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Using multiple data sets for public health tracking of work-related injuries and 

illnesses in California 

Authors: 

Lauren Joe1 MPH, Rachel Roisman1 MD, MPH, Stella Beckman1,2 MPH, Martha Jones3,4 PhD, 

MPA, John Beckman5, Matt Frederick5, Robert Harrison1 MD, MPH 

Corresponding author: 

Robert Harrison, California Department of Public Health, Division of Environmental and 

Occupational Disease Control, 850 Marina Bay Parkway, Building P3, Richmond, CA 94607; 

email: Robert.Harrison@ucsf.edu 

ABSTRACT 

Background: Research suggests the U.S. Bureau of Labor Statistics Survey of Occupational 

Injuries and Illnesses underestimates the magnitude of workplace injuries and illnesses. 

Enumerating workplace injuries and illnesses may be improved utilizing multiple state-based 

data sources. 

Methods: Using California-based datasets (workers’ compensation claims, health care facility 

data, and physician reports), we enumerated unique cases of amputations and carpal tunnel 

syndrome (2007-2008), and evaluated the datasets for usefulness in occupational health 

tracking by performing record linkage across all datasets and calculating match rates between 

them. 

Results: 6,892 amputation and 39,589 CTS cases were identified. Match rates between the 

datasets ranged from 34.0-45.6% (amputations) and 3.0-43.5% (CTS). Enumerated amputation 

and CTS cases from state-based sources were about five and ten times greater than the BLS SOII 

estimates (1,390 and 3,720). 

Conclusions: Successful demonstration of this state level approach has broad implications for 

improving federal and state efforts to track and prevent work-related injuries and illnesses. 

Conflict of interest statement: 

The authors do not have any conflicts of interest. 

KEYWORDS 

Surveillance; Carpal Tunnel Syndrome; Amputation; Occupational; Record Linkage

1California Department of Public Health, Richmond, CA 

2

School of Public Health, University of California, Berkeley, CA 

3California Department of Industrial Relations, Oakland, CA 

4Center for Medicine, Health and Society, Vanderbilt University, Nashville, TN 

5

Impact Assessment Incorporated, La Jolla, CA 
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INTRODUCTION 

Occupational health surveillance, also referred to as tracking, relies on worker injury and 

illness reporting, and surveillance efforts are a necessary foundation for addressing workplace 

hazards through targeted prevention strategies. The identification of occupational injury and 

illness trends is useful for prioritization of occupational risks and follow-up prevention and 

intervention strategies. As previously demonstrated in the literature, workplace injuries and 

illnesses are not accurately identified for a variety of reasons, including harassment, fear of 

employer retaliation, ignorance, lack of training in occupational health among health care 

providers, and administrative barriers (Azaroff et al. 2002; Probst and Estrada 2010). Despite 

these limitations, valuable information can be garnered from existing tracking systems, and 

understanding how these systems differ in their ability to capture the totality of worker injuries 

and illnesses enables us to better utilize them to protect workers and prevent workplace 

injuries and illnesses. 

At the national level, the U.S. Bureau of Labor Statistics (BLS) conducts the Census of 

Fatal Occupational Injuries (CFOI) and the Survey of Occupational Injury and Illness (SOII). The 

CFOI has been counting every work-related fatal injury occurring in the U.S. since 1992 and is a 

comprehensive and valuable tool for collecting detailed data on workplace deaths. Although 

death is the most severe outcome of occupational injuries, occupational morbidity is just as 

important to track, as injuries and illnesses can be devastating and result in lifelong disability. 

Reducing causes of morbidity may also prevent death, as morbidity risks may have the potential 

to escalate into life-threatening risks when left unaddressed. The SOII has been the basis of 

epidemiologic surveillance of workplace injuries and illnesses since 1972. It differs from the 

CFOI in that the SOII is not a census, but based on a survey method that provides estimates of 

injuries and illnesses (BLS 2008). Many attributes of the SOII system make it ideal for tracking 

workplace injuries and illnesses. For example, the comprehensive nature of reporting and 

sampling characteristics can generate state-based data and annual rates that can be used to 

evaluate the impact of interventions over time. However, recent studies have suggested that 

the SOII may undercount injuries and illnesses and may benefit from the additional 
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ascertainment of cases using state-based data sources (Leigh et al. 2004; Rosenman at al. 2006; 

Boden and Ozonoff 2008; Boden and Ozonoff 2008a). 

State-based data sources that are not available at the national level can be used to 

target specific cases and/or work sites for investigations, thereby coordinating efforts at the 

individual worker and work site levels to reduce the burden of workplace injuries, diseases, and 

deaths. One example is the California Occupational Pesticide Illness Prevention Program, which 

utilizes reported cases of acute pesticide illness from various sources to identify the specific 

occupations and types of pesticides that are related to health problems in workers (CDPH OHB 

2009). With funding from the National Institute for Occupational Safety and Health (NIOSH) 

and the California Department of Public Health (CDPH), this program collects and examines 

various reports to learn more about occupational pesticide poisoning and how to prevent toxic 

exposures. This system allows for timely identification of pesticide poisoning outbreaks, which 

are investigated to provide assistance to employees and their employers, and to develop 

educational materials and recommendations to prevent future outbreaks of similar nature from 

occurring in the future (CDC 2011). 

In addition to identifying instances of acute injury and illness, occupational health 

surveillance data can be used to strengthen ongoing public health action that has already begun 

through the efforts of workers themselves. Using surveillance data to characterize the burden 

of specific occupational injuries and illnesses can provide the scientific evidence needed to 

propose policy change and long-term solutions. With the adoption of electronic data systems 

for hospital discharge, emergency department, ambulatory surgery, and workers’ 

compensation claims, we have an opportunity to improve the ability of state and federal 

agencies to perform coordinated and timely surveillance that can more closely estimate the 

true nature and extent of workplace morbidity and mortality. Whether the data are utilized to 

provide routine injury-specific surveillance, to track emerging and acute occupational injuries, 

or to provide scientific evidence to strengthen worker-initiated public health efforts, it is 

important to understand the various data systems available so that they can be harnessed most 

effectively. 
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In order to demonstrate the utility and feasibility of using multiple state-based data sets 

for injury-specific occupational health tracking, the CDPH Occupational Health Branch (OHB) 

enumerated cases of work-related amputations and carpal tunnel syndrome (CTS) that 

occurred in California in 2007 and 2008. We focused on three datasets that are available for 

tracking work-related illness and injury in California: the Workers’ Compensation Information 

System (WCIS), Doctors’ First Reports of Occupational Injury and Illness (DFRs), and health care 

facility data collected by the California Office of Statewide Health Planning and Development 

(OSHPD), which includes ambulatory surgery, inpatient discharge, and emergency department 

visits. We also utilized a fourth dataset that includes cases from the BLS SOII (2007 and 2008) 

that were provided to CDPH under a cooperative agreement with the BLS for this analysis and 

are not typically available for routine surveillance at the state level. The overall purpose of this 

study was to examine these various sources of work-related injury and illness reporting to 

improve our ability to perform occupational public health prevention and intervention 

activities. 

MATERIALS AND METHODS 

Data Sources 

The four sources of data utilized for this analysis have varying administrative purposes 

and data elements. A summary of case inclusion criteria from the four data sources is 

presented in Table 1. 

Bureau of Labor Statistics (BLS) Survey of Occupational Illness and Injuries (SOII) 

The SOII is an annual survey of a sample of workplace establishments utilizing data 

collected on Occupational Safety and Health Administration (OSHA) injury and illness logs 

(known as OSHA 300 Logs) maintained by employers. The SOII uses a survey design to estimate 

the number and frequency of work-related injury and illness in the U.S. and participating states, 

and includes detailed data on industry and the nature and circumstances of illness or injury. In 

addition to these data, the SOII collects descriptive case information, including the 

demographic characteristics of the injured and ill workers who require at least one day of 
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recuperation away from work. Under a cooperative agreement with the BLS to conduct a pilot 

study to enumerate cases of amputations and CTS for the years 2007 and 2008, BLS provided 

the requested SOII microdata to our research team. Each case in the SOII dataset was assigned 

a code indicating the specific type of injury or illness based on the Occupational Injury and 

Illness Classification System (OIICS, BLS 2008). Amputation cases were extracted from the SOII 

dataset using the following OIICS nature of injury codes: “0310,” “0311,” or “0319.” CTS cases 

were extracted using the OIICS nature of injury code: “1241.” The SOII estimated a total of 

1,390 amputation cases of amputation and 3,720 cases of CTS in California for the years 2007 

and 2008. 

California Division of Workers’ Compensation (DWC) Workers’ Compensation Information 

System (WCIS) 

WCIS has been collecting workers’ compensation data in electronic format since March 

2000. Claims administrators must submit electronic First Reports of Occupational Injury (FROI) 

to the California Department of Industrial Relations, Division of Workers’ Compensation (DWC), 

within five working days after knowledge of the injury or illness. Though WCIS is an 

administrative database, the data elements it contains make it a valuable tool for occupational 

health surveillance purposes, including narrative text describing the injury (Sorock et al. 1997). 

