Evaluation of exposure biomarkers in offshore workers exposed to low benzene and toluene concentrations

Purpose: Characterize ethylbenzene and xylene air concentrations, and explore the biological exposure markers (urinary t,t-muconic acid (t,t-MA) and unmetabolized toluene) among petroleum workers offshore. Offshore workers have increased health risks due to simultaneous exposures to several hydrocarbons present in crude oil. We discuss the pooled benzene exposure results from our previous and current studies and possible co-exposure interactions. Methods: BTEX air concentrations were measured during three consecutive 12-h work shifts among 10 tank workers, 15 process operators, and 18 controls. Biological samples were collected pre-shift on the first day of study and post-shift on the third day of the study. Results: The geometric mean exposure over the three work shifts were 0.02ppm benzene, 0.05ppm toluene, 0.03ppm ethylbenzene, and 0.06ppm xylene. Benzene in air was significantly correlated with unmetabolized benzene in blood (r=0.69, p<0.001) and urine (r=0.64, p<0.001), but not with urinary t,t-MA (r=0.27, p=0.20). Toluene in air was highly correlated with the internal dose of toluene in both blood (r=0.70, p<0.001) and urine (r=0.73, p<0.001). Co-exposures were present; however, an interaction of metabolism was not likely at these low benzene and toluene exposures. Conclusion: Urinary benzene, but not t,t-MA, was a reliable biomarker for benzene at low exposure levels. Urinary toluene was a useful biomarker for toluene exposure. Xylene and ethylbenzene air levels were low. Dermal exposure assessment needs to be performed in future studies among these worker

Fremtidens HR: en helsefremmende arbeidsplass

Tema: I denne bacheloroppgaven har vi dannet en arbeidsmodell for hvordan norske bedrifter optimalt sett kan tilrettelegge for god ansatthelse. Problemstillingen er:”Hva består tilrettelegging for helsefremmende tiltak av blant norske bedrifter, og hvordan implementeres disse?” Formål: Formålet er å avdekke hvordan norske bedrifter tilrettelegger for fysisk helse på arbeidsplassen, for å fremme en arbeidsmodell for hvordan helseprogrammer best mulig kan implementeres. Teoretisk utgangspunkt: Det teoretiske rammeverket består av to deler: effekter av et helseprogram, og hvordan disse skapes. Dette innebærer strukturelle, kulturelle og individuelle aspekter knyttet til helse, web-basert tilretteleggelse, evaluering og oppfølging og helse i rekrutteringssammenheng. Videre vil teorien bli knyttet opp mot empirisk data. Metode: Vi har valgt et kvalitativt forskningsdesign. Dette har gitt oss dypere innsikt på de aktuelle forskningsområdene. Data er innsamlet gjennom dybdeintervjuer med strategisk utvalgte intervjuobjekter. Fremgangsmåte: Prosessen tok utgangspunkt i et foreløpig rammeverk basert på eksisterende teori på området. Vi utarbeidet casebeskrivelser av bedriftene og gjennomførte dybdeintervjuer for å danne et bilde av helseprogrammer i norske bedrifter. Kombinasjonen av disse funnene dannet grunnlag for forslag og en tilhørende intervert arbeidsmodell for optimalisering, tilrettelegging og implementering av et helseprogram. Resultater: Våre funn avdekket at norske bedrifter ikke har et strukturert helseprogram. Det tilrettelegges tilstrekkelig i form av fasiliteter, men det er likevel områder som er mangelfulle. Blant annet finnes det ikke rutiner for evaluering og oppfølging. Funnene har imidlertid reist ny kunnskap, som for eksempel et integrert helseteam i fremtidens HR-avdeling

Validation of a full-shift benzene exposure empirical model developed for work on offshore petroleum installations on the Norwegian continental shelf

Workers on offshore petroleum installations might be exposed to benzene, a carcinogenic agent. Recently, a full-shift benzene exposure model was developed based on personal measurements. This study aimed to validate this exposure model by using datasets not included in the model. The exposure model was validated against an internal dataset of measurements from offshore installations owned by the same company that provided data for the model, and an external dataset from installations owned by another company. We used Tobit regression to estimate GM (geometric mean) benzene exposure overall and for individual job groups. Bias, relative bias, precision, and correlation were estimated to evaluate the agreement between measured exposures and the levels predicted by the model. Overall, the model overestimated exposure when compared to the predicted exposure level to the internal dataset with a factor of 1.7, a relative bias of 73%, a precision of 0.6, a correlation coefficient of 0.72 (p = 0.019), while the Lin’s Concordance Correlation Coefficient (CCC) was 0.53. The model underestimated exposure when compared to the external dataset with a factor of about 2, with a relative bias of −45%, a precision of 1.2, a correlation coefficient of 0.31 (p = 0.544), and a Lin’s CCC of 0.25. The exposure model overestimated benzene exposure in the internal validation dataset, while the precision and the correlation between the measured and predicted exposure levels were high. Differences in measurement strategies could be one of the reasons for the discrepancy. The exposure model agreed less with the external dataset.publishedVersio

