Title: Use of multiple data sources for surveillance of work-related amputations in 

Massachusetts, comparison with official estimates and implications for national surveillance 

Running title: Multisource surveillance of work-related amputations 

Authors: 

Letitia Davis, ScD, EdM (lead author) 

Director, Occupational Health Surveillance Program 

Massachusetts Department of Public Health 

Kathleen Grattan, MPH

Epidemiologist, Study Coordinator, Occupational Health Surveillance Program 

Sangwoo Tak, ScD, MPH* 

Assistant Director, Southern California NIOSH Education and Research Center, UCLA

Lucy Bullock, BA* 

Al Ozonoff, PhD 

Director, Center for Patient Safety and Quality Research, Boston Children’s Hospital 

Leslie I. Boden, PhD 

Professor of Environmental Health, Boston University School of Public Health 

*Formerly with the MA Department of Public Health 

Institution where work performed: Massachusetts Department of Public Health 

Lead author contact information: 

Letitia Davis, ScD, Ed M, 

Director, Occupational Health Surveillance Program 

Massachusetts Department of Public Health 

250 Washington Street, 6th floor 

Boston, MA 02018 

Phone: 617 624-5626 

Fax: 617-624 5696 

Email: letitia.Davis@state.ma.us

Conflict of interest: None. 

Word count: 5,649 
2

Use of multiple data sources for surveillance of work-related amputations in Massachusetts, 

comparison with official estimates and implications for national surveillance 

Abstract 

Background: Accurate surveillance of work-related injuries is needed at national and state 

levels. We used multiple sources for surveillance of work-related amputations, compared findings 

with Survey of Occupational Injuries and Illnesses (SOII) estimates, and assessed generalizability 

to national surveillance.

Methods: Three data sources were used to enumerate work-related amputations in 

Massachusetts, 2007-2008. SOII eligible amputations were compared with SOII estimates.

Results: 787 amputations were enumerated, 52% ascertained through hospital records only, 

exceeding the SOII estimate (n=210). The estimated SOII undercount was 48% (95% CI: 36-

61%). Additional amputations were reported in SOII as other injuries, accounting for about half 

the undercount. Proportionately more SOII estimated than multisource cases were in 

manufacturing and fewer in smaller establishments. 

Conclusion: Multisource surveillance enhanced our ability to document work-related 

amputations in Massachusetts. While not feasible to implement for work-related conditions 

nationwide, it is useful in states. Better understanding of potential biases in SOII is needed.

Key words: work-related amputations, injury surveillance, underreporting, undercounting 
3

Introduction 

Occupational injuries and illnesses are a significant public health problem in the United States, 

imposing substantial human and economic costs [Leigh, 2011; Biddle, 2009]. Timely, accurate 

measures of the extent, nature and causes of work-related injuries and illnesses are essential to 

allocate limited prevention resources effectively and monitor progress in meeting prevention 

goals at both national and state levels. National surveillance data are needed to inform national 

prevention priorities, yet, relying on national statistics can obscure safety and health concerns that 

may be specific to a state. State surveillance data are needed to inform state occupational injury 

and illness prevention priorities and can also be a powerful means of garnering the support of 

local stakeholders necessary to address identified safety and health problems [CSTE, 2001].

The Census of Fatal Occupational Injuries (CFOI), a comprehensive national-state system using 

multiple data sources for case ascertainment, has been in place since the early 1990s [BLS, 

2013]. However, surveillance of nonfatal occupational injuries and illnesses in the United States 

remains fragmented with significant gaps [IOM and NRC, 2009]. Official national and state 

estimates of nonfatal injuries and illnesses are based on the Survey of Occupational Injuries and 

Illnesses (SOII), conducted annually by the U.S. Bureau of Labor Statistics (BLS) with state 

agency partners [BLS, 2104]. Data from logs of work-related injuries and illnesses required to be 

maintained by the Occupational Safety and Health Administration (OSHA) are collected from a 

sample of approximately 250,000 private industry and state and local government establishments 

nationwide. Recordable injuries and illnesses include those resulting in medical treatment 

beyond first aid, loss of consciousness, one or more days away from work, restricted work 

activity or job transfer or other specific conditions [Wiatrowski, 2014, in this issue]. SOII 

excludes approximately 14% of the workforce, including the self-employed, federal and 

household workers, workers on small farms, and, until 2008, state and municipal workers who 

make up an additional 12% of the workforce [Dye et al., 2011]. It is well recognized that SOII 

does not capture long latency occupational illnesses, and there is increasing evidence that injuries 
4

are also underreported [Nestoriak and Pierce, 2009; Ruser, 2010; Lipscomb et al., 2013]. 

Estimates of underreporting range widely, from 20% - 70% [Conway and Svenson, 1998; Leigh 

et al., 2004; Rosenman et al., 2006; Boden and Ozonoff, 2008]. A number of factors may 

discourage reporting by either workers or employers [Lessin and McQuiston, 2013]. There is also 

concern about differential reporting depending on characteristics of the worker, the injury or the 

employer establishment, as well as by geographic region [Azaroff et al., 2002; Azaroff et al., 

2004; Friedman and Forst, 2007; Mendeloff and Burns, 2013]. Alternative approaches to 

nonfatal occupational injury and illness surveillance need to be explored. 

The present study, part of a three-state effort sponsored by BLS, was undertaken by the 

Massachusetts Department of Public Health (MDPH) to pilot use of multiple data sources for 

surveillance of work-related amputations in Massachusetts, compare multisource findings with 

state estimates from the SOII, and assess the generalizability of this approach to national

surveillance. BLS chose amputations for this study as an example of a serious work-related 

health condition that should be relatively easy to recognize and attribute to work. 

Methods

Surveillance case definition

A work-related amputation was defined as the loss of a protruding body part involving bone loss 

due to a traumatic incident with evidence of work-relatedness. Eligible body parts included all or 

part of the upper or lower limbs. If bone loss could not be determined in the available records, it 

was assumed and the case was considered to meet the surveillance case definition. This approach 

was consistent with the BLS protocol for assigning amputation codes according to the 

Occupational Injury and Illness Classification System (OIICS) in place during the study period 

[BLS, 2007].1 Amputations can occur immediately during an incident or be delayed days or 

 

1

 According to OIICS coding protocols in place during 2007-08, an injury reported as an amputation 

involving a limb without mention of bone loss should be coded as an amputation ( Nature of Injury Code: 
5

weeks after the incident [Anderson et al., 2010]. Eligible cases included amputations in which the 

injury incident occurred during 2007 – 2008 and involved workers 15 years or older employed in 

Massachusetts establishments, including the self-employed. 

Data sources

Data from three sources - workers’ compensation records, hospital administrative records, and 

SOII - were used for case ascertainment. MDPH entered into data use and confidentiality 

agreements with the three government agencies maintaining these records and obtained approval 

from the MDPH Institutional Review Board. As shown in Table I, the data sources differed in 

terms of populations covered, severity of injury captured, and data coding systems used. All 

sources were initially reviewed for work-related amputations with preliminary designation as 

probable or possible (see below). Additional information from medical records was collected for 

amputations ascertained using hospital administrative records. Subsequently, exclusion criteria 

were applied to arrive at the final set of work-related amputations. 