Claims must be reported to WCIS if a claims administrator receives any of the following: 

Employer’s First Report or Doctors’ First Report of Occupational Injury or Illness, an application 

for adjudication, or any indication that an injury requiring medical treatment by a physician 

occurred. Self-employed individuals are not required to report to WCIS. 

CDPH accesses the WCIS database by requesting data from the DWC based on various 

predefined criteria. In this study, WCIS extraction criteria were based on case definitions for 

amputations and CTS developed in conjunction with other collaborators (BLS, Boston 

University, Washington State Department of Labor and Industries, Massachusetts Department 

of Public Health). For claims identified as potential amputations or CTS with an injury date in 

2007-2008, the dataset used in this study was comprised of extracts from WCIS containing 

claims data on injury type, employee name, employer name, and benefit payments, and 
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medical billing data on clinical procedure and diagnosis codes. At the time of analysis, 2008 

was the most recent year available with complete WCIS claims data. Our original amputation 

extract from the WCIS database included all claims with “amputation” in the nature of injury 

field, with amputation-related keywords in the injury description field, or with appropriate 

diagnosis or procedure codes in the medical billing data. Our original CTS extract from the 

WCIS database included all claims with “carpal tunnel syndrome” in the nature of injury field, 

CTS-indicating or -related keywords in the injury description field, or appropriate diagnosis or 

procedure codes in the medical billing data (Supplemental Material A). 

Manual review of a sample of amputation and CTS claims revealed that the original 

extracts contained some claims that were not amputations or CTS. As a result, detailed case 

classification schemes were developed using a combination of values in the following fields: 

diagnosis, procedure, nature of injury, part of body, cause of injury, and injury description. The 

final case classification scheme for amputations (classified as probable or uncertain cases) and 

CTS (classified as probable, possible, or uncertain cases) are detailed in Supplemental Material 

B. Medical records for a sample of amputation and CTS claims were used to validate the case 

classification schemes. Fifty-three amputation and 59 CTS medical records were reviewed 

independently by two physicians, and positive and negative predictive values (PPV, NPV) were 

calculated. NPVs for amputations and CTS were greater than 0.5, thus cases classified as 

“uncertain” based on the case classification schemes were removed (1,508, or 22.8%, of the 

amputation cases, and 12,106, or 29.3%, of the CTS cases) from the record linkage analysis to 

reduce chances of misclassification. (Joe et al. 2012; Roisman et al. 2013) 

Health Care Facility Data 

Hospital Discharge (HD), Emergency Department (ED), and Ambulatory Surgery (AS) data 

are collected by the California OSHPD from all licensed health care facilities in California, 

approximately 5,000. Data related to financial performance, utilization, patient characteristics, 

and services provided are publically available, and are often utilized by health care facilities to 

monitor patient outcomes and hospital performance. Under a Data Use Agreement, the CDPH 

Center for Health Statistics provided our research team with more detailed data, including ICD-
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9 codes, social security numbers (SSN), dates of birth, and dates of service. Each dataset (HD, 

ED, and AS) has a primary diagnosis and primary procedure field, as well as up to ten additional 

diagnosis and procedure fields. Due to concerns about misclassification, we limited our analysis 

to cases for which amputation or CTS was the primary diagnosis or procedure code, using the 

same ICD-9 codes utilized for the extraction of WCIS claims (Supplemental Material A). Only 

work-related cases were included in the analysis and were determined by the designation of

workers’ compensation as the payer or by a “place of occurrence” code consistent with a 

workplace. 

The OSHPD dataset contains social security numbers (SSN) but no names, making it 

challenging to match to other datasets (e.g., SOII has names but no SSN). Therefore, we utilized 

a health care data service (SearchAmerica) to obtain first and last names for the SSNs in our 

OSHPD dataset. The service identified names for 96.2% of the OSHPD cases, which we 

subsequently used for matching with the other data sets (see below). 

Doctors’ First Reports of Occupational Injury or Illness (DFR)

DFRs have been a reporting source for California work-related injury and illness data 

since 1949, and have been used routinely by CDPH under numerous CDC/NIOSH surveillance 

cooperative agreements since 1987. DFRs must be completed within five days by all physicians 

in California who suspect work-related injury or illness. They contain detailed case and 

employer information, and are submitted to the workers’ compensation insurance carrier (or 

administrator if self-insured), who then must forward the DFRs to the California Department of 

Industrial Relations (DIR). CDPH obtains the DFRs under a Memorandum of Understanding with 

DIR. Approximately 600,000 DFRs are received annually. DFRs are reviewed manually, sorted 

into selected categories for data analysis and follow-up, and then the remaining DFRs are 

archived. The CTS DFR cases were available for the present analysis as they had been collected 

for 2007 and 2008 as part of CDPH’s ongoing surveillance activities. Amputation cases had not 

been collected and were not available for analysis. All CTS DFR cases were classified into 4 

categories (Definite, Probable, Possible, and Uncertain) based on criteria previously developed 

by CDPH OHB as part of an earlier CTS surveillance project (Supplemental Material C). 
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Data Linkage 

A summary of the variables used for record linkage across datasets is provided in Table 

1. We linked cases across datasets in order to identify cases that appeared in more than one 

dataset using an iterative process with record linkage software (Jurczyk et al. 2008; Jurczyk et 

al. 2008a). We conducted deterministic record linkage based on exact SSN when possible, and 

probabilistic record linkage based on criteria when a deterministic match using SSN was not 

successful (Mason and Tu 2008; Meray et al. 2007). The linkage process for each dataset was 

iterative such that remaining unmatched records were matched again in order to maximize the 

number of matches identified (Supplemental Material D). After linkage was performed to 

obtain raw matches, matches were de-duplicated and refined (described below) to restrict the 

linkages to one-to-one matched pairs for the purpose of enumerating unique cases of 

amputation and CTS across the datasets. 

For each series of record linkage, many different cases of amputation or CTS in a dataset 

matched to a single case in the other dataset. For example, in the WCIS-OSHPD linkage series, 

multiple OSHPD cases matched to a single WCIS case (resulting in duplicate OSHPD cases), and 

multiple WCIS cases matched to a single OSHPD case (resulting in duplicate WCIS cases). In 

order to enumerate the unique number of cases across the different datasets, these duplicates 

were removed to obtain a final list of one-to-one matches. This de-duplication process differed 

with each linkage series based on the variables available in each dataset (Supplemental 

Material D details this stepwise process for each linkage series). The most common variables 

utilized for de-duplication were dates of injury (SOII, WCIS, DFRs), dates of admittance or 

service (OSHPD), and case classification (WCIS). Linkages were also refined so that the matches 

retained were limited to amputation or CTS cases, resulting in a final list of one-to-one, unique 

matches for each linkage series that could be used for enumeration. 

Calculating Match Rates and Case Enumeration 

Cases in WCIS and OSHPD that remained unmatched after performing record linkage to 

all datasets were de-duplicated based on exact SSN when possible, or first name, last name, 

and birthdate. Match rates between datasets were calculated by dividing the number of 
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unique matches by the total number of de-duplicated cases in each dataset. Cases were 

enumerated by counting the number of unique amputations and CTS that appeared in the 

WCIS, DFR, and OSHPD datasets. We included both lost-time and non-lost-time cases from the 

WCIS, DFR, and OSHPD datasets. For purposes of enumeration, we also included cases 

involving mining, railroad and water transportation, temporary employment, membership 

organizations and small agricultural establishments, which are excluded from the SOII. As SOII 

represents a sample of cases only, these were not included in the final enumeration. 

This study received Common Rule approval (Code of Federal Regulations 45 46.111) by 

the State of California, Health and Human Services Agency, Committee for the Protection of 

Human Subjects. 

RESULTS 

Data Linkage 

BLS Survey of Occupational Illness and Injuries (SOII) 

In total, 65.9%of SOII amputation and 60.4% of CTS cases were linked to the WCIS data 

set (Table 2). Thus, approximately one-third of amputation and CTS cases from the OSHA 300 

Logs could not be found in our workers’ compensation database. As the SOII cases in our 

analysis represent work-related lost-time injuries, we expect these workers to have filed 

workers’ compensation claims for medical treatment and/or lost work time. We would 

particularly expect that a workers compensation claim would be filed for acute traumatic 

amputations where the connection with work is usually obvious. 

Only 29.9% of SOII amputation and 27.0% of SOII CTS cases were linked to the OSHPD 

data set (Table 2). This low match rate is to be expected, as most SOII cases of amputation or 

CTS may not be treated in an emergency department, or require inpatient hospitalization or 

surgery. In contrast, only a small fraction (3.7%) of SOII CTS cases could be linked to the DFR 

data set. We expect that most SOII-eligible CTS cases would seek physician care and a DFR 

should be filed under existing California regulations. Many physicians may fail to recognize 
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work-related injuries and illnesses and/or submit a DFR, and some insurers may not send the 

DFRs to DIR as required. 

