Hierarchizing Determinants of Sick Leave

Pain, Conflicted Feelings About Work, and Sickness Absence: A Prospective Study of the Effects of Number of Pain Sites and Role Conflicts on Medically Certified Sickness Absence

We investigated associations between the number of pain sites (NPS) and role conflict with medically certified, pain-related sickness absence (SA) in employees of Norwegian enterprises (N = 5,654). Latent profile analyses identified exposure profiles based on 3 types of role conflict (work-role conflict, work-life conflict, and emotional dissonance). Multinomial logistic regressions estimated effects on absence (short-term absence of less than 56 days, long-term absence of more than 56 days) during 1 year after survey. Effects of the NPS on absence were compared across exposure profiles. Results suggested the NPS and all types of role conflict predicted absences separately. Mutually adjusted regressions revealed unique contributions of the NPS to the short-term and long-term absence (odds ratio [OR] 1.24, 95% confidence interval [CI] 1.18, 1.30 and OR 1.51, 95% CI 1.37, 1.66) and of work-role conflict to the short-term absence (OR 1.18, 95% CI 1.03, 1.35). Latent profile analyses identified 4 exposure profiles ("1 unconflicted," "2 dissonant, otherwise medium," "3 conflicted, medium dissonance," "4 conflicted and dissonant"). Profiles 3 and 4 exhibited elevated risk of SA, with the strongest baseline-adjusted effects for profile 4 (short-term absence OR 1.90, 95% CI 1.40, 2.57, long-term absence OR 1.95, 95% CI 1.15, 3.31). Effects of the NPS on short-term absence were stronger for profile 4 versus profile 1 (OR 1.38 vs 1.24, P < .001). Our findings suggest that addressing role conflicts may prevent pain-related absence, possibly also for individuals already experiencing pain. PERSPECTIVE: This article elucidates the connections between role conflicts associated with work roles, the NPS, and SA due to pain. This should help organizations prevent pain-related absences from work and improve working conditions for workers who remain occupationally active in spite of pain problems.

A Literature Review to Identify Effective Web- and App-Based mHealth Interventions for Stress Management at Work

BACKGROUND

Persistent job-related stress can be harmful to physical and mental health and has a sizable financial burden on society. Face-to-face interventions are effective in reducing stress but have the disadvantage of high costs and time requirements. mHealth solutions may be an effective alternative to provide stress management interventions at work. Occupational health professionals need information on which mHealth apps are effective for employees to manage job-related stress. The aim of this review is to provide an overview of effective web- and app-based interventions for reduction of job-related stress and stress-related symptoms.

METHOD

A literature review was conducted in the databases PubMed, PsycINFO, CINAHL Complete, and IEEEXplore.

FINDINGS

A total of 24 articles describing 19 products were found. All products showed effectiveness in trials in improving mental and/or physical health and reducing stress. Most products have a course-like structure with a duration from 1 to 8 weeks. The products use various methods such as psychoeducation and education on stress, cognitive restructuring, emotional regulation, problem-solving, goal setting, gratitude, breathing, or mindfulness techniques. Most products use more than one method and most mixed material such as text on web pages, text messages, videos, reading and audio material, and games.

CONCLUSION/APPLICATION TO PRACTICE

Overall, effective mHealth products were identified for the intervention of acute and chronic stress. Occupational health practitioners can use these 19 evidence-based mHealth products when advising organizations on health promotion of employees to reduce stress symptoms and promote health and well-being.

A statistical algorithm for outbreak detection in multisite settings: an application to sick leave monitoring

Motivation

Public health authorities monitor cases of health-related problems over time using surveillance algorithms that detect unusually high increases in the number of cases, namely aberrations. Statistical aberrations signal outbreaks when further investigation reveals epidemiological significance. The increasing availability and diversity of epidemiological data and the most recent epidemic threats call for more accurate surveillance algorithms that not just detect aberration times but also detect locations. Sick leave data, for instance, can be monitored across companies to identify companies-related aberrations. In this context, we develop an extension to multisite surveillance of a routinely used aberration detection algorithm, the quasi-Poisson regression Farrington Flexible algorithm. The new algorithm consists of a negative-binomial mixed effects regression model with a random effects term for sites and a new reweighting procedure reducing the effect of past aberrations.

Results

A wide range of simulations shows that, compared with Farrington Flexible, the new algorithm produces better false positive rates and similar probabilities of detecting genuine outbreaks, for case counts that exceed historical baselines by 3 SD. As expected, higher surges lead to lower false positive rates and higher probabilities of detecting true outbreaks. The new algorithm provides better detection of true outbreaks, reaching 100%, when cases exceed eight baseline standard deviations. We apply our algorithm to sick leave rates in the context of COVID-19 and find that it detects the pandemic effect. The new algorithm is easily implementable over a range of contrasting data scenarios, providing good overall performance and new perspectives for multisite surveillance.

Availability and implementation

All the analyses are performed in the R statistical software using the package glmmTMB. The code for performing the analyses and for generating the simulations can be found online at the following link: https://github.com/TomDuchemin/mixed_surveillance.

Contact

a.noufaily@warwick.ac.uk.

Developing Action Plans Based on Machine Learning Analysis to Prevent Sick Leave in a Manufacturing Plant

OBJECTIVE

We aimed to develop action plans for employees' health promotion based on a machine learning model to predict sick leave at a Japanese manufacturing plant.

