Modeling the Major Ailments of Petroleum Workers with Associated Risk Factors using Logistic Regression Approach
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 135 | Views: 354

Research Paper | Statistics | Sri Lanka | Volume 5 Issue 8, August 2016 | Popularity: 6.8 / 10


     

Modeling the Major Ailments of Petroleum Workers with Associated Risk Factors using Logistic Regression Approach

K.G.D.P. Gunarathne, A.M.C.H. Attanayake, D.D.M. Jayasundara


Abstract: Petroleum workers directly expose to both petroleum vapors and liquid petroleum products. Long-term exposures to the petroleum products have been shown to affect functioning of different systems of the body. The effects may result in accelerated decline in lung functions, increase in skin rashes, increase in visual problems and increase graying hair. This study was aimed to model the major ailments, Cough, Visual Problem, Skin Rash and Graying Hair with associated risk factors, Age, Service, Use of Gasmask, Use of Goggles, Use of Gloves, Use of Cigarette, Use of Alcohol and Having Animals. Data were collected from a 90 petroleum workers, of one of the main petroleum storing and distributing centres of Sri Lanka. The analysis was carried out using binary and ordinal logistic regression. From the fitted logistic regression models, it can be concluded that the long-term exposure to the petroleum products causes bad health effects on petroleum workers. Further, based on the results it could conclude that safety wears help to prevent from that ailments.


Keywords: Ailments, Logistic Regression Models, Petroleum Products, Petroleum Workers, Risk Factors


Edition: Volume 5 Issue 8, August 2016


Pages: 250 - 253


DOI: https://www.doi.org/10.21275/ART2016738


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K.G.D.P. Gunarathne, A.M.C.H. Attanayake, D.D.M. Jayasundara, "Modeling the Major Ailments of Petroleum Workers with Associated Risk Factors using Logistic Regression Approach", International Journal of Science and Research (IJSR), Volume 5 Issue 8, August 2016, pp. 250-253, https://www.ijsr.net/getabstract.php?paperid=ART2016738, DOI: https://www.doi.org/10.21275/ART2016738

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