Exploratory analysis of Spanish energetic mining accidents

International Journal of Occupational Safety and Ergonomics (JOSE) 2012, Vol. 18, No. 2, 209–219 Exploratory Analysis of Spanish Energetic Mining Accidents Lluís Sanmiquel Dpto. de Ingeniería Minera y Recursos Naturales, Universitat Politecnica de Catalunya, ` Barcelona, Spain Modesto Freijo Dpto. de Ingeniería Eléctrica, Universitat Politecnica de Catalunya, Barcelona, Spain ` Josep M. Rossell Dpto. de Matemática Aplicada III, Universitat Politecnica de Catalunya, Barcelona, Spain ` Using data on work accidents and annual mining statistics, the paper studies work-related accidents in the Spanish energetic mining sector in 1999–2008. The following 3 parameters are considered: age, experience and size of the mine (in number of workers) where the accident took place. The main objective of this paper is to show the relationship between different accident indicators: risk index (as an expression of the incidence), average duration index for the age and size of the mine variables (as a measure of the seriousness of an accident), and the gravity index for the various sizes of mines (which measures the seriousness of an accident, too). The conclusions of this study could be useful to develop suitable prevention policies that would contribute towards a decrease in work-related accidents in the Spanish energetic mining industry. energetic mining risk index average duration index 1. INTRODUCTION In the total number of Spanish economic sectors, mining is one of those with a high index of annual incidences, i.e., the number of accidents per 100 000 workers. In 2006, Spanish mining had an incidence index 4.7 times higher than all economic sectors [1, 2, 3]. If the indices of work accidents in Spanish mining are compared with those of other countries, we can see that their values are also much higher. Specifically, in 2006, the index of annual incidence was 8.9 times higher than gravity index mining accidents in the USA [4] and 20.4 times higher than in the State of Queensland (Australia) [5]. The Spanish mining sector can be classified into two types, energetic and nonenergetic mining. Energetic mining includes activities related to extraction and agglomeration of coal, extraction of uranium and thorium, and extraction and preparation of any solid fuel. Petrol and gas extraction are not included. Nonenergetic mining includes the extraction and preparation of metallic and nonmetallic minerals and quarry products such as limestone, marble, granite, sand and gravel or clay, with the We would like to thank the National Institute of Safety and Hygiene in Work for allowing us to use the annual general databases for accidents in Spain in the mining sector from 1999–2007. We also wish to thank the Director General of Energetic Policies and Mining of the Spanish Ministry of Industry, Tourism and Commerce for giving access to the mining statistics data from 1999–2006. Correspondence and requests for offprints should be sent to Lluís Sanmiquel, Polytechnical University of Catalunya, Avda. Bases de Manresa, 61–73, 08242 Manresa (Barcelona), Spain. E-mail: sanmi@emrn.upc.edu. 210 L. SANMIQUEL ET AL. purpose of producing aggregates, cement, ornamental stones, concrete or ceramic products. The indices of annual incidences in energetic mining [1] are significantly larger than in nonenergetic mining [2]. In 2006, this index was 2.7 times higher than in the nonenergetic sector. Most accidents are caused by human error [6], and many of those are experienced by relatively few workers [7]. The first event that immediately precedes an accident is mostly caused by environment factors, while the second event, i.e., the event that takes place immediately before the first one, is mainly attributed to behaviour