Fatalities Cost in Mining Technical Guide
Abstract
To better understand the burden imposed by fatal accidents in the mining
industry, it is necessary to develop measures of the economic component of
loss to complement existing surveillance research efforts. The Fatalities
Cost in Mining web application, developed by the NIOSH Mining Program, uses
an adapted version of a well-known cost-of-injury methodology to estimate
the societal cost of an individual fatality based on key characteristics of
the fatally injured miner. The user can select fatality characteristics
(demographics, incident, employer, etc.) and reporting year (2008–2017
currently available) to generate a report and view the average societal
costs per fatality as well as the total cost for all fatalities matching
the criteria. For example, from 2008 through 2017 there were 404 fatal
accidents with a median cost of $1.42 million per fatality and a total
societal cost of $554.16 million. Researchers, occupational health
professionals, workplace safety organizations, and labor unions have proven
to be willing and avid users of past NIOSH estimates. This Technical Guide
reviews the Fatalities Cost in Mining web app, including its development
and how it can be used to estimate the economic burden of fatal mining
injuries.
Introduction
It is commonly recognized that there are costs involved with fatal injury
to workers. These costs generally address the overall costs to victims,
families, employers, organizations, and to society as a whole. This
wide-ranging national burden imposed by occupational fatalities consists of
numerous areas of personal and public life that can include social costs,
organizational costs, and even intangible personal costs which can include
suffering and grief. It affects personal well-being, family, and
relationships with coworkers, communities, companies, and governments.
This burden also includes an economic component of loss. Surveillance data
is vitally important for understanding the occupational safety and health
of mine workers, and analyzing this data helps in identifying accident
trends over time, the magnitude of occupational injuries, identifying risk
factors, and setting priorities for prevention research. However, there is
a need to better understand the burden levied by fatal occupational
injuries and to continue to develop measures of the economic component of
loss. The work described in this paper adds an economic dimension to
existing mining fatal injury research. The focus is on monetary costs of
fatal occupational injury, which largely consist of lost wages and
benefits, but also includes the direct costs of medical care and the
indirect costs of the decedent’s household production.
This work represents a continuation of previous research by the National
Institute for Occupational Safety and Health (NIOSH) that attempted to
establish the economic consequences of workplace injury (NIOSH, 2009,
2011). It builds on existing research on the occupational injury economic
burden as it focuses on the mining industry and uses specific data from
MSHA datasets to make specific burden estimates for individual fatal
injuries. And, some of the initial results are captivating. Over 2008–2017,
the costs from 404 premature deaths exceeded $554 million, which can
provide motivation to reduce the severe toll of occupational fatalities on
our nation’s workers, institutions, and communities. Researchers and
concerned parties within the occupational and public health professions,
academia, organizations focusing on workplace safety, labor unions, and the
business community have all proven to be willing and avid users of this
data and have used this research to continue their efforts, in concert with
continuing NIOSH research efforts, to reduce the great toll that fatal
injury imposes on our nation, workplaces, and workers (NIOSH, 2011).
Methods
Identifying Fatal Occupational Injuries in Mining
In the United States, the Mine Safety and Health Administration (MSHA)
requires all mines and their independent contractors who perform work on
mine property to report all occupational injuries (not including
first-aid), illnesses, and fatalities as required under the Code of Federal
Regulations, Title 30 Part 50 (Notification, investigation, reports and
records of accidents, injuries, illnesses, employment, and coal production
in mines, 2014). The Accident/Injury/Illness data files and the
Address/Employment data files are released annually to the public on the
MSHA web site. These data are in the public domain and are provided in
statistical analysis software format (SPSS) by the NIOSH Mining Program
(NIOSH, 2018). These are the most comprehensive publicly available mining
injury data available.
For this work, the Accident/Injury/Illness data files for 2008 through 2017
were combined and only fatal injuries were selected. This identified 404
fatal mining injuries for this time period. The mine IDs were matched to
those in the Address/Employment data files, and they were merged into to
one data file in order to get mine characteristic information for these
fatal injuries.
