Examination of the relationship between theory-driven policies and allowed lost-time back claims in...

WFC 2013 AWARD WINNING PAPER

EXAMINATION OF THE RELATIONSHIP BETWEEN

THEORY-DRIVEN POLICIES AND ALLOWED LOST-TIME BACK

CLAIMS IN WORKERS' COMPENSATION: A SYSTEM

DYNAMICS MODEL

Jessica J. Wong, DC, a Marion McGregor, DC, PhD,b Silvano A. Mior, DC, PhD, c

and Patrick Loisel, MDd

a Research AsDisability PrevenInstitute of TechChiropractic Coll

b Director ofCanadian Memori

c Research ScMemorial Chirop

d Professor, DaToronto DivisioChiropractic Coll

Submit requeFCCS(C), 54 Sh2Z9 (e-mail: w.je

Paper submittaccepted July 5,

0161-4754/$3Copyright ©http://dx.doi.o

ABSTRACT

Objective: The purpose of this study was to develop a model that evaluates the impact of policy changes on thenumber of workers' compensation lost-time back claims in Ontario, Canada, over a 30-year timeframe. The model wasused to test the hypothesis that a theory- and policy-driven model would be sufficient in reproducing historical claimsdata in a robust manner and that policy changes would have a major impact on modeled data.Methods: The model was developed using system dynamics methods in the Vensim simulation program. Thetheoretical effects of policies for compensation benefit levels and experience rating fees were modeled. The model wasbuilt and validated using historical claims data from 1980 to 2009. Sensitivity analysis was used to evaluate themodeled data at extreme end points of variable input and timeframes. The degree of predictive value of the modeleddata was measured by the coefficient of determination, root mean square error, and Theil's inequality coefficients.Results: Correlation between modeled data and actual data was found to be meaningful (R2 = 0.934), and themodeled data were stable at extreme end points. Among the effects explored, policy changes were found to berelatively minor drivers of back claims data, accounting for a 13% improvement in error. Simulation results suggestedthat unemployment, number of no-lost-time claims, number of injuries per worker, and recovery rate from backinjuries outside of claims management to be sensitive drivers of back claims data.Conclusion: A robust systems-basedmodel was developed and tested for use in future policy research inOntario's workers'compensation. The study findings suggest that certain areas within and outside the workers' compensation system need to beconsidered when evaluating and changing policies around back claims. (J Manipulative Physiol Ther 2014;37:7-21)
Key Indexing Terms: Workers' Compensation; Back Pain; Policy; Economics; Nonlinear Models
sociate, UOIT-CMCC Centre for the Study oftion and Rehabilitation, Universitynology (UOIT) and the Canadianege (CMCC), Toronto, Ontario, CanaEducation, Year II, Undergraduateal Chiropractic College, Toronto, Ontarientist, Department of Research,ractic College, Toronto, Ontario, Canlla Lana School of Public Health, Unn of Graduate Studies, Canadianege, Toronto, Ontario, Canada.sts for reprints to: Jessica J. Wongeila Crescent, Richmond Hill, ON, [email protected]).ed May 15, 2013; in revised form Jun2013.6.002014 by National University of Healtrg/10.1016/j.jmpt.2013.07.009

orkers' compensation boards in Canada are

l

,.

l

,

;

.

Wproviders of rehabilitation and income replace-ment benefits to persons who had workplace

accidents or industrial diseases.1 Workers' compensationboards are funded by payroll contributions from insuredfirms.1 Claims in workers' compensation, particularly relatedto occupational back pain, are costly and burdensome tosociety. In Canada, the cost of all workers' compensationclaims was Can $7.8 billion in 2009, of which Can $5.2billion was paid in wage replacement and Can $1.9 billionwas for health care and rehabilitation.2 In Ontario, theaverage benefit cost per insured employee escalated fromCan$578.86 in 2000 toCan $731.68 in 2009.2 Occupational backpain has been the most prevalent workplace injury claim inOntario for the past 21 years, comprising 26.9% to 28.4% ofall claims.3 Such claims not only impact the economic burdenon workers' compensation boards but also the worker

of OntarioMemoriada.Educationio, CanadaCanadianada.iversity ofMemoria

BSc, DCanada L4B

e 20, 2013

h Sciences

7

mailto:[email protected]

http://dx.doi.org/10.1016/j.jmpt.2013.07.009

8 Journal of Manipulative and Physiological TherapeuticsWong et alJanuary 2014Theory-Driven Policies in Workers' Compensation

through loss of income and employers through loss ofproductivity.4,5

Financial compensation in workers' compensation hasbeen associated with a poorer prognosis in back pain and anumber of other conditions.6-11 A systematic review foundstrong evidence for a negative effect of high compensationon the duration of work absenteeism for back pain.8 Thiswas based on 2 high-quality studies included in thereview. The studies found that workers with back injuriesand receiving higher compensation (ie, greater than Can$50 per day in Quebec and US $350 per week inMichigan) were more likely to have chronic workdisability than those receiving lower compensation.12,13

Another study combined randomized and observationalcohorts (for subjects declining randomization) of compen-sated and noncompensated workers comparing surgery vsnonoperative treatment for lumbar disk herniations.9 Inthat study, the initial significant improvements in backpain, function, and satisfaction of surgery were found to bediminished at 2 years in the workers' compensation group.9

However, the noncompensated group maintained theirinitial improvements at 2 years, even after adjusting forpotential confounders.9

Reasons for the negative effect of compensation on theprognosis of back pain are unclear. There are arguably 2contrasting theoretical frameworks that aim to help examineoccupational back pain: (1) a theory focusing primarily onhealth-related factors and (2) another theory focusingmainly on economic incentives and financial impact.14 Insupport of a health-related theory, a systematic reviewidentified prognostic factors for work disability in occupa-tional back pain, including time since onset, functionaldisability, psychological distress, pain reports, and previousepisodes.15 In contrast, literature supporting an economictheory, consistent with the literature reviewed above,suggests that compensation schemes may affect behavioramong workers and employers through incentives anddisincentives to influence outcomes.16,17 For example,economic theory proposes that an increase in compensationfor claims may be associated with an increased likelihood ofreported illnesses by workers, including back pain.16 Withan increase in compensation, it may be that the workerdeems the injury sufficiently significant to report.16 This isbased on the theory that employees may be incentivized toeither report more minor injuries or to take fewerprecautions.16 These behavioral effects of the economictheory may also influence the employer to increase effortsto prevent workplace injuries or contest claims whenclaims-related costs are high.16 Therefore, compensatedback pain may be influenced by financially incentivizedbehavior in addition to influencing the clinical prognosis.

