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Predictors of Work Injuries in Mines – A Case Control Study
Dr. J. MaitiAssistant ProfessorIndian Institute of TechnologyKharagpur, India
• Introduction• Objectives of the Study• Literature Review• Determinants of Work Injuries• Design of Questionnaires • General Description of Mines Studied• Data Collection• Applications to Mines• Conclusions
Outline of Presentation
Introduction
Mining is a hazardous profession associated with high level of accidents, injuries and illnesses
For example, in Indian coal mines the fatal and serious bodily injury rates per 1000 persons employed for the years 2001 and 2002 were 0.30, 0.28 and 1.14, 1.21 respectively
Several causes starting from personal to technical factors are responsible for such high injury experience rates in mines
There is a critical need of study to identify these factors and to evaluate their effects on accident/injury occurrences in a multivariate situation
Objectives of the Study
Identification of the causative factors associated with work injuries in mines representing the social, technical and personal characteristics of the workers.
Evaluation of the risk of injuries to the underground coal mine workers, controlling their social, technical and personal characteristics.
Evaluation of sequential relationships amongst personal, social and technical factors and work injuries
Implementation of the findings to case study mines.
Literature ReviewSelected literature on quantitative analysis of mine safety studies (1970-2005)
Quantitative Techniques Literature
1. Classification Based Analysis
2. Correlation and Bivariate Regression Analysis
3. Reliability and Risk Analysis
4. Cost-Benefit Analysis
5. Time Series Analysis
6. Multivariate Analysis
1. Barry and Associates, 1971 and 1972; Traube and Hooper, 1972; Pfleider and Krug, 1973; Adkins and Hargreaves, 1977; Tierney, 1977; Bendersky and Klein, 1980; Basanez and Mikel, 1986; Long, 1987; Conway et al., 1988; Unger, 1988; Bhattacherjee et al., 1992, Bise, 1992; Maiti et al., 1997; H.B.Sahu & B.K.Pal, 2000; S.C.Batra & V.Mahajan, 1997; TN.Singh, 1997; S.Krishnamurty, 1997; D.P.Tripathi & A.K.Patra, 1996.
2. Sinha et al., 1974; FlorJancic, 1981; National Safety Council, 1982; Root, 1983; Bennett and Passmore, 1984; Grawson and Wang (1985); Goodman, 1986; Sounder, 1988; Randolph, 1989; Dhar et al., 1997.
3. Cooley and Hill, 1981; Hill and Stanek, 1981; Kerkering and McWilliams, 1987; Goodman and Kissel, 1989; Bhattacherjee, 1991, Bhattacherjee et al., 1992; Grayson et al., 1992; Foster and Edwards, 1995; Goswami, 1996; G.C.Simpson, 1996; Maiti & Bhattacherjee, 2001a, Maiti, 2003, 2005.
4. Stilley et al., 1976; Julian, 1980; Florjancic et al., 1981; Seidle, 1984; Spencer, 1984; Haislip, 1987; Finkel, 1987; Peters, 1991; Bhattacherjee, 1996.
5. Zebetakis and Zalar,1982; Bhattacherjee et al., 1994.
6. Bennett, 1982; Bennett and Passmore, 1984; Bennett and Passmore, 1986; Phiri, 1989; Maiti et al., 1999, Maiti and Bhattacherjee, 1999; Bhattacherjee & Maiti, 2000a; Maiti & Bhattacherjee, 2000b; Maiti & Bhattacherjee,2001b, Maiti, Chatterjee and Bangdiwala, 2004, Paul, Maiti and Furjouh 2005.
The Salient Geological and Mining Related Information for the Case Study Mines Geological and Mining
InformationMine 1 Mine 2
Seam characteristics
Depth: 65-90 mThickness: 10 m (approx.)
Dip : South 69o eastGradient : 1 in 17
Depth : 65-90 mThickness :10 m (approx.)
