Pub Date : 2024-04-27DOI: 10.1101/2024.04.26.24306427
Doddy Izhar, David Kusmawan, Budi Aswin
Fatigue during work among oil and gas employees can have dangerous effects on wellbeing, economics, safety, and health. This cross-sectional survey was conducted in July and August of 2022 at two national oil and gas companies located in Muara Jambi and Jambi City. A convenience sample of 116 respondents was selected in total. To address the study hypotheses, partial least square-structural equation modeling (PLS-SEM) was employed. This study aims to determine the relationship between the risk of job weariness among Indonesian oil and gas workers in Jambi Province and the direct and indirect impacts of mental workload, sociodemographic characteristics, and sleep quality. Personality data has a significant and negative direct impact on occupational weariness at alpha 5% and with a path value of -0.203 (p-value: 0.047), corroborating the earlier hypothesis. For the second hypothesis, the path coefficient value of 0.462 (p-value: 0.000) clearly shows that sleep quality has an impact on occupational weariness. In order to improve sleep hygiene and address personality factors like age and length of employment, fatigue risk management strategies can be combined with those that are currently being used to control job tiredness.
{"title":"Do Sleep Quality Can Be the Intervening Factors of Personality Data to Occupational Fatigue?","authors":"Doddy Izhar, David Kusmawan, Budi Aswin","doi":"10.1101/2024.04.26.24306427","DOIUrl":"https://doi.org/10.1101/2024.04.26.24306427","url":null,"abstract":"Fatigue during work among oil and gas employees can have dangerous effects on wellbeing, economics, safety, and health. This cross-sectional survey was conducted in July and August of 2022 at two national oil and gas companies located in Muara Jambi and Jambi City. A convenience sample of 116 respondents was selected in total. To address the study hypotheses, partial least square-structural equation modeling (PLS-SEM) was employed. This study aims to determine the relationship between the risk of job weariness among Indonesian oil and gas workers in Jambi Province and the direct and indirect impacts of mental workload, sociodemographic characteristics, and sleep quality. Personality data has a significant and negative direct impact on occupational weariness at alpha 5% and with a path value of -0.203 (p-value: 0.047), corroborating the earlier hypothesis. For the second hypothesis, the path coefficient value of 0.462 (p-value: 0.000) clearly shows that sleep quality has an impact on occupational weariness. In order to improve sleep hygiene and address personality factors like age and length of employment, fatigue risk management strategies can be combined with those that are currently being used to control job tiredness.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1101/2024.04.16.24305910
Evangelos Ntontis, Richard Williams, Katarzyna Luzynska, Abigail Wright, Anastasia Rousaki
Background Extreme events (e.g., floods, hurricanes) can overwhelm healthcare workers and systems. Similarly, healthcare workers were particularly affected during the COVID-19 pandemic, and high levels of distress and mental ill health have been reported.
{"title":"Stressors and lessons for future support for healthcare staff facing adverse challenges: A systematic review of qualitative research conducted in the UK during the COVID-19 pandemic","authors":"Evangelos Ntontis, Richard Williams, Katarzyna Luzynska, Abigail Wright, Anastasia Rousaki","doi":"10.1101/2024.04.16.24305910","DOIUrl":"https://doi.org/10.1101/2024.04.16.24305910","url":null,"abstract":"<strong>Background</strong> Extreme events (e.g., floods, hurricanes) can overwhelm healthcare workers and systems. Similarly, healthcare workers were particularly affected during the COVID-19 pandemic, and high levels of distress and mental ill health have been reported.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"2013 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background Antimicrobial resistant (AMR) organisms in environment may harm people. This study assessed the phenotypic and genotypic characteristics of AMR organisms from drinking and wastewater.