The effects of economic incentives on back claims inworkers' compensation are complex. Thus, policy researchin back claims is challenged by the multifactorial nature ofthe problem. Studies have attempted to understand the acute

impact of policy changes on workplace characteristics,including independently measuring workforce productivity,duration of unemployment, and effectiveness of return-to-work interventions.18-23 Systematic reviews have alsoattempted to synthesize this information but often combineheterogeneous compensation systems12 and include inter-ventions specific to their studied jurisdiction and workplacecharacteristics.24,25 However, policymakers require a highlevel of understanding of policy effects and economicincentives when considering changing workers' compensa-tion reimbursement schemes. Changes are often aimed atdecreasing claims and costs in the short term in response tolimited resources. Instead, policymakers may benefit fromexamining interactions between various factors, incentives,and interventions over a longer timeframe.

Policy changes that involve long delays between relatedfactors and health outcomes, such as in chronic diseases,can be examined through systems-based ecologicalmodeling.26-29 For example, system dynamics modelingmathematically accounts for interactions of relevant vari-ables over time.24 Previous system dynamics projects havedeveloped multifactorial prevention strategies in cardiovas-cular disease, diabetes, immunization, HIV, and rural healthby exploring the effects of risk factors on diseaseprevalence. 30-38 Other studies have used modelingapproaches to study workers' compensation but are limitedto conceptual models not yet tested,39 narrow modelboundaries,40-43 or short-time horizons.44 Recently, Sok-laridis et al45 explored risk factors from a socioeconomicperspective by interviewing a range of workplace stake-holders for relevant factors in Ontario's workers' compen-sation, but the influence of such factors has not yet beentested. There is therefore a need for a systems-based modelwith a long timeframe to study the impact of policy on backclaims in workers' compensation.

There are additional advantages to using systemdynamics modeling to help guide policy research inworkers' compensation. First, simulation models helpinterpret historic trends through identifying pathways inwhich certain factors operate. This helps improve theunderstanding of complex effects among wide rangingfactors over many years. Second, simulation models create avirtual environment in which alternative interventions canbe studied without having to commit large amounts oflimited resources. Policymakers and planners can virtuallycreate a wide range of scenarios and demonstrate the relativeimpact of different interventions. Finally, simulated data canhelp support the development of future objectives andplausible solutions of the organization. The model couldpotentially help minimize discrepancies between objectivesand anticipated trends over a combination of variables.

Despite the need for a systems-wide approach, there areno studies that use multifactorial and longitudinal metho-dology to study policy impact on workers' compensation inCanada. The primary objective of this study was to develop

Table 1. Outline of relevant WSIB policy changes from 1980 to 200947

Year Description of policy change a Input to model variable b

Prior to 1985 Compensation payable was 75% gross Compensation payable, 0.9Temporary benefits payable before MMR Temporary benefits, 1Experience rating program absent Employer experience rating, 1

After 1985 Compensation payable was 90% net Compensation payable, 0.9Temporary benefits payable before MMR Temporary benefits, 1Experience rating program present Employer experience rating, 0.9

After 1990 Compensation payable was 90% net Compensation payable, 0.9Temporary benefits may not be payable before MMR Temporary benefits, 0.7Experience rating program present Employer experience rating, 0.9

After 1998 Compensation payable was 85% net Compensation payable, 0.85Temporary benefits reviewed and adjusted each year Temporary benefits, 0.7Experience rating program present Employer experience rating, 0.9

MMR, Maximum medical recovery; WSIB, Workplace Safety and Insurance Board.a Compensation payable was calculated as percentage of wage replacement in net income; temporary benefits and experience rating program were

calculated as a weighting out of 1 (direction of weighting was dependent on the respective model equation).b These values were used in mathematical equations that modeled the proposed relationships of economic theory in workers’ compensation.

9Wong et alJournal of Manipulative and Physiological TherapeuticsTheory-Driven Policies in Workers' CompensationVolume 37, Number 1

a model of the effect of economic theory-driven policies onthe number of lost-time back claims in Ontario's workers'compensation board over a 30-year timeframe. This testedthe hypothesis that an economic theory-driven model wouldreproduce historical claims data driven by workers'compensation policy changes. The secondary objectivewas to determine the robustness, utility, and flexibility ofthe model in evaluating proposed policy changes. It washypothesized that the behavior of modeled data would bestable over an extended timeframe and at extremes ofvariable input.

METHODS

A system dynamics modeling approach was selected tocapture the dynamic complexity between chronic diseasesand policy changes.24,28 Complex feedback effects betweenvariables in workers' compensation policies and the numberof back claims were explored over a 30-year timeframe witha broad model boundary. The model was developed usingthe Vensim Professional Windows version 5.5 c simulationenvironment (Harvard, MA). The mathematical model used ahost of differential equations (rates of change) to form anoutput of modeled data (levels over time), aiming tomathematically account for interactions of relevant variablesover time.24 Ethics approval was obtained from the CanadianMemorial Chiropractic College. Aggregate secondary datathat were publicly accessible from the Workplace Safety andInsurance Board (WSIB) in Ontario, Canada, and StatisticsCanada were used for the model formulation.

DataYearly aggregated totals of lost-time back claims in

Ontario's WSIB from 1980 to 2009 were used as referencedata for the study outcome.5,46 The primary outcome ofinterest was the predicted annual number of lost-time back

claims from 1980 to 2009 as per the model output. Thesedata were selected because of the following:

1. aggregated data at the population level strengthen thegeneralizability of the model to the entire populationrather than subgroups (eg, age, sex);

2. back injuries are considered difficult to diagnoseand therefore potentially susceptible to economicincentives;17 and

3. back claims make up the highest proportion ofworkplace injuries.5,46

The 30-year timeframe was selected to capture 3 timepoints of WSIB policy changes in benefit compensationschemes (1985, 1990, 1998) because these policies mayclosely relate to economic theory. These policy changesformed 4 key eras at the WSIB, known as the Pre-Weiler(before 1985), Pre-Wage Loss Weiler (1985-1990), WageLoss Weiler (1990-1998), and Wage Loss Jackson (1998 topresent) (Table 1).47 For example, workers with permanentdisability between 1985 and 1990 received compensationequal to 90% of net preinjury earnings.47 We used all 30years of data to build and validate the model.

The model timeframe began in 1980 to capture baselinetrends and any potential anticipatory effects of policy changes.It has been suggested that 1 month before policy changes,individuals may alter behavior in favor of the policy change asan anticipatory effect. This was evidenced in the seminalworkof Harvey and Durbin when analyzing the implementation ofseat belt legislation in Great Britain.48 Using this logic, withthe first WSIB policy change taking effect on April 1, 1985,an anticipatory effect would have occurred earlier in 1985.However, policy changes in WSIB are less advertised in thegeneral public, and workers were likely not aware of policychanges initially, omitting an anticipatory effect. Never-theless, starting the model at 1980 was a conservativeapproach to capture a potential anticipatory effect.