factors [8]. This paper considers several characteristics. Specifically, it analyses the relationship between work-related accidents and the age of workers, occupational experience and the size of the mining work centre (defined by the number of employees). Many researchers studied the effect of age and mining experience on the occurrence of accidents; different conclusions followed. The National Research Council found a strong negative link between age and the seriousness of the lesions of injured workers in coal mines [9]. Younger injured workers seemed more seriously injured than the older ones. Bennett did not find any relationship between age and the seriousness of the injury; the author concluded that less experienced miners were slightly more likely to have an accident than more experienced ones [10]. However, Bennett and Passmore showed that older injured miners were more likely to have serious or fatal injuries than younger ones [11]. Butani noted that injuries in the coal industry were more related to experience than to age, and less experience increased the probability of an accident [12]. Specifically, this study concludes that there exists a significant occupational experience effect with the largest increase in risk occurring in workers over 50 years old with under one year of occupational experience. In their study on transport injuries in small coal mines, Hunting and Weeks reported an increased risk of injury with less experience but no age effect [13]. They also observed that small coal mines had the highest rates of transport-related injuries. Moreover, small mines had a greater share of fatal and JOSE 2012, Vol. 18, No. 2 permanently disabling injuries, whereas the large mines had a greater share of injuries involving no lost workdays. On the other hand, some studies showed how accidents in the workplace could be attributed to personal and environmental factors [14, 15, 16, 17]. Groves, Kecojevic and Komljenovic classified accidents taking into account the machine or equipment used when the accident happened [18]. A similar study has been impossible with our available mining data. In this paper, information on the machine or equipment has been substituted with seven frequent types of accidents occurring in underground and surface mining. We can note that a wide variety of results has been offered in the literature depending on the available mining data. In this article, we have presented and analysed data on accidents in the Spanish energetic mining sector in 1999–2008. The conclusions may be used in planning appropriate safety programs and measures such as engineering, enforcement, education or technological advances, to warn against injuries or fatal accidents in the energetic mining sector in the future. 2. METHODS 2.1. Study Population The study population comprised accidents that took place in the Spanish energetic mining sector in 1999–2008, within the work schedule (we did not consider accidents which happened on one’s way to or from work) which caused the injured worker to lose at least one workday. The data were obtained from the annual digital database of the Ministry of Employment and Social Security with ArcGis 9.21. It is important that the annual accident database does not supply separate information about underground mining activities and surface ones for 1999–2002. However, this information is available for 2003–2008, and the two kinds of mining activities can be distinguished. In this paper, we analyse the overall accidents which happened in the indicated period or only the accidents corre- ACCIDENTS IN SPANISH ENERGETIC MINING sponding to 2003–2008, according to information from the database. The percentage of workers in the Spanish energetic mining who were divided into seven age groups and into six types of work centres, were obtained from the yearbooks of work statistics of Spain’s Ministry of Work and Immigration from 1999–2008 and the yearbooks of mining statistics of Spain’s Ministry of Industry, Tourism and Commerce for 1999–2006. 