Workers’ compensation (WC) data

Almost all employers in Massachusetts are required to maintain workers’ compensation insurance 

and submit reports of injuries and illnesses resulting in five or more lost workdays (starting on 

day of incident if injury occurs before noon) to the Massachusetts Department of Industrial

Accidents (MDIA). The MDIA dataset of lost time claims includes employee name, address, 

gender, date of birth and occupation; employer name and address; date of injury; American 

National Standards Institute [ANSI, 1969] codes for nature of injury and affected body part; and a 

brief narrative description of how the injury occurred. ANSI codes are selected by the persons 

completing the reporting forms (e.g., employer, attorney, insurer, employee) from a list of codes 

provided on the back of the reporting form. Claims awarded cannot be distinguished from claims 

filed. 

 

031). An injury reported as an amputation involving a limb that did not involve bone loss should be coded 

as an avulsion (Nature of Injury code: 033). 
6

ANSI Nature of Injury code 100 (amputation) and/or any variation of the words ‘amputation’, 

‘avulsion’, ‘deglove,’ ‘cut off’, ‘sever’, ‘detach’, ‘rip off’, ‘lost tip’ in the incident narrative were 

used to search MDIA files for 2007-2009 to identify amputations with dates of injury during 

2007-2008. The incident narrative for each record meeting the search criteria was reviewed. 

Records in which the narrative clearly indicated that the injury was not an amputation were 

excluded. Records with ANSI code 100 and/or information in the narrative that an amputation 

had occurred were defined as probable amputations. Remaining records (i.e. records with Other 

Nature of Injury codes and text suggestive of amputations) were defined as possible amputations. 

It was not possible to determine bone loss status of amputations involving tips of fingers or toes 

based on the WC records. North American Industry Classification (NAICs) codes and 

establishment size category were obtained from state and publically available data sources. 

Occupation was coded manually by trained MDPH coders according to the Standard Occupation 

Classification (SOC). Information about source of injury was abstracted from the incident 

narrative and coded according to OIICS. All amputations were assumed to be work-related. 

Massachusetts Survey of Occupational Injuries and Illnesses

The Massachusetts Department of Labor Standards (DLS) collaborates with BLS to conduct the 

SOII in Massachusetts. Following BLS protocols, data from on-site OSHA logs and 

supplementary materials are collected from a sample of over 5,000 establishments annually 

(approximately 4% of the 130,000 establishments and 33% of the workforce statewide). Details 

on nature of injury, body part, source of injury/illness, event or exposure, and date of injury, as 

well as worker demographics (birth date, age, gender, race/ethnicity combined) are collected only 

for injuries or illnesses resulting in one or more lost workday (starting on the day after the 

incident) and are included in the case and demographic file. Nature, body part, source and event 

are coded according to OIICS by DLS staff. Also included is information about patient name, 
7

occupation, employer name and address, and establishment size category. Industry and 

occupation are coded according to NAICS and SOC, respectively. 

OIICS Nature of Injury codes for amputation (0310, 0311 or 0319) were used to identify 

amputations in the Massachusetts SOII case and demographic data for 2007 and 2008. All 

amputations so identified were considered probable amputations. The entire two year file of all 

MA SOII cases was retained for matching with other sources to allow for identification of 

amputations reported in SOII as other injuries.

 

Hospital Case Mix Data 

The Massachusetts Division of Health Care Policy and Finance2 maintains three separate datasets 

of hospital encounters in all non-federal acute care hospitals for purposes of rate setting, 

surveillance and research: inpatient hospitalizations (HD); outpatient observation stays (OOS) 

and emergency department (ED) visits. For any single hospital encounter, the datasets are 

mutually exclusive with a single case being recorded at the highest level of care (HD > OOS > 

ED). A single patient may have multiple encounters for the same injury over time and possibly at 

different hospitals. The data are collected on a federal fiscal year basis (October 1 – September 

31); data for the full calendar year are available for surveillance purposes approximately 18 

months after close of the calendar year. 

Each HD record includes up to 15 clinical diagnoses and 15 procedures coded according to the 

International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). 

Injury cases are also assigned External Cause of Injury codes (E-codes) at hospitals. Hospitals 

may also assign “V” codes – supplementary classifications of factors influencing health status. 

Records include a medical record number and information about patient demographics, (age, sex, 

 

2

 Renamed the Center for Health Information and Access in 2012. 
8

race, ethnicity and zip code of residence), zip code of employer and primary payer. They have 

neither patient nor employer names nor street addresses. 

The OOS and ED datasets, though similar, have several notable differences. Both have fewer 

fields for diagnostic (up to 6) and procedure codes (up to 4); both have no employer zip codes. 

OOS data allow either CPT or ICD-9-CM codes to be used for procedures; and the ED data set 

has a short narrative text field to state reason for visit. 

These three administrative datasets were merged for this analysis and are hereafter collectively 

referred to as Case Mix (CM) data. Nature of Injury codes for traumatic amputations (ICD-9 CM: 

885-887, 895, 896, 897) and amputation procedure codes in any of the available diagnostic or 

procedure fields were used to identify probable amputations with dates of hospital encounter 

(admission for HD) in 2007-08. Several amputation-related diagnostic and procedure codes, such 

as stump complication (ICD-9-CM: 997.6) were used to identify possible amputations (see 

Supplemental Material, online only). 

Probable and possible amputations with WC listed as primary payer, select E and V codes and/or 

key terms in the ED ‘reason for visit’ narratives (see Supplemental Material online only) were 

designated as likely work-related. All other traumatic amputations and probable amputations 

identified through procedure codes only with a primary diagnostic code of traumatic injury were 

designated as uncertain work-related. Probable amputations identified through procedure codes 

only with a primary diagnostic code of disease and no indication of work-relatedness were 

considered not work-related and excluded from further analysis. All possible amputations with no 

indication of work-relatedness were likewise excluded (Table II). 

Medical records
9

MDPH has legal authority to collect medical records for select health conditions, including 

amputations, from Massachusetts hospitals [105 CMR 300.193]. Using medical record numbers, 

medical records were requested by mail from hospitals for all probable and possible amputations 

that were likely or uncertain work-related in the CM data to obtain patient and employer names 

and addresses needed to match cases with the other data sources and additional information to 

assess work-relatedness and bone loss. Over an 11 month period, records were obtained from 

hospitals or, abstracted on-site at the request of hospitals (3 hospitals) for 98% of the cases. 

Based on medical record review, MDPH data abstractors made an assessment of work-relatedness 

(likely/uncertain/not work-related) independent of payer information. CM amputations were 

subsequently considered likely work-related if identified as such through either payer information 

(WC) or medical record review. Residents in occupational medicine assisted in review of clinical 

information to assign bone loss status (yes/no/indeterminate) to all cases considered likely or 

uncertain work-related.

Once medical records were obtained, personal identifiers were used to identify repeat encounters 

for the same injuries in the CM dataset and create an injury level CM file for matching with other 

data sources. Amputations clearly not work-related, those that occurred before the study period, 

and other injuries miscoded as amputations were excluded prior to matching.

Merging data sets and development of final multisource file

The full SOII file was merged with files of: 1) probable and possible WC amputations, and 2) 

probable and possible CM amputations, both likely and uncertain work-related, to identify 

matched cases using FRIL®v.2.1.4. FRIL is a software program that uses the probabilistic 

linkage approach to perform record linkage over large data sources which incorporates features, 

including Soundex, for maximizing the efficiency and accuracy of the linkages [Jurczyk et al., 

2008; NARA, 2007]. Iterative deterministic and probabilistic linkages were carried out using 

personal (name, birth date, age, date of injury) and employer name (see Supplemental Materials, 
10

online only), with extensive manual review of linked cases in which we considered additional 

variables including addresses and gender. Personal and employer identifiers of SOII-only cases 

were used to search the full MDIA file to identify WC amputations reported as other injuries.

