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

К численному решению задач о деформации анизотропных пластин с отверстием

Представлен подход к расчету напряженно-деформированного состояния пластин с отверстием, основанный на численной параметризации двусвязной области, сведении исходной нелинейной краевой задачи к последовательности линейных двухмерных и интегрировании последних устойчивым численным методом. Исследуется влияние формы и месторасположения отверстия на напряженно-деформированное состояние квадратной пластины.Представлено підхід до розрахунку напружено-деформованого стану пластин, що послаблені отвором. Підхід базується на числовій параметризації двозв’язної області, зведенні вихідної нелінійної задачі до послідовності лінійних двомірних та останніх до одномірних, інтегрування яких проводиться стійким числовим методом. Досліджується вплив форми та місцезнаходження отвору на напружено-деформований стан квадратної пластини.The study presents an approach to stressed-state analysis of plates with apertures which is based on the numerical parameterization of a doubly-connected area, reduction of the initial nonlinear boundary-value problem to a sequence of linear two-dimensional problems and integrating the latter by the stable numerical method. The effect of form and place of an aperture on stressed state of a square plate has been analyzed

External validation of a prediction model and decision tree for sickness absence due to mental disorders

Purpose: A previously developed prediction model and decision tree were externally validated for their ability to identify occupational health survey participants at increased risk of long-term sickness absence (LTSA) due to mental disorders. Methods: The study population consisted of N = 3415 employees in mobility services who were invited in 2016 for an occupational health survey, consisting of an online questionnaire measuring the health status and working conditions, followed by a preventive consultation with an occupational health provider (OHP). The survey variables of the previously developed prediction model and decision tree were used for predicting mental LTSA (no = 0, yes = 1) at 1-year follow-up. Discrimination between survey participants with and without mental LTSA was investigated with the area under the receiver operating characteristic curve (AUC). Results: A total of n = 1736 (51%) non-sick-listed employees participated in the survey and 51 (3%) of them had mental LTSA during follow-up. The prediction model discriminated (AUC = 0.700; 95% CI 0.628–0.773) between participants with and without mental LTSA during follow-up. Discrimination by the decision tree (AUC = 0.671; 95% CI 0.589–0.753) did not differ significantly (p = 0.62) from discrimination by the prediction model. Conclusion: At external validation, the prediction model and the decision tree both poorly identified occupational health survey participants at increased risk of mental LTSA. OHPs could use the decision tree to determine if mental LTSA risk factors should be explored in the preventive consultation which follows after completing the survey questionnaire

Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population

PurposeThis study investigated if and how occupational health survey variables can be used to identify workers at risk of long-term sickness absence (LTSA) due to mental disorders.MethodsCohort study including 53,833 non-sicklisted participants in occupational health surveys between 2010 and 2013. Twenty-seven survey variables were included in a backward stepwise logistic regression analysis with mental LTSA at 1-year follow-up as outcome variable. The same variables were also used for decision tree analysis. Discrimination between participants with and without mental LTSA during follow-up was investigated by using the area under the receiver operating characteristic curve (AUC); the AUC was internally validated in 100 bootstrap samples.Results30,857 (57%) participants had complete data for analysis; 450 (1.5%) participants had mental LTSA during follow-up. Discrimination by an 11-predictor logistic regression model (gender, marital status, economic sector, years employed at the company, role clarity, cognitive demands, learning opportunities, co-worker support, social support from family/friends, work satisfaction, and distress) was AUC = 0.713 (95% CI 0.692-0.732). A 3-node decision tree (distress, gender, work satisfaction, and work pace) also discriminated between participants with and without mental LTSA at follow-up (AUC = 0.709; 95% CI 0.615-0.804).ConclusionsAn 11-predictor regression model and a 3-node decision tree equally well identified workers at risk of mental LTSA. The decision tree provides better insight into the mental LTSA risk groups and is easier to use in occupational health care practice

Framingham score and work-related variables for predicting cardiovascular disease in the working population