背景 环境中的抗微生物（AMR）生物可能会对人类造成危害。本研究评估了饮用水和废水中的 AMR 微生物的表型和基因型特征。
{"title":"Molecular characterization of antimicrobial resistance organisms from drinking water and wastewater in a metropolitan city","authors":"Khursheda Akhtar, Nasreen Farhana, Alamgir Hossain, Fahmida Khanam","doi":"10.1101/2024.04.12.24305711","DOIUrl":"https://doi.org/10.1101/2024.04.12.24305711","url":null,"abstract":"<strong>Background</strong> Antimicrobial resistant (AMR) organisms in environment may harm people. This study assessed the phenotypic and genotypic characteristics of AMR organisms from drinking and wastewater.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1101/2024.04.09.24305578
Zhenqiu Liu, Igor Shuryak
Accurately evaluating the disease risks after low-dose ionizing radiation (IR) exposure are crucial for protecting public health, setting safety standards, and advancing research in radiation safety. However, while much is known about the disease risks of high-dose irradiation, risk estimates at low dose remains controversial. To date, five different parametric models (supra-linear, linear no threshold, threshold, quadratic, and hormesis) for low doses have been studied in the literature. Different dose-response models may lead to inconsistent or even conflicting results. In this manuscript, we introduce a data-driven deep neural network (DNN) model designed to evaluate dose-response models at low doses using Life Span Study (LSS) data. DNNs possess the capability to approximate any continuous function with an adequate number of nodes in the hidden layers. Being data-driven, they circumvent the challenges associated with misspecification inherent in parametric models. Our simulation study highlights the effectiveness of DNNs as a valuable tool for precisely identifying dose-response models from available data. New findings from the LSS study provide robust support for a linear quadratic (LQ) dose-response model at low doses. While the linear no threshold (LNT) model tends to overestimate disease risk at very low doses and underestimate health risk at relatively high doses, it remains a reasonable approximation for the LQ model, given the minor impact of the quadratic term at low doses. Our demonstration underscores the power of DNNs in facilitating comprehensive investigations into dose-response associations.
{"title":"Dose-Response after Low-dose Ionizing Radiation: Evidence from Life Span Study with Data-driven Deep Neural Network Model","authors":"Zhenqiu Liu, Igor Shuryak","doi":"10.1101/2024.04.09.24305578","DOIUrl":"https://doi.org/10.1101/2024.04.09.24305578","url":null,"abstract":"Accurately evaluating the disease risks after low-dose ionizing radiation (IR) exposure are crucial for protecting public health, setting safety standards, and advancing research in radiation safety. However, while much is known about the disease risks of high-dose irradiation, risk estimates at low dose remains controversial. To date, five different parametric models (supra-linear, linear no threshold, threshold, quadratic, and hormesis) for low doses have been studied in the literature. Different dose-response models may lead to inconsistent or even conflicting results. In this manuscript, we introduce a data-driven deep neural network (DNN) model designed to evaluate dose-response models at low doses using Life Span Study (LSS) data. DNNs possess the capability to approximate any continuous function with an adequate number of nodes in the hidden layers. Being data-driven, they circumvent the challenges associated with misspecification inherent in parametric models. Our simulation study highlights the effectiveness of DNNs as a valuable tool for precisely identifying dose-response models from available data. New findings from the LSS study provide robust support for a linear quadratic (LQ) dose-response model at low doses. While the linear no threshold (LNT) model tends to overestimate disease risk at very low doses and underestimate health risk at relatively high doses, it remains a reasonable approximation for the LQ model, given the minor impact of the quadratic term at low doses. Our demonstration underscores the power of DNNs in facilitating comprehensive investigations into dose-response associations.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Burnout of healthcare workers is of increasing concern as workload pressures mount. Burnout is usually conceptualised as resulting from external pressures rather than internal resilience and although is not a diagnosable condition, it is related to help seeking for its psychological sequelae. Objective: To understand how staff support services can intervene with staff heading for burnout, it is important to understand what other intrapsychic factors that are related to it. Methods: A diary tool was used by staff in a region of England to self monitor their wellbeing over time. The tool explores many areas of mental health and wellbeing and enabled regression analysis to predict which of the various factors predicted scores on the burnout item. Findings: Burnout can be best explained with independent variables including depression, receptiveness, mental wellbeing, and connectedness (p<0.05) using a multiple linear regression model. It was also shown that 71% of the variance present in the response variable, i.e. burnout, explained by independent variables. There is no evidence found for multicollinearity in our regression models confirmed by both the Spearman Rank Correlation and the Variance Inflation Factor methods. Conclusion: We showed how burnout can be explained using a handful number of factors including emotional and mental health indicators. Clinical implications: The findings suggest a simple set of items can predict burnout and could be used for screening. The data suggests attention to four factors around social safeness, grounding and care in the self, hope and meaning and having sufficient energy could form the basis of attention in weelbeing programs.
