A study of the shift in fatal construction work-related accidents during 2012–2019 in Turkey

Olcay, Zeynep Feride; Sakalli, Ahmet Ebrar; Temur, Sertaç; Yazıcı, Ahmet

doi:10.1080/10803548.2021.1900503

Cited by 21 publications

(9 citation statements)

References 31 publications

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“…Therefore, it is probable that the number of injured people without any insurance who refused to be hospitalized might be considerably higher due to financial matters. In this regard, it has been well-elucidated that by better insurance coverage of the workers, the rate of fatal occupational accidents have remarkably decreased, a fact that indirectly refers to the willingness of the injured cases to be visited by a physician after an event (20,21). Expectedly, Social security insurance was the most common type of insurance followed by Rural insurance that was consistent with the previous studies in Iran (6,11).…”

Section: Discussionsupporting

confidence: 82%

Epidemiological Study of Patients with Workplace Accidents Referred In Isfahan

Samani¹,

Safaee²

2022

JHS

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Background and Purpose: Work-related accidents are those events that occur while on duty in the workplace. Due to the lack of accurate information about the epidemiological status of occupational injuries in Isfahan, the present study was conducted to investigate the epidemiology of workplace accidents. Materials and Methods: This was a retrospective cross-sectional study assessing the medical records of all patients injured in the workplace in 2018. The list of all work accident patients was collected from the Hospital archives that accounted for 836 cases. The information about age, sex, the season in which the accident has occurred; type of insurance, nationality, marital status, occupation, and the type of injury were extracted. Afterward, they were statistically analyzed using qualitative variables as well as Chi-square or independent t-test. A P-value of less than 0.05 was considered significant. Results: Based on the findings, the 836 assessed cases had the mean age of 34.43±12.73 years old and were predominantly males (n=812, 97.2%). They were mostly bachelor (n=462, 55.26%) and aged less than 40 years old (57.4%). Soft tissue injuries (53.94%), amputation (13.63%), upper (13.27%) and lower (7.77%) extremities fractures ranked the highest types of injuries. 342 (40.9%) of the cases did not have any insurance, while Social security insurance (48.3%) was the most common one among the supported workers with insurances. Conclusion: According to the present study, the highest number of hospitalizations due to workplace injuries were related to less than 40 years old young adults who were mostly bachelor, experienced soft tissue injuries. Most of the patients were under Social security insurance coverage, while the significant rate of 40% without any insurance surprised us. The results are consistent with the previous studies. Further investigations with more precise assessments are strongly recommended.

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Section: Discussionsupporting

confidence: 82%

Epidemiological Study of Patients with Workplace Accidents Referred In Isfahan

Samani¹,

Safaee²

2022

JHS

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“…According to the International Labor Organization (2020), Turkey is the 9th most unsafe country in terms of fatality rates. A considerable portion of these accidents occurred in small and medium-sized residential projects (Gurcanli et al, 2015) and Olcay et al (2021) reported that falling from height accounted for the vast majority of the fatal accidents in the country. Baradan et al (2019) underlined the significant (i.e.…”

Section: Accidents and Safety Issues In Construction Sitesmentioning

confidence: 99%

“…A considerable portion of these accidents occurred in small and medium-sized residential projects (Gurcanli et al. , 2015) and Olcay et al. (2021) reported that falling from height accounted for the vast majority of the fatal accidents in the country.…”

Section: Introductionmentioning

confidence: 99%

Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods

Koç

Ekmekçioğlu²,

Gürgün³

2022

ECAM

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PurposeCentral to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.Design/methodology/approachOccupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.FindingsThe results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.Practical implicationsThe proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.Social implicationsPractitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.Originality/valueThe literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.

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“…Many studies conducted in different countries and in different sectors reveal that the main cause of accidents is human error-behaviour [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] ]. National culture has long been recognized as important in explaining behaviour (Ng, Lee & Soutar 2007).…”

Section: Introductionmentioning

confidence: 99%

The relationship of Value dimensions in Turk Society with fatalistic tendencies, safety motivation, risk perception and safety performance

Ebrar Sakalli,

Arikan

2024

Heliyon

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A study of the shift in fatal construction work-related accidents during 2012–2019 in Turkey

Cited by 21 publications

References 31 publications

Epidemiological Study of Patients with Workplace Accidents Referred In Isfahan

Epidemiological Study of Patients with Workplace Accidents Referred In Isfahan

Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods

The relationship of Value dimensions in Turk Society with fatalistic tendencies, safety motivation, risk perception and safety performance

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