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                  <text>Safety and Health at Work Vol. 16 Issue 3 2025</text>
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                <text>Approach to Chemical Health Risk Assessment for Maintenance&#13;
Workers at Electronics Manufacturing Facilities</text>
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                <text>chemical hazard&#13;
exposure probability&#13;
maintenance works (MW)&#13;
multiple chemical health risk&#13;
occupational health risk assessment</text>
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                <text>Maintenance workers in the electronics manufacturing industry are exposed to a diverse&#13;
combination of gaseous and particulate substances, yet specific guidance for assessing their chemical&#13;
health risks remains limited. The aims of this study are to develop a stepwise framework approach for&#13;
qualitatively assessing hazard severity, exposure probability, and chemical health risks for maintenance&#13;
workers at electronic manufacturing facilities, and to propose a method for categorizing these factors&#13;
through occupational health risk assessment (OHRA).&#13;
Methods: We developed a stepwise framework for qualitatively assessing the health risks posed by&#13;
chemicals to maintenance workers in electronics facilities. This framework employs a semiquantitative&#13;
approach using ordinal scores to evaluate the severity of chemical hazards and the likelihood of&#13;
exposure, particularly in situations where representative measurement data for multiple chemicals are&#13;
unavailable.&#13;
Results: The framework assesses hazard severity qualitatively by considering the number of chemicals&#13;
and the presence of carcinogenic, mutagenic, or reprotoxic substances or sensitizing hazards. A qualitative approach to estimating the likelihood of exposure is recommended, focusing on the effectiveness&#13;
of engineering controls, the frequency and duration of maintenance works and tasks (MW), and the use&#13;
of air jets. Two examples of a qualitative chemical OHRA for maintenance workers are given, one for a&#13;
moderate hazard chemical integrating several exposure factors, and the second for MW on an ion&#13;
implanter.&#13;
Conclusion: Further research is needed to validate this method and to develop additional practical&#13;
guidance for the assessment of chemical health risks associated with MW.</text>
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                <text>Kyong-Hui Lee 1&#13;
, Sangjun Choi 2,3,q, Kyung Ehi Zoh 4,5&#13;
, Dong-Hee Koh 6&#13;
, Won Kim 7&#13;
,&#13;
Dong-Uk Park</text>
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                <text>US Army 65th Medical Brigade, Force Health Protection and Preventive Medicine Unit, Republic of Korea 2Graduate School of Public Health and Healthcare Management, The Catholic University of Korea, Seoul, Republic of Korea 3 Catholic Institute for Public Health and Healthcare Management, Seoul 06591, Republic of Korea 4Department of Environmental Health, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea 5 National Cancer Control Institute, National Cancer Center, Goyang, 10408, Republic of Korea 6Department of Occupational and Environmental Medicine, Severance Hospital, Yonsei University, Seoul, Republic of Korea 7Wonjin Institute for Occupational and Environmental Health, Seoul, Republic of Korea 8Department of Environmental Health, Korea National Open University, Seoul, Republic of Korea</text>
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                <text>Fajar Bagus Wijanarko</text>
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                <text>Effect of Prolonged Use of Different Facemasks on Their Physical&#13;
Performance and Physiological Impact on the Wearer</text>
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                <text>breathing resistance&#13;
facemask&#13;
facial skin temperature&#13;
penetration&#13;
transepidermal water loss</text>
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                <text>Most published studies on potential facemask physical performances and physiological&#13;
impairments had shorter observation periods, and the observed occupational physical activities had&#13;
limited relevance to real occupational work. Thus, our study aimed to assess the impact of prolonged&#13;
mask use on its physical performances and the associated physiological responses in wearers.&#13;
Methods: The recruitment of study participants took place between November 2022 and March 2023.&#13;
Facemask penetration and breathing resistance measurements were obtained at 4 and 8 hours using the&#13;