{"title":"Understanding emotional and health indicators underlying the burnout risk of healthcare workers","authors":"Elçin Güveyi, Garry Elvin, Angela Kennedy, Zeyneb Kurt, Petia Sice, Paras Patel, Antoinette Dubruel, Drummond Heckels","doi":"10.1101/2024.04.11.24305661","DOIUrl":"https://doi.org/10.1101/2024.04.11.24305661","url":null,"abstract":"Background: Burnout of healthcare workers is of increasing concern as workload pressures mount. Burnout is usually conceptualised as resulting from external pressures rather than internal resilience and although is not a diagnosable condition, it is related to help seeking for its psychological sequelae.\u0000Objective: To understand how staff support services can intervene with staff heading for burnout, it is important to understand what other intrapsychic factors that are related to it.\u0000Methods: A diary tool was used by staff in a region of England to self monitor their wellbeing over time. The tool explores many areas of mental health and wellbeing and enabled regression analysis to predict which of the various factors predicted scores on the burnout item.\u0000Findings: Burnout can be best explained with independent variables including depression, receptiveness, mental wellbeing, and connectedness (p<0.05) using a multiple linear regression model. It was also shown that 71% of the variance present in the response variable, i.e. burnout, explained by independent variables. There is no evidence found for multicollinearity in our regression models confirmed by both the Spearman Rank Correlation and the Variance Inflation Factor methods.\u0000Conclusion: We showed how burnout can be explained using a handful number of factors including emotional and mental health indicators.\u0000Clinical implications: The findings suggest a simple set of items can predict burnout and could be used for screening. The data suggests attention to four factors around social safeness, grounding and care in the self, hope and meaning and having sufficient energy could form the basis of attention in weelbeing programs.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1101/2024.04.11.24305677
Irene S. Gabashvili
Volatile Organic Compounds (VOCs) triggering respiratory irritation are implicated in conditions such as Trimethylaminuria (TMAU) and “people are allergic to me” (PATM) which occur in otherwise healthy individuals without clear syndromic associations. Despite the absence of established non-targeted non-challenge-based diagnostic procedures, recent studies have identified discriminatory VOCs associated with PATM using gas chromatography-mass spectrometry. Breath VOCs, originating from the bloodstream, hold promise for non-invasive diagnosis.
{"title":"Propylene Oxide in Exhaled Breath as a Marker for Discriminating TMAU-like Conditions from TMAU","authors":"Irene S. Gabashvili","doi":"10.1101/2024.04.11.24305677","DOIUrl":"https://doi.org/10.1101/2024.04.11.24305677","url":null,"abstract":"Volatile Organic Compounds (VOCs) triggering respiratory irritation are implicated in conditions such as Trimethylaminuria (TMAU) and “people are allergic to me” (PATM) which occur in otherwise healthy individuals without clear syndromic associations. Despite the absence of established non-targeted non-challenge-based diagnostic procedures, recent studies have identified discriminatory VOCs associated with PATM using gas chromatography-mass spectrometry. Breath VOCs, originating from the bloodstream, hold promise for non-invasive diagnosis.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1101/2024.04.04.24305326
Emmanuel Fort, Nicolas Connesson, Julien Brière, Amina Ndiaye, Blandine Gadegbeku, Barbara Charbotel
Introduction According to the 2018–2019 People Mobility Survey, work-related journeys (commuting and on-duty journeys) account for approximately 25% of all journeys. The use of non-motorized (nm) and motorized (m) personal mobility devices (PMDs) has steadily increased since their introduction into the French market in the last decade.