Overall, 70.5% of all SOII amputation cases and 66.9% of all SOII CTS cases were linked 

to at least one other dataset. Thus, about one in three cases of amputation and CTS reported 

by employers on their OSHA 300 Logs were not recorded elsewhere – including physician 

reports or workers’ compensation claims. 

Workers’ Compensation Information System (WCIS) 

Only a small percentage of WCIS claims were linked to SOII cases (2.9% of amputation 

claims and 0.9% of CTS claims, Table 2). This is expected, as the SOII is a relatively small sample 

of all cases, and the WCIS is a statewide system that is designed to capture all claims. We found 

34.0% of WCIS amputation claims and 10.5% of WCIS CTS claims were linked to an OSHPD case. 

This finding is to be expected, as most work-related injuries are not treated in an emergency 

room, admitted to a hospital, or require surgery. A large majority of work-related cases of 

amputations (65.9%) and CTS (86.5%) were found only in WCIS (Figures 1 and 2). 

Health Care Facility Data (OSHPD) 

Only 45.6% of OSHPD amputation cases and 24.5% of OSHPD CTS cases were linked to a 

WCIS claim (Table 2). All work-related cases requiring ambulatory surgery should have a 

workers’ compensation claim, as health care providers typically must obtain authorization from 

the workers’ compensation insurance carrier prior to surgery. Overall, 54.4% of amputation 

cases and 73.8% of CTS cases were found only in OSHPD (Figures 1 and 2). 

Doctors’ First Reports of Occupational Injury or Illness (DFR)

Only 43.5% of DFR CTS cases were linked to WCIS claims (Table 2). After recognizing an 

injury or illness as work-related, California regulation requires that physicians submit a DFR to 

the workers’ compensation insurance carrier. The DFR then becomes a basis for the insurance 

carrier to submit an electronic FROI to DWC’s WCIS database. Therefore, we expect that all DFR 

cases, absent an administrative problem in claims management or an immediate determination 
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that the injury was not work-related, would be matched to a claim in the WCIS database. 

Overall, 46.0% of CTS cases were uniquely identified from DFRs (Figure 2). 

Enumeration 

For the years 2007 and 2008, a total of 6,892 amputation cases were identified from 

WCIS and OSPHD (Figure 1), and 39,589 CTS cases were identified from WCIS, OSPHD and DFRs 

(Figure 2). Of the 6,892 amputation cases, almost half (3,216 or 46.9%) were found only in the 

WCIS. Of the 39,589 CTS cases, almost two-thirds (25,193 or 63.6%) were found only in the 

WCIS. Amputation and CTS cases identified from California’s state-based data systems (6,895 

and 39,589) were about five and ten times greater, respectively, than the number estimated 

from the BLS SOII (1,390 and 3,720) for 2007 and 2008. 

DISCUSSION 

There are several unexpected findings from the linkage of work-related amputations 

and CTS in California that deserve discussion. First, more than one-third of SOII cases of CTS

and amputations could not be found in WCIS as a workers’ compensation claim. SOII cases

represent injured employees who have notified their employer of their injury. If these 

employees had a work-related injury requiring medical care beyond first aid, a claim should 

have been filed with the employer’s workers’ compensation insurance carrier to pay for 

medical care and indemnity payments of temporary or permanent disability where appropriate. 

CTS is often a cumulative injury that occurs over months to years, and it is conceivable that 

some SOII cases filed workers’ compensation claims in other years. However, amputations are 

usually immediate and fairly obvious injuries, and we expect that a workers’ compensation 

claim should be filed for these cases within weeks of the incident. 

There are several reasons why SOII cases may not be found as workers’ compensation 

claims. Workers’ compensation claims may not be filed if the treating physician does not 

recognize the injury as work-related (e.g., does not submit a DFR), if the employer does not

notify the workers’ compensation insurance carrier of the work-related injury, or if the 

employee procures medical treatment outside of the workers’ compensation system. In some 
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cases, employers may pay medical providers directly for the injured workers’ care. A recent 

survey of Log 300 reporting in Washington State suggests that some employers simply list all 

workers’ compensation claims on their OSHA 300 Log, others carefully follow the OSHA 

recordkeeping guidelines, and others have difficulties interpreting the applicable regulations 

and how they apply (particularly among small employers) (Wuellner and Bonauto 2013). For 

example, some employers may list a case on the OSHA Log 300 to meet the recordkeeping 

requirements, but then make a separate administrative decision about whether to notify their 

workers’ compensation insurer about an injury. The extent to which these issues arise in OSHA 

recordkeeping and workers’ compensation claims reporting in California is not known, and 

would require follow-up surveys of both employers and employees to uncover the actual chain 

of events that finally records a work-related injury or illness or not. 

Based on the OSHPD linkage results, one of two cases of work-related amputation and 

three of four cases of work-related CTS that required an emergency room visit or surgery could 

not be found in the workers’ compensation claims database. Injured workers with amputations 

or with CTS that need surgery are likely suffering from more severe injuries, and therefore we 

expect that the majority of these cases would have filed a workers’ compensation claim. In 

addition, health care providers usually require insurance authorization prior to proceeding with 

a surgical procedure. Informal telephone interviews with several ambulatory surgery center 

billing departments confirmed that prior insurance authorization is indeed obtained. There are 

several possible reasons why we were not able to find OSHPD cases in WCIS. For CTS cases 

requiring surgery, workers’ compensation claims may have been filed in prior years when the 

case was initially identified as work-related. Indeed, when 2007-2008 ambulatory surgery CTS 

cases were matched to prior years, approximately 66% of cases were matched in WCIS (results 

not shown). The ambulatory surgery center may initially expect payment for the surgical 

procedure from the workers’ compensation insurance company, but after additional review 

these cases may be determined not to be work-related and final payment for the procedure is 

made from another source. In-depth surveys of workers’ compensation provider billing 

practices, administrative procedures and workers’ compensation claims practices are needed to 

determine the extent to which these issues explain the large discrepancy in case identification. 
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Third, three of five CTS cases reported by physicians on the DFR could not be found in 

the workers’ compensation claims database. Under California law, all physicians are required 

to submit a DFR to the workers’ compensation insurance carrier for a suspected work-related 

injury and illness. The DFR is one basis for creating and submitting a FROI to the WCIS. 

Therefore, we expect that every CTS DFR should be matched to a workers’ compensation claim. 

The cumulative nature of CTS suggests that some workers’ compensation claims may have been 

filed in years prior or subsequent to the DFR. It is possible that some physicians file DFRs for 

work-related injuries (including CTS) but a workers’ compensation claim is never established, or 

there is inconsistent interpretation by insurance carriers of reporting requirements. Since DFRs 

are required to be filed for any suspected work-related injury or illness, it is possible for there 

to be no associated workers’ compensation claim file if the injury or illness was later deemed to 

be not work-related. Additional studies are needed to track the “life of an injury” from 

physician reporting to detection in the workers’ compensation claims database. 

The enumeration of all work-related amputation and CTS cases suggests that the 

number of these injuries that occur annually in California is much greater than those estimated 

by the BLS SOII. The greatest proportion of all cases was found only in the workers’ 

compensation database, which includes cases with and without lost work time (days away from 

work). Likewise, many cases were found only in the OSHPD database, which includes hospital 

discharge, emergency department, and ambulatory surgery data, representing cases that are 

more severe in nature. A subset of cases is reported only by physicians on the DFR, perhaps 

representing suspected injuries or those requiring only medical treatment. Although not 

included in the overall enumeration, there were SOII cases that were not detected elsewhere as 

well.

These findings suggest that there are numerous pathways by which work-related 

injuries may be reported, each of which adds to the overall estimated magnitude of workrelated injury and illness in California. After an employee is injured at work, workers’ 

compensation regulations trigger numerous administrative requirements involving many 

individuals including the injured worker, the worker’s supervisor, the employer’s personnel or 

human resources manager, the insurance claims administrator, and the health care provider. 
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With approximately 1.3 million workplaces in 2008 (California Employment Development 

Department), over 200 insurers that wrote workers compensation premiums in 2012 (California 

Department of Insurance 2013), and 136,000 health care providers in California (Medical Board 

of California, Osteopathic Medical Board of California, California Board of Registered Nurses, 

and California Physician Assistant Committee), it is not surprising that multiple data sources are 

needed to ascertain the burden of work-related injury and illness. 

