Hand eczema-related presenteeism and sickness absence: A cross-sectional population-based study.

BACKGROUND: To date, hand eczema (HE)-related presenteeism has never been assessed within the general population, and general population-based studies on HE-related sickness absence are limited. OBJECTIVES: To assess the prevalence of HE-related presenteeism and sickness absence, and factors associated with HE-related presenteeism, within the Dutch general population. METHODS: Within the Lifelines Cohort Study, participants with HE in the last year (aged 18-65 in 2020), were identified by a questionnaire including questions regarding HE-related presenteeism and sickness absence. Socio-demographic factors were collected from 2006 to 2020. RESULTS: Out of the 3.703 included participants with HE, 2.7% (n = 100) reported HE-related presenteeism, with 19.8% (n = 57) among those with severe-to-very-severe HE. HE-related sickness absence was reported by 0.5% (n = 20) and 5.9% (n = 17), respectively. Logistic regression analyses, adjusted for age and sex, showed negative associations between HE-related presenteeism and higher educational attainment, higher income (>€2500) and higher occupational skill level, and positive associations for high-risk occupations, chronic HE, moderate and severe-to-very-severe HE (compared to almost clear), atopic dermatitis and occupational wet exposure. CONCLUSIONS: A high prevalence of HE-related presenteeism was found among participants with severe-to-very-severe HE. Future studies should focus on longitudinal associations with the clinical course of HE, as HE-related presenteeism might aggravate symptoms of HE.

Gender differences in long term sickness absence.

PURPOSE: Sickness absence is a major public health problem, given its high cost and negative impact on employee well-being. Understanding sickness absence duration and recovery rates among different groups is useful to develop effective strategies for enhancing recovery and reducing costs related to sickness absence. METHODS: Our study analyzed data from a large occupational health service, including over 5 million sick-listed employees from 2010 to 2020, out of which almost 600,000 cases were diagnosed by an occupational health physician. We classified each case according to diagnosis and gender, and performed descriptive statistical analysis for each category. In addition, we used survival analysis to determine recovery rates for each group. RESULTS: Mean sickness duration and recovery rate both differ significantly among groups. Mental and musculoskeletal disorders had the longest absence duration. Recovery rates differed especially during the first months of sickness absence. For men the recovery rate was nearly constant during the first 1.5 year, for women the recovery rate was relatively low in the first three months, and then stayed nearly constant for 1.5 year. CONCLUSION: Across almost all diagnostic classes, it was consistently observed that women had longer average sickness absence durations than to men. Considering mental disorders and diseases of the musculoskeletal system, women had relatively lower recovery rates during the initial months compared to men. As time progressed, the recovery rates of both genders converged and became more similar.

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.

Job Demands, Work Functioning and Mental Health in Dutch Nursing Home Staff during the COVID-19 Outbreak: A Cross-Sectional Multilevel Study.

COVID-19 posed enormous challenges for nursing home staff, which may have caused stress and mental health problems. This study aimed to measure the prevalence of mental health problems among nursing home staff and investigate the differences in job demands, work functioning and mental health between staff with and without COVID contact or COVID infection and across different levels of COVID worries. In this cross-sectional study, 1669 employees from 10 nursing home organizations filled in an online questionnaire between June and September 2020. The questionnaire measured the participants' characteristics, COVID contact, infection and worries, job demands, work functioning, depressive symptoms and burnout. Differences were investigated with multilevel models to account for clustering at the organization level. Of the participants, 19.1% had high levels of depressive symptoms and 22.2% burnout. Job demands, work functioning, depressive symptoms and burnout differed between participants who never worried and participants who often or always worried about the COVID crisis. Differences were smaller for participants with and without COVID contact or infection. Most models improved when clustering was accounted for. Nursing homes should be aware of the impact of COVID worries on job demands, work functioning and mental health, both at the individual and organizational level.

Sustainable employability of teachers with hearing loss.

OBJECTIVE: In many countries the retirement age is rising. Consequently, age-related hearing loss is an increasing occupational health problem. This study examined the association between hearing loss and sustainable employability of teachers. DESIGN: For this cross-sectional study a survey and an online hearing screening test were used. Sustainable employability was measured with the Capability Set for Work Questionnaire (CSWQ), examining seven work values. CSWQ-scores of teachers with poor, insufficient, and good hearing were investigated with ordinal regression analyses. Work values and discrepancies between the importance and achievement of the values were examined by chi-square tests. STUDY SAMPLE: Dutch teachers (N = 737) of whom 146 (20%) had insufficient and 86 (12%) poor hearing. RESULTS: Teachers with insufficient (OR = 0.64; 95% CI 0.46-0.89) and poor (OR = 0.55; 95% CI 0.36-0.83) hearing had lower CSWQ-scores compared with good hearing teachers. Adjustment for covariates, in particular for self-rated health, attenuated the associations. Compared with good hearing teachers, teachers with poor hearing reported more discrepancies in using their knowledge and skills and setting their own goals at work. CONCLUSIONS: Hearing loss was negatively associated with sustainable employability of teachers. This emphasises the importance of assessing the hearing status of teachers.

