Occupations and Sickness-Related Absences during the COVID-19 Pandemic

Protective action in the workplace in the time of COVID-19: The role of worker representation

BACKGROUND

This study addresses the contribution of worker representation to health and safety in the pandemic context. To do so, we examine whether the self-reported presence of representatives in workplaces is associated with the implementation of anti-COVID-19 protective action and with which type of measures their existence is most strongly associated (individual, collective or organizational). The article also explores how the presence of worker representatives and anti-COVID-19 protective measures are distributed according to workers' socio-professional characteristics and company features.

METHODS

This is a cross-sectional study based on an online survey conducted in Spain (n = 19,452 workers). Multiple Correspondence Analysis was used for the multivariate description while the association between worker representation and protective measures was assessed by robust Poisson regressions.

RESULTS

The maps resulting from the Multiple Correspondence Analysis allow for the identification of patterns of inequalities in protection, with a clear occupational social class divide. The regression models show that protective measures are applied more frequently where worker representatives exist, this association being particularly strong in relation to organizational measures.

CONCLUSIONS

The presence of worker representation is systematically associated with a greater presence of protective measures, which could have implications for the reduction of social inequalities resulting from labor-management practices.

The chronically ill in the labour market - are they hierarchically sorted by education?

BACKGROUND

The chronically ill as a group has on average lower probability of employment compared to the general population, a situation that has persisted over time in many countries. Previous studies have shown that the prevalence of chronic diseases is higher among those with lower levels of education. We aim to quantify the double burden of low education and chronic illness comparing the differential probabilities of employment between the chronically ill with lower, medium, and high levels of education and how their employment rates develop over time.

METHODS

Using merged Norwegian administrative data over a 11-year period (2008-2018), our estimations are based on multivariable regression with labour market and time fixed effects. To reduce bias due to patients' heterogeneity, we included a series of covariates that may influence the association between labour market participation and level of education. To explicitly explore the 'shielding effect' of education over time, the models include the interaction effects between chronic illness and level of education and year.

RESULTS

The employment probabilities are highest for the high educated and lowest for chronically ill individuals with lower education, as expected. The differences between educational groups are changing over time, though, driven by a revealing development among the lower-educated chronically ill. That group has a significant reduction in employment probabilities both in absolute terms and relative to the other groups. The mean predicted employment probabilities for the high educated chronic patient is not changing over time indicating that the high educated as a group is able to maintain labour market participation over time. Additionally, we find remarkable differences in employment probabilities depending on diagnoses.

CONCLUSION

For the chronically ill as a group, a high level of education seems to "shield" against labour market consequences. The magnitude of the shielding effect is increasing over time leaving chronically ill individuals with lower education behind. However, the shielding effect varies in size between types of chronic diseases. While musculoskeletal, cardiovascular and partly cancer patients are "sorted" hierarchically according to level of education, diabetes, respiratory and mental patients are not.

Association between work sick-leave absenteeism and SARS-CoV-2 notifications in the Netherlands during the COVID-19 epidemic

BACKGROUND

Alternative data sources for surveillance have gained importance in maintaining coronavirus disease 2019 (COVID-19) situational awareness as nationwide testing has drastically decreased. Therefore, we explored whether rates of sick-leave from work are associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) notification trends and at which lag, to indicate the usefulness of sick-leave data for COVID-19 surveillance.

METHODS

We explored trends during the COVID-19 epidemic of weekly sick-leave rates and SARS-CoV-2 notification rates from 1 June 2020 to 10 April 2022. Separate time series were inspected visually. Then, Spearman correlation coefficients were calculated at different lag and lead times of zero to four weeks between sick-leave and SARS-CoV-2 notification rates. We distinguished between four SARS-CoV-2 variant periods, two labour sectors and overall, and all-cause sick-leave versus COVID-19-specific sick-leave.

