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This chapter presents an overview of fatal and nonfatal injuries reported to MSHA during 1986-1995. Reportable injuries follow the criteria for reporting specified in 30 CFR 50. These criteria are listed in appendix A. The data used for these analyses came from the MSHA accident, injury, and illness data closeout files for the years 1986-1995. The MSHA data are described in appendix A and methods used in data analysis in appendix B. There may be slight discrepancies between the figures presented here and other presentations of MSHA data for the following reasons: (1) injuries to nonemployees have been excluded from analysis; and (2) injuries identified by MSHA after the files were closed out are not included. Within the MSHA data, there are differences in reporting requirements for operators and contractors. Contractors are required to report only those injuries that occur during certain categories of work (described in appendix A). For this reason, data on injuries among employees of operators and contractors in the MSHA database should not be considered directly comparable. As discussed in the "Introduction," the MSHA data on fatal injuries differ from the CFOI data (chapter 1) because of varying definitions used for the mining industry. As compared to the CFOI data, MSHA data exclude injuries that occurred off of mine property and include injuries occurring at mills and processing plants on mine property. Tables 4-1 through 4-9 and figure 4-1 provide comparisons of injury frequencies and rates among the commodities. These comparisons are followed by sections 4A through 4E, which provide more detailed tables and figures for each commodity separately. The method used to calculate injury rates is described in appendix B. Since denominator data for occupations (lob titles) were available for 1986 only, estimates of rates by occupation for the entire 10 year period are based on extrapolation from 1986 data. For this reason, estimates of injury by occupation should be interpreted with caution. Fatal Injuries A total of 1,078 fatal on-the-job injuries were reported to MSHA during the 10-year period 1986- 1995. The average annual fatal injury rate for all commodities combined during this period was 30.9 per 100,000 full-time equivalent workers. Mine operators accounted for 908 (84%) of the fatalities and had an average annual fatal injury rate of 28.2 per 100,000. While mine contractors comprised only 16% (n = 170) of the total number of fatalities, their average annual fatal injury rate was 2.3 times higher than that of mine operators (65.0 per 100,000). Table 4-1 presents the number and rates of fatal injuries by commodity for operators and contractors. Contractors in metal, nonmetal, stone, and sand and gravel are grouped together, since MSHA data on employment were not available for rate calculations for these commodities separately. The highest average annual fatality rate for the period, 66.5 per 100,000, occurred among metal and nonmetallic mineral mining contractors, followed closely by coal contractors (63.1 per 100,000). Among mine operators, the highest average annual fatal injury rate was in coal (38.9 per 100,000); the lowest was in nonmetal (14.3 per 100,000). Table 4-2 shows the average annual fatal injury rate by type of operation for each commodity for mine operators only. Of commodities with underground mines, all except stone had higher fatality rates at underground than at surface operations of these mines. The highest fatality rate for any operation occurred in surface operations at underground stone mines (124.5 fatal injuries per 100,000 workers). Stone had the second highest fatality rate for underground operations at underground mines (77.9 per 100,000). The highest rate for underground operations at underground mines occurred in metal (87.6 per 100,000). Table 4-3 shows the number and rate of fatal injuries by U.S. Bureau of the Census occupational groups (described in appendix D) for the 10 occupation-commodity groups with the highest fatal injury rates. These data should be interpreted with caution because the denominator used in calculating the rates was based on an extrapolation from data for the year 1986 only, as described in appendix B. Extractive occupations are represented in this group for three commodities: coal, metal, and sand and gravel. Construction trades are included for coal, sand and gravel, and nonmetal. The distribution of fatal injuries by commodity and work activity for mine operators is presented in table 4-4. A description of the work activity categories is in appendix C. Overall, the leading work activity at the time of injury was using or operating tools and machinery (26.4%) followed by constructing, repairing, and cleaning (24.0%), vehicular operations (18.8%), and materials handling (12.6%). The distribution varied by commodity. The largest percentage of fatal injuries occurred while operating tools and machinery in both coal and stone; during vehicular operations in metal; and during constructing, repairing, and cleaning activities in nonmetal and sand and gravel. Table 4-5 shows the distribution of fatal injuries by commodity and MSHA accident classification for mine operators. A description of the MSHA accident classifications appears in appendix E. The largest proportion of injuries overall occurred during powered haulage (28.5%). The