2.2. Description of the Methodology The study was divided into underground and surface mining, but this only applied to 2003–2008, since in 1999–2002 the location of the accidents was unknown (see section 2.1.). We considered the workers’ age and the size of the work centre (defined by the number of employees) where the accident took place. There were seven age groups (16–24, 25–29, 30–34, 35–39, 40–44, 45–54 and ≥55 years), six sizes of work centres (1–9, 10–19, 20–49, 50–99, 100–499 and ≥500 workers) and six experience groups (0–12, 13–60, 61–120, 121–180, 181–240 and ≥241 months) for seven kinds of accidents. A risk index is defined as the ratio of the percentage of injuries attributed to a given subpopulation (age group or size of work centre) to the percentage of the total workforce represented by that subpopulation [12]. A risk index of 1 corresponds to an average risk, while a value greater than 1 indicates a higher risk for that group. Thus, to calculate the risk index, we needed to know the percentage of accidents that happened in each age groups or work centre, and the percentage of workers in them. We also calculated the average duration index (ADI) for each age group (1999–2007) and for each size of the work centre (1999–2006); it indicated the seriousness of accidents. Once the risk index and the ADI for the age groups were calculated, we analysed the relationship between the risk index (as an indicator of the incidence of accidents in a population) and the ADI (as an indicator of the severity of the 1 211 accidents), and both the age and the size of the work centres. This was done with the nonparametric statistical Spearman rank correlation. The mean was calculated for the total population in 1999–2006 or 1999–2008. Throughout this paper, analyses were conducted at a .05 significance level. The results were analysed in two ways. The Mann–Whitney U test was used to observe if there was a significant difference between underground and surface mining in the distribution of the ADI, depending on the occupational experience of the injured parties. Spearman rank correlation coefficient was used to analyse the relationship between the ADI and the workers’ experience in the seven types of the most frequent accidents in underground and surface mining. 3. RESULTS AND DISCUSSION 3.1. Age and Experience of Injured Workers Table 1 shows data on the percentage of workers in 1999–2008 per year, per age group, for the seven age groups. The results in Table 2 show a significantly greater risk in the 16–24, 25–29, 30–34 and 35–39 age groups, with the highest estimated risk indices. At the same time, the risk index was significantly lower for the 45–54 and, especially, ≥55 groups. Spearman rank correlation coefficients, calculated for each year, show that the incidence of accidents (expressed with the risk index) is correlated with the workers’ age: all coefficients are over the critical value of .714. The correlation is negative, i.e., when the workers’ age increases, the incidence of accidents decreases (Table 2). Table 2 illustrates accidents for each age group, the risk index and Spearman rank correlation coefficient for each year between 1999 and 2008. To analyse the possible relationship between the seriousness of the accidents and the age of the workers, the ADI for each age group was calculated with data from Table 3 (Table 4). Spearman rank correlation coefficients were higher than the critical value, except in 2005 and 2006. This indi- http://www.esri.com/software/arcgis/arcgisserver/ JOSE 