Other studies of work-related injuries and illnesses in California have suggested a 

significant BLS SOII undercount ranging from 25 to 40 percent (Boden and Ozonoff 2008; Boden 

and Ozonoff 2008a). The undercount estimates in our analyses may differ from these previous 

studies due to the addition of other data sources in addition to workers’ compensation claims, 

specific endpoints (amputations and CTS), and use of a different WCIS extraction criteria. While 

Boden and Ozonoff utilized capture-recapture methods to estimate the undercount of WCIS 

and the BLS SOII, our analysis focused on enumerating endpoint-specific cases and evaluating 

the utility of state-based data sources for tracking occupational injuries and illnesses. From the 

public health perspective, the BLS SOII undercount reflects an employer-based system of 

reporting that provides a partial description of the actual burden of work-related injury and 

disability in the U.S. Many authors have described barriers to reporting of work-related injuries 

and illnesses by workers themselves, and these cases will never be reported anywhere (Azaroff 

et al. 2002) or detected by our public health system. Indeed, in California there is no systematic 

collection of worker-reported injury or illnesses directly to the California Department of Public 

Health or other state regulatory authorities. 

There are a number of limitations in our analyses. First, because of the large number of 

cases reported in California, we adopted strict matching criteria and could not review individual 

matches by hand. Therefore, there may have been matches that we missed. Due to the large 

number of cases and frequency of “close” matches on SSN or birth date, it was not feasible to 

include matching criteria that accounted for single digit differences or transpositions in these 

data. The numbers of matched pairs with one and two digit differences in SSN when matching 

the full WCIS and OSHPD data files would be significantly larger and prohibit manual review. 

Pairs of dates of birth with digit differences are similarly numerous. 
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Second, our analyses included both accepted and denied workers’ compensation claims. 

The overall claims denial rate in WCIS was approximately 8% in 2007-2008 (California 

Department of Industrial Relations 2013). It is not known to what extent claim denial may

influence OSHA 300 Log reporting and subsequent inclusion in the SOII survey for a sampled 

employer. 

Third, the case definitions for CTS and amputation may be subject to misclassification 

due to inaccurate physician diagnosis, administrative claims processing, or other unknown 

factors. Although we reviewed records and confirmed the medical diagnoses in a sample of CTS 

and amputation cases, we did not ascertain the extent to which other diagnoses (such as 

tendinitis or avulsion) might be “true” cases of CTS or amputation. Matching was performed 

between datasets using our a priori case definition, and the extent to which matching results 

may vary using related diagnoses is unknown. 

Finally, a number of challenges are intrinsic to each data set that we utilized. The WCIS 

industry information is incomplete and/or has inconsistent coding; for example, some 

employers provide the same corporate mailing address on every claim, while other employers 

provide the physical address of the establishment where the injured employer works. On 

different claims, a single employer might use different company names, different Federal 

Employer Identification Numbers, or different industry or class codes, even sometimes for the 

same employee at the same location. It is thus sometimes difficult to determine which claims 

come from the same employers. Occupation coding is not feasible as a unique “class code” is 

assigned for administrative purposes. The sheer size of the WCIS dataset means that individual 

claim review is not feasible for all records. 

OSHPD data reporting requirements do not include employer information or a workrelated variable other than expected payer (where workers’ compensation is one of ten 

different possible categories). This limits our ability to identify work-related injuries among 

workers who are uninsured and to characterize disparities that exist in these injuries based on 

workers’ compensation coverage status (Berdahl and Zodet 2010; Nicholson et al. 2008). Use 

of E-coding is not feasible for identifying work-relatedness due to the limited use of this field by 

health care providers, and the date of injury is not a variable included in the OSHPD dataset so 
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was not available for matching to SOII and WCIS. Furthermore, due to changes in California 

licensing requirements, beginning in 2008, physician-owned ASCs are not required to report to 

OSHPD. 

The DFR is a paper-based form completed by physicians, and some information may be 

unavailable due to illegible handwriting or incomplete fields. There are no automatic quality 

checks on the DFR, thus resulting in inaccurate data completion or misdiagnosis. 

CONCLUSION 

An ongoing system using multiple data sources can add to federal and state efforts to prevent 

work-related injuries and illnesses. The total number of amputations and CTS in California is 

significantly greater than the BLS SOII estimates, suggesting that a multisource surveillance 

system is a valuable adjunct to employer-based reporting. From the public health perspective, 

BLS SOII data are a valuable tool that can be used to analyze trends and compare relative risk of 

injuries and illness across industries and occupations. The BLS SOII system was not designed for 

use by public health departments for case identification, disease outbreak detection, or work 

site investigations. In contrast, the workers’ compensation claims system may be used by 

public health agencies for case identification, leading to workplace interventions that can 

prevent additional cases. Physician reports are a valuable and timely source of clinical 

information about both individual and multiple cases from a work site, leading to investigations 

of injuries and disease outbreaks. Hospital discharge, emergency department, and ambulatory 

surgery records may identify cases of severe work-related injury or disease that require public 

health action. Developing and maintaining occupational epidemiology as a core component of 

public health capacity at the state level is critical to the use of these data sources in the ongoing 

prevention of work-related injuries and illnesses. 
Page 17 of 40

ACKNOWLEDGEMENTS 

This project was supported by a U.S. Bureau of Labor Statistics grant, “Enumerating 

Workplace Injuries and Illnesses with Multiple Data Sources” (RFO BLS-0910-002). We wish to 

acknowledge other state and federal collaborators: Darrin Adams, Les Boden, Dave Bonauto, 

Letitia Davis, Kathleen Grattan, John Ruser, John Russell, Eric Sygnatur, SangWoo Tak, and Sara 

Wuellner. 

This work was supported in part by an appointment to the Applied Epidemiology 

Fellowship Program administered by the Council of State and Territorial Epidemiologists (CSTE) 

and funded by the Centers for Disease Control and Prevention (CDC) Cooperative Agreement 

Number 5U38HM000414-5. 
Page 18 of 40

REFERENCES

Azaroff LS, Levenstein C, Wegman DH. September 2002. Occupational injury and illness 

surveillance: Conceptual filters explain underreporting. American Journal of Public Health: 

1421–1429. 

Berdahl TA and Zodet M. 2010. Medical care utilization for work-related injuries in the United 

States 2002-2006. Medical Care. 48: 645-651. 

Boden LI and Ozonoff A. June 2008. Capture-Recapture Estimates of Nonfatal Workplace 

Injuries and Illnesses. Annals of Epidemiology: 500-506. 

Boden LI and Ozonoff A. August 2008a. Reporting Workers’ Compensation Injuries in California: 

How Many are Missed? The California Commission on Health and Safety and Workers’ 

Compensation. 

Bureau of Labor Statistics. September 2008. Occupational Safety and Health Statistics. In: BLS 

Handbook of Methods: United States Department of Labor, Bureau of Labor Statistics webpage. 

Retrieved from: https://www.bls.gov/opub/hom/homch9.htm. Date accessed: 12/30/2010. 

California Board of Registered Nurses. September 2013. Monthly Statistics webpage. Retrieved 

from: http://www.rn.ca.gov/about_us/stats.shtml. Date accessed: 2/6/2014 

California Department of Industrial Relations. June 2013. In: Table 9. California Workers’ 

Compensation Claims: FROI and SROI Data Summary, by Year of Injury 2000-2012, California 

Department of Industrial Relations Worker’s Compensation Information System Database 

Tables webpage. Retrieved from: http://www.dir.ca.gov/dwc/wcis/WCIS_tables/Table9.xls. 

Date accessed: 2/4/2014. 

California Department of Insurance, Rate Specialist Bureau. June 2013. In: California Life and 

Annuity Insurance Industry 2012 Market Share Report, California Department of Insurance 

Market Share Reports webpage. Retrieved from: http://www.insurance.ca.gov/0400-

news/0200-studies-reports/0100-market-share/2012/upload/LifeMktShr2012.pdf. Date 

accessed: 2/7/2014. 

California Department of Public Health, Occupational Health Branch (CDPH OHB). August 2009. 

In: Occupational Pesticide Illness in California, 1998-2007, CDPH OHB Tracking Pesticide-Related 

Illness and Injury – Publications webpage. Retrieved from: 

http://www.cdph.ca.gov/programs/ohsep/Documents/pestillness.pdf. Date accessed: 

5/29/2013. 

California Employment Development Department, Quarterly Census of Employment and 

Wages. In: Size of Business Data – 2003-present, California Employment Development 
Page 19 of 40

Department webpage. Retrieved from: 

http://www.labormarketinfo.edd.ca.gov/Content.asp?pageid=138. Date accessed: 2/5/2014. 

California Physician Assistant Committee. In: PAC Annual Report, 2011-2012, California 

Physician Assistant Committee Forms and Publications webpage. Retrieved from: 

http://www.pac.ca.gov/forms_pubs/annual_report_1112.pdf. Date accessed: 2/6/2014. 

Centers for Disease Control and Prevention (CDC). October 2011. Acute Illness and Injury from 

Swimming Pool Disinfectants and Other Chemicals --- United States, 2002—2008. MMWR. 

Morbidity and Mortality Weekly Reports. 60:1343-1347. 

