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.)