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Evaluation of human error in workers of an Iranian copper mine during the COVID-19 pandemic using the CREAM

Abstract

BACKGROUND:

The outbreak of COVID-19 has adversely affected both global economy and public health around the world. These effects have also been observed in many workplaces, including mines.

OBJECTIVE:

This study aimed to examine the human error of copper miners during the pandemic.

METHOD:

This descriptive-analytical, cross-sectional study was performed on 192 workers of a copper mine in Iran.. For this, occupation tasks were firstly analyzed using the Hierarchical Task Analysis (HTA), and then the human error in different subunits was assessed using the basic Cognitive Reliability and Error Analysis Method (CREAM). The prevalence of COVID-19 among miners was determined by assessing positive PCR test records.

RESULTS:

The probability of human error in the operational subunits including mining, crushing, processing, and support subunits was estimated to be 0.0056, 0.056, 0.0315, and 0.0177, respectively. All three operational units were found to be in the scrambling control mode. The support unit was determined to be in the tactical control mode. Approximately 50% of all workers had been infected with COVID-19, with the highest prevalence in support units.

CONCLUSION:

The results suggest that during the COVID-19 pandemic, copper miners are at higher risk of human error induced by poor working conditions. Therefore, it is recommended to employ some management strategies such as promotion of safety, health monitoring, and adopting supportive measures to control occupational stresses and therefore the probability of human error in the mine’s operational units.

1Introduction

The outbreak and rapid spread of the COVID-19 infection have adversely affected the global economy, public health, and everyday life since 2020. Workplaces are considered as the primary hotspots for the transmission of this virus [1]. For this reason, health authorities have put much effort into preventing transmission since the beginning of the pandemic [2]. Before the outbreak of COVID-19, it had been proven that working in the illness state can prevent recovery; it also increase the risk sickness absence in the future [3]; negatively effect on productivity; and increase the rate of work errors, accidents, and injuries not only for the person herself/himself but also for her/his colleagues [4, 5]. On the other hand, it is suggested that an increased workload, long working hours, and occupational stressors caused by the COVID-19 pandemic not only can increase the risk of infection but also can pose enormous concerns for the health of employees in the future [6]. The COVID-19 pandemic has also adversely affected the livelihood of employees, job opportunities, and economic stability in many industries, including the mining sector. It has been suggested that COVID-19 can have more severe short-, medium-, and long-term economic, physical, and mental health implications for the mining industry than for many other sectors [7]. Indeed, poor job security or wage cuts tend to put more stress on low-earning workers [8]. According to statistics, from 2004–2015, a total of 20,731 mine accidents occurred in China, with an average of 1.7 deaths per accident [9]. Many factors such as stress coping capacity, psychological and physiological performance, and life events can affect the rate of human error and ultimately health-threatening incidents [10]. Regardless of the COVID-19 pandemic, the rate of injury and mortality in harsh work environments of the mining industry has been consistently higher than in the other industries [11]. Miners tend to work in fairly dangerous environments almost every day, which explains why this industry still has higher levels of accidents and injuries than the others [12, 13]. In recent years, various studies have been conducted on the causes of human error in occupational environments [14], but many of them lack a proper analysis of external factors that may increase the chance of errors such as life events or psycho-cognitive conditions [15]. Events that affect a worker’s everyday life and living conditions (such as the COVID-19 pandemic) can easily provoke emotional disorders, which may affect the worker’s mental and even physical health condition [16]. At the very least, a poor mental state can increase the likelihood of serious non-fatal injuries such as musculoskeletal problems and slipping and falling, which in turn, lead to lost working days [17].