Friedman L, Krupczak C, Brandt-Rauf S, Forst L. 2012. Occupational amputations in Illinois 2000–

2007: BLS vs. data linkage of trauma registry, hospital discharge, workers’ compensation 

databases and OSHA citations. Injury. 44:667-73. 

Joe L, Beckman S, Roisman R, Frederick M, Beckman J, Rempel D, Jones M, Harrison R. June 

2012. Using an Administrative Workers’ Compensation Claims Database for Occupational 

Health Surveillance in California: Validation of a Case Classification Scheme for Carpal Tunnel 

Syndrome (Abstract 1326952). Council of State and Territorial Epidemiologists - Annual 

Conference. 

Jurczyk P, Lu JJ, Xiong L, Cragan JD, Correa A. 2008. FRIL: A tool for comparative record linkage. 

AMIA Annual Symposium Proceedings: 440-444.

Jurczyk P, Lu JJ, Xiong L, Cragan JD, Correa A. 2008a. Fine-grained record integration and linkage 

tool. Birth Defects Research Part A: Clinical and Molecular Teratology. 82: 822-829. 

Leigh JP, Marcin JP, Miller TR. 2004. An estimate of the U.S. government’s undercount of nonfatal occupational injuries. Journal of Occupational and Environmental Medicine. 46: 10–18. 

Mason CA and Tu S. 2008. Data linkage using probabilistic decision rules: a primer. Birth Defects 

Research Part A: Clinical and Molecular Teratology. 82: 812-821. 

Medical Board of California. Physician and Surgeon License by County, 2011-2012, webpage. 

Retrieved from: http://mbc.ca.gov/About_Us/Statistics/Licenses_by_County.aspx. Date 

accessed: 2/6/2014. 

Meray N, Reitsma JB, Ravelli AC, Bonsel GJ. 2007. Probabilistic record linkage is a valid and 

transparent tool to combine databases without a patient identification number. Journal of 

Clinical Epidemiology. 60: 883-891.

Mustard CA, Chambers A, McLeod C, Bielecky A, Smith PM. May 2012. Comparison of data 

sources for the surveillance of work injury. Occup Environ Med. 69: 317-324. 
Page 20 of 40

Nicholson V., Bunn TL, Costich JF. Jun 2008. Disparities in work-related injuries associated with 

worker compensation coverage status. Am J Ind Med. 51: 393-398.

Osteopathic Medical Board of California. In: OMBC 2012 Oversight Report, Osteopathic Medical 

Board of California Forms and Publications webpage. Retrieved from: 

http://www.ombc.ca.gov/forms_pubs/sunset_2012.pdf. Date accessed: 2/6/2014. 

Probst TM and Estrada AX. 2010. Accident under-reporting among employees: Testing the 

moderating influence of psychological safety climate and supervisor enforcement of safety 

practices. Accident Analysis and Prevention. 42: 1438-1444. 

Roisman R, Joe L, Frederick M, Beckman S, Beckman J, Jones M, Harrison R. May 2013. Using an 

Administrative Workers’ Compensation Claims Database for Occupational Health Surveillance in 

California: Validation of a Case Classification Scheme for Amputations. Use of Workers’ 

Compensation Data for Occupational Safety and Health: Proceedings from June 2012 

Workshop: 121-125.

Rosenman KD, Kalush A, Reilly MJ, Gardiner JC, Reeves M, Luo Z. April 2006. How Much WorkRelated Injury and Illness is Missed by the Current National Surveillance System?” Journal of 

Occupational and Environmental Medicine: 357–365. 

Sorock GS, Smith GS, Reeve GR, Dement J, Stout N, Layne L, Pastula ST. 1997. Three 

perspectives on work-related injury surveillance systems. American Journal of Industrial 

Medicine. 32: 116-128.

Wuellner S and Bonauto D. November 5, 2013. Occupational injury recordkeeping among BLS 

sampled establishments; Implications for national surveillance. Conference presentation at: 

2013 American Public Health Association Annual Meeting, Boston, MA. 
Page 21 of 40

FIGURES 

Figure 1. Enumeration Results for Amputation Cases in 2007-2008 from Workers’ Compensation 

Information System (WCIS) and Health Care Facility Data (OSHPD) 

WCIS

3,216 (65.9%) 

unmatched

OSHPD

1,984 (54.4%) 

unmatched

1,662

matched

6,862 amputation cases
Page 22 of 40

Figure 2. Enumeration Results for CTS Cases in 2007-2008 from Workers’ Compensation Information 

System (WCIS), Health Care Facility Data (OSHPD), and Doctors’ First Reports of Occupational Injury or 

Illness (DFR) 

WCIS

25,193 (86.5%) 

unmatched

DFR

1,062 (46.0%) 

unmatched

OSHPD

9,254 (73.8%) 

unmatched

2833 801

140

306

39,589 CTS cases
Page 23 of 40

TABLES 

Table I. Dataset Inclusion Criteria and Data Linkage Elements 

Dataset 

(2007-2008)

Inclusion Criteria Data Linkage 

CTS Amputation Elements

SOII - Nature of injury: OIICS code for 

CTS

- Nature of injury: 3 OIICS codes 

for amputation

Name

Employer name

Date of birth

Date of injury

WCIS - Nature of injury: 4 WCIO codes

- Cause of injury: 4 WCIO codes

- Part of body: 8 WCIO Part of 

body codes

- Injury description: “carpal”, 

“CTS”, “numbness”, or “tingling”

- Procedure: CPT code for CT 

release 

- Diagnosis: ICD-9 code for CTS

- Nature of injury: WCIO code

- Injury description: “bony loss”, 

“cut off”, “amputation” or some 

variation in text

- Diagnosis: 5 Diagnosis related 

group codes; 68 ICD9-CM codes

- Procedure: 83 CPT; 46 ICD-9-CM 

codes; 124 Healthcare procedure 

coding system codes

Name

Employer name

Date of birth

Date of injury

OSHPD - Payer: Workers’ compensation 

or work-related ICD-9 e-code

- Primary procedure: CPT code 

for CT release 

- Primary diagnosis: ICD-9 code 

for CTS

- Payer: Workers’ compensation 

or work-related ICD-9 e-code

- Primary diagnosis: 5 Diagnosis 

related group codes; 68 ICD9-CM 

codes

- Primary procedure: 83 CPT; 46 

ICD-9-CM codes; 124 Healthcare 

procedure coding system codes

SSN

Name

Date of birth

DFR - Symptoms: paresthesia, 

hypoesthesia, pain, 

burning/numbness affecting 

median nerve of hand(s)

- Physical exam findings: Tinel’s 

sign, Phalen’s test, 

diminished/absent sensation to 

pin prick in median nerve 

distribution of hand; positive 

median nerve compression 

- Electrodiagnostic findings

(NCS/EMG): Median nerve 

dysfunction across the CT

N/A SSN

Name

Employer name

Date of birth
Page 24 of 40

Table II. Overall Record Linkage Results by Dataset for Cases of Amputation and CTS that Occurred in 

2007-2008 

Dataset SOII WCIS OSHPD DFR

Amputation cases 217 4,881 3,646 (n/a)

Matched to WCIS OSHPD SOII OSHPD SOII WCIS

Records matched 143 65 143 1,662 65 1,662

Match rate (%) 65.9 29.9 2.9 34.0 1.8 45.6

CTS cases 459 29,133 12,533 2309

Matched to WCIS OSHPD DFR SOII OSHPD DFR SOII WCIS DFR SOII WCIS OSHPD

Records matched 277 124 17 277 3,069 1,005 124 3,069 377 17 1,005 377

Match rate (%) 60.4 27.0 3.7 0.9 10.5 3.4 1.0 24.5 3.0 0.7 43.5 16.3
Page 25 of 40

Supplemental Material 

Section A. WCIS Extraction Criteria

Table A1. Amputation Extraction Criteria for WCIS

Criteria Variable Values 

Nature of Injury 02 - Amputation

Injury Description Has “bony loss”, “cut off”, “amputation”, or some variation in the 

text 

Diagnosis Related Group Codes 5 DRG codes

Diagnosis Codes 68 ICD9-CM Disease Codes

Procedure Codes 83 CPT Codes; 46 ICD-9 CM Procedure Codes; 124 Possible 

Healthcare Procedure Coding System Codes

The original amputation extract from the WCIS database included all claims with Nature of Injury = 

“Amputation”, or with appropriate keywords in the Injury Description, or with appropriate diagnosis or 

procedure codes in the medical billing data. 