A set of decision rules was developed and applied to the merged files to rule out amputations and 

create the final multisource file of work-related amputations with known or indeterminate bone 

loss. Major decision rules were as follows. Any case reported as: 1) an amputation in the SOII, 

and/or 2) a probable amputation in the WC, and/or 3) a probable amputation in CM file and likely 

work-related was retained unless there was specific medical record information ruling it out as a 

bone loss amputation. We excluded possible WC amputations that matched with SOII cases 

coded as other injuries. We also excluded CM-only amputations with uncertain workrelatedness, employed outside of Massachusetts, or with dates of injury incident prior to the study 

period. 

Data analysis

Proportions of cases captured by data source were computed and frequency distributions of cases 

by demographic, injury and employment characteristics were compared across data sources using 

weighted SOII estimates rather than raw counts for these comparisons.

Comparison with SOII estimates

Not all work-related amputation cases are eligible for inclusion in the SOII case and demographic 

file. Each multisource case was categorized as SOII eligible, ineligible or unknown eligible 

considering both injury and worker eligibility. Eligible injuries included amputations resulting in 

one or more lost workday. All WC cases and all CM cases resulting in hospitalization or with 

amputations involving body parts other than fingers were assumed to meet this SOII lost time 

criterion. CM-only cases with any part of the fingers identified as the body part were considered 

unknown eligible on the basis of lost time. Eligible workers included those working in 
11

establishments covered by the SOII. All WC and CM cases clearly involving federal or household 

workers, workers on small farms or the self- employed (all years) or state and municipal workers 

in 2007 were considered ineligible. WC and CM cases with insufficient information to determine 

whether the worker was employed in one of these categories were considered unknown eligible. 

The count of multisource SOII eligible cases was compared to the published SOII estimate of 

amputation cases for the two-year period to generate a measure of the SOII undercount. 

Because some multisource SOII eligible amputations were reported as other injuries in the SOII, 

a revised SOII estimate was computed treating these “other injuries “ as amputations and using 

SOII sample weights. The count of SOII eligible cases was compared to this revised SOII 

estimate to generate a measure that hereafter is referred to as the SOII underreport. 

Additional analyses were carried out to assess the effect of making different assumptions about 

multisource cases with unknown SOII eligibility. Results of capture-recapture analyses of these 

data are reported elsewhere [Tak et al., this issue]. 

To assess whether published SOII findings were representative of all amputations eligible for 

inclusion the SOII, we compared the distribution of multisource SOII eligible cases by 

demographic, injury and employment characteristics with the distribution based on published 

SOII estimates. We also compared distributions of the published and revised SOII estimates. 

Results

Counts of amputations meeting initial search criteria by data source and included in the final 

enumeration of work-related amputations are presented in Figure I. A total of 787 unique cases 

meeting the surveillance case definition were identified after matching and application of final 

decision rules. The largest percentage (82%) were ascertained through CM data, followed by WC 

(45%), and SOII (13%) (Table III). Of the 106 cases captured in the SOII data, 43 (41%) were 

reported as “other injuries” with the largest numbers reported as cuts/lacerations (24) and 
12

crushings (5). Likewise 23 (7%) of the WC cases were reported in WC as other injuries. Notably, 

406 (52%) of the unique cases were ascertained through CM records only. Of these, 321 were 

identified through ED records. 

The SOII estimated number of work-related amputations in Massachusetts during the two year 

study period was 210 (95% CI: 158-262). The multisource count of 787 work-related amputations 

was 3.7 times higher than this estimate. 

The distribution of cases by demographic, injury and employment characteristics by data source 

is presented in Table IV. The distribution of SOII cases in this table is based on the published 

SOII estimates. Age and gender distributions were largely similar across data sources. As 

expected, CM cases included a substantial fraction of self-employed (10%), and there were 

proportionately more serious cases (those involving body parts other than fingers or toes) among 

the WC and SOII cases. Missing race and ethnicity information in WC and SOII data sets 

precluded meaningful comparisons across data sources as did missing information on occupation 

among WC and CM cases. There was a high percentage of SOII estimated cases in 

manufacturing (54%) compared to WC (31%), and CM (24%). There was a low percentage of 

SOII estimated cases in smaller establishments (< 50 employees) compared to WC cases. 

Establishment size information was missing for over 40% of the CM cases. 

Source of injury information was available for 86% of cases. The leading sources for all cases 

were machinery (31%) and saws (22%). Distributions by source were similar for WC and CM 

cases, whereas among SOII estimated cases, saws accounted for only 7% of the cases and 

machinery accounted for 46%.

SOII undercount 
13

Of the 787 multisource cases, 406 were SOII eligible, 96 were ineligible, and 285 had unknown 

SOII eligibility. Comparison of multisource SOII cases with and SOII estimated cases is depicted 

in Figure II. The ratio of the multisource SOII eligible cases (406) to the SOII published estimate 

(210) was 1.93, yielding a minimum estimate of the undercount of 48% (95% CI: 36-61%). Of

the 285 multisource cases with unknown SOII eligibility, the majority (n=189) were SOII eligible 

workers (employed in establishments covered by the SOII) but with insufficient information to 

determine lost time. Including all work-related amputations among SOII eligible workers

regardless of lost time (n=595) produced an undercount estimate of 65% (95% CI: 56-73%).

 

As described, to assess “underreporting” we included amputations reported by employers but as 

other injuries in the SOII in generating a revised SOII estimate. When these “uncounted 

amputations” were taken into account using the revised SOII estimate (310), the estimated 

underreport of clearly eligible cases was 24%. When all cases involving SOII eligible workers 

regardless of lost work-time (n=595) were included, the estimated underreport was 48%.

The distributions of multisource SOII eligible cases and published SOII estimated cases were 

similar with respect to gender, age and body part injured. (Absolute differences in percentages 

ranged from ±0.0 - 4.5%; data not shown). However, compared to multisource SOII eligible 

cases, proportionately more SOII estimated cases were employed in manufacturing (53% vs. 

33%) and proportionately fewer were employed in establishments with less than 50 employees. 

(24% vs. 45%). (Table V). As shown in Table V, the distributions of published and revised SOII 

estimated cases by establishment characteristics were similar. They were likewise similar across 

demographic and worker characteristics. (Absolute differences in percentages ranged from ±0 - 

5.8%; data not shown). 

Discussion
14

In this study, we found that combining information from multiple data sources to enumerate 

work-related amputations in Massachusetts was feasible and provided useful, otherwise 

unavailable information to inform prevention efforts. Findings add to the evidence and 

understanding of undercounting in the SOII and provide insights about the challenges to 

implementing a multisource approach to surveillance of non-fatal occupational injuries 

nationally. 

