AMERICAN JOURNAL OF INDUSTRIAL MEDICINE 50:155–172 (2007)

The Economic Burden of Carpal Tunnel Syndrome:Long-Term Earnings of CTS Claimants in

Washington State

Michael Foley, MA,� Barbara Silverstein, PhD, MPH, and Nayak Polissar, PhD

Background The long-term earnings losses borne by injured workers, beyond thosecovered by workers’ compensation insurance, are rarely estimated. The post-claimearnings of a cohort of carpal tunnel syndrome (CTS) claimants are tracked over a periodof 6 years and compared to the earnings of claimants with either upper extremity fracturesor dermatitis.Methods Quarterly earnings records of 4,443 workers in Washington State who filedclaims with the State Fund in 1993 or 1994 for CTS are compared to those of 2,544 withupper-extremity fracture claims and 1,773 with medical-only dermatitis claims.Multivariate regression was used to identify the effect of injury type on earnings fromthat of other potential predictors.Results CTS claimants recover to about half of their pre-injury earnings level relative tothat of comparison groups after 6 years; they also endured periods on time-loss three timeslonger than claimants with upper extremity fractures. CTS surgery claimants had betteroutcomes than those who did not have surgery. Earnings recovery fractions among CTSclaimants were better for workers who: (1) were younger; (2) had stable pre-claimemployment; (3) lived in the Puget sound area; (4) worked for large businesses; (5) workedin non-construction/transportation industries; or (6) were in the higher pre-injuryearnings categories. Cumulative excess loss of earnings of the 4,443 CTS claimants was$197 million to $382 million over 6 years, a loss of $45,000–$89,000 per claimant. Thisunderscores the importance of prevention, early diagnosis, and accommodation for returnto work. Am. J. Ind. Med. 50:155–172, 2007. � 2007 Wiley-Liss, Inc.

KEY WORDS: carpal tunnel syndrome; occupational injuries; occupational diseases;economic impacts; workers’ compensation; cost of illness; disability; employment

INTRODUCTION

The full economic loss that results from work-related

injury goes far beyond the direct covered medical costs,

vocational rehabilitation expenditures, pensions, and wage-

replacement costs that comprise the reported cost of a

workers’ compensation claim. Employers bear a portion of

the full economic loss in the form of indirect cost including

production interruption, accident investigation, and the

recruiting, and training of replacement workers. On the

injured workers’ side there are economic losses borne by

the workers’ families: the loss of the ability to perform

family and social roles, depression, living/working with pain,

impacts to disability and welfare systems, and loss of the

worker’s contribution to community life [Keogh et al., 2000].

Perhaps most significantly, there is the long-term loss of

earnings, which extends beyond the period of wage

� 2007Wiley-Liss, Inc.

Safety and Health Assessment and Research for Prevention (SHARP) Program,Washing-ton State Department of Labor and Industries, Olympia,Washington

*Correspondence to: Michael Foley, Safety and Health Assessment and Research forPrevention (SHARP) Program, Washington State Department of Labor and Industries,P.O. Box 44330, Olympia,WA 98504-4330. E-mail: folm235@lni.wa.gov

Accepted 7 December 2006DOI10.1002/ajim.20430. Published online inWiley InterScience

(www.interscience.wiley.com)

replacement, and is due to the worker’s injury-related loss of

function or skills. Several recent studies, applying a human

capital approach, use data on earnings from state employment

security departments for injured workers and matched control

groups [Biddle, 1998; Boden and Galizzi, 1999; Reville,

1999; Reville et al., 2002]. One technique for estimating long-

term earnings loss is to use multiple regression analysis to

compare the earnings of workers with accepted time-loss

claims to those of workers with medical-only claims.

Covariates such as age, gender, industry, and occupation are

included in these models in order to isolate the separate impact

of injury type on earnings [Biddle, 1998; Boden and Galizzi,

1999]. Another approach is to choose a set of uninjured

workers from state unemployment insurance databases,

matched to each injured worker by initial earnings and firm

or industry [Reville, 1999; Reville et al., 2002]. These studies

show that workers who have time-loss injuries are likely to

experience substantial income losses that continue long after

their wage-replacement benefits end [Reville et al., 2001].

Using a sample of workers with permanent disability claims,

Reville estimated 5-year earnings losses averaged $23,692;

for workers with the most seriously disabling injuries losses

over $90,000 over the same period of time [Reville, 1999].

These earnings losses, together with the unpaid fringe benefits

that workers would have received, provide a lower-bound

estimate of the economic loss to society from the worker’s

reduced output. Although this provides an estimate of the lost

output, it cannot capture the qualitative portion of the burden

of injury.1 It should be noted here that the burden of work-

related injury is measured from the perspective of society as a

whole, rather than from either the worker or the employer’s

point of view. Since wage replacement payments made by the

State Fund to workers with time-loss claims are transfers from

one part of society to another, they are not counted in the

estimates presented here. It is assumed that the economy is at

or near full-employment, so that the loss to society of one

worker’s output due to injury is not offset by the gain to society

from replacing that output by employing a formerly idle

worker. Using a combination of the human capital method

described above and the results of several national surveys,

Leigh has estimated an overall economic loss of about

$155 billion (in 1992 dollars) for all work-related injury and

illness [Leigh et al., 2000]. About half of this cost was

attributed to lost earnings. Using somewhat different methods,

Miller arrived at a similar estimate [Miller, 1997]. The long-

term earnings losses experienced by injured workers may be

due to incomplete recovery of physical function prior to return

to work, or they may be due to labor market effects of the

worker’s injury-related absence which may persist long after

the worker recovers. These effects include loss of pre-injury

job, loss of seniority or of investment in job-specific skills.

They may also be due to stigma attaching to the worker with

long periods of injury-related absence. Such workers may

come to be viewed as being ‘‘injury prone’’ or ‘‘unreliable’’

causing the worker to have more difficulty finding employ-

ment or advancement. There is also evidence that the longer

the duration of time-loss the more frequent the episodes of

unemployment after the initial return to work [Galizzi and

Boden, 1996; Eakin et al., 2003].

MATERIALS AND METHODS

In this study the human capital approach is used to

estimate the burden of work-related carpal tunnel syndrome

(CTS). The approach is similar to that used by Reville in that

the earnings of CTS claimants are compared to those of

claimants with other injury types. Ideally, these earnings

should be compared to those of uninjured workers matched

by gender, age, and occupation/industry to the set of injured

workers. Unfortunately it is not possible to obtain data

on gender or age for uninjured workers from the Washington

State Employment Security database. This information is

available, however, for all workers who file a workers’

compensation claim, which allows a comparison of the

earnings profiles of workers with a CTS-related claim to

those of workers with other kinds of injury claims while

controlling for a large number of earnings-related factors

independent of injury type. Two categories of injury were

chosen as comparison groups: workers with an upper-

extremity fracture that resulted in four or more lost work-

days; and workers who filed medical-only claims for

dermatitis. It is assumed that the medical-only dermatitis

claimants would be the claimant cohort most similar to

uninjured workers in terms of their long-term earnings.

While this approach does not permit a calculation of the full

earnings loss a worker bears when they suffer work-related

CTS, any fraction of earnings lost in excess of that suffered

by the workers in the two comparison groups gives a lower

bound estimate of the loss caused by the injury [Biddle, 1998;

Boden and Galizzi, 1999]. Estimates of work-related CTS

incidence based upon frequency of claims are likely to be just

a fraction of the true number of cases, as survey-based

evidence has documented [Biddle et al., 1998; Morse et al.,

2001]; and the total burden of these cases extends well

beyond the direct costs associated with the claims themselves

[Keogh et al., 2000].

Workers’ Compensation Data

Washington State employers, except the federal govern-

ment, and employers of railroad and long-shore workers, are

1 This might be achieved by using other techniques such as ‘‘contingentvaluation,’’ where individuals are asked what they would be willing to payto reduce their risk of an injury or illness by a given amount (Smith andDesvouges, 1987). Another approach quantifies the ‘‘risk premium’’ paidto workers exposed to job hazards (Viscusi, 1993). These approaches havebeen widely applied to fatal risks to derive ‘‘value of life’’ measures, butbecause of their subjectivity, computational complexity, and because theyestimate the value of changes in risk at the margin instead of the totalsocial cost of illness, we define burden as the quantifiable loss to societyof the output of injured workers as measured by lost earnings.