Theoretical Basis of Societal Cost Estimation
The cost to society of a fatal mining injury was approximated using the
cost-of-injury method, which combines direct and indirect costs to
calculate the total lifetime societal cost of a mining fatality. Direct
costs measure the opportunity cost of resources used for medical treatment,
while indirect cost measures the value of resources lost due to the
premature occupational fatal injury (Segel, 2006). Medical expenses were
used to estimate a one-time direct cost of the fatality. The indirect
lifetime cost of a fatal mining injury is determined by using the human
capital approach, which calculates the present value of lost future
compensation (earnings and benefits) and household production. The indirect
cost calculations were built on a model developed by Rice (1965), modified
by Biddle (2004), and published by NIOSH (2009; 2011). Indirect costs are
calculated for each fatality by accounting for: the victim’s probability of
survival to the next year, annual wages at time of death, benefits at time
of death, expected wage growth after death, lost household production, and
the real discount rate. The indirect cost is an annual series of costs
starting at the decedent’s age at death () and ending at a maximum age of
67 (). All costs are initially calculated in dollars of the year of death
( and then adjusted for inflation using the GDP deflator (). This
adjustment allows the costs of multiple fatalities (which may have occurred
in different years) to be reported in a selected common year’s dollars.
The mathematical representation of indirect costs have been previously
described by Biddle (2004) as:
Where:
PVF = present discounted value of loss per individual due to a fatal
occupational injury
(n) = probability that an individual of age y, race q,
and sex s will survive to age n
q = race of the individual (all races used)
s = sex of the individual
n = age if the individual had survived
= median annual compensation of an employed person of sex s,
specific occupation at death j, and age n (includes wages
and benefits by detailed industry and wage growth adjustments)
j = specific occupation of individual at death
= mean annual imputed value of home production (h) of a person of
sex s and age n
g = earnings growth rate attributable to overall productivity
y = age of the individual at death
r = real discount rate (3%)
This is easier to understand if the total cost calculation is broken down
into three parts: the direct cost, the sum of the indirect costs (from age
of death to age 67), and adjusting for inflation.
Direct Cost
The direct cost has one component: the medical expense adjusted by medical
care consumer price index (CPI). Medical expenses were only included in the
first year calculations because over 90% of fatalities occurred within one
day of the accident (MSHA, 2018).
NIOSH-sponsored, preliminary unpublished estimates assessed the average
medical cost of a fatal occupational mining injury in 2013 at approximately
$70,000. Therefore, the medical expense associated with each fatality is
estimated at $70,000 in = 2013 dollars. This is adjusted using the medical
care CPI () to determine what the medical expense would have been if it
had occurred in rather than in .
Medical Expense = $70,000
= average medical care consumer price index for year x (BLS,
2018a)
= year of death
= year of medical expense = 2013 dollars
For this model, .
Indirect Cost
The indirect costs of the fatal mining injuries are derived by calculating
the present value of lost household production and future compensation of
the miner until the decedent would have reached the estimated retirement
age of 67 years, also accounting for the probability of survival. For those
who died at or over 67 years of age, only a single year of indirect cost is
included in the indirect cost calculation.
Employee compensation is broken down into wage value and benefit value, and
therefore, the indirect cost has three components: the wage value, the
benefit value, and the household production value. For each year in which
the indirect cost is calculated (between and ), these three values are
summed and then adjusted for the time value of money using the real
discount rate. This adjustment is required because the indirect cost
represents a future value relative to . Sixty-seven years was selected as
the maximum work age since it is the full retirement age to collect Social
Security (SSA, 2018). Indirect cost calculations can be represented as:
= 67
= Age of death
= age iteration if decedent did not die
The survival probability represents the chance that the decedent would have
survived an additional year ( to ) if he or she had not died at (Arias et
al., 2017). Since MSHA does not report a decedent’s race, the survival
probability for “all” races is used.
Wages
The wage value estimates the annual income of the decedent if he or she had
not died. The model assumes that the decedent had worked full-time in the
same occupation until retirement age. The wage value is estimated from base
earnings, economy-wide productivity growth, and life-cycle wage growth. The
wage value estimate is based on the Bureau of Labor Statistics’ (BLS)
Occupational Employment Statistics (BLS, 2018b) annual median wage of
individuals sharing the decedent’s occupation based on Standard
Occupational Classification codes in . The decedent’s age and gender are
used to account for deviation from the median BLS wage value as follows:
Annual wage is the median annual gross earnings (before taxes and other
deductions), based on occupation by state. This is multiplied by a wage
adjustor to account for deviations from the median value based on age and
sex.