Fig 1. General causal loop diagram based on economic theory. Legend: +, positively related (positive feedback); − inversely related(negative feedback).


Modeling ProceduresTheoretical Premise and Boundary Formulation. Modeling began

by formulating a population-based model representative ofrelevant concepts from economic theory.16,17 Therefore,model variables were selected based on theorized beha-vioral effects on the number of claims in workers'compensation. These concepts (Fig 1) describe incentiveand disincentive effects of benefit compensation levels foremployees and experience rating schemes for employersand associated workers' compensation policies between1980 and 2009. The 3 main behavioral effects described byeconomic theory are categorized with respect to the worker,employer, and health care provider:

1. The worker is affected by compensation benefitpayments, where the incentive to submit claims isgreater with higher compensation benefit payments.Compensation benefit payment is payment made bythe respective workers' compensation board for atime of work absence linked to a job-related injury orillness submitted as a claim.

2. The employer is affected by experience ratingprograms, where the incentive to decrease or contestclaims is greater with higher experience rating fees.The experience rating program is a method foradjusting premium rates to reflect an employer'sclaims history. It is intended to incentivize safety andpromote injury prevention in the workplace todecrease claims.

3. The health care provider is affected by treatmentreimbursement schemes, where the incentive toprolong treatment for a claimant is greater withhigher treatment reimbursement schemes.

Endogenous (defined as included or inside of24) modelvariables were based on these potential behavioral effects atthe worker and employer level. Input into policy-relatedvariables was based on (1) percentage of net income inwage replacement for employee compensation (eg, 90% ofnet income was converted to 0.9 of 1) and (2) estimatedweighting representative of the proposed impact fortemporary benefits and experience rating (Table 1). Speci-

fically, the following estimated weightings were made foreach temporary benefits and experience rating: (1)temporary benefits payable before maximum medicalrecovery were given full weighting (ie, 1 of 1 using themathematical function of multiplication), whereas thosewith additional restrictions before maximum medicalrecovery were given lesser weighting (ie, 0.7/1); (2)experience rating program was not present before 1985and was therefore given no weighting (ie, 1 of 1 using themathematical function of division), whereas experiencerating fees were present after 1985 and were therefore givena weight (ie, 0.9/1). Unemployment levels were includediteratively after consideration of expert opinion and furtherinvestigation of the theory-based literature.49 Additionalmodel variables were identified through expert input andentered into the model through an iterative process.

Exogenous (defined as excluded or outside of24) modelvariables were the policies regarding payment schemes tohealth care providers for compensation-related treatments.It was assumed that provider care remained the same giventhe same patient condition. Although economic theoryproposes a theoretical relationship between workers'compensation payment schemes for health care providersand the incentive to provide longer treatment, which maydelay return to work or recovery,17 this would likely affectduration rather than number of lost-time back claims.Therefore, based on economic theory, the model did notinclude variables related to health care.

AnalysisThe modeled data were compared with historical claims

data through (1) statistical analysis for validity, (2) visualcomparison of the graphical data for validity, and (3)sensitivity analysis for robustness.

Model Validity. Validity of the modeled data was assessedstatistically with (1) coefficient of determination (R2) and(2) Theil's inequality coefficient (U). The coefficient ofdetermination (R2) is a statistical test that determines theproportion of variability in the actual data set that isaccounted for by the modeled data. This is accomplished bymeasuring the variance between modeled and actualnumber of lost-time back claims over time. An R2 of 0.8

Fig 2. System dynamics model of back claim processes—variable and relationship. WSIB, Workplace Safety and Insurance Board.


was considered meaningful correlation between modeledand actual data, and an R2 of 1 was considered an exactreplication of the actual series by the model.24

Theil's inequality coefficient is a statistical test thatmeasures how well a time series of estimated valuescompares to a corresponding time series of observedvalues.50 This is accomplished by calculating the meansquare error and calculating the inequality coefficient (U).The U is equal to the square root of the sum of the squaredifferences between actual and predicted data, divided bythe sum of the actual squared data. The smaller thedifference, the closer the numerator is to zero, whichindicates a more representative model when compared withactual data. Theil's decomposition of mean square errorscomprises 3 inequality coefficients that sum to 1:

1. Um is the proportion attributed to bias and isconsidered the most important coefficient of the 3coefficients.

2. Us is the proportion attributed to differences invariance. A high variance proportion results fromfluctuations around the trend line.

3. Uc is the inequality attributed to a difference incovariation. Unless phase differences are observed,this coefficient is considered unsystematic error andshould be the largest value of the proportions.

Impact of Policy. To test the hypothesis of whether policychanges significantly impacted the trends in claims data,policy-related variables were used in 2 model simulations:(1) with no policy changes (ie, input into policy-relatedvariables are constant throughout study period) and (2) withpolicy changes (ie, input into policy-related variableschange during study period as per historical WSIB policychanges) (Table 1). The 2 model simulations werecompared based on root mean square error values, thecoefficient of determination, and Theil's inequality coeffi-cients.50 Root mean square error is the measure of thedifferences between values predicted by the model and thevalues actually observed. The difference in root meansquare errors provides a value representative of the changein error between the 2 model simulations.

Sensitivity Analysis. Sensitivity analysis involved testing themodel's integrity at the extreme end points of variable inputand timeframes.51 We used a best and worst case sensitivityanalysis by changing 1 parameter at a time in 2 modelsimulations: (1) 100% for policy input (ie, compensationlevels and experience rating fees) and (2) 10% for policyinput (ie, compensation levels and experience rating fees).Best and worst case sensitivity analysis aims to providebounds for the behavior that will most likely be faced.51

The output was also extended by 100 years to evaluatestability. These values were selected for pragmatic reasons.

image of Fig�2


Tests of robustness are not intended to predict outcomes buttest mathematically inconsistent behavior and strengthenconfidence in the model even under extreme conditions. Forexample, a short-time boundary may exclude an importanttrend that would have contributed to the key variables in themodel but is instead omitted from the model. Theboundaries within which the model data can be consideredrobust were identified.

Fig 3. Total Ontario population—variables.
RESULTS
ModelFigure 2 provides a schematic representation of the model.