Background: The Framingham score is commonly used to estimate the risk of cardiovascular disease (CVD). This study investigated whether work-related variables improve Framingham score predictions of sickness absence due to CVD. Methods: Eleven occupational health survey variables (descent, marital status, education, work type, work pace, cognitive demands, supervisor support, co-worker support, commitment to work, intrinsic work motivation and distress) and the Framingham Point Score (FPS) were combined into a multi-variable logistic regression model for CVD sickness absence during 1-year follow-up of 19 707 survey participants. The Net Reclassification Index (NRI) was used to investigate the added value of work-related variables to the FPS risk classification. Discrimination between participants with and without CVD sickness absence during follow-up was investigated by the area under the receiver operating characteristic curve (AUC). Results: A total of 129 (0.7%) occupational health survey participants had CVD sickness absence during 1-year follow-up. Manual work and high cognitive demands, but not the other work-related variables contributed to the FPS predictions of CVD sickness absence. However, work type and cognitive demands did not improve the FPS classification for risk of CVD sickness absence [NRI = 2.3%; 95% confidence interval (CI) -2.7 to 9.5%; P = 0.629]. The FPS discriminated well between participants with and without CVD sickness absence (AUC = 0.759; 95% CI 0.724-0.794). Conclusion: Work-related variables did not improve predictions of CVD sickness absence by the FPS. The non-laboratory Framingham score can be used to identify health survey participants at risk of CVD sickness absence

Validation of reference genes for quantitative RT-PCR studies in porcine oocytes and preimplantation embryos

<p>Abstract</p> <p>Background</p> <p>In the developing embryo, total RNA abundance fluctuates caused by functional RNA degradation and zygotic genome activation. These variations in the transcriptome in early development complicate the choice of good reference genes for gene expression studies by quantitative real time polymerase chain reaction.</p> <p>Results</p> <p>In order to identify stably expressed genes for normalisation of quantitative data, within early stages of development, transcription levels were examined of 7 frequently used reference genes (<it>B2M, BACT, GAPDH, H2A, PGK1, SI8</it>, and <it>UBC</it>) at different stages of early porcine embryonic development (germinal vesicle, metaphase-2, 2-cell, 4-cell, early blastocyst, expanded blastocyst). Analysis of transcription profiling by geNorm software revealed that <it>GAPDH, PGK1, S18</it>, and <it>UBC </it>showed high stability in early porcine embryonic development, while transcription levels of <it>B2M, BACT</it>, and <it>H2A </it>were highly regulated.</p> <p>Conclusion</p> <p>Good reference genes that reflect total RNA content were identified in early embryonic development from oocyte to blastocyst. A selection of either <it>GAPDH </it>or <it>PGK1</it>, together with ribosomal protein <it>S18 </it>(<it>S18</it>), and <it>UBC </it>is proposed as reference genes, but the use of <it>B2M, BACT</it>, or <it>H2A </it>is discouraged.</p

Predicting long-term sickness absence among retail workers after four days of sick-listing

Objective This study tested and validated an existing tool for its ability to predict the risk of long-term (ie, ≥6 weeks) sickness absence (LTSA) after four days of sick-listing. Methods A 9-item tool is completed online on the fourth day of sick-listing. The tool was tested in a sample (N=13 597) of food retail workers who reported sick between March and May 2017. It was validated in a new sample (N=104 698) of workers (83% retail) who reported sick between January 2020 and April 2021. LTSA risk predictions were calibrated with the Hosmer-Lemeshow (H-L) test; non-significant H-L P-values indicated adequate calibration. Discrimination between workers with and without LTSA was investigated with the area (AUC) under the receiver operating characteristic (ROC) curve. Results The data of 2203 (16%) workers in the test sample and 14 226 (13%) workers in the validation sample was available for analysis. In the test sample, the tool together with age and sex predicted LTSA (H-L test P=0.59) and discriminated between workers with and without LTSA [AUC 0.85, 95% confidence interval (CI) 0.83–0.87]. In the validation sample, LTSA risk predictions were adequate (H-L test P=0.13) and discrimination was excellent (AUC 0.91, 95% CI 0.90–0.92). The ROC curve had an optimal cut-off at a predicted 36% LTSA risk, with sensitivity 0.85 and specificity 0.83. Conclusion The existing 9-item tool can be used to invite sick-listed retail workers with a ≥36% LTSA risk for expedited consultations. Further studies are needed to determine LTSA cut-off risks for other economic sectors