2012, Vol. 18, No. 2 212 L. SANMIQUEL ET AL. TABLE 1. Energetic Mining Population by Age Group (1999–2008) Age Group (%) Year 16–24 25–29 30–34 35–39 40–44 1999 3.09 7.73 19.07 31.96 2000 2.75 6.59 18.13 31.32 2001 2.41 6.02 16.87 2002 2.01 6.04 15.44 2003 2.24 5.97 2004 1.68 5.88 2005 1.92 2006 2007 2008 M 45–54 ≥55 26.29 9.79 2.06 29.12 10.44 1.65 30.72 30.72 10.84 2.41 30.87 32.21 11.41 2.01 14.18 30.60 32.84 12.69 1.49 12.61 30.25 32.77 14.29 2.52 5.77 11.54 29.81 32.69 15.38 2.88 2.13 5.32 11.70 28.72 32.98 17.02 2.13 2.33 5.81 11.63 26.74 32.56 18.60 2.33 2.53 6.33 12.66 24.05 34.18 18.99 1.27 2.37 6.27 15.15 30.07 31.06 13.01 2.07 TABLE 2. Distribution of Accidents by Age Group and Estimated Risk Index (1999–2008) Age Group (%) Risk Index Spearman Correla16–24 25–29 30–34 35–39 40–44 45–54 ≥55 tion Year 16–24 25–29 30–34 35–39 40–44 45–54 ≥55 1999 4.31 8.33 20.82 33.63 26.76 5.52 0.64 1.39 1.08 1.09 1.05 1.02 0.56 0.31 –.964 2000 4.41 8.04 20.59 31.67 29.09 5.54 0.66 1.60 1.22 1.14 1.01 1.00 0.53 0.40 –1.000 2001 2.58 6.74 19.03 34.04 32.15 4.87 0.59 1.07 1.12 1.13 1.11 1.05 0.45 0.25 –.750 2002 2.87 7.43 16.65 32.85 34.56 5.22 0.41 1.42 1.23 1.08 1.06 1.07 0.46 0.21 –.964 2003 2.79 7.99 14.70 33.22 35.01 5.84 0.44 1.25 1.34 1.04 1.09 1.07 0.46 0.30 –.857 2004 2.37 7.79 13.55 34.38 35.80 5.61 0.49 1.41 1.33 1.07 1.14 1.09 0.39 0.19 –.893 2005 2.49 7.55 13.73 34.55 35.12 5.87 0.70 1.29 1.31 1.19 1.16 1.07 0.38 0.24 –.964 2006 2.81 7.60 13.76 33.37 34.81 7.03 0.62 1.32 1.43 1.18 1.16 1.06 0.41 0.29 –.964 2007 3.43 7.25 15.89 30.83 34.67 7.30 0.62 1.48 1.25 1.37 1.15 1.07 0.39 0.27 –.964 2008 3.93 7.88 16.63 27.25 34.88 8.47 0.96 1.55 1.24 1.31 1.13 1.02 0.45 0.76 –.929 M 3.33 7.71 17.44 32.86 32.25 5.81 0.60 1.40 1.23 1.15 1.09 1.04 0.45 0.29 –1.000 cates a positive correlation between the workers’ age and the seriousness of the accident. This coincides with Bennet and Passmore’s results [11]. Table 5 shows the ADIs for the seven most frequent types of accidents in underground and surface energetic mining, according to the experience of the injured workers. We calculated the Spearman rank correlation coefficient for each type of accident to evaluate the relationship between experience and the ADI. The null hypothesis can be rejected only for accident 50 (contact with a cutting, piercing, hard or rough material agent), for underground energetic mining. In the other kinds of accidents in underground and surface mining, the null hypothesis cannot be rejected because the Spearman rank JOSE 2012, Vol. 18, No. 2 correlation coefficients are under the critical value of .786. So, for the other types of accidents, there is no statistically significant relationship between the injured parties and the seriousness of the accidents expressed through the ADI. To compare the ADI for each group, considering experience in underground and surface energetic mining, we used the Mann–Whitney U test. The results showed the null hypothesis could not be rejected for accident 31 (blow, or hitting something as a result of a fall), 32 (blow as a result of a fall, or crashing into an immovable object), 71 (physical overexertion of the musculoskeletal system) and the remaining types of accidents (considered as one type). We concluded that there was no significant difference between ACCIDENTS IN SPANISH ENERGETIC MINING 213 TABLE 3. Distribution of Lost Workdays and Nonfatal Accidents by Age Group (1999–2008) Lost Workdays Year 16–24 25–29 30–34 35–39 40–44 45–54 ≥55 Total 1999 9635 18 237 48 436 85 356 72 940 16 206 2313 253 123 2000 9544 18 735 43 832 73 243 71 067 17 211 2078 235 710 2001 5033 14 252 47 890 89 861 90 407 15 850 1544 264 837 2002 5178 13 938 32 095 69 485 76 711 12 164 851 210 422 2003 4248 13 455 23 494 59 635 66 526 11 623 863 179 844 2004 