Ashis Bhattacherjee

Relationships of job and some individual characteristics to occupational injuries in employed people: a community-based study.

Relationships of Job and Some Individual Characteristics to Occupational Injuries in Employed People: A Community‐Based Study: Ashis Bhattacherjee, et al. Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India—This study assessed the associations of job and some individual factors with occupational injuries among employed people from a general population in north‐eastern France; 2,562 workers were randomly selected from the working population. A mailed auto‐questionnaire was filled in by each subject. Statistical analysis was performed with loglinear models. The annual incidence rate of at least one occupational injury was 4.45%. Significant contributing factors for occupational injuries were job category (60.8%), sex (16.2%), regular psychotropic drug use (8.5%), age groups (7.5%), and presence of a disease (7.0%). The men had higher risk than the women (adjusted odds‐ratio 1.99, 95% CI 1.43.2.78). Compared to executives, intellectual professionals and teachers, labourers had the highest risk (6.40, 3.55. 11.52). They were followed by farmers, craftsmen and tradesmen (6.18, 2.86.13.08), technicians (3.14, 1.41. 6.70), employees (2.94, 1.59.5.48) and other subjects (3.87, 1.90.7.88). The young (.29 yr) showed an increased risk. Similar odds‐ratios were observed for regular psychotropic drug use (1.54, 1.16.2.05) and the presence of a disease (1.50, 1.11.2.02). Univariate analysis showed that smoking habit, overweight and excess alcohol use were also associated with injuries. The loglinear model results showed that there were associations between some of these independent factors. It was concluded that job, sex, young age, smoking habit, excess alcohol use, overweight, psychotropic drug use, and disease influenced the occupational injuries. Preventive measures concerning work conditions, risk assessment and job knowledge should be conducted in overall active population, especially in men, young workers, smokers, alcohol users, overweight workers and in individuals with a disease or psychosomatic disorders.

Relationships of working conditions and individual characteristics to occupational injuries: a case-control study in coal miners.

Relationships of Working Conditions and Individual Characteristics to Occupational Injuries: A Case‐Control Study in Coal Miners: Apurna Kumar Ghosh, et al. Department of Mining Engineering, National Institute of Technology, India—This study assessed the relationship of age, poor perception of working condition, poor safety environment, poor management and supervision, risk‐taking behavior, emotional instability, negative job involvement, job dissatisfaction, job stress, and poor safety performance of workers to occupational injuries. This case‐control study was conducted on 202 male coal miners with at least one occupational injury during a five‐year period and 202 male controls with no occupational injury, matched on the job. A standardized questionnaire administered by individual interviewers was used. Data were analysed by the logistic regression method. For all workers combined, the factors with significant adjusted odds ratios (ORs) found were: 30–45 and >45 yr age groups (OR vs. <30 yr age group: 1.80, 95% CI 1.02–3.17 and 2.59, 1.38–4.85 respectively), poor perception of working conditions (1.61, CI 1.00–3.18), emotional instability (2.33, 1.04–5.22), job stress (1.83, 1.00–3.46) and poor safety performance of workers (3.10, 1.45–6.63). No significant interaction was found between these risk factors and the job. It was concluded that older age, poor perception of work conditions, poor work environment, and human behavioral factors played significant roles in occupational injuries. This information would help in implementing preventive programs to improve working conditions and management quality and to help the workers to develop positive psychological traits, but workers with negative traits such as emotional instability and older workers should be employed in less demanding jobs.