Unfortunately, there is a limited information on the assessment of mining accidents in Iran in terms of human error. Human errors are often the outcomes of human physiological and psychological limitations, such as forgetfulness, negligence, attention deficit, low motivation, carelessness, and recklessness [14, 18]. Unfortunately, the studies conducted on such principles are limited and provide only qualitative descriptions. Miners, especially who works at control devices, equipment, and systems in various operational units, are exposed to the high psychological factors, resulting in augmentation of their error probability [19, 20]. The Cognitive Reliability and Error Analysis Method (CREAM) technique is known as an effective and useful method to determine the cognitive errors with high probability, owning to a detailed theoretical background; focusing on cognitive and psychological, structural, and cognitive contexts of human behavior even in mining. Therefore, this study aimed to identify and assess human error in one of Iran’s copper mines during the COVID-19 pandemic in order to adopt effective control strategies to minimize the occurrence of such errors.

2Materials and method

This descriptive, analytical cross-sectional study was conducted on the copper miners working in one of Iran’s biggest copper mines. The cases were selected by means of complete enumeration sampling with at least one-year work experience. The subjects who were not interested in participating or completing the questionnaires were excluded. A total of 192 questionnaire was collected, from which the COVID-19 infections-related data were extracted by mine’s health, safety, and environment (HSE) unit. Upon observing and interviewing the unit heads as well as the safety experts, the tasks related to the units of mining, crushing, processing, and support wherein more accident are occurred were included in the study. Regarding the complexity of occupational tasks, heavy workload, and high stress of working in a mine, the analysis was carried out using the CREAM by the following steps:

  • 1. Hierarchical Task Analysis (HTA): CREAM starts with the analysis of work activities using HTA [21]. Inspired from human factors, HTA is a structured, objective method to describe users’ performance especially cognitive tasks. This approach offers an understanding of the tasks users need to perform to reach the desired goals determined by operational plans or guidelines. In this approach, the tasks can also be divided into several sub-groups to facilitate the analysis describing the interactions of users and therefore, focusing on goals and plans [22]. In this study, three operational units of mining, crushing, processing, and support units were analyzed using HTA. For this, the operators’ tasks were divided into sub-tasks. Figure 1 illustrates the analysis of the fire load operator in the mining unit as an example. Similarly, the crushing and processing units were analyzed [23].

  • 2. Assessment of Common Performance Conditions (CPCs): After task analysis, the general characteristics of each task along with the working conditions affecting performance was assessed using the CPC table (Table 1). The condition effect is described as terms of improved, reduced, or no significant (NS). Afterwards, the total number of these conditions is determined for each specified task. CPC is still a basic comprehensive structure of the features for working conditions, indicating the quality of tasks’ performance as well as the related probability of error [24].

  • 3. Determination of Total Cognitive Failure Probability (CFPt): This step starts with computing a score of control mode (called β) by subtracting the total number of conditions that improve performance from the total number of conditions that reduce performance. This score is then substituted into the following equations to estimate the total human error probability using the CREAM matrix [25, 26].

    (1)
    β=X-Y=R-I
    Where, β refers to control mode index, X and Y are the number of reduced and improved influence indexes, respectively, and K is the constant coefficient.
    CFP=CFPmax/10Kβmax
    βmax=9,βmin=-7
    CFPmax=1.0,CFPmin=0.0001,
    K=0.25,CFP=0.0056,
    CFP=CFP.×100.25β
    (2)
    CFP=0.0056×100.25β

  • 4. Regarding the β scores (the highest and lowest values of 9 and -7 for reducing and improving of the performance, respectively) that reduce and improve performance (9 and -7), CFPt values were obtained and the control modes were determined from accordingly (Table 2 and Fig. 2).

Fig. 1

Analysis of the cognitive tasks of mining (fire load) operator.

Analysis of the cognitive tasks of mining (fire load) operator.
Table 1

Context influence index for CPCs

Expected effectLevelCPC name
Adequacy of organizationVery efficientImproved
EfficientNS
InefficientReduced
DeficientReduced
Working conditionsAdvantageousImproved
CompatibleNS
IncompatibleReduced
Adequacy of human-machine interaction and operational supportSupportiveImproved
AdequateNS
TolerableNS
InappropriateReduced
Availability of procedures/ plansAppropriateNS
AcceptableNS
InappropriateReduced
Number of simultaneous goalsFewer than capacityImproved
Matching current capacityNS
More than capacityReduced
Available timeAdequateImproved
NormalNS
Temporarily inadequateNS
Continuously inadequateReduced
Time of dayDay-time (adjusted) Night-timeNS
(Unadjusted)Reduced
Adequacy of training and preparationAdequate, high experienceImproved
Adequate, low experienceNS
A little inadequateReduced
InadequateReduced
Crew collaboration qualityVery efficientImproved
EfficientNS
InefficientReduced
DeficientReduced
Table 2