Jan 5, 2000

This chapter presents MSHA data on mine employment and active mining operations during 1986-1995. Figures 3-1 through 3-10 show, for each commodity, the location of mine operations in 1995, and employee hours and active mining operations by year during 1986-1995 for mine operators. For coal and nonmetal operators, the number of active mining operations and the number of employee hours decreased over the 10-year period. For metal operators, the number of active operations decreased, while the number of employee hours increased. For stone and sand and gravel operators, the number of active operations and the number of employee hours remained about the same. Figures 3-11 and 3-12 show that over the 10-year period, there was an increase in the proportion of hours worked by contractors in coal mining, while the overall employment decreased. For metal, nonmetal, stone, and sand and gravel combined, the proportion of contractor hours increased, with overall employment also increasing. Table 3-1 shows the distribution of hours worked during 1986-1995 by commodity and type of operation. For coal, the majority of hours worked were in underground mines. For metal, nonmetal, and stone, the largest number of hours worked were in mills and preparation plants. For sand and gravel, all work hours other than office were in surface mines. [ ]

Jan 5, 2000

This chapter presents data on occupational exposures from several sources. Data from compliance samples obtained by MSHA inspectors are presented in table 6-1 for coal mine dust and silica dust, and in tables 6-2 through 6-6 for metal fumes. Since these samples were taken for compliance monitoring rather than as part of a survey of the industry, it is difficult to predict how well they indicate actual exposures for all mining operations. In the coal industry, 7.8% of all respirable dust samples during 1986-1995 were above the permissible exposure limit (PEL). Of silica dust samples obtained during the 10-year period, 23.7% were above the PEL in coal, 16% in metal, 10.8% in nonmetal, 9.1% in stone, and 7.6% in sand and gravel. Of metal fume samples, silver samples showed the largest percentage above the PEL-approximately 48% of samples in both metal and nonmetal. Tables 6-7 and 6-8 present data on noise exposures from the MSHA "dual threshold" survey, which was published in the Federal Register [61 Fed. Reg. 66347 (1996)] as part of a proposed rule change for occupational noise exposure in mining. This study examined a group of samples obtained during 1991-1995 and compared the percent of samples that were above two separate specified limits. The first limit was the current noise standard, a time-weighted average of 90 dBA, calculated to include only exposures at 90 dBA and above. The second limit was a time-weighted average of 85 dBA, calculated to include exposures at 80 dBA and above. Tables 6-8 and 6-9 refer to musculoskeletal overload conditions examined in the National Occupational Health Survey of Mining (NOHSM) [NIOSH 1996]. Table 6-8 shows the operational definitions for each condition; table 6-9 shows the percentage of the workforce potentially exposed, by commodity. Across all commodities, a large proportion of workers were exposed to musculoskeletal overloads due to positioning of the neck and back; positioning and motion of the forearms, arms, and shoulders; heavy lifting; and positioning and movement of the lower limbs. The recorded overloads were defined in the survey and did not exceed any NIOSH, MSHA, or OSHA guidelines for musculoskeletal overloads. Further information on this survey appears in appendix A.

Jan 5, 2000

This chapter presents proportionate mortality ratios (PMRs) derived from death certificate data in the National Occupational Mortality Surveillance (NOMS) database for the years 1986 through 1993. At the time this report was prepared, 1993 was the latest year for which data were available. Industry is classified under the 1980 U.S. Bureau of the Census classification system [Bureau of the Census 1982]; cause of death, under the International Classification of Diseases, Ninth Revision (ICD-9) [World Health Organization 1977]. A PMR of 100 indicates a mortality risk for a particular industry group that is similar to that for all industry groups combined. A PMR over 100 suggests a higher risk; a PMR under 100 suggests a lower risk. PMRs are subject to many factors that affect their reliability and validity and may not be comparable across populations with very different causes of death. An elevated PMR for a specified cause of death among an occupational or industry group may indicate a relationship between a work exposure and the cause of death specified. However, there are many limitations of PMRs as a measure of risk; they are most useful for generating hypotheses about exposure-disease relationships, which can then be tested in other studies. Selected PMRs are presented separately for males and females for coal mining, metal mining, nonmetallic mineral mining and quarrying (which includes nonmetal, stone, and sand and gravel), and for oil and gas extraction. All PMRs have been adjusted for age and race. In general, PMRs are presented only if the lower 95% confidence limit exceeded 100. If a PMR in an ICD-9 subcategory was above 100, however, the PMR for the larger category into which the subcategory falls is always presented and may be less than 100. PMRs can be expected to be elevated for conditions that have a high prevalence in particular occupational groups. PMRs showed elevated values for conditions known to affect miners, such as coal workers' pneumoconiosis, pneumoconiosis due to silica, tuberculosis, chronic pulmonary heart disease, and injuries. Additional findings of interest were- • An elevated PMR for myocardial infarction among male coal miners (table 2-1); • Elevated PMRs for neurologic disease in general and anterior horn cell disease, in particular among male metal miners (table 2-3); • An elevated PMR for rheumatic heart disease in male metal miners (table 2-3); • Elevated PMRs for malignant neoplasms of the digestive organs and peritoneum and for malignant neoplasms of the colon and rectum in female nonmetal miners (table 2-6); and • Elevated PMRs for non-A, non-B hepatitis and arteriosclerotic heart disease among male oil and gas workers (table 2-7).

