Henrik Albert Kolstad

Background: Identifying and addressing long-term health and societal challenges after COVID-19 is a research priority. Objectives: To create an international, multidisciplinary COVID-19 database, and synthesise long-term outcomes, predictors and costs. Design: Systematic identification of COVID-19 data sets and meta-analysis of individual participant data on long-term outcomes after COVID-19. Setting: Contributed data were collected in clinical, community and research settings. Interventions: Interventions from original studies were included as covariates in models. Data sources: MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, Web of Science, PsycInfo ^® (American Psychological Association, Washington, DC, USA), Cumulative Index to Nursing and Allied Health Literature, World Health Organization Global Index Medicus, Epistemonikos, LitCOVID; World Health Organization International Clinical Trials Registry Platform; ClinicalTrials.gov and supplementary searches for studies (November 2019–November 2021) were searched for studies on > 10 people from cohort, case-control, survey or randomised controlled trial studies, across any setting, describing validated assessment instruments, symptoms, hospitalisation, discharge destination or mortality beyond 28-days after COVID-19 onset. Data were extracted by two independent reviewers. Methods: Principal investigators contributed fully anonymised individual participant data. Demography, equity and symptoms were described. Assessment instruments were mapped to the International Classification of Functioning, Disability and Health. Factors associated with outcomes at 3–6 months, 9–12 months and beyond 12 months of index infection, for n > 500 individual participant data and > 1 data set were described using ratio of difference, point estimates, odds ratio and 95% confidence interval, as appropriate. The Mixed Methods Appraisal Tool described study quality; models were appraised using a Grading of Recommendations Assessment, Development and Evaluation-informed approach; heterogeneity was described using I ² . Outcome measures: Included overall perception of health, multidomain cognitive function, anxiety, depression, stress, post-traumatic stress disorder, fatigue, strength, walking ability, mobility, coping with daily life, breathlessness, mortality, later hospitalisation and health-related quality of life. Results: PRECIOUS collated 116 data sets from 40 countries (individual participant data = 62,849), comprising 20 randomised controlled trials, 13 case-control, 60 cohort 2 longitudinal, 1 survey and 20 other study types. Participants’ median age was 58 years interquartile range (45–68); 34,185 (54.4%) were female; 158 unique symptoms and 137 unique assessment instruments were captured, predominantly describing International Classification of Function, Disability and Health-body functions. Women had poorer outcomes across 30/37 models, compared with men. In 15/37 models, pre-existing lung disease and increasing age were associated with poorer outcomes; hospitalisation, diabetes and chronic kidney disease were each associated with poorer outcomes in 8/37 models. Initial hospitalisation resulted in lower health-related quality of life that did not recover for up to 2 years after initial infection. Heterogeneity was low in 34/37 models; 22/37 models were of moderate and 11/37 were of low quality. Limitations: Use of secondary data limits available covariates, outcomes and time points to those included in primary data sets; evidence was primarily based on high-income countries. There was a lack of data on longer-term healthcare resource use to estimate the costs to the healthcare system. Conclusions: PRECIOUS contributes to the overall picture of long-term COVID-19 outcomes beyond the long-COVID condition and highlights poorer long-term outcomes in women and people with pre-existing comorbidities. Future work: Evidence gaps included healthcare resource use, isolation, loneliness, societal participation and return to work outcomes. Data are needed on the role of health inequity on long-term outcomes. Study registration: This study is registered as PROSPERO (CRD42020224323, IRAS ID: 293578). Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR132895) and is published in full in Health and Social Care Delivery Research ; Vol. 14, No. 29. See the NIHR Funding and Awards website for further award information.

Background: Epoxy polymers, widely used in industries such as wind turbine production, are common causes of allergic contact dermatitis (ACD). However, gaps remain mostly regarding the type of occupational exposure (specific tasks) and protective strategies. Objectives: To evaluate epoxy resin sensitisation in a wind turbine blade plant and assess its associations with tasks, medical history, clinical patterns, and personal protective equipment (PPE) use. Methods: A cross‐sectional study was performed in a large wind turbine blade facility. A group of volunteer workers completed a questionnaire on occupational/medical history, PPE use, and symptoms. Onsite patch testing included the European Baseline‐, epoxy‐, and isocyanate series. Data were analysed using SPSS and R ( p < 0.05). Results: Among 131 workers tested (7.4% of the workforce), seven (5.3%), all from high‐exposure tasks, showed positive reactions to epoxy compounds: Bisphenol F resin (5), Bisphenol A resin (4), and hardeners/additives (5). Hand dermatitis was the predominant ACD pattern, with a median time to onset of 8 months after employment. No associations were found between sensitisation and previous medical history or PPE compliance. Conclusions: The ACD to epoxy resin is a significant concern in wind turbine blade manufacturing. Accurate diagnosis requires patch testing with epoxy‐specific series, beyond the European Baseline series. Prevention should extend beyond PPE, addressing airborne exposure and cross‐contamination.