Multisource pilot 

Given access to personal identifiers in the WC and SOII data, and in the medical records obtained 

for CM amputations, coupled with establishment and incident information, we were successful in 

merging information across the three data sources with a high degree of certainty about matched 

cases. Each of the three data sources used for case ascertainment had difference exclusions, and, 

as found in previous studies, the multisource approach identified many more work-related 

amputations than any single data source [Friedman et al, 2012; Largo and Rosenman, 2013]. The 

finding that over half of the 787 unique multisource cases (52%) were identified solely by CM 

records underscores the value of using health system data as a complement to the SOII and WC 

records more conventionally used for surveillance of work-related injuries and illnesses. Recent 

Danish and Canadian studies likewise highlight the added value of using hospital records to 

document work-related injuries [Fleming et al., 2014; Mustard et al., 2012]. The CM records 

offered the important advantage of providing information about amputations among workers 

covered by WC but not included in the WC lost time claim file, including workers with less 

serious amputations and potentially some workers with more serious injuries who may not have 

used the WC system to pay for their care [Azaroff et al, 2013]. CM records were also the only 

source of information on amputations among the self-employed. Additionally, CM records 

provided information on race and ethnicity, variables not available in WC records and commonly 

missing in SOII. 
15

There were, however, a number of challenges in using CM records, the most time consuming of 

which was the need to collect medical records to obtain additional information necessary to 

merge the CM records with data from other sources, assess work-relatedness and SOII eligibility 

and characterize cases by industry and occupation, variables missing in the CM datasets. Another 

challenge was the high proportion of all CM encounters for work-related amputations that were 

repeat encounters for the same injury (15%), a factor important to consider when making simple 

count comparisons between hospital administrative data and information from other sources. 

Lack of specific information on work-relatedness, independent of payer source, not only in the 

CM file, but also in the medical records was another limitation. 

Comparison with SOII estimates 

Central goals of population-based surveillance are to describe the magnitude and distribution of 

public health problems, generating information to target and evaluate prevention efforts. In 

comparing two approaches to surveillance of an adverse health event, keys questions, therefore, 

are whether the two approaches provide similar information with respect to both these dimensions 

of the problem. 

In this study, we found that the multisource count of work-related amputations in Massachusetts 

was 3.7 times the SOII estimated number of cases during the study period. In short, the 

multisource approach revealed a far larger problem. This difference was explained in part by 

SOII-ineligibility of some cases, such as the 8% self-employed, a well known gap in the official 

surveillance system. Yet, even when comparison between approaches was restricted to cases 

clearly eligible for the SOII, there were nearly twice as many multisource cases as the SOII 

estimate; and the minimum estimate of the undercount was 48% [95% CI: 36-61%]. 
16

An important finding in this study, however, was that many amputation cases were reported in the 

SOII as other injuries – thus these injuries were “reported” by employers, but not counted as 

amputations. Possible explanations include employers' lack of knowledge about the exact nature 

of injuries, delayed amputations following earlier injuries for which employers failed to update 

OSHA logs, or reporting or coding errors. When these “uncounted” amputations were taken into 

account using the revised SOII estimate, the estimated underreport of clearly eligible cases was 

24%. Reporting of amputations as other injuries on the OSHA logs thus appeared to account for 

about half of the estimated undercount among clearly SOII eligible cases. A number of additional 

factors likely contributed to the undercount. Employer in the SOII sample may not have reported 

all amputations of which they had knowledge; employers may have been unfamiliar with recordkeeping requirements, or they may not have been aware of injuries because workers did not report 

their injuries for any number of factors that can serve as barriers to worker reporting. There may 

also have been SOII sampling limitations. 

Our estimated range of underreporting was on the lower end of previous estimates based on 

studies examining all work-related injuries and illnesses, [Rosenman et al., 2006; Boden and 

Ozonoff, 2008; Boden, this issue]. This was not unanticipated given the serious nature of 

amputations and comparatively straightforward attribution to work. Largo and Rosenman [2013], 

using multiple data sources to track work-related amputations in Michigan, estimated that SOII 

undercounted amputations (excluding those among the self-employed) by 57%, a finding fairly 

similar to our estimate of the eligible amputation undercount (48%). Friedman et al. [2012], 

using multiple data sources to track work-related amputations in Illinois, found a much higher 

congruence between multisource counts and SOII estimates, but he did not have access to data 

from all emergency departments.

As noted, in comparing different approaches to surveillance, it is important to assess whether the 

approaches yield similar information about not only the overall magnitude but also the 
17

distribution of the problem. While different approaches to surveillance may provide different 

estimates of magnitude, as they did in this study, they may still provide similar views of the 

distribution and therefore identify similar prevention priorities. Unfortunately, our ability to 

compare the distribution of SOII estimated and multisource amputations with respect to worker, 

employer, injury and incident characteristics was limited due to the small number of amputations 

in the SOII sample, the lack of available standard errors for the SOII estimates and missing 

information on key variables in different data sources. Nonetheless, our findings were suggestive 

of several differences that raise important questions about representativeness of the SOII 

estimates that should be further investigated, most notably the high proportion of SOII estimated 

amputations in manufacturing compared to multisource cases. There also appeared to be 

proportionately fewer SOII estimated cases in establishments less with less than 50 employees 

compared to multisource SOII eligible cases. This finding, albeit limited by missing information, 

is consistent with several previous studies that have found that undercounting is greater in small 

establishments [Mendeloff et al.; 2006; Dong et al., 2011]. Possible factors believed to vary by 

industry and influence employer record-keeping include misclassification of workers as 

independent contractors, use of temporary workers, use of injury rates to qualify for contracts, 

and employer lack of knowledge about completing OSHA logs, which may be a particular 

concern among smaller employers who do not routinely complete these logs unless selected by 

BLS to participate in the SOII. Injury severity may also vary by industry and could also influence 

these patterns. 

Strength and limitations

A strength of this study was the use of SOII records in addition to WC and hospital 

administrative data, and the inclusion of more comprehensive ED data than used in other 

multisource studies of work-related amputations [Largo and Rosenman, 2013; Friedman et al., 

2012]. The collection and clinician review of medical records for almost all CM cases was a 

strength as was the systematic approach to classifying multisource cases according to bone loss 
18

and SOII eligibility status for comparison with SOII estimates. The approach of generating a 

revised SOII estimate allowed for distinguishing between undercounting in the SOII 

and employer failure to record the injury at all on OSHA logs (underreporting), an important 

distinction when examining the extent to which SOII captures specific work-related conditions. 

This study had many limitations, especially the difficulty in defining amputations consistently 

across datasets. The issue of bone loss and the evolution of injuries over the course of time (initial 

crushing injury that subsequently becomes an amputation) were problematic. We addressed this 

as best we could by using the best available information for each case, and applying a standard set 

of decision rules. This difficulty in defining amputations highlighted the fundamental challenge 

of comparing non-clinical information reported by employers and clinical information from 

hospitals.

Missing information on other key variables was also problematic, not only for comparing workrelated amputations by case characteristics across data sources as described, but also for case 

ascertainment and determination of SOII eligibility. In the SOII file, information was mostly 

complete, with the exception of information about race/ethnicity, which is not a mandatory 

reporting element. However, WC and CM are both administrative data sources not intentionally 

designed for occupational health surveillance. Key information such as and work-relatedness 

(CM), occupation (CM and WC), industry (CM) and lost time (CM) was often missing. With 

respect to case ascertainment, our general approach was to handle missing information 

conservatively, i.e., exclude amputations that we could not reasonably and confidently assume 

should be included. For example, we excluded the over 200 unsure work-related CM amputations 

from the final multisource enumeration. With respect to SOII eligibility in our analysis of the 

SOII undercount, we computed a range of estimates including a minimum estimate limiting cases 

to those with known SOII eligibility. It is also possible that some of the WC and SOII cases for 

which we did not have confirmatory medical records were not amputations. However, we do not 
19

believe this to be a substantial concern as all SOII reported amputations for which we had 

medical records and 92% of WC cases with medical records met the surveillance case definition. 