Jan 5, 2000

An examination of the Census of Fatal Occupational Injuries (CFOI) data for the period 1992-1995 shows that 659 fatalities occurred in the mining industry as defined by the SIC. Oil and gas accounted for slightly more than one-half of all fatalities (figure 1-1). The coal industry had the highest fatality rate for the 4-year period, with 35.6 deaths per 100,000 full-time workers, followed by metal (23.1 per 100,000), oil and gas (23.1 per 100,000), and nonmetal (20.1 per 100,000). The fatality rate for all U. S. industries during this same period was 5.1 per 100,000 full-time workers. Figure 1-2 compares the fatal injury rates by year for the four major industry sectors of mining to the fatality rate for all industries combined. Figure 1-3 compares age-specific fatality rates in the mining industry to those seen for U. S. industry as a whole. Fatality rates for all industries combined were lowest for young workers and steadily increased in relation to workers' ages. In the mining industry, fatality rates by age group show a U-shaped curve. Young workers under 20 years of age had the highest injury rate (54.6 per 100,000), followed by workers aged 20-24 (50.8 per 100,000) and workers over age 65 (37.7 per 100,000). Table 1-1 shows the distribution of injuries by type of injury event. The mining industry, in comparison with all industries, had a considerably larger proportion of fatalities classified as caught in or crushed by collapsing materials (9.1% and 1.9%, respectively) and fires and explosions (10.0% and 3.1%, respectively). Transportation events comprised the largest proportion of fatal injuries in both the mining industry and all industries. Within the mining industry only, transportation accounted for the largest proportion of fatalities in all mining sectors except coal. A more detailed examination of the transportation events within the mining industry showed that highway incidents (50.5%) were the most common, followed by nonhighway incidents (22.2%), pedestrian incidents (10.6%), and those incurred during rail, water, and air transport (15.7%). Incidents involving caught in or crushed by collapsing materials comprised a higher percentage of injuries in the coal and metal sectors, while fall-from-height incidents were more frequent in the oil and gas sector. The classification system used in CFOI for type of injury event is not compatible with any variables contained in the MSHA data; therefore, direct comparisons are not attempted. [ ]