Exploring the relation between modelled and perceived workload of nurses and related job demands, job resources and personal resources; a longitudinal study.

AIM: Calculating a modelled workload based on objective measures. Exploring the relation between this modelled workload and workload as perceived by nurses, including the effects of specific job demands, job resources and personal resources on the relation. DESIGN: Academic hospital in the Netherlands. Six surgical wards, capacity 15-30 beds. Data collected over 15 consecutive day shifts. METHODS: Modelled workload is calculated as a ratio of required care time, based on patient characteristics, baseline care time and time for non-patient related activities, and allocated care time, based on the amount of available nurses. Both required and allocated care time are corrected for nurse proficiency. Five dimensions of perceived workload were determined by questionnaires. Both the modelled and the perceived workloads were measured on a daily basis. Linear mixed effects models study the longitudinal relation between this modelled and workload as perceived by nurses and the effects of personal resources, job resources and job demands. ANOVA and post-hoc tests were used to identify differences in modelled workload between wards. RESULTS: Modelled workload varies roughly between 70 and 170%. Significant differences in modelled workload between wards were found but confidence intervals were wide. Modelled workload is positively associated with all five perceived workload measures (work pace, amount of work, mental load, emotional load, physical load). In addition to modelled workload, the job resource support of colleagues and job demands time spent on direct patient care and time spent on registration had the biggest significant effects on perceived workload. CONCLUSIONS: The modelled workload does not exactly predict perceived workload, however there is a correlation between the two. The modelled workload can be used to detect differences in workload between wards, which may be useful in distributing workload more evenly in order prevent issues of over- and understaffing and organizational justice. Extra effort to promote team work is likely to have a positive effect on perceived workload. Nurse management can stimulate team cohesion, especially when workload is high. Registered nurses perceive a higher workload than other nurses. When the proportion of direct patient care in a workday is higher, the perceived workload is also higher. Further research is recommended. The findings of this research can help nursing management in allocating resources and directing their attention to the most relevant factors for balancing workload.

Validation of the Work Role Functioning Questionnaire 2.0 in cancer patients.

OBJECTIVE: The Work Role Functioning Questionnaire 2.0 (WRFQ), measuring the percentage of time a worker has difficulties in meeting the work demands for a given health state, has shown strong reliability and validity in various populations with different chronic conditions. The present study aims to validate the WRFQ in working cancer patients. METHODS: A validation study of the WRFQ 2.0 was conducted, using baseline data from the longitudinal Work Life after Cancer study. Structural validity (Confirmatory Factor Analysis, CFA), internal consistency (Cronbach's alpha) and discriminant validity (hypothesis testing) were evaluated. RESULTS: 352 working cancer patients, most of them diagnosed with breast cancer (48%) and 58% in a job with mainly non-manual tasks, showed a mean WRFQ score of 78.6 (SD = 17.1), which means that they had on average difficulties for 78.6% of the time they spent working. Good internal consistency (α = 0.96) and acceptable to good fit for both the four and five-factor model (CFA) was found. The WRFQ distinguished between cancer patients reporting good vs. poor health (80.3 vs. 73.0, p = 0.001), low vs. high fatigue (82.0 vs. 72.2, p < 0.001), no vs. clinical depression (80.4 vs. 58.8, p < 0.001) and low vs. high cognitive symptoms (86.1 vs. 64.7, p < 0.001). CONCLUSIONS: The WRFQ 2.0 is a reliable and valid instrument to measure work functioning in working cancer patients. Further psychometric research on responsiveness is needed to support its use in health practice.

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.

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.

Purpose This 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. Methods Cohort 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. Results 30,857 (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). Conclusions An 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.

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.

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.

Development of Prediction Models for Sick Leave Due to Musculoskeletal Disorders.

Purpose The aim of this study was to develop prediction models to determine the risk of sick leave due to musculoskeletal disorders (MSD) in non-sick listed employees and to compare models for short-term (i.e., 3 months) and long-term (i.e., 12 months) predictions. Methods Cohort study including 49,158 Dutch employees who participated in occupational health checks between 2009 and 2015 and sick leave data recorded during 12 months follow-up. Prediction models for MSD sick leave within 3 and 12 months after the health check were developed with logistic regression analysis using routinely assessed health check variables. The performance of the prediction models was evaluated with explained variance (Nagelkerke's R-square), calibration (Hosmer-Lemeshow test) and discrimination (area under the receiver operating characteristic curve, AUC) measures. Results A total of 376 (0.8%) and 1193 (2.4%) employees had MSD sick leave within 3 and 12 months after the health check. The prediction models included similar predictor variables (educational level, musculoskeletal complaints, distress, supervisor social support, work-home interference, intrinsic motivation, development opportunities, and work pace). The explained variances were 7.6% and 8.8% for the model with 3 and 12 months follow-up, respectively. Both prediction models showed adequate calibration and discriminated between employees with and without MSD sick leave 3 months (AUC = 0.761; Interquartile range [IQR] 0.759-0.763) and 12 months (AUC = 0.740; IQR 0.738-0.741) after the health check. Conclusion The prediction models could be used to determine the risk of MSD sick leave in non-sick listed employees and invite them to preventive consultations with occupational health providers.