Additionally, our methods did not allow us to identify cases treated in settings other than 

hospitals, such as urgent care centers, or in out-of-state hospitals, and captured by neither SOII 

nor WC. For this reason alone, the final multisource count is likely an underestimate of the true 

burden of work-related amputations in Massachusetts.

Amputations were chosen for this surveillance research study because it was assumed that, 

compared to other work-related conditions with more complex etiologies, work-related 

amputations would be comparatively straightforward to document. While we experienced some 

challenges in defining amputations, this assumption still holds and the specific findings presented 

here cannot necessarily be generalized to other work-related injuries and illnesses. 

Generalizability of these multisource surveillance methods to national surveillance 

While these findings are limited to one health condition in one state, they suggest that multisource 

surveillance can substantially enhance our understanding of the magnitude and distribution of 

work-related injuries and illnesses. Multisource approaches that include hospital administrative 

records can fill well known gaps in the SOII by including the self-employed and other excluded 

populations, provide information on data elements not available in any one data source, and shed 

light on the magnitude and distribution of the SOII undercount. Our experience, however, also 

suggests that a surveillance system for all nonfatal work-related conditions that would attempt to 

collect comparable data from all states using multiple data sources, similar to CFOI, is not 

currently feasible. The most challenging barrier is lack of comparability of data sources across 

states. Limited data access and lack of timeliness are also obstacles. 

A significant barrier is the fact that multisource surveillance, as implemented in this and other 

similar studies, relies heavily on WC data, either directly through access to the claims or 
20

indirectly by using payment information in hospital records to identify probable cases. There are 

widely recognized and substantial differences between WC laws and WC data collection systems 

across the states that limit data comparability [Sengupta et al., 2012; NIOSH, 2013; NIOSH, 

2014]. Lack of comparability is more likely for illnesses, which may not be compensated in all 

states, and less serious injuries, as waiting periods for lost wage payments vary substantially 

across states. We also relied heavily on hospital electronic datasets for case ascertainment. While 

all states have inpatient hospital datasets, outpatient datasets are not in place in all states [Barrett 

and Steiner, 2013]. Variation in states laws governing data access and data sharing between 

agencies is likewise a barrier. While in theory these differences are not insurmountable, they 

remain practical obstacles in the near future. 

Timeliness, defined as time between the surveillance period end and surveillance report, is 

impacted by delays in initial access to data sources as well as the time involved in processing the 

data. In this study, the delay in access to the CM data was a significant barrier to timely release of 

surveillance findings. Collecting medical records from over 80 hospitals also proved to be highly 

time consuming taking almost 12 months. If data collection is conducted routinely, the delays can 

be substantially reduced [Largo and Rosenman, 2013]. Alternatively, if public health regulations 

required active reporting of work-related conditions with personal and employer identifiers, as 

they do in Michigan, it may not be necessary to follow-back to hospitals for medical records. This 

would necessitate regulatory changes across the states. Changes in national policy, such as the 

current proposal to promote inclusion of industry and occupation codes in the Uniform Bill 

[Taylor and Frey, 2013], may provide new opportunities in the states in the long run. The ongoing 

effort to incorporate occupational information in electronic health records may likewise open up 

new long-term opportunities for more efficient data collection in the future [IOM, 2011; CSTE, 

2012].
21

Despite the challenges in implementing multisource surveillance of work-related health 

conditions nationally, we believe that it is extremely useful at the state level for both case-based 

and population based surveillance to inform intervention activities. Such state level efforts 

conducted on an ongoing basis can fill surveillance gaps and contribute significantly to our 

understanding of the national burden. It will be more important for some health conditions than 

others, and the choice of surveillance targets should reflect a combination of state and national 

priorities for protecting workers’ health [CSTE, 2001]. 

Acknowledgements

This study was funded through a cooperative agreement with the Bureau of Labor Statistics: OS 

200447-10-75-J-25. Research Assistant: Yaritza Roberts. 
22

References

ANSI. American National Standards Institute. 1969. Methods of recording basic facts relating to 

the nature and occurrence of work injuries. New York, NY: American National Standards 

Institute.

Anderson NJ, Bonauto DK, Adams D. 2010. Work-related amputations in Washington State, 

1997-2005. Am J Ind Med 53:693-705. 

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

Conceptual filters explain underreporting. Am J Public Health 92(9):1421-1429.

Azaroff LS, Lax MB, Levenstein C, Wegman DH. 2004. Wounding the messenger: The new 

economy makes occupational health indicators too good to be true. Int J Health Serv 34(2):271-

303. 

Azaroff AS, Davis LK, Naparstek R, Hashimoto D, Laing JR, Wegman DH. 2013. Barriers to 

Use of Workers’ Compensation for Patient Care at Massachusetts Community Health Centers. 

Health Serv Res. Aug 48(4):1375-92. 

Barrett M, Steiner C. 2013. Healthcare Cost and Utilization Project (HCUP)

E Code Evaluation Report, 2001-2010. Rockville, MD: U.S. Agency for Healthcare Research and 

Quality. Available at: http://www.hcup-us.ahrq.gov/reports/methods/methods.jsp. Accessed 

August 13, 2013.

Biddle E. 2009. The cost of fatal injuries to civilian workers in the United States, 1992 – 2001. 

Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and 

Prevention, National Institute for Occupational Safety and Health. DHHS (NIOSH) Publication 

No.: 2009-154.
23

BLS. Bureau of Labor Statistics. 2007. Occupational Injury and Illness Classification Manual, v. 

1.01. Washington D.C.: U.S. Department of Labor, BLS.

BLS. Bureau of Labor Statistics. 2012. BLS Handbook of Methods. Chapter 9.Occupational 

Safety and Health Statistics (updated 2012). Washington D.C.: U.S. Department of Labor. 

⑙䑌佄䕏䠃䑗块卖ᴒቚ婚ᅅ佖ᅊ剙ቒ単䔒䭒倒升䤒䭒偆䬜ᅓ䝉̃⑆䙈噖䡇̶䡓坈偅䡕̔ᔏ̕ጔᔑ

BLS. Bureau of Labor Statistics. 2013. Census of Fatal Occupational Injuries (CFOI) Program 

Description. Washington D.C.: U.S. Department of Labor, BLS, CFOI. Available at 

䭗坓嘝ሒ婚娑䕏嘑䩒夒䱌䤒剖䭉䑗ᐑ䭗們̃⑆䙈噖䡇̰䑕䙋̕᠏̕ጔᘑ̀

BLS. Bureau of Labor Statistics. 2014. Survey of Occupational Injuries and Illnesses (SOII) 

Program Description. Washington D.C.: U.S. Department of Labor, BLS. Available at 

䭗坓嘝ሒ婚娑䕏嘑䩒夒䱌䤒剖䭆䑖䠔ᅋ坐ᄃ̤䙆䡖噈䜃⥈䕕塄啜̔ᜏ̕ጔᘑ

Boden LI, Ozonoff A. 2008. Capture-recapture estimates of nonfatal workplace injuries and 

illnesses. Ann Epidemiol,18 (6):500-506. 

Boden, LI, 2014. Capture-recapture estimates of the undercount of workplace injuries and 

illnesses: sensitivity analysis. Am J Ind Med 57:1090–1099

Conway H, Svenson J. 1998. Occupational injury and illness rates, 1992-1996: Why they fell. 