Jan 5, 2000

This surveillance report summarizes data on work-related fatal and nonfatal injuries, illnesses, and hazardous exposures in the mining industry for the 10-year period 1986-1995. The term "surveillance," as used in public health, may be new to many readers of this report. With regard to occupational safety and health, the goal of surveillance is to describe the occurrence of work-related injuries, illnesses, and known hazardous exposures; to identify new hazards that may occur due to the introduction of new technology or other factors; and to assess the effects of preventive measures designed to improve worker safety and health. Surveillance provides an overall picture, which can be used to focus resources on areas most in need of prevention programs or further research. Surveillance generally requires the use of multiple sources of data, all of which have their own strengths and limitations. Surveillance in the U. S. mining industry is complicated by the fact that U.S. national data systems that provide information on mining are based on two different definitions of the mining industry. One is established by the Mine Safety and Health Administration (MSHA), the other by the Standard Industrial Classification (SIC) System [Office of Management and Budget 1987] and the 1980 U. S. Bureau of the Census (BOC) Classification System [U.S. Bureau of the Census 1982]. The mining industry as defined by MSHA is based on the regulatory jurisdiction of that agency. Reporting requirements for injuries, illnesses, and workplace exposures are stipulated under the Federal Coal Mine Health and Safety Act of 1969 and the Federal Mine Safety and Health Amendments Act of 1977. Of note is that the oil and gas extraction industry falls outside of MSHA jurisdiction. The Occupational Safety and Health Administration (OSHA) was charged with enforcement in the oil and gas industry under the Occupational Safety and Health Act of 1970. Under the SIC, the definition of mining covers the extraction of naturally occurring minerals, including liquids (petroleum), gases (natural gas) and solids (coal, metal, and nonmetallic minerals). Exploration and development of mineral properties are also included in the SIC definition of the mining industry. The SIC was developed by the Office of Management and Budget for use in classifying establishments based on the type of economic activity in which they are engaged and serves as the industry classification standard for all establishment based Federal economic statistics. This classification is important for occupational safety and health surveillance because it also serves as the industry classification system for all data on work-related injuries and illnesses collected by the U.S. Bureau of Labor Statistics (BLS). The U.S. Bureau of the Census uses an industry coding scheme similar to that used by the SIC. For the mining industry, the categorization is identical in both systems. Death certificate data made available by the National Center for Health Statistics (NCHS) are categorized under this system. Death certificate data are particularly important for surveillance of mortality from work-related illnesses. Key differences between the SIC/BOC categorization system and the MSHA system can be summarized as follows: • MSHA excludes the oil and gas industry, which is classified as part of the mining industry under SIC/BOC. • MSHA excludes work that is done off of mine property; work off of mine property is included under SIC/BOC if it is performed by an establishment falling into one of the SIC/BOC mining industry codes. • MSHA includes mine-associated mills and processing plants; these are classified under the manufacturing industry, rather than mining, according to the SIC/BOC coding structure. Since the data collected by MSHA and the data collected by BLS and NCHS using the SIC/BOC classification system are both important sources of information on the mining industry and in many cases serve different purposes, data from both systems are presented in this report. In general, data using the SIC/BOC system are comparable across U.S. industries and should be used when comparing mining to other U.S. industries. Much of the data in this report are presented in the form of frequencies or rates. A frequency provides only a count of the number of cases without providing any information about the risk of occurrence. Rates provide an estimate of the risk of injury or illness. The reader is cautioned, however, that rates based on small numbers can be unstable; thus, any inferences should be drawn with care. In this report, no rates are presented for groups in which there were fewer than three cases. The incidence of most injury and illness conditions differs by age, sex, and race. For this reason, data in many surveillance reports are adjusted for these demographic factors. It was not possible to do this for most of the rates in this report because information on demographics was not available. With the exception of the proportionate mortality ratios (PMRs), all data presented are unadjusted. A detailed account of the sources of data is in appendix A. The methods used in data selection and analysis are described in appendix B.

Jan 5, 2000

By Gerrit V. R. Goodman, Charles D. Taylor, Edward D. Thimons, Jay F. Colinet

The importance of controlling respirable dust and methane gas levels in underground coal mining cannot be underestimated. While respirable dust can significantly affect the occupational health of underground coal miners, methane gas accumulations pose significant safety concerns for these same workers. Water sprays and machine mounted dust scrubbers offer effective control of respirable dust exposures and methane gas accumulations. Water must not only be applied carefully to avoid dust rollback to the machine operator but must create sufficient turbulence to remove dead zones that could contain high concentrations of methane gas. While the flooded-bed dust scrubber has been generally responsible for decreased worker exposures to respirable dusts, this device has proved effective in controlling methane levels at the face. This paper reviews practical applications of water sprays and dust scrubbers to control respirable dust and methane gas on continuous miner faces.

Jan 4, 2000

By David C. Byrne

PURPOSE: Evaluate practical technological advances in level-dependent hearing protectors and inexpensive personal acoustic monitors. RESEARCH SUMMARY: The results of previous NIOSH research show that miners develop hearing loss much more quickly than nonoccupationally noise-exposed individuals and that miners experience a greater severity of hearing loss than would be expected for nonoccupational noise-exposed persons of the same age and gender. Using hearing thresholds at 4,000 Hz as an indicator, coal miners experience hearing loss 2.5 to 3 times greater than would be expected for persons not exposed to occupational noise. At age 50, 90% of coal miners and 49% of metal/nonmetal miners were found to have a hearing impairment. In comparison, only 9% of a nonoccupationally exposed control group had a hearing impairment at age 50. There are numerous barriers to the prevention of hearing loss among miners. In an effort to overcome some of these obstacles, this work was initiated to evaluate practical technological advances in level-dependent hearing protectors and inexpensive personal acoustic monitors. Experiments will involve analysis of this new technology under both laboratory and field conditions. Laboratory components will consist of controlled studies using an anthropometrically correct test fixture as well as human subjects to evaluate the adequacy/effectiveness of various hearing protective devices. Electroacoustical performance testing of new noise monitors will also be conducted in a laboratory setting. Subsequent field studies will determine the actual usefulness and practicality of the personal protective/monitoring equipment as used in different mining situations. It is anticipated that increased on-the-spot awareness of hazardous noise levels and the availability of better-suited hearing protectors will provide a degree of protection beyond what is currently attained in different mining environments. Therefore, the intent of this project is to identify and adapt new acoustical technologies and products that have a direct application to individual mine workers. With increased knowledge of these technological advances, a greater demand for similar devices will likely occur, which, in turn, will lead to a better understanding and awareness of hearing loss prevention methods throughout the mining community. The ultimate goal is for widespread integration of useful hearing loss prevention products and devices into the daily routines of miners