Psychosocial work characteristics and long-term sickness absence due to mental disorders

Background: Psychosocial work characteristics are associated with all-cause long-term sickness absence (LTSA). Aims: This study investigated whether psychosocial work characteristics such as higher workload, faster pace of work, less variety in work, lack of performance feedback, and lack of supervisor support are prospectively associated with higher LTSA due to mental disorders. Methods: Cohort study including 4877 workers employed in the distribution and transport sector in The Netherlands. Psychosocial work characteristics were included in a logistic regression model estimating the odds ratios (OR) and 95% confidence intervals (CI) of mental LTSA during 2-year follow-up. The ability of the regression model to discriminate between workers with and without mental LTSA was investigated with the area under the receiver operating characteristic curve (AUC). Results: Tow thousand seven hundred and eighty-two (57%) workers were included in the analysis; 73 (3%) had mental LTSA. Feedback about one’s performance (OR = 0.82; 95% CI 0.70–0.96) was associated with mental LTSA. A prediction model including psychosocial work characteristics poorly discriminated (AUC = 0.65; 95% CI 0.56–0.74) between workers with and without mental LTSA. Conclusions: Feedback about one’s performance is associated with lower rates of mental LTSA, but it is not useful to measure psychosocial work characteristics to identify workers at risk of mental LTSA

Focus Group Study Exploring Factors Related to Frequent Sickness Absence

INTRODUCTION:Research investigating frequent sickness absence (3 or more episodes per year) is scarce and qualitative research from the perspective of frequent absentees themselves is lacking. The aim of the current study is to explore awareness, determinants of and solutions to frequent sickness absence from the perspective of frequent absentees themselves. METHODS:We performed a qualitative study of 3 focus group discussions involving a total of 15 frequent absentees. Focus group discussions were audiotaped and transcribed verbatim. Results were analyzed with the Graneheim method using the Job Demands Resources (JD-R) model as theoretical framework. RESULTS:Many participants were not aware of their frequent sickness absence and the risk of future long-term sickness absence. As determinants, participants mentioned job demands, job resources, home demands, poor health, chronic illness, unhealthy lifestyles, and diminished feeling of responsibility to attend work in cases of low job resources. Managing these factors and improving communication (skills) were regarded as solutions to reduce frequent sickness absence. CONCLUSIONS:The JD-R model provided a framework for determinants of and solutions to frequent sickness absence. Additional determinants were poor health, chronic illness, unhealthy lifestyles, and diminished feeling of responsibility to attend work in cases of low job resources. Frequent sickness absence should be regarded as a signal that something is wrong. Managers, supervisors, and occupational health care providers should advise and support frequent absentees to accommodate job demands, increase both job and personal resources, and improve health rather than express disapproval of frequent sickness absence and apply pressure regarding work attendance

Coping styles relate to health and work environment of Norwegian and Dutch hospital nurses:A comparative study

Nurses exposed to high nursing stress report no health complaints as long as they have high coping abilities. The purpose of this study was to investigate coping styles in relation to the health status and work environment of Norwegian and Dutch hospital nurses. This comparative study included a random sample of 5400 Norwegian nurses and a convenience sample of 588 Dutch nurses. Coping, health, and work environment were assessed by questionnaire in both samples and associations were investigated bivariately and multi-variately. We found that active problem-solving coping was associated with the health and work environment of Norwegian nurses but not with the health and work environment of Dutch. Passive coping (avoiding problems or waiting to see what happens) was found to relate to poor general health, poor mental health, low job control, and low job support in both Norwegian and Dutch nurses. Improvements in the nursing work environment may not only result in better mental health, but may also reduce passive coping