{"title":"Work-related road traffic crashes: emergence of new modes of personal journey. Analysis based on data from a register of road traffic crashes","authors":"Emmanuel Fort, Nicolas Connesson, Julien Brière, Amina Ndiaye, Blandine Gadegbeku, Barbara Charbotel","doi":"10.1101/2024.04.04.24305326","DOIUrl":"https://doi.org/10.1101/2024.04.04.24305326","url":null,"abstract":"<strong>Introduction</strong> According to the 2018–2019 People Mobility Survey, work-related journeys (commuting and on-duty journeys) account for approximately 25% of all journeys. The use of non-motorized (nm) and motorized (m) personal mobility devices (PMDs) has steadily increased since their introduction into the French market in the last decade.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1101/2024.04.04.24305319
Shanshan Zuo, Vidhya Sasitharan, Gian Luca Di Tanna, Judith M. Vonk, Maaike De Vries, Moustafa Sherif, Balázs Ádám, Juan Carlos Rivillas, Valentina Gallo
Objective Exposure to pesticides is a risk factor for various diseases, yet its association with biological aging remains unclear. We aimed to systematically investigate the relationship between pesticide exposure and biological aging.
{"title":"Is exposure to pesticides associated with biological aging? A systematic review and meta-analysis","authors":"Shanshan Zuo, Vidhya Sasitharan, Gian Luca Di Tanna, Judith M. Vonk, Maaike De Vries, Moustafa Sherif, Balázs Ádám, Juan Carlos Rivillas, Valentina Gallo","doi":"10.1101/2024.04.04.24305319","DOIUrl":"https://doi.org/10.1101/2024.04.04.24305319","url":null,"abstract":"<strong>Objective</strong> Exposure to pesticides is a risk factor for various diseases, yet its association with biological aging remains unclear. We aimed to systematically investigate the relationship between pesticide exposure and biological aging.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-03DOI: 10.1101/2024.04.02.24305133
Silvana Figar, Analia Ferloni, Amparo Saravi, Adriana R. Dawidowski, Valeria I. Aliperti, Ignacio Bressán, Florencia De Florio, Jimena Vicens, Nahuel Braguinsky Golde, Natalia K. Garcia, Glenda Pazur, Guillermo E. Hough, Adrián C. Gadano
Introduction An increasing number of rural communities express perception of health damage from glyphosate and other agrochemicals. We measure the presence of glyphosate in the human body, in order to create, together with the local community, a systemic model that highlights modifiable causal socio-environmental conditions.
{"title":"Presence of glyphosate in urine due to environmental exposure among populations of agro-industrial areas in Buenos Aires, Argentina","authors":"Silvana Figar, Analia Ferloni, Amparo Saravi, Adriana R. Dawidowski, Valeria I. Aliperti, Ignacio Bressán, Florencia De Florio, Jimena Vicens, Nahuel Braguinsky Golde, Natalia K. Garcia, Glenda Pazur, Guillermo E. Hough, Adrián C. Gadano","doi":"10.1101/2024.04.02.24305133","DOIUrl":"https://doi.org/10.1101/2024.04.02.24305133","url":null,"abstract":"<strong>Introduction</strong> An increasing number of rural communities express perception of health damage from glyphosate and other agrochemicals. We measure the presence of glyphosate in the human body, in order to create, together with the local community, a systemic model that highlights modifiable causal socio-environmental conditions.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"125 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.1101/2024.03.06.24303821
Carolyn Gigot, Nora Pisanic, Kristoffer Spicer, Meghan F Davis, Kate Kruczynski, Magdielis Gregory Rivera, Kirsten Koehler, D. J. Hall, Devon J. Hall, Christopher D Heaney
Background: The COVID-19 pandemic has disproportionately affected workers in certain industries and occupations, and the workplace can be a high risk setting for SARS-CoV-2 transmission. In this study, we measured SARS-CoV-2 antibody prevalence and identified work-related risk factors in a population primarily working at industrial livestock operations. Methods: We used a multiplex salivary SARS-CoV-2 IgG antibody assay to determine infection-induced antibody prevalence among 236 adult (>=18 years) North Carolina residents between February 2021 and August 2022. We used the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System (NIOCCS) to classify employed participants' industry and compared infection-induced IgG prevalence