Jan 2, 2000

By Nicholas Kyriazi, John P. Shubilla

The National Institute for Occupational Safety and Health (NIOSH), Pittsburgh Research Laboratory, has undertaken a study to determine how well self-contained self-rescuers (SCSRs), deployed in accordance with Federal regulations (30 CFR 75.1714), hold up in the underground environment with regard to both physical damage and aging. This report presents findings regarding laboratory-tested SCSRs in the sixth phase of testing from mid-1996 to early 1998. The SCSRs were tested on human subjects and on a breathing and metabolic simulator. These results indicate that most of the apparatus, if they pass their inspection criteria, perform satisfactorily. However, the deployed CSE SR-100s exhibited significantly higher inhaled CO2 levels than new units, as in the previous phase. This will cause higher ventilation rates in most users, which will, in turn, result in higher breathing pressures, possibly causing users to prematurely remove the apparatus. CSE Corp. has developed a noise test that can identify apparatus suffering from chemical-bed degradation causing the early CO2 breakthrough. This test was added to the inspection criteria for the SR-100. In addition, several of the MSA Portal-Packs that passed their inspection criteria were found to have KO2 dust in their mouthpieces. Further investigations by NIOSH and the Mine Safety and Health Administration confirmed these findings, which resulted in the decertification of the apparatus and their removal from service.

Jan 1, 2000

By Linda L. Chasko, Charles P. Lazzara, Gary Braselton, Ronald S. Conti

Preparedness is an important element of any underground mine's strategic plan in dealing with an unexpected event, such as a fire. A fully implemented fire preparedness and response plan is essential in reducing the probability and seriousness of a mine fire. This report describes the development of an underground coal mine fire preparedness and response checklist (MFPRC). The checklist is a data collection instrument for profiling both the fire prevention and response capabilities of a mine site and usually requires 3 to 4 days to complete. The checklist encompasses conditions, procedures, and equipment that have frequently been identified as the primary or contributing causes of underground coal mine fires. At least 1 day is needed underground to evaluate the water system. This entails measurements of water flows and pressures at fire hydrants, and water throw distances of fire hose and nozzles at several locations (mains and branch lines). A few of the other topics that are discussed with mine personnel include detection and suppression systems, combustible materials, mine rescue and fire brigades, and firefighting equipment. The MFPRC was developed by the National Institute for Occupational Safety and Health (NIOSH),Pittsburgh Research Laboratory. Under a Cooperative Research and Development Agreement (CRADA) with Cyprus Amax, Twenty mile Coal Co. (Oak Creek, CO), the checklist was field tested and further refined. Additional field tests were conducted at several other operating coal mines.

Jan 1, 2000

To provide a practical method to reduce hand and arm injuries during roof bolting operations.

Jan 1, 2000

This chapter explores leadership behavior in a life-threatening situation-fire in a coal mine. Previous chapters have discussed the database of interviews with miners who escaped from underground fires. Researchers raised questions such as: (1) Who led the miners out of the mine? (2) Did leadership make a difference in the escapes? (3) Was the escape leader the hierarchal leader? (4) What, if any, characteristics did the escape leaders possess? Subsequently, researchers analyzed the mine fire database from a group interaction perspective to address the leadership questions posed. Leadership has been one of the most researched topics of human behavior in the twentieth century. Studies have ranged from individual characteristics of leaders, to situational leadership, to interaction of leader and follower, suggesting different leadership techniques for different followers. The question that emerges here is: Are there different types of leadership that "fit" different kinds of situations? In a crisis situation like that examined in the present study, such information about leadership may significantly improve the chances of escape. To address these issues, the study team looked at the formal authority structure before each fire, considered leadership behavior or lack of leadership during the escapes, and examined those conditions associated with the emergence of leadership. According to Bardo [ 1978], "Emergent behaviors are those forms of action, and the norms, values and beliefs governing those actions, that rise out of the disaster situation." This chapter discusses previous studies in the area of crisis leadership and examines the emergent behaviors of leaders under duress during the mine fire escapes. Previous Studies The research on leadership during emergency situations has consisted mainly of simulation and field studies, with the principal concern being escape from building fires. During the 1980s, Hayashi [1988] created a computer simulation model to evaluate leader behavior in a fire. Although his purpose was to aid in planning for disaster prevention, his findings are relevant because they address the issue of situational leadership in crisis-where a leader changes his or her behavior to fit the situation. Essentially, his simulation model was designed to judge the actions and thinking of leaders. The simulation was tried by 101 subject/leaders 4 times each. The simulation consisted of amaze containing the leader, an informal leader, and 50 evacuees. Interestingly, the results indicated that the leader's actions were not dictated by circumstances. Any differences in behavior were attributed to the individual characteristics of each leader. The study also showed that the worse the situation became, the less individual