Relations between the context influence index and the control mode

Control modeCFPtContext influence
index
Strategic0.00005 <  P <  0.01–7 to –4
Tactic0.001 <  P <  0.1–3 to 1
Opportunistic0.01 <  P <  0.52–5
Scrambled0.1 <  P <  1.06–9
Fig. 2

Context influence index and control modes.

Context influence index and control modes.

3Results

The demographic analysis of the population showed that 100% of the participants were male (71% married and 29% single). The workers mean age was found to be 35±8 years, of which about 50% had been infected with COVID-19, with the highest prevalence in support units (53.3% ) (Table 3).

Table 3

Percentage of COVID-19 among miners

Task nameNumberCOVID-19%
Processing451516.6
Mining351213.4
Crushing351617.7
Support units774853.3
Total1929047

The results of the nine CPCs in the four subunits of mining, crushing, processing, and support (Table 4) showed a negative impact of the working condition on the performance in three former units. Nevertheless, no significant impact was found on the performance of the workers in the support subunit.

Table 4

Context influence index for CPCs

CPC nameMiningCrushingProcessingSupport units
Adequacy of organizationNSNSReducedNS
Working conditionsReducedReducedReducedNS
Adequacy of human-machine interaction and operational supportReducedReducedNSNS
Availability of procedures/ plansNSReducedNSNS
Number of simultaneous goalsReducedNSReducedNS
Available timeReducedNSNSNS
Time of dayImprovedImprovedImprovedImproved
Adequacy of training and preparationNSReducedReducedNS
Crew collaboration qualityNSReducedImprovedReduced

The results of basic CREAM-Method are presented in Table 5. According to the results shown in Table 4, for example the in crushing unit total number of CPCs that will reduce performance (ΣR) is equal to 5 and the total CPCs that will improve performance (ΣI) is equal to 1. The Control Mode Index (β), according to Equation 1, would be 4. According to Fig. 2 the opportunistic control mode is selected for control mode type. As well as, based on Equation 2 the CFPt would be 0.056. As shown in Table 2 this number is in opportunistic control mode area. A similar calculation has been performed for the processing, mining and Support units and the CFPt in these cases would be 0.0177, 0.0315, and 0.0056, respectively. Also the control modes processing and mining units fall into opportunistic control and Support units in tactical control, based on CFPt values.

Table 5

CREAM basic method results

Task nameControl modeValue of β CFPt
ProcessingOpportunistic20.0177
MiningOpportunistic30.0315
CrushingOpportunistic40.056
Support unitsTactic00.0056

According to the basic CREAM analysis, among four mining subunits, crushing and support were found to be the highest and lowest probability for human error, respectively. In the β diagram, the crushing subunit was specified on the border between scrambling and opportunistic control modes and all other subunits fell in the scrambling control mode (Table 5).