156 Foley et al.

required to obtain workers’ compensation insurance through

the Washington State Department of Labor and Industries

(L&I) industrial insurance system unless they are able to self-

insure. The L&I State Fund provides workers’ compensation

to approximately 160,000 employers and approximately

66% of the covered workforce. The remaining 400 (primarily

large) employers self-insure and employ approximately one-

third of the Washington workforce.2

All Washington workers’ compensation State Fund

claims receive codes for nature, type, body part, source,

and associated source of injury or illness based upon the

American National Standards Institute (ANSI) Z16.2 coding

system [ANSI, 1986].3 These codes are assigned early in the

claims administration process, and represent an initial

definition of the primary injury or illness. ANSI codes can

be used individually or in combination with each other to

identify specific injury or illness claims. ANSI codes can be

combined with ICD-9 diagnosis and CPT procedure codes to

develop case criteria as well.

L&I maintains claims databases for both State Fund and

self-insured employers, however the information collected

from self-insured employers is more limited, has incomplete

claim costs and lost workdays data and does not allow

identification of medical-only cases by ANSI or ICD-9

codes. This led us to restrict this study only to State Fund

claims. The exclusion of self-insured employers means that

claimants employed by the very largest employers in

Washington State were not included in the analysis.

The approach to defining cases of carpal tunnel

syndrome for purposes of this study was to start with a

definition that was narrowly focused upon a limited set of

ANSI codes, which restricted cases to those with an onset

related to overexertion. This first group was then augmented

by drawing in those claims with a more expanded set of ANSI

codes related to the upper extremities. Finally, claims were

added which had diagnosis (ICD-9) or procedure (CPT)

codes consistent with CTS, but not the relevant ANSI codes.

These definitions are displayed in Table I.

The definition of Group 1 has the advantage of being

related specifically to claims related to the wrist or hand area,

and only covering injuries that are classified early in the case

history as being carpal tunnel syndrome. Earnings outcomes

for this group may be affected by this early case diagnosis, so

these are analyzed separately. The disadvantage of restricting

ourselves to this narrow definition of CTS is that it misses

many cases which later turn out to be CTS, but which are not

classified as such because claims are assigned ANSI codes

relatively early in the case history. In addition, with ANSI

TABLE

I.SelectionC

odesforCTS

Cohort,byCTSGroup

Group1

Group2

Group3

Numberofcases

2,123

1,043

1,277

ANSInature

562:Bell’sPalsy

andotherdiseasesofthenervesandperipheralganglia[CTS]

[562],190,260,310,400,560,561,580,995,999:dislocation,inflammationof

joints,tendonsorm

uscles;strains,sprains;multipleinjuries;conditionsofner-

vous

system

;sym

ptom

sofill-defined

conditions;otherinjurynec;unclassi-

fied,notdetermined

Any

AND

AND

AND

ANSIbodypart

320^

350:wrist,hand,fingers

[320^350],300,310,311,313,315,318,398,450:upperextremities;arm(s)

abovew

rist;upperarm;elbow;forearm;arm-multiple;shoulder(s)

Any

AND

AND

AND

ANSIType

120^

124,129:over-exertioninlifting,pushing,pulling,wielding,throwing,

carrying,NEC

[120^124,129],082,083,085,086,100:rubbed

orabradedby

objectsbeing

handled,vibratingobjects,repetitionofpressure;bodilyreaction

Any

AND

AND

ICD-9code

354.0:carpaltunnelsyndrome

354.0:carpaltunnelsyndrome

354.0:carpaltunnelsyndrome

OROR

CPTcode

Any

64721,29848:carpaltunnelreleasesurgery;arthroscopicsurgeryon

thew

rist

64721,29848:

carpal

tunnelrelease

surgery;arthroscopicsurgeryon

thew

rist

2 The L&I State Fund offers elective workers’ compensation coverage forself-employed workers and household employers with two workers orless, and other defined exemptions listed in Revised Code of WashingtonTitle 51. This segment of the workforce accounts for approximately 7% ofthe total employed.

3 A complete description of the L&I claims management database can befound in Silverstein et al. [2002].

Long-Run Economic Burden of CTS 157

coding, only one body area is listed, usually the one identified

as the major area of concern. This is an important limitation

in comparison with the ICD-9 and CPT codes, which come

from medical bills submitted over the course of the claim’s

history, and which can refer to multiple body areas.

Previous research in musculoskeletal disorders con-

ducted with the Washington State workers’ compensation

claims data has shown that the set of ANSI and ICD-9 and

CPT codes in the third column of Table I bring in additional

CTS cases, and this augmented set of cases is called Group

2 [Silverstein et al., 2002]. The selection codes for Group

2 can overlap partially with those for Group 1. For example, a

case with a nature code 562 (Bell’s Palsy and other Diseases

of the Nerves, etc) and a type code of 120 (overexertion), but

a body part code of 398 (arm-multiple areas) would be a

Group 2 case, as long as the diagnosis code is ‘‘354.0’’ (CTS)

or there is a medical procedure code of ‘‘64721’’ (carpal

tunnel release surgery) or ‘‘29848’’ (arthroscopic procedure

on the wrist). The disadvantage of this augmented definition

is that sometimes these cases involve injuries to multiple

body parts, which means that the fraction of the impact on

the worker’s earnings that is related to their carpal tunnel

condition is unknown.

Finally, there are also CTS cases that do not receive a

combination of ANSI codes that would allow one to include

them in either of the above groups. They may have CTS-

related codes for body part or type or nature, but not all three.

However, at some point in their case history these workers do

receive either a medical diagnosis of CTS (ICD-9 354.0) or

a medical procedure related to CTS is performed (CPT

64721 or 29848). This is more likely to occur in workers with

multiple injuries where the main initial injury is something

other than CTS. The selection codes for Group 3 can overlap

partially with those for Groups 1 and 2. For example, a case

with a nature code of 160 (contusion) and a body part code of

331 (upper arm) and a type code of 030 (fall from elevation)

would not be either a Group 1 or a Group 2 case. But if there

was an ICD-9 code of 354.0 on the claimant’s medical bills,

then this is a Group 3 CTS case. These three CTS subgroups

form a comprehensive set of all State Fund compensable

CTS cases filed in the years 1993 and 1994, a total of

4,443 claims.4

Compensable upper-extremity fractures and medical-

only dermatitis claims were chosen as comparison groups.

Fracture cases were used in order to have a comparison group

which, like CTS, involved a significant interference with

ability to work and which often results in a significant period

of lost workdays. This is also a group where, unlike CTS,

the injury is traumatic, with visible signs and unmistakable

work-relatedness, and where the diagnosis and treatment

would follow rapidly after injury onset. Medical-only

dermatitis claims were chosen because a comparison group

was needed with a condition which would not be expected to

result in interference with the worker’s ability to work or

result in long-term productivity loss. This group is a close

approximation to an uninjured control group for whom a full

set of demographic and industry characteristics that predict

earnings could still be obtained.5

For dermatitis the ANSI coding criterion was only that

the claim must have one of the nature codes associated with

dermatitis (Z16.2 nature codes 180–184, 189). This resulted

in 1,773 eligible dermatitis cases being selected. For upper

extremity fractures the coding criteria was any claim with an

ANSI Nature code of 210 (fracture) and with one of the ANSI

Body Part codes associated with the upper extremities:

(Z16.2 Body Part codes 300, 310, 311, 313, 315, 318, 320,

330, 340, 350, 398 or 450). Using these criteria resulted in

2,544 fractures cases.

Data for workers’ compensation claims for carpal tunnel

syndrome, upper extremity fractures, and dermatitis filed

during the calendar years 1993 and 1994 were extracted in

January 1999 from the L&I Industrial Insurance claims

database. Data elements captured included: social security

number; date of injury; birth date; age; claim status

(‘‘compensable’’ lost time claim of 4 or more days or

medical treatment claim only); gender; total cost of claim;

days of time-loss paid; dollar amount of time-loss payments;

dollar amount of medical aid payments; industry of claimant;

county of claimant; ANSI z16.2 codes for body area; nature;

and type of disorder, number of claims filed by the claimant

between 1991 and 1998, and whether the claimant received a

surgical procedure related to CTS. Claim costs and time-loss

days reported here reflect actual totals for closed claims. For

claims that were not closed, costs and time-loss days reflect

actual totals paid through January 1999 plus total future

estimated costs and time-loss days, as calculated by agency

case reserve staff.

Claimant Earnings Profiles

A total of 9,305 claims were identified in the State Fund

database. The quarter in which the claim was filed divides

time into ‘‘pre-injury period’’ and ‘‘post-injury period’’ for

each claimant. These claimants were linked by social

security number (SSN) to their quarterly earnings records

as reported by their employers to the Washington State

Employment Security Department (ESD), which manages

unemployment insurance. ESD collects information on

wages and hours worked per quarter for each employee in

the state. In this way, quarterly earnings profiles for the

9,305 claimants were assembled, beginning with the first

4 More than 98% of ‘‘compensable’’ CTS claims involve four or more lostworkdays; however there are a small number of cases where no time-lossdays are incurred.