Mon Labor Rev 121:36-58. 

CSTE. Council of State and Territorial Epidemiologists. 2001. The Role of the States in a 

Nationwide Comprehensive Surveillance System for Work-related Diseases, Injuries, and 

Hazards: a report from the NIOSH-CSTE Surveillance Planning Work Group. Available at 

www.cste.org/OH/pdfffiles/NIOSH.pdf. Accessed April 20, 2011. 

CSTE. Council of State and Territorial Epidemiologists. 2012. Position statement 12-OH-01: 

Inclusion of Occupation and Industry as core data elements in Electronic Health Record (EHR) 
24

systems and in recommended elements in other minimum data sets. Available at 

http://c.ymcdn.com/sites/www.cste.org/resource/resmgr/PS/12-OH-01FINAL.pdf. Accessed 

May 6, 2013.

Dong XS, Fujimoto A, Ringen K, Stafford E, Platner JW, Gittleman JL, Wang X. 2011. Injury 

underreporting among small establishments in the construction industry. Am J Ind Med 54:339-

349. 

Dye CE, Derk SJ, Jackson LL. 2011. Occupational injuries among U.S. workers missed by 

employer-based reporting. Poster presented at the National Occupational Injury Research 

Symposium, Morgantown WV, 18 – 20 October. 

Fleming L, Nielsen, KJ, Rasmussen K, Lauritsen JM. 2014. Patterns of work-injuries: cases 

admitted to emergency room treatment compared to cases reported to the Danish Working 

Environment Authority during 2003-2010. Occup Environ Med 71(2):97-103.

Friedman LS, Forst L. 2007. Occupational injury surveillance of traumatic injuries in Illinois, 

using the Illinois trauma registry: 1995-2003. J Occup Environ Med 49(4):401-10.

Friedman L, Krupczak C, Brandt-Rauf, 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(5):667-73.

 

IOM. Institute of Medicine. 2011. Incorporating occupational information in electronic health 

records: Letter report. Washington, DC: The National Academies Press. 

IOM and NRC. Institute of Medicine and National Research Council. 2009. Traumatic injury 

research at NIOSH. Committee to review the NIOSH Traumatic Injury Research Program. Rpt. 

No 6, Review of research programs of the National Institute for Occupational Safety and Health. 
25

Washington DC: The National Academies Press. 

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

linkage, American Medical Informatics Association (AMIA) 2008 Annual Symposium 

Proceedings:440-444.

Largo TW, Rosenman KD. 2013. Michigan Work-related Amputations, 2008. J Occup Environ 

Med 55(3): 280-285.

Leigh JP, Marcin JP, Miller TR. 2004. An estimate of the U.S.Government’s undercount of 

nonfatal occupational injuries. J Occup Environ Med 46:10-18. 

Leigh JP. 2011. Economic burden of occupational injury and illness in the United States. 

Millbank Q 89(4):728-772. Available at 

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3250639/pdf/milq0089-0728.pdf. Accessed 

August 2, 2013.

Lessin N, McQuiston TH. 2013. An inverse relationship between injuries and fatalities: What is 

surprising – and what is not. Am J Ind Med 56(5):505-508.

Lipscomb HJ, Nolan J, Patterson D, Sticca V, Myers DJ. 2013. Safety, incentives, and the 

reporting of work-related injuries among union carpenters: "you're pretty much screwed if you get 

hurt at work". Am J Ind Med 56(4): 389-399.

Mendeloff J, Nelson C, Ko K, Haviland A. 2006. Small businesses and workplace fatality risk. 

Santa Monica, CA: RAND Corporation. Available at 

http://www.rand.org/content/dam/rand/pubs/technical_reports/2006/RAND_TR371.pdf. 

Accessed July 24, 2013.
26

Mendeloff J, Burns R. 2013. States with low non-fatal injury rates have high fatality rates and 

vice-versa. Am J Ind Med 56(5): 509-519. 

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

for the surveillance of work injury. Occup Environ Med 69(5):317-24.

NARA. National Archives and Records Administration. 2007. The soundex indexing system. 

Available at http://www.archives.gov/research/census/soundex.html . Accessed August 13, 2013.

 

Nestoriak N, Pierce B. 2009. Comparing workers’ compensation claims with establishments’ 

responses to the SOII. Mon Labor Rev. 132(5):57-64. . Available at 

䭗坓嘝ሒ婚娑䕏嘑䩒夒剓塅ቐ何ሕጓᰒጘቄ啗ᝉ塏休升䤃̤䙆䡖噈䜃ⵘ先̕༃ᔓᐓᄃ

NIOSH: National Institute for Occupational Safety and Health. 2013. Use of Workers’ 

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

DHHS (NIOSH) Publication No. 2013-147. Cincinnati, OH. Available at 

http://www.cdc.gov/NIOSH/docs/2013-147/pdfs/2013–147.pdf. Accessed January 24, 2014. 

NIOSH. National Institute for Occupational Safety and Health. 2014. Workers’ Compensation 

Insurance: A Primer for Public Health. Utterback DF, Myeres AR and Wurzelbacher SJ. DHHS

(NIOSH) Pub No. 2014-110. Cincinnati, OH. 

Rosenman KD, Kalush A, Reilly MJ, Gardiner JC, Reeves M, Luo Z. 2006. How much work 

related injury and illness is missed by the current national surveillance system? J Occup Environ 

Med 48(4):357-365. 

 

Ruser J. 2010. Allegations of Undercounting in the BLS Survey of Occupational Injuries and 

Illnesses, In 2010 JSM Proceedings, Statistical Computing Section, Alexandria, VA: American 

Statistical Association.
27

Sengupta I, Reno V, Burton JF, Baldwin M. 2012. Workers’ Compensation: Benefits, coverage, 

and costs, 2010. Washington D.C.: National Academy of Social Insurance (NASI). Available at 

http://www.nasi.org/sites/default/files/research/NASI_Workers_Comp_2010.pdf. Accessed May 

6, 2013.

Tak S, Grattan K, Boden LI, Ozonoff A, Davis L. 2014 Impact of differential injury reporting on 

the estimation of the total number of work-related amputations. Am J Ind Med 57:1144–1148. 

Taylor JA, Frey LT. 2013. The need for industry and occupation standards in hospital discharge 

data. J Occup Environ Med 55(5): 495-499. 

Wiatrowski, WJ. 2014. The BLS survey of occupational injuries and illnesses: a primer. 

Am J Ind Med 57:1085–1089.
28

TABLES 

Table I. Attributes of data sources used for multisource surveillance of work-related amputations, 

Massachusetts, 2007-08

Attribute Data Source

Workers’ compensation 

records (WC)

Hospital 

administrative 

(Case Mix) recordsa

(CM)

Survey of Occupational 

Injuries and Illnesses 

(SOII)

Data type (N 

records) Census (N=91,283) Census (N= ~7 million) Sample (n= ~17,700)

Scope/ injury 

severity

Lost wage claims filed

for injuries resulting ≥5 lost 

workdays

Discharges from acute 

care, non-federal 

hospitals

Employer reported cases 

with ≥I lost workday

Injury/illness coding 

system ANSI ICD-9 and CPT OIICS

Excluded 

populations

Police, firefighters, the 

military, and federal 

government, maritime, 

railroad, and part time 

household workersb

and the self-employed

None

The military, federal 

government, small farm, 

and household workers, 

state and municipal 

workers (2007 only) and 

the self-employed

a Includes data from three hospital administrative datasets: Inpatient hospitalizations, Outpatient observation stays, and 

Emergency department visits.

b Some of these groups are covered under other compensation programs.