= Median annual wage for a given occupation by state for the year of death
= Adjustment to median earnings by age and sex of decedent
The published median earnings from the BLS Current Population Survey (CPS)
for a particular age group was assigned to the median age of that age group
(BLS, 2018c). To derive an earnings value for each age, the difference
between sequential age groups was calculated as:
Earnings x+1– Earnings x
Earnings = CPS published age group earnings
This difference was evenly distributed within the age group by dividing by
the number of ages in that age category. Using this quotient, subtract from
median income for ages below median, and add to median income for ages
above it for each age category. This now creates median incomes for each
age. Then the proportion of the median age earnings for each age was
determined by dividing by the median income for the entire year to get a
proportion of median earnings. The process was repeated for both males and
females and for each year of earnings data.
In order to model annual changes in wage, two more adjustments are made to
the adjusted wage. First, Employment Cost Index (ECI) accounts for changes
due to economy-wide productivity growth. ECI is an indicator of employee
costs to businesses and is prepared by the BLS. The employment cost index
for wages (ECIW) estimates economy-wide productivity growth, or how much
earnings would rise jointly with the growth of the entire U.S. economy
(BLS, 2018d). It measures the change in cost of labor and includes changes
in wages.
The ECI uses a current-cost approach, where annual costs are calculated
based on the current price of benefits. Because this model has the
potential to forecast decedent wages for up to 50 years, it uses a
long-term productivity growth rate, the average of the percent changes in
ECI from 1976–2017 for wages and 1980–2017 for benefits. This
inflation-free change in wages and benefits represents an annual proxy for
a change in productivity (NIOSH 2011).
= the employment cost index for wage rate for year x. This is the 12-month
proportional change in ECI for wages and salaries ending in December of the
specified year.
= the average ECIW for goods-producing industries reported over 1976–2017.
For this model, it is 0.0930.
Second, the life cycle wage growth rate accounts for changes in wages due
to employee experience. This rate was based on mean wages from the
historical income tables of the Current Population Survey (CPS) for the
years 2010 through 2017 (BLS, 2018c). Mean wages were presented in constant
dollars by sex and BLS age group for each year. The rate of change for mean
wages was determined for each sex and race within a specific age group.
Wages for the initial age group (x) were subtracted from the wages of the
next age group (x+1) and divided by the initial age group wage: (x+1)-x/x.
This process was repeated for male and female. In this model, it was
assumed that the salary growth rate is constant within age groups–in equal
increments for each year of age within that age group.
= Salary growth due to experience of individual workers
In order to account for fluctuations in economic conditions over time, the
average ECIW for the years, 1976–2017 is used. For the first year, it is
assumed that the death occurred in the middle of the year, so only
calculate for half of a year:
For the subsequent years, the first-year wage value is used. The ECI wage
rate and life cycle growth rate are compounded as follows:
Benefit Value
To get an accurate representation of the market value for compensation, the
value of employee benefits is added to indirect cost. These benefits
include the employers’ share of insurance (life, health, and disability),
retirement and savings contributions, and legally required payments (Social
Security, Medicare, Unemployment, and Workers’ Compensation). Employer
Costs for Employee Compensation (ECEC) measures the average cost to
employers for benefits per employee hour worked (BLS, 2018e). Benefits are
estimated from the adjusted wage.
As previously mentioned, the Employment Cost Index (ECI) accounts for
changes due to economy-wide productivity growth. The employment cost index
for benefits (ECIB) estimates how much benefits would change jointly with
the growth of the entire U.S. economy (BLS 2018f). It measures the change
in employee benefit costs. In order to account for fluctuations in economic
conditions over time, the average ECIB for the years, 1980–2017 is used.
The first-year benefit value is calculated as:
For subsequent years, the first-year value is reused. The ECIB (ECI benefit
rate) is compounded as follows:
ECEC = Employer costs for employee compensation
= the ECEC benefit rate for year x. This is the ECEC for benefits,
expressed as a proportion of total compensation. These include insurance
plans, retirement, and legally required benefits.