We want to thank Drs. Burstyn & Luta (1) for their recognition of our recent study (2) suggesting that estimates of breast cancer risk following retrospective self-reported night shift work are inflated by recall bias. The main strength of the study was a gold standard based on individual, prospective, objective and detailed information on night shift work that allowed validation of self-reported night shift work obtained after breast cancer was diagnosed – the usual situation for case–control studies (3). The study confirmed what textbooks have long taught but rarely documented empirically (4–6). We also want to thank Drs. Burstyn & Luta for their advice on how we could have utilized this precious dataset not only for simple but also probabilistic and Bayesian quantitative bias analyses. Even if highly instructive, this may still require strong statistical involvement. Using data provided in our paper, Drs. Burstyn & Luta’s bias-corrected odds ratio (OR) estimate of breast cancer following night shift work was centered around 1.0 (95% credible interval 0.3–1.7) and suggests that recall bias could completely, and not only partly as in our analysis (OR 1.05; correctly computed 95% confidence interval 0.88–1.27), explain the observed associations between night shift work and breast cancer found in case–control studies with retrospective self-reported exposure information. This finding strengthens our concern that breast cancer studies based on retrospective self-reports of night shifts may not provide convincing evidence. The gold standard of this validation study was based on a cohort of healthcare workers with day-by-day night shift information from a pay roll register and has earlier been used for breast cancer risk assessment showing no increased risk (7). A recent Swedish study using comparable data neither showed an overall increased risk (8). However, these studies included only information on recent night shift work. The next step should be a follow up of the cohorts when information on more distant night shift work becomes available with an emphasis on analyses that explore the timing of shift work on breast cancer risk. References 1. Burstyn I, Luta G. Advice on better utilization of validation data to adjust odds ratios for differential exposure misclassification (recall bias). Scand J Work Environ Health – online first. https://doi.org/10.5271/sjweh.4226 2. Vestergaard JM, Haug JN, Dalbøge A, Bonde JP, Garde AH, Hansen J et al. Validity of self-reported night shift work among women with and without breast cancer. Scand J Work Environ Health. 2024;50(3):152–7. https://doi.org/10.5271/sjweh.4142. 3. Cordina-Duverger E, Menegaux F, Popa A, Rabstein S, Harth V, Pesch B et al. Night shift work and breast cancer: a pooled analysis of population-based case-control studies with complete work history. Eur J Epidemiol. 2018; 33(4):369–79. https://doi.org/10.1007/s10654-018-0368-x. 4. Berrington de González A, Richardson DB, Schubauer-Berigan MK. Statistical methods in cancer research, Volume V. Bias assessment in case–control and cohort studies for hazard identification. Lyon, France: International Agency for Research on Cancer; 2024. 5. Checkoway H, Pearce N, Kriebel D. Research Methods in Occupational Epidemiology. New York: Oxford University Press 2004. p372. 6. Rothman KJ. Modern Epidemiology. Boston/Toronto: Little, Brown and Company; 1986. p358. 7. Vistisen HT, Garde AH, Frydenberg M, Christiansen P, Hansen AM, Hansen J et al. Short-term effects of night shift work on breast cancer risk: a cohort study of payroll data. Scand J Work Environ Health. 2017;43(1):59–67. https://doi.org/10.5271/sjweh.3603. 8. Gustavsson P, Bigert C, Andersson T, Kader M, Härmä M, Selander J et al. Night work and breast cancer risk in a cohort of female healthcare employees in Stockholm, Sweden. Occup Environ Med. 2023;80(7):372–6. https://doi.org/10.1136/oemed-2022-108673. Henrik Albert Kolstad, MD,1, 2 Jesper Medom Vestergaard, MIT,1 Jens Peter Bonde, MD,3 Sadie Costello, PhD,4 Annett Dalbøge, PhD,1 Åse Marie Hansen, PhD,5, 6 Ann Dyreborg Larsen, PhD,6 Anne Helene Garde, PhD 5, 6 1 Occupational and Environmental Medicine, Danish Ramazzini Centre, Aarhus University Hospital, Aarhus, Denmark. 2 Department of Clinical Medicine, Aarhus University, Denmark. 3 Department of Occupational and Environmental Medicine, Bispebjerg and Frederiksberg Hospital, Denmark. 4 Environmental Health Science, School of Public Health, University of California, Berkeley, USA. 5 The National Research Centre for the Working Environment, Denmark. 6 Department of Public Health, University of Copenhagen, Denmark. Correspondence to: Henrik Kolstad, Occupational and Environmental Medicine, Danish Ramazzini Centre, Aarhus University Hospital, Aarhus, Denmark. [E-mail: kolstad@clin.au.dk]