Dip : South 69o eastGradient : 1 in 17
Immediate roof Shale and sandstone Shale and sandstone
Roof support Road ways- props and roof bolting; Junction- cogs (wooden, steel),
Jafri; Gallery and freshly exposed roof- cog and
props
Road ways- props and roof bolting; Junction- cogs (wooden, steel);
Gallery and freshly exposed roof- conventional (roof bolting)
Mining Method Bord and Pillar (Depillaring) Bord and Pillar (Depillaring)
Face Mechanization Conventional Conventional andSemi-mechanized
Haulage Endless rope haulage Belt conveyor
Gassiness Degree-1 Degree-2
Average wet bulb temperature
28o C 26o C
Average dry bulb temperature
29o C 26.8o C
Percentage of oxygen at the place of working
20% 19%
Type of ventilation Antitropal Antitropal
Production, Employment and Injury Statistics of the Sample Mine, for the Five Year Periods, 1998-2002
Mine 1 Mine 2 Total
Average Production (Tonne/Year)
91527 113289 204816
Average Manpower (Underground)
592 408 1000
No. of accident in five yearsFatalSeriousReportableTotal
55
173183
05
98103
510
271286
Frequency rates[1] per 1000 miners
61.82 30.80 57.20
[1] The frequency rate of occupational injuries is the number of injury occurrences expressed as a rate per thousand employees. Such rates were calculated using the following formula: Number of annual occupational injury cases X 1,000 Number of employees
Data collection Data were collected through accident/injury
reports available at the mines and through a questionnaire survey.
Interview was taken for individual miners from different categories of workers from both the mines (Mine 1 and Mine 2).
Two groups namely Non-Accident Group (NAG) and Accident Group(AG) of workers were identified to study the influence of different factors contributing mine accident/injury amongst the workers.
Data collection (Contd.)
For most of the mine workers who were not fluent in reading and writing the questions were read out. It took 45-60 minutes to fill up the questionnaire forms for an individual participant.
Out of 175 participants from case group, 150 miners’ answer matched the inclusion criteria of the study. Inclusion criteria consist of proper identifying information and proper response to each of the questionnaires.
Data collection (Contd.)
Through frequency matching 150 participants were chosen randomly from the participants in the control group whose answers matched the inclusion criteria of the survey.
Overall, of the 375 participants, 175 miners participated from case group and 200 miners participated from control group with an overall response rate of 80%.
Identified factors No. of questionsasked to the mine workers
No. of questionsretained after reliability and validity
testReliability
Risk taking
Negative affectivity
Job Dissatisfaction
Impulsivity
Depression
Job stress
Safety training
Safety practice
Safety equipment availability and maintenance
Co- worker’s support
Supervisory Support
Management worker interaction
Production pressure
Physical hazards
Safe work behavior
11
15
13
12
5
12
8
27
9
7
7
14
4
15
8
9
11
12
8
5
8
6
19
8
5
7
10
4
11
8
0.82
0.83
0.83
0.71
0.65
0.67
0.66
0.80
0.72
0.64
0.71
0.84
0.79
0.65
0.67
Reliability and Validity Test of the Collected Data
Logistic Regression
Structural Equation Modeling
Models applied for this study
Variables and Category Description
Indicator Covariates used in Logistic Regression
AGEAGE0 ≤mean years (RC)
AGE1 >mean years
EXPERIENCEEXP0 ≤ mean years (RC)
EXP1 > mean years
IMPULSIVITYIMP0 ≤mean score (RC)
IMP1 > mean score
NEGATIVE AFFECTIVITY N_A0 ≤ mean score (RC)
N_A1 > mean score
RISK TAKINGRISK_TK0 ≤ mean score (RC)
RISK_TK1 > mean score
DEPRESSIONDEPR0 ≤ mean score (RC)
DEPR1 > mean score
X1
01
X2
01
X3
01
X4
01
X5
01
X6
01
Logistic ModelDescription of Variables Used in Logistic Regression Model (RC) indicates reference category
Predictor categoricala variables r Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Demographics Age Experience