5 Selecting a control group of workers without any claims from theEmployment Security Department’s reported earnings database would beunsatisfactory because these records do not indicate the worker’s genderor age, which are important predictors of earnings.

158 Foley et al.

quarter of 1990 and continuing through the fourth quarter of

2001. To reduce the loss of claimants from the database due

to retirement, and to focus on workers having pre-injury

earnings records in the ESD database, workers whose age at

time of injury was under 16 or over 70 were excluded.

Claimants with no reported earnings in the quarter of injury

were also excluded, as this quarter is used as a benchmark to

compare to post-injury earnings. Finally, to ensure that any

earnings impact observed is not related to some other work-

related injury, we excluded those claimants with any previous

lost-time claim from 1990 up to the first claim for the

target condition in the study period. These exclusions

together yielded 8,760 earnings records available for study:

4,443 CTS claimants, 2,544 fracture claimants, and 1,773

claimants with medical-only dermatitis. Calendar quarters

were converted to quarters before/after the quarter of injury

to define time elapsed since injury when looking across

claimants injured in different calendar quarters. All earnings

were converted into year 2001 dollars using the National

Urban Wage Earners Consumer Price Index. In addition to

providing claimants’ quarterly earnings, ESD wage records

provided information on the claimants’ employer at the

time of injury. This included the total employment of the

establishment as well as the four-digit standard industrial

classification (SIC) code. Finally, this database allows one to

track breaks in the claimants’ employment history over the

40 quarters of the study period.

When a claimant has no reported earnings for a given

quarter, these were treated as having zero earnings. It is

recognized that in some cases these claimants may have

simply left Washington State but are working elsewhere, or

they may have taken jobs not covered by unemployment

insurance, or they may have become self-employed. Due to the

natural turnover in the labor market a proportion of any set of

workers who all have earnings records in any given index

quarter, will have earnings records in adjacent quarters that

will decline the further away from the index quarter one looks,

either forward or backward in time. Since a worker’s absence

from the ESD wage records is regarded as their having zero

earnings for that quarter, the time-profile of the cohort’s

median earnings slopes downwards as one moves in either

direction away from the index quarter. This would be true even

for a set of uninjured workers. In the case of the three injury

cohorts, approximately 20% of these workers have no reported

earnings in the fourth quarter prior to their quarter of injury.

The downward slope after injury is thus due both to natural

labor force turnover (e.g., retirement, self-employment, and

out-migration) and to the effect of injury preventing their

return to work. Thus the median earnings profiles presented

below are likely underestimates of the true earnings of these

claimants. Nevertheless, they do focus on the comparative

change in earnings between those of the group whose injuries

are relatively minor—the medical-only claimants—and those

of the groups whose injuries caused them to lose four or more

days from work and to receive wage-replacement benefits—

the time-loss or ‘‘compensable’’ claims. Reasons for leaving

employment that are unrelated to injury may vary system-

atically across the three injury cohorts. To isolate the

independent impact of the injury on CTS claimants’ earn-

ings, we controlled for the variation in worker characteristics

which might be related to the probability of their leaving

formal employment separately from the impact of their

injury. In this way the excess loss of earnings of the CTS

cohort over that of either fractures or dermatitis claimants is

seen as an indication of the economic burden of CTS.

Multivariate Analysis

In order to detect the independent contribution of injury

type to long-term economic burden, post-injury earnings as a

percent of pre-injury earnings level were modeled on the set

of predictors described in Table II.

Using linear regression, the following model was

estimated:

LOGðYYR6=YPREÞ ¼ aþ b1 � INJURYTYPE þ b2 � AGE

þ b3�EMPLOYERSIZE þ b4�SEX

þ b5�AGE � SEX þ b6�SECTOR

þ b7�REGION þ b8�PRESTABLE

In this model YYR6/YPRE is the individual worker’s ratio

of average quarterly earnings in the year beginning 6 years

after the quarter of injury to that of average quarterly earnings

in the year prior to injury. Claimants with less than $100 in

average quarterly earnings in the year prior to their injury

were dropped from this model.6 For each variable in the

model, the group expected to have the highest post-injury

earnings recovery is used as the reference category. These

are: dermatitis, youngest age group, male, large employer,

fixed-site industry, Puget Sound region, and stable pre-injury

employment history. Other worker characteristics may also

be expected to affect the time profile of their earnings, but

were unavailable for inclusion in the model, such as

education level, race, or occupation. But since each worker’s

earnings in the sixth year post-injury is taken into the model

as a fraction of their own earnings level in the year prior to

their injury, this makes each worker serve as their own-

matched control, which helps to compensate for the missing

variables. Although only the results from the model at 6 years

post-injury are presented, this model was also fitted for 1 and

3 year(s) post-injury in order to see whether the predictors

retain the same approximate impacts on earnings recovery

and these results are discussed below.7

6 Since earnings in period YYR6 could be zero, we adjusted these claimants’earnings recovery fraction from 0 to 0.0001 prior to the log-transformation.

7 ‘‘Sixth year post-injury’’ is defined as quarters 25–28 after the quarter ofinjury; ‘‘3 years post-injury’’ means quarters 13–16; ‘‘1 year post-injury’’includes quarters 5–8.

Long-Run Economic Burden of CTS 159

In addition to this model each possible two-way

interaction between predictors was tested whose main effect

was statistically significant at the 6-year point. For each

interaction statistical significance was determined by com-

paring the model as shown above with a model including

all of these terms plus the additional interaction (such as

injury type and age category). Of all the possible two-way

interactions that might be added to the model above, only two

(age category by injury type and age category by pre-injury

employment stability) were statistically significant at the

0.05 level. None of the interactions were statistically

significant at the more conservative 0.01 level. With 20

possible two-way interaction terms available beyond the

age� gender interaction already included, the risk of a false

positive is high, so the number of interactions in the model

presented in Table V-a was limited to the only one that was

statistically justified.

In addition, a logistic regression model of the probability

of having a surgery was fitted as a function of CTS subgroup,

pre-injury earnings category, age group, sex, employer size,

sector, region, and pre-employment stability.

Cumulative Earnings Loss

The multivariate regression model outlined above

estimates the impact of injury type on post-injury earnings

recovery, at 6 years post-injury, for CTS claimants relative to

fractures and dermatitis claimants. This estimate controls for

the impact of differences across injury cohorts in age, gender,

size of employer and other predictors. Similarly, to portray

the cumulative earnings loss of the whole CTS cohort over 1,

3, and 6 years post-injury, the difference in earnings recovery

was compared across the three injury cohorts by fitting the

multivariate regression model to each post-injury quarter

separately. For each CTS case the individual’s income loss

attributable to CTS relative to either fracture or dermatitis

was calculated. In order to estimate the loss for each post-

injury quarter T, a multivariate linear regression model for the

log ratio of post-injury income at quarter T and pre-injury

income was fitted. The following model was used.

LRATIOT ¼ LOGðYT=YPREÞ ¼ aT þ b1T

� FRACTURE þ b2T�CTS þ b3T

� AGE25�34þb4T � AGE35�39þb5T

� AGE50þ þ b6T�EMPLOYERSIZESMALL þ b7T

� EMPLOYERSIZEMEDIUMþb8T

� SEX þ b9T�AGE25�34 � SEX þ b10T

� AGE35�39 �SEX þ b11T�AGE50þ�SEXþb6T

� SECTOR þ b7T�REGION þ b8T

� PRESTABLE þ e

where YT is the observed income in the post-injury quarter T

and YPRE is the mean observed income in the year prior to

injury. To estimate the loss attributable to CTS in quarter T

the individual’s estimated income was calculated as if they

had had a fracture instead of CTS (denoted by YTjFRAC).