TSI Model 8130 Automated Filter Tester and the INSPEC Breathing Resistance Rig. Facial skin temperature and transepidermal water loss were quantified using thermal imaging cameras and vapometers,&#13;
respectively.&#13;
Results: A total of 216 workers participated in the study. A significant reduction in penetration&#13;
(p &lt; 0.001, partial Ƞ2 = 0.1) and an increase in breathing resistance (p &lt; 0.001, partial Ƞ2 = 0.9) were&#13;
observed only in case of surgical masks worn by cleaners after 8 hours. Facial skin temperature&#13;
increased after 8 hours for KF94 (p &lt; 0.001, partial Ƞ2 = 0.2), surgical mask (p &lt; 0.001, partial Ƞ2 = 0.4),&#13;
and cloth mask (p &lt; 0.001, partial Ƞ2 = 0.2). All three facemasks had a statistically significant interaction&#13;
with use on facial skin temperature. Higher transepidermal water loss was only observed for the cloth&#13;
mask (F (p = 0.034, partial Ƞ2 = 0.02).&#13;
Conclusion: Our findings suggest that prolonged use of face masks can lead to a deterioration in&#13;
penetration, breathing resistance, and physiological impairment for the mask wearer. The implications&#13;
are particularly critical for high—occupational activity jobs requiring prolonged use of masks.</text>
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                <text>Baderin Osman 1&#13;
, Muhammad Zubir Yusof 2,*, Haalah Mahmud 1</text>
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                <text>Consultation Research and Development Department, National Institute of Occupational Safety and Health, Bandar Baru Bangi, Selangor, Malaysia 2Department of Community Medicine, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan, Pahang, Malaysia</text>
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                <text>Development and Application of Standard Codes for the National&#13;
Exposure Surveillance System Utilizing Korea’s Nationwide Exposure&#13;
Database</text>
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                <text>Exposure condition category&#13;
Job-exposure matrix&#13;
Standard occupation classification&#13;
Standard process classification</text>
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                <text> The Work Environment Measurement Database (WEMD) is a valuable system for occupational exposure surveillance. However, its use is limited due to the lack of proper standardization of&#13;
exposure-related variables. Hence, we developed a new standard process classification (SPC), standard&#13;
occupation classification (SOC), and exposure condition category (ECOC) codes to establish an exposure&#13;
surveillance system using the WEMD. Additionally, we assessed the feasibility of constructing a jobexposure matrix (JEM) using standardized codes.&#13;
Methods: The SPC and SOC were reclassified based on similarity from an exposure perspective, using&#13;
established codes refined through reviews by industrial hygiene experts. The ECOC codes were based on&#13;
the conceptual exposure assessment model. Ten experts conducted a pilot project to evaluate the&#13;
applicability of the newly reclassified SPC, SOC, and ECOC codes.&#13;
Results: We developed 77 SPC, 82 SOC, and 12 ECOC codes, which were assigned to over 98% of the data&#13;
by experts, demonstrating their practical applicability. A JEM linking industry, occupation, process, and&#13;
exposure condition was constructed into an interactive dashboard based on expert evaluations,&#13;
demonstrating feasibility and enabling better interpretation of exposure levels through user-controlled&#13;
variables. Exposure levels varied significantly across ECOC groups, showing a clear linear trend with&#13;
higher exposures in conditions representing greater exposure potential, such as proximity to the source&#13;
and lack of control measures.&#13;
Conclusion: The newly developed standardized codes are easily applicable by industrial hygienists and</text>
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                <text>Sangjun Choi 1,2,*, Ju-Hyun Park 3&#13;
, Dong-Hee Koh 4&#13;
, Dae Sung Lim 5&#13;
, Hwan-Cheol Kim 6&#13;
,&#13;
Jin-Ha Yoon 7&#13;
, Dong-Uk Park 8&#13;
, Su Min Oh 1&#13;
, Hoekyeong Seo 9</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="128487">
