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Methods Underlying New Workplace Injury and Illness Rates by Demographic

Group

by Brooks Pierce

Bureau of Labor Statistics

Originally Posted: March 26, 2008

Beginning with the 2006 survey year, BLS began publishing new estimates of injury and illness rates for days-away-fromwork cases by certain demographic characteristics. This article describes the methodology used to produce these new data

and provides some illustrative examples of how to use the statistics.

Introduction

BLS annually produces statistics detailing nonfatal work-related injuries and illnesses. As part of that annual publication

cycle, BLS reports nonfatal injury and illness counts requiring days away from work for certain demographic groups and for

certain incident characteristics. For example, BLS reported that in 2005, an estimated 415,880 incidents resulting in days

away from work occurred to females working in private industry in the United States.1 Of these incidents, an estimated

136,340 were classified as being musculoskeletal disorders.2

BLS also reports nonfatal injury and illness days-away-from-work rates, primarily by industry and case characteristics. Injury

and illness rates are calculated using estimated injury totals in conjunction with hours worked data provided by establishment

respondents to the Survey of Occupational Injuries and Illnesses (SOII). For example, BLS reported that in 2005, an

estimated 2.6 nonfatal amputations occurred per 10,000 full-time equivalent workers that resulted in days away from work in

private sector manufacturing in the United States.3

Prior to November 2007, however, BLS did not produce injury rates estimates by demographic group. The SOII survey

instrument asks respondents the total number of hours worked by all employees, but it does not ask respondents to

separately report total hours worked by workers in the various demographic groups. Therefore, constructing an estimate of

the injury rate among, for example, female workers would require either a change to the survey instrument and a subsequent

increase in the burden on survey respondents or access to external estimates of hours worked totals separately by gender.

Beginning in November 2007, BLS began publishing new estimates of injury and illness rates for days-away-from-work cases

by certain demographic characteristics for the 2006 survey year.4 These estimates are constructed by incorporating data on

employment and hours worked, from sources external to the SOII, along with SOII estimates of injury and illness counts. This

article describes the statistics produced and the data sources and methods used to produce them. It also presents some

illustrative statistics and describes several important caveats for data users.

What New Estimates Are Produced?

Beginning with the 2006 survey year, the SOII program began publishing private sector estimates of injury and illness rates

for days-away-from-work cases by gender, by age group, by occupation, and by occupational group.5 Statistics are available

at the national level and at the State level for States that participate in the SOII. Occupational estimates are available at the

detailed Standard Occupational Classification (SOC) level for national statistics, and at the major group level for State and

national level statistics.6 Injury and illness rates also are available by demographic characteristics and certain injury

characteristics, such as the nature, event, and source of the incident, and the part of body affected. For example, BLS now

publishes an estimated injury and illness rate for all days-away incidents occurring to female workers, as well as for

musculoskeletal disorders occurring to female workers.

Methods

The SOII program incidence rates for case characteristics represent the number of injuries and illnesses per 10,000 full-time

workers and are calculated as follows:
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incidence rate = (N÷EH) × 20,000,000,

where

N = number of injuries and illnesses with the given characteristic, 

EH = total hours worked during the calendar year, and 

20,000,000 = base for 10,000 equivalent full-time workers (working 40 hours per week, 50 weeks per year).

To apply this formula for constructing incidence rates for a particular subsample of the data, BLS uses the N and EH totals

relevant to the subsample of interest.

For example, consider the previously mentioned rate of 2.6 amputations per 10,000 FTE workers in private sector U.S.

manufacturing. To construct that estimate, BLS uses the formula above with N set equal to the SOII survey’s total reported

number of amputations occurring in U.S. private manufacturing, and with EH set equal to the SOII survey’s total reported

number of hours worked by employees in U.S. private manufacturing. The base of 20,000,000 is applied and rates are

reported per 10,000 FTE workers.