YTjFRAC was calculated from the above multivariate

model as

YTjFRAC¼YT exp½PREDðLRATIOTjFRACÞ�PREDðLRATIOTÞ�¼YT exp½b1T�b2T�

where PRED(LRATIOTjFRAC) and PRED(LRATIOT) are

predicted values from the multivariate linear regression for

quarter T. PRED(LRATIOTjFRAC) is the individual’s pre-

dicted value using all of their covariate values as they are,

TABLE II. Definitions of Predictors Used inMultivariate Analysis

Variable Definition

INJURYTYPE Type of injury (CTS, fracture, or dermatitis)CTSGROUP Subgroup of CTS claimants (specific, augmented, or other)AGE Age of the claimant at time of claim; categories are16^24, 25^36, 37^49, and 50 or overEMPLOYERSIZE Size of the claimant’s employer asmeasured by employment; categories are1^10,11^49, 50 or overa

SEX Gender of claimantSECTOR Claimant worked in a fixed-site or non-fixed site industry; non-fixed industry includes construction and transportation; all other industries are

considered fixedREGION Geographic location of claimant’s place of employment; categories are: the Puget Sound area (King, Pierce, Snohomish,Thurston, and Kitsap

counties) and non-Puget sound area (all other counties)PRESTABLE Indicateswhether claimant had earnings in all sixquarters prior to the quarter of claimSURGERY Indicateswhether CTS claimant had either a carpal tunnel release surgery or an arthroscopic procedurePREINJURYEARNINGS Categorizeseachclaimantby their averageearningsover the fourquartersprior to theirquarterofclaim; categoriescorrespondto thequartiles

of the earnings distribution: Less than $2,743; $2,743^$5,093; $5,093^$7,748; and $7,748 andmore

aTests failed to reject the null hypothesis that a fourth size category (200 or more) was no different from the category 50^200 employees, so these were combined.

160 Foley et al.

but changing injury type from CTS to fracture. PRE-

D(LRATIOT) is the individual’s predicted value using all of

their covariate values as they are and keeping the injury type

as CTS. The estimated quarterly loss LOSSTjCTS-FRAC is the

difference between the observed income YT and YTjFRAC.

The individuals’ quarterly estimated losses were then

summed over all post-injury quarters and individuals to

provide a total estimate of excess earnings loss for the CTS

group during the periods of 1, 3, and 6 years following the

quarter of injury.8

All dollar amounts are expressed in year 2001 values,

and losses were discounted at 3% per year so that they are in

present value form at the time of injury. All statistical

analyses were carried out in SAS, version 9.1, and in the

statistical language R, version 2.01.

RESULTS

Demographic and employment-related characteristics

of claimants vary substantially when they are grouped by

injury type (see Table III). Compared with either fractures or

dermatitis claimants, CTS claimants tend on average to be

older, to be more steadily employed—defined as having

reported earnings in six out of six quarters prior to the quarter

of injury, and are more likely to be female. A disproportio-

nately greater number of CTS claimants reside in the Puget

Sound area of Washington State (defined as King, Pierce,

Snohomish, Thurston, and Kitsap counties) than either of the

other injury cohorts; and they tend to have higher pre-injury

earnings than do claimants in the other cohorts. Pre-injury

earnings also decline as one moves across the three CTS

subgroups: $5,404 for Group 1, $4,646 for Group 2, and

$4,587 for Group 3. Claimants with fractures are more likely

to work in ‘‘non-fixed site’’ industries—typically construc-

tion and transportation—and to work for smaller employers.

They are also the group with the highest proportion of males.

The claimants with dermatitis were the youngest, were most

likely to work for a large, fixed-site employer, and had the

lowest pre-injury earnings of the three cohorts. There was a

great deal of overlap in the industries in which claimants

worked across the three injury cohorts: six of the top eight

industries in both fractures and dermatitis show up in the

top eight for CTS. But there was a noticeable difference in

ranking among the top five for each: fractures tended to occur

more in the non-fixed site industries (e.g., construction and

transportation), while health care and retail trade played a

bigger part in both CTS and dermatitis cases. By two-digit

standard industrial code (SIC) the top five industries for

fractures are: SIC 17 (construction-trades), SIC 15 (con-

struction-residential); SIC 24 (sawmills and logging); SIC 01

(agriculture); SIC 58 (restaurants). These account for 40% of

the total cases. By two-digit SIC code the top five industries

for CTS are: SIC 17 (construction-trades), SIC 58 (restau-

rants); SIC 80 (health care); SIC 54 (grocery stores); and SIC

24 (sawmills and logging). These account for about 30% of

the total. By two-digit SIC code the top five industries for

dermatitis are: SIC 58 (restaurants); SIC 01 (agriculture);

SIC 80 (health care); SIC 17 (construction-trades); and SIC

51 (wholesale trade-non-durable). These account for 40% of

the claims. For the three CTS groupings there was no

difference in industry composition: by two-digit SIC the

three subgroups overlapped each other by 70%–90%, with

SIC 58, 17, and 80 appearing in the top four in all three

subgroups.

TABLE III. Characteristics of Claimants by InjuryType

Carpal tunnelsyndrome Fractures

Dermatitis(med-only)

Total claimants 4,443 2,544 1,773Percentmale 43% 84% 62%Median age 38 34 30Percent Puget sound 58% 54% 49%Percent fixed industry 73% 66% 85%Percent large firm 54% 44% 62%Percent with pre-injury earnings stability 66% 55% 52%Median pre-injury quarterly earnings $5,032 $4,116 $3,438Median earnings as percent of pre-injury level: Year1a 69.6% 102.3% 95.2%Median earnings as percent of pre-injury level: Year 3a 62.3% 103.1% 94.1%Median earnings as percent of pre-injury level: Year 6a 52.9% 98.4% 83.3%

aYear1is quarters 5^8 after the quarter of injury; Year 3 is13^16 quarters after injury; Year 6 is 25^28 quarters after the quarterof injury.

8 One-year loss estimate used quarters 1–4 post-injury; 3-year estimateused quarters 1–12; 6-year estimate used quarters 1–24.

Long-Run Economic Burden of CTS 161

Time-Loss Duration

Since the length of time a claimant is not able to work

following injury is a significant factor in determining their

long-run earnings, the issue of time-loss was examined in

more detail for the two time-loss injury categories: CTS and

fractures. In particular, we looked at the distribution of time-

loss by injury type and other demographic characteristics to

see whether time-loss varied systematically with other

potential predictors of long-run earnings. It should be noted

that time-loss days is not a measure of time elapsed from

injury until return to work. It simply measures days of State

Fund paid time-loss incurred while the claim is still open.

As Table IV shows, workers with CTS claims are on time-

loss far longer than are those with fractures. Within the CTS

cohort the distribution of time-loss shifts to progressively

longer and longer duration when comparing Group 1 to either

Group 2 or Group 3. Those CTS claimants who undergo a

carpal tunnel surgical procedure have about a 30% longer

period of time-loss than those who do not have a surgery, with

most of this difference concentrated among claimants in

Group 1. There is a stronger age-related increase in time-loss

duration for the CTS cases than is true of the fractures, except

for the oldest age cohort. Although women account for a

larger fraction of CTS cases than men, it is the men who tend

to remain on time-loss status longer. There is no significant

difference by sex for duration of time-loss among the fracture

cases. Large differences in time-loss duration also appear for

TABLE IV. Time-Loss Days Paid to Claimants, by Demographic Characteristics and OtherAspects of the Claim and Claimant

Cases Median time-loss days paid

NCTS NFRAC

By injury typea

CTS 4,443 138Fracture 2,544 46

NCTS NFRAC All cases Caseswith surgery Caseswithout surgeryBy CTS categorySpecific* 2,123 101 105 74Augmented* 1,044 165 182 154Other* 1,277 238 228 256

Cases Median time-loss days paid

NCTS NFRAC CTS Fracture

By age group16^24 years old* 359 621 109 4325^36 years old* 1,539 888 144 4237^49 years old* 1,860 689 145 5150^70 years old 685 346 127 64

By sexMale*b 1,909 2,136 155 45Female*,b 2,534 408 122 46

Bypre-injury stabilityContinuous* 2,945 1,402 113 42Non-continuous* 1,498 1,142 197 50

By industry sectorAll other* 3,247 1,670 123 42Construction andtransportation*

878 841 188 55

By employer size200þworkers*,b 1,174 413 107 4250^199workers*,b 1,240 701 130 3911^49workers*,b 1,216 849 165 511^10workers 813 581 162 51

*Differences between categories are statistically significant at the 5% level.aTable excludes dermatitis claims because these were all medical-only claims by design.bSignificant for CTS cases only.

162 Foley et al.

both CTS and fractures cohorts between those with

continuous employment history before their injuries and

those with sporadic employment histories. For both CTS

and fractures, the median duration of time-loss post-injury is

longer for those who are employed in the construction and

transportation industries than it is for those employed in

fixed-site industries such as manufacturing, retail, or

services. The relative increase in time-loss is substantially

larger for CTS cases than for the fractures. Finally, there is a

clear, inverse relationship between the size of the employer

and the median length of time-loss for both injury cohorts.