2270 9149 16 241 46 469 50 023 8551 1087 133 790 2005 2779 8892 17 048 35 815 38 206 7878 1031 111 649 2006 2411 5832 11 563 27 040 32 702 6403 655 86 606 2007 1988 6247 15 304 30 394 30 467 8785 1025 94 210 2008 2557 5458 12 438 20 520 25 733 7828 1574 76 108 45 643 114 195 268 341 537 818 554 782 112 499 13 021 1 646 299 Year 16–24 25–29 30–34 35–39 40–44 45–54 ≥55 Total 1999 495 955 2391 3859 3075 634 74 11 483 2000 464 847 2162 3329 3060 581 70 10 513 2001 235 614 1735 3101 2929 444 54 9112 2002 215 557 1248 2462 2589 391 30 7492 2003 182 520 959 2164 2283 381 29 6518 2004 136 450 782 1983 2066 322 28 5767 2005 114 345 628 1582 1606 268 32 4575 2006 108 295 534 1295 1350 273 24 3879 2007 133 280 615 1194 1344 281 24 3871 2008 139 279 589 964 1234 298 34 3537 2221 5142 11 643 21 933 21 536 3873 399 66 747 total Nonfatal Accidents total TABLE 4. Distribution of Average Duration Index for Nonfatal Injuries by Age Group (1999–2008) Average Duration Index Year 16–24 25–29 30–34 35–39 40–44 45–54 ≥55 Spearman Correlation 1999 19.46 19.10 20.26 22.12 23.72 25.56 31.26 .964 2000 20.57 22.12 20.27 22.00 23.22 29.62 29.69 .893 2001 21.42 23.21 27.60 28.98 30.87 35.70 28.59 .787 2002 24.08 25.02 25.72 28.22 29.63 31.11 28.37 .893 2003 23.34 25.88 24.50 27.56 29.14 30.51 29.76 .929 2004 16.69 20.33 20.77 23.43 24.21 26.56 38.82 1.000 2005 24.38 25.77 27.15 22.64 23.79 29.40 32.22 .464 2006 22.32 19.77 21.65 20.88 24.22 23.45 27.29 .714 2007 14.95 22.31 24.88 25.46 22.67 31.26 42.71 .893 2008 18.40 19.56 21.12 21.29 20.85 26.27 46.29 .893 20.55 22.21 23.05 24.52 25.76 29.05 32.63 1.000 total JOSE 2012, Vol. 18, No. 2 214 L. SANMIQUEL ET AL. TABLE 5. Average Duration Index for 7 Types of Accident-Related Injuries Depending on Experience in Underground and Surface Mining (2003–2008) Average Duration Index Experience in Underground Mining Nonfatal injuries Fatalities 71 42 50 32 40 41 31 All Types of Accidents 5084 0 4925 4 3874 0 1353 0 1317 0 882 1 840 3 23 970 20 26 647 20.23 23.20 14.60 63.60 14.88 45.92 14.79 16.29 –.071 37 606 27.79 24.67 37.75 07.24 41.42 16.27 25.79 17.06 –.143 14 498 16.44 16.77 19.95 18.95 15.98 14.61 10.97 18.20 –.464 22 190 26.42 23.84 24.17 29.81 26.41 25.26 26.67 51.25 .750 569 774 23.77 24.56 26.17 22.42 23.29 24.01 21.50 27.80 –.071 Lost workdays (nonfatal injuries) 120 680 122 033 80 711 Type of accident (total) 23.74 24.78 20.83 0–12 months 23.81 25.43 21.69 13–30 months 25.95 23.82 28.30 31–60 months 23.45 21.78 20.27 61–120 months 22.64 25.97 18.79 121–180 months 25.97 22.39 18.63 181–240 months 22.34 23.46 16.86 ≥241 months 27.26 32.64 15.09 Spearman correlation .143 .179 –.964 Average Duration Index Experience in Surface Mining Nonfatal Injuries Fatalities Lost workdays (nonfatal injuries) Type of accident (total) 0–12 months 13–30 months 31–60 months 61–120 months 121–180 months 181–240 months ≥ 241 months Spearman correlation 71 424 0 42 211 1 31 116 0 32 73 0 44 57 0 41 49 0 63 43 0 All Types of Accidents 1372 3 10 266 24.21 21.49 20.69 30.03 21.46 22.64 27.44 26.65 .464 6111 28.96 24.59 37.93 28.73 32.38 24.71 40.47 26.95 .214 3264 28.14 27.57 28.05 26.95 38.63 14.83 32.37 21.83 –.214 1662 22.77 20.69 19.00 17.33 49.50 18.20 22.25 32.33 .393 1286 22.56 31.26 20.50 14.54 16.20 28.00 23.50 14.50 –.429 660 13.47 13.72 07.33 28.75 04.40 12.25 13.25 14.00 .107 1292 30.05 24.29 22.50 38.63 28.00 27.00 46.83 00.00 .071 34 513 25.16 23.94 23.18 27.61 23.47 24.94 28.98 26.06 .393 Notes. 