Table A2. CTS Extraction Criteria for WCIS 

Criteria 

Variable Code Major Category WCIO Description*

Nature of Injury

49 Sprain or Strain Sprain or Tear

52 Sprain or Strain Strain or Tear

78 Computer Cumulative Trauma Carpal Tunnel Syndrome

80 Other Cumulative Trauma All Other Cumulative Injuries, NOC

Cause of Injury

60 Strain Strain or Injury by, NOC

94 Rubbed or Abraded Repetitive Motion

97 Strain Repetitive Motion

98 Miscellaneous Causes Cumulative, NOC

Part of Body

30 Upper Extremities Multiple Upper Extremities

33 Upper Extremities Lower Arm

34 Upper Extremities Wrist

35 Upper Extremities Hand

36 Upper Extremities Finger(s) 

37 Upper Extremities Thumb

39 Upper Extremities Wrist(s) & Hand(s)

90 Multiple Body Parts Multiple Body Parts

Injury Description

Contains a variation of the term "carpal", "CTS", etc. or "numbness" or 

"tingling"

*Source: https://www.wcio.org/Document%20Library/InjuryDescriptionTablePage.aspx

The original CTS extract from the WCIS database included all claims with Nature of Injury = “Carpal 

Tunnel Syndrome”, or with appropriate keywords in the Injury Description, or with appropriate 

diagnosis or procedure codes in the medical billing data. 
Page 26 of 40

Section B: WCIS Case Classification 

Amputation Case Classification 

Cases obtained from WCIS (using the amputation extraction criteria agreed upon in conference calls) 

were classified as probable or uncertain cases of amputation. Classification data were obtained from 

medical billing data (diagnosis and procedure codes), nature of injury, part of body, and injury 

description. Diagnosis for amputation and amputation-related procedure codes were considered 

probable amputation codes. The lists of probable diagnosis and procedure codes are in Tables B1 and 

B2. 

A nature of injury (NOI) signifying amputation was also considered indicative of an amputation case. 

Both part of body and injury description were used as “criteria variables” to support probable codes and

the nature of injury code. Acceptable values for these variables, which are consistent with a diagnosis of 

amputation, are in Table B3. 

A classification scheme was established by reviewing cases and examining characteristics that suggested 

the level of probability that an extracted case was a true amputation case (Table B4). The scheme was 

confirmed on a preliminary basis by further review of classified cases to ensure that the majority of 

cases in a given class appeared to be properly classified. The expanded table of the case classification 

scheme shows all the possible combinations of the variables and the resulting case classification (Table 

B5). Further testing of the classification scheme by reviewing a sample of medical records is described in 

the body of the main article. 

Table B1: List of probable amputation diagnosis codes

Description ICD-9-CM

Amp of Thumb 885, 885.0, 885.1

Amp of Finger 886, 886.0, 886.1

Amp of Arm/hand 887

Amp of Arm/hand 

unilateral

887.0, 887.1, 887.2, 887.3, 887.4, 

887.5

Amp of Arm/hand 

bilateral 887.6, 887.7

Amp of Toe 895, 895.0, 895.1

Amp of Foot 896

Amp of Foot, unilateral 896.0, 896.1

Amp of Foot, bilateral 896.2, 896.3

Amp of Leg 897

Amp of Leg, unilateral 897.0, 897.1, 897.2, 897.3, 897.4, 

897.5

Amp of Leg, bilateral 897.6, 897.7

Lower limp amp status V49.70

Upper limb amp status V49.60
Page 27 of 40

Table B2: List of probable amputation procedure codes 

Description CPT ICD-9-CM

Amp of upper limb

23900, 23920, 23921, 24900, 24920, 

24925, 24930, 24931, 24940, 25900, 

25905, 25907, 25909, 25915, 25920, 

25922, 25924, 25927, 25929, 25931

84

Amp finger 26910, 26951, 26952 84.01

Amp thumb 84.02

Amp upper limb 84.03 – 84.09

Amp lower limb 27290, 27295, 27590, 27591, 27592, 

27594, 27596, 27598, 28800, 28805

84.1; 84.12 –

84.19

Amp lower limb NOS 84.1

Amp toe 28810, 28820, 28825 84.11

Amputation, NOS 84.91

Amp tuft of distal 

phalanx 11752

Table B3. Acceptable values for criteria variables of amputation case classification scheme 

Criteria Variable Acceptable Values*

Part of Body

30 - Multiple Upper Extremities 50 - Multiple Lower Extremities

31 - Upper arm 52 - Upper Leg

32 - Elbow 53 - Knee

33 - Lower Arm 54 - Lower Leg

34 - Wrist 55 - Ankle

35 - Hand 56 - Foot

36 - Finger(s) 57 - Toe(s)

37 - Thumb 58 - Great Toe

38 - Shoulder(s) 90 - Multiple Body Parts

39 - Wrist(s) & Hand(s)

Injury Description

Contains “amputate”

Contains “severed” (without “tendon”)

*Numeric codes are WCIO codes 
Page 28 of 40

Table B4. Amputation case classification scheme 

Strong Injury Description and/or 

Acceptable Part of Body

Neither Strong Injury Description nor 

Acceptable Part of Body 

Nature of Injury = Amputation and/or 

Probable Code (Procedure or Diagnosis) Probable Uncertain

Table B5. Expanded amputation case classification scheme, all possible combinations 

Probable Code

Nature of 

Injury = 

Amputation

Strong Injury 

Description

Acceptable Part 

of Body

Case 

Classification

Yes Yes Yes Yes Probable

Yes Yes Yes No Probable

Yes Yes No Yes Probable

Yes No Yes Yes Probable

Yes No Yes No Probable

Yes No No Yes Probable

No Yes Yes Yes Probable

No Yes Yes No Probable

No Yes No Yes Probable

Yes Yes No No Uncertain

Yes No No No Uncertain

No Yes No No Uncertain

Probable case: 

[Probable code and/or NOI = Amputation] and a [strong ID and/or acceptable POB] 

Uncertain case: 

Does not have a strong injury description or acceptable POB. 
Page 29 of 40

Carpal Tunnel Syndrome Case Classification 

Cases obtained from WCIS using the CTS extraction criteria (agreed upon in conference calls) were 

classified as probable, possible, or uncertain cases of CTS. Classification data were obtained from 

medical billing data (procedure or diagnosis codes), nature of injury, cause of injury, part of body, and 

injury description. Procedure codes for carpal tunnel release1 were considered indicative of CTS, as well 

as diagnosis codes for CTS or mononeuritis of upper limb2. The remaining variables were used as 

“criteria variables” to support procedure or diagnosis codes, and values for these variables consistent 

with a diagnosis of CTS were considered acceptable as shown in Table B6. 

A case classification scheme (Table B7) was established by reviewing cases and examining characteristics 

that suggested the level of probability that an extracted case was a true case of carpal tunnel syndrome 

as described below. The scheme was confirmed on a preliminary basis by further review of classified 

cases to ensure that the majority of cases in a given class appeared to be properly classified. Further 

testing of the classification scheme by reviewing a sample of medical records is described in the body of 

the main article. 

Table B6. CTS extraction criteria from WCIS 

Criteria Variable Code Major Category Description

Nature of Injury

49 Sprain or Strain Sprain

52 Sprain or Strain Strain

78 Computer Cumulative Trauma Carpal Tunnel Syndrome

80 Other Cumulative Trauma All Other Cumulative Injuries

Cause of Injury

60 Strain Strain or Injury by, NOC

94 Rubbed or Abraded Repetitive Motion

97 Strain Repetitive Motion

98 Miscellaneous Causes Cumulative, NOC

Part of Body

30 Upper Extremities Multiple Upper Extremities

33 Upper Extremities Lower Arm

34 Upper Extremities Wrist

35 Upper Extremities Hand

36 Upper Extremities Finger(s) 

37 Upper Extremities Thumb

39 Upper Extremities Wrist(s) & Hand(s)

90 Multiple Body Parts Multiple Body Parts

Injury Description

Contains a variation of the term "carpal","CTS", etc. or "numbness" or 

"tingling"

 

1

 CPT codes 64721 or 29848, or ICD-9-CM code 04.43 

2

 ICD-9-CM 354.0 or 354 
Page 30 of 40

Table B7. CTS case classification scheme 

Procedure 

code

ICD-9 Dx 

Code

Number of Acceptable Criteria Variables

4 3 2 1 0

64721 or 

29848 Any Probable Probable Possible Uncertain Uncertain

Any 354 or 354.0 Probable Probable Possible Uncertain Uncertain

Other or N/A Other or N/A Possible Uncertain Uncertain Uncertain Uncertain

Probable case: 

1. Procedure code is 64721, 29848, or 04.43 and 3 or 4 out of 4 criteria variables acceptable 

2. Diagnosis code is 354 or 354.0 and 3 or 4 out of 4 criteria variables acceptable 

Possible case: 

1. Procedure code is 64721, 29848, or 04.43 and 2 criteria variables acceptable 

2. Diagnosis code is 354 or 354.0 and 2 criteria variables acceptable 

3. 3 or 4 out of 4 criteria variables acceptable (no ICD-9)

Uncertain case: 

1. Procedure code is 64721, 29848, or 04.43 and 0 or 1 criteria variables acceptable 

2. Diagnosis code is 354 or 354.0 and 1 or 0 criteria variables acceptable 

3. 2, 1, or 0 out of 4 criteria variables acceptable (no procedure or diagnosis codes available) 
Page 31 of 40

Section C: DFR CTS Case Classification Criteria 

A- Symptoms of CTS: paresthesia, hypoesthesia, pain, burning or numbness affecting at least part of the 

median nerve distribution of the hand(s);3 symptoms should have lasted at least one week or, if 

intermittent, have occurred on multiple occasions. 

