Electrical Injury in Construction Workers: A Special Focus on Injury with Electrical Power

This paper analyses the risks and safety measures for electrical injuries among construction workers. It includes a literature review, analysis of qualitative and quantitative data, alternative research strategies, and initial research ideas.

Safety, health and environment 1
Safety health and environment
Electrical injury in construction workers: a
special focus on injury with electrical power

Safety, health and environment 2
Contents
Review of published paper....................................................................................................................3
Literature Review..................................................................................................................................3
Analysis of Qualitative and Quantitative data.......................................................................................3
Alternative research strategies..............................................................................................................4
Descriptive research strategy:...........................................................................................................4
Applied research strategy:.................................................................................................................4
Predictive research strategy:.............................................................................................................4
Exploratory research strategy:...........................................................................................................4
Initial research ideas..............................................................................................................................4
References.............................................................................................................................................5

Safety, health and environment 3
Review of published paper
A journal paper with title name “Injuries among electric power industry workers, 1995-2013” was chosen.
This paper was published in “Journal of Safety Research”, volume 60 in 2016. This is the peer reviewed
journal which comes under Elsevier. The paper was written by Vitaly Volberga, Tiffani Fordycec, Megan
Leonhardb, Gabor Mezeic, Ximena Vergarad and Lovely Krishene. All of them are academic researcher
from United States. The paper is attached in the appendix.
According to the paper electric power industry has very unpredictable work environment. The workers
which are working there are in great danger. The work they do there is very risky and demanding. In this
paper analysis of the risks and safety measures were done on the basis of various surveys. The authors
divided the injuries caused in the past on the basis of age, sex, occupational group and injury type. For this
they chose the Electric Power Research Institute’s (EPRI) Occupational Health and Safety Database
(OHSD). In these database injuries, medical claims and personnel data was recorded from 18 different
countries. This data was from 1995 to 2013. In the result it was found that the injury rate was 3.20 injuries
per 100 employees in a year and it was decreasing by the year. It was 1.31 injuries per 100 employees in
the year 2013. It was found that according to the occupation, welders, meter readers and line workers
were most prone to injuries. Male workers were recorded with more injuries but in case of meter readers
women were more injured. Injury rate was higher among the workers of age group between 21 to 30
years. In case of welders and machinist workers who were older (65+), were more with injuries. In the end
it was suggested that targeted safety measures need to be taken based on the study. This can prevent
many fatalities and injuries for electric industry workers.
Literature Review
It is the research, observation and analysis of the work done in the area which we have chosen. It analyse
the literature available in the given topic. The main purpose of writing a literature review is to present the
literature in an organised way. It does not contain any new idea or original work. It tells us about the
previous and present research of our topic and what were their limitations.
It is very important to write literature review in the research because it tells us that about the research
that has already been done in the area which we have chosen for research. It also let the reader know
that the author has read the previous and present topics related to his research. A literature review saves
time by letting the author not to research on the same topic again.
There are many risks and injuries while working in an electrical company. They can produce serious
problems like cardiac arrhythmia, hypoxia, cardiopulmonary arrest and renal failure (Cooper and Price,
2002). If a person had an accident with electricity it can cause both psychological and neurological effects
which in turn can affect their life in a serious manner (Noble et al., 2006). The exposure to alternating
current is more dangerous as compared to direct current (Cooper, 1995). The exit wounds which are
caused by the current when it leaves the body are small of direct current as compared to alternating
current (Salehi et al.,2013). Young workers are more prone to injuries specially workers between the age
of 16 to 19 years (Janicak, 2008). In a research survey done by Lombardi and co-authors in 2009, it was
claimed that non-fatal injuries were 98.8% of the total cases of injuries. Another factor is that once the

Safety, health and environment 4
worker is injured there are very less chances for them to return back to work (Wesner and Hickie, 2013;
Theman et al., 2008; Stergiou-Kita et al., 2014).
Analysis of Qualitative and Quantitative data
As the name itself suggest, the data which gives information about the quantity is called quantitative data
and the data which gives information about the quality of the data is called qualitative data. The
information that can be measured in the form of numbers falls into the category of quantitative data, for
example, number of electric companies, injuries caused to the workers in present year, etc. It deals with
the data which is generally in the form of how much and how many.
Qualitative data covers the quality of the information. In simpler terms, the data which cannot be
measured is called qualitative data. Examples of qualitative data are what type of injuries happens to
electric workers, what kind of workers are more prone to the injuries, etc. As it is seen from the examples
the data which is generally in the form of type of or kind of comes in the category of qualitative data.
According to the journal paper used in this report analysis were done for both qualitative and quantitative
data. Injuries calculated per year are the quantitative analysis of the data. They were analysed using
Poisson distribution, where upper and lower limits were analysed using Fleiss method. Analysis of injuries
based up on the occupational group was done qualitatively. For this, mechanism of injury and which body
part was affected with injury were analysed. Generally, qualitative analysis is non-statistical process and it
is required when in-depth knowledge of topic is provided, whereas, in case of quantitative analysis
statistical methods and tabulation method are used (Bryman, 2006).
Alternative research strategies
Apart from qualitative and quantitative research strategies can be of different types (Openlearn, 2018).
Some of them are discussed here:
Descriptive research strategy:
This strategy is used when giving the description of a situation. While describing the relation between the
growth of a plant with the amount of sunlight and water this strategy can be used.
Applied research strategy:
It is used by a company or government to find a solution of a problem, for example a research to find out
that what is the best scheme to motivate physically handicapped children.
Predictive research strategy:
It is used to predict that what will happen in the future. This is generally used by the companies to predict
the sale of their new product at special time and at the end of year.
Exploratory research strategy:
If a company is launching a new product into the market, they will need an idea, finance and a team to
learn about the market. In this case this type of research will be best suited.

Safety, health and environment 5
Initial research ideas
Environment, health and safety are very important aspects of any organization. They must be given equal
importance as compared to any other criterion. In case of construction workers it becomes very
important. Research ideas for their safety health and environment are given in various researches. As
discussed according to the journal paper that an analysis was done on the database of injuries of the
workers. This database was studied in detail and conclusions were drawn from it. For example according
to the research it was found that in case of meter readers, there were more injuries in women as
compared to men. Hence, special scheme or targeted policies can be formed to remove such injuries.
Similarly, it was found that in case of welders older workers were more prone to risks. Therefore, they can
be shifted to another division according to their age group.
Conclusion
A research process is used to propose a research idea, literature review, implement that idea, applying
any research methodology and finding the results. In this assignment, a research paper related to electric
injuries was selected which was critically analysed. After that a literature review was presented to find out
the work done in that area. It was found that most of the workplaces lack safety measures. Previous years
data was collected and then both qualitative and quantitative analysis was done. Other research
strategies were explained with example. The importance of targeted safety measures at electric industries
was explained in the initial research idea.