Introduction: Outdoor workers are exposed to high levels of solar ultraviolet radiation (UVR). UVR causes skin cancer and is a risk factor for cataract and other short- and long-term health effects, but there are significant knowledge gaps regarding the exposure-response relations based on quantitative measures of UVR exposure. We developed a quantitative UVR job-exposure matrix (JEM) for the general working population of Europe. Methods: Three experts from each of Northern, Central, and Southern Europe rated duration of outdoor work for all 372 occupations defined by the International Standard Classification of Occupations from 1988 (ISCO-88(COM)). A systematic literature search identified 12 studies providing 223 sets of summary workday UVR exposure for 49 ISCO-88(COM) occupations based on 75,711 personal workday measurements obtained from 2,645 participants and reported as arithmetic mean standard erythemal dose (SED). We combined the expert ratings with the measured occupational UVR exposure data and estimated harmonized workday UVR exposures for all 372 occupations in a linear mixed effects model. Results: Monotonically increasing workday UVR exposure of 0.68, 1.57, 1.80, and 2.49 SED were seen by increasing expert ratings of 0, 1 to 2, 3 to 4, and ≥5 h of daily outdoor work. The UVR exposure showed a 6-fold increase from lowest to highest exposed occupation. Farm hands, roofers, concrete placers, and other occupations within craft and related trades were among the highest exposed, while bartenders, wood-processing-plant operators, and several white-collar occupations who typically work indoor were among the lowest exposed. Conclusion: This quantitative JEM for solar UVR exposure proves able to provide substantial discrimination between occupations, shows good agreement with expert assessments, and may facilitate epidemiological studies characterizing the exposure-response relation between occupational solar UVR exposure and different health effects.

Aims: Exposure to air pollution including diesel engine exhaust (DEE) is associated with increased risk of acute myocardial infarction (AMI). Few studies have investigated the risk of AMI according to occupational exposure to DEE. The aim of this study was to evaluate the association between occupational exposure to DEE and the risk of first-time AMI. Methods and results: The study was a register-based cohort study of the Danish working population and included 903 415 individuals aged 35–50 years in 1995. Exposure estimates of DEE were assigned by linking a quantitative DEE job exposure matrix with the individual job history (1976–2017). National registers provided data on AMI throughout the follow-up period (1996–2018). The incidence rate ratios (IRRs) for AMI were computed using Poisson regression while adjusting for demographics, comorbidities, socioeconomic factors, ambient air pollution and occupational exposure to noise, physically demanding work, and job strain. A total of 35 511 cases of AMI occurred during the follow-up period of 19 357 326 person-years. Incidence rate ratios for cumulative exposure to DEE in the fully adjusted model were 1.04 [95% confidence intervals (CI): 1.00–1.08] for exposure levels between the 50th and 75th percentiles, and 1.08 (95% CI: 1.04–1.12) for exposure levels ≥75th percentile. Recent exposure to DEE was associated with an increased IRR in the highest exposed quartile [IRR = 1.15 (95% CI: 1.05–1.27)], but only when compared within exposed workers. Conclusion: Increasing exposure to DEE was associated with increasing IRR for first-time AMI across different exposure measures. The results indicate that AMI should be included in the risk assessment of DEE when establishing occupational exposure limits.