Negative personality Impulsivity Negative affectivity Depression Risk taking
Safe work behavior Safe work behavior
Job stress Job stress
Job dissatisfaction Job dissatisfaction
Work hazards Production pressure Physical hazards
Safety environment Safety training Safety practice Safety equipment availability and maintenance
Social support Co-worker support Supervisory support Management worker interaction
Predictive accuracy (%)Total R2
R2
0.27*** 0.25***
0.21*** 0.37*** 0.08 0.35***
-0.22***
0.17***
0.31***
0.17*** 0.24***
-0.25***-0.36***-0.30***
-0.08-0.27***-0.29***
0.410.19
56.7 0.026*** 0.026***
0.75* 0.15
0.41 1.03***-0.14 0.59**
67.3 0.189*** 0.163***
0.77* 0.13
0.35 1.11***-0.29 0.39
-0.34
-0.22
0.48*
69.3 0.206*** 0.017**
0.70* 0.22
0.40 0.92***-0.09 0.41
-0.42
-0.25
0.48*
0.410.57**
69.3 0.228*** 0.022**
0.75* 0.15
0.50 0.90***-0.16 0.38
-0.30
-0.12
0.55*
0.40 0.53*
0.46-0.52-0.24
68.30.241***0.013
0.76* 0.15
0.55 0.93***-0.18 0.36
-0.26
-0.12
0.53*
0.39 0.52*
0.47-0.48 0.27
0.19 0.14-0.34
68.70.244*** 0.003
Note. For Models 1 – 6, standardized regression coefficients (β) are reported. * P < 0.10, ** P 0.05, *** P 0.01. a category (0) represents the reference group. For example, AGE (0) is the reference group of age variable. The parameter (β) for AGE (1) is estimated with reference to AGE (0).
Multivariate Logistic Regression Results Predicting Work Injury
Older age group is 2.14 times more likely to be injured than the younger age group.
Negatively affected workers are 2.54 times more prone to injuries.
Highly job dissatisfied workers are 1.71 times more likely to become injured.
Results
Results
Workers who perceive higher level of physical hazards are 1.69 times more likely to be injured.
Highly impulsive and more risk taking workers are 1.73 and 1.44 times more likely to be injured (but none of them were statistically significant).
Conclusions
The national level accident/injury statistics in Indian mines showed that for the last 25 years, there is no apparent improvement in safety in mines.
This has instigated the need for studies beyond engineering control of safety in mines.
Studies on the effect of sociotechnical factors on work injuries is present day needs for Indian mines.
Conclusion (Contd.)
The case study results showed that the accident-involved workers are aged (OR = 2.14), negatively affected (OR = 2.54), more job dissatisfied (OR = 1.71), and perceive the physical hazards more harmful
(OR = 1.69)
Conclusions (Contd.)
The sequential interrelationships amongst factors reveals the keys factors as social support (total effect = -0.14) work hazards (total effect = 0.15) safety environment (total effect = -
0.16) job dissatisfaction (total effect =
0.29)
T H A N K U
The analysis of the data is based on logistic regression procedure. The logistic model allows the estimation of the probability of a coal miner with given characteristics (e.g. age, experience, risk taking, negative affectivity, job stress, safety training, safety practice, etc) that will have an accident resulting in an injury.
Following the coding scheme of the variables mentioned in previous Table, the logistic model is thus specified as follows:
Probability of an injury = ( age, experience, risk taking, negative affectivity, job stress, safety training, safety practice, etc)
The logistic regression equation for this study can be expressed as: P ( X1 , X2, .........., Xk ) = 1/ [ 1 + exp{ (0 + 1 X1 + …….+ k Xk)}]
where X1, X2, …………. , Xk are the variables of interest (age, experience, …….., management worker interaction with their categories) being used to provide P(X1, X2, …………., XK), the probability of an accident/injury in question.
1, 2, 3,….k = corresponding parameters of Xj, for j = 1, 2, …, k.
The parameters of the logistic model were estimated by maximum likelihood method suggested by Cox (1970).
Logistic Model Specification
Methodologies applied for this study (Contd.)
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