31—blow, or hitting something as a result of a fall; 32—blow as a result of a fall, or crashing into an immovable object; 40—crashing into or hitting a moving object; 41—being hit by an object or projected fragments; 42—being hit by a falling object or one that is detached; 44—crash or blow against a moving object, including vehicles (immovable worker); 50—contact with a cutting, piercing, hard or rough material agent; 71—physical overexertion of the musculoskeletal system; 63—being trapped or flattened. energetic underground and surface mining for those four types of accidents. This is not the case for accident 41 (being hit by an object or projected fragments) or 42 (being hit by a falling object or one that is detached), since the null hypothesis is rejected because in both cases the U values are under or equal to the critical value. 3.2. Size of the Mines Table 6 shows the percentage of workers by year and size of mine, for 1999–2006. The workforce JOSE 2012, Vol. 18, No. 2 is divided into six sizes of mines (in number of workers) classified in percentage per year. Tables 6–7 show how the total number of accidents does not coincide exactly with the number of those in Table 2. This is so because in the classification according to size, the database field has missing values. Mining data for 2007–2008 were not published yet and, consequently, we could not calculate either the percentage of workers according to the size of the mines or the risk index for those years. ACCIDENTS IN SPANISH ENERGETIC MINING TABLE 6. Energetic Mining Population by Size of Mine (1999–2006) Size of Mine (%) Year 1–9 10–19 20–49 50–99 100–499 ≥500 1999 0.24 0.79 3.77 4.31 21.64 69.25 2000 0.22 0.84 4.09 5.87 23.09 65.88 2001 0.20 1.20 3.94 4.73 21.25 68.68 2002 0.36 0.94 4.60 5.78 22.70 65.61 2003 0.32 1.18 3.89 6.20 22.32 66.10 2004 0.20 1.41 5.76 5.70 24.62 62.31 2005 0.31 1.78 3.35 6.83 33.63 54.09 2006 0.29 1.45 4.32 5.72 37.19 51.04 M 0.27 1.20 4.22 5.64 25.80 62.87 The results in Table 7 indicate a significantly greater risk for 1–9 workers (especially), 10–19, 20–49 and 50–99 workers, with the estimated risk indices of 3.33, 1.25, 1.26 and 1.32, respec- 215 tively. At the same time, risk was significantly lower for the 100–499 category, with an index of .76. It should be noted that 100–499 workers (especially) and ≥500 workers were the safest categories in 1999–2006. The 1–9 workers category was more dangerous because the risk index was significantly higher than for the other groups. These results coincide with studies that established that the proportion of accidents was greater in small mines [13, 20]. Spearman coefficients calculated for distributions of the risk index according to the size of the mines showed that there was no correlation between accidents and the size of the mines. The null hypothesis can be rejected for 1999 and 2002 only. For other years, Spearman rank correlation coefficients were high (except for 2004), but they TABLE 7. Distribution of Accidents by Size of Mine and Estimated Risk Index (1999–2006) Size of Mine (%) Risk Index Year 1–9 10–19 20–49 50–99 100–499 ≥500 1–9 10–19 20–49 50–99 100–499 ≥500 1999 1.33 1.69 4.58 4.46 18.01 69.93 5.47 2.13 1.21 1.04 0.83 1.01 –.943 2000 1.33 1.80 4.16 7.46 17.80 67.45 6.13 2.13 1.02 1.27 0.77 1.02 –.786 2001 0.54 1.20 3.73 6.40 19.88 68.26 2.70 1.00 0.95 1.35 0.94 0.99 –.600 2002 0.75 1.59 5.74 6.18 18.19 67.55 2.05 1.68 1.25 1.07 0.80 1.03 –.943 2003 1.07 1.29 4.68 8.61 19.53 64.81 3.40 1.10 1.20 1.39 0.88 0.98 –.714 2004 0.71 1.04 5.06 8.96 18.62 65.62 3.58 0.74 0.88 1.57 0.76 1.05 –.143 2005 0.72 1.53 5.98 9.95 20.34 61.48 2.29 0.86 1.78 1.46 0.60 1.14 –.543 2006 0.67 1.86 8.74 7.71 24.74 56.29 2.34 1.28 2.02 1.35 0.67 1.10 –.771 M 0.89 1.50 5.33 7.47 19.64 65.17 3.33 1.25 1.26 1.32 0.76 1.04 –.714 Spearman Correlation TABLE 8. Distribution of Accidents and Lost Workdays by Size of Mine Categories (1999–2008) Lost Workdays Nonfatal Accidents Year 1–9 10–19 20–49 50–99 100–499 ≥500 1–9 10–19 20–49 50–99 100–499 ≥500 1999 3348 4755 12 308 10 048 46 903 141 777 140 177 481 468 1885 7336 2000 3251 4732 9824 15 