Safety, health and environment 6
References
Bryman, A. (2006) Integrating quantitative and qualitative research: how is it done? Sage journals, 6(1),
pp. 97-113
Cooper, M.A. (1995) Emergent Care in Lightning and Electrical Injuries. Seminars in Neurology, 15, pp.
268-278.
Cooper, M.A. and Price, T.G. (2002) Electrical and Lightning Injuries. 5th ed. St.Louis, MO.
Janicak ,C.A. (2008) Occupational Fatalities Due to Electrocution in the Construction Industry. Journal of
Safety Research, 39, pp. 617-621.
Noble, J., Gomez, M., and Fish, J.S. (2006) Quality of Life and Return to Work Following Electrical Burns.
Burns, 32, pp. 159-164.
Openlearn, (2018) Understanding different research perspectives [online] Available from:
http://www.open.edu/openlearn/money-management/understanding-different-research-perspectives/
content-section-6 [Accessed 15/06/2018].
Salehi, S.F., Fatemi, M.J., Asadi, K., Shoar, S., Ghazarian, A.D. and Samimi, R. (2014) Electrical Injury in
Construction Workers: A Special Focus on Injury with Electrical Power. Burns, 40, pp. 300-304.
Stergiou-Kita, M., Mansfield, E., Bayley, M., Cassidy, J.D., Colantonio, A., Gomez, M., Jeschke, M., Kirsh, B.,
Kristman, V., Moody, J. and Vartanian, O. (2014) Return to Work After Electrical Injuries: Workers’
Perspectives and Advice to Others. Journal of Burn Care Research, 35, pp. 498-507.
Theman, K., Singerman, J., Gomez, M., and Fish, J.S. (2008) Return to Work After Low-Voltage Electrical
Injury. Journal of Burn Care Research, 6, pp. 959-964.
Wesner, M.L. and Hickie, J. (2013) Long-Term Sequelae of Electrical Injury. Canadian Family Physician, 59,
pp. 935-939.

Safety, health and environment 7
Appendix
JSR-01352; No of Pages 8
Journal of Safety Research xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
Journal of Safety Research
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j s r
1Q1 Injuries among electric power industry workers, 1995–2013
2Q2 Vitaly Volberg, a Tiffani Fordyce, a, Megan Leonhard, b Gabor Mezei, c Ximena Vergara, d Lovely
Krishen e
3 a Exponent, 475 14th St #400, Oakland, CA 94612, United States
4 b Exponent, 15375 SE 30th Place, Suite 250, Bellevue, WA 98007, United States
5 c Exponent, 149 Commonwealth Drive Menlo Park, CA 94025, United States
6 d The Electric Power Research Institute (EPRI), 3420 Hillview Ave, Palo Alto, CA 94304, United States
7Q3 e EPRI, 942 Corridor Park Blvd, Knoxville, TN 37932, United States
8
9 a r t i c l e i n f o
10 Article history:
11 Received 22 March 2016
12 Received in revised form 6 July 2016
13 Accepted 17 November 2016
14 Available online xxxx
198765
40Q4 Keywords:
41 Injury surveillance
42 Utility
43 Electrical
44 Occupational injury
45 Non-fatal injury
46 Fatal injury
50487
49
a b s t r a c t
Introduction: Workers in the electric power industry face many risks of injury due to the high diversity of work
20
tasks performed in potentially hazardous and unpredictable work environments. Method: We calculated injury
21 rates by age, sex, occupational group, and injury type among workers in the Electric Power Research
Institute’s 22 (EPRI) Occupational Health and Safety Database (OHSD), which contains recordable injury,
medical claims, 23 and personnel data from 18 participating electric power companies from 1995 to 2013.
Results: The OHSD 24 includes a total of 63,193 injuries over 1,977,436 employee-years of follow-up, for an
overall injury rate of 3.20 25 injuries per 100 employee-years. Annual injury rates steadily decreased from
1995 to 2000, increased sharply 26 in 2001, and subsequently decreased to their lowest rate of 1.31 injuries per
100 employee-years in 2013. 27 Occupations with the highest injury rates were welders (13.56 per 100
employee-years, 95% CI 12.74–14.37), 28 meter readers (12.04 per 100 employee-years, 95% CI 11.77–
12.31), and line workers (10.37 per 100 29 employee-years, 95% CI 10.19–10.56). Males had an overall
higher injury rate compared to females (2.74 vs. 30 1.61 per 100 employee-years) although some occupations,
such as meter reader, had higher injury rates for 31 females. For all workers, injury rates were highest for
those in the 21 to 30 age group (3.70 per 100 employee- 32 years) and decreased with age. Welders and
machinists did not follow this trend and had higher injury rates 33 in the 65+ age group. There were 63
fatalities over the 1995 to 2013 period, with 21 fatalities (33.3%) occurring 34 among line workers.
Conclusions: Although injury rates have decreased over time, certain high-risk groups 35 remain (i.e., line
workers, mechanics, young males, older welders and machinists, and female meter readers). 36 Practical
applications: Protective measures and targeted safety programs may be warranted to ensure their safety 37
in the workplace. 38 © 2016 Published
by Elsevier Ltd. 39
51 1. Introduction
52 Workplace injuries and illnesses in the United States have
declined
53 over the past decade, but limited data on injury trends within the
54 electric power industry are available. Although the U.S. Bureau
of
55 Labor Statistics (BLS) provides injury estimates for the
utilities sectors,
56 reporting an overall injury rate of 1.8 cases per 100 employee-
years
57 for 2013, this estimate is averaged over several diverse sub-
industries
58 including electric power generation, transmission and
distribution,

Safety, health and environment 8
59 natural gas distribution, and water sewage systems, which are
likely
60 to have differing occupational hazards and associated risks (U.S.
61 Bureau of Labor Statistics, 2013). Further, little is known about
specific
62 risk factors and vulnerable sub-populations that may have
particularly
63 high injury rates within the electric power industry.
Corresponding author.
E-mail address: [e-mail] (T. Fordyce).
http://dx.doi.org/10.1016/j.jsr.2016.11.001
0022-4375/© 2016 Published by Elsevier Ltd.
The current analysis uses data gathered by the Electric Power 64
Research Institute (EPRI) Occupational Health and Safety Database
65 (OHSD) and is intended to update and expand upon an earlier
publica- 66 tion characterizing injuries in the electric power industry
(Kelsh et al., 67 2004). The OHSD program has been described
previously (EPRI, 2012, 68 2015; Kelsh et al., 2004; Yager, Kelsh,
Zhao, & Mrad, 2001). Briefly, the 69 OHSD was created in 1999 to
provide more detailed information 70 about the occurrence of
workplace injury among workers in the electric 71 power industry
(EPRI, 2001, 2004; Kelsh et al., 2004; Yager et al., 2001). 72 Its main
objectives are to: (a) monitor trends of injury and illness over 73 time,
across job characteristics, and worker demographics; (b) identify 74
high-risk occupations and work environments; (c) quantify costs and
75 lost time caused by work-related injuries and illnesses; (d) identify
76 and prioritize injury/illness issues that merit focused research
efforts; 77
and (e) evaluate the effectiveness of prevention programs. 78
Workers in the electric power industry face many potential risks of 79
injury, including injuries from hazardous and unpredictable work
envi- 80 ronments, physically demanding maintenance and repair
activities, 81
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research
(2016), http:// dx.doi.org/10.1016/j.jsr.2016.11.001