Correlated physical and mental health composite scores for the RAND-36 and RAND-12 health surveys: can we keep them simple?

Background: The RAND-36 and RAND-12 (equivalent to versions 1 of the SF-36 Health Survey and SF-12 Health Survey, respectively) are widely used measures of health-related quality of life. However, there are diverging views regarding how to create the physical health and mental health composite scores of these questionnaires. We present a simple approach using an unweighted linear combination of subscale scores for constructing composite scores for physical and mental health that assumes these scores should be free to correlate. The aim of this study was to investigate the criterion validity and convergent validity of these scores. Methods: We investigated oblique and unweighted RAND-36/12 composite scores from a random sample of the general Norwegian population (N = 2107). Criterion validity was tested by examining the correlation between unweighted composite scores and weighted scores derived from oblique principal component analysis. Convergent validity was examined by analysing the associations between the different composite scores, age, gender, body mass index, physical activity, rheumatic disease, and depression. Results: The correlations between the composite scores derived by the two methods were substantial (r = 0.97 to 0.99) for both the RAND-36 and RAND-12. The effect sizes of the associations between the oblique versus the unweighted composite scores and other variables had comparable magnitudes. Conclusion: The unweighted RAND-36 and RAND-12 composite scores demonstrated satisfactory criterion validity and convergent validity. This suggests that if the physical and mental composite scores are free to be correlated, the calculation of these composite scores can be kept simple.publishedVersio

Exposure to benzene at work and the risk of leukemia: a systematic review and meta-analysis

Background A substantial number of epidemiologic studies have provided estimates of the relation between exposure to benzene at work and the risk of leukemia, but the results have been heterogeneous. To bridge this gap in knowledge, we synthesized the existing epidemiologic evidence on the relation between occupational exposure to benzene and the risk of leukemia, including all types combined and the four main subgroups acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML). Methods A systematic literature review was carried out using two databases 'Medline' and 'Embase' from 1950 through to July 2009. We selected articles which provided information that can be used to estimate the relation between benzene exposure and cancer risk (effect size). Results In total 15 studies were identified in the search, providing 16 effect estimates for the main analysis. The summary effect size for any leukemia from the fixed-effects model was 1.40 (95% CI, 1.23-1.57), but the study-specific estimates were strongly heterogeneous (I2 = 56.5%, Q stat = 34.47, p = 0.003). The random-effects model yielded a summary- effect size estimate of 1.72 (95% CI, 1.37-2.17). Effect estimates from 9 studies were based on cumulative exposures. In these studies the risk of leukemia increased with a dose-response pattern with a summary-effect estimate of 1.64 (95% CI, 1.13-2.39) for low (< 40 ppm-years), 1.90 (95% CI, 1.26-2.89) for medium (40-99.9 ppm-years), and 2.62 (95% CI, 1.57-4.39) for high exposure category (> 100 ppm-years). In a meta-regression, the trend was statistically significant (P = 0.015). Use of cumulative exposure eliminated heterogeneity. The risk of AML also increased from low (1.94, 95% CI, 0.95-3.95), medium (2.32, 95% CI, 0.91-5.94) to high exposure category (3.20, 95% CI, 1.09-9.45), but the trend was not statistically significant. Conclusions Our study provides consistent evidence that exposure to benzene at work increases the risk of leukemia with a dose-response pattern. There was some evidence of an increased risk of AML and CLL. The meta-analysis indicated a lack of association between benzene exposure and the risk of CML

Evaluation of exposure biomarkers in offshore workers exposed to low benzene and toluene concentrations