                <text>Graduate School of Public Health and Healthcare Management, The Catholic University of Korea, Seoul, Republic of Korea 2 Catholic Institute for Public Health and Healthcare Management, Seoul, Republic of Korea 3Department of Statistics, Dongguk University, Seoul, 04620, Republic of Korea 4Occupational and Environmental Medicine, Severance Hospital, Yonsei University, Seoul, Republic of Korea 5 Hansung Health and Safety Technology Co., Ltd., Daejeon, Republic of Korea 6Department of Occupational and Environmental Medicine, Inha University, Incheon, Republic of Korea 7Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea 8Department of Environmental Health, Korea National Open University, Seoul, Republic of Korea 9Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency, Ulsan, Republic of Korea</text>
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                <text>Fajar Bagus Wijanarko</text>
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                  <text>Safety and Health at Work Vol. 16 Issue 3 2025</text>
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            <description>A name given to the resource</description>
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                <text>Risk for Diabetes From Long Working Hours and Night Work in the&#13;
United States: Prospective Associations and Machine Learning&#13;
Techniques</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>diabetes&#13;
long working hours&#13;
machine learning&#13;
night work&#13;
prospective cohort&#13;
</text>
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                <text>Diabetes contributes significantly to death in the U.S., with many working-age individuals&#13;
affected. This research determined the independent and joint associations of long working hours and&#13;
night work with diabetes risk in U.S. workers, and their contribution to risk prediction.&#13;
Methods: This prospective study included 1,454 workers from the Midlife in the United States (MIDUS)&#13;
study with 9-year follow-up. Long working hours included those working 55 or more hours per week.&#13;
Night work involved those working 16 or more nights per year. Diabetes was determined by selfreported diagnosis or treatment. Multivariable Poisson regression analysis was applied to examine&#13;
the prospective association of these work-related factors at baseline with incident diabetes. A gradient&#13;
boosting machine learning model was used to investigate the contributions of both factors in predicting&#13;
incident diabetes.&#13;
Results: Long working hours (RR and 95% CI = 1.60 [1.04, 2.46], p &lt; 0.05) and night work (RR and 95%&#13;
CI = 1.66 [1.05, 2.62], p &lt; 0.05) were independently associated with the risk for diabetes, while controlling for baseline covariates. Gradient boosting analysis suggested long working hours and night work&#13;
facilitated diabetes incidence. Exposure to both long working hours and night work increased the risk&#13;
for diabetes (RR and 95% CI = 3.02 [1.64, 5.58], p &lt; 0.001), suggesting additive interaction.&#13;
Conclusion: Organizations may consider reducing hours on duty and improving shift systems for primary prevention of diabetes.</text>
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                <text>Elizabeth Keller 1&#13;
, Liwei Chen 2&#13;
, Feng Gao 1,3&#13;
, Jian Li 1,2,4,5,*</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="128436">
                <text>Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, United States 2Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, United States 3Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, United States 4 School of Nursing University of California Los Angeles, Los Angeles, United States 5Department of Public Health Nursing, Faculty of Public Health, Mahidol University, Bangkok, Thailand&#13;
</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
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                <text>28 May 2025</text>
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            <description>An entity responsible for making contributions to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="128438">
                <text>Elizabeth Keller 1&#13;
, Liwei Chen 2&#13;
, Feng Gao 1,3&#13;
, Jian Li 1,2,4,5,</text>
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            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
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                  <text>Safety and Health at Work Vol. 16 Issue 3 2025</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>From Safety Climate to Life Satisfaction: The Mediating Role of Job&#13;
Stress and Job Satisfaction</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="128379">
                <text>Job satisfaction&#13;
Job stress&#13;
Life satisfaction&#13;
Occupational health and safety&#13;
Safety climate</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
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                <text>Healthcare workers and occupational health and safety (OHS) professionals are among the&#13;