Safety, health and environment 9
2 V. Volberg et al. / Journal of Safety Research xxx (2016) xxx–xxx
82 working long shifts, working in emergency situations, and driving. The
83 initial report using EPRI OHSD data was based on 528,133 employee-
84 years and 11,166 injuries over the 1995 to 2002 period and identified
85 welders, meter readers, and line workers at highest risk of injury
86 (Kelsh et al., 2004). Subsequent publications using OHSD data charac-
87 terized risks, risk factors, and costs associated with thermal burns and
88 neck injuries and factors distinguishing severity of sprain and strain
89 injuries among electric utility workers (Fordyce, Kelsh, Lu, Sahl, &
90 Yager, 2007; Fordyce, Morimoto, Coalson, Kelsh, & Mezei, 2010; Kelsh
91 et al., 2009).
92 The goals of the current analyses were to characterize injury and
93 illness rates using the current OHSD data, which includes a total of
94 1,977,436 employee-years and 63,193 recordable injuries over the
95 1995 to 2013 time-period. We examined injury rates over time and by
96 age, sex, and occupation, to determine risk factors for injury and identify
97 vulnerable sub-populations with high injury rates.
98 2. Methods
99 Definitions, classification methodology, and data standardization
100 methodology used in the OHSD have been previously described in detail
101 (EPRI, 2012, 2015; Kelsh et al., 2004; Yager et al., 2001). In brief, the
102 OHSD currently includes data from 18 companies, comprising a total
103 of 1,977,436 employee-years of follow up and 63,193 reportable indi-
104 vidual injuries. Participation in the EPRI OHSD program is voluntary.
105 Both small and large companies are present in the database with
106 the five largest companies comprising over 60% of all workers. Three
107 categories of data, including personnel files, reportable injury files, and
108 medical claim files were requested from each participating electric
109 power company and compiled to generate the EPRI OHSD data set.
110 Employee date of birth, sex, hire date, job code, job title, and work loca-
111 tion or business unit were abstracted from company personnel files for
112 each of the study years 1995 to 2013 and each employee was assigned a
113 unique identifier. Occupation and work location were defined by the
114 employee’s record status on January 1 of any particular year and entered
115 into the database.
116 Basic work history and demographic data for all company em-
117 ployees and not just injured employees were used to calculate injury
118 rates. In addition to personnel data, injury event information (location,
119 accident description, injury mechanism), data about the injury itself
120 (body region, nature of injury), and claims information (work days
121 lost, medical costs) were requested and incorporated into the database.
122 Location refers to a worker’s primarily work location and may or may
123 not represent where an injury took place. A standardized coding system
124 for injury mechanism was developed using a combination of injury
125 source codes (e.g., vehicle collision, fall, “struck by”) and data contained
126 in accident descriptions. The mechanism of injury classification charac-
127 terizes the event leading to the worker’s injury and usually represents
128 the immediate or preceding cause based on temporality; however, the
129 mechanism of injury may or may not represent the underlying or
130 preventable cause. Data for nature of injury and body region injured
131 were coded and classified into a standard common format based pri-
132 marily on Bureau of Labor Statistics guidelines (EPRI, 2001). The OHSD
133 contains 26 categories for nature of injury (e.g., sprains and strains, frac-
134 tures and dislocations, heat and thermal burns) and 15 categories for
135 body region injured (e.g., back and trunk, hand and finger). From over
136 35,000 unique reported job titles, we created 22 specific job categories
137 using an occupational classification system previously developed for
138 electric power industry workers (Kelsh, Kheifets, & Smith, 2000).
139 Unclassifiable primary work location codes and missing nature of injury
140 and injured body region information were updated based on a thorough
141 review of the narrative accident description when the relevant informa-
142 tion was provided.
143 All reported lost time and “recordable” injury/illness claims have
144 been included in the injury analyses. The Occupational Safety and
145 Health Administration (OSHA) definition of a “lost time injury or illness”
requires that a worker miss one full day of work (or shift) after the 146 injury date.
An OSHA recordable injury involves medical attention 147 “beyond first aid” or
loss of consciousness or results in days away 148 from work, restricted work
activity, or job transfer. Because some 149
utilities could not provide reports on less severe, first-aid-only, or 150
non-injury events, the EPRI OHSD database excludes such data. 151
To ensure data confidentiality, the OHSD program policy restricts 152 use of
the data to peer-reviewed health and safety research proposals 153 only and does
not distribute personnel and individual records. In 154 addition, all personal
identifiers were removed from data records and 155 the name of each participating
company was replaced with generic 156
identifiers. 157
3. Statistical analyses 158
Injury rates are expressed as the number of injuries and illnesses per 159
100 employees during a year of follow-up. The rate per 100 employee- 160 years
is equivalent to that used for OSHA reporting purposes, which 161 estimates rates
per 200,000 work hours (OSHA 300 rate). Although 162 injury rates estimate the
relative occurrence and risk of injury, they do 163 not directly reflect the severity
of an injury. Time lost from work, mea- 164 sured by full time equivalents (FTEs),
can be used as a proxy to examine 165 injury severity. FTEs lost was defined as
the total number of days lost 166 divided by 240 workdays which assumes an
average of four weeks off 167 per year for workers (Kelsh et al., 2004). For
recordable injuries where 168 no lost time was reported, 0.002 FTEs lost, which is
equivalent to one 169 half day lost, was assigned to represent an approximate
midpoint of 170 the potential time away from work. Fatality rates are expressed
per 171
100,000 employee-years. 172
To date, six companies have provided data for the entire 19-year 173 period.
Six additional companies have provided data for the majority 174 of the past 10
years. One company (Company N) provided only total 175 employee data for the
1995 to 1999 period and did not report demo- 176 graphic or job description data.
Thus, data for company N for this period 177 are excluded from rate calculations,
with the exception of overall OHSD 178
injury rates. 179
Given the deviance criteria (degrees of freedom ratio close to one) 180
and the dispersion estimate criteria (over-dispersion parameter equal 181
to zero), the calculation of confidence intervals assumes an underlying 182
Poisson distribution. Upper and lower 95% confidence limits were esti- 183
mated using the methods described by Fleiss (Fleiss, 1981). To investi- 184
gate injury trends over time, a Poisson regression model was fit to the 185 data,
adjusting for the observation time per year. For trends in FTE loss 186 rates over
time, a negative binomial regression model fit the data best 187 based on deviance
and dispersion estimate criteria. To address the 188 sex-specific differences in
injury rates between occupations, we per- 189 formed an age-adjusted Mantel–
Haenszel analysis to estimate injury- 190 rate ratios by occupation (Fleiss, 1981).
For the three occupations with 191 the highest injury rates, mechanisms of injury
and body regions of 192 injury were analyzed. Additionally, an analysis of injury
by seasons 193 was performed. We defined winter as December through February,
194 spring as March through May, summer as June through August, and 195
fall as September through November. 196
4. Results 197
The majority of electric power industry workers were male (73.4%), 198
providing a total of 1,451,143 employee-years of observation (Table 1). 199
Female workers accounted for 22.9% of the workforce and 452,260 200
employee-years. Sex was not reported for 3.7% of the study population. 201 The
majority of workers were between 41 and 60 years of age (58.9%), 202 with
31.1% of the workforce 40 years or younger and only 5.4% 203
61 years or older. 204
The most common injury type was sprains and strains, accounting 205 for
40.9% of all injuries (Table 2). Sprains and strains were the primary 206
contributor to reported medical costs at 43.7%. Although representing 207
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research (2016), http://
dx.doi.org/10.1016/j.jsr.2016.11.001