4Discussion

4.1Basic method-CREAM

The Cognitive Failure Probability (CFP) and Probable Control Modes (PCM) were determined in the operational units (viz. mining, crushing, and processing) and Support units in one of Iran’s copper mines during the COVID-19 pandemic, using Cognitive Reliability and Error Analysis Method (CREAM). The final goal of the basic method-CREAM was to identify and assess human error and increase performance reliability and to decrease CFPt. In order to reach proper results, the control mode type should move from opportunistic mode to strategic mode. Mazloumi et al. used CREAM technique to analyze a Petrochemical Control Room in Iran. In this study, CPCs factors were analyzed and demonstrated that “number of simultaneous work”, “time of day (circadian rhythm)” and “adequacy of training and experience” are related to reduction of performance reliability. They suggested In order to increase performance reliability, the instruction for emergency situation should be used and the shift work schedule should be noted. Also improved the quality of training courses [27]. In this study the analysis of the basic method CREAM for the two operational unit (mining and crushing) demonstrated that “Adequacy of human-machine interaction and operational support” and “Working conditions” are related to the reduction of performance reliability (Table 4). Therefore, it is necessary In order to increase performance reliability, the workers have sufficient skills in task. Another strategy is to improved working condition. According to the basic CREAM outcomes (Table 5), although the operational units (i.e. mining, crushing, and processing) were in the opportunistic control mode, taking some measures e.g., moving toward tactical and strategic modes are still needed to achieve the best strategic plan to improve of working conditions [28]. Since workplace design plays a significant role in enhancing such conditions, mine officials can take some actions in this setting as well [29].

4.1COVID-19 pandemic

Due to a relative high prevalence of COVID-19 disease among the miners from the start of this pandemic (about 50% infection, Table 3), this study investigated the it’s on the employers’ performance [30]. On the one hand, the analysis of the CPCs results in three operational units of mining, crushing, and processing revealed that working conditions could have the most significant effect on an employer performance, and therefore, on human errors in such facilities, which was consistent with the nature and characteristics of the industry concerned [31]. On the other hand, working in the time of COVID-19 showed a negative effect on working conditions. The results are in close agreement with the previous reports on increasing of stress level under critical COVID-19 conditions [32]. Since work stress can negatively effect on the miners’ performance as reported elsewhere [29]. Such psychosocial factor (i.e. stress level) then can induce more human error probability (HEP) in working settings as reported in several studies [33–35]. In fact, the psychosocial factors have shown significant effect on the safety, HEP, and catastrophic accidents in the mine [19, 36]. Despite these findings, a few studies have focused on the intensified effect of the COVID-19 pandemic in the mining sector. Regarding the importance of the issue; the unreliability of the human element; and the limited research performed on the subject of human error in the mining profession, especially in the time of COVID-19 it seems that mine managers and HSE units need to consider adopting some more and better measures to minimize the risk of error in this harsh occupation.

4.3Limitations and future research directions

Although this study aimed to identify and assess human error in one of Iran’s copper mines during the COVID-19 pandemic using the CREAM method, there are still some limitations regarding time and resource factors. The CREAM method focused on the impact of environmental conditions or Performance Shaping Factors (PSFs) on human errors when calculating the Human Reliability Analysis (HRA) for a given task. However, in addition to the influence of environmental conditions or PSFs, human error was also affected by Human Inherent Factors (HIFs). Therefore, future research can explore this issue in order to discover which factors are most effective on human errors.

5Conclusion

The results of this study showed that the COVID-19 pandemic has increased the human error risk in a high percentage of copper miners. From the parameters’ studied, poor working condition was found to be the main effective one influencing on the occurrence of human error, which in turn, was influenced by COVID-19. The probability of human error and subsequent accidents, therefore, can be greatly reduced by taking appropriate management strategies such as promotion of safety and health monitoring (as either quantity or quality standpoints), and adopting supportive measures for controlling occupational stresses in such operational units.

Ethical approval

This cross-sectional study was conducted on miners from June to September 2021 at Koomehmine Pars (Nasim Copper), Bardaskan, Iran. The study protocol was approved by the Research Ethics Committee of Tarbiat Modares University (registration code: IR.MODARES. REC.1401.080).

Informed consent

Written informed consent was obtained from all participants.

Conflict of interest

The authors do not have any conflicts of interest.

Acknowledgments

The authors appreciate the authorities of the University for Financial Support, as well as the mine management and staff (Koomehmine Pars (Nasim Copper), Bardaskan, Iran) for their assistance in this project.

Funding

This study was part of a PhD thesis and was financially supported by Tarbiat Modares University (Grant no: 1601432).