B- Objective evidence in the affected hand(s) or wrist(s), either: 

 physical exam findings - positive Tinel’s4, positive Phalen’s5, diminished or absent sensation to pin prick in 

median nerve distribution of the hand, positive median nerve compression (aka Durkin’s);

OR

 electrodiagnostic findings (NCS/EMG) indicative of median nerve dysfunction across the carpal tunnel (old 

or new NCS/EMG with positive CTS reported) 

1 - Definite CTS 

A - symptom specific to the median nerve distribution reported 

B - positive nerve conduction results reported by physician 

OR

A - positive Tinel’s, Phalen’s, pin prick, atrophy in median nerve distribution, or median nerve compression (aka 

Durkin’s) reported 

B - positive nerve conduction results reported by physician 

2 - Probable CTS 

A - symptom specific to the median nerve distribution reported 

B - positive Tinel’s, Phalen’s, pin prick, atrophy in median nerve distribution or median nerve compression (aka 

Durkin’s) reported

OR

A - no symptom specific to the median nerve distribution reported 

B - positive nerve conduction results reported by physician 

3 - Possible CTS 

A - symptom specific to the median nerve distribution reported 

B - no positive test results reported 

OR

A - no symptom specific to the median nerve distribution reported 

B - positive Tinel’s, Phalen’s, pin prick, atrophy in median nerve distribution or median nerve compression (aka 

Durkin’s) reported

4 - Uncertain CTS 

A - no symptoms reported 

B - no positive test results reported 

(physician diagnosis of CTS or rule out CTS with no other information) 

 

3

 Median nerve distribution: palmar side of thumb, index finger, middle finger, and radial half of ring finger; dorsal 

(back) side of the same digits above the proximal interphalangeal joint; and radial half of palm. Pain and paresthesia 

may radiate proximally into the arm. 

4

 Tinel’s sign – paresthesia elicited or accentuated by gentle percussion over the carpal tunnel. 

5 Phalen’s test – paresthesia elicited or accentuated by maximal passive flexion of the wrist for one minute. 
Page 32 of 40

Section D: Data Linkage Methods 

The matching software used is an open source tool available online (http://fril.sourceforge.net/) called 

Fine-grained Records Integration and Linkage Tool (FRIL). The variables available for use in record 

linkage are presented by dataset in Table D1. As summarized in Tables D2a-f, the record linkage process 

for each dataset pair was iterative such that remaining unmatched records were matched again to 

maximize the number of matches identified. Deterministic record linkage based on exact SSN was 

performed when possible, and probabilistic record linkage was used when SSN was not available (Table 

D2a). Tables D3a-e summarize the record linkage sensitivity analyses conducted to determine the most 

appropriate match score for each record linkage iteration. The match score is a number calculated by 

FRIL for each matched pair based on the record linkage configuration or criteria set by the user (Tables 

2a-f). After linkage was performed to obtain raw matches, matches were deduplicated and refined 

based on the criteria summarized by Tables D4a-b. 

Table D1. Variables Used for Matching across Datasets 

Variable SOII WCIS DFR OSHPD

Case Name (First and Last)    

Employer Name   

Case Social Security Number (SSN)  

Date of Birth (DOB)    

Date of injury (DOI)*  

* Date of injury was used to deduplicate matches where multiple cases in a dataset linked to one case in another 

dataset, in order to obtain matches at a ratio of 1:1 (see Table D4). 

Tables D2a-f: Criteria used for iterative record linkage 

Table D2a: Summary of criteria used for iterative record linkage

Criteria # WCIS Variable SOII variable Comparison type Parameters

A SSN SSN Exact N/A

B First name First name Edit distance* Match level start=0.2, 

end=0.4 

C Last name Last name Edit distance Match level start=0.2, 

end=0.4 

D Date of birth Date of birth Exact N/A

E Employer name Employer name Edit distance Match level start=0.2, 

end=0.4 

F First name Last name Edit distance Match level start=0.2, 

end=0.4 

G Last name First name Edit distance Match level start=0.2, 

end=0.4 

* Metric of difference between two character strings based on the number of operations needed to transform one 

string into the other. 

Table D2b: WCIS-OSHPD Linkage
Page 33 of 40

Iteration # Criteria Used (Weight) Match Score 

Accept Level

Linkage 

Method

Raw CTS 

Matches

Raw Amp 

Matches

1 A (100) 100 Nested loop 

join* 8988 3040

2

B (34)

C (33)

D (33)

100 (CTS)

90 (Amp)

Nested loop 

join 172 89

* Compares each record to all other records (computationally intensive, not suitable for larger data sets. 

Table D2c: SOII-OSHPD Linkage

Iteration # Criteria Used (Weight) Match Score 

Accept Level

Linkage 

Method

Raw CTS 

Matches

Raw Amp 

Matches

1

B (34)

C (33)

D (33)

75 Nested loop 

join 524 205

2

B (25)

C (25)

D (50)

100 Nested loop 

join 168 15

Table D2d: WCIS-SOII Linkage

Iteration # Criteria Used (Weight) Match Score 

Accept Level

Linkage 

Method

Raw CTS 

Matches

Raw Amp 

Matches

1

B (25)

C (25)

D (25)

E (25)

75 Nested loop 

join 452 195

2

D (25)

E (25)

F (25)

G (25)

75 Blocked* by 

DOB 4 2

* Stratifies by DOB and compares records with the same DOB with one another.

Table D2e: WCIS-DFR Linkage

Iteration 

#

Criteria Used (Weight) Match Score 

Accept Level

Linkage 

Method

Raw CTS 

Matches

1 A (100) 100 Nested loop 

join 2107

2

B (25)

C (25)

D (25)

E (25)

65 Nested loop 

join 251

3

D (25)

E (25)

F (25)

G (25)

65 Nested loop 

join 4

Table D2f: OSHPD-DFR Linkage
Page 34 of 40

Iteration 

#

Criteria Used (weight) Match Score 

Accept Level

Linkage 

Method

Raw CTS 

Matches

1 A (100) 100 Nested loop 

join 867

2

B (34)

C (33)

D (33)

75 Nested loop 

join 70

Tables D3a-e: Matching Methods Sensitivity Analyses 

Table D3a: WCIS-OSHPD Amputation Iterative Record Linkage Sensitivity Analysis

Total match 

pairs, all scores

Acceptable match pairs based on manual review* (from 

random sample, if applicable)

WCIS-OSHPD Amputation Iteration 1 (n=3040 total match pairs)

Match Score n % n %

100 3040 100 50 (50 pairs sampled) 100 (of sample)

WCIS-OSHPD Amputation Iteration 2 (n=92 total match pairs)

Match Score n % n %

100 88 95.65 50 (50 pairs sampled) 100 (of sample)

91** 1 1.09 1 100

77 2 2.17 0 0

70 1 1.09 0 0

* Manual review based on first name, last name, date of birth, and employer name (if applicable)

** Acceptable score

WCIS-OSHPD Amputations Linkage (Table D 3a) 

The first iteration was performed using exact SSN, yielding 3,040 raw matches. Review of a sample 

(n=50) of these showed that they were acceptable matches based on first name, last name, and date of 

birth. 

For the second iteration, there were 89 raw matches. Of these, 88 had a confidence level of 100 and 

one had a confidence level of 91. A manual review of a sample of those with a confidence level of 100 

(n=50 sample) and the record with a level of 91 (n=1) confirmed that they were good matches based on 

first name, last name, and date of birth. The three matched pairs below a score 91 did not match on 

first or last name or date of birth, and were therefore not considered acceptable raw matches. 
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Table D3b: WCIS-OSHPD CTS Iterative Record Linkage Sensitivity Analysis

Total match 

pairs, all scores

Acceptable match pairs based on manual review* (from 

random sample, if applicable)

WCIS-OSHPD CTS Iteration 1 (n=8988 total match pairs)

Match Score n % n %

100 8988 100 50 (50 sampled) 100 (of sample)

WCIS-OSHPD CTS Iteration 2 (n=197 total match pairs)

Match Score n % n %

100** 176 89.34 50 (50 sampled) 100 (of sample)

91 6 3.05 0 0

85 8 4.06 0 0

78 2 1.02 0 0

77 5 2.54 0 0

* Manual review based on first name, last name, date of birth, and employer name (if applicable)

** Acceptable score

WCIS-OSHPD CTS Linkage (Table D3b) 

The first iteration was performed using exact SSN, yielding 8,988 raw matches. Review of a random 

sample (n=50) of these showed that they were acceptable matches based on first name, last name, and 

date of birth. 