Definitions and explanations of the selected sectors, modelvariables, and the modeled variable effects are providedbelow. After documenting the variable effects, modelconstants and variable rates were calibrated to the historicalclaims data as closely as possible. An adjustmentwasmade tothe model timeframe intervals to account for variations inWSIB claims documentation. It was suspected that lost-timeclaims are recorded up to March of the following year,thereby extending past 12 months. In addition, a change indocumentation of claims volumes occurred before 1990,5

which appeared to coincide with WSIB's shift from paper toelectronic claims management in 1989. However, WSIB wasunable to locate specific documentation that defined thebacklog referred to in the 1997 statistical supplement (WSIB,personal communication).

Sector for Total Ontario WorkersTotal Ontario population model variable was defined as

the annual number of Ontario residents during the studyperiod and modeled by considering population increases(birth and immigration), population decreases (death andemigration), and population growth rate (Fig 3). This wascontrasted with the actual data for the Ontario population asrecorded by Statistics Canada.52 This sector served to bindthe model at the population level.

Sector for Total Ontario Workers and Total Back InjuriesTotal Ontario workers variable was defined as the

annual number of persons employed in Ontario during thestudy period (Figs 4A and B). The employment rate andleave rate were modeled to directly increase and decreasethe number of Ontario workers, respectively. This wascontrasted with the actual annual number of employedworkers in the Ontario labor force as documented byStatistics Canada.53 Unemployed workers variable wasdefined as the number of persons in Ontario activelylooking for a job, and its actual data were directly enteredinto the model. A portion of those who were unemployedmay also become lost from the labor force. The employ-ment, unemployment, and loss from labor force rates were

based on data regarding labor force in Ontario fromStatistics Canada.53 An additional feedback loop was alsoentered with respect to employment rate and number ofunemployed workers. With employment rates low, thenumber of persons actively job seeking increases, therebyincreasing unemployment rates,49 which was representedas a negative feedback loop in the model.

Total back injuries variable was defined by the annualnumber of accidents or conditions related to the back, whichwere registered with WSIB during the study period. Thiswas contrasted with the actual data for total back injuries,which was approximated by multiplying the ratio of lost-time back claims to total lost-time claims with the totalaccidents and illnesses recorded by WSIB that year.5,46

Since it was approximated, it was anticipated that themodeled data might not be as highly correlated to theapproximated actual data. The “injury per people” (ie,number of injuries sustained per worker) and injury ratewere modeled to directly increase the number of backinjuries, whereas the recovery rate (ie, rate of back painresolution before any claim submission) was modeled todirectly decrease the number of back injuries.

Sector for Submitted Back ClaimsSubmitted number of back claims variable was defined by

the annual number of submitted claims related to the back(Figs 5A and B). This was contrasted with the actual datafor submitted back claims, which were approximated bymultiplying the ratio of lost-time back claims to total lost-time claims with the total submitted claims recorded byWSIB that year.5,46 Since it was approximated, it wasagain anticipated that the modeled data and the approxi-mated data might not be as highly correlated. As suggestedby economic theory in workers' compensation, the moralhazard effect and reporting effect were modeled to directlyincrease the submitted number of back claims, which maybe increased by the employee total compensation(composed of compensation payable and temporarybenefits) (Fig 6). The moral hazard effect is a situationwhere workers take fewer precautions against the risk ofworkplace injuries as the risk becomes more fully insured,and employers may be viewed as assuming more

image of Fig�3

Fig 4. A, Total Ontario workers and total back injuries—variables and relationship. B, Total back injuries—actual vs modeled data(Color version of figure is available online.)


responsibility for the injuries.16,17 The reporting effect is asituation where workers tend to report more workplaceinjuries that they now deem significant enough to reportrather than work through them, in light of the compensa-tion available.16,17

Sector for Allowed Lost-time Back ClaimsNumber of allowed lost-time back claims variable was

defined by the annual number of lost-time back claimsduring the study period (Fig 7A). The reference data fornumber of allowed lost-time back claims were the annualnumber of lost-time back claims recorded by WSIB.5,46 Assuggested by economic theory, employer work modifica-tions and rate of contesting claims were modeled to directlydecrease the number of lost-time back claims (Fig 6),which, in turn, were increased by the fees from higheremployer experience rating schemes. Resolution rate wasmodeled to directly decrease the number of lost-time back

.

claims and was based on the mean percentage of claims thatresolve in 1 year's duration.

AnalysisThe coefficient of determination for most model

variables was above 0.8 and considered high (Table 2).For the outcome of interest (ie, number of allowed lost-timeback claims) in the final model with policy changes, thecoefficient of determination between modeled data andactual data was found to be meaningful (R2 = 0.934).Statistical evaluation was conducted for 2 other relevantmodel variables' outcomes (total back injuries andsubmitted number of back claims), and the coefficients ofdetermination were 0.887 and 0.632, respectively.

When comparing the model simulation without policychanges to the simulation with policy changes, thecoefficients of determination were 0.882 and 0.934,respectively (Figs 7B and C). By calculating the

image of Fig�4

Fig 5. A, Submitted number of back claims—variables and relationship. B, Submitted number of back claims—actual vs modeled data(Color version of figure is available online.)

ig 6. Feedback loops associated with theory-driven policies. Legend: +, positively related (positive feedback); −, inversely relatedegative feedback). (Color version of figure is available online.)


F(n

proportion of root mean square error when comparing themodel simulation without policy changes to the simulationwith policy changes, policy changes accounted for a 13%improvement in the root mean square error in the modeleddata This suggests that policy changes were relativelysmall drivers of back claims data overall.

.

Evaluation of model robustness by extending thetimeframe to 2109 indicated that the model data werestable over time. Extreme variable input (10% and 100%)into policy changes indicated that the modeled data werestable and provided reasonable boundaries for modelrobustness (Fig 8).

image of Fig�5

image of Fig�6

Fig 7. A, Number of allowed lost-time back claims—variables and relationship. B, Number of allowed lost-time back claims beforepolicy changes—actual vs modeled data. C, Number of allowed lost-time back claims after policy changes—actual vs modeled dataWSIB, Workplace Safety and Insurance Board. (Color version of figure is available online.)


During model optimization, a number of variables actedas sensitive levers to the model output in the number ofallowed lost-time back claims variable. Two variables,number of unemployed workers and number of no lost-time

.

claims, were entered into the model directly to improvemodel data fit. Sensitive drivers to the modeled systemincluded 2 other variables: injuries per worker and recoveryof back injuries before submission of claims (denoted as

image of Fig�7

Fig 8. Modeled data at extreme variable input and timeframe—actual vs modeled data. (Color version of figure is available online.)

