.5 122 0.00 0.981

M4 M1, w/o Antecedent (other than Prior Wk Injury/Illness)3Wkhr3Outcome paths

934.8 107 0.00 0.939

M4A M1, w/o Demo3Wkhr3Outcome paths 210.6 80 0.00 0.986M4B M1, w/o Comp Type3Wkhr3Outcome paths 768.2 80 0.00 0.950M4C M1, w/o Job Type3Wkhr3Outcome paths 115.6 80 0.00 0.992M4D M1, w/o Prior Health3Wkhr3Outcome paths 123.5 83 0.00 0.992M4E M1, w/o Health Risk Behav3Wkhr3Outcome paths 119.3 80 0.00 0.992M5 M1, w/o All paths involving Wkhrs & Prior Wk Injury/Illness3Outcome

paths1184.1 155 0.00 0.922

M5A M5, w/ corresponding Prior Wk Injury/ Illness (4) direct paths reinserted 1115.2 151 0.00 0.927M6 M1, w/o Antecedent (other than Prior Wk Injury/Illness)3Outcome

direct paths4419.3 146 0.00 0.710

M6A M6, w/ corresponding Prior Physical/Mental health & disease (3) directpaths reinserted

1003.1 143 0.00 0.934

Difference Tests

Model Contrast Test of 2diff dfdiff Pdiff BBN-diff

M2M1 All Wkhr direct effects 52.6 27 0.005 0.003M2AM1 40 Wkhr direct effects only 14.5 9 0.100 0.001M2BM1 Overtime direct effects only 30.9 18 0.05 0.002M3M1 All Wkhr direct effects All Prior Injury/Illness3Wkhr effects 68.5 39 0.005 0.004M3AM1 All Wkhr direct effects All Prior Injury/Illness3Wkhr effects All

Prior Injury/Illness3Outcome effects226.1 75 0.001 0.015

M4M1 All Wkhr direct effects All Antecedent (other than Prior Injury/Illness)3Outcome paths

878.4 60 0.001 0.057

M4AM1 All Wkhr direct effects All Demo3Wkhr effects 154.5 33 0.001 0.010M4BM1 All Wkhr direct effects Comp Type3Wkhr effects 711.8 33 0.001 0.040M4CM1 All Wkhr direct effects Job Type3Wkhr effects 59.2 33 0.250 0.004M4DM1 All Wkhr direct effects All Prior Health3Wkhr effects 67.1 39 0.005 0.004M4EM1 All Wkhr direct effects All Health Risks3Wkhr effects 62.9 36 0.025 0.004M5M1 All effects involving Wkhrs All Prior Wk Injury/Illness3Outcome

effects1127.7 108 0.001 0.074

M5AM1 All effects involving Wkhrs All Prior Wk Injury/Illness3OutcomeeffectsPrior Acute Injuries3Acute Injury Outcomes Prior MusculoInjuries3Musculo Injury Outcomes Prior Other Injuries3Acute OtherOutcomes Prior Disability Episodes3Disability Episode Outcomes

1058.8 104 0.001 0.069

M6M1 All Antecedent (other than Prior Wk Injury/Illness)3Outcome effects 4362.9 99 0.001 0.286M6AM1 All Antecedent (other than Prior Wk Injury/Illness)3Outcome

effectsPrior Physical Health3Physical Health Outcomes Prior Men-tal Health3Mental Health Outcomes Prior Disease Count3DiseaseCount Outcomes

946.7 96 0.001 0.062

BBN is Bentler-Bonett normed fit index; Wkhr, workhour; Demo, demographic; Comp, compensation; diff, difference.


its presence across a wider range ofmeasurescontradicted the litera-ture by specifying significantlygreater rates of adverse events at thelow end of the workhour continuum,with this rate decreasing as hoursincreased up until the 40-hour markwhere it stayed steady up through thehigh end of the workhour continuum.Here, employees did not even haveto work the first hour to have anappreciable risk of being linked withan adverse outcome. Stage 2 resultsinstead tied this riskfor those out-comes where it was observedprimarily to antecedent factors whoseorigins preceded any effects stemmingfrom the number of hours worked.These antecedents helped determinethe amount of time worked (frequentlyleading to workweeks of less than 40hours), but appeared unlikely to havebeen influenced or shaped by the latter.

Empirical testing often leads torefinements in theory, and this casestudy supports a call for revisitinghow work schedules are conceivedas affecting employee health, safetyand productivity. Previous studies inthis area have generally shown littlediscrimination in investigating longworkhour effects. The modal ap-proach to assessment has been tanta-mount to hypothesizing workhourimpact as an equal opportunitythreat to which the broad range ofemployees are increasingly andsteadily vulnerable across the spec-trum of workplace outcomes as theirhours increase above the normalworkload. From the proverbial viewat 30,000 feet, the evidence forlong workhour effects amassedacross these studies has been equiv-ocal and inconsistent. This casestudy suggests that this modal ap-proach may have been a contributor.