For the second iteration, the acceptance level was set at 100 which yielded 172 raw matches, after 

removing linkages between first names “Maria” in one data set and “Mario” in the other (n=6). Manual 

review of a sample of these confirmed that they were acceptable matches based on first name, last 

name, and date of birth. All matches below 100 did not match first name, last name or date of birth, 

and were not considered acceptable matches. 
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Table D3c: SOII-OSHPD Amputation Iterative Record Linkage Sensitivity Analysis

Total match 

pairs, all scores

Acceptable match pairs based on manual review* (from 

random sample, if applicable)

WCIS-OSHPD Amputation Iteration 2 (n=2408 total match pairs)

Match Score n % n %

100 200 8.31 50 (50 sampled) 100 (of sample)

85 1 0.04 1 100

78 1 0.04 1 100

77** 3 0.12 3 100

67 2184 90.7 0 (70 sampled) 0

66 19 0.79 0 0

WCIS-OSHPD Amputation Iteration 2 (n=77 total match pairs)

Match Score n % n %

100** 15 19.48 15 100

75 62 80.52 0 0

* Manual review based on first name, last name, date of birth, and employer name (if applicable)

** Acceptable score

SOII-OSHPD Amputations Linkage (Table D3c) 

Because this match is limited to the use of only three variables (first name, last name, and date of birth), 

our acceptance level was conservative and subject to manual review of the linkages. We were also 

limited in the number of iterations that could be successfully performed with only three variables 

available. 

For the first iteration, the acceptance level was set at 75 because manual review of the 205 raw matches 

with a confidence level above 75 revealed that they were good matches based on first name, last name, 

and date of birth. Manual review of a sample of matches below 75 revealed that these were not 

acceptable matches. Most of these unacceptable matches did not match on date of birth but matched 

only on common first and last names, so we are certain that these were not true matches (confidence 

scores of 66 and 67). 

The weight of exact date of birth was increased for the second iteration, which linked the unmatched 

BLS SOII records from the first iteration to the full OSHPD amputation dataset. The acceptance level was 

set at 100 because manual review of the 15 raw matches with a confidence level of 100 revealed that 

they were good matches based on first name, last name, and date of birth. Manual review of a sample 

of matches below 100 revealed that these were not acceptable matches. However, these 15 matches 

were already identified in the first iteration and therefore did not contribute to the total number of 

linkages found.
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Table D3d: SOII-OSHPD CTS Iterative Record Linkage Sensitivity Analysis

Total match pairs, 

all scores

Acceptable match pairs based on manual review* 

(from random sample, if applicable)

WCIS-OSHPD Amputation Iteration 2 (n=524 total match pairs, acceptance level=75)

Match Score n % n %

100 513 97.9 50 (50 pairs sampled) 100

96 4 0.76 4 100

91 2 0.38 2 100

88 1 0.19 1 100

85 4 0.76 4 100

Below 75** - - 0 (70 pairs sampled) 0

WCIS-OSHPD CTS Iteration 2 (n=168 total match pairs)

Match Score n % n %

100** 168 100.00 50 (50 pairs sampled) 100

Below 100 - - 0 (70 pairs sampled) 0

* Manual review based on first name, last name, date of birth, and employer name (if applicable)

** Acceptable score

SOII-OSHPD CTS Linkage (Table D3d) 

For the first iteration, acceptance level was set at 75 which yielded 524 raw matches. Of these matches, 

96% had a confidence level of 100, and a manual review of a sample of these confirmed that they were 

acceptable matches based on first name, last name, and date of birth. The remaining 4% had 

confidence levels that ranged from 85 to 96. Manual review of these matches confirmed that these 

were acceptable matches as well. A sample (n=70) of matches below 75 revealed that these were not 

acceptable. Most of these unacceptable matches did not match on date of birth but matched only on 

common first and last names, so we are certain that these are not true matches. 

The weight of exact date of birth was increased for the second iteration, which linked the unmatched 

BLS SOII records from the first iteration to the full OSHPD amputation dataset. Acceptance level was set 

at 100 because manual review of a sample of the 168 raw matches with a confidence level of 100 

revealed that they were acceptable matches, while a sample (n=70) of matches below 100 were not. 

However, these matches were already identified in the first iteration and therefore did not contribute to 

the total number of linkages found. 
Page 38 of 40

Table D3e: WCIS-SOII Iterative Record Linkage Sensitivity Analysis

Total match pairs, all 

scores

Acceptable match pairs based on manual 

review* (from random sample, if applicable)

WCIS-SOII all Iteration 1 (n=39052 total match pairs)

Match Score n % n %

100 12274 31.43 50 (50 sampled) 100.00

77 to 99 4901 0.14 50 (50 sampled) 100.00

76 250 0.64 96 (100 sampled) 96.00

75** 20465 52.4 86 (100 sampled) 86.00

74 25 0.06 14 56.00

71 to 73 346 0.45 - -

70 112 0.29 63 56.25

69 19 0.05 8 42.11

68 424 1.09 54 12.74

67 117 0.3 79 67.52

66 51 0.13 26 50.98

65 68 0.17 11 16.18

WCIS-SOII all Iteration 2 (n=405 total match pairs)

Match Score n % n %

100 64 15.8 64 100.00

76 to 99 30 0.25 30 100.00

75* 290 71.6 95 (100 sampled) 95.00

72 2 0.49 1 50.00

70 3 0.74 0 0.00

65 to 69 16 0.49 7 43.75

* Manual review based on first name, last name, date of birth, and employer name (if 

applicable)

** Acceptable score

WCIS-SOII all Linkage (Table D3e) 

For the first iteration, acceptance level was set at 75 based on manual review of different levels of 

acceptance scores starting at a score of 65. Only 16% of matches with a score of 65 were acceptable 

matches after review. Cases from each score were reviewed until the proportion of acceptable matches 

appeared to be maximized and proportion of bad matches was minimized. Of matches with a score of 

74, only 56% of matches with a score were acceptable matches, while 85% of a sample of 100 pairs with 

a score of 75 were acceptable matches. Of a sample of 100 pairs with a score of 76, 96% were 

acceptable matches. Therefore, 75 was chosen as an appropriate acceptance level for this iteration. 

There were 37,890 raw matches identified: 452 (1.19%) had a CTS endpoint in SOII, and 195 (0.51%) had 

an amputation endpoint in SOII. 

The second iteration of matching was blocked, or stratified, by exact date of birth in order to link the 

records in a more computationally efficient manner. First names were compared to last names in order 
Page 39 of 40

to pick up any matched cases whose name may have been entered incorrectly into the databases. An 

acceptance level was determined by manual review in the same manner as the first iteration. Of a 

sample of 100 cases with a score of 75, 95% were acceptable matches, while only 50% or less of the 

cases from lower scores were good matches. There were 384 raw matches identified: 4 (1.04%) had a 

CTS endpoint in SOII, and 2 (0.52%) had an amputation endpoint in SOII. 

Table D4: Deduplication for Enumeration 

Table D4a: WCIS – SOII Linkage De-duplication and Refinement for Enumeration

Amputation CTS

Raw matches 197 456

SOII duplicates removed because 

 Not amputation/CTS in WCIS 

 Not highest case class in WCIS

 Not closest injury date

28 99

WCIS duplicates removed because 

 Not closest injury date 0 10

One-to-one matches for enumeration 169 347

TableD4b: WCIS – OSHPD Linkage De-duplication and Refinement for Enumeration

Amputation CTS

Raw matches 3129 9160

WCIS duplicates removed because 

 service/admit date (in OSHPD) was not closest to 

injury date (in WCIS)

482 508

OSHPD duplicates removed because 

 not highest case class in WCIS

 service/admit date was not closest to injury date

415 1740

Matches removed because

 service date occurred before injury date 30 n/a

Matches removed because

 not amputation or CTS in WCIS 293 1322

One-to-one matches for enumeration 1909 4167
Page 40 of 40

Table 4c: SOII – OSHPD Linkage De-duplication and Refinement for Enumeration

Amputation CTS

Raw matches 205 524

SOII duplicates removed because

 service/admit date (in OSHPD) was not closest to 

injury date (in WCIS)

37 134

OSHPD duplicates removed because

 service/admit date was not closest to injury date 0 9

Matches removed because

 service date occurred before injury date or 8 n/a

One-to-one matches for enumeration 159 381

Amputation/CTS in SOII (used for enumeration) 72 (45.28%) 132 

(34.65%)

Non-amputation/CTS in SOII 87 (54.72%) 249 

(65.35%)

Table 4d: DFR – OSHPD De-duplication

CTS

Raw matches 937

DFR duplicates removed because

 not primary procedure/diagnosis in OSHPD

 not first OSHPD case

13

DFR date of injury not in 2007-2008 72

One-to-one matches for enumeration 625