RESULTS

The correlation coefficients between weekly all-cause sick-leave and SARS-CoV-2 notification rate at optimal lags were between 0.58 and 0.93, varying by the variant period and sector (overall: 0.83, lag -1; 95% CI [0.76, 0.88]). COVID-19-specific sick-leave correlations were higher than all-cause sick-leave correlations. Correlations were slightly lower in healthcare and education than overall. The highest correlations were mostly at lag -2 and -1 for all-cause sick-leave, meaning that sick-leave preceded SARS-CoV-2 notifications. Correlations were highest mostly at lag zero for COVID-19-specific sick-leave (coinciding with SARS-CoV-2 notifications).

CONCLUSION

All-cause sick-leave might offer an earlier indication and evolution of trends in SARS-CoV-2 rates, especially when testing is less available. Sick-leave data may complement COVID-19 and other infectious disease surveillance systems as a syndromic data source.

Causes and Risk Factors for Absenteeism among Medical Staff in German Specialized Lung Clinics during the COVID Pandemic

BACKGROUND

Staff shortages pose a major challenge to the health system.

OBJECTIVES

The objective of this study was to clarify the role of different causative factors we investigated on staff absenteeism during the COVID pandemic.

METHODS

The prospective multicentre cohort study assessed the private and professional impact of the pandemic on health care workers (HCWs) using a specially developed questionnaire. HCWs from 7 specialist lung clinics throughout Germany were surveyed from December 1 to December 23, 2021. The current analysis addresses pandemic-related absenteeism.

RESULTS

1,134 HCW (55% female; 18.4% male, 26.3% not willing to provide information on age or gender) participated. 72.8% had received at least one vaccination dose at the time of the survey, and 9.4% reported a COVID infection. Of those with positive tests, 98% reported home quarantine for median (IQR) 14 (12-17) days; 10.3% of those who ultimately tested negative also reported quarantine periods of 14 (7-14) days. 32.2% of vaccinated respondents reported absenteeism due to vaccine reactions of 2 (1-3) days. Overall, 37% (n = 420) of HCW reported pandemic-related absenteeism, with 3,524 total days of absenteeism, of which 2,828 were due to illness/quarantine and 696 to vaccination effects. Independent risk factors for COVID-related absenteeism ≥5 days included already having COVID, but also concern about long-term effects of COVID (OR 1,782, p = 0.014); risk factors for vaccine-related absenteeism ≥2 days included concerns of late effects of vaccination (OR 2.2, 95% CI: 1.4-3.1, p < 0.000).

CONCLUSION

Staff shortages due to quarantine or infections and vaccine reactogenicity have put a strain on German respiratory specialists. The fact that staff concerns also contributed to absenteeism may be helpful in managing future pandemic events to minimize staff absenteeism.

Bullying before and during the COVID-19 pandemic

The rates of bullying during the COVID-19 pandemic, a time of unprecedented public health and social restrictions, were compared to earlier times when students attended school in person. Several studies indicated a notable decrease in the prevalence of bullying victimization and perpetration during the pandemic, particularly when online learning was implemented. But studies from countries with fewer social restrictions indicated increases in rates of bullying during the pandemic. Mixed results regarding prevalence rates for some bullying forms (e.g., cyberbullying) were also found. Racialized youth and LGBTQ+ youth reliably reported higher rates of bullying victimization during the pandemic, consistent with pre-pandemic patterns. Reasons for the inconsistencies in findings likely relate to diverse methods, timeframes, and sampling techniques, as well as different experiences with pandemic social restrictions. More longitudinal studies are needed to assess whether bullying involvement did in fact "change" during, compared to before, the pandemic. The findings point to the importance of peer relationships and hint at the potential of increased teacher supervision as a bullying prevention strategy.

Drivers and Barriers to Implementing the Internet of Things in the Health Care Supply Chain: Mixed Methods Multicase Study

BACKGROUND

Over the past 2 years, the COVID-19 pandemic has placed enormous pressure on the health care industry. There has been an increase in demand and, at the same time, a shortage of supplies. This has shown that supply chain management in the health care industry cannot be taken for granted. Furthermore, the health care industry is also facing other major challenges, such as the current labor market shortage. In the literature, the Internet of Things (IoT) is highlighted as an effective tool to build a more resilient and efficient supply chain that can manage these challenges. Although using IoT in supply chain management has been extensively examined in other types of supply chains, its use in the health care supply chain has largely been overlooked. Given that the health care supply chain, compared to others, is more complex and is under growing pressure, a more in-depth understanding of the opportunities brought by IoT is necessary.