In producing demographic incidence rates, BLS similarly takes the N and EH totals to refer to the demographic group

subsample of interest. Estimates for the numerator term N, the number of injuries and illnesses, are available directly from

the SOII survey. Estimates for the denominator term EH are derived jointly from the SOII and external data. The denominator

term EH that is relevant to the demographic group is derived by multiplying the total hours worked for all employees (from the

SOII survey), and the fraction of hours worked by members of the demographic group (from the external data).

In symbols, the incidence rate for workers of demographic group j is

incidence rate for group j = (N j ÷ EH j) × 20,000,000 

 = (N j ÷ (EH × f j )) × 20,000,000,

where

N j = number of injuries and illnesses occurring to group j workers, 

EH = total hours worked during the calendar year by workers, 

EH j = total hours worked during the calendar year by workers in group j, 

f j = fraction of all hours worked attributable to workers in group j, and 

20,000,000 = base for 10,000 equivalent full-time workers (working 40 hours per week, 50 weeks per year).

For example, to construct an estimate of the injury rate for female workers in U.S. private industry, set Nj equal to the SOII

survey’s total reported number of injuries and illnesses occurring to females in U.S. private industry, and set EHj equal to an

estimate of the total reported number of hours worked by female employees in U.S. private industry. BLS constructs this

estimate of EHj by calculating the proportion of all hours worked that are worked by females (fj, from external data), and

applying that proportion to the SOII estimate for total hours worked by all employees (EH).7

This method apportions the SOII total hours worked estimates across demographic groups in proportion to the hours worked

totals by demographic group as observed in the external data. This apportioning guarantees that the weighted average of the
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demographic group incidence rates returns the overall incidence rate. This method works best where the external data can

deliver accurate estimates for the proportion of hours worked that are worked by the demographic group in question, for the

population of workers within scope of the SOII.

Incidence Rates By Age Group Or Gender

The Current Population Survey (CPS) is the external data source providing hours worked totals for constructing injury and

illness rate estimates by age group or gender. The CPS is conducted monthly by the Census Bureau for BLS and is a

household survey used primarily to produce unemployment rates and employment totals. Each month, the survey of about

60,000 households collects a substantial amount of information on individual demographic and employment characteristics.

Demographic characteristics include gender and age. Employment information includes occupation, industry, selfemployment status, and hours worked variables for individuals’ main and secondary jobs.8

The CPS data have several advantages for the purpose at hand. CPS monthly surveys are pooled to give annual totals,

providing a fairly large data set. The CPS sample design gives representative annual estimates for adult employment at the

State or national level, which are the geographic areas of interest for the SOII. CPS employment detail can be used to narrow

the CPS sample to a scope similar to that of the SOII survey. Finally, the CPS reported hours data allow an estimate of total

hours worked to be constructed that mimics the SOII concept of hours at risk for injury.

To produce hours worked estimates for a sample comparable to the SOII scope, certain classes of workers must be excluded

from the CPS data, principally the unincorporated self-employed and government workers.9 The CPS microdata reports age

and gender for individuals, along with hours worked on individuals’ main and secondary jobs. Using the CPS microdata

weights, summing hours worked across individuals of like age, or of like gender, provides estimates of the proportion of hours

worked by a particular demographic group (the fj of the previous equation).

Table 1 and table 2 give some example statistics using 2005 data.10 Table 1 shows rates by gender, while table 2 shows

rates by age group. The statistics in each table refer to national data for the private sector. The first column of numbers in

each table shows the published injury and illness count, the second column shows the demographic group’s employment

fraction from CPS data, and the third column shows the injury rate.

Table 1 shows that the bulk of injuries and illnesses resulting in days away from work occur to male workers. Male workers

who are in scope experienced nearly twice as many incidents as did female workers. However, males worked approximately

59 percent of all hours worked by private sector employees in 2005, as measured by the CPS. That implies that injury and

illness rate differences by gender are likely to be smaller, proportionally, than the differences in the injury and illness counts

shown in the first column. In fact, the estimated injury and illness rate for men of 152.7 is roughly 37 percent greater than the

rate of 111.4 for women.