Table 2. Summary statistics used to compare actual data to model-generated data

Model VariableInequalitycoefficient (U)

Bias proportion(Um)

Variance proportion(Us)

Covariationproportion (Uc)

Coefficient ofdetermination (R2)

Total back injuries 0.104 0.698 0.012 0.290 0.887Submitted back claims 0.131 0.273 0.121 0.606 0.632Allowed lost-time back claims

before policy changes0.107 0.003 0.128 0.869 0.882

Allowed lost-time back claimsafter policy changes

0.080 0.001 0.007 0.992 0.934


variables “injury per people” and “recovery rate,” respec-tively, in Fig 4A). Through visual comparison of graphicaldata, small changes to these variables appeared to have amajor impact on data fit of modeled data toward actual data.

DISCUSSION

Up to this point in time, there have been no studies thatuse multifactorial and longitudinal methodology to studypolicy impact on workers' compensation in Canada. Thisstudy examined the effect of WSIB policies, modeled oneconomic theory, on the number of lost-time back claimsfrom 1980 to 2009. By using a systems-wide, population-based model, policy changes accounted for a 13%improvement of error in the modeled data when comparedwith modeled data based on no policy changes. Mostmodeled variables had high correlation with the actual data,with the exception of the variable for submitted number ofback claims. We anticipated the lower correlation becausethis variable was iteratively modeled against estimated data.

There are arguably 2 competing theories in occupationalback pain: one focusing on biomedical and psychosocialfactors and the other emphasizing economic or administrativeissues, while each appearing to downplay the importance ofthe other.14 Although our study focused on economicconcerns, some biomedical-related factors, such as injury

rate, recovery rate, and work modifications, were required tocontextualize the intended economically driven behaviors.This suggests that a combination of biomedical, psychoso-cial, and economic factors needs to be considered within abroader perspective when examining policy impact on backclaims. Our model demonstrated the complex pathways inwhich policy changes operate over a long timeframe andhelped elucidate their impact on back claims data.

Although considered a minor driver to the system, thedegree of impact from policy is consistent with previousstudies not using a systems approach. In a time series modelmeasuring injury claim rates after a premium discountprogram was implemented in Finnish farmers' workers'compensation, the incentive resulted in a significant (10.2%)reduction in the overall injury claim rate (P b .05).54 Aliterature synthesis by Loesler et al14 found that an increase of10% in workers' compensation benefits was related to a 1%to 11% increase in claim frequency. In a systematic reviewexamining the effectiveness of experience rating andoccupational health and safety regulation, there wasmoderateevidence that thedegree of experience rating reduces injuries.55

This finding was based on studies using mostly crude proxymeasures or natural experiments, suggesting that experiencerating is a key policy lever that needs to be further exploredin research.55 Our innovative systems-based model alsofound evidence of decreased claims with higher experience

image of Fig�8


ratings, although with relatively minor impact when coupledwith employee compensation schemes.

By using a system dynamics approach and boundingthe model at the population level, our research identifiedadditional drivers not previously reported as majorinfluences to the number of lost-time back claims.Potential explanations for each identified driver arediscussed below.

UnemploymentThe literature on the effect of unemployment rates on

workers' compensation claims has been conflicting. Usinglogistic and quantile regression to investigate predictors ofdisability in population-based administrative data on newworkers' compensation claims, Stover et al56 found thathigher unemployment rate was a predictor of long-termdisability. Conversely, in a study of Ontario lost-time backclaims between 1975 and 1993 that were regressed onunemployment rates using time series methods, Brookeret al57 found that back claims' rates increased during workboom periods and decreased during recessionary periods.Unlike these findings, our model simulations suggest thatthe number of lost-time back claims increased with higherunemployment rates. This difference may be due to Brookeret al including only unemployment data between 1975 and1993, whereas we used unemployment data spanning 1980to 2009 and considered other factors.

There are a number of hypotheses that describe thepotential impact of unemployment rates on claim rates.These hypotheses span from the economic climate affectingreporting decisions or altering composition of the work-force, to altering the work environment or stress levels thatchange the risk of being injured.57 Most of the hypothesessuggest that lower unemployment rates during the boomperiods would increase the number of claims, unless thefear of lay-off increases the stress of workers andsubsequently increases the risk of injury.57 Our findingsprovide support for this latter scenario.

Multiple Back Injuries and Recovery RateMultiple back injuries and recovery rate were also

identified as sensitive drivers of the modeled data. A greaternumber of back injuries per worker or a lower recovery ratewas found to increase the predicted number of lost-timeback claims. This highlights the importance of decreasingor resolving recurrent injuries before potential claimsubmission. Similarly, a previous prospective cohortstudy found that occupational management resulted inlower injury claim incidence, duration, and costs whencompared with standard care and early intervention.58 Thissuggests that occupational management (minimal clinicalintervention, reassurance, education, early return to work,and work accommodation) before claims management by

the workers' compensation board may play a key role infacilitating recovery among injured workers.

Efforts to facilitate early injury intervention areparticularly important for addressing recurrent back injuriesin workers. A multivariable logistic regression analysisfrom a representative, population-based survey found that ahistory of work-related low back injury was positivelyassociated with increasing severity of prevalent low backpain.59 In a Quebec sample of 2342 compensated spineinjury claimants followed for 3 years, 36.3% had at least 1recurrence after the initial claim, with longer absences fromwork than those without a recurrence.60 This study alsofound a trend of gradually increasing durations of absenceon each subsequent recurrence, accompanied by increasesin cost.60 Similarly, a descriptive and survival analysis offactors related to single vs multiple compensation claimsfound that initial claims of repeat claimants resulted insignificantly lower costs and work disability than repeatclaims.61 This suggests that a substantial proportion ofinjured workers experience a second occupational injury,with potentially longer durations of work disability andgreater economic burden than those with single claims. Inturn, these workers may need additional support from theworkplace and workers' compensation.