Jan 1, 2000

The notion of wayfinding, as conceptualized by planners, geographers, and psychologists, denotes the ability of an individual to move from one point to another through physical space. In order to achieve this movement, a person relies on a cognitive map of spatial representations [Passini 1984]. Which features of this cognitive mapping will be stressed depend, in part, on the researcher's perspective. A planner, for instance, would tend to emphasize the effect of physical structures on mobility. A psychologist, on the other hand, might focus on individual differences in how human minds encompass and represent physical space [Evans et al. 1984]. There is yet another dimension to wayfinding that needs consideration, and it rests upon the idea that reality, as experienced by human beings, is mediated: "[People] have preselected and preinterpreted this world by a series of commonsense constructs ... which help them find their bearings in their natural and socio-cultural environment and to come to terms with it" [Schutz 1967]. These "common sense constructs" are arrived at socially and constitute the agreed-upon schemas that guide people's everyday activities. According to this principle, cognition is governed by some nonlogical factors that reflect not only individual procedures but collective ones as well. These group strategies, which are shaped by shared rules and values, influence "the information gathered, the ways it is processed, the inferences that are drawn, the options that are being considered, and those that are finally chosen" [Etzioni 1992]. From this perspective, cognitive maps, rather than being individual-centered templates of environmental images [Rovine and Weisman 1989] or representations of spatial relationships [Evans 1980], are partially group-centered schematic processes. As such, they are subject to reinterpretation, revision, and outside intervention [Kaplan and Kaplan 1982]. As intermediaries between the environment and behavior, cognitive maps serve as bases for decision-making. Traditionally, it has been assumed that good maps facilitate correct decisions, which in turn leads to optimal performance during wayfinding [Hunt 1984]. Given the argument that there is a social (non¬cognitive) facet to cognitive mapping, however, this image of a cognitive map as some sort of static reference construct that motivates individual action is too narrow and mechanistic. If cognition involves less a knowledge of the environ¬ment than it does the process of "giving it meaning through imposing an order on it" [Rapoport 1976], then wayfinding behavior is not j ust a function of setting and individual differences, but is also a function of one's "normative-affective" structure [Etzioni 1992]. Rapoport [1976] used such an assumption as the base for a set of hypotheses about the connection between "external demands" and "organismic factors." One significant assertion deriving from Rapoport's ideas is that environmental

Jan 1, 2000

This surveillance report summarizes data on work-related fatal and nonfatal injuries, illnesses, and hazardous exposures in the mining industry for the 10-year period 1986-1995. The term ?surveillance,? as used in public health, may be new to many readers of this report. With regard to occupational safety and health, the goal of surveillance is to describe the occurrence of work-related injuries, illnesses, and known hazardous exposures; to identify new hazards that may occur due to the introduction of new technology or other factors; and to assess the effects of preventive measures designed to improve worker safety and health. Surveillance provides an overall picture, which can be used to focus resources on areas most in need of prevention programs or further research. Surveillance generally requires the use of multiple sources of data, all of which have their own strengths and limitations. Surveillance in the U.S. mining industry is complicated by the fact that U.S. national data systems that provide information on mining are based on two different definitions of the mining industry. One is established by the Mine Safety and Health Administration (MSHA), the other by the Standard Industrial Classification (SIC) System [Office of Management and Budget 1987] and the 1980 U.S. Bureau of the Census (BOC) Classification System [U.S. Bureau of the Census 1982].