= the Employment Cost Index for benefits for year x. It is the 12-month
proportional change in ECI for benefits, ending in December of the
specified year. It estimates economy-wide productivity growth. It measures
the change in cost of labor and includes changes in wages as well as
employee benefit costs.
<ECIB> = the average ECIB from 1980–2017 for private
industry. For this calculator, it is 1.147%.
Household Production Value
The value of household production losses associated with a fatally injured
miner were derived from time diary data captured in the National Human
Activity Pattern Survey (NHAPS), a study sponsored by the U.S.
Environmental Protection Agency and conducted from 1992–1994. The survey
inquired about daily activities completed over a 24-hour period (Klepeis et
al., 2001). Activities were grouped into five large categories: household
production, providing care, hygiene and personal care, leisure, and
employment and education (Expectancy Data, 2000). Household production is
defined as activities that could produce benefit for all members of the
household and includes cooking, housework, cleanup, chores, plant and
animal care, home and auto maintenance, and purchasing goods and services.
Providing care includes childcare, playing with children, transporting
children, and providing care to others. The market replacement value for
this time is reported in 1998 dollars and based on total compensation
(hourly wages plus the employer’s legally required benefit costs). For this
mining fatality cost model, household production and providing care
activities were calculated by each age and sex category and multiplied by
365 to obtain annual values. Dollar values are adjusted by the ECI wage
rate for all industries (BLS, 2018g).
= x
= market replacement value of household production and providing care.
Household production is reported in = 1998 dollars. It is adjusted to using
the ECI wage rate ().
=
= Employment cost index for household production.
= the ECI wage rate for all industries in year x. Previously in this model,
it was only from goods-producing industries.
Adjusting for Inflation and Discounting
Gross domestic product (GDP) is the value of the goods and services
produced in the United States. The GDP deflator measures the level of price
inflation with respect to a base dollar year. To adjust for inflation, the
sum of the direct and indirect costs are multiplied by the quotient of the
GDP deflator of the year of death divided by the GDP deflator of the dollar
year used to measure the societal cost. This is represented as:
= GDP deflator for year x (BEA, 2018)
= year of death
= dollar year
The social discount rate is used for public health evaluations, and it is a
rate at which society is willing to exchange present costs for future
benefits. The discount rate appropriate for use in economic analyses of
health-care interventions is 3% (Neumann et al., 2016). The discount rate
already accounts for inflation, so there is no need for further adjustment.
The work in this paper uses the three percent discount rate; however, it is
recommended to recalculate cost estimates using alternative discount rates
to demonstrate the effect of initial assumptions regarding appropriate
societal rate. It is appropriate to use a rate between zero and ten percent
in a sensitivity analysis.
The overall lifetime societal costs of fatal occupational injury in mining
is calculated by combining the present discounted value of loss per
individual (indirect costs) with the medical expenditures related to the
fatal injury (direct costs).
The cost model explained above was written into a computer program that
uses key variables from the fatal injuries sorted from the MSHA
accident/injury/illness files (including occupation, state, year, gender,
age, etc.) to make the cost estimates. Reports can be run using many of the
MSHA mine-specific variables such as state, number of employees working at
the mine, subunit, commodity, and accident/injury/illness specific
variables (including accident classification, source of injury, and the
decedent’s occupation, mining experience, and age).
Example Results
All cost estimates are in 2017 dollars and are discounted at 3%. Median and
mean costs are included to help show cost distribution. The median provides
a good estimate for a single fatal injury. However, the mean can shed light
on the high cost of fatal injuries. For example, if the mean is much higher
than the median, then there is at least one cost outlier or a very
high-cost estimate that is driving up the mean cost.
Table 1 displays the annual counts and median, mean, and total cost
estimates for fatal mining injuries from 2008–2017 for all mining
commodities. Counts, average costs, and total costs have been on a downward
trend since 2014; however, 2017 has the highest median cost and second
highest mean cost during the 2008-2017 time period.