472 43 806 131 631 129 175 403 722 1724 6547 2001 1510 3188 7823 13 173 47 536 179 492 47 105 326 559 1738 5970 2002 1215 2475 9583 11 269 37 498 141 554 54 115 416 448 1318 4894 2003 1757 1900 7992 12 574 34 458 118 790 69 82 301 553 1255 4160 2004 1541 1415 7923 12 592 29 353 80 966 41 60 291 516 1074 3785 2005 1341 2153 6543 11 981 23 769 65 862 33 70 273 453 930 2816 2006 505 2200 7905 6577 24 044 45 348 26 72 338 299 959 2184 2007 577 3557 10 343 8773 37 819 33 119 21 140 425 389 1344 1551 2008 745 2461 10 014 8526 29 533 24 829 41 80 386 421 1178 1431 601 1076 3640 4828 13 405 40 674 total 15 790 28 836 90 258 110 985 354 719 963 368 JOSE 2012, Vol. 18, No. 2 216 L. SANMIQUEL ET AL. were lower than the critical value (.829). All the coefficients were negative. To analyse the possible relationship between the seriousness of the accidents and the size of the mines, we calculated the ADI for each group. Spearman rank correlation coefficients were lower than the critical value, except in 2002 and 2005 (the two coefficients had different signs). This indicates that there is no correlation between the size of the mines (defined by the number of TABLE 9. Distribution of Average Duration Index for Nonfatal Injuries by Size of Mine Categories (1999–2008) Average Duration Index Year 1–9 10–19 20–49 50–99 100–499 ≥500 Spearman Correlation 1999 23.91 26.86 25.59 21.47 24.82 19.32 –.543 2000 25.20 27.04 24.32 21.34 25.32 20.08 –.543 2001 32.13 30.36 24.00 23.52 27.32 30.05 –.486 2002 22.50 21.52 23.04 25.15 28.45 28.92 .943 2003 25.46 22.89 26.55 22.74 27.46 28.53 .600 2004 37.59 23.58 27.13 24.36 27.31 21.37 –.486 2005 40.64 30.76 23.88 26.27 25.50 23.38 –.829 2006 19.42 30.56 23.32 22.00 25.05 20.76 .029 2007 27.48 25.41 24.34 22.55 28.14 21.35 –.429 2008 18.17 30.76 25.94 20.25 25.07 17.35 –.371 26.27 26.80 24.80 22.99 26.46 23.69 –.429 M TABLE 10. Distribution of Accidents and Worked Hours by Size of Mine Category (1999–2006) Lost Workdays Year 1–9 10–19 20–49 50–99 100–499 ≥500 1999 3348 4755 12 308 10 048 46 903 141 777 2000 3251 4732 9824 15 472 43 806 131 631 2001 1510 3188 7823 13 173 47 536 179 492 2002 1215 2475 9583 11 269 37 498 141 554 2003 1757 1900 7992 12 574 34 458 118 790 2004 1541 1415 7923 12 592 29 353 80 966 2005 1341 2153 6543 11 981 23 769 65 862 2006 505 2200 7905 6577 24 044 45 348 14 468 22 818 69 901 93 686 287 367 905 420 total Worked Hours Year 1–9 10–19 20–49 50–99 100–499 ≥500 1999 79 783 183 880 839 619 761 356 4 778 611 12 861 750 2000 175 422 164 031 767 758 1212 769 4 512 380 11 982 640 2001 63 654 204 159 688 551 769 283 3 740 067 11 577 285 2002 76 788 153 575 715 691 998 240 3 302 615 97 84 091 2003 66 338 152 578 529 969 841 758 3 125 267 8 831 090 2004 39 264 152 694 674 034 582 418 3 087 323 7 076 267 2005 46 661 298 114 467 478 966 063 4 711 932 7 262 752 2006 46 896 250 110 591 405 757 276 5 398 199 6 600 115 594 806 1 559 141 5 274 504 6 889 163 32 656 395 75 975 991 total JOSE 2012, Vol. 18, No. 2 ACCIDENTS IN SPANISH ENERGETIC MINING 217 TABLE 11. Distribution of Gravity Index for Nonfatal Injuries by Size of Mine (1999–2006) Gravity Index Year 1–9 10–19 20–49 50–99 100–499 ≥500 Spearman Correlation 1999 41.96 25.86 14.66 13.20 9.82 11.02 –.943 2000 18.53 28.85 12.80 12.76 9.71 10.99 –.886 2001 23.72 15.62 11.36 17.12 12.71 15.50 –.486 2002 15.82 16.12 13.39 11.29 11.35 14.47 –.543 2003 26.49 12.45 15.08 14.94 11.03 13.45 –.543 2004 39.25 9.27 11.75 21.62 9.51 11.44 –.314 2005 28.74 7.22 14.00 12.40 5.04 9.07 –.543 2006 M 10.77 8.80 13.37 8.69 4.45 6.87 –.771 24.32 14.63 13.25 13.60 8.80 11.92 –.886 workers) with the seriousness of the accidents. Table 9 shows the values of the ADI for six sizes of mines. Table 9 was calculated from the data in Table 8. There was no important variation between