The results here indicate that theprobability of an adverse eventandthe type of eventinstead differedby the characteristics of the em-ployee and the type of work in ques-tion as well as the number of hoursworked. Working long hours did in-crease the probability of an acute orother work-related injury, but pri-

marily among hourly males with ahistory of disease and disability epi-sodes. Long hours similarly in-creased the likelihood of onset of anew disease diagnosis, but mostlyamong older employees paradoxi-cally in better prior health status butalso with a history of at least onemedical condition. Both sets of char-acteristics became operative as riskfactors only in the presence of work-hours well above the 40 to 48 hourbenchmark.

Previous studies have also rarelyattempted to place the impact of longworkhours in larger context. Yet, ourtesting found that antecedent factorswith arguably little to no link to thenumber of hours worked exerted themajor influence on adverse out-comes, far exceeding the collectiveimpact brought to bear by the entirearray of direct and indirect effectsinvolving workhours. This patternobtained even when prior work inju-ries and illnessesherein treated aslargely a function of past hoursworked which itself may be a gener-ous assumptionwere taken intoaccount, either as direct effects onoutcomes or as indirect effects influ-encing outcomes by way of work-hours. This pattern also obtainedeven when workhours were treatedas mediating influences for the ante-cedents. When considered as directinfluences, the age, gender and jobcharacteristics of employees andwhether or not they had one or morepre-existing diseases, or were at priorrisk for overweight, or had poor priorhealth status figured much moreprominently in the incurral of ad-verse health, safety or productivityevent. This held not only for healthstatus, presenteeism and disabilityoutcomes but also even for work-related injuries and illnesses.

Our results, as such, do not lead tothe conclusion that the number ofhours worked is unimportant as adeterminant of these outcomes. But,they do underscore the need to sup-plant a workforce-wide focus with acarefully calibrated strategy for incor-porating this fundamental feature of

work performance into the broaderhealth, safety and productivity missionat ITEC. The prospects for this strat-egy will be maximized if it predicatedon a relatively precise grasp of whenand under what circumstances longworkhours become detrimental to em-ployee outcomes.

To inform this calibration with thebest information available, the keyvariables differentiating when longworkhours do and do not increaserisk need to be identified, and theirinterplay with workhours as well asother contextual variables better un-derstood. Two especially promisingcandidates in this regard stood out inthis analysis: the prior health of em-ployees and their type of compensa-tion. Whether employees were sickor healthy at the outset, and whetherthey held an hourly or salaried posi-tion, in turn, played pivotal roles inthe subsequent likelihood of theirincurring an adverse event as well asthe type of event they incurred whenworking long hours.

As such, further testing comparingthe fit of the workhour causal model(M1) separately on these pairs ofgroups could be of real benefit to thecalibration task. These tests need tobe open to the possibility that use ofa segmented characteristic approachin lieu of a workforce-wide focusmay uncover new aspects to the work-hour/outcome relationships observedhere. They need to be sensitive tothe interplay between workhoursand antecedent factors that directlyinfluence worker health, safety andproductivity, may help determine thenumber of hours actually worked, andare modifiable through intervention.They also need to maintain scrutiny ofworkhours in larger context by com-paring their impact other factorsknown to contribute to adverse events.

ITECs use of voluntary overtimeduring this study period offers aplausible basis for reconciling ourresults with previous work, most no-tably the Dembe et al study. It is notunreasonable to assume that the de-cision to work long hours was oftenprompted or accompanied by a pos-


itive orientation toward work. If em-ployees worked long hours becausethey wanted to, not because theywere (or saw themselves as) com-pelled to, they could well have had amindset, an energy level etc. whosenet effect reduced the risk of anadverse development. This hypothe-sis could not be tested with the mea-sures available here and is thus anintriguing item for future research.Regardless, our results suggest theimportance of controlling for volun-tary versus mandatory overtime.Working long hours may simply bemuch less onerous when freely chosen.

Two other, take-home lessons rec-onciling our results with the Dembeet al. study would appear pertinentfor future research. First, prior healthstatus and health risksconcepts notmeasured in their analysisplayedsubstantial roles in the results here,and their addition to the roster ofmeasures for future studies wouldnow seem well indicated. Second,the steps taken to pit workhoursagainst other competing factorsproved useful when evaluating therelative importance of workhours.The perspectives that emerge fromthis exercise can play a crucial rolein determining the extent to whichnew results should be incorporatedinto company policy.

The current study, however, fallsshort of being definitive in its ownright. Its limitations include designflaws: the retrospective, secondarycharacter of this analysis and theshortness of the length of the expo-sure period. A prospective study de-sign with primary data collectioncentered on the impact of workhoursas the primary study issue wouldimprove the capacity for causal in-ference. As for exposure to workhourimpact, even though we were in ef-fect able to expand the period from 1to 3 months to 5 to 7 months and totest for change, this period may nothave been long enough to give ad-verse consequences a chance to reg-ister. Lengthening the observationperiod, and systematically testing forcritical thresholds of time for the

long workhour effect to develop,would strengthen confidence in thestudy results.

More generally, the survey for thisstudy focused only on the assessmentof average hours worked in a typicalweek. Yet, work schedules at ITECdiffer in ways other than simply theextent of overtime, including use ofdouble shifts extended hour shifts(eg, 1012 hour shifts versus 8 hourshifts) and blends

Documents

Allen, Slavin, Bunn - Long Workhour Impact - JOEM Feb 2007