The 11 states with the highest number of fatal occupational mining injuries
and the estimated lifetime societal costs are shown in Table 2. West
Virginia led all states by far with 83 fatal injuries with an estimated
total cost of $118.989 million. Kentucky had the second highest number of
fatal injuries with 53 during the 2008–2017 time period and the second
highest total cost with an estimated $81.016 million. Illinois had the
highest median cost at $1.721 million per fatal injury, and the highest
mean cost with $1.577 million per fatal injury.
Fatal injury counts and associated lifetime societal costs by MSHA canvass
are shown in Table 3. Bituminous coal mining had the most fatal injuries
from 2008–2017 with 206 fatal injuries and the highest societal cost
totaling over $302 million. Coal mining made up over half of the fatal
injuries and half of the total societal costs for the entire mining
industry during this time period. Stone mining was a distant second with 81
fatal injuries and total societal costs just over $98 million.
Table 1: Number and lifetime societal costs (represented in millions of
U.S. dollars) of fatal mining injuries by year, 2008–2017, for all mining commodities.
Year
|
Count
|
Median
|
Mean
|
Total
|
2008
|
53
|
$1.261
|
$1.232
|
$65.317
|
2009
|
35
|
1.200
|
1.241
|
43.418
|
2010
|
72
|
1.500
|
1.468
|
105.695
|
2011
|
37
|
1.483
|
1.511
|
55.903
|
2012
|
36
|
1.487
|
1.422
|
51.180
|
2013
|
42
|
1.457
|
1.304
|
54.756
|
2014
|
47
|
1.546
|
1.402
|
65.876
|
2015
|
29
|
1.367
|
1.302
|
37.747
|
2016
|
25
|
1.274
|
1.304
|
32.588
|
2017
|
28
|
1.641
|
1.488
|
41.677
|
Total
|
404
|
$1.424
|
$1.372
|
$554.157
|
Table 2: Number and lifetime societal costs (represented in millions of
U.S. dollars) of fatal mining injuries for states with the highest
fatal injury counts, 2008–2017, for all mining commodities.
State
|
Count
|
Median
|
Mean
|
Total
|
West Virginia
|
83
|
$1.422
|
$1.434
|
$118.989
|
Kentucky
|
53
|
1.680
|
1.529
|
$81.016
|
Pennsylvania
|
20
|
1.554
|
1.422
|
28.445
|
Nevada
|
19
|
1.511
|
1.478
|
28.088
|
Alabama
|
18
|
1.604
|
1.543
|
27.772
|
Texas
|
18
|
1.254
|
1.346
|
24.234
|
Illinois
|
17
|
1.721
|
1.577
|
26.802
|
Virginia
|
12
|
1.544
|
1.459
|
17.509
|
Arizona
|
11
|
1.543
|
1.304
|
14.341
|
New York
|
10
|
1.374
|
1.388
|
13.882
|
California
|
10
|
1.175
|
1.013
|
10.132
|
Table 3: Number and lifetime societal costs (represented in millions of
U.S. dollars) of fatal mining injuries, 2008–2017, by MSHA Canvass or product class.
MSHA Canvass
|
Count
|
Median
|
Mean
|
Total
|
Coal – Bituminous
|
206
|
$1.492
|
$1.466
|
$302.057
|
Stone
|
81
|
1.257
|
1.214
|
98.329
|
Metal
|
47
|
1.580
|
1.467
|
68.939
|
Sand and Gravel
|
43
|
1.179
|
1.086
|
46.718
|
Nonmetal
|
25
|
1.101
|
1.358
|
33.957
|
Coal – Anthracite
|
2
|
2.078
|
2.078
|
4.157
|
The MSHA subunit indicates the location at a mine where the fatal injury
occurred. Table 4 shows that 167 fatal injuries occurred underground with a
total societal cost estimate of $257.813 million. Strip and open pit mining
had the second highest counts and total costs with 156 fatal injuries at a
cost of $192.670 million.
Table 4: Number and lifetime societal costs (represented in millions of
U.S. dollars) of fatal mining injuries, 2008-2017, by MSHA subunit or work location of fatally injured miner.