Table 2 carries out a similar exercise using age groups. Injury and illness case counts are higher among prime-aged workers

than they are at the youngest and oldest age ranges. Clearly this is due to the fact that there are more prime-age workers,

and that prime-age workers, on average, work more per year. The second column of the table quantifies these differences in

work by age group, using CPS data. For example, workers aged 16 to 19 work approximately 3.2 percent of the total hours

worked by employees of any age. Column 3 shows that incidence rates are much more uniform across age groups than the

injury and illness counts might lead one to believe. Of further interest is the fact that the incidence rates are 144.1 and 144.5

for the 16- to 19-year olds and 20- to 24-year olds, respectively, versus a rate of 122.4 for the 65 years and older age group.

Incidence Rates By Occupation

To produce incidence rates by occupation or occupational aggregate, BLS combines employment count estimates from the 

Occupational Employment Statistics (OES) program with hours worked data from the CPS. Combining these two data

sources introduces some additional complexity to the occupational calculations, but the OES data possess some advantages

that make the extra complexity worth undertaking.
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The OES is a large establishment survey expressly designed to produce State and national level estimates of occupational

employment counts.11 The OES detailed occupational codes follow the Standard Occupational Classification (SOC) system,

which the SOII program also uses. The CPS coding scheme is also based on the detailed SOC codes, but in some instances

the CPS data are collapsed across SOC codes. Furthermore, the OES occupational reports are likely to be more comparable

to SOII occupational reports, because both the SOII and OES survey are establishment surveys.

OES data provide only employment counts estimates, however, rather than total hours worked estimates. Furthermore, the

OES scope excludes certain agricultural industries that are within the scope of the SOII program. Therefore, the CPS data

are needed to provide estimates of average hours worked per job, as well as to provide occupational hours worked estimates

in those agricultural industries outside the scope of OES.12

The estimated proportion of hours worked by employees in an occupation (the fj of the previous equation) is constructed from

estimates of total hours worked by occupation using CPS and OES data. The estimate for total hours worked by employees

in an occupation is the sum of two terms. The first term represents total hours worked by those in the occupation that are in

scope for both the OES and SOII programs. This equals the OES occupational employment estimate for the occupation

multiplied by the CPS estimate for average hours worked per year for workers in the occupation and in scope for the private

sector OES tabulations. The second term represents the total hours worked by those in the occupation that are in scope of

the SOII program but working in the agricultural industries that are out of scope for the OES program. This second term

equals the CPS estimate for the total hours worked for occupational employees that are in those agricultural industries

outside the OES scope.13

In symbols, the proportion fj of hours worked that are attributable to employees in a given occupation j is given by

f j = TH j⁄∑TH j = ((N j × h j) + A j)⁄∑TH j, where

TH j = external data estimate of total hours worked by employees in occupation j, N j = OES survey employment count in occupation j, h j = CPS average hours worked per year in occupation j jobs in industries in scope to the SOII and OES

surveys, and 

A j = CPS total hours worked by employees in occupation j jobs in those agricultural industries in scope to the

SOII survey and out of scope to the OES survey. 14

The occupation’s fraction of all hours worked, fj, essentially apportions the total hours worked estimates that are derived

directly in the SOII. For higher level occupational aggregates, the j subscripts in this formula are taken to refer to the

occupational aggregate rather than to a detailed occupation, with the exception that the hj is defined to be an average across

detailed occupations using OES employment counts as weights.

To give some flavor for the resulting incidence rates, tables 3, 4, and 5 give select occupational rate statistics. Table 3 shows

rates at the occupational group level, for the private sector in the United States and for the private sector in California. The

key point of interest in this table is that the calculated occupational rates tend to be lower than average for white collar

occupations, and they tend to be higher for blue collar occupations. For example, the rate of 30.5 for management, business,

and financial occupations is less than one-fourth of the overall national rate of 135.7 in 2005. On the other hand, the rates for

construction and extraction occupations and for transportation and material moving occupations are more than twice the

overall national rate. These patterns also seem to hold roughly in the State of California as well.
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Table 4 gives incidence rates for those detailed occupations estimated to have more than 20,000 injuries or illnesses