key professions where a strong safety climate is critical. A robust safety climate is theorized to enhance&#13;
life satisfaction. Both job stress and job satisfaction play influential roles in shaping the effects of safety&#13;
climate on life satisfaction. This study aims to examine the mediating roles of job stress and job satisfaction in the relationship between safety climate and life satisfaction among healthcare workers and&#13;
OHS professionals.&#13;
Methods: The study utilized data from 610 healthcare workers and OHS professionals employed across&#13;
Türkiye. Structural equation modelling analysis was performed to explore the relationship between&#13;
safety climate and life satisfaction and to assess the mediating effects of job stress and job satisfaction.&#13;
Results: The findings indicated that job satisfaction serves as a full mediator in the relationship between&#13;
safety climate and life satisfaction.&#13;
Conclusion: Given the mediating role of job satisfaction in the link between safety climate and life&#13;
satisfaction, organizations should prioritize strategies aimed at improving job satisfaction to enhance&#13;
overall employee well-being</text>
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                <text>Department of Occupational Health and Safety, Kırklareli University, Kırklareli, Türkiye 2Department of Business Administration, Bursa Technical University, Bursa, Türkiye</text>
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                  <text>Safety and Health at Work Vol. 16 Issue 3 2025</text>
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                <text>Ineffectiveness of Water Mist Spray to the Upper Body of Healthy&#13;
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Core temperature&#13;
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                <text>Global warming has increased heat-related illness concerns. Water misting is used for&#13;
workplace cooling, but its effect on core body temperature is unclear.&#13;
Methods: We examined whether spraying water mist on the upper body suppresses the increase in core body&#13;
temperature in hot and humid environments. Nine healthy adult men exercised for 60 min on a bicycle&#13;
ergometer at four metabolic equivalents under two conditions: warm-humid environment (30 �C ambient&#13;
temperature [Ta], 85% relative humidity [RH]) and hot-dry (HD) environment (40 �C Ta, 30% RH). Water mist&#13;
(MIST) or no mist (CON) was applied to the back of the head, neck, and dorsum of hands every 10 min.&#13;
Results: Rectal temperature changes were similar between conditions, but peak temperatures were&#13;
slightly higher with MIST (38.32�C vs. 38.14�C, p ¼ 0.074) in HD environments. The estimated sweat&#13;
volume was lower with MIST, especially in HD settings.&#13;
Conclusion: Water mist does not prevent core temperature increases and should be avoided in extremely&#13;
hot environments</text>
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                <text>Kimie Fukuzawa 1,2&#13;
, Kimiyo Mori 1,3&#13;
, Kahori Hashimoto 1,4&#13;
, Riho Tanaka 1,5&#13;
,&#13;
Chikage Nagano 1,*, Seichi Horie 1</text>
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              <elementText elementTextId="128351">
                <text>1Department of Health Policy and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health (UOEH),&#13;
Kitakyushu, Japan&#13;
2 Fuji Electric Co., Ltd., Kawasaki Plant, Kanagawa, Japan&#13;
3 Kobe Steel, Ltd., Chofu Works, Yamaguchi, Japan&#13;
4 Mitshubishi Heavy Industries, Ltd, Tokyo, Japan&#13;
5Daihatsu Motor Co., Ltd, Osaka, Japan</text>
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                <text>Fajar Bagus Wijanarko</text>
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                  <text>Safety and Health at Work Vol. 16 Issue 3 2025</text>
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            <description>A name given to the resource</description>
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                <text>Differences in Upper Extremity Muscle Activity, Neck Angle, and Body&#13;
Discomfort Between Tablet Usage With Virtual Keyboard and&#13;
Bluetooth Keyboard</text>
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            <description>The topic of the resource</description>
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                <text>Bluetooth keyboard&#13;
Muscle activity&#13;
Neck angle&#13;
Tablet&#13;
Virtual keyboard</text>
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                <text> The objective was to compare the differences in muscle activity of the upper extremities,&#13;