The beneficial impact of employer size on time-loss is far

greater for CTS cases than it is for fractures.

Earnings Recovery

Figure 1 shows median quarterly earnings, as a percent

of earnings in the quarter of their claim, for each cohort. This

is tracked for the forty-quarter period starting three years

prior to the quarter of injury and continuing for 7 years post-

injury. Looking at how these earnings profiles differ by injury

illustrates the impact of the injury in a way not easily

appreciated by means of the multivariate models alone

because it shows how the deficit in earnings among CTS

claimants evolves over time. Earnings for the CTS cohort are

broken out into the three subgroups discussed above

(‘‘specific,’’ ‘‘augmented,’’ and ‘‘other’’) because large and

statistically significant differences develop between them.

Earnings for all cohorts except dermatitis are similar

prior to the quarter of injury. However, earnings post-injury

follow distinctly different tracks across the injury types. The

earnings of the dermatitis cohort only gradually decline,

probably due to natural labor market exit as discussed above.

The story is similar for the compensable fractures cases.

While their earnings decline sharply for the first quarter post-

injury, due to their temporary disability, they return to more-

than-full earnings beginning with the second post-injury

quarter. After that, their earnings fraction gradually declines,

but they retain the highest fraction of pre-injury earning

power of any of the injury cohorts. For the CTS group, the

post-injury course of earnings is sharply different. All three

CTS subgroups lose a much larger share of their pre-injury

earnings than is the case with the dermatitis or fractures

groups. And there is no obvious post-injury recovery after a

short-term disability, as with the fractures. CTS Group 1

earnings stabilize at roughly 60% of the pre-injury level after

1 year. For the ‘‘augmented’’ CTS subgroup the loss of

earnings is greater: their median earnings fall to roughly

30% of pre-injury levels before stabilizing. Finally, for CTS

Group 3, the most broadly defined category of CTS case,

earnings fall continuously and approach zero within roughly

3 years after the claim is filed.

Another important predictor of earnings recovery for the

CTS claimants is whether or not they undergo surgery to

relieve pressure on the median nerve. Figure 2 shows the

earnings profiles for the CTS claimants broken out by

‘‘surgery’’ versus ‘‘no surgery.’’ Out of all claimants with a

CTS designation, 55.4% received a carpal tunnel release

procedure. About 1.4% received an arthroscopic procedure.

These proportions varied by whether the claimant was

classified as a CTS case by upper-extremity specific ANSI

codes or by an ICD-9 code alone. In particular, 77.2% of the

‘‘CTS specific’’ group underwent a carpal tunnel release

procedure; the corresponding proportion for the other two

FIGURE 1. Medianquarterlyearningsaspercentof injuryquarter.

Long-Run Economic Burden of CTS 163

subgroups was 43.3% for the ‘‘CTS augmented’’ group and

40.1% for the ‘‘CTS other’’ group. As Figure 2 shows, the

earnings outcomes for CTS claimants who underwent

the carpal tunnel release procedure were strikingly better

than for those who did not have the surgery even though they

had more time-loss. It should be noted that the claimants who

did not receive surgery had significantly lower earnings prior

to their CTS claim than did those who went on to have the

surgery. In Group 1 the median pre-injury earnings of those

claimants who went on to have surgery was $5,664 versus

$4,542 for those who did not. Similar differences were found

in Groups 2 and 3. But as Figure 2 shows even when

controlling for this difference, the earnings gap widens after

the injury. In the logistic regression model it was found

that higher pre-injury earnings was positively associated

with having surgery (OR¼ 1.44; 95% confidence interval

1.14–1.83) even when controlling for CTS subgroup, age

group, sex, employer size, sector, region, and pre-employ-

ment stability in the model. Older claimants were more

likely to have surgery (over age 50: OR¼ 3.03; CI 2.44–

4.10). Females were less likely to have surgery

(OR¼ 0.73; CI 0.62–0.85). No relationship was found

between the size of the claimant’s employer, pre-injury

employment stability, or industry sector and likelihood of

having surgery.

Earnings profiles for claimants with CTS also varied by

gender when comparing fixed-site employment to non-fixed-

site employment. As Figure 3 shows, male claimants with

CTS show a marked difference between those working in

fixed-site industries and those working in non-fixed

site industries, with males in non-fixed industries doing

especially poorly. For females with CTS there is no such

inter-sector difference.

Multivariate Analysis

Based upon the results presented in the earnings charts

and in the univariate descriptive analysis above, a variety of

multivariate model specifications were tested over various

time periods. The underlying hypothesis is that those

claimants with carpal tunnel syndrome suffer a greater loss

in their ability to return to work at full earnings following

injury as compared to claimants with either an upper

extremity fracture or a medical-only dermatitis claim.

However, several other factors are included which may be

important in determining both the level and trend of

an individual’s earnings over time: gender, age, the

differing demands of working in non-fixed-site industries

such as construction, the size and region of the employer, and

the worker’s own history of work stability prior to injury.

Studying the recovery of earnings allows us to isolate the

impact of injury on the worker’s ability to resume their full

productive potential after they have returned to work. Once

again the critical comparison to be made is the impact of CTS

on the recovery of earnings as compared to that of the other

two injury types. The model was run for three post-injury

periods: 1 year, 3 years, and 6 years post-injury. Since the

primary interest is the long-term economic impact of CTS,

only the results for earnings 6 years after the quarter of injury

are reported in Table V-a. However, results for all three post-

injury time periods are discussed below.

FIGURE 2. ImpactofsurgeryonCTSmedianquarterlyearnings.

164 Foley et al.

The multivariate regression results accord well with

those seen in the univariate tables: at 6 years post-injury,

claimants with CTS have much poorer outcomes than do

claimants with either fractures or dermatitis. CTS claimants

recover only 45% of their pre-injury earnings level as

compared to the rate of recovery for dermatitis claimants

(exp[coefficient]¼ 0.45; CI 0.34–0.59, P< 0.001). The

same model, estimated at 1-year and 3-years post-injury,

shows that for CTS claimants there is evidence of a greater

initial impact of the injury on earnings, followed by a gradual,

and partial, recovery relative to the other two cohorts. One

year after claim filing, their relative earnings recovery (RER)

is 0.29 (0.23–0.36) as compared to dermatitis; at 3 years

this relative recovery is 0.35 (0.27–0.45). Other predictors

with significant negative impacts on earnings recovery are:

older age, smaller-size employer, working in a non-fixed

industry sector, working outside of the Puget Sound region,

and having an unstable pre-injury employment history. The

age effect is less pronounced at 1 year and 3 years post-injury

than it is at 6 years. For all age groups other than the youngest

the RER declines with time after injury. The deficit grows

particularly for workers over age 50: falling from an RER of

FIGURE 3. A: Females with CTS: median earnings by fixed-site or non-fixed-site Industry.B: Males with CTS: median earnings by

fixed-site ornon-fixed-site industry.

Long-Run Economic Burden of CTS 165

0.47 at 1 year post-injury to 0.18 at 3 years and 0.07 at 6 years.

For workers aged 16–24, males have substantially better

earnings growth 6 years post-injury than do females (0.59;

0.36–0.98). However, this advantage disappears for workers

between the ages of 25 and 49, for whom there is no

statistically significant, or substantive, difference between

males and females. Female workers aged 50 and over recover

their earning power much more successfully than do older

males (2.58; 1.52–4.39).

The age-gender interaction changes as workers

are considered at 1, 3, and 6 years post-injury. Whereas at

the 1-year point no statistically significant difference are

found across the four age groups between males’ and

females’ earnings recovery, a pattern emerges at the 3-year

mark that shows younger males doing better as compared to

young females, and females over 50 doing better than males

over 50. By the 6-year point, this pattern is even more

entrenched.

One three-way interaction, age by gender by injury type,

was found to be statistically significant at the 6-year post-

injury point, though it was not included in the model depicted

in Table V-a because its inclusion does not change the injury

group impacts on earnings. Instead, this interaction is more

easily seen by examining how the age–gender interaction

terms vary when the same regression model is fitted to each

injury cohort separately (CTS, fractures, and dermatitis).

Each injury cohort shows the same pattern of age by gender

effects as shown in Table V-a: older workers recover less than

younger workers, and the gender differential consistently

shows that younger males recover more earning power

than younger females, while older females recover more than

older males. Only the magnitude of the age-gender interac-

tion effect differs across the three injury cohorts: the gradient

of the age-gender differential is steeper for CTS than for

either the dermatitis or fractures cohorts.