resulting in days away from work. These high incident count occupations tend to represent a large fraction of the total hours

worked within the scope of the survey. For example, the combined OES-CPS estimate is that about 2 percent of all hours

worked in scope in 2005 were worked by laborers. The incidence rates for most of these detailed occupations are quite high

relative to the national aggregate rate of 135.7. Two interesting exceptions are retail salespersons, with a rate of 102.6, and

registered nurses, whose rate of 132.5 is very much on par with the national level figure of 135.7. It appears that the high

counts of injuries and illnesses to retail salespersons and registered nurses can be attributed to the fact that these

occupations have large employment and hours worked totals.

Table 5 shows the injury and illness counts and rates for select detailed occupations, with the occupations chosen to have

the highest rates, subject to the condition that the occupation have at least one-tenth of 1 percent of all employment. All

occupations listed in table 5 have an incidence rate above 350 per 10,000 workers. The top three listed occupations all have

an incidence rate above 500 per 10,000 workers. Other occupations that appear on this list but not in table 4 are roofers;

industrial machinery mechanics; cooks, institution and cafeteria; butchers and meat cutters; and welders, cutters, solderers

and brazers. Note also that several of the occupations shown in table 4 also appear in table 5. This implies that the high

incidence of injuries to, say, nursing aides, orderlies and attendants is not simply due to high employment in that occupation.

The high incidence reflects a high estimated rate of injury or illness for those workers as well.

Incidence Rates By Demographic Characteristic And Incident Characteristic

The SOII program publishes a substantial amount of detailed data on the particular characteristics associated with injury and

illness episodes. Injury and illness counts and incidence rates are published for the nature and source of the episode, the

part of body affected, and the event characteristic. As an example, data collected on incident characteristics are used to

determine how many injury and illness cases are due to musculoskeletal disorders (MSDs).15

Adapting the previously given formulas to produce incidence rates by demographic group and incident characteristic is

straightforward, because no additional hours worked estimates are required. Simply let the injury count estimate refer to the

particular case characteristics of interest, and let the hours estimate still refer to the given demographic group.

For example, to compute an incidence rate for MSDs among females, use the number of MSD incidents occurring to females

as the numerator. For the denominator, use the estimated total hours worked by female employees. This denominator is the

same denominator used for the overall incidence rate among females.

Table 6 shows the gender-specific national MSD rates for 2005. Note that a greater fraction of females’ than males’ injuries

are classified as MSDs. Nevertheless, the incidence rate of MSDs is roughly comparable for men and women, if not slightly

higher among males. Males appear to be at least as much at risk for MSDs as are women.

Caveats

The incidence rate statistics described in this article are somewhat different from most others produced by the Survey of

Occupational Injuries and Illnesses (SOII) program, because they require external data. As a result, special care should be

exercised when using them. Statisticians often distinguish between two sorts of error: sampling error and nonsampling error.

Describing these sources of error may help users understand the quality of the demographic rate statistics.

Sampling error refers to the fact that estimates are based on a survey and not a census. Therefore any particular estimate

will not likely equal the population’s true value, but will instead exhibit some variation from that true value. Constructing

statistics using external data therefore involves sampling error from the external data as well as sampling error in the SOII. All

of the three surveys described here--the SOII, the OES survey, and the CPS--take steps to minimize sampling error by using

appropriate sampling design and estimation techniques. The SOII survey further screens publication reports to suppress

reporting of statistics with high injury count sampling error. In the current context, the data user should treat extreme values

with caution in situations where small samples are a possible issue. No estimates of sampling error were calculated for the
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new injury and illness rate statistics presented in this article; therefore, statistical statements made could not be validated

given the unavailability of estimates of sampling error.

Nonsampling error refers to all other errors that can affect survey estimates. Examples include such possible factors as

biased reporting by respondents, incorrect coding of survey responses, programming errors in estimation, and so forth.

Although nonsampling error is inherently difficult to gauge and measure, all survey programs continually take steps to detect

and mitigate such errors.