OBJECTIVE

This study aims to address this research gap by identifying and ranking the drivers of and barriers to implementing IoT in the health care supply chain.

METHODS

We conducted a 2-stage study. In the first, exploratory stage, a total of 12 semistructured interviews were conducted to identify drivers and barriers. In the second, confirmatory stage, a total of 26 health care supply chain professionals were asked in a survey to rank the drivers and barriers.

RESULTS

The results show that there are multiple financial, operational, strategy-related, and supply chain-related drivers for implementing IoT. Similarly, there are various financial, strategy-related, supply chain-related, technology-related, and user-related barriers. The findings also show that supply chain-related drivers (eg, increased transparency, traceability, and collaboration with suppliers) are the strongest drivers, while financial barriers (eg, high implementation costs and difficulties in building a business case) are the biggest barriers to overcome.

CONCLUSIONS

The findings of this study add to the limited literature regarding IoT in the health care supply chain by empirically identifying the most important drivers and barriers to IoT implementation. The ranking of drivers and barriers provides guidance for practitioners and health care provider leaders intending to implement IoT in the health care supply chain.

Associations between an international COVID-19 job exposure matrix and SARS-CoV-2 infection among 2 million workers in Denmark

OBJECTIVES

This study investigates the associations between the Danish version of a job exposure matrix for COVID-19 (COVID-19-JEM) and Danish register-based SARS-CoV-2 infection information across three waves of the pandemic. The COVID-19-JEM consists of four dimensions on transmission: two on mitigation measures, and two on precarious work characteristics.

METHODS

The study comprised 2 021 309 persons from the Danish working population between 26 February 2020 and 15 December 2021. Logistic regression models were applied to assess the associations between the JEM dimensions and overall score and SARS-CoV-2 infection across three infection waves, with peaks in March-April 2020, December-January 2021, and February-March 2022. Sex, age, household income, country of birth, wave, residential region and during wave 3 vaccination status were accounted for.

RESULTS

Higher risk scores within the transmission and mitigation dimensions and the overall JEM score resulted in higher odds ratios (OR) of a SARS-CoV-2 infection. OR attenuated across the three waves with ranges of 1.08-5.09 in wave 1, 1.06-1.60 in wave 2, and 1.05-1.45 in those not (fully) vaccinated in wave 3. In wave 3, no associations were found for those fully vaccinated. In all waves, the two precarious work dimensions showed weaker or inversed associations.

CONCLUSIONS

The COVID-19-JEM is a promising tool for assessing occupational exposure to SARS-CoV-2 and other airborne infectious agents that mainly spread between people who are in close contact with each other. However, its usefulness depends on applied restrictions and the vaccination status in the population of interest.

A Comparison of the 1918 and 2019 Pandemics in the United States

ABSTRACT

There are eerie similarities between the 1918 Spanish influenza and 2019 COVID-19 pandemics that are somewhat surprising and disheartening, given that the time interval between the 2 pandemics is more than 100 years. This article covers the national response, etiology and pathophysiology, disease course and treatments, nursing shortages, healthcare responses, sequelae following infections, and economic and social impacts of both pandemics. Understanding the development and course of both pandemics will inform clinical nurse specialists about the changes that need to be made to be better prepared to recognize the changes that need to be made to prepare for the next pandemic.

Explaining the higher COVID-19 mortality rates among disproportionately Black counties: A decomposition analysis

Background

Why is COVID-19 mortality higher in counties with a disproportionately large (>13.4%) share of Black residents (hereafter "Black counties") relative to others ("non-Black counties")? Existing literature points to six categories of determinants: (1) social distancing, (2) COVID-19 testing, (3) socioeconomic characteristics, (4) environmental characteristics, (5) prevalence of (pre-existing) chronic health conditions, and (6) demographic characteristics. The relative importance of these determinants has not yet been thoroughly examined.