Safety, health and environment 10
V. Volberg et al. / Journal of Safety Research xxx (2016) xxx–xxx 3
t1:1 Table 1
t1:2 Distribution of sex and age, EPRI OHSD 1995–2013.
t1:3 Employee-years Percentage of OHSD
t1:4 Sex
t1:5 Female 425,260 22.9
t1:6 Male 1,451,143 73.4
t1:7 Unknown 74,033 3.7
t1:8 Age group (years)t1:9
t1:10 b20 14,944 0.8
t1:11 21–30 202,767 10.3
t1:12 31–40 396,159 20.0
t1:13 41–50 621,654 31.4
t1:14 51–60 544,667 27.5
t1:15 61–65 75, 898 3.8
t1:16 65+ 31,571 1.6
t1:17 Unknown 89,776 4.5
t2:1 Table 2
t2:2 Distribution of injuries and medical costs, EPRI OHSD 1995–2013.
t2:3 Injury type Percentage of Injuries Percent of Medical Costs
t2:4 Sprains, strains 40.9% 43.7%
t2:5 Cut, laceration, puncture 16.0% 4.2%
t2:6 Contusion, bruise 9.0% 4.2%
t2:7 Scratches, abrasions 5.7% 0.7%
t2:8 Fracture/dislocation 5.5% 12.3%
t2:9 CTD/RSI 4.7% 10.9%
t2:10 Hearing loss or impairment 3.0% 0.9%
t2:11 Bite 3.0% 0.2%
t2:12 Respiratory 2.2% 0.4%
t2:13 Dermatitis/skin 1.4% 0.2%
t2:14 Burn heat/thermal 1.3% 2.9%
t2:15 Burn, flashburn 0.7% 8.4%
t2:16 Electric shock, electrocution 0.6% 2.4%
t2:17 CTD/RSI Carpal Tunnel Disorder/Repetitive Stress Injury.
208 only 2.0% of injuries, burns had the highest cost per incident, accounting
209 for 11.3% of total medical costs.
210 Commonly affected body regions were back and trunk (17.8%); hand
211 and finger (14.3%); head (excluding eyes, 9.9%); upper extremities,
212 including arm, forearm and elbow (8.3%); neck and shoulder (8.2%);
213 and knees (8.0%) (Fig. 1). There was a statistically significant increase
214 in the proportion of injuries to the head, excluding eyes, with increasing
215 age (pb0.01). The unadjusted distributions of injuries were similar
216 between males and females, with a few notable exceptions. Injuries to
217 the wrist and upper extremities were more frequent among female
workers (14.1% and 12.2% vs. 3.3% and 8.1%, respectively), while injuries 218 to
the back and trunk (17.5% vs. 12.6%), head (11.1% vs. 4.6%), and eyes 219
(5.0% vs. 1.8%) were more frequent among male workers. 220
5. Overall injury rates and FTEs lost 221
The overall injury rate over the 1995 to 2013 period was 3.20 per 222
100 employee-years (95% CI 3.17–3.22) (Fig. 2). Annual injury rates 223 steadily
decreased from 1995 to 2000, increased sharply in 2001, and 224 subsequently
steadily decreased to their lowest rate of 1.31 injuries 225 per 100 employee-years
in 2013. For 2013, the current data-reporting 226 year, injury rates (1.31 per 100
employee-years, 95% CI 1.22–1.40) 227 were significantly lower than the peak
injury rates from 1995 (4.70 228 per 100 employee-years, 95% CI 4.57–4.82).
They were also significantly 229 lower compared to the 2012 injury rate of 2.06
per 100 employee-years 230 (95% CI 1.97–2.16). For the entire 19-year study
period, the annual 231
injury rate declined by an average 5% per year (p b 0.01). 232
Annual total FTEs lost have shown substantial variability and no con- 233
sistent trend over the 19-year reporting period (p = 0.11). However, 234 there was
a steady decline from the peak in 2003 of 28.19 FTEs per 235 10,000 employee-
years to the current, 2013, rate of 6.83 FTEs lost per 236 10,000 employee-years
(p b 0.001); the lowest in the history of the 237
OHSD (data not shown). 238
6. Injury rates and FTEs lost by job classification 239
Occupations with the highest injury rates were welders (13.56 per 240 100
employee-years, 95% CI 12.74–14.37), meter readers (12.04 per 241 100
employee-years, 95% CI 11.77–12.31), and line workers (10.37 per 242
100 employee-years, 95% CI 10.19–10.56) (Fig. 3). Line workers 243 (19.5%),
mechanics (12.8%), and meter readers (12.3%) accounted for 244 the highest
proportions of injuries among all of the occupational groups 245 and were among
the highest FTEs lost (61.20, 25.42, and 57.08 per 246 10,000 employee-years,
respectively). Although welders made up a 247 relatively small proportion of the
workforce (b1% of total employee- 248 years), of the injuries that had occurred
(1.7%), they had the highest 249
observed injury rate and the fifth highest FTE loss rate (25.42 per 250 10,000
employee-years). Occupations with the lowest injury rates 251 were engineers
(0.65 per 100 employee-years, 95% CI 0.60–0.69) and 252
managers (0.42 per 100 employee-years, 95% CI 0.38–0.45). 253
7. Injury rates and FTEs lost by sex 254
Over the 1995 to 2013 period, males had higher injury rates (2.74 255 per 100
employee-years, 95% CI 2.71–2.76 vs. 1.61 per 100 employee- 256
Fig. 1. Distribution of injuries by injured body region, EPRI OHSD 1995–2013.
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research (2016), http://
dx.doi.org/10.1016/j.jsr.2016.11.001

