Analysis of occupational accidents with agricultural machinery in the period 2008–2010 in Austria

Kogler, Robert; Quendler, Elisabeth; Boxberger, Josef

doi:10.1016/j.ssci.2014.10.004

Cited by 44 publications

(16 citation statements)

References 25 publications

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“…The main purpose of incident analysis is the derivation and development of preventative measures from injury data. To meet that goal, analysis of injury reports can significantly improve the information about incidents [9]. Applying proper analytical tools aimed at discovering the causes of occupational incidents is essential to gain useful information that contributes to preventing these incidents in future [10].…”

Section: Introductionmentioning

confidence: 99%

Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

2019

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Agribusiness industries are among the most hazardous workplaces for non-fatal occupational injuries. The term “post-incident state” is used to describe the health status of an injured person when a non-fatal occupational injury has occurred, in the post-incident period when the worker returns to work, either immediately with zero days away from work (medical state) or after a disability period (disability state). An analysis of nearly 14,000 occupational incidents in agribusiness operations allowed for the classification of the post-incident state as medical or disability (77% and 23% of the cases, respectively). Due to substantial impacts of occupational incidents on labor-market outcomes, identifying factors that influence the severity of such incidents plays a significant role in improving workplace safety, protecting workers, and reducing costs of the post-incident state of an injury. In addition, the average costs of a disability state are significantly higher than those of a medical state. Therefore, this study aimed to identify the contributory factors to such post-incident states with logistic regression using information from workers’ compensation claims recorded between 2008 and 2016 in the Midwest region of the United States. The logistic regression equation was derived to calculate the odds of disability post-incident state. Results indicated that factors influencing the post-incident state included the injured body parts, injury nature, and worker’s age, experience, and occupation, as well as the industry, and were statistically significant predictors of post-incident states. Specific incidents predicting disability outcomes included being caught in/between/under, fall/slip/trip injury, and strain/injury by. The methodology and estimation results provide insightful understanding of the factors influencing medical/disability injuries, in addition to beneficial references for developing effective countermeasures for prevention of occupational incidents.

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Section: Introductionmentioning

confidence: 99%

Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

2019

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“…As mentioned, the number of accidents in agriculture is consistently high at a global level [8,13,44,45]. In the EU, agriculture is considered a high-risk industry; together with manufacturing, construction, and transportation, it accounted for about 15% of all fatal accidents at work, as reported by official statistics [46].…”

Section: Background and Motivationsmentioning

confidence: 99%

The Impact of Human Error in the Use of Agricultural Tractors: A Case Study Research in Vineyard Cultivation in Italy

et al. 2018

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Recently, standards and regulations concerning occupational safety have become more and more rigorous. Nevertheless, the number of accidents and victims has not decreased significantly, as reported by official statistics. In Italy, the agricultural sector is certainly one of the most affected by this situation, especially taking into account the occurrence of serious injuries and fatalities related to the use of tractors. The main reasons for such a situation can be ascribed to the peculiarities of agricultural operations. Therefore, when analyzing the root causes of agricultural accidents, a user-centered approach is needed in order to make the development of health and safety interventions easier and more effective. Based on this, the present paper proposes a practical case study research focused on integrating the factor of human error into the risk assessment procedures of agricultural activities in vineyard cultivation. Such an approach allowed us to consider the impact of human error-while performing work activities (e.g., the use of a tractor)-on hazards and related hazardous events in a thorough manner. The proposed approach represents a novelty in the sector of the safety assessment of agricultural activities, providing a first valuable basis for further analysis and implementation by researchers and practitioners.

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“…where R is the risk, F is the frequency of occurrence, calculated as number of occupational accidents divided by day, and S is the seriousness, calculated as the average days of prognosis. Equation (5) in Table 5 summarizes the Risk estimation for all the identified clusters. Risk shows a wide range of variation, that is, from 1.6 for "CL1-1" to 12 for "CL3".…”

Section: Opportunities For Prevention: Skm Data Clusteringmentioning

confidence: 99%

“…The results of this approach are published regularly in official reports, by National Health and Safety Authorities, and they highlight such useful and general information on the trends of occupational accidents as: The classes of workers most exposed to accidents, gender effects, the role of the educational level, the age of the injured and various other parameters. In addition, ESAW data have also been analyzed, with reference to a specific field of activity, through a statistics approach to analyze the cause-effect mechanism [3], and information about the trend of accidents and "typical" accidents have been reported in the recent work by Dzwiarek et al [4] and Kogler et al [5]. However, these kinds of analyses are only useful to a certain extent to enhance the prevention of accidents in the work environment, as observed by Palamara et al [6] and Comberti et al [7], because they do not produce a risk assessment outcome [8].…”

Section: Introductionmentioning

confidence: 99%

Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry

Comberti

Demichela

Baldissone

et al. 2018

Safety

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Data on occupational accidents are usually stored in large databases by worker compensation authorities, and by the safety and prevention teams of companies. An analysis of these databases can play an important role in the prevention of accidents and the reduction of risks, but it can be a complex procedure because of the dimensions and complexity of such databases. The SKM (SOM K-Means) method, a two-level clustering system, made up of SOM (Self Organizing Map) and K-Means clustering, has obtained positive results in identifying the dynamics of critical accidents by referring to a database of 1200 occupational accidents that had occurred in the wood industry. The present research has been conducted to validate the recently presented SKM methodology through the analysis of a larger data set of more than 4000 occupational accidents that occurred in Piedmont (Italy), between 2006 and 2013. This work has partitioned the accidents into groups of different accident dynamics families and has quantified the severity and frequency of occurrence of these accidents. The obtained information may be of help to Company Managers and National Authorities to better address preventive measures and policies concerning the clusters that have been identified as being the most critical within a risk-based decision-making framework.

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Analysis of occupational accidents with agricultural machinery in the period 2008–2010 in Austria

Cited by 44 publications

References 25 publications

Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

The Impact of Human Error in the Use of Agricultural Tractors: A Case Study Research in Vineyard Cultivation in Italy

Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry

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