Characterize ethylbenzene and xylene air concentrations, and explore the biological exposure markers (urinary t,t-muconic acid (t,t-MA) and unmetabolized toluene) among petroleum workers offshore. Offshore workers have increased health risks due to simultaneous exposures to several hydrocarbons present in crude oil. We discuss the pooled benzene exposure results from our previous and current studies and possible co-exposure interactions. BTEX air concentrations were measured during three consecutive 12-h work shifts among 10 tank workers, 15 process operators, and 18 controls. Biological samples were collected pre-shift on the first day of study and post-shift on the third day of the study. The geometric mean exposure over the three work shifts were 0.02 ppm benzene, 0.05 ppm toluene, 0.03 ppm ethylbenzene, and 0.06 ppm xylene. Benzene in air was significantly correlated with unmetabolized benzene in blood (r = 0.69, p &lt; 0.001) and urine (r = 0.64, p &lt; 0.001), but not with urinary t,t-MA (r = 0.27, p = 0.20). Toluene in air was highly correlated with the internal dose of toluene in both blood (r = 0.70, p &lt; 0.001) and urine (r = 0.73, p &lt; 0.001). Co-exposures were present; however, an interaction of metabolism was not likely at these low benzene and toluene exposures. Urinary benzene, but not t,t-MA, was a reliable biomarker for benzene at low exposure levels. Urinary toluene was a useful biomarker for toluene exposure. Xylene and ethylbenzene air levels were low. Dermal exposure assessment needs to be performed in future studies among these workers

Effects of Interventions to Prevent Work-Related Asthma, Allergy, and Other Hypersensitivity Reactions in Norwegian Salmon Industry Workers (SHInE): Protocol for a Pragmatic Allocated Intervention Trial and Related Substudies

Background: Workers in the salmon processing industry have an increased risk of developing respiratory diseases and other hypersensitivity responses due to occupational exposure to bioaerosols containing fish proteins and microorganisms, and related allergens. Little is known about effective measures to reduce bioaerosol exposure and about the extent of skin complaints among workers. In addition, while identification of risk factors is a core activity in disease prevention strategies, there is increasing interest in health-promoting factors, which is an understudied area in the salmon processing industry. Objective: The overall aim of this ongoing study is to generate knowledge that can be used in tailored prevention of development or chronification of respiratory diseases, skin reactions, protein contact dermatitis, and allergy among salmon processing workers. The main objective is to identify effective methods to reduce bioaerosol exposure. Further objectives are to identify and characterize clinically relevant exposure agents, identify determinants of exposure, measure prevalence of work-related symptoms and disease, and identify health-promoting factors of the psychosocial work environment. Methods: Data are collected during field studies in 9 salmon processing plants along the Norwegian coastline. Data collection comprises exposure measurements, health examinations, and questionnaires. A wide range of laboratory analyses will be used for further analysis and characterization of exposure agents. Suitable statistical analysis will be applied to the various outcomes of this comprehensive study. Results: Data collection started in September 2021 and was anticipated to be completed by March 2023, but was delayed due to the COVID-19 pandemic. Baseline data from all 9 plants included 673 participants for the health examinations and a total of 869 personal exposure measurements. A total of 740 workers answered the study’s main questionnaire on demographics, job characteristics, lifestyle, health, and health-promoting factors. Follow-up data collection is not completed yet. Conclusions: This study will contribute to filling knowledge gaps concerning salmon workers’ work environment. This includes effective workplace measures for bioaerosol exposure reduction, increased knowledge on hypersensitivity, allergy, respiratory and dermal health, as well as health-promoting workplace factors. Together this will give a basis for improving the work environment, preventing occupational health-related diseases, and developing occupational exposure limits, which in turn will benefit employees, employers, occupational health services, researchers, clinicians, decision makers, and other stakeholders.publishedVersio

Occupational Benzene Exposure in the Norwegian Offshore Petroleum Industry, 2002-2018