neck angles, and body discomfort during tablet use between virtual and Bluetooth keyboards.&#13;
Methods: Twenty-four female volunteers were randomly assigned to use a tablet with keyboard for&#13;
typing articles. The first order was randomly selected for 30 min, with a 30-minute break before the next&#13;
keyboard layout test began.&#13;
Results: The result showed a statistically significant difference in the muscle activity of the upper&#13;
trapezius while using a tablet between the two types of keyboards (p &lt; 0.001). In addition, there were&#13;
statistically significant differences in muscle activities of the upper trapezius (p &lt; 0.05), extensor digitorum communis (EDC) (p &lt; 0.001), and flexor digitorum superficialis (p &lt; 0.001), craniohorizontal&#13;
angle (p &lt; 0.001) and craniovertebral angle (p &lt; 0.001), and body discomfort in the neck and shoulder&#13;
areas before and after using the tablet with each keyboard (p &lt; 0.05).&#13;
Conclusion: Tablet use with the two types of keyboards induced an increase in muscle activity, neck&#13;
flexion angles, and body discomfort, which may contribute to the occurrence of musculoskeletal disorders (MSDs). This study may serve as a basis for developing guidelines to reduce the risk of MSDs during&#13;
tablet use</text>
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                <text>Praphatson Sengsoon 1,2,*, Kanruethai Threesittidath 1,2&#13;
, Thunyaporn Singhadacha 1&#13;
,&#13;
Suchanat Saengthong 1</text>
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                <text>1Department of Physical Therapy, School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat, Thailand&#13;
2 Movement Sciences and Exercise Research Center, Walailak University (MoveSE-WU), Nakhon Si Thammarat, Thailan</text>
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Exposure assessment&#13;
Influencing factors&#13;
Occupational exposure&#13;
Welding operations</text>
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                <text> Welding fumes from industrial operations pose serious health risks. This study aimed to&#13;
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Advanced REACH Tool (ART) for dust exposure assessment in building materials enterprises.&#13;
Methods: The dust exposure of 18 building materials enterprises was compared using ART model assessments and measured data. Multiple covariance analysis identified key influencing factors consistent&#13;
with field conditions. Finally, correlation and regression analyses evaluated the relationships between&#13;
these factors and dust exposure.&#13;
Results: The ART model showed the highest agreement with field data at the 95th percentile. Correlation and regression analyses identified moisture content, surface contamination/emissions, and dust&#13;
particle size as the main influencing factors.&#13;
Conclusion: Mitigating occupational health risks requires prioritized control of moisture content, followed by dust particle size and surface contamination/emission. Targeted control of these parameters&#13;
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                <text>School of Emergency Management and Safety Engineering, China University of Mining &amp; Technology (Beijing), Beijing, China</text>
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                  <text>Safety and Health at Work Vol. 16 Issue 3 2025</text>
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                <text>Confounding Effects of Lifestyle Factors in Cancer Risk Estimation for&#13;
Occupational Radiation Exposure</text>
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                <text>Confounding Effects of Lifestyle Factors in Cancer Risk Estimation for&#13;
Occupational Radiation Exposure</text>
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                <text> The confounding effect of lifestyle factors is an important concern in occupational studies,&#13;
particularly when the risk magnitude is relatively small. This study aimed to evaluate the potential&#13;
confounding effects of lifestyle factors on the association between radiation exposure and cancer&#13;
incidence.&#13;
Methods: Data from all Republic of Korean diagnostic medical radiation workers enrolled in the national&#13;
dose registry were merged with cancer incidence records up to 2018. Excess relative risks (ERRs) for&#13;
cancer were calculated using Poisson regression models to quantify the radiation dose-response relationship. Major lifestyle factors were imputed using multiple imputations by chained equations based&#13;
on survey data. The confounding effects were assessed by comparing ERRs before and after adjustment&#13;