Methods

We built a dataset consisting of 21 sub-indicators across the six categories of determinants for 3108 US counties and their COVID-19 mortality over the period of January 22, 2020-December 31, 2020. Applying the Gelbach's decomposition, we quantified which determinants were most (or least) associated with the COVID-19 mortality disparity between Black and non-Black counties.

Results

We find that COVID-19 death rates were 26 percent higher in Black counties compared to non-Black counties. This disparity was almost completely explained by the six categories of determinants included in our model. Decomposition analyses indicate that county-level demographic and population health characteristics explained most of this disparity. Among all sub-indicators considered, the greater proportion of females and smaller proportion of rural residents in Black counties were the two largest contributors to the COVID-19 mortality gap between Black and non-Black counties. Proportions of diabetic residents, uninsured residents, and the degree of income inequality also significantly contributed to the gap in COVID-19 mortality.

Conclusion

The COVID-19 mortality gap between Black and non-Black counties was largely explained by pre-pandemic differences in demographic and population health characteristics. Policies aiming to reduce the prevalence of chronic conditions and uninsured residents in Black counties would have helped narrow the COVID-19 mortality gap between Black and non-Black counties in 2020.

Immigrant Legal Status among Essential Frontline Workers in the United States during the COVID-19 Pandemic Era

Emerging evidence suggests that the COVID-19 pandemic has extracted a substantial toll on immigrant communities in the United States, due in part to increased potential risk of exposure for immigrants to COVID-19 in the workplace. In this article, we use federal guidance on which industries in the United States were designated essential during the COVID-19 pandemic, information about the ability to work remotely, and data from the 2019 American Community Survey to estimate the distribution of essential frontline workers by nativity and immigrant legal status. Central to our analysis is a proxy measure of working in the primary or secondary sector of the segmented labor market. Our results indicate that a larger proportion of foreign-born workers are essential frontline workers compared to native-born workers and that 70 percent of unauthorized immigrant workers are essential frontline workers. Disparities in essential frontline worker status are most pronounced for unauthorized immigrant workers and native-born workers in the secondary sector of the labor market. These results suggest that larger proportions of foreign-born workers, and especially unauthorized immigrant workers, face greater risk of potential exposure to COVID-19 in the workplace than native-born workers. Social determinants of health such as lack of access to health insurance and living in overcrowded housing indicate that unauthorized immigrant essential frontline workers may be more vulnerable to poor health outcomes related to COVID-19 than other groups of essential frontline workers. These findings help to provide a plausible explanation for why COVID-19 mortality rates for immigrants are higher than mortality rates for native-born residents.

Mothers' Nonstandard Work Schedules and Children's Behavior Problems: Divergent Patterns by Maternal Education

Increasing evidence has demonstrated that nonstandard work schedules are more prevalent among the less-educated population, and mothers' nonstandard work schedules have adverse influences on children's development. Yet, we have known relatively little about how such impacts differ across the educational distribution. Using data from the Fragile Families and Child Wellbeing Study, random and fixed effects regression results revealed a general "pattern of disadvantage" in the sense that detrimental influences of mothers regularly working nonstandard schedules on children's behavior were concentrated among those born to mothers without high school education, a "truly disadvantaged" group in the contemporary United States. In addition, regular nonstandard schedules appeared to play a mixed role in the behavioral development of children who had college-educated mothers, depending on the specific type of nonstandard schedule. These findings suggest that children born to the least-educated mothers experience compounded disadvantages that may reinforce the intergenerational transmission of disadvantages and also illustrate that negative implications of nonstandard work schedules for child wellbeing may extend to the more advantaged group.