Jan 1, 2000

By Todd M. Ruff

An average of 13 mine workers are killed each year by being run over or pinned by mobile mining equipment. At surface mines, these accidents commonly involve large dump trucks that drive over a smaller vehicle or a person that is in the dump truck’s blind spot. One method of detecting a person or another vehicle in a blind spot is to use some type of sensor technology such as radar or radio-frequency identification (RFID). Researchers at the Spokane Research Laboratory of the National Institute for Occupational Safety and Health tested a number of commercially available and experimental sensors that monitor obstacles in a vehicle’s blind spots. None of the sensors had been previously applied to the specific problem of rigid-frame surface mining trucks. This report documents the procedures and results of tests conducted after RFID and radar systems were mounted on a 50-ton-capacity dump truck. It was determined that both RFID and radar technology show promise for detecting obstacles in the blind spots of mining equipment; however, more development work is needed to meet the unique requirements of mining equipment and the mine environment.

Jan 1, 2000

By Ovsei Volberg

Motion capture involves recording the position and global orientation of joint sensors of a real object, in most cases a real person performing some human activities. This information is usually recorded at uniformly spaced instances of time, or as it is often called frame-by-frame. Then the recorded motion data sets are processed and mapped into a skeleton hierarchy of a virtual, computer simulated human figure to control the motion of the virtual human in the computer simulation. In the first part of the paper we review several new techniques developed to facilitate the manipulation, noise reduction, storage and reuse of captured data, which have a potential to reduce the overall cost of motion simulation and improve its realism. In the second part of the paper we consider the real life problem of reducing a worker?s risk from being hit by underground mining machinery in a confined space. We formulate a set of requirements to motion editing for this particular task and analyze the limitation of existing techniques

Jan 1, 2000

Various aspects of judgment and decision-making are key themes in this book. The model presented here serves as a loose structure for the chapters that follow this one. The notion of a model is introduced because growing research interest in the subjective aspects of group and individual behavior has led to a debate over whether judgment is a skill that can be understood scientifically. A related point of contention is whether such an understanding could lead to the development of methods for estimating people's ability to make good decisions during an emergency. There is some literature that supports the potential usefulness of this approach. However, little agreement seems to have been reached on how to define and operationalize even those basic concepts necessary to assess the soundness of decisions from within their environmental and group contexts [Jensen and Benel 1977; Godden and Baddeley 1979; Baumann and Bourbonnais 1982; Brecke 1982; Stone et al. 1985]. A look at the real-world process is clearly needed. The need to attempt a better understanding of judgment and decision-making properties stems from those occasions in the existence of an organization when there is a lot at stake. The process of decision-making (which is part of the exercise of judgment) has been analyzed in situations such as corporate takeovers [Janis and Mann1977], military combat [Begland1979], clinical emergencies [Baumann and Bourbonnais 1982], and aviation events [Billings and Reynard1984]. The fundamental assumption of these analyses is that, while there are untold successes, there are also notable numbers of failures resulting from decisions that can be ascribed to one or more errors in judgment. From a cognitive perspective, any person engaged in decision-making (either alone or in a group) is actively involved in a process characterized by certain elements. These were mentioned in chapter 1, but are reiterated briefly at this point: (1) detection of a problem, (2) definition or diagnosis, (3) consideration of available options, (4) choice of what is perceived as the best option given recognized needs, and (5) execution of the choice based on what has transpired [Flathers et al. 1982; Baumann and Bourbonnais 1982]. At any moment in this process, there are factors at play that have a large impact on one's ability to solve complex problems in a limited time: (1) an internal state [Hedge and Lawson 1979] is the sum of a person's psychomotor skills, knowledge, attitudes, etc.; (2) uncertainty [Brecke 1982] is caused by faulty or incomplete information received from the external environment; (3) stress [Biggs 1968; Jensen and Benel 1977] is generated both by the problem at hand and any background problem that may exist; and (4) complexity, as it is used here, refers to the number of elements involved that must be attended to. These variables are depicted in figure 5.1, and their relationship to each other and to an outcome is indicated. This schema is designed to suggest interaction, because while the judgment and decision-making process may be conceptualized as discrete stages, experience tells us that this is not the way people function in real-world situations.