Safety, health and environment 11
4 V. Volberg et al. / Journal of Safety Research xxx (2016) xxx–xxx
Fig. 2. Injury rate per 100 employee-years by year, EPRI OHSD 1995–2013.
257 years, 95% CI 1.57–1.65) and more FTEs lost (13.86 per 10,000
258 employee-years, 95% CI 13.25–14.46 vs. 10.63 per 10,000 employee-
259 years, 95% CI 9.67–11.57) compared to females. Several occupations
260 had higher rates of injury among females compared to males; these
261 occupational groups included line workers (11.36 per 100 employee-
262 years, 95% CI 9.14–13.48 vs. 8.66 per 100 employee-years, 95% CI
263 8.49–8.83), meter readers (14.10 per 100 employee-years, 95% CI
264 13.25–14.93 vs. 9.12 per 100 employee-years, 95% CI 8.87–9.38), and
265 plant and equipment operators (3.31 per 100 employee-years, 95% CI
266 2.90–3.72 vs. 2.48 per 100 employee-years. 95% CI 2.40–2.57). An
267 age-adjusted Mantel–Haenzel analysis by occupation indicated that
268 females have higher injury rates than males for three non-office
269 related occupations: meter readers, security, and plant and equipment
270 operators (Fig. 4). Custodians and cooks had slightly higher rates,
271 which were not statistically significant.
272 8. Injury rates and FTEs lost by age
273 For all workers, injury rates were highest among those aged 21 to
274 30 years, at 3.70 per 100 employee-years (95% CI 3.62–3.79) (Fig. 5).
275 Workers in the 41 to 50 and 51 to 60 age groups made up the majority
276 of the worker population (61.8%) and had injury rates of 3.19 per
100 years (95% CI 3.14–3.23) and 2.54 per 100 employee-years (95% 277 CI
2.50–2.58), respectively. Injuries in these age groups accounted for 278 the most
total FTEs lost, 872.3 and 896.9, respectively. Injury rates 279 for trade
occupations tended to decrease with age and were lowest in 280 those aged 65 or
older (0.94 per 100 employee-years, 95% CI 0.84– 281 1.05). Welders did not
follow this trend and had higher injury rates 282 among the youngest population
and oldest population (71.43 per 100 283 employee years and 50.00 per 100
employee years, respectively). How- 284 ever, welders less than 20 and welders
older than 65 combined repre- 285 sented less than 1% of the total employee-years
for that occupation. 286 The majority of injuries to welders over 65 were due to
falls on the 287 same level (66.7%) with hands/fingers being most commonly
injured. 288 The majority of injuries to welders under 20 were indicated as struck
289
by (60.0%), with injuries to the head and eyes. Injury rates across most 290
other occupational age groups, including office-based staff, were rela- 291
tively constant across workers aged 31–60 years. 292
9. Additional analysis of welders, meter readers, and line workers 293
Welders, meter readers, and line workers had the highest injury 294 rates of all
occupations in the OHSD. For meter readers and line workers, 295 over half of all
injuries were classified as sprains and strains or cuts, 296
Fig. 3. Injury rate per 100 employee-years by job classification, EPRI OHSD 1995–2013.
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research (2016), http://
dx.doi.org/10.1016/j.jsr.2016.11.001

Safety, health and environment 12
V. Volberg et al. / Journal of Safety Research xxx (2016) xxx–xxx 5
Fig. 4. Female to male injury rate ratios controlled for age and 95% confidence intervals by job classification, EPRI OHSD 1995–2013.
297 lacerations, or punctures. For welders, over half of all injuries were clas-
298 sified as sprains and strains; scratches, abrasions; or cuts, lacerations
299 or punctures. Amongst welders, the three most common mechanisms
300 of injury, struck by (30.9%); overexertion, body motion (18.4%); and
301 contact with temperature extremes (7.1%), accounted for over half of
302 all injuries. The majority of struck by injuries had body region listed as
303 eyes (60.3%), followed by hand/finger (10.1%). For overexertion, body
304 motion, back/trunk (46.3%), hand/finger (10.7%), and neck/shoulder
305 (10.7%) were the most common body regions injured. Upper extremi-
306 ties including the arm, forearm, and elbow comprised a quarter
307 (25.0%) of all contact with temperature extreme injuries and hands/
308 fingers represented 19.1%. The top three mechanisms of injury for line
309 workers, overexertion, body motion (39.4%), struck by (12.2%), and
310 fall on the same level (12.1%), account for over 60% of all injury. The
most prevalent body regions injured for overexertion, body motion 311 injuries
were back/trunk (36.3%), neck/shoulder (14.6%), and knees 312 (10.2%). For
struck by injuries, head excluding eyes (25.7%), hand/finger 313 (15.2%), eyes
(10.9%), and feet/toes (10.0%) were the most common 314 body regions affected.
For a fall on the same level the knees (21.9%), 315 ankle (17.9%), or back/truck
(17.0%) were most common. Amongst 316 meter readers, the top three
mechanisms of injury, animal or insect 317 bite (30.1%), overexertion, body
motion (23.4%), and fall on the same 318 level (19.0%), accounted for over 70%
of all causes of injury. Animal or 319 insect bite injuries were most common to
other lower extremities 320 (34.2%) and hand/finger (20.4%). Overexertion, body
motion injuries 321 were most frequently to the back/trunk (25.2%), feet/toe
(15.7%), and 322
knees (14.7%). For falls on same level ankles (24.4%), knees (17.6%), 323
back/trunk (15.9%) were the most common body regions injured. 324
Fig. 5. Distribution and injury rate per 100 employee-years by age group, EPRI OHSD 1995–2013.
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research (2016), http://
dx.doi.org/10.1016/j.jsr.2016.11.001

