{"title":"Investigation of Occupational Health and Safety Levels in Genetic Disease Centers in Istanbul.","authors":"Vedat Caner, Ferdi Tanir","doi":"10.1002/jcla.70015","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Genetic disorders significantly impact public health and quality of life, necessitating precise and timely diagnosis for effective risk management and treatment. Genetic diagnostic centers (GDCs) play a critical role in this process but face numerous occupational health and safety (OHS) challenges. The classification of GDCs based solely on biosafety levels is insufficient for assessing their overall OHS conditions. This study aims to systematically evaluate OHS practices in GDCs and propose a new classification approach based on hazard dimensions.</p><p><strong>Methods: </strong>This cross-sectional study was conducted in 15 GDCs in Istanbul, including two public and 13 private facilities with 75 employees. Data were collected through a structured survey with 49 statements covering seven hazard dimensions. Regression and correlation analyses were used to assess the impacts and interrelationships of these dimensions on risk management. Principal Component Analysis (PCA) was applied for dimensionality reduction, and the k-Nearest Neighbours (k-NN) algorithm classified laboratories into safety levels.</p><p><strong>Results: </strong>Personal protective equipment had the highest impact on risk management (56.3%), while physical security had the lowest (34.8%). Among the 21 identified hazard relationships, 18 were very strong and three were strong. PCA reduced the data into three primary components, explaining 81.9% of the variance. The k-NN algorithm achieved a classification accuracy of 93.33%, consolidating six hazard dimensions into three and categorizing centers into three safety levels.</p><p><strong>Conclusion: </strong>The findings emphasize the need for an updated OHS classification for GDCs beyond biosafety levels. Integrating hazard dimensions into safety assessments can improve risk management and enhance laboratory safety standards.</p>","PeriodicalId":15509,"journal":{"name":"Journal of Clinical Laboratory Analysis","volume":" ","pages":"e70015"},"PeriodicalIF":2.6000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Laboratory Analysis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/jcla.70015","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Genetic disorders significantly impact public health and quality of life, necessitating precise and timely diagnosis for effective risk management and treatment. Genetic diagnostic centers (GDCs) play a critical role in this process but face numerous occupational health and safety (OHS) challenges. The classification of GDCs based solely on biosafety levels is insufficient for assessing their overall OHS conditions. This study aims to systematically evaluate OHS practices in GDCs and propose a new classification approach based on hazard dimensions.
Methods: This cross-sectional study was conducted in 15 GDCs in Istanbul, including two public and 13 private facilities with 75 employees. Data were collected through a structured survey with 49 statements covering seven hazard dimensions. Regression and correlation analyses were used to assess the impacts and interrelationships of these dimensions on risk management. Principal Component Analysis (PCA) was applied for dimensionality reduction, and the k-Nearest Neighbours (k-NN) algorithm classified laboratories into safety levels.
Results: Personal protective equipment had the highest impact on risk management (56.3%), while physical security had the lowest (34.8%). Among the 21 identified hazard relationships, 18 were very strong and three were strong. PCA reduced the data into three primary components, explaining 81.9% of the variance. The k-NN algorithm achieved a classification accuracy of 93.33%, consolidating six hazard dimensions into three and categorizing centers into three safety levels.
Conclusion: The findings emphasize the need for an updated OHS classification for GDCs beyond biosafety levels. Integrating hazard dimensions into safety assessments can improve risk management and enhance laboratory safety standards.
期刊介绍:
Journal of Clinical Laboratory Analysis publishes original articles on newly developing modes of technology and laboratory assays, with emphasis on their application in current and future clinical laboratory testing. This includes reports from the following fields: immunochemistry and toxicology, hematology and hematopathology, immunopathology, molecular diagnostics, microbiology, genetic testing, immunohematology, and clinical chemistry.