The Prediction of Industrial Accident Rate in Korea: A Time Series Analysis / 한국직업건강간호학회지
Korean Journal of Occupational Health Nursing
; : 65-74, 2016.
Article
Dans Ko
| WPRIM
| ID: wpr-197500
Responsable en Bibliothèque :
WPRO
ABSTRACT
PURPOSE: The purpose of this study is to predict industrial accident rate using time series analysis. METHODS: The rates of industrial accident and occupational injury death were analyzed using industrial accident statistics analysis system of the Korea Occupational Safety and Health Agency from 2001 to 2014. Time series analysis was done using the most recent data, such as raw materials of Economically Active Population Survey, Economic Statistics System of the Bank of Korea, and e-National indicators. The best-fit model with time series analysis to predict occupational injury was developed by identifying predictors when the value of Akaike Information Criteria was the lowest point. Variables into the model were selected through a series of expertises' consultations and literature review, which consisted of socioeconomic structure, labor force structure, working conditions, and occupational accidents. RESULTS: Indexes at the meso- and macro-levels predicting well occurrence of occupational accidents and occupational injury death were labor force participation rate for ages 45-49 and budget for small scaled work-place support. The rates of industrial accident and occupational injury death are expected to decline. CONCLUSION: For reducing industrial accident continuously, we call for safe employment policy of economically active middle aged adults and support for improving safety work environment of small sized workplace.
Texte intégral:
1
Indice:
WPRIM
Sujet Principal:
Orientation vers un spécialiste
/
Budgets
/
Accidents du travail
/
Santé au travail
/
Emploi
/
Blessures professionnelles
/
Corée
Type d'étude:
Health_economic_evaluation
/
Prognostic_studies
Limites du sujet:
Adult
/
Humans
Pays comme sujet:
Asia
langue:
Ko
Texte intégral:
Korean Journal of Occupational Health Nursing
Année:
2016
Type:
Article