ANSI: American National Standards Institute 

ICD-9: International Classification of Disease, 9tth revision 

CPT: Current Procedural Terminology codes 

OIICS: Bureau of Labor Statistics, Occupational Injury and Illness Classification System 

Table II. Initial designation of work-relatedness of probable and possiblea amputations identified 

using Case Mix records

Amputation group Initial work-relatedness designation

Probable and possible amputations with an indication of 

work-relatedness: Workers’ Compensation listed as 

primary payer, and/or select E and V codesb, and/or key 

terms in the ED “reason for visit” free text fieldc

Likely work-related

Probable amputations identified through diagnostic codes 

for traumatic amputations, with no indication of workrelatedness Uncertain work-related

Probable amputations identified through procedure codes 

only, with a primary diagnostic code of injury to a limb or 

digit and with no indication of work-relatedness

Uncertain work-related

Probable amputations identified through procedure codes 

only, with a primary diagnostic code of disease and no 

indication of work-relatedness

Not work-related

Possible amputations with no indication of workrelatedness Not-work-related

E-code: Code for external cause of injury

V-code: Code for factor influencing health status or contact with health services

ED: Emergency department visit data set 

a See Supplemental Materials, online only, for list of diagnostic and procedure codes used to define possible and probable 
29

amputations. 

b E800-E807 (with 4th digit of 0); E830-E838 (with 4th digit of 6); E850-845 (with 4th digit of 2 or 8); E849.3 (industrial place 

or premise); V26.1;V71.3 (see Supplemental Materials, online only).

c All variations of the following words: work, work injury, work-related.

Table IV. Distribution of work-related amputationsa by data source and by worker, injury, and employment 

characteristics, Massachusetts, 2007 - 08

All Sources

%

(N = 787)

CM

WC

%

(N = 351)

Published

SOII 

estimate 

% 

(n = 210)

All

%

(N = 646)

CM-Onlyb

%

(N = 406)

Worker characteristics

Gender

Female 8.3 6.5 6.7 10.3 5.1

Male 91.2 93.5 93.4 89.2 93.9

Unknown 0.5 0.0 0.0 0.6 1.0

Age

<18 years 0.4 0.3 0.3 0.6 - -

18-24 13.6 12.9 10.8 16.5 11.8

25-44 43.5 43.6 41.6 48.4 52.3

45-64 38.6 39.8 43.3 31.7 33.3

65+ 3.2 3.4 3.9 2.3 2.6

Unknown 0.6 0.0 0.0 0.0 - -

Race/ethnicity

White Non-Hispanic 60.2 70.9 74.9 43.9 47.7

Black Non-Hispanic 5.1 5.6 3.0 7.4 9.0

Hispanic 12.2 14.6 13.3 10.5 10.9

Table III. Number and Percent of Work-Related Amputations*

Captured by Data Source**, Massachusetts 2007 - 2008

Data Source N (N)a %

CM 646b 82.1

WC 351 44.6

SOII 106 (63) 13.4

TOTAL 787 100.0

SOII CM WC 47 (34) 6.0

SOII CM 24 (3) 3.1

SOII WC 29 (20) 3.7

WC CM 169 21.5

CM-only 406 51.6

WC-only 106 13.5

SOII-only 6 (6) 0.8

SOII, Survey of Occupational Injuries and Illnesses; CM, hospital case mix; WC, 

workers’ compensation.

* Includes amputations with known (76%) and indeterminate (24%) bone loss. 

** Numbers of SOII cases reported here are raw numbers, not weighted estimates. 

a Numbers in parentheses are the numbers of SOII cases reported as amputations in 

SOII.

b Seventy-eight percent of the CM cases had workers’ compensation recorded as the 

primary payer.
30

Other 5.0 5.7 5.7 4.0 9.6

Unknown 17.5 3.3 3.2 34.2 22.8

Injury characteristics

Body part

Finger 90.2 90.9 94.8 86.0 89.5

Other upper extremity 2.0 1.7 0.5 3.7 8.4

Toe 3.3 3.4 3.9 2.3 1.1

Other lower extremity 1.9 2.2 0.7 2.9 1.1

Other

c 0.6 0.1 0.0 1.1

--

Unknown 1.9 1.70.0 4.0--

Employment characteristics

Employment type

Wage/salary

Public 4.7 4.2 3.2 6.5--

Private 70.0 65.2 48.3 92.9 100.0

Self-employed 8.4 10.1 15.8 0.6 0.0

Unknown 16.9 20.6 32.8 0.0--

Occupation (SOC)

Management (11) 1.7 1.4 1.0 2.3 3.4

Food preparation and serving related (35) 2.7 1.4 0.7 4.0 5.3

Building and grounds cleaning and 

maintenance (37) 4.7 4.8 4.2 5.7

--

Construction and extraction (47) 14.9 17.0 19.0 10.0 21.1

Installation, maintenance and repair (49) 4.7 5.0 3.0 6.6 8.7

Production (51) 14.9 13.6 6.4 22.5 47.3

Transportation and material moving (53) 7.0 7.1 3.4 10.8 11.0

Other

d 2.9 2.2 1.5 4.0

--

Unknown 46.6 47.5 60.8 34.2--

Industry sector (NAICS)

Agriculture, forestry, fishing, and hunting 

(11) 1.4 1.7 2.2 0.3

--

Construction (23) 20.6 23.2 23.6 17.4 25.9

Manufacturing (31-33) 24.3 23.7 16.7 30.5 52.7

Wholesale/retail trade (42, 44-45) 8.6 7.9 7.9 10.0 4.6

Transportation and warehousing (48-49) 1.9 2.0 2.0 1.7 2.1

Administrative and support and waste 

management and remediation services (56) 7.2 6.3 5.7 9.7 3.0

Healthcare and social assistance (62) 2.2 1.7 1.0 2.8 3.5

Accommodation and food services (72) 3.0 1.9 2.0 4.3 1.7

Public administration (92) 3.3 2.9 2.7 4.3--

Other services (except public administration) 

(81)

e 2.4 2.5 2.2 2.8

--

Otherf 7.1 5.1 4.4 10.3 2.0

Unclassifiable (99) 16.1 19.2 28.3 3.4 4.6

Unknown 1.8 1.9 1.2 2.6--

Establishment size

Less than 10 employees 15.5 16.6 15.3 17.1--

10—49 20.6 19.5 14.5 28.2 23.9

50—250 18.7 16.7 9.9 25.6 59.1

250 + 7.4 5.7 3.2 10.5 17.1

Unknown 37.9 41.5 57.1 18.5--
31

CM: Hospital Case Mix

WC: Workers’ Compensation

SOII: Survey of Occupational Injuries and Illnesses

SOC: Standard Occupational Classification

NAICS: North American Industry Classification system

“- -“ indicates the estimated percentage (%) is less than one.

a Includes amputations with known (76%) and indeterminate (24%) bone loss.

b Includes cases identified solely through hospital administrative records and not captured by other data sources. 

c

Includes cases reported with multiple body parts and 1 case with a miscoded body part. 

d Includes architecture and engineering; education, training, and library; healthcare support; protective services; personal care and 

services; sales and related; office and administrative support; and farming, fishing, and forestry.

e Includes repair and maintenance; personal and laundry services; religious, grant making, civic, professional, and similar orgs.; and 

private households.

f Includes: mining, quarrying, and oil and gas extraction; utilities; information; finance and insurance; real estate, rental and leasing; 

professional, scientific, and technical services; management of companies and enterprises; educational services; and arts, 

entertainment, and recreation.