by participant industry and with the North Carolina general population. We also combined antibody results with reported SARS-CoV-2 molecular test positivity and vaccination history to identify evidence of prior infection. We used logistic regression to estimate odds ratios of prior infection by potential work-related risk factors, adjusting for industry and date. Results: Most participants (55%) were infection-induced IgG positive, including 71% of animal slaughtering and processing industry workers, which is 1.5 to 4.3 times higher compared to the North Carolina general population, as well as higher than molecularly-confirmed cases and the only other serology study we identified of animal slaughtering and processing workers. Considering questionnaire results in addition to antibodies, the proportion of participants with evidence of prior infection increased slightly, to 61%, including 75% of animal slaughtering and processing workers. Participants with more than 1000 compared to 10 or fewer coworkers at their jobsite had higher odds of prior infection (adjusted odds ratio [aOR] 4.5, 95% confidence interval [CI] 1.0 to 21.0). Conclusions: This study contributes evidence of the severe and disproportionate impacts of COVID-19 on animal processing and essential workers and workers in large congregate settings. We also demonstrate the utility of combining non-invasive biomarker and questionnaire data for the study of workplace exposures.
{"title":"SARS-CoV-2 antibody prevalence by industry, workplace characteristics, and workplace infection prevention and control measures, North Carolina, 2021 to 2022","authors":"Carolyn Gigot, Nora Pisanic, Kristoffer Spicer, Meghan F Davis, Kate Kruczynski, Magdielis Gregory Rivera, Kirsten Koehler, D. J. Hall, Devon J. Hall, Christopher D Heaney","doi":"10.1101/2024.03.06.24303821","DOIUrl":"https://doi.org/10.1101/2024.03.06.24303821","url":null,"abstract":"Background: The COVID-19 pandemic has disproportionately affected workers in certain industries and occupations, and the workplace can be a high risk setting for SARS-CoV-2 transmission. In this study, we measured SARS-CoV-2 antibody prevalence and identified work-related risk factors in a population primarily working at industrial livestock operations. Methods: We used a multiplex salivary SARS-CoV-2 IgG antibody assay to determine infection-induced antibody prevalence among 236 adult (>=18 years) North Carolina residents between February 2021 and August 2022. We used the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System (NIOCCS) to classify employed participants' industry and compared infection-induced IgG prevalence by participant industry and with the North Carolina general population. We also combined antibody results with reported SARS-CoV-2 molecular test positivity and vaccination history to identify evidence of prior infection. We used logistic regression to estimate odds ratios of prior infection by potential work-related risk factors, adjusting for industry and date.\u0000Results: Most participants (55%) were infection-induced IgG positive, including 71% of animal slaughtering and processing industry workers, which is 1.5 to 4.3 times higher compared to the North Carolina general population, as well as higher than molecularly-confirmed cases and the only other serology study we identified of animal slaughtering and processing workers. Considering questionnaire results in addition to antibodies, the proportion of participants with evidence of prior infection increased slightly, to 61%, including 75% of animal slaughtering and processing workers. Participants with more than 1000 compared to 10 or fewer coworkers at their jobsite had higher odds of prior infection (adjusted odds ratio [aOR] 4.5, 95% confidence interval [CI] 1.0 to 21.0). Conclusions: This study contributes evidence of the severe and disproportionate impacts of COVID-19 on animal processing and essential workers and workers in large congregate settings. We also demonstrate the utility of combining non-invasive biomarker and questionnaire data for the study of workplace exposures.","PeriodicalId":501555,"journal":{"name":"medRxiv - Occupational and Environmental Health","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140074578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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