Given the size of the earnings recovery deficit for

workers with CTS over age 50, the question is raised whether

the earnings recovery estimate for CTS at 6 years is driven

mainly by the collapse of earning power for the older age

group. The model in Table V-a was re-estimated excluding all

workers over 50 and only a small change was found in

estimated earnings recovery from that of the fully populated

model: the CTS recovery fraction rises from 0.45 to

0.51 relative to dermatitis claimants.

Although not all results of all possible interaction terms

are reported, because their inclusion in the model does not

substantively change the magnitudes of the injury recovery

ratios, there were statistically significant interactions

between age and injury type which were consistent with

the univariate results: older workers did relatively worse in

each injury category than did younger workers, and older

workers with CTS had the lowest earnings recovery. In both

CTS and fractures there was a substantial loss of earnings

among workers over age 50 relative to those of workers over

50 with dermatitis. However, the loss for CTS claimants was

about 30% greater than for fractures.

The interaction of sex and industry sector showed that

males in the construction and transportation sectors fared

worse relative to males in the fixed-site sector (�25%).

Females in the construction and transportation sectors

showed a similar deficit (�16%) relative to their counterparts

in the fixed-site sector, but this difference was not statistically

significant, perhaps due to the small numbers of female

claimants in the non-fixed sector.

It was expected that a significant difference would be

found between how males and females would fare across the

different injury types. But when an interaction term for sex

and injury type was included in the model, the results did not

support this: there was no significant interaction between sex

and injury type.

Finally we tested whether workers’ earnings recovered

differently across injury types between the fixed and non-

fixed industry sectors.9 Across all injury types workers in the

non-fixed sector had poorer long-term earnings recovery. But

the deficit in earnings recovery for CTS claimants in the non-

fixed sector, at 35% relative to CTS claimants in the fixed

sector, was three times larger than it was for workers with

fractures or dermatitis. In addition, this deficit was the only

one that was statistically significant.

The recovery pattern is potentially dependent on yet

higher order interactions (three-way and higher), yet this

sample size, though very large, is not sufficient to reliably

decide among the possibilities. Clearly the role of age and

gender and their combination, plus their combination with

other factors is important, as also indicated by the discussion

above of various age or gender interactions. All of these

three-way interactions involving age and gender give results

consistent with those in Table V-a, though the magnitudes of

the effects vary somewhat. Thus, Table V-a fairly represents

the effect of the variables considered, and some more

variations on the theme may be discovered with considerably

larger sample sizes.

Sub-Groups of CTS Claimants

The income recovery model was re-estimated for the

CTS claimants only, broken out by CTS sub-group, to see

whether there were systematic differences between the three

sub-groups in their recovery of their pre-injury earnings

levels and to examine the impact of having a carpal tunnel

release surgery. Table V-b has the results of this model. All of

the predictors from Table V-a retain their signs and

approximate magnitudes, and only the impact of subgroup

and surgery is discussed here.10

9 ‘‘Non-fixed’’ industry sector refers to the construction and transportationindustries.

10 For space reasons, therefore, we have omitted all but these predictorsfrom Table V-b.

166 Foley et al.

TABLE V-a. Multiple RegressionModel of Relative Earnings Recovery as a Function of InjuryType, Demographics,Region and Industry Predictors, 6 Years Post-Injury

Variable CategoryRelative earnings

recoveryLower 95%

CIUpper 95%

CIGlobalPP-value

Injury type <0.001CTS 0.45 0.34 0.59Fractures 0.85 0.64 1.14Dermatitis 1.00 Reference category

Agea <0.00150 and over 0.07 0.05 0.1035^49 0.57 0.42 0.7725^34 0.68 0.51 0.9124 and under 1.00 Reference category

Sexa 0.6Female 1.06 0.85 1.32Male 1.00 Reference category

Female compared to maleby age at injury

<0.001*

50 and over 2.58 1.52 4.3935^49 1.03 0.72 1.4525^34 0.98 0.68 1.4024 and under 0.59 0.36 0.98

Size of employer 0.02Small 0.72 0.55 0.94Medium 0.79 0.63 0.99Large 1.00 Reference category

Sector 0.03Non-fixed 0.75 0.59 0.97Fixed 1.00 Reference category

Region <0.001Non-Puget 0.61 0.50 0.74Puget 1.00 Reference category

Pre-injury employment <0.001Non-stable 0.39 0.32 0.48Stable 1.00 Reference category

aMarginal effects of age and sex from the model without the age-sex interaction.*P-value for the interaction of age and sex.

TABLE V-b. Multiple RegressionModel of Relative Earnings Recovery as a Function of CTS Subgroup and SurgeryStatus

Variable Category Relative earnings recovery Lower 95%CI Upper 95%CI PP-value

CTS group Group 3 0.52 0.37 0.74 <.0001Group 2 0.72 0.51 1.04 0.08Group1 1.00 Reference category

SurgeryNo 0.62 0.46 0.83 0.002Yes 1.00 Reference category

Six years post-injury: estimates adjusted for age, gender, age-gender interaction, employer size, sector, Puget/non-Puget, stable/non-stable variables. CTS claimants only.

Long-Run Economic Burden of CTS 167

Claimants in CTS Group 3 have much poorer long-term

outcomes than do claimants with more specifically defined

CTS codes. As before, other predictors with significant

negative effects on recovery of earnings are: older age,

smaller-size employer, working in a non-fixed industry

sector, working outside of the Puget Sound region and having

a sporadic pre-injury employment history. In addition,

claimants who had a carpal tunnel release surgery experi-

enced much better earnings recovery at the 6-year mark. The

effect of surgery on earnings recovery did not vary

significantly across the three CTS subgroups.

These results raise the question whether the deficit in

earnings recovery for CTS claimants is driven mostly by the

performance of CTS Groups 2 and 3. The model was then

restricted to CTS Group 1, fractures and dermatitis claimants.

The results parallel those of Table V-a in that all predictors

retain their signs and levels of significance as before.

Claimants in CTS Group 1 do relatively better than CTS

claimants as a whole, as compared to the fractures and

dermatitis claimants. However, they still show a substantial

shortfall in their earnings recovery at 6 years post-claim

relative to that of the other two injury type groups: 38% less

recovery than that of dermatitis claimants and nearly 20%

less recovery than that of the fractures cohort. If the analysis

is restricted to the CTS Group 1 claimants who had surgeries

we find they recover about 31% less of their pre-injury

earnings after 6 years as do the dermatitis claimants

(P< 0.03).

A regression model was also estimated in which the total

number of days of paid time-loss at claim closure was a

function of the same set of predictors in the same form as used

in the earnings recovery model, with the single addition of

pre-injury earnings level as a predictor. Dermatitis claimants

were not included in this model as they had no days of paid

time-loss. All claims were closed prior to the 6-year

measurement point, so no further accumulation of paid

time-loss occurs with these claims after the time of the

analysis. As with the earnings recovery model all claimants

are present and included in the analysis, whether or not they

have returned to work. The results of this model support the

hypothesis that claimants with CTS experience a substan-

tially greater burden than do claimants with upper extremity

fractures: controlling for other predictors, their time-loss was

more than 300% longer for CTS than for fractures

(exp[coefficient]¼ 4.36; CI 3.96–4.80; P< 0.0001). Other

impacts also accorded with the earnings recovery model:

older workers, employees of smaller businesses and those

working in the construction or transportation sectors had

longer periods of time-loss following their claims. Time-loss

was slightly less for women than for men. Geographic region

and pre-injury employment stability were not associated with

length of time-loss. Finally, claimants’ length of time-loss

was inversely related to their pre-injury earnings level, with

each successively higher income quartile having shorter

time-loss than the one below it. Claimants in the highest pre-

injury earnings quartile have about half the time-loss as those

in the lowest quartile.11

In addition to its impact on time-loss we also tested

whether pre-injury earnings level predicted long-term earn-

ings recovery. Since pre-injury earnings forms the denomi-

nator of the dependent variable in the model tested above, it

could not be included directly in the model. But median

earnings recovery percentages at 6 years broken out by injury

type and pre-injury earnings quartile show that, for CTS and

fractures, those with the highest pre-injury earnings also have

higher rates of earnings recovery than those with lower pre-

injury earnings. This is especially true for CTS claimants,

with 64.8% earnings recovery for the highest quartile versus

23% recovery for the lowest quartile. There is a smaller, but

still notable difference for the fractures claimants (87%

recovery for the highest earning group vs. 62% for the

lowest). For the dermatitis cohort there is not a substantive

difference between the highest earning claimants and those in

the third quartile. However, for this group the lowest pre-

injury earnings quartile actually had the highest rate of

earnings recovery after 6 years, at 156% of pre-injury levels.