Table V. Distributions of multisource work-related amputations eligible for inclusion in the SOII 

and SOII estimates of amputations, by industry sector and establishment size, 

Massachusetts, 2007 – 08

Multisource SOII 

eligible cases

%

(N=406)

Published SOII 

estimate

%

(n=210)

Revised SOII 

estimate

%

(n=310)a

Industry sector (NAICS)

Agriculture, forestry, fishing (11) 0.7 - - 1.9

Construction (23) 18.0 25.9 20.3

Manufacturing (31-33) 33.0 52.7 54.2

Wholesale/retail trade (42, 44-45) 10.3 4.6 11.3

Transportation and warehousing (48-49) 2.0 2.1 - -

Administrative support and waste 

management (56)

8.4 3.0 1.9

Healthcare and social assistance (62) 3.4 3.5 3.0

Accommodation and food services (72) 4.2 1.7 1.9

Public Administration (92) 1.2 - - - -

Other services (81) 3.0 - - - -

Other 8.4 2.0 1.7

Unclassifiable (99) / unknown 7.4 4.6 3.1

Establishment size

Less than 10 employees 17.0 - - - -

10-49 employees 27.6 23.9 20.9

50-250 employees 27.6 59.1 63.5

250+ employees 11.3 17.1 15.7

Unknown 16.5 - - - -

SOII: Survey of Occupational Injuries and Illnesses

NAICS: North American Industry Classification System

“- -“ indicates the estimated percentage (%) is less than one.

a Revised SOII estimate computed including amputation cases reported in SOII as other injuries and applying SOII sample 

weights. 
32

Supplemental Materials

SI. Table 1. Diagnostic and procedure codes 1,2 used to identify probable and possible amputations in 

the Hospital Administrative (Case Mix) Records 

ICD-9 CM diagnostic codes (probable): 

885 – traumatic amp of thumb (complete, partial)

886 – traumatic amp of other finger(s) (complete, 

partial)

887 - traumatic amp of arm & hand (complete, 

partial)

895 - traumatic amp of toe(s) (complete, partial)

896 – traumatic amp of foot (complete, partial)

897 – traumatic amp of leg(s) (complete, partial)

ICD-9 CM procedure codes (probable): 

84.0 – 84.09 Amputation of upper limb

84.1-84.19 Amputation of lower limb

84.91 Amputation, NOS

84.2 – 84.29 Reattachment of extremity

CPT procedure codes3 (probable):11752 23900 23920 

23921 24900 24920 24925 24930 24931 24940 25900 

25905 25907 25909 25915 25920 25922 25924 25927 

25929 25931 26910 26951 26952 27290 27295 27590 

27591 27592 27594 27596 27598 27880 27881 27882 

27884 27886 27888 27889 28800 28805 28810 28820 

28825 54120 54125 54130 54135 69110 69120

ICD-9 CM diagnostic codes (possible):

997.6 Amputation stump complication 

(excludes txt for current traumatic amp 

& phantom limb syndrome)

905.9 Late effect of traumatic amputation

V49.6 Upper limb amputation status

V49.7 Lower limb amputation status

ICD-9 CM procedure codes (possible):

84.3 Amputation stump revision

84.40-84.48 Prosthetic limb device (fitting or implanting)

CPT procedure codes3(possible)

20802 20805 20808 20816 20822 20824 20827 20838 

24935

1 Generic ICD-9-CM Hospital Version 2008 (vols. 1-3), Channing Publishing, LTD., 2007; Current Procedural Terminology 

 (CPT) 2001, American Medical Association, 2000.

2 Current Procedural Terminology (CPT) Professional Edition 2001, American Medical Association, 2000.

3 CPT procedure codes available only in the Outpatient Observation Stay visit data set (OOS).

SI Table 2. Descriptions of ICD-9 CM External Cause of Injury (E-codes) and Supplemental 

information (V- codes) reported in Hospital Administrative (Case Mix) data that were 

considered as indicators that the amputation occurred at work

E-code Code Description

E 800-E807 (with 4th digit of 0) Injury sustained by an employee in a railway 

accident/incident 

E830-E838 (with 4th digit of 6) Injury sustained by a longshoreman (docker/stevedore) in 

a watercraft or water transport accident/incident

E840-E845 (with 4th digit of 2 or 8) Injury sustained by the flight or ground crew in an aircraft 

or air transport accident/incident 

E 849.3 Denotes that the injury occurred at an industrial place or 

premise

V-code

V62.1 Adverse effects of work environment

V71.3 Observation following accident at work
33

SI Table 3. Data elements used in the linkage of cases from three sources, Workers’ 

Compensation (WC), Hospital Administrative (Case Mix) and Related Medical Records, and

Survey of Occupational Injuries and Illnesses (SOII)

Employee (worker) information

First name of employee/worker

Last name

Abbreviated name (first letter of first name + first 4 letters of last name)

Soundex of last name (using first 4 letters)

Date of birth

Age

Gendera

Worker address (WC & Case Mix/medical record only)a

Injury/illness (medical) information

Date of injury and Admission date (Case Mix/medical record only)

Employer information

Employer Name

Abbreviated employer name (first 6 letters)

Employer Address (street, city/town, zipcode) a

a Denotes data elements that were used only for manual review and verification of matched cases.
FIGURE 1. Flow Diagram of amputation cases and encounters meeting initial selection criteria to inclusion in the final file of work-related amputations.

a

Numbers of SOII cases are raw counts, not weighted estimates.

b

Includes amputations with known (76%) and indeterminate (24%) bone loss.

Met initial 

selection 

criteria

Survey of Occupational 

Injuries and Illnesses 

(SOII)

a

N=63

N=106

43 SOII “other 

injuries” matched 

with WC or CM 

probable 

amputations

Workers’ Compensation 

(WC)

N= 447 

N= 351

20 cases 

matched with 

CM cases 

involving no 

bone loss

23 cases reported 

as other injuries 

that matched with 

SOII amputations

11 possible 

cases matched 

with SOII “other 

injuries”

88 unmatched 

possible cases 

and repeats

Hospital Administrative 

Case Mix (CM) Records 

N= 2,630

Amputations

N=2,243

387 (15%) repeat 

encounters

193 (9%) cases not 

amputations 

62 (3%) cases not 

in study period

33 (2%) cases 

employed out of 

state

826 (37%) cases 

not work-related; 

247 (11%) 

uncertain workrelated 

N=646

236 cases with 

evidence of no 

bone loss

787b

Final count of unique 

work-related amputations
Figure 2. Number of multisource work-related amputations eligible for inclusion in the SOII compared to published and revised SOII estimates 

of amputations, Massachusetts, 2007 – 2008.

a

Includes amputations with known (76%) and indeterminate (24%) bone loss. 

b

Range in parentheses is the 95% confidence interval for the SOII estimate. 

c

Revised SOII estimate (310) computed including amputations reported as other injuries such as cuts/lacerations on OSHA logs and in SOII. 

406 

SOII eligible

210

285

Unknown 

eligible

96 Ineligible

0

100

200

300

400

500

600

700

800

900

Multisource amputations Published

SOII estimate

Revised SOII estimate

787a

310

Revisedc

210 (158-262)b

Number of work-related amputations