MSHA Subunit
|
Count
|
Median
|
Mean
|
Total
|
Underground operations
|
167
|
$1.607
|
$1.544
|
$257.813
|
Strip or open pit
|
156
|
1.257
|
1.235
|
192.670
|
Mill or preparation plant
|
46
|
1.353
|
1.365
|
62.809
|
Surface operations at underground mine
|
18
|
0.987
|
1.006
|
18.109
|
Dredge
|
10
|
1.317
|
1.305
|
13.045
|
Office
|
3
|
0.802
|
1.213
|
3.638
|
Independent shops and yards
|
2
|
1.106
|
1.106
|
2.213
|
Auger
|
1
|
1.820
|
1.820
|
1.820
|
Culm bank
|
1
|
2.038
|
2.038
|
2.038
|
Societal costs can also be evaluated according to the size of the mine.
MSHA provides the average number of employees in their address/employment
data set. The average number of employees employed per year can be used as
a surrogate for mine size. Table 5 shows the number of fatal injuries and
their associated societal costs by average number of employees working at
the mine. While the distribution seems even, as the smallest three employee
categories (generally considered “small” mines) have similar values to the
largest employee category, mines that employed an average of 250 or more
employees per year, which had the most fatal injuries, 91, and the highest
societal cost at over $132 million.
Besides mine-specific information, costs can be reported by
accident-specific details. Table 6 shows the most common mine worker
activity that the decedent was performing when the fatal accident occurred.
Sixty-two fatal injuries occurred during machine maintenance and repair,
the most common mine worker activity associated with a fatality, with a
total societal cost of over $87 million. It is worth noting that the second
highest mine worker activity is “unknown,” which means that it is not clear
what the fatally injured miner was doing when the injury occurred. These
unknown activities contributed to over $53.5 million in societal cost.
Table 5:
Number and lifetime societal costs (represented in millions of U.S.
dollars) of fatal mining injuries, 2008-2017, by average number of employees working at mine.
Number of Employees at Mine
|
Count
|
Median
|
Mean
|
Total
|
Fewer than 5
|
19
|
$1.113
|
$1.158
|
$22.021
|
5-9
|
34
|
1.304
|
1.168
|
39.720
|
10-19
|
40
|
1.157
|
1.254
|
50.146
|
20-34
|
35
|
1.640
|
1.472
|
51.528
|
35-49
|
23
|
1.299
|
1.366
|
31.422
|
50-99
|
83
|
1.422
|
1.428
|
118.561
|
100-149
|
34
|
1.404
|
1.358
|
46.188
|
150-249
|
43
|
1.431
|
1.396
|
60.049
|
250 or more
|
91
|
1.562
|
1.452
|
132.122
|
Table 6: Number and lifetime costs (represented in millions of U.S.
dollars) of fatal mining injuries, 2008–2017, by most common mine
worker activity of fatally injured miner.
Mine Worker Activity
|
Count
|
Median
|
Mean
|
Total
|
Machine maintenance/repair
|
62
|
$1.568
|
$1.414
|
$87.665
|
Unknown
|
35
|
1.591
|
1.531
|
53.583
|
Operate haulage truck
|
32
|
1.368
|
1.280
|
40.957
|
Walking/running
|
26
|
1.125
|
1.096
|
28.505
|
Operate continuous miner
|
23
|
1.888
|
1.756
|
40.393
|
Operate bulldozer
|
14
|
1.116
|
1.117
|
15.639
|
Handling supplies or material, load and unload
|
13
|
1.457
|
1.379
|
17.927
|
Operate utility truck
|
12
|
1.369
|
1.455
|
17.458
|
Inspect equipment
|
10
|
1.680
|
1.603
|
16.028
|
Operate power shovel /dragline / backhoe
|
10
|
1.072
|
1.296
|
12.957
|
Accident classifications of fatal injuries are displayed in Table 7.
Powered haulage is a category that covers the haulage of materials and
personnel, and includes haul trucks and conveyor belts. Powered haulage was
involved in 116 fatal accidents and accrued a total societal cost of
$150.702 million. Machinery was involved in 79 fatal accidents with a total
societal cost of approximately $106.815 million. Falling material is
separated into three different classifications, but when combined, they are
responsible for 92 fatal injuries and total almost $135 million in societal
costs.