The demographic rate statistics are constructed by joining two or more surveys. It is possible and indeed likely that this

merging of data induces some nonsampling error in the joint product that is present in none of the surveys separately.

Although care has been taken to minimize this error, it is not entirely avoidable. It is part of the price of producing these

statistics. This section is designed to identify some of this nonsampling error.

One set of possible error involves different scopes in the various survey data used. The exclusions imposed in the CPS

microdata, as well as the special tabulations produced by the OES program, are designed to mimic the scope of the SOII.

Although all indications are that this effort has been largely successful, there are still some remaining differences. The SOII

survey excludes agricultural establishments with fewer than 11 employees, whereas the CPS survey does not. The CPS

scope is for the adult population, meaning for ages 16 years and older, whereas the SOII records injury and illness cases for

employees confirmed younger than 16 years of age. Also, the CPS identifies State location based on household residence,

whereas the OES and SOII surveys identify State location based on place of employment. Finally, the exclusions imposed in

the CPS data so that it mimics the scope of the SOII are largely derived from individual responses to questions rather than

more directly through employer administrative reports. For instance, the self-employed are inherently out of scope for the

SOII because the SOII universe is primarily based on administrative records from State unemployment insurance programs.

In contrast, the CPS sample requires exclusion of the self-employed via an individual response to a question detailing the

class of job held by the worker. Those different avenues should arrive at similar definitions of scope, but they are not

guaranteed to match scope exactly in the different surveys.

The issue of scope can also arise where the different data sources have different definitions governing which employees are

in a given group. This is not likely to be an issue for age and gender definitions, but it may arise for occupational definitions.

For example, the CPS data on occupation are based on self-reports rather than establishment reports. Although the OES

survey and SOII use the same coding structure, they may effectively code occupations differently simply because of different

respondent detail or different methods of slotting respondent information into occupations.16 Generally speaking, this should

be less of a problem at higher levels of occupational aggregation, or in detailed occupations that are well defined. Another

example involves the situations in which CPS coding structure is less fine than the SOC detailed occupational structure used

in SOII and OES. In those situations, part of the information that is used to construct an estimate of total hours worked in a

particular detailed occupation is based on a slightly broader occupational detail in the CPS.

A final type of nonsampling error that can occur is the possibility that hours worked reports by individual respondents in the

CPS are based on inherently different concepts than hours worked reports by establishments. It is possible that employers,

were they asked, might give different responses to hours worked questions than do their employees, and that furthermore

such a difference could lead to different estimated hours worked totals within a demographic group.

Due to these possible sources of nonsampling error, data users should use the new rate statistics with caution, especially in

situations where large scope differences are possible between the surveys. In the majority of situations, however, such scope

differences are likely to be relatively small, and the new rate statistics can aid in our understanding and potentially in

mitigating workplace injuries and illnesses.

Brooks Pierce

Research Economist, Compensation Research and Program Development Group, Office of Compensation and Working

Conditions, Bureau of Labor Statistics.

Telephone: (202) 691-7525; E-mail: Pierce.Brooks@bls.gov.
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Notes

1 See Nonfatal Occupational Injuries and Illnesses Requiring Days Away from Work, 2005, USDL 06-1982 (U.S. Department of Labor),

November 17, 2006, table 1; available on the Internet at http://www.bls.gov/iif/oshwc/osh/case/osnr0027.pdf.

2 See Case and Demographic Characteristics for Work-related Injuries and Illnesses Involving Days Away From Work, table 11; available on

the Internet at http://www.bls.gov/iif/oshwc/osh/case/ostb1655.pdf.

3 See Nonfatal Occupational Injuries and Illnesses Requiring Days Away from Work, 2005, USDL 06-1982 (U.S. Department of Labor),

November 17, 2006, table 7; available on the Internet at http://www.bls.gov/iif/oshwc/osh/case/osnr0027.pdf.