Jan 1, 2000

It is suggested in chapter 1 that emergency activities (including escape) are not individualistic. They tend to be group responses. If escapes from mine fires are group activities, then preparation for such events must take group behavior into account. This chapter explores the hypothesis that the miners who escaped from the three mines under study did so as members of groups. For development of this chapter, the database was examined for evidence of the existence of escape groups and for instances when individualistic behavior was paramount. Illustrations of group and/or individualistic behavior were analyzed and representative examples are provided in the following discussion. The nature of groups was discussed in chapter 3 and the following working definition was offered: "We mean by a group a number of persons who communicate with one another often over a span of time, and who are few enough so that each person is able to communicate with all the others, not at second-hand, through other people, but face-to-face" [Homans 1950]. To determine whether or not groups existed during the fire evacuations, it is important that the concept of group be clearly defined. Therefore, the discussion started in chapter 3 will be elaborated here. The defining characteristics of group given in chapter 3 were taken from Homans [1950]. They include size, person-to-person com¬munication, feelings that members have for each other, explicit and implicit rules for behavior, and common activities. An additional characteristic that is sometimes used to define groups is cohesiveness. Kiesler and Kiesler [1970] state, "Cohesiveness would include not only the attraction that the group holds for its members but also any other force operating on the individual to stay in the group." Variables said to contribute to cohesiveness include "(1) the attractiveness of a group for its members," and "(2) the coordination of the efforts of the members" [Keisler 1970]. One source of the attraction to a group occurs when "the goals or exterior tasks confronting the group are consistent with those of the individual person, and can best be handled by group action" [Cartwright and Zander 1968, in Davis 1969]. In other words, a common goal and a coordinated effort mounted to achieve that goal contributes toward the creation of a cohesive group. As discussed in the earlier review of research (chapter 1), groups have frequently been studied in laboratory or simulation settings. These methods allowed control over variables of interest. Kiesler and Kiesler [1970] state, however, that they "find group variables conceptually imprecise and experimentally difficult to work with." Experimental control, in other words, is difficult to achieve in the study of groups. The richness of the naturalistic mine fire data may provide an opportunity for an examination of groups that cannot be found in laboratories or simulations because this environment was not

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This book has employed miners' narratives to illustrate basic concepts about the escape process. One of the most powerful means by which people make sense of their experiences is through the telling and internalization of stories [Bruner 1990]. By couching one's own and others' motives and actions in terms of a coherent narrative, a person is able to learn from mistakes and plan future behaviors that may help ensure survival. A growing body of research suggests that decision-making skills used to deal with emergency situations can be taught and assessed by simulations based on narratives from the real world [Bransford et al. 1986; Brecke 1982; Brener 1984; Connolly et al. 1989; Halff et al. 1986; Jones and Keith 1983; Lacefield and Cole 1986]. Such techniques have been used to address the decision-making of medical personnel, civil and military flight crews, and even people involved in broader life events such as political and military situations [Babbott and Halter 1983; Dugdale et al. 1982; Farrand et al. 1982; Gilbert 1975; McGuire 1985; McGuire et al. 1976; Flathers et al. 1982; Giffin and Rockwell 1984; Jensen 1982; Janis and Mann 1987]. Given the validity of this method of study and the promise it holds for helping people improve the quality of their responses to nonroutine occurrences, it is perhaps surprising that there have been no studies of emergency decision-making among blue-collar workers prior to those conducted by the present authors and their colleagues. The purpose of this chapter is to describe underground coal miners' decision-making performance on a table-top simulation whose problem structure is derived from interviews with a group of eight miners who escaped from the 5 Left section at Brownfield Mine. The exercise was constructed by a panel of domain experts (mine safety and rescue personnel) with the assistance of an educational psychologist. The simulation includes actual predicaments with wise and unwise decision alternatives that, in the opinion of these domain experts, are characteristic of such escapes. Results reported in this chapter are the scores of a sample of experienced mine workers who completed the simulation. Because the exercise is a series of objective performance tasks coupled with detailed and immediate feedback, this simulation can be used to teach and refresh critical escape skills, as well as to provide data concerning the proficiency of miners at the time of exercise administration.

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PURPOSE: Evaluate practical technological advances in level-dependent hearing protectors and inexpensive personal acoustic monitors. RESEARCH SUMMARY: The results of previous NIOSH research show that miners develop hearing loss much more quickly than nonoccupationally noise-exposed individuals and that miners experience a greater severity of hearing loss than would be expected for nonoccupational noise-exposed persons of the same age and gender. Using hearing thresholds at 4,000 Hz as an indicator, coal miners experience hearing loss 2.5 to 3 times greater than would be expected for persons not exposed to occupational noise. At age 50, 90% of coal miners and 49% of metal/nonmetal miners were found to have a hearing impairment. In comparison, only 9% of a nonoccupationally exposed control group had a hearing impairment at age 50.

Jan 1, 2000