Predictors of Work Injuries in Mines – A Case Control Study

Predictors of Work Injuries in Mines – A Case Control Study Dr. J. Maiti Assistant Professor Indian Institute of Technology Kharagpur, India

Outline of Presentation • 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

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 Review Selected literature on quantitative analysis of minesafety studies (1970-2005)

The Salient Geological and Mining Related Information for the Case Study Mines

Production, Employment and Injury Statistics of the Sample Mine, for the Five Year Periods, 1998-2002 [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%.

Reliability and Validity Test of the Collected Data

Models applied for this study Logistic Regression Structural Equation Modeling

Logistic Model Description of Variables Used in Logistic Regression Model (RC) indicates reference category

Multivariate Logistic Regression Results Predicting Work Injury 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).

Results • 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 • 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

Methodologies applied for this study (Contd.) Logistic Model Specification 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).

Predictors of Work Injuries in Mines – A Case Control Study