for lifestyle factors.&#13;
Results: The baseline ERR for cancer incidence per Sievert was 0.44 (95% CI: -0.94, 1.83) after adjusting&#13;
for attained age, sex, birth year, and employment duration. Further adjustment for lifestyle factors&#13;
(smoking status, alcohol consumption, body mass index, physical exercise, sleep duration, and night&#13;
shift work) did not substantially modify this risk coefficient, with change-in-estimate values ranging&#13;
from 0% to 13.6%. Sensitivity analyses conducted with the survey-based cohort and sex-stratified analyses yielded consistent results.&#13;
Conclusion: Our study found little evidence of significant confounding effects from unmeasured lifestyle&#13;
factors on cancer risk when basic registry data variables were adjusted among medical radiation&#13;
workers. Further studies are warranted to investigate the impact of unmeasured and unknown confounders to improve the accuracy of radiation risk estimates</text>
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                <text>Eun Jung Park 1,2&#13;
, Ye Jin Bang 1&#13;
, Won Jin Lee 1,2</text>
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                <text>1Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea 2Graduate School of Public Health, Korea University, Seoul, Republic of Korea</text>
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Carcinogen&#13;
CAREX&#13;
Job-exposure matrix&#13;
Occupational exposure</text>
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                <text>Reliable exposure data are critical for occupational cancer prevention and epidemiological&#13;
studies. We developed the Korean CARcinogen EXposure (K-CAREX) to provide exposure prevalence and&#13;
intensity for 20 carcinogens classified as Group 1 by the International Agency for Research on Cancer&#13;
(IARC). This study extends K-CAREX to include IARC Group 2A agents.&#13;
Methods: National data sources, including the Work Environment Measurement Database (WEMD),&#13;
Special Health Examination Database (SHED), and Work Environment Condition Survey (WECS), provided basic exposure data. A stepwise estimation approach was employed to refine exposure prevalence&#13;
estimates by industry, derived from the WEMD, SHED, and WECS. WEMD was used to estimate exposure&#13;
intensity by industry, whereas SHED was used to assess exposure prevalence by occupation. Matched&#13;
data, representing workers who wore samplers and underwent health examinations for the same&#13;
carcinogen in the same year, were used to estimate exposure intensity by occupation.&#13;
Results: A total of 18 IARC Group 2A agents were included. Exposure prevalence was estimated for all 18&#13;
carcinogens by industry, and exposure intensity was estimated for 16. The number of exposed workers&#13;
was estimated, with 162,153 workers exposed to lead, for example. Exposure prevalence and intensity&#13;
were also assessed for 18 and 16 carcinogens, respectively, by occupation.&#13;
Conclusion: The K-CAREX database was developed to estimate exposure prevalence and intensity by&#13;
industry and occupation for 18 IARC Group 2A agents. These findings provide valuable data for cancer&#13;
prevention and epidemiological studies. Future work may expand K-CAREX to include IARC Group 2B&#13;
agents and other carcinogenic agents</text>
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                <text>Dong-Hee Koh 1,*, Sangjun Choi 2,3&#13;
, Ju-Hyun Park 4&#13;
, Sang-Gil Lee 5&#13;
, Hwan-Cheol Kim 6&#13;
,&#13;
Inah Kim 7&#13;
, Jeehee Min 8&#13;
, Yujin Kim 5&#13;
, Jihye Lee 5&#13;
, Dong-Uk Park 9</text>
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                <text>1Department of Occupational and Environmental Medicine, Severance Hospital, Yonsei University, Seoul, Republic of Korea 2Graduate School of Public Health and Healthcare Management, The Catholic University of Korea, Seoul, Republic of Korea 3 Catholic Institute for Public Health and Healthcare Management, Seoul, Republic of Korea 4Department of Statistics, Dongguk University, Seoul, Republic of Korea 5Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency, Ulsan, Republic of Korea 6Department of Occupational and Environmental Medicine, Inha University, Incheon, Republic of Korea 7Department of Occupational and Environmental Medicine, College of Medicine, Hanyang University, Seoul, Republic of Korea 8Department of Occupational and Environmental Medicine, Hanyang University Hospital, Seoul, Republic of Korea 9Department of Environmental Health, Korea National Open University, Seoul, Republic of Korea</text>
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