References

[1] 

Probst TM , Lee HJ , Bazzoli A , Jenkins MR , Bettac EL . Work and Non-Work Sickness Presenteeism: The Role of Workplace COVID-19 Climate. Journal of Occupational and Environmental Medicine. 2021;63(8):713. 10.1097/JOM.0000000000002240.

[2] 

Bergström G , Bodin L , Hagberg J , Lindh T , Aronsson G , Josephson M . Does sickness presenteeism have an impact on future general health?. International Archives of Occupational and Environmental Health. 2009;82(10):1179–90. https://doi.org/10.1007/s00420-009-0433-6

[3] 

Skagen K , Collins AM . The consequences of sickness presenteeism on health and wellbeing over time: a systematic review. Social Science & Medicine. 2016;161:16977. https://doi.org/10.1016/j.socscimed.2016.06.005

[4] 

Kinman G . Sickness presenteeism at work: prevalence, costs and management. 2019. 10.1093/bmb/ldy043

[5] 

Niven K , Ciborowska N . The hidden dangers of attending work while unwell: A survey study of presenteeism among pharmacists. International Journal of Stress Management. 2015;22(2):207. https://doi.org/10.1037/a0039131

[6] 

Kinman G , Grant C . Presenteeism during the COVID-19 pandemic: risks and solutions. Occupational Medicine. 2021;71(6-7):243–4. https://doi.org/10.1093/occmed/kqaa193

[7] 

Laing T . The economic impact of the Coronavirus (Covid-Implications for the mining industry. The extractive industries and society. 2020;7(2):580–2. https://doi.org/10.1016/j.exis.2020.04.003

[8] 

Jowitt SM . COVID-19 and the global mining industry. SEG Discovery. 2020(122):33–41. https://doi.org/10.5382/SEGnews.2020-122.fea-02

[9] 

Sun J , Qian X . Analysis of coal mine accidents in China during 2004–2015. Ind Mine Autom. 2016;42(11):1–5.

[10] 

Martins Floris L , Gomes Carvalho E , Ramos Faustino BC , Leal Calegario CL . Workplace Accidents as a Consequence of Human Error: An Empirical Study in a Gold Mine. Revista FSA. 2021;18. http://dx.doi.org/10.12819/2021.18.1.4

[11] 

Kumar P , Gupta S , Agarwal M , Singh U . Categorization and standardization of accidental risk-criticality levels of human error to develop risk and safety management policy. Safety Science. 2016;85:88–98. https://doi.org/10.1016/j.ssci.2016.01.007

[12] 

Asfaw A , Mark C , Pana-Cryan R . Profitability and occupational injuries in US underground coal mines. Accident Analysis & Prevention. 2013;50:778–86. https://doi.org/10.1016/j.aa2012.07.002

[13] 

Masterson EA , Tak S , Themann CL , Wall DK , Groenewold MR , Deddens JA , et al.. Prevalence of hearing loss in the United States by industry. American Journal of Industrial Medicine. 2013;56(6):670–81. https://doi.org/10.1002/ajim.22082

[14] 

Liu H-Z , Zhang L , Wang Y-Q . Control model for errors caused by human and barrier analysis. Industrial Engineering Journal-Guangzou-. 2007;10(6):13.

[15] 

Zhang W . Causation mechanism of coal miners’ human errors in the perspective of life events. International Journal of Mining Science and Technology. 2014;24(4):581–6. https://doi.org/10.1016/j.ijmst.2014.06.002

[16] 

Krenek M , Maisto SA . Life events and treatment outcomes among individuals with substance use disorders: A narrative review. Clinical Psychology Review. 2013;33(3):470–83. https://doi.org/10.1016/j.cpr.2013.01.012

[17] 

Monforton C , Windsor R . An impact evaluation of a federal mine safety training regulation on injury rates among US stone, sand, and gravel mine workers: an interrupted time-series analysis. American Journal of Public Health. 2010;100(7):1334–40. https://doi.org/10.2105/AJPH.2009.178301

[18] 

Skalle P , Aamodt A , Laumann K . Integrating human related errors with technical errors to determine causes behind offshore accidents. Safety Science. 2014;63:179–90. https://doi.org/10.1016/j.ssci.2013.11.009