Societal costs can also be estimated based on characteristics of the
deceased miner. Table 8 shows the number of fatal injuries and the
associated estimated societal costs by categories of years of total mining
experience. The category of four to nine years of total mining experience
had the most fatal injuries, with 91, and the highest societal costs, with
over $145 million. The second most fatal injuries occurred in the 27–49
years of mining experience category. This category had 74 fatal injuries
and total societal costs of approximately $72 million. Even though this
category had the second highest number of fatal injuries, the total costs
were much lower than the lower categories of mining experience. This is
likely due to the correlation of higher experience with older age. On
average, miners with this much work experience are going to be older than
miners with much less mining experience and the societal costs in this
model stop calculating at age 67, which is the estimated age of retirement.
Table 7: Accident classification, number and lifetime costs
(represented in millions of U.S. dollars) of fatal mining injuries,
2008-2017, for all mining commodities.
Accident Classification
|
Count
|
Median
|
Mean
|
Total
|
Powered haulage
|
116
|
$ 1.273
|
$1.299
|
$150.702
|
Machinery
|
79
|
1.548
|
1.352
|
106.815
|
Falling, rolling, or sliding rock or material of any
kind
|
36
|
1.282
|
1.272
|
45.805
|
Slip or fall of person (from an elevation or on the
same level)
|
36
|
1.146
|
1.143
|
41.135
|
Ignition or explosion of gas or dust
|
32
|
1.543
|
1.544
|
49.399
|
Fall of face, rib, pillar, side, or high wall (from in
place)
|
31
|
1.774
|
1.667
|
51.664
|
Fall of roof, back, or brow (from in place)
|
25
|
1.505
|
1.430
|
35.745
|
Electrical
|
14
|
1.845
|
1.753
|
24.541
|
Table 8: Years of total mining experience of fatally injured miner,
number and lifetime costs (represented in millions of U.S. dollars) of
fatal mining injuries, 2008-2017 for all mining commodities.
Years of Experience
|
Count
|
Median
|
Mean
|
Total
|
Fewer than 1
|
52
|
$1.728
|
$1.649
|
$85.754
|
1-3
|
56
|
1.675
|
1.578
|
88.346
|
4-9
|
91
|
1.690
|
1.601
|
145.706
|
10-17
|
66
|
1.479
|
1.402
|
92.560
|
18-26
|
52
|
1.085
|
1.080
|
56.171
|
27-49
|
74
|
0.983
|
0.977
|
72.294
|
50 or more
|
1
|
0.075
|
0.075
|
0.075
|
Unknown
|
12
|
1.141
|
1.104
|
13.249
|
Discussion
The cost-of-injury method for measuring economic burden of fatal mining
injuries estimates the potential costs that could be saved if the fatal
injury did not occur. Knowledge of these costs can help policy makers
decide which mining commodities or accident causes need to be addressed
first through prevention efforts or increased health and safety research.
The Upper Big Branch mine disaster occurred in April 2010 and resulted in
twenty-nine fatal injuries. This would have an impact on West Virginia, the
coal commodity, the ignition/explosion accident classification, and the
2010 fatal injury count and cost data. The web application allows the
display of costs by mine ID, and therefore the societal costs of mine
disasters can be calculated. The Upper Big Branch Mine-South has a mine ID
of 46-08436, and the twenty-nine fatal injuries from the April 2010
explosion had an estimated total societal cost of $45.491 million and a
median cost of $1.591 million per deceased miner.
These are the only current mining-specific fatal injury burden estimates
for the U.S. that the author has been able to identify. Much of the
mining-related burden estimates are from twenty-five-year-old data that
combines mining with oil and gas, and gives one aggregate cost for a
ten-year period (NIOSH 2009, 2011). It is difficult to compare these
current estimates to previous cost estimates; however, the mean and median
total costs are in line with the previous NIOSH estimates after adjusting
for inflation.
The Bureau of Mines sponsored the development of the Accident Cost
Indicator Model (ACIM) in the late 1970s. The ACIM (DiCanio and Nakata,
1976) provides cost estimates for underground coal mines based on publicly
available data on wages, workers’ compensation claims, medical payments,
investigation costs, and other direct and indirect costs. It was designed
to provide a useful guideline on the magnitude of costs suffered by
individuals, mining industry, and society. The last known published results
from this model were from Randolph and Boldt’s (1997) work on haulage
accidents where the six haul truck related fatal injuries had an estimated
total cost of $2.58 million.