This may be related to this group’s younger age composition

as well as the minimal impact of their illness.

Perhaps the best summary measure of the overall

difference in the burden suffered by CTS claimants relative

to fractures and dermatitis claimants is the difference in

cumulative long-term earnings losses. This is presented in

Table VI. If CTS claimants’ post-injury earnings had

followed the same trajectory of recovery as did those of

fractures or dermatitis claimants, controlling for differences

across injury cohorts in the covariates, they would have

earned between $197 million and $382 million more than

they did over a 6-year period after injury. On a per claimant

basis, this is an average of between $45,762 and $88,894 in

cumulative lost earnings over the 6-year period. Further-

more, as a comparison of the disparity at 1, 3, and 6 years

11 We used ordinary least-squares (OLS) regression analysis to determinethe association of time-loss duration to injury type, controlling for theother predictors. While the data on duration could also have beenanalyzed using survival analysis methods, the OLS regression approachis preferable for several reasons. First, none of the individual workers’time-loss durations were censored. Second, the OLS model compares theactual duration of time-loss across different categories of workers.Survival analysis, by contrast, is well suited for comparing the risk(hazard ratio) of an event between categories of claimants. For these datathe event in question would be the end of the time-loss period. But such acomparison is of less interest to this study than the relative length oftime-loss duration across injury types. Since the end of time-loss does notnecessarily imply an immediate or successful return to work, comparingearnings losses is a better method for measuring the impact of injury.Nevertheless, a survival analysis using a Cox proportional hazardsregression model was performed to estimate the likelihood of the end oftime-loss as a function of injury type, with sex, age, pre-injury earningsstability, and level of pre-injury earnings as covariates, using PROCPHREG in SAS v9.1. This model estimated a hazard ratio for fracturesrelative to CTS of 0.432 (CI: 0.408–0.457, P< 0.0005). So at a givenpoint in time, the hazard of a fractures claim reaching the end of time-loss status in the workers’ compensation system was about 2.3 timeshigher than that for a CTS claim.

168 Foley et al.

shows, the deficit continues to grow over time. There is no

evidence of any convergence of earnings.12

DISCUSSION

The main finding of this analysis is that CTS claimants

return to a much lower fraction of their pre-injury earnings

than do claimants with either medical-only dermatitis or lost-

time upper extremity fractures, and they endure much longer

periods of time-loss than do claimants with upper extremity

fractures. These results remain even when controlling for

other predictors of time-loss or earnings. CTS claimants

experience more than three times as much time-loss as

claimants with fractures. Six years after filing the claim, the

average fraction of pre-injury earnings achieved by the CTS

cohort is less than half that of the dermatitis claimants, and

about 45% below that of the fractures cohort. These

differences capture only the gap in take-home earnings

between the cohorts. Any excess loss of fringe benefits by the

CTS cohort is not included in this comparison, but can be

expected to amplify the shortfall. Part of this deficit may

be related to the length of time which elapses from onset of

symptoms to treatment. Those with CTS are less likely to

seek immediate treatment compared to fractures. It should be

kept in mind, however, that the term ‘‘time-loss’’ only

captures the number of days for which the claimant received

wage replacement payments from L&I. Any time lost prior to

the start of wage replacement benefits payments is not

captured. If the claimant has subsequent interruptions in

their employment due to an inability to handle their

customary tasks, this is also not captured by this measure

unless the claim is re-opened and wage replacement

payments resume. A better overall measure of economic

impact, which incorporates the impact of post-closure breaks

in employment, is the long-term quarterly earnings of the

claimant measured at 6 years after the claim is filed.

The number of lost workdays for the set of CTS cases

analyzed in this study is much higher than that which is

reported in the Bureau of Labor Statistics (BLS) Survey of

Occupational Injuries and Illnesses, based upon surveyed

OSHA 300 employer logs. Whereas the CTS cases involve

between 101 and 238 days of paid time-loss, BLS lost

workday cases have a median of about 32 lost workdays in

2003. This disparity also appears for upper extremity

fractures: the set of time-loss workers’ compensation claims

have a median of 46 days of paid time-loss. The median lost

workdays for upper extremity fractures cases reported by the

BLS was only 10. This may be due to several reasons. First,

unlike the set of workers’ compensation claims, BLS cases

are based upon employer reports and may include a wide

range of hand/wrist conditions not medically diagnosed as

carpal tunnel syndrome. Second, BLS cases include those

with only 1–3 lost workdays, whereas State Fund time-loss

claims must involve at least 4 lost workdays. Third, the self-

insured employers were excluded from this analysis.

Because of their larger size, they tend to offer more

opportunities for early return to work. Fourth, the BLS

Survey reports days away from work only up to 1 year

following the injury event, while claims can still accumulate

lost days more than 1 year post-injury. Finally, it is possible

that, in order for a worker to be willing to take on the cost in

unreplaced wages and perhaps the stigma of filing a workers’

compensation claim, their case must first develop to a more

severe stage, with positive electrodiagnostic findings, result-

ing in more lost workdays than is the case with the reports

surveyed by the BLS [Azaroff et al., 2002].

A second result is that those CTS claimants with specific

ANSI codes which originally define CTS as the primary

injury have better earnings recovery and shorter time-loss

than those with only a diagnosis code on one of their medical

bills consistent with CTS. This may be a function of the

claims administration system. When a claim is first

processed, the ANSI coding is based upon the primary

condition of the claimant, for example, ‘‘nerve condition

affecting the wrist.’’ If this specific ANSI code is not present

it may indicate that the primary condition is unrelated to

CTS, or it may mean that the case is more complicated and

takes longer to diagnose and treat. The interval from the date

of injury reported on the claim to the date a medical provider

first makes a diagnosis of CTS ranges across the three

subgroups from a median of 14 days for Group 1, to 71 days

for Group 2, and to 133 days for Group 3. Once diagnosed,

however, there is no variation across the subgroups in the

interval from diagnosis until surgery. This interval is about

135 days for all subgroups. The presence of a CTS-related

ICD-9 code on one of many medical bills for a claimant may

indicate an acute traumatic event such as a fall from elevation

or a motor vehicle crash, where CTS is only one part of

a complicated case requiring more recuperation time.

However, lengthier recuperation may also result from a

12 If we restrict the CTS cohort just to Group 1, the subset whose claimswere classified early in the case history as being related specifically to thewrist or hand area by ANSI z16.2 codes, we find a per claimantcumulative loss of between $22,125 and $50,727 over the 6 year post-injury period.

TABLE VI. Cumulative ExcessMean Earnings Loss for CTS ClaimantsCompared to Fracture and Dermatitis Claimants

Periodelapsed

CTS relative to fractures CTS relative to dermatitis

Per claimant Group Per claimant Group

One yeara $12,821 $55,105,855 $22,143 $95,172,460Three yearsa $31,374 $134,847,030 $49,800 $214,038,515Six yearsa $45,762 $196,685,843 $88,894 $382,065,630

aIn this table post-claim time periods were defined as follows: ‘‘One Year’’¼quarters1^4; ‘‘ThreeYears’’¼quarters1^12; ‘‘Six Years’’¼quarters1^24.

Long-Run Economic Burden of CTS 169

lack of prompt diagnosis and treatment of CTS, or may

raise issues of appropriate or timely claim administration.

An analysis of the relationship between length of time-

loss and delays in claim processing, as measured by the

length of time taken to pass certain significant claims

administration landmarks (such as date of assignment of

employer liability, date of first time-loss payment, and date of

surgery) might reveal significant opportunities to improve

long-term worker outcomes. Inadequate treatment due to

delayed identification and poor claim administration lead to

substantially greater morbidity for CTS claimants [Daniell

et al., 2005].

It was also found that males with CTS who work in

non-fixed industries, such as construction or transportation

fare less well than workers in any other gender-industry

combination. Both the construction and transportation

industries are predominantly male. The heavy manual work

often involves vibration and hand-intensive tasks. Many jobs

also involve skills acquired over many years of employment.

When these skills are compromised by CTS, it may be much

more difficult to return to work than in other industries.

Another factor potentially hindering recovery of earning

potential is that construction is comprised disproportionately

of small firms, with an average of about 7 employees as

compared to the all-industry average of about 15 employees

per firm. These employers may believe they do not have the

light duty positions available which would be appropriate for

a recovering worker. In general, the size of employer is

inversely related to time-loss and recovery of earnings. Large

employers tend to have a greater diversity of work positions

available so accommodation of the worker may be easier

during the recuperation period. Because self-insured

employers were excluded, the analysis of CTS earnings

recovery among workers employed by large firms is missing

those working for some of the very largest employers in

Washington State. This also means that we were unable to

examine whether differences in cost incentives for employers

who self-insure may alter their policies so as to accommodate

workers’ return to work earlier than is seen among those

employers in the State Fund, independently of employer

size.