Relationship between job, lifestyle, age and occupational injuries

BACKGROUND Physical job demands (PJD), age, disability and lifestyle may influence the risk of occupational injury. AIM To assess the relationships between PJD, lifestyle and injury in workers of various ages. METHODS A total of 2888 randomly selected workers from northeastern France, aged >or=15, completed a postal questionnaire. The PJD score was defined as the total number of the following reported job demands: using pneumatic tools, other vibrating hand tools, hammers, machine tools or vibrating platforms and exposure to manual handling tasks, awkward postures, high pace of work, high physical workload, work at heights, work in adverse climates or exposure to noise, cold or heat. Data were analysed using logistic regression. RESULTS Nine per cent of subjects reported an injury during the previous 2 years. The PJD score was related to the injury rate for workers aged >or=45: crude odds ratio (OR) 3.5 (95% confidence interval = 1.5-8.0) for PJD = 1, 5.0 (2.2-11.3) for PJD = 2-3 and 14.5 (6.5-32.2) for PJD >or=4, versus PJD = 0. Lower ORs were found for those aged <30 (1.4, 4.2 and 9.9, respectively) and 30-44 (1.5, 4.4 and 6.5, respectively). The differences between age groups remained when controlling for all factors studied. Obesity, smoking and musculoskeletal disorders were associated with injury risk in workers aged >or=45 (adjusted ORs 1.7-2.6). Smoking was also an injury risk factor for workers aged <30. CONCLUSIONS PJD and lifestyle have a higher impact on injury rates among older workers than among younger ones. Injury prevention should address reducing PJD and improving relevant lifestyle factors, especially for older workers.

Genetic algorithms for feature selection of image analysis-based quality monitoring model: An application to an iron mine

Measuring the quality parameters of materials at mines is difficult and a costly job. In this paper, an image analysis-based method is proposed efficiently and cost effectively that determines the quality parameters of material. The image features are extracted from the samples collected from a mine and modeled using neural networks against the actual grade values of the samples generated by chemical analysis. The dimensions of the image features are reduced by applying the genetic algorithm. The results showed that only 39 features out of 189 features are sufficient to model the quality parameter. The model was tested with the testing data set and the result revealed that the estimated grade values are in good agreement with the real grade values (R^2=0.77). The developed method was then applied to a case study mine of iron ore. The case study results show that proposed image-based algorithm can be a good alternative for estimating quality parameters of materials at a mine site. The effectiveness of the proposed method was verified by applying it on a limestone deposit and the results revealed that the method performed equally well for the limestone deposit.

Relationships of Job Hazards, Lack of Knowledge, Alcohol Use, Health Status and Risk Taking Behavior to Work Injury of Coal Miners: A Case-Control Study in India

Relationships of Job Hazards, Lack of Knowledge, Alcohol Use, Health Status and Risk Taking Behavior to Work Injury of Coal Miners: A Case‐Control Study in India: Bijay Mihir Kunar, et al. Department of Mining Engineering, Indian Institute of Technology, India—Objective is to assess the relationships of job hazards, individual characteristics, and risk taking behavior to occupational injuries of coal miners. This case‐control study compared 245 male underground coal miners with injury during the previous two‐year period with 330 matched controls without injury during the previous five years. Data were collected via face‐to‐face interview and analyzed using the conditional logistic model. Handling material, poor environmental/working conditions, and geological/strata control‐ related hazards were the main risk factors: adjusted ORs 5.15 (95% CI 2.42–10.9), 2.40 (95% CI 1.29–4.47), and 2.25 (95% CI 1.24–4.07) respectively. Their roles were higher among the face‐workers than among the non‐face‐workers. No formal education, alcohol consumption, disease, big‐family, and risk‐taking behavior were associated with injuries (2.36≤ORs≤10.35), and the findings were similar for both face and non‐face workers. Prevention should focus on handling material, poor environmental condition, especially addressing workers with no formal education, alcohol consumption, disease, big family size, and risk‐taking behavior.

Loglinear model for analysis of cross-tabulated coal mine injury data

Mine accidents/injuries can be cross-classified against the variables of interest in a contingency table and their associations can be assessed through aggregate statistics. However, there is a need to develop a coherent and structured procedure for analysis of accident data, which will allow one to identify associations between two or more variables multivariately. In this study, the loglinear model, which has been proposed as a mathematical representation of the contingency table, was applied to accident data from a group of coal mines to assess the associations/interactions between two or more variables multivariately through their main and interaction effects. The case study results revealed that the variables ‘occupation’ and ‘workplace location’ were highly associated with degree of injury. It was also clearly indicated that the workers with more than 20 years of experience exhibited high injury rate patterns. While designing training programs for miners, focused attention should be given to specific categories of workers to reduce accident/injuries at the case study mines.