This current model will be maintained on the NIOSH Mining web page as a web
application called Fatalities Cost in Mining. Users will be able
to select characteristics of the fatal injury (occupational, mine,
incident, and employer), select a dollar year and discount rate for
calculations, then generate a cost report based on victim, mine, and/or
accident related variables. A few examples of these reports are shown in
the Results section. This model is expected to be updated annually and
maintained for years to come.
Limitations
In light of the many advantages of the method, the cost-of-injury
methodology has some limitations. It can show which kinds of fatal injuries
may require increased prevention resources. This methodology does not
measure benefits, but provides the framework and important cost information
when conducting cost-effectiveness or cost-benefit analyses, which can
determine the best course of action for prevention.
As previously mentioned, the entire burden of a fatal mining injury is not
captured by this model, as intangible losses of premature death are not
included. The more extended nature of the degree of the fatal occupational
injury problem includes the cost of suffering and role loss. These
subjective and personal components are difficult to measure and are not
considered in these calculations.
It is also important to note that cost-of-injury method is just one way to
measure economic burden. Other notable methods including willingness-to-pay
and willingness-to-accept are commonly used to determine the value of a
statistical life and tend to provide higher cost estimates for fatal
injuries. The cost-of-injury method used for developing these estimates
produces a conservative estimate for the lifetime economic costs of fatal
mining injuries. The estimates are based on many factors and are subject to
the limitations of the model used as well as the limitations associated
with the level of detail of the some of the data in the calculations. The
wage data is not specific to the mining industry as the data are from state
averages by occupation. The mining-specific wage data acquired by NIOSH was
not robust enough to use for specific mining occupations throughout the
United States. Additionally, specific benefits data by industry and
occupation would improve burden cost estimates.
Finally, these estimates are only for fatal mining injuries. They do not
include deaths from mining related illnesses or diseases, as these are not
normally included in the MSHA fatality data. They can be extremely
difficult to estimate due to unknown exposures as well as long latency
periods. Therefore, this work focused only on fatal injuries.
Despite the acknowledged limits of the burden estimates, they do offer
important applied value. The cost estimates provide information to policy
and decision makers on relevant costs of fatal mining occupational injuries
in relation to costs and selection of prevention programs. These cost
outcomes represent income that is not received and medical expenses
incurred because of a fatal injury and, therefore, have direct impact on
state, regional, and national economic measures of goods and services
production, such as GDP and other national income measures. The estimates
can be used to plan, enhance, and prioritize mining injury prevention and
control programs, policy analysis, evaluations of health and safety
interventions, and promotion for a safer work environment.
Conclusion
The cost model and data sources used in the Fatalities Cost in Mining methodology in this research combine to
form an effective tool for determining the societal cost of fatal mining
injuries. These cost estimates can be used to determine the specific burden
of injury to particular demographic groups, as well as the circumstances of
injury. This can be extremely useful in determining the cost to various
constituencies—to the country, states, and geographical regions, and to
mining commodities and occupations. In addition, this work provides the
frequency of fatal mining events to indicate the extent of the occupational
fatal injury problem. The contribution of this societal cost estimation
data, in identifying the financial cost of fatal mining occupational
injuries, constitutes a major component in defining the scope of these
fatal injuries. The use of economic losses, such as those calculated using
the Fatalities Cost in Mining model described in this paper,
provides an additional measure to existing societal measures of frequency
and rate of injury to assist in defining the overall dimensions of
traumatic workplace fatalities in the mining industry.
Acknowledgements
This work would not have been possible without the groundwork laid by the
late Elyce A. Biddle, PhD, valuable feedback provided by NIOSH ERSO group,
and the programming work of Steven C. Williams and Jonathan E. Fritz.
Disclaimer
The findings and conclusions in this document are those of the author and
do not necessarily represent the official position of the National
Institute for Occupational Safety and Health, Centers for Disease Control
and Prevention. Mention of any company or product does not constitute
endorsement by NIOSH.
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