[19] 

Vitório DM , Masculo FS , Melo MO . Analysis of mental workload of electrical power plant operators of control and operation centers. Work. 2012;41(Supplement 1):2831–9. 10.3233/WOR-2012-0531-2831

[20] 

Pan X , Lin Y , He C . A review of cognitive models in human reliability analysis. Quality and Reliability Engineering International. 2017;33(7):1299–316. https://doi.org/10.1002/qre.2111

[21] 

Annett J , Duncan K , Stammers R , Gray M . Task Analysis. (HMSO). London; 1971.

[22] 

Annett J , Duncan KD . Task analysis and training design. 1967. ED019566.

[23] 

Annett J . Hierarchical Task Analysis. Handbook of Cognitive Task Design, Ed. E. Hollnagel, Ch. 2.

[24] 

He X , Wang Y , Shen Z , Huang X . A simplified CREAM prospective quantification process and its application. Reliability Engineering & System Safety. 2008;93(2):298–306. https://doi.org/10.1016/j.ress.2006.10.026

[25] 

Marseguerra M , Zio E , Librizzi M . Quantitative developments in the cognitive reliability and error analysis method (CREAM) for the assessment of human performance. Annals of Nuclear Energy. 2006;33(10):894–910. https://doi.org/10.1016/j.anucene.2006.05.003

[26] 

Hollnagel E . Cognitive reliability and error analysis method (CREAM). Elsevier; 1998 Jan 23.

[27] 

Mazloumi A , Ziarani MH . Determining Human Error Global Causes in a Petrochemical Control Room with a Cognitive Analytical Approach-CREAM. International Journal of Occupational Hygiene. 2017;9(4):223–34. http://ijoh.tums.ac.ir

[28] 

Mahdi Rezaie F , Fakoor Saghih AM , Motahari Farimani N . A novel hybrid approach based on CREAM and fuzzy ANP to evaluate human resource reliability in the urban railway. Journal of Transportation Safety & Security. 2020Mar21:1- https://doi.org/10.1080/19439962.2020.1738611

[29] 

McPhee B . Ergonomics in mining. Occupational Medicine. 2004;54(5):297–303. https://doi.org/10.1093/occmed/kqh071

[30] 

Malekpour F , Ebrahimi H , Yarahmadi R , Mohammadin Y , Kharghani Moghadam SM , Soltanpour Z . Prevention measures and risk factors for COVID-19 in Iranian workplaces. Work. 2021 Jun 5(Preprint):1–4. 10.3233/WOR-205045.

[31] 

Feng Y , Chen H , Zhang Y , Jing L . The hybrid systems method integrating human factors analysis and classification system and grey relational analysis for the analysis of major coal mining accidents. Systems Research and Behavioral Science. 2019;36(4):564–79. https://doi.org/10.1002/sres.2571

[32] 

aha K , Torous J , Caine ED , De Choudhury M . Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media. Journal of Medical Internet Research.. 2020;22(11):e22600. https://preprints.jmir.org/preprint/22600

[33] 

Bussier MJ , Chong HY . Relationship between safety measures and human error in the construction industry: working at heights. International Journal of Occupational Safety and Ergonomics. 2022;28(1):162–73. https://doi.org/10.1080/10803548.2020.1760559

[34] 

Kumar P , Gupta S , Gunda YR . Estimation of human error rate in underground coal mines through retrospective analysis of mining accident reports and some error reduction strategies. Safety Science. 2020;123:104555. https://doi.org/10.1016/j.ssci.2019.104555

[35] 

Ghosh AK , Bhattacherjee A , Chau N . Relationships of working conditions and individual characteristics to occupational injuries: a case-control study in coal miners. Journal of Occupational Health. 2004;46(6):470–80. https://doi.org/10.1539/joh.46.470

[36] 

Stanton NA , Hedge A , Brookhuis K , Salas E , Hendrick HW , editors. Handbook of human factors and ergonomics methods. CRC press; 2004 Aug. 30.