Differences in treatment of CTS may also account for

part of the variation in length of time-loss and earnings.

CTS claimants who had carpal tunnel release surgery had

better earnings outcomes than those who did not have

surgery. This is in spite of the fact that CTS claimants who

underwent surgery had longer periods of time-loss than those

without surgery, even when controlling for CTS subgroup.

This does not, by itself, indicate that more CTS claimants

should be getting surgeries. It may be that those CTS

claimants who have surgery represent less complicated,

more definitive cases, such as those having a positive

nerve conduction velocity (NCV) test, which is required

in Washington State, where treatment is relatively

straightforward.13 This is supported by the finding that

CTS claimants with specific ANSI codes for CTS as their

primary condition are much more likely to have surgery. An

implication of this finding is that early reporting and NCV

testing may lead to better earnings outcomes. However, it is

not known what time-loss or earnings recovery for claimants

in the CTS surgery group would have been had they not had

surgeries.

The large relative loss of earnings for the non-surgery

CTS cases as compared to the surgery CTS cases and the

fractures and dermatitis cohorts may indicate that workers

without some visible signs of injury or illness are more likely

to experience long delays in obtaining proper recognition of

their condition and appropriate treatment. The fact that this

group also tends to have lower pre-injury earnings levels may

also indicate that members of this group are less able or

willing to overcome any barriers to reporting their disorders

promptly, perhaps for fear of lost wages, job loss, or

discrimination.

There is no information in this dataset about what, if any,

ergonomic improvements have been made to accommodate

the CTS claimants’ return to work. Nor is it known if they

return to the same or to a different job. Variation in employer

accommodation is believed to play an important role in

preserving workers’ long-term earnings potential.

High pre-injury earnings are associated with shorter

time-loss and better return to pre-injury earnings levels. They

are also associated with a higher probability of receiving a

carpal tunnel release surgery even when controlling for CTS

subgroup and industry sector. One reason for this difference

may be that high-wage workers are harder to replace, so

employers press for quick treatment and are more likely to

make necessary accommodations to keep them working as

compared to unskilled, lower wage workers. High pre-injury

earnings levels may also denote higher levels of education

and greater comfort or experience with reporting symptoms

of ill health to physicians. Higher income workers also may

press for early return because for them the fraction of pre-

injury earnings that are replaced by workers’ compensation

payments is lower than it is for lower wage workers. A lack of

access to health care resources and paid sick leave benefits,

which are not uncommon among the lower wage working

population, could lead some workers to avoid reporting their

CTS symptoms until the severity of their condition is

relatively high.

Earnings recovery and time-loss also varied system-

atically with demographic and economic characteristics of

workers. The chances for successful recovery of earnings are

13 In the Washington State workers’ compensation system’s AttendingDoctor’s Handbook, there have been specific criteria since 1993 forallowing carpal tunnel surgery, including positive electrodiagnosticfindings. Prior to this, surgery was sometimes conducted without meetingclinical guidelines. The prevalence of surgery usage among CTSworkers’ compensation cases can be expected to vary across the UnitedStates and other countries.

170 Foley et al.

much poorer for older workers across all injury types,

especially for workers over age 50. This, together with the

result that older workers face much longer time-loss, sup-

ports recent findings that older workers who are injured face

higher risk for early retirement than uninjured older workers

[Pransky et al., 2005]. In addition, the recovery of earnings

varies by gender across age groups: younger males do better

than younger females; but older females do better than older

males. Several questions are raised by this last result: are

older males more likely to be eligible to receive Social

Security Disability Insurance payments? For older males

with CTS, are their cases more severe by the time they file

claims? Do they develop the disorder working in occupations

in which it is more difficult to accommodate their return to

work in their accustomed jobs? Earnings recovery was better

for workers in large firms and in the fixed industry sector.

Workers with unstable pre-injury employment histories did

have more trouble recovering their earning potential,

although this was not due to enduring longer periods of

time-loss. Also unlike time-loss, earnings recovery did differ

significantly by region: workers in the Puget Sound area had

much better success recovering to their pre-claim earning

potential than did workers outside of this urbanized region.

This could be related to the long-standing differential across

Washington State in labor market conditions. The Puget

Sound area typically enjoys lower unemployment rates and

higher average wages than other areas of the State. The

opportunities to find alternative occupations more suited to

workers’ post-injury capacities may be better in the much

denser labor market of the Puget Sound region.

We also tested whether the direct relationship between

higher pre-injury earnings and better long-term earnings

recovery varied by injury type. The result was clear: higher

pre-injury earnings made a much bigger difference to long-

term earnings recovery for CTS than it did for either fractures

or dermatitis. It is evident that some combination of

employee accommodation, early diagnosis and appropriate

treatment is associated with higher wage workers, allowing a

much better long-term outcome than is the case for lower

wage CTS claimants. For fractures and dermatitis claimants

there is evidently less room for these other factors to affect

claimants’ probability of successful return to work.

Finally, when controlling for differences in demographic

and other income predictors across the three injury types, the

cumulative excess loss of earnings of the 4,443 CTS

claimants included in this study amounted to between $197

million and $382 million over the 6-year period post-injury

for which data were collected, a per claimant earnings loss of

between $45,000 and $89,000 compared to workers with

fractures or dermatitis, respectively. These losses are similar

in magnitude to those reported in other studies for the most

seriously disabling injuries [Reville et al., 2001]. Wage

replacement payments partially and temporarily cushion the

burden of this loss to individual workers during the period in

which their claims are open. But from society’s perspective

this loss is a lower bound estimate of the full burden, since it is

based upon a comparison to two cohorts of workers who are

themselves recovering from injuries rather than upon a

comparison to an uninjured control population. Moreover,

the cumulative loss measured at 6 years is likely to be much

smaller than lifetime cumulative loss, since annual losses

remained large even at the end of this period. Because median

age at injury was only 38 for the CTS cohort, a potentially

large proportion of the lifetime earnings loss is not captured

here. Even so, these losses are very large when compared

with the direct cost of a CTS claim as measured only by State

Fund medical costs and wage replacement payments: the

median total direct cost per claim for CTS in the State Fund

over the period 1993–2001, as reported by L&I was $5,235

[Silverstein et al., 2003].14 Finally, there is no evidence that

the gap in earnings between CTS claimants and workers with

fractures or dermatitis converges as time passes.

The losses of CTS claimants compared to those of

fractures claimants, also raises the issues of differences in

injury visibility, reporting, and timeliness of treatment. This

would suggest that policies are needed which favor primary

prevention through ergonomic assessment and job improve-

ment, and which encourage the early reporting of hand/wrist

symptoms such as numbness and tingling before progression

of the disorder leads to clinical CTS and long-term loss of

work ability.

CONCLUSION

The reported cost of a workers’ compensation claim

reflects only a part of the full burden of a work-related injury

or illness. A portion of this burden falls on the employer in the

form of indirect costs to replace the injured worker. On the

workers’ side the burden can take many forms, both

economic and social. The focus of this study was on the

workers’ long-term loss of earnings. The results show that

long-term earnings losses far exceed the reported direct costs

of CTS claims: their cumulative excess loss of earnings

amounted to between $197 million and $382 million over the

6 years following the claim, a loss of between $45,000 and

$89,000 for each CTS claimant in the study. Because the

losses captured by this study only cover 6 years following

injury, lifetime cumulative loss is likely to be much larger,

since annual losses were still quite large at the end of

this period and median age at injury was only 38. CTS

claimants recover to a much lower fraction of their pre-injury

earnings than do claimants with either dermatitis or upper

extremity fractures, and they endure much longer periods of

time-loss than do claimants with upper extremity fractures.

Given the magnitude of these uncompensated losses, and

the devastating effect they can have on workers and their

14 In 2001 dollars.

Long-Run Economic Burden of CTS 171

households, this study points to the need for a renewed

emphasis on primary prevention, early reporting and diagno-

sis, appropriate treatment, and assistance for employers to

accommodate an early and successful return to work.

ACKNOWLEDGMENTS

Michael Foley and Barbara Silverstein conducted this

research while employed by the Washington State Depart-

ment of Labor and Industries. We acknowledge the

invaluable statistical assistance of Blazej Neradilek. Edmund

Rauser provided programming help. We thank Randy Clark

and two anonymous referees for their thoughtful reviews and

comments.

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