No-Lost-Time ClaimsOur study found that the number of no-lost-time claims

was a sensitive driver of the system leading to lost-timeclaims. To further understand the burden of workers'compensation claims, research studies have begun examin-ing no-lost-time claims in recent years. While lost-timeclaims have been decreasing over the years, the number ofno-lost-time claims has remained relatively steady.5 Smithet al62 examined the demographic and labor market risksfor a number of work-related injuries and illnesses throughsecondary analysis of accepted Ontario claims from 1991 to2006. This study found that while mining, utilities, andconstruction industries had an increased probability ofreporting no-lost-time claims, they had a decreasedprobability of reporting lost-time claims.62 When examin-ing trends in health care usage and expenditures of Ontariono-lost-time claims from 1991 to 2006 using a regressionanalysis, health care expenditures per claim declinedbetween 1991 and 1997, but then increased between 1998and 2006.63

There are potential explanations to the reported trends inno-lost-time claims. The marked change in no-lost-timeclaims between 1997 and 1998 coincided with theintroduction of occupational health and safety legislationpromoting early return to work in Ontario. The increaseduse of workplace accommodation since 1998 is a possibleexplanation for the relatively stable rate of no-lost-timeclaims in Ontario. In comparison with lost-time claims, theincreasing health care costs associated with no-lost-time


claims may be a result of increasing secondary preventionactivities at the workplace. As a potentially incentivizedbehavior, WSIB gives greater weighting to lost-time claims(with higher costs) than no-lost-time claims (with lowercosts) in determining whether a firm is administered asurcharge or refund on their payments for workers'compensation coverage.63 This may incentivize employersto report claims as no-lost-time claims as opposed to lost-time claims or increase workplace accommodation so thatemployees return to work soon after an occupationalinjury.64,65 This potential incentive effect requires furtherresearch, particularly around the effect of policy onworkers' compensation claims, and subsequent impact onwork disability.

Our model serves as the first step in using systemdynamics modeling for policy research in workers'compensation. Future studies using this model can virtuallyinvestigate alternative scenarios to understand the relativeimpact of different interventions on lost-time back claims.Although models are not intended to predict future trends,they can compare different simulations to a baselinesimulation to determine their relative effectiveness.30 Thepredicted trends observed in our model could serve as areference point in these future studies. Overall, our studyprovides a strategic model in workers' compensation thatmay help in understanding how combined interventionscould be balanced to achieve system goals.

LimitationsOur study has a number of limitations. Model variables

were selected to represent key concepts of economic theoryin workers' compensation as previously described.16,17 Thetheory-based model excluded variables related to healthcare, which remains an oversimplification of this compo-nent in workers' compensation. The model includes anumber of assumptions, where 2 model variables wereestimated based on proportions of back claims to all claimsdata due to the lack of availability of actual data. Usingaccurate values in these 2 model variables may haveprovided a more precise evaluation of back claims andtheory-driven policies. However, WSIB does not reportannual data on the number of back injuries in workers or thenumber of back claims submitted. Workplace Safety andInsurance Board administrative data may be prone tomisclassification bias or reporting bias, particularly under-reporting of claims to possibly decrease premiums ofemployers' firms, which has been previously raised in thiscontext.55 However, the experience rating program wasimplemented in 1984 and has gradually changed over theyears, so underreporting of claims would have likelyimpacted claim volumes over the majority of the studyperiod and are unlikely to have changed specific trends anddrivers in our model. We also estimated the model input fortemporary benefits and experience rating fees. However,

only minor differences in the modeled back claims werefound when comparing the 2 model simulations (ie,simulated with and without policy changes). Therefore, itis not likely that small modifications to these values woulddrastically change the results. Furthermore, the modeleddata included 5 years of data preceding policy changes toallow for the anticipatory effect of policy. A substantialanticipatory effect would have been observed as a trenddifference associated with policy, before the policy cameinto effect. Such an effect was not observed in the actualdata trend lines. Thus, any anticipatory effect associatedwith the policy changes studied is believed to be minimal.Future study at a more granular level would be required tounderstand this further.

It has been proposed that “all models are wrong but someare useful.”66 Sensitivity testing can only increase theconfidence in the model's ability to mimic historical data.51

In light of this, the intent of modeled data is to engageinterested parties in reflective thought on important relation-ship in the system. Our model may help direct policy changeand analysis, given that the system remains stable and underthe assumption that all key variables have been included.With aggregate data, the target population of all lost-timeback claims has been assumed to be homogenous in themodel, although aggregate data have a degree of hetero-geneity.67 Examining individual patient data could revealother factors that influence the decision to submit a WSIBclaim, including physical and psychosocial factors.

In addition, it would have been helpful to have new datato retest the model. This would further determine howrobust it is in determining trends associated with otherworkers' compensation systems and evaluate general-izability. The 30 years of available data was used to buildand validate this model, as the dynamic hypotheses of thisinvestigation were intended to test the impact of policychange. The 30-year timeframe allowed for the opportunityto test 3 individual policy changes. As a result, however,generalizability was not assessed, and this remains alimitation of our study.

CONCLUSION

The model that we developed examined the complexeffects around policies and compensation to provide a uniqueunderstanding and contextualize certain strategies to mitigatework disability from back pain. Our findings suggest thatpolicy changes are relatively minor drivers of back claims,and the impact of other identified drivers also needs to beconsidered. These drivers were identified as unemploymentrates, multiple back injuries and recovery rates, and numberof no-lost-time claims. The broad perspective of ourmodeling is considered a key methodological strength. Thismodel may provide a viable approach to evaluating policychanges in workers' compensation in Ontario.

Practical Applications• This study found that correlation betweenmodeled and actual data was meaningful, andmodeled data were stable at extreme endpoints.

• Policy changes were found to be relativelyminor drivers of back claims data. Simulationresults suggested unemployment rates, num-ber of no-lost-time claims, and multiple backinjuries to be sensitive drivers of back claimsdata.


FUNDING SOURCES AND POTENTIAL CONFLICTS OF INTEREST

No funding sources or conflicts of interest were reportedfor this study.

CONTRIBUTORSHIP INFORMATION

Concept development (provided idea for the research):JJW, MM, PLDesign (planned the methods to generate the results):JJW, MMSupervision (provided oversight, responsible for orga-nization and implementation, writing of the manuscript):MM, SAM, PLData collection/processing (responsible for experiments,patient management, organization, or reporting data):JJW, MMAnalysis/interpretation (responsible for statistical analy-sis, evaluation, and presentation of the results): JJW,MM, PLLiterature search (performed the literature search): JJWWriting (responsible for writing a substantive part of themanuscript): JJWCritical review (revised manuscript for intellectualcontent, this does not relate to spelling and grammarchecking): MM, SAM, PL

REFERENCES

1. Campolieti M, Lavis JN. Disability expenditures in Canada,1970-1996: trends, reform efforts and a path for the future.Can Public Policy 2000;26:241-64.

2. Association of Workers’ Compensation Boards of Canada.Key statistical measures, 2007. Accessed March 18, 2011:http://www.iwh.on.ca/compensation-fact-sheets.

3. Workplace Safety and InsuranceBoard. Statistical support to theannual reports, 1988-2009. Accessed March 18, 2011: http://www.wsib.on.ca/en/community/WSIB/230/ArticleDetail/24338?vgnextoid=8037a5596b8d7210VgnVCM100000449

c710aRCRD&vgnextchannel=f2e5007c39867210VgnVCM100000449c710aRCRD.