Purpose Workers on offshore petroleum installations are at risk of being exposed to benzene which is carcinogenic to humans. The present study aimed to assess the time trend of full-shift benzene exposure from 2002 to 2018 in order to characterize benzene exposure among laboratory technicians, mechanics, process operators, and industrial cleaners, and to examine the possible determinants of benzene exposure. Methods A total of 924 measurements of benzene exposure from the Norwegian petroleum offshore industry were included. The median sampling duration was 680 min, ranging from 60 to 940 min. The overall geometric mean (GM) and 95% confidence interval, time trends, and determinants of exposure were estimated using multilevel mixed-effects tobit regression analyses. Time trends were estimated for sampling duration below and above 8 h, both overall and for job groups. The variability of exposure between installation and workers was investigated in a subset of data containing worker identification. Results The overall GM of benzene exposure was 0.004 ppm. When adjusting for job group, design of process area, season, wind speed, and sampling duration, industrial cleaners had the highest exposure (GM = 0.012). Laboratory technicians, mechanics, and process operators had a GM exposure of 0.004, 0.003, and 0.004 ppm, respectively. Overall, the measured benzene exposure increased by 7.6% per year from 2002 to 2018. Mechanics had an annual increase of 8.6% and laboratory technicians had an annual decrease of 12.6% when including all measurements. When including only measurements above 8 h, mechanics had an increase of 16.8%. No statistically significant time trend was found for process operators. Open process area, high wind speed, and wintertime were associated with reduced exposure level. Conclusions An overall increase in measured exposure was observed from 2002 to 2018. The increase may reflect changes in measurement strategy from mainly measuring on random days to days with expected exposure. However, the time trend varied between job groups and was different for sampling duration above or below 8 h. Industrial cleaners had the highest exposure of the four job groups while no differences in exposure were observed between laboratory technicians, mechanics, and process operators. The design of the process area, job group, wind speed, and season were all significant determinants of benzene exposure.publishedVersio

Benzene Exposure From Selected Work Tasks on Offshore Petroleum Installations on the Norwegian Continental Shelf, 2002-2018

OBJECTIVES: Work on offshore petroleum installations may cause exposure to benzene. Benzene is a carcinogenic agent, and exposure among workers should be as low as reasonably practicable. We aimed to assess short-term (less than 60 min) benzene exposure from the most frequent work tasks on offshore installations on the Norwegian continental shelf and identify determinants of exposure. In addition, we aimed to assess the time trend in task-based benzene measurements from 2002 to 2018. METHODS: The study included 763 task-based measurements with a sampling duration of less than 60 min, collected on 28 offshore installations from 2002 to 2018. The measurements were categorized into 10 different tasks. Multilevel mixed-effect Tobit regression models were developed for two tasks: sampling and disassembling/assembling equipment. Benzene source, season, indoors or outdoors, design of process area, year of production start, sampling method, and work operation were considered as potential determinants for benzene exposure in the models. RESULTS: The overall geometric mean (GM) benzene exposure was 0.02 ppm (95% confidence intervals 95%(CI: 0.01-0.04). The pipeline inspection gauge (PIG) operation task was associated with the highest exposure, with a GM of 0.33 ppm, followed by work on flotation cells, disassembling/assembling, and sampling, with GMs of 0.16, 0.04, and 0.01 ppm, respectively. Significant determinants for the disassembling/assembling task were work operation (changing or recertifying valves, changing or cleaning filters, and breaking pipes) and benzene source. For sampling, the benzene source was a significant determinant. Overall, the task-based benzene exposure declined annually by 10.2% (CI 95%: -17.4 to -2.4%) from 2002 to 2018. CONCLUSIONS: The PIG operation task was associated with the highest exposure out of the ten tasks, followed by work on flotation cells and when performing disassembling/assembling of equipment. The exposure was associated with the type of benzene source that was worked on. Despite the decline in task-based exposure in 2002-2018, technical measures should still be considered in order to reduce the exposure

Validation of a full-shift benzene exposure empirical model developed for work on offshore petroleum installations on the Norwegian continental shelf

Workers on offshore petroleum installations might be exposed to benzene, a carcinogenic agent. Recently, a full-shift benzene exposure model was developed based on personal measurements. This study aimed to validate this exposure model by using datasets not included in the model. The exposure model was validated against an internal dataset of measurements from offshore installations owned by the same company that provided data for the model, and an external dataset from installations owned by another company. We used Tobit regression to estimate GM (geometric mean) benzene exposure overall and for individual job groups. Bias, relative bias, precision, and correlation were estimated to evaluate the agreement between measured exposures and the levels predicted by the model. Overall, the model overestimated exposure when compared to the predicted exposure level to the internal dataset with a factor of 1.7, a relative bias of 73%, a precision of 0.6, a correlation coefficient of 0.72 (p = 0.019), while the Lin’s Concordance Correlation Coefficient (CCC) was 0.53. The model underestimated exposure when compared to the external dataset with a factor of about 2, with a relative bias of −45%, a precision of 1.2, a correlation coefficient of 0.31 (p = 0.544), and a Lin’s CCC of 0.25. The exposure model overestimated benzene exposure in the internal validation dataset, while the precision and the correlation between the measured and predicted exposure levels were high. Differences in measurement strategies could be one of the reasons for the discrepancy. The exposure model agreed less with the external dataset