Academic Neurology and the COVID-19 Pandemic: Resilience, Hope, and Solutions

The SARS-CoV-2 (COVID-19) viral pandemic dramatically affected human health, health care delivery, health care workers, and health care research worldwide. The field of academic neurology was no exception. In this 2022 Presidential Plenary, we discuss the challenges faced by neurologists and neuroscientists professionally and personally. We review the threats posed by the pandemic to neuroscience research activities, materials, productivity, and funding. We then discuss the impact of the pandemic on clinical trials for neurologic diseases. Restrictions to patient enrolment due to limited in-person access to laboratory testing, imaging, and study visits led to delay in both clinical trial enrolment and study completion but also to innovative new means to engage clinical trial participants remotely and to strategies to critically appraise the frequency and design of trial-related patient evaluations. Clinical care was also challenged by initial pandemic prioritization of urgent visit and inpatient care and the rapid pivot to telehealth for most other neurology care encounters. Front-line neurology care teams faced their fears of infection, with the first few months of the pandemic being characterized by uncertainty, inconsistent national health care strategies, limited personal protective equipment, and an alarming rate of human illness and death caused by COVID-19. The personal and societal toll of the pandemic is incalculable. Across research and clinical neurology providers, women and particularly those with young families juggled the impossible balance of career and family care as schools closed and children required home-based education. Shining through this dark time are lessons that should shape a brighter future for our field. We are resilient, and the advances in neuroscience and neurology care continue to advance improved neurologic outcomes. The National Institutes of Health devised multiple support strategies for researchers to help bridge the pandemic. Telehealth, clinical trial designs that are more participant-centric with remote monitoring, and flexible work schedules are strategies to rebalance overworked lives and improve our engagement with our patients. As we re-emerge, we have the chance to reframe our field.

Close encounters during a pandemic: Social habits and inter-generational links in the first two waves of COVID-19

Social habits are ingrained in a community and affect human behaviour. Have they played any role in the spread of the pandemic? We use high-frequency data for 220 regions in 15 European countries from March to December 2020 to compare the association between social contacts outside the family and within inter-generational families, on the one hand, and cases and excess mortality on the other. We find that a standard deviation increase in the percentage of people having daily face-to-face contacts outside the household is associated with 5 new daily cases and 2.6 additional weekly deaths, while the incidence of inter-generational households exhibits a less robust association with both COVID-19 transmission and mortality. We compare results across the first and the second wave of pandemic and show that differences are related to the average age of the most affected groups. Our findings are robust to the inclusion of a number of controls, fixed effects, the chosen sample of countries, and the estimation method. We argue that type and frequency of social interactions are interweaved with a region culture and habits and are informative on the potential transmission of contagion and on its lethality.

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

The virus that causes COVID-19 changes over time, occasionally leading to Variants of Interest (VOIs) and Variants of Concern (VOCs) that can behave differently with respect to detection kits, treatments, or vaccines. For instance, two vaccination doses were 61% effective against the BA.1 predominant variant, but only 24% effective when BA.2 became predominant. While doses still confer protection against severe disease outcomes, the BA.5 variant demonstrates the possibility that individuals who have received a few doses built for previous variants can still be infected with newer variants. As previous vaccines become less effective, new ones will be released to target specific variants and the whole process of vaccinating the population will restart. While previous models have detailed logistical aspects and disease progression, there are three additional key elements to model COVID-19 vaccination coverage in the long term. First, the willingness of the population to participate in regular vaccination campaigns is essential for long-term effective COVID-19 vaccination coverage. Previous research has shown that several categories of variables drive vaccination status: sociodemographic, health-related, psychological, and information-related constructs. However, the inclusion of these categories in future models raises questions about the identification of specific factors (e.g., which sociodemographic aspects?) and their operationalization (e.g., how to initialize agents with a plausible combination of factors?). While previous models separately accounted for natural- and vaccine-induced immunity, the reality is that a significant fraction of individuals will be both vaccinated and infected over the coming years. Modeling the decay in immunity with respect to new VOCs will thus need to account for hybrid immunity. Finally, models rarely assume that individuals make mistakes, even though this over-reliance on perfectly rational individuals can miss essential dynamics. Using the U.S. as a guiding example, our scoping review summarizes these aspects (vaccinal choice, immunity, and errors) through ten recommendations to support the modeling community in developing long-term COVID-19 vaccination models.