the different sizes of mines. To obtain information about for the possible relationship between the seriousness of the accidents and the size of the mines, we calculated the gravity index for each group. Table 11 shows that index; it was developed from the data in Table 10. Spearman correlation coefficients were lower than the critical value (.886), except in 1999, 2000, and the mean values in 1999–2006. This indicates a poor negative correlation between the size of the mine and the seriousness of the accidents, expressed as the gravity index. Furthermore, there is an important variation of the gravity index among the different sizes of mine. Thus, smaller mines had a greater proportion of accidents with more serious injuries. This coincides with Hunting and Weeks [13], Fabiano, Currò and Pastorino [19] and Saari [20]. 4. CONCLUSIONS We can draw the following conclusions. · The incidence of work accidents in Spanish energetic mining decreases, whereas the age of the injured workers increases. However, the seriousness of the injuries caused by the accidents increases with age. Both results were significant. Using these results, the competent administrations and prevention services of the Spanish energetic mining sector should programme specific safety training and information for the youngest workers (especially those under 29 years). The causes of the most serious accidents of the oldest workers should also be analysed (especially those over 54 years). A possible explanation could be that the older workers take longer to recover from the same injuries than the younger ones. · We did not observe any relationship between the seriousness of the accidents and the experience of the injured parties. The analysis was carried out for the seven most frequent kinds of accidents in underground and surface mining. There was a relationship between the seriousness of the accidents and the workers’ occupational experience in accident type 50 (contact with a cutting, piercing, hard or rough agent) in underground mining only. Thus, the negative consequences of the accidents produced by type 50 decrease, whereas the workers’ occupational experience increases. · Accident 71 (physical overexertion of the musculoskeletal system) and 42 (being hit by a falling object or one that is detached) are the most frequent in underground and surface mining. Those two types of accidents caused the highest number of lost workdays, specifically, 242 713 and 16 377 lost workdays in underground and surface mining, respectively. It should be taken into account that in underground mining there is the highest ADI by the group of workers with more experience (>240 months), with values of 32.64 and 27.26 for accidents 42 and 71, respectively. Thus, in energetic underground mining for 1999–2008, JOSE 2012, Vol. 18, No. 2 218 L. SANMIQUEL ET AL. for workers with over 240 months of occupational experience (usually 40–45 years old), the results showed that the injuries caused by overexertion produced more lost workdays by the oldest workers. This is why people who are responsible for the organization of the work in different mines (underground and surface) in the Spanish energetic mining sector should consider the age of the workers who have to carry out specific jobs involving physical effort. · The results seem to show that mining centres with a low number of workers had a higher incidence of accidents than those with more workers. This incidence is especially important in mines with under 10 workers, with an average risk index of 3.33 in 1999–2006. · We cannot confirm that the seriousness of the accidents, expressed as ADI, increases or decreases with the size of the mining work centres, due to low statistical significance. 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