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Trends in Occupational Infectious Diseases in South Korea and Classification of Industries According to the Risk of Biological Hazards Using K-Means Clustering

Author

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  • Saemi Shin

    (Research Institute of Health Sciences, Korea University, Seoul 02841, Korea)

  • Won Suck Yoon

    (Allergy and Immunology Center, Korea University, Seoul 02841, Korea)

  • Sang-Hoon Byeon

    (School of Health and Environmental Science, Korea University, Seoul 02841, Korea)

Abstract

Against the backdrop of the COVID-19 pandemic, it is necessary to identify these risks and determine whether the current level of management is appropriate to respond to the risk of biological hazards depending on the occupation. In this study, the incidence and fatality rates of occupational diseases were calculated using industrial accident statistics of South Korea, and trends by year using joinpoint regression and relative risk by industry using k-means clustering were evaluated for infectious diseases. We found that infectious diseases had the third highest incidence and fourth highest fatalities among all occupational diseases. In the incidence rate, joinpoints appeared in 2009 and 2018, and the annual percent change changed to 7.79, −16.63, and 82.11. The fatality rate showed a consistent increase with an annual percent change of 4.37, but it was not significant. Industries were classified into five groups according to risk, and the legal control measures of certain industries were not sufficient. Follow-up studies are needed to rectify the structural limitations of industrial accident statistics.

Suggested Citation

  • Saemi Shin & Won Suck Yoon & Sang-Hoon Byeon, 2022. "Trends in Occupational Infectious Diseases in South Korea and Classification of Industries According to the Risk of Biological Hazards Using K-Means Clustering," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:11922-:d:921035
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    References listed on IDEAS

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    1. Dahlan Abdullah & S. Susilo & Ansari Saleh Ahmar & R. Rusli & Rahmat Hidayat, 2022. "The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1283-1291, June.
    2. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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