4 Nonfatal Occupational Injuries and Illnesses Requiring Days Away from Work, 2006, USDL 07-1741 (U.S. Department of Labor), November

8, 2007; available on the Internet at http://www.bls.gov/news.release/pdf/osh2.pdf.

5 All estimates in this note are for injury and illness cases resulting in days away from work.

6 For more information, see 2000 Standard Occupational Classifications (SOC) System, on the Internet at http://www.bls.gov/soc/home.htm.

7 For some injury and illness cases, the case characteristic is missing. For example, gender may not be reported for each case. The incidence

rate formula adjusts for such non-reports by imputing characteristics for those cases and including the imputed total in the Nj count. These

adjustments are numerically very small, as case characteristics are missing only infrequently.

8 For more information on the CPS, see the CPS home page at http://www.bls.gov/cps/home.htm. The CPS sample each month consists of

short overlapping panels: households are in the survey for 4 consecutive months, out for 8 months, and then back in sample for 4 final

consecutive months. The new estimates of injury and illness rates for days-away-from-work cases by demographic characteristics utilize the

public use CPS microdata, which are available on the Internet at http://dataferrett.census.gov/index.html.

9 The scope of the CPS data after these exclusions approximates the SOII survey scope, but there are some remaining differences in scope.

These differences are discussed in the section below on caveats.

10 The 2005 incidence rates by demographic group presented in this article are for illustrative purposes. BLS has not generated a

comprehensive set of such rates for 2005. Survey year 2006 is the first for which a full set of incidence rates by demographic group have been

published.

11 The Occupational Employment Statistics (OES) program provides OSHS with unpublished tabulations for the private sector subsample

required by the SOII scope. For more information, see the OES home page at http://www.bls.gov/oes/home.htm.

12 The OES program scope includes logging and agricultural support activities for animal and crop production (NAICS industries 1151, 1152,

and 1133), and excludes other industries in NAICS sector 11. For more on NAICS, see North American Industry Classification System

(NAICS) at BLS, available on the Internet at http://www.bls.gov/bls/naics.htm.

13 Exclusions to CPS data to fit SOII scope are, as in the case of the gender and age group statistics, primarily self-employed and government

workers. The SOII scope excludes farms with fewer than 11 employees, but CPS data do not identify establishment size and hence that

exclusion in the CPS data cannot be replicated.

14 Combining CPS and OES data requires merging those data at the detailed occupational level. The CPS occupational classification is in

some instances coarser than the detailed SOC codes used in the SOII. In these instances the Aj and hj of this equation must be imputed at the

detailed SOC level using CPS data at the slightly coarser level dictated by the CPS classification. In these instances annual average hours

worked in the detailed SOC code (the hj) is imputed to be the average observed in the coarser CPS occupational classification. Agricultural

sector hours worked (the Aj) is imputed by apportioning the CPS agricultural sector hours worked total for the coarser occupational level

across the detailed SOC codes. The apportionment is in proportion to the employment distribution observed in the OES. This method assumes

that the agricultural and nonagricultural sectors have similar detailed SOC distributions within the given CPS level of occupational detail. Most

detailed SOC codes have little agricultural sector employment relative to the employment observed in OES, and hence apportioning the Aj has

little effect on the estimates. For detailed occupations that are primarily agricultural and hence in sectors outside of OES scope, BLS reports

incidence rates at the coarser level of detail observed in the CPS rather than at the detailed SOC level.

15 The U.S. Department of Labor defines a musculoskeletal disorder (MSD) as an injury or disorder of the muscles, nerves, tendons, joints,

cartilage, or spinal discs. MSDs do not include disorders caused by slips, trips, falls, motor vehicle accidents, or similar accidents.