Objective: Within the scope of the Exposome Project for Health and Occupational Research on applying the exposome concept to working life health, we aimed to provide a broad overview of the status of knowledge on occupational exposures and associated health effects across multiple noncommunicable diseases (NCDs) to help inform research priorities. Methods: We conducted a narrative review of occupational risk factors that can be considered to have “consistent evidence for an association,” or where there is “limited/inadequate evidence for an association” for 6 NCD groups: nonmalignant respiratory diseases; neurodegenerative diseases; cardiovascular/metabolic diseases; mental disorders; musculoskeletal diseases; and cancer. The assessment was done in expert sessions, primarily based on systematic reviews, supplemented with narrative reviews, reports, and original studies. Subsequently, knowledge gaps were identified, e.g. based on missing information on exposure–response relationships, gender differences, critical time-windows, interactions, and inadequate study quality. Results: We identified over 200 occupational exposures with consistent or limited/inadequate evidence for associations with one or more of 60+ NCDs. Various exposures were identified as possible risk factors for multiple outcomes. Examples are diesel engine exhaust and cadmium, with consistent evidence for lung cancer, but limited/inadequate evidence for other cancer sites, respiratory, neurodegenerative, and cardiovascular diseases. Other examples are physically heavy work, shift work, and decision latitude/job control. For associations with limited/inadequate evidence, new studies are needed to confirm the association. For risk factors with consistent evidence, improvements in study design, exposure assessment, and case definition could lead to a better understanding of the association and help inform health-based threshold levels. Conclusions: By providing an overview of knowledge gaps in the associations between occupational exposures and their health effects, our narrative review will help setting priorities in occupational health research. Future epidemiological studies should prioritize to include large sample sizes, assess exposures prior to disease onset, and quantify exposures. Potential sources of biases and confounding need to be identified and accounted for in both original studies and systematic reviews.

Introduction: Upper respiratory tract infections (URTI) are common and a common cause of sick-leave for healthcare workers, and furthermore pose a threat especially for patients susceptible to other diseases. Sufficient use of respiratory protective equipment (RPE) may protect both the workers and the patients. The COVID-19 pandemic provided a unique opportunity to study the association between use of RPE and URTI in a real-life setting. The aim of this study was to examine if failure of RPE or non-compliance with RPE guidelines increases the risk of non-COVID-19 URTI symptoms among healthcare workers. Methods: In a longitudinal cohort study, we collected self-reported data daily on work tasks, use of RPE, and URTI symptoms among healthcare workers with patient contact in 2 Danish Regions in 2 time periods during the COVID-19 pandemic. The association between failure of RPE or non-compliance with RPE guidelines and URTI symptoms was analyzed separately by generalized linear models. Persons tested positive for severe acute respiratory syndrome coronavirus 2 were censored from the analyses. The 2 waves of data collection were analyzed separately, as there were differences in recommendations of RPE during the 2 waves. Results: We found that for healthcare workers performing work tasks with a risk of transmission of viruses or bacteria, failure of RPE was associated with an increased risk of URTI symptoms, RR: 1.65[0.53–5.14] in wave 1 and RR: 1.30[0.56–3.03] in wave 2. Also non-compliance with RPE guidelines was associated with an increased risk of URTI symptoms compared to the use of RPE in wave 1, RR: 1.28[0.87–1.87] and wave 2, RR: 1.39[1.01–1.91]. Stratifying on high- versus low-risk tasks showed that the risk related to failure and non-compliance was primarily associated with high-risk tasks, although not statistically significant. Discussion: The study was conducted during the COVID-19 pandemic and thus may be affected by other preventive measures in society. However, this gave the opportunity to study the use of RPE in a real-life setting, also in departments that did not previously use RPE. The circumstances in the 2 time periods of data collection differed and were analyzed separately and thus the sample size was limited and affected the precision of the estimates. Conclusion: Failures of RPE and non-compliance with RPE guidelines may increase the risk of URTI, compared to those who reported use of RPE as recommended. The implications of these findings are that the use of RPE to prevent URTI could be considered, especially while performing high-risk tasks where other prevention strategies are not achievable.

Pigeon breeders are exposed to high levels of fine particulate organic matter in the pigeon lofts. A total of 6,704 pigeon breeders and their 1:30 sex and age-matched referents from the general Danish population were followed from 1980 or first year of membership in the Danish Racing Pigeon Association, until first event of myocardial infarction, emigration, death, or end of study, on December 31, 2013. Information on outcomes and covariates was obtained by record linkage with national registers. Stratified Cox regression models estimated the hazard ratio of myocardial infarction, adjusted for occupation and residence at the start of follow-up. Compared with referents, pigeon breeders had an adjusted hazard ratio of 1.14 (95% CI: 1.05-1.22) for myocardial infarction. Exposure to pigeon-derived organic dust may increase the risk of myocardial infarction, but this finding needs to be corroborated.