4. Shearer B. Compensation policy and worker performance:identifying incentive effects from field experiments. J EurEcon Assoc 2003;1:503-11.

5. Lalive R, Ours JV, Zweimuller J. How changes in financialincentives affect the duration of unemployment. Rev EconStud 2006;73:1009-38.

6. Harris I, Mulford J, Solomon M, van Gelder JM, Young J.Association between compensation status and outcome aftersurgery. JAMA 2005;293:1644-52.

7. Koljonen P, ChongC,YipD.Difference in outcome of shouldersurgery between workers’ compensation and nonworkers’compensation populations. Int Orthop 2009;33:315-20.

8. Steenstra IA, Verbeek JH, Heymans MW, Bongers PM.Prognostic factors for duration of sick leave in patients sicklisted with acute low back pain: a systematic review of theliterature. Occup Environ Med 2005;62:851-60.

9. Atlas SJ, Toteson TD, Blood EA, Skinner JS, Pransky GS,Weinstein JN. The impact of workers’ compensation onoutcomes of surgical and nonoperative therapy for patientswith a lumbar disc herniation SPORT. Spine 2010;35:89-97.

10. Spearing NM, Connelly LB. Is compensation “bad forhealth”? A systematic meta-review. Inj Int J Care 2010;41:683-92.

11. Sholten-Peeters GGM, Verhagen AP, Bekkering GE, et al.Prognostic factors of whiplash-associated disorders: a sys-tematic review of prospective cohort studies. Pain 2003;104:303-22.

12. Abenhaim L, Rossignol M, Gobeille D, Bonvalot Y, Fines P,Scott S. The prognostic consequences in the making of theinitial medical diagnosis of work-related back injuries. Spine1995;20:791-5.

13. Oleinick A, Gluck JV, Guire K. Factors affecting first return towork following a compensable occupational back injury. AmJ Ind Med 1996;30:540-55.

14. Loesler JD, Henderlite SE, Conrad DA. Incentive effects ofworkers’ compensation benefits: a literature synthesis. MedCare Res Rev 1995;52:34-59.

15. Crook J, Milner R, Schultz IZ, Stinger B. Determinants ofoccupational disability following a low back injury: a criticalreview of the literature. J Occup Rehabil 2002;12:277-95.

16. Gunderson M, Hyatt D. Some benefit considerations in workers’compensation. Accessed March 1, 2011: http://www.llbc.leg.bc.ca/public/pubdocs/bcdocs/345628/gunderson_hyatt_consider.pdf.

17. Hirsch BT. Incentive effects of workers’ compensation. ClinOrthop Relat Res 1997;336:33-41.

18. McNaughton HK, Sims A, Taylor WJ. Prognosis for peoplewith back pain under a no-fault 24-hour-cover compensationscheme. Spine 2000;25:1254-8.

19. Ammendolia C, Cassidy D, Steensta I, et al. Designing aworkplace return-to-work program for occupational low backpain: an intervention mapping approach. BMC MusculoskeletDisord 2009;10:65.

20. Loisel P, Abenhaim L, Durand P, et al. A population-based,randomized clinical trial on back pain management. Spine1997;22:2911-8.

21. Anema JR, Steenstra IA, Bongers PM, et al. Multidisciplinaryrehabilitation for subacute low back pain: graded activity orworkplace intervention or both? A randomized controlledtrial. Spine 2007;32:291-8.

22. Steenstra IA, Anema JR, van Tulder MW, Bongers PM, deVet HC, van Mechelen W. Economic evaluation of a multi-stage return to work program for workers on sick-leave due tolow back pain. J Occup Rehabil 2006;16:557-78.

http://www.iwh.on.ca/compensation-fact-sheets

http://www.wsib.on.ca/en/community/WSIB/230/ArticleDetail/24338?vgnextoid=8037a5596b8d7210VgnVCM100000449c710aRCRD&vgnextchannel=f2e5007c39867210VgnVCM100000449c710aRCRD





http://www.llbc.leg.bc.ca/public/pubdocs/bcdocs/345628/gunderson_hyatt_consider.pdf




23. Durand MJ, Vezina N, Loisel P, Baril R, Richard MC, DialloB. Workplace interventions for workers with musculoskeletaldisabilities: a descriptive review of content. J Occup Rehabil2007;17:123-36.

24. Sterman JD. Business dynamics: system thinking andmodeling for a complex world. Irwin/McGraw-Hill: Boston,Mass; 2000.

25. Sterman JD. Learning from evidence in a complex world. AmJ Public Health 2006;96:505-14.

26. Homer JB, Hirsch GB. System dynamics modeling for publichealth: background and opportunities. Am J Public Health2006;96:452-8.

27. Leischow SJ, Milstein B. Systems thinking and modeling forpublic health practice. Am J Public Health 2006;96:403-5.

28. Sterman JD. System methodologies for solving real-worldproblems: applications in public health. Accessed March 18,2011: http://videocast.nih.gov/Summary.asp?file=13712.

29. Homer J, Jones A, Seville D, Essien J, Milstein B, Murphy D.The CDC’s diabetes systems modeling project: developing anew tool for chronic disease prevention and control. AccessedMarch 1, 2011: h t tp : / /www.susta iner .org /pubs/Diabetes_System%28ISDC04%29.pdf.

30. Hirsch G, Homer J, Evans E, Zielinski A. A system dynamicsmodel for planning cardiovascular disease interventions. Am JPublic Health 2010;100:616-22.

31. Rwashana AS, Williams DW, Neema S. System dynamicsapproach to immunization healthcare issues in developingcountries: a case study in Uganda. Health Informatics J 2009;15:95-107.

32. Miller RL, Levine RL, McNall MA, Kharmarko K, ValentiMT. A dynamic model of client recruitment and retention incommunity-based HIV prevention programs. Health PromotPract 2011;12:135-46.

33. Rwashana AS, Williams DW. Enhancing immunization cover-age through health information systems: a system dynamicsapproach. Stud Health Techol Inform 2007;130:247-56.

34. Milstein B, Jones A, Homer JB, Murphy D, Essien J, SevilleD. Charting plausible futures for diabetes prevalence in theUnited States: a role of system dynamics simulation modeling.Prev Chronic Dis 2007;4:A52.

35. Atun RA, Lebcir RM, Drobniewski F, McKee M, Coker RJ.High coverage with HAART is required to substantiallyreduce the number of deaths from tuberculosis: systemdynamics simulation. Int J STD AIDS 2007;18:267-73.

36. Jones AP, Homer JB, Murphy DL, Essien JD, Milstein B,Seville DA. Understanding diabetes population dynamicsthrough simulation modeling and experimentation. Am JPublic Health 2006;96:488-94.