Safety, health and environment 13
6 V. Volberg et al. / Journal of Safety Research xxx (2016) xxx–xxx
325 10. Seasonal analysis
326 Of all injuries in the OHSD, 88.0% provided the data necessary to
327 determine season of injury. There was little change in injury by season
328 with only a slightly higher proportion of injury in summer (27.2%)
329 and a slight reduction in injury seen in the winter months (22.8%).
330 This pattern did not differ between sexes. Workers under 60 years of
331 age had the lowest proportion of injury in the winter months (22.5%),
332 while workers 61 and older had the lowest proportion of injury in the
333 fall (19.0%). Summer had the highest frequency of injury for those
334 50 years and younger (27.7%). For workers ages 51 to 64, spring had
335 the highest proportion of injury (27.1%). Workers 65 and older experi-
336 enced the highest proportion of injury in winter (39.6%). Of these win-
337 ter injuries, 37.0% were indicated as due to falls on the same level. Of the
338 three highest risk occupations, meter readers and line workers followed
339 the same pattern as the overall cohort with the highest proportion of
340 injury in summer and lowest in winter. Welders, on the other hand,
341 experienced the highest proportion of injury in the spring (28.7%).
342 11. Fatalities
343 The fatality rate was 3.18 per 100,000 employee-years for the entire
344 19-year study period (95% CI 2.37–3.95). Of the 63 total fatalities, line
345 workers (21 deaths, 33.3%), maintenance workers (8 deaths, 12.7%),
346 and mechanics (4 deaths, 6.3%) accounted for the highest proportion
347 of deaths. The highest fatality rate was for line workers (18.03 per
348 100,000 employee-years, 95% CI 11.16–27.56), which was significantly
349 higher compared to most other occupational groups. Most of the fatali-
350 ties involved male workers (n = 52, 82.5%), two involved female
351 workers (3.2%), and a total of nine had an unknown sex. The most com-
352 mon sources of fatality were motor-vehicle collisions (n = 16, 25.4%)
353 and contact with an electric current (n = 16, 25.4%).
354 12. Discussion
355 Data from OHSD indicate that injury rates among electric power
356 industry workers have tended to decrease over the 1995 to 2013 period.
357 Although there was an increase in total injury rate in 2001, this was
358 likely due at least in part to a change in OSHA reporting requirements
359 that was announced in January 2001 (Occupational Safety and Health
360 Administration, 2001). Injury rates for the current reporting period
361 (2013) are similarly low when compared to the 2013 BLS injury rate
362 for the entire utilities sector (U.S. Bureau of Labor Statistics, 2013).
363 Although injury rates have decreased over time, certain high-risk
364 groups remain. Injury rates varied more than 30-fold across occupational
365 groups examined, with the highest risk of injury among workers in the
366 craft/trade occupations and the lowest injury risk among office-based
367 staff. Line workers and meter readers tended to have the highest FTE
368 loss rates, potentially reflecting the severity of the injuries occurring
369 among these workers, or the mobile nature of the work. Other high-
370 risk groups included male workers overall, younger workers (aged 21–
371 30 years), and older welders.
372 In comparing these results to an earlier analysis of OHSD data span-
373 ning the 1995 to 2002 period, many of these trends and high risk popu-
374 lations have persisted (Kelsh et al., 2004). Male workers, meter readers,
375 welders, and line workers have continued to have higher risk of injury,
376 although the rank order between meter readers and welders has
377 switched. Sprains and strains continue to be a common injury type
378 amongst these occupations, indicating the continued need for develop-
379 ment and implementation of prevention programs accounting for
380 the majority of medical costs. For these occupations, the back and
381 truck was the primary body region associated with overexertion, body
382 motion injuries. An analysis of occupation risk factors and back injury
383 determined that weight lifted per hour, trunk twists per hour, weight
384 lifted per day, frequency of lift, trunk motions per hour, and trunk flex-
385 ions per hour were significantly associated with occurrence of back
injury among laborers performing manual material handling tasks 386 (Craig et
al., 2013). Although narrative descriptions of injury are incon- 387 sistently
present and/or complete in the EPRI OHSD, future analyses of 388 this field may
provide further information on tasks being performed 389
when injured or equipment being used. 390
Welders were most likely to have a “stuck by” mechanism of injury 391 with
the eyes as the body region. Lombardi reported that almost 72% of 392 struck by
injuries to the eyes among welders were due to airborne 393 objects (Lombardi et
al., 2005). OSHA reports that helmet protection 394 alone is not sufficient to
prevent eye injuries to welders and that proper 395 eye glasses are a warranted
prevention measure suggesting develop- 396 ment and implementation of an eye
protection program (Braun, 397 2007). Over a third of all stuck by injuries among
line workers were to 398 the head and eyes, indicating that similar head and facial
protective 399 wear policies could reduce the occurrence of injuries. In the present
400 analysis, meter readers, line workers and mechanics had high propor- 401
tions of FTEs lost. A recent study of this population assessing injury 402 severity
reported that meter readers had the highest severe injury rate 403 (2.26 per 100
employee-years), followed by line workers (1.99 per 404 100 employee-years) and
mechanics (1.17 per 100 employee-years) 405
(Fordyce et al., 2016). 406
Welders in the youngest and oldest age groups had elevated injury 407 rates
and may represent vulnerable sub-populations. Welders 20 and 408 under were
most likely to be struck by an object to the head or face, 409 indicating that
increased emphasis on helmet and safety glasses use 410 may benefit this
population. On the other hand, welders over 65 were 411 primarily injured by falls
on the same level, an injury mechanism 412 which represents a small proportion
of total injury among all welders. 413 Thus, prevention strategies may need to be
specifically tailored for this 414 population. Decreased mobility among older
welders may contribute 415
to the increased risk of falls and further exploration of factors leading 416
to fall injury in this population is warranted. However, prevention strat- 417
egies targeted primarily to welders in these age groups are not likely to 418
lead to considerable reductions in injuries amongst all welders since 419
these age groups represent less than 1% of all welder employee-years. 420
Data on fatalities in the utility sector remain sparse. The fatality rate 421
of 3.18 per 100,000 employee-years estimated in the present analysis is 422
substantially smaller than that of results from a study using death certif- 423
icate data, which reported a fatality rate of 13.2 per 100,000 employee- 424
years (Loomis, Dufort, Kleckner, & Savitz, 1999). Follow-up in that 425
cohort covered 1950 to 1986, however, and likely reflects higher 426 mortality risk
compared to the 1995 to 2013 period of EPRI OHSD mon- 427 itoring. As in the
previous analysis using 1995 to 2002 OHSD data, line 428
workers had the highest risk of death compared to all other occupations, 429
accounting for 15 of the 53 deaths after 2002, indicating that increased 430
safety measures may be justified. 431
When stratified by sex, injury rates in certain trade occupations (line 432
workers, meter readers, and plant and equipment operators) were 433 higher for
females compared to males. Among non-office type occupa- 434 tions, after
controlling for age, only the injury risk ratio comparing 435 female to male meter
readers remained significant. Reasons for this 436 sex-dependent difference in
injuries among meter readers are unclear; 437 ergonomic issues and/or training
(formal or informal) may be contrib- 438 uting factors and further research is
required. Injuries to the wrist and 439 upper extremities were more prevalent
among females than males. 440 This is partially explained by the association
between CTD/RSI and 441 office-related job types which are more common
amongst women. In 442 comparison to the analysis of OHSD data from 1995 to
2002, some of 443 the present female to male injury risk ratios have undergone
drastic 444 shifts (Kelsh et al., 2004). For example, representatives shifted from an
445 odds ratio less than one, to an odds ratio of 1.4. These changes may be 446
due to the shifting gender composition of certain job titles over time, 447
indicating that periodic updating 448
Analysis of injuries by season indicated that, overall, there is little 449
variation in the proportion of injury due to seasonal factors. Reductions 450
in the proportion of injury during winter may be due to fewer days 451
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research (2016), http://
dx.doi.org/10.1016/j.jsr.2016.11.001