16 Some occupational estimates are suppressed where it is believed that issues of scope or definition might be more important. Examples

include detailed occupations in which the external data define the occupation to be out of scope. For example, all CPS workers coded as

farmers also were coded as being unincorporated self-employed workers.
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Table 1. U.S. private sector nonfatal injury and illness rate per 10,000 full-time workers by gender, 2005

Gender Nonfatal injuries and illnesses Employment fraction Rate of nonfatal injuries and illnesses

Male 814,250 0.588 152.7

Female 415,880 0.412 111.4

Total 1,234,680 1 135.7

Table 2. U.S. private sector nonfatal injury and illness rate per 10,000 full-time workers by age group, 2005

Age group Nonfatal injuries and illnesses Employment fraction Rate of nonfatal injuries and illnesses

16 to 19 41,530 0.032 144.1

20 to 24 133,760 0.103 144.5

25 to 34 290,500 0.235 136.9

35 to 44 311,830 0.256 135.0

45 to 54 282,310 0.233 134.7

55 to 64 135,290 0.116 129.4

65 and older 27,050 0.025 122.4

Total 1,234,680 1.000 135.7

Table 3. U.S. and California private sector nonfatal injury and illness rate per 10,000 full-time workers by occupational

group, 2005

Occupational Group

United States California

Nonfatal injuries and

illnesses

Rate of nonfatal injuries

and illnesses

Nonfatal injuries

and illnesses

Rate of nonfatal injuries

and illnesses

Management, business, and

financial occupations 28,110 30.5 4,980 41.8

Professional and related

occupations 83,060 61.5 8,400 51.5

Service occupations 247,270 169.2 26,510 161.8

Sales and related occupations 80,020 74.5 8,880 69.8

Office and administrative

support occupations 91,400 60.3 14,510 79.7

Farming, fishing, and forestry

occupations 15,540 165.7 5,210 173.9

Construction and extraction

occupations 152,490 288.6 19,540 306.4

Installation, maintenance, and

repair occupations 107,770 243.8 11,730 275.3

Production occupations 173,440 191.3 14,830 168.7

Transportation and material

moving occupations 253,570 321.2 26,210 314.1

Total 1,234,680 135.7 141,340 132.5
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Table 4. U.S. private sector nonfatal injury and illness rate per 10,000 full-time workers for selected occupations, 2005

Occupation Nonfatal injuries and

illnesses

Employment

fraction

Rate of nonfatal injuries and

illnesses

Laborers and freight, stock, and material

movers, hand 92,240 0.02 504.7

Truck drivers, heavy and tractor-trailer 65,930 0.017 419.9

Nursing aides, orderlies, and attendants 52,150 0.011 543.4

Construction laborers 39,270 0.008 517.6

Truck drivers, light or delivery services 32,740 0.01 360.3

Retail salespersons 32,300 0.035 102.6

Janitors and cleaners, except maids and

housekeeping cleaners 31,440 0.012 283.7

Carpenters 31,270 0.009 396

Maintenance and repair workers, general 23,170 0.011 239

Stock clerks and order fillers 23,060 0.013 196.6

Registered nurses 20,100 0.017 132.5

Note: Occupations are selected to have 20,000 or more injuries and illnesses resulting in days away from work in 2005.

Table 5. U.S. private sector nonfatal injury and illness rates per 10,000 full-time workers for selected occupations, 2005

Occupation Nonfatal injuries and illnesses Rate of nonfatal injuries and illnesses

Nursing aides, orderlies, and attendants 52,150 543.4

Construction laborers 39,270 517.6

Laborers and freight, stock, and material movers,

hand 92,240 504.7

Roofers 4,540 455.3

Industrial machinery mechanics 10,040 446.8

Truck drivers, heavy and tractor-trailer 65,930 419.9

Carpenters 31,270 396

Cooks, institution and cafeteria 6,460 396

Butchers and meat cutters 4,340 395.9

Welders, cutters, solderers, and brazers 12,700 367.7

Note: These selected occupations have the highest injury and illness rates, from among those occupations accounting for at least one-tenth of

1 percent of all employment.

Table 6. U.S. private sector nonfatal injury and illness rates per 10,000 full-time workers for musculoskeletal disorders

by gender, 2005

Gender All cases Rate, all cases MSD cases MSD rate

Male 814,250 152.7 238,630 44.8

Female 415,880 111.4 136,340 36.5

Total 1,234,680 135.7 375,540 41.3

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