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KMID : 1156220220480040236
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Journal of Environmental Health Sciences
2022 Volume.48 No. 4 p.236 ~ p.243
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Evaluation of the Relationship between the Exposure Level to Mixed Hazardous Heavy Metals and Health Effects Using Factor Analysis
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Kim Eun-Seop
Moon Sun-In
Yim Dong-Hyuk
Choi Byung-Sun
Park Jung-Duck
Eom Sang-Yong
Kim Yong-Dae
Kim Heon
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Abstract
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Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body.
Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter.
Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers.
Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model.
Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals.
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KEYWORD
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Heavy metals, smelter, factor analysis, multiple linear regression
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