Safety, health and environment 14
V. Volberg et al. / Journal of Safety Research xxx (2016) xxx–xxx 7
452 worked due to holidays. However, in an analysis of occupational injury
453 rates by seasons, the Bureau of Labor and Statistics reports that lower
454 end of year injury rates may be due to difficulty in identifying the injury,
455 or difficulty identify the injury as work-related, or inability to identify in
456 time for reporting (Brooks, 2013). In our analysis of seasonality by age
457 group, workers over 65 experienced the highest proportion of injury
458 during winter months, the majority of which were due to falls. Raising
459 awareness of increased fall risk, primarily amongst older workers,
460 during winter months and determining contributing factors leading
461 to these falls may reduce this increased risk of injury during winter
462 among older workers.
463 Several studies of aging construction worker populations showed
464 that older workers typically experience fewer injuries and accidents,
465 although when they do occur, injuries are more severe (Farrow &
466 Reynolds, 2012; Jones, Latreille, Sloane, & Staneva, 2013; Schwatka,
467 Butler, & Rosecrance, 2012). Similar to the present findings, Lipscomb
468 et al. (2003) reported that carpenters 45 and older were more likely
469 to experience falls on the same level compared to workers 30 years
470 and younger (Lipscomb, Li, & Dement, 2003). A study of male railway
471 workers indicated that older employees ages 50 to 55 had a higher
472 risk of fall, injury resulting from handling equipment, and injuries
473 from a collision with moving objects (Chau et al., 2010). Given that
474 the relative proportion of older workers in the labor force has been in-
475 creasing in the United States, it is important to further characterize
476 risk factors for injury among older workers, especially in this under
477 studied electric power worker population, to guide creation of targeted
478 safety measures (Rogers & Wiatrowski, 2005).
479 Injury rates over all EPRI OHSD companies have declined over
480 the 19-year study period. This corresponds with trends observed by
481 the BLS in nationally collected data on utilities as well as other industries
482 such as manufacturing and retail trades (U.S. Bureau of Labor Statistics,
483 2014a). The observed decline over time may be due to various aspects of
484 company health and safety programs, potentially including improved
485 safety awareness, increased attention to health and safety by manage-
486 ment, and improved safety program implementation. The decline may
487 be, in part, a result of the increasing use of contractors (historically
488 employed for high injury-risk work) whose injury and illness records
489 are not maintained by electric power companies and are thus not in-
490 cluded in the EPRI OHSD database.
491 A limitation of this study is the use of voluntarily reported industry
492 data. The quality, representativeness, and accuracy of these data are
493 dependent on several factors. The OHSD data depends on the quality
494 of information captured and provided by the participating companies
495 to the EPRI research program. Given variations in coding of injury
496 reports and medical claims across different U.S. states and electric
497 power companies, database development and standardization of
498 variables has been dynamic and classification error may have occurred.
499 In addition, there are recognized reporting biases that cause under-
500 reporting of workplace injuries including worker perceptions that an
501 injury is not serious enough to report and/or that their job security
502 will be affected, and an employer perception that the injury was not
503 work-related (Capelli-Schellpfeffer, Floyd, Eastwood, & Liggett, 2000;
504 Floyd et al., 2004). Due to heavy regulation and the unionized nature
505 of electric power companies, however, these types of reporting bias
506 may be less likely to occur (Kelsh et al., 2004; U.S. Bureau of Labor
507 Statistics, 2014b). Missing data in fields such as age or occupation
508 could introduce bias, particularly in our analysis of age adjusted female
509 to male injury rate ratios by occupation. Unknown self-selection factors
510 may affect our study since only 18 of over 200 investor-owned electric
511 utility companies in the United States provide data to the OHSD.
512 Furthermore, events that do not result in an injury to a worker, such
513 as damage to equipment or near-misses, are not reported in the OHSD
514 due to incomplete and less systematic reporting of these events. Despite
515 these limitations, the EPRI OHSD provides a unique resource to examine
516 occupational injury/illness and characterization of severity of injury/
517 illness unique to the electric utility industry.
The EPRI OHSD has several important strengths and advantages over 518
larger nationwide and statewide injury reporting systems (Sorock, 519
Ranney, & Lehto, 1996). The OHSD contains basic work history and 520
demographic data for all employees and not just for those injured, 521
allowing for more precise calculation of injury risks and rates. The 522
detailed information and circumstances for each injury occurrence can 523
be used to better understand relationships between occupation, injury 524
type, injury source, and other factors. Overall, the OHSD data can be 525
used to identify risk factors associated with injury and help characterize 526
high injury-risk sub-populations, providing employers with informa- 527
tion to prioritize health and safety efforts and identify areas where 528
further research or interventions are required. Our results reinforce 529
some of the associations seen in other industries between age, sex, 530
and risk of injury (U.S. Bureau of Labor Statistics, 2014c) and contribute 531
to understanding the etiology of occupational injuries among workers 532
in the electric power industry. 533
Acknowledgement 534
We thank all the participating utility companies and their health and 535
safety staff who assisted in assembling the data for the EPRI OHSD pro- 536
gram. 537
538
Funding source 539
540
This research was funded by the Electric Power Research Institute 541
(EPRI), an independent private and nonprofit center for energy and 542
environmental research, which is supported principally by electric util- 543
ity companies. Although electric utility companies contribute to funding 544
for EPRI, EPRI independently determines avenues for research funding. 545
Data voluntarily provided by participating electric power companies 546
to EPRI was used for this analysis. Primary development of study design, 547
analysis completion, interpretation of data, and manuscript writing 548
were undertaken by Exponent employees. 549
References 550
Braun, T. (2007). Occupational health & safety. Preventing eye injuries when welding. 551
[cited 2016 July 1]; Available from: https://ohsonline.com/Articles/2007/02/ 552
Preventing-Eye-Injuries-When-Welding.aspx 553
Brooks, P. (2013). The seasonal timing of work-related injuries. Joint statistical meetings 554Q5
(Montreal, Quebec, Canada). 555
Capelli-Schellpfeffer, M., Floyd, H. L., Eastwood, K., & Liggett, D. P. (2000). How we can 556
better learn from electrical accidents. IEEE Industry Applications Magazine, 5, 16–23. 557
Chau, N., Wild, P., Dehaene, D., Benamghar, L., Mur, J. M., & Touron, C. (2010). Roles of age, 558
length of service and job in work-related injury: a prospective study of 446 120 559
person-years in railway workers. Occupational and Environmental Medicine, 67(3), 560
147–153. 561
Craig, B. N., Congleton, J. J., Beier, E., Kerk, C. J., Amendola, A. A., & Gaines, W. G. (2013). 562
Occupational risk factors and back injury. International Journal of Occupational Safety 563
and Ergonomics, 19(3), 335–345. 564
EPRI (2001). Injury and illness among the electric energy workforce, 1995–2000. Palo Alto, 565
CA: Electric Power Research Institute. 566
EPRI (2004). Electric energy industry workforce—Occupational health and safety trends 2004. 567
Palo Alto, CA: Electric Power Research Institute. 568
EPRI (2012). Occupational health and safety annual report, 2012: Occupational health and 569
safety trends among electric energy workers, 1995–2011. Palo Alto, CA: Electric Power 570
Research Institute. 571
EPRI (2015). EPRI Occupational Health and Safety Annual Report, 2014 Injury and Illness 572
Among the Electric Energy Workforce, 1995–2013. Palo Alto, CA: Electric Power 573
Research Institute. 574
Farrow, A., & Reynolds, F. (2012). Health and safety of the older worker. Occupational 575
Medicine (London), 62(1), 4–11. 576
Fleiss, J. L. (1981). Statistical methods for rates and proportions. New York: John Wiley & Sons. 577
Floyd, H. L., II, Andrews, J. J., Capelli-Schellpfeffer, M., Neal, T. E., Liggett, D. P., & Saunders, 578
L. F. (2004). Safeguarding the electric workplace. IEEE Industry Applications Magazine, 579
10, 18–24. 580
Fordyce, T. A., Kelsh, M., Lu, E. T., Sahl, J. D., & Yager, J. W. (2007). Thermal burn and elec- 581
trical injuries among electric utility workers, 1995–2004. Burns, 33(2), 209–220. 582
Fordyce, T. A., Leonhard, M. J., Watson, H. N., Mezei, G., Vergara, X. P., & Krishen, L. (2016). 583
An analysis of fatal and non-fatal injuries and injury severity factors among electric 584
power industry workers. American Journal of Industrial Medicine. 585
Fordyce, T. A., Morimoto, L., Coalson, J., Kelsh, M. A., & Mezei, G. (2010). Neck injuries among 586
electric utility workers, 1995–2007. Journal of Occupational and Environmental Medicine, 587
52(4), 441–449. 588
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research (2016), http://
dx.doi.org/10.1016/j.jsr.2016.11.001