METHODS

A random forest model was developed to predict sick leave. We developed plans for workers' health promotion based on variable importance and partial dependence plots.

RESULTS

The model showed an area under the receiving operating characteristic curve of 0.882. The higher scores on the Brief Job Stress Questionnaire stress response, younger age, and certain departments were important predictors for sick leave due to mental disorders. We proposed plans to effectively use the Brief Job Stress Questionnaire and provide more support for younger workers and managers of high-risk departments.

CONCLUSIONS

We described a process of action plan development using a machine learning model, which may be beneficial for occupational health practitioners.

Thériault W, Longpré J, Thibodeau A, Hocine MN, Fravalo P. Distinct Microbiotas Are Associated with Different Production Lines in the Cutting Room of a Swine Slaughterhouse

The microorganisms found on fresh, raw meat cuts at a slaughterhouse can influence the meat's safety and spoilage patterns along further stages of processing. However, little is known about the general microbial ecology of the production environment of slaughterhouses. We used 16s rRNA sequencing and diversity analysis to characterize the microbiota heterogeneity on conveyor belt surfaces in the cutting room of a swine slaughterhouse from different production lines (each associated with a particular piece/cut of meat). Variation of the microbiota over a period of time (six visits) was also evaluated. Significant differences of alpha and beta diversity were found between the different visits and between the different production lines. Bacterial genera indicative of each visit and production line were also identified. We then created random forest models that, based on the microbiota of each sample, allowed us to predict with 94% accuracy to which visit a sample belonged and to predict with 88% accuracy from which production line it was taken. Our results suggest a possible influence of meat cut on processing surface microbiotas, which could lead to better prevention, surveillance, and control of microbial contamination of meat during processing.

Praise and recognition from supervisors buffers employee psychological strain: A two-sample investigation with tourism workers

BACKGROUND

Focusing on employees with psychological strain, this research draws on Fredrickson's 'undoing hypothesis' to examine praise and recognition from one's supervisor as an organizational resource.

OBJECTIVE

A model is tested in which psychological strain is a mediator in the positive relationship between role demands and employees' intentions to take sick leave and seek medical advice, and positions supervisor praise and recognition as a buffer of psychological strain on such intentions.

METHODS

The model was tested using two Australian samples in the tourism sector, consisting of motel workers (n = 104) and museum workers (n = 168).

RESULTS

For museum workers, but not motel workers, there was a positive indirect effect of each role demand on sick leave intentions through psychological strain that weakened as a function of supervisor praise and recognition. The proposed moderated mediated model was supported for both samples in regards to intentions to seek medical advice.

CONCLUSIONS

This research contributes new evidence regarding the antecedents of employees' intentions to take sick leave and seek medical advice for work stress-related problems. It also contributes to the limited evidence regarding supervisor praise and recognition as a protective factor for employees exhibiting the symptoms of psychological strain.

Strategy for finding occupational health survey participants at risk of long-term sickness absence

BACKGROUND

When resources are limited, occupational health survey participants are usually invited to consultations based on an occupational health provider's subjective considerations. This study aimed to find health survey participants at risk of long-term (i.e., ≥ 42 consecutive days) sickness absence (LTSA) for consultations with occupational health providers (OHPs).

METHODS

The data of 64 011 non-sicklisted participants in occupational health surveys between 2010 and 2015 were used for the study. In a random sample of 40 000 participants, 27 survey variables were included in decision tree analysis (DTA) predicting LTSA at 1-year follow-up. The decision tree was transferred into a strategy to find participants for OHP consultations, which was then tested in the remaining 24 011 participants.

RESULTS

In the development sample, 1358 (3.4%) participants had LTSA at 1-year follow-up. DTA produced a decision tree with work ability as first splitting variable; company size and sleep problems were the other splitting variables. A strategy differentiating by company size would find 75% of the LTSA cases in small (≤99 workers) companies and 43% of the LTSA cases in medium-sized (100-499 workers) companies. For large companies (≥500 workers), case-finding was only 25%.

CONCLUSIONS

In small and medium-sized companies, work ability and sleep problems can be used to find occupational health survey participants for OHP consultations aimed at preventing LTSA. Research is needed to further develop a case-finding strategy for large companies.

Modeling sickness absence data: A scoping review

The identification of sick leave determinants could positively influence decision making to improve worker quality of life and to reduce consequently costs for society. Sick leave is a research topic of interest in economics, psychology, health and social behaviour. The question of choosing an appropriate statistical tool to analyse sick leave data can be challenging. In fact, sick leave data have a complex structure, characterized by two dimensions: frequency and duration, and involve numerous features related to individual and environmental factors. We conducted a scoping review to characterize statistical approaches to analyse individual sick leave data in order to synthesise key insights from the extensive literature, as well as to identify gaps in research. We followed the PRISMA methodology for scoping reviews and searched Medline, World of Science, Science Direct, Psycinfo and EconLit for publications using statistical modeling for explaining or predicting sick leave at the individual level. We selected 469 articles from the 5983 retrieved, dated from 1981 to 2019. In total, three types of model were identified: univariate outcome modeling using for the most part count models (438 articles), bivariate outcome modeling (14 articles), such as multistate models and structural equation modeling (22 articles). The review shows that there was a lack of evaluation of the models as predictive accuracy was only evaluated in 18 articles and the explanatory accuracy in 43 articles. Further research based on joint models could bring more insights on sick leave spells, considering both their frequency and duration.