Ore grade estimation of a limestone deposit in India using an Artificial Neural Network

This study describes a method used to improve ore grade estimation in a limestone deposit in India. Ore grade estimation for the limestone deposit was complicated by the complex lithological structure of the deposit. The erratic nature of the deposit and the unavailability of adequate samples for each of the lithogical units made standard geostatistical methods of capturing the spatial variation of the deposit inadequate. This paper describes an attempt to improve the ore grade estimation through the use of a feed forward neural network (NN) model. The NN model incorporated the spatial location as well as the lithological information for modeling of the ore body. The network was made up of three layers: an input, an output and a hidden layer. The input layer consisted of three spatial coordinates (x, y and z) and nine lithotypes. The output layer comprised all the grade attributes of limestone ore including silica (SiO2), alumina (Al2O3), calcium oxide (CaO) and ferrous oxide (Fe2O3). To justify the use of the NN in the deposit, a comparative evaluation between the NN method and the ordinary kriging was performed. This evaluation demonstrated that the NN model decisively outperformed the kriging model. After the superiority of the NN model had been established, it was used to predict the grades at an unknown grid location. Prior to constructing the grade maps, lithological maps of the deposit at the unknown grid were prepared. These lithological maps were generated using indicator kriging. The authors conclude by suggesting that the method described in this paper could be used for grade-control planning in ore deposits.

Rock-type classification of an iron ore deposit using digital image analysis technique

In this paper, the rock types of an iron ore deposit were classified using the digital image analysis technique. The image acquisition and analysis of blasted rocks were conducted in a laboratory for six different rock types. A total of 189 features were extracted from the individual rock samples using best-suited segmentation technique selected by validation study. The neural network technique was applied for rock classification model using image features. Five principal components, which accounts for 95% of total data variance, were selected as input parameters for the model. The misclassification error of the model for testing data was 2.4%.

General regression neural network residual estimation for ore grade prediction of limestone deposit

Abstract The aim of the present paper is to provide an improved estimator for the ore grade prediction of a limestone deposit in India. A generalised modelling framework with the help of general regression neural network and the ordinary kriging was formulated to capture the spatial variability of the deposit. In this platform, spatial variability of the deposit is assumed to be characterised by three major components: spatial trend component, regionalised component, and purely random component. The general regression neural network (GRNN) model was used to capture the spatial trend component, and the ordinary kriging technique was implemented to capture the regionalised component. The GRNN model was developed using the spatial coordinates (Northing, Easting and Elevation) as the input parameters and the grade attributes (CaO, Al2O3, Fe2O3 and SiO2) as the output parameters. The performance of the GRNN residual kriging model was tested using a testing data set, and the outputs of this model were compared with the outputs of the GRNN model, and the ordinary kriging. The comparative results show that the GRNN residual kriging model provided significant improvement over the ordinary kriging, however, it only shows a marginal improvement over the GRNN model.

Time series analysis of coal mine accident experience

This study investigates several forecasting techniques that can be useful to mine safety managers for studying mine accident rate behavior. Three time series models were studied for extrapolation of accident rates. These models are applied to historical accident incidence data from a coal mine. Further, a method is presented for evaluating the three models for the selection of an appropriate model. For this particular mine application, it is concluded that the more complex Box-Jenkins ARMA model as well as first order autoregressive model do not give better results than the simple exponential smoothing model. However, when the random variations or autocorrelations in the accident experience rates between periods are different, the models may predict differently. As such, specific models must be developed for each mine on the basis of statistical analysis of the mine accident experience data over time. Moreover, the importance of incorporating human judgement to interpret the results of statistical forecasting cannot be overemphasized. Integration of policy or operating changes, which may impact mine safety performance, with statistical forecasting techniques is essential to arrive at a realistic prediction of future performance.