Safety, health and environment 15
8 V. Volberg et al. / Journal of Safety Research xxx (2016) xxx–xxx
589 Jones, M. K., Latreille, P. L., Sloane, P. J., & Staneva, A. V. (2013). Work-related health risks
590 in Europe: Are older workers more vulnerable? Social Science & Medicine, 88, 18–29.
591 Kelsh, M. A., Fordyce, T. A., Lau, E. C., Mink, P. J., Morimoto, L. M., Lu, E. T., & Yager, J. W.
592 (2009). Factors that distinguish serious versus less severe strain and sprain injuries:
593 an analysis of electric utility workers. American Journal of Industrial Medicine, 52(3),
594 210–220.
595 Kelsh, M. A., Kheifets, L., & Smith, R. (2000). The impact of work environment, utility, and
596 sampling design on occupational magnetic field exposure summaries. AIHAJ, 61(2),
597 174–182.
598 Kelsh, M. A., Lu, E. T., Ramachandran, K., Jesser, C., Fordyce, T. A., & Yager, J. W. (2004).
599 Occupational injury surveillance among electric utility employees. Journal of
600 Occupational and Environmental Medicine, 46(9), 974–984.
601 Lipscomb, H. J., Li, L., & Dement, J. M. (2003). Falls among union carpenters. American
602 Journal of Industrial Medicine, 44(2), 148–156.
603 Lombardi, D. A., Pannala, R., Sorock, G. S., Wellman, H., Courtney, T. K., Verma, S., & Smith,
604 G. S. (2005). Welding related occupational eye injuries: A narrative analysis. Injury
605 Prevention, 11, 174–179.
606 Loomis, D., Dufort, V., Kleckner, R. C., & Savitz, D. A. (1999). Fatal occupational injuries
607 among electric power company workers. American Journal of Industrial Medicine,
608 35(3), 302–309.
609 Occupational Safety and Health Administration (2001). OSHA Revises Recordkeeping
610 Regulations. [cited 2016 July 1]; Available from: https://www.osha.gov/pls/
611 oshaweb/owadisp.show_document?p_table=NEWS_RELEASES&p_id=200
612 Rogers, E., & Wiatrowski, W. (2005, October). Injuries, illnesses, and fatalities among older
613 workers. Bureau of Labor and Statistics, Occupational Safety and Health, Monthly
614 Labor Review, 24–30.
615 Schwatka, N. V., Butler, L. M., & Rosecrance, J. R. (2012). An aging workforce and injury in
616 the construction industry. Epidemiologic Reviews, 34, 156–167.
617 Sorock, G. S., Ranney, T. A., & Lehto, M. R. (1996). Motor vehicle crashes in roadway
618 construction workzones: An analysis using narrative text from insurance claims.
619 Accident Analysis and Prevention, 28(1), 131–138.
620 U.S. Bureau of Labor Statistics (2013). Incidence rate and number of nonfatal occupational
621 injuries by industry and ownership. [cited 2016 July 1]; Available from: http://www.
622 bls.gov/iif/oshwc/osh/os/ostb3966.pdf
623 U.S. Bureau of Labor Statistics (2014a). Employer-reported workplace injury and illness
624 summary. [cited 2016 July 1]; Available from: http://www.bls.gov/news.release/
625 osh.nr0.htm
626 U.S. Bureau of Labor Statistics (2014b). U.S. Bureau of Labor Statistics. Union Members –
627 2014. [cited 2016 July 1]; Available from: http://www.bls.gov/news.release/pdf/
628 union2.pdf
629 U.S. Bureau of Labor Statistics (2014c). Occupational employment statistics — Power
630 generation, transmission and distribution. [cited 2016 July 1]; Available from:
631 http://www.bls.gov/oes/current/naics4 221100.htm
632 Yager, J. W., Kelsh, M. A., Zhao, K., & Mrad, R. (2001). Development of an occupational
633 illness and injury surveillance database for the electric energy sector. Applied
634 Occupational and Environmental Hygiene, 16(2), 291–294.
Dr. Vitaly Volberg is a Senior Scientist in Exponent's Health Sciences Center for Epidemi- 635
ology, Biostatistics, and Computational Biology. He has 8 years of experience with 636
epidemiological study design, data collection and analysis, and grant and manuscript 637
preparation. His prior work has focused on risk factors for childhood obesity, with 638
emphasis on environmental exposures. 639
640
Dr. Tiffani Fordyce is a Managing Scientist in Exponent's Health Sciences Center for 641
Epidemiology, Biostatistics, and Computational Biology. She has 15 years of experience 642
with research protocols and procedures, epidemiological study design and execution, 643
statistical analyses, database development, data collection and management. Dr. Fordyce 644
has conducted many occupational health and safety studies, including multiple large 645
cohort mortality studies and case-control studies. 646
647
Ms. Megan Leonhard is a Scientist in Exponent's Health Sciences Center for Epidemiology 648
and Computational Biology. Ms. Leonhard has expertise in epidemiological study design, 649
data analysis and management, data analysis program use, and surveillance system use. 650
She also has extensive experience in literature review particularly related to injury, 651
trauma, and rare disease. 652
653
Dr. Gabor Mezei has over 25 years of experience in health research including epidemio- 654
logical studies of both clinical outcomes and environmental and occupational health 655
issues. Previously, at the Electric Power Research Institute, he was responsible for leading 656
a multidisciplinary scientific research program aimed at addressing potential human 657
health effects associated with residential and occupational exposure to power frequency 658
and radiofrequency EMF. 659
660
Ximena Vergara leads the EPRI Electric and Magnetic Fields and Radiofrequency Fields 661
Health Assessment and Safety Program. Dr. Vergara received a Bachelor of Arts in chemis- 662
try from the University of Chicago and a Master of Public Health in environmental health 663
science (industrial hygiene) from the University of California, Los Angeles (UCLA), School 664
of Public Health. In 2012, she completed her Ph.D. in epidemiology at UCLA. Dr. Vergara is a 665
member of the Society for Epidemiologic Research, American Industrial Hygiene Associa- 666
tion and American Public Health Association. Her research focuses on electromagnetic and 667
radiofrequency field exposure assessment and epidemiology, occupational hygiene and 668
injury surveillance. 669
670
Dr. Lovely Krishen is a senior technical leader and program manager of health and safety 671
at the Electric Power Research Institute. She is an experienced R&D manager with a back- 672
ground in technology management, research and development, and training for industry, 673
government, and academia. Her previous research experience has primarily focused on: 674
Systems-level Safety, Reliability, and Quality Assurance, Human Health, Environments 675
and Safety Risk Management for Human Space flight programs at NASA. 676
677
Please cite this article as: Volberg, V., et al., Injuries among electric power industry workers, 1995–2013, Journal of Safety Research (2016), http://
dx.doi.org/10.1016/j.jsr.2016.11.001