37. Atun RA, Lebcir R, Drobniewski F, Coker RJ. Impact of aneffective multidrug-resistant tuberculosis control programmein the setting of an immature HIV epidemic: system dynamicssimulation model. Int J STD AIDS 2005;16:560-70.

38. Hoard M, Homer J, Manley W, Furbee P, Hague A,Helmkamp J. Systems modeling in support of evidence-based disaster planning for rural areas. Int J Hyg EnvironHealth 2005;208:117-25.

39. Lipscomb HJ, Loomis D, McDonald MA, Argue RA, Wing S.A conceptual model of work and health disparities in theUnited States. Int J Health Serv 2006;36:25-50.

40. Fulton-Kehoe D, Franklin G, Weaver M, Cheadle A. Years ofproductivity lost among workers in Washington state:modeling disability burden in workers’ compensation. Am JInd Med 2000;37:656-62.

41. Campolieti M. Multiple state hazard models and workers’compensation claims: an examination of workers’ compensa-tion data from Ontario. Accid Anal Prev 2000;33:197-202.

42. Apalit N. The work ratio—modeling the likelihood of returnto work for workers with musculoskeletal disorders: a fuzzylogic approach. Work 2010;36:77-87.

43. Kniesner TJ, Leeth JD. Separating the reporting effectsfrom the injury rate effects of workers’ compensationinsurance: a hedonic simulation. Ind Labor Relat Rev 1989;42:280-93.

44. Sears JM, Heagerty PJ. Including injured workers withoutcompensated time loss in Cox regression models: analyzingtime loss using all available data. J Occup Rehabil 2008;18:225-32.

45. Soklaridis S, Ammendolia C, Cassidy D. Looking upstream tounderstand low back pain and return to work: psychosocialfactors as the product of system issues. Soc Sci Med 2010;71:1557-66.

46. Workplace Safety and Insurance Board. Statistical support tothe annual reports, 1980-1987, Toronto, Ontario.

47. Workplace Safety and Insurance Board. Overview of Workers’Compensation in Ontario: 1980 to present. Accessed May 10,2011: http://www.wsib.on.ca/files/Content/FundingReviewFROverviewWorkersComp/OverviewWorkersCompensation.pdf.

48. Harvey AC, Durbin J. The effects of seat belt legislation onBritish road casualties: a case study in structural time seriesmodeling. J R Stat Soc 1986;149:187-227.

49. Breslin FC, Tompa E, Mustard C, Zhao R, Smith P, Hogg-Johnson S. Association between the decline in workers’compensation claims and workforce composition and jobcharacteristics in Ontario, Canada. Am J Public Health 2007;97:453-5.

50. Theil H. Applied Economic Forecasting. North HollandPublishing Company 1966: Amsterdam.

51. Tank-Nielsen C. Sensitivity analysis in system dynamics.Accessed May 12, 2011: http://www.systemdynamics.org/conferences/1976/proceed/tank733.pdf.

52. Statistics Canada. Estimates of population, 1980-2009. AccessedDecember 8, 2011: http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=0510001&paSer=&pattern=&stByVal=1&p1=1&p2=37&tabMode=dataTable&csid=.

53. Statistics Canada. Labour force survey estimates, 1980-2009.Accessed February 6, 2012: http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=2820087&paSer=&pattern=&stByVal=1&p1=1&p2=37&tabMode=dataTable&csid=.

54. Rautiainen RH, Ledolter J, Sprince NL, et al. Effects ofpremium discount on workers’ compensation claims inagriculture in Finland. Am J Ind Med 2005;48:100-9.

55. Tompa E, Trevithick S, McLeod C. Systematic review of theprevention incentives of insurance and regulatory mechanismsfor occupational health and safety. Scand J Work EnvironHealth 2007;33:85-95.

56. Stover B, Wickizer TM, Zimmerman F, Fulton-Kehoe D,Franklin G. Prognostic factors of long-term disability in aworkers’ compensation system. J Occup Environ Med 2007;49:31-40.

57. Brooker AS, Frank JW, Tarasuk VS. Back pain claim ratesand the business cycle. Soc Sci Med 1997;45:429-39.

58. Lemstra M, Olszynski WP. The effectiveness of standard care,early intervention, and occupational management in workers’compensation claims. Spine 2004;29:1573-9.

59. Hincapie CA, Cassidy JD, Cote P. Is a history of work-relatedlow back injury associated with prevalent low back pain anddepression in the general population? BMC MusculoskeletDisord 2008;9:22.

60. Rossignol M, Suissa S, Abenhaim L. The evolution ofcompensated occupational spinal injuries: a three-year follow-up study. Spine 1992;17:1043-7.

http://videocast.nih.gov/Summary.asp?file=13712

http://www.sustainer.org/pubs/Diabetes_System%28ISDC04%29.pdf

http://www.sustainer.org/pubs/Diabetes_System%28ISDC04%29.pdf

http://www.wsib.on.ca/files/Content/FundingReviewFROverviewWorkersComp/OverviewWorkersCompensation.pdf



http://www.systemdynamics.org/conferences/1976/proceed/tank733.pdf

http://www.systemdynamics.org/conferences/1976/proceed/tank733.pdf

http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=0510001&paSer=&pattern=&stByVal=1&p1=1&p2=37&tabMode=dataTable&csid=







61. Ruseckaite R, Collie A. Repeat workers’ compensationclaims: risk factors, costs and work disability. BMC PublicHealth 2011;11:492.

62. Smith P, Hogg-Johnson S, Mustard C, Chen C, Tompa E.Comparing the risk factors associated with serious versus lessserious work-related injuries in Ontario between 1991 and2006. Am J Ind Med 2012;55:84-91.

63. Smith PM, Chen C, Hogg-Johnson S, Mustard CA, Tompa E.Trends in the health care use and expenditures associated withno lost-time claims in Ontario: 1991 to 2006. J Occup EnvironMed 2011;53:211-7.

64. Thomason T, Pozzebon S. Determinants of firm workplacehealth and safety and claims management practices. Ind LaborRelat Rev 2002;55:286-307.

65. Kralj B. Employer responses to workers compensationinsurance experience rating. Relat Ind-Ind Relat 1994;49:41-61.

66. Box GEP, Draper NR. Empirical model-building and responsesurfaces. Hoboken: Wiley; 1987. p. 688.

67. Lyman GH, Kuderer NM. The strengths and limitations ofmeta-analyses based on aggregate data. BMC Med ResMethodol 2005;5:14.

Examination of the relationship between theory-driven policies and allowed lost-time back claims in...

Documents

Transcript of Examination of the relationship between theory-driven policies and allowed lost-time back claims in...