Predicting long-term sickness absence among employees with frequent sickness absence.

PURPOSE: Frequent absentees are at risk of long-term sickness absence (SA). The aim of the study is to develop prediction models for long-term SA among frequent absentees. METHODS: Data were obtained from 53,833 workers who participated in occupational health surveys in the period 2010-2013; 4204 of them were frequent absentees (i.e., employees with ≥ 3 SA spells in the year prior to the survey). The survey data of the frequent absentees were used to develop two prediction models: model 1 including job demands and job resources and model 2 including burnout and work engagement. Discrimination between frequent absentees with and without long-term SA during follow-up was assessed with the area under the receiver operating characteristic curve (AUC); (AUC) ≥ 0.75 was considered useful for practice. RESULTS: A total of 3563 employees had complete data for analyses and 685 (19%) of them had long-term SA during 1-year follow-up. The final model 1 included age, gender, education, marital status, prior long-term SA, work pace, role clarity and learning opportunities. Discrimination between frequent absentees with and without long-term SA was significant (AUC 0.623; 95% CI 0.601-0.646), but not useful for practice. Model 2 showed comparable discrimination (AUC 0.624; 95% CI 0.596-0.651) with age, gender, education, marital status, prior long-term SA, burnout and work engagement as predictor variables. Differentiating by gender or sickness absence cause did not result in better discrimination. CONCLUSIONS: Both prediction models discriminated significantly between frequent absentees with and without long-term SA during 1-year follow-up, but have to be further developed for use in healthcare practice.

Effect of an eHealth Intervention to Reduce Sickness Absence Frequency Among Employees With Frequent Sickness Absence: Randomized Controlled Trial.

BACKGROUND: Frequent sickness absence-that is, 3 or more episodes of sickness absence in 1 year-is a problem for employers and employees. Many employees who have had frequent sickness absence in a prior year also have frequent sickness absence in subsequent years: 39% in the first follow-up year and 61% within 4 years. Moreover, 19% have long-term sickness absence (≥6 weeks) in the first follow-up year and 50% within 4 years. We developed an electronic health (eHealth) intervention, consisting of fully automated feedback and advice, to use either as a stand-alone tool (eHealth intervention-only) or combined with consultation with an occupational physician (eHealth intervention-occupational physician). OBJECTIVE: This study aimed to evaluate the effect of the eHealth intervention, with or without additional occupational physician consultation, to reduce sickness absence frequency for employees with frequent sickness absence, versus care as usual (CAU). METHODS: This study was a three-armed randomized controlled trial. Employees with frequent sickness absence received invitational letters, which were distributed by their employers. The primary outcome measure was the number of register-based sickness absence episodes 12 months after completing the baseline questionnaire. Secondary outcome measures were register-based total sickness absence days and self-assessed burnout, engagement, and work ability. In a process evaluation 3 months after baseline, we examined adherence to the intervention and additional actions such as general practitioner and occupational physician visit, communication with the manager, and lifestyle change. RESULTS: A total of 82 participants were included in the analyses, 21 in the eHealth intervention-only group, 31 in the eHealth intervention-occupational physician group, and 30 in the CAU group. We found no significant difference in sickness absence frequency between the groups at 1-year follow-up. Sickness absence frequency decreased in the eHealth intervention-only group from 3 (interquartile range, IQR 3-4) to 1 episode (IQR 0.3-2.8), in the eHealth intervention-occupational physician group from 4 (IQR 3-5) to 3 episodes (IQR 1-4), and in the CAU group from 3 (IQR 3-4) to 2 episodes (IQR 1-3). For secondary outcomes, we found no significant differences between the intervention groups and the control group. The process evaluation showed that only 3 participants from the eHealth intervention-occupational physician group visited the occupational physician on invitation. CONCLUSIONS: Among employees with frequent sickness absence, we found no effect from the eHealth intervention as a stand-alone tool in reducing sickness absence frequency, nor on total sickness absence days, burnout, engagement, or work ability. This might be due to low adherence to the intervention because of insufficient urgency to act. We cannot draw any conclusion on the effect of the eHealth intervention tool combined with an occupational physician consultation (eHealth intervention-occupational physician), due to very low adherence to the occupational physician consultation. An occupational physician consultation could increase a sense of urgency and lead to more focus and appropriate support. As this was the first effectiveness study among employees with frequent sickness absence, strategies to improve recruitment and adherence in occupational eHealth are included. TRIAL REGISTRATION: Netherlands Trial Register NTR4316; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4316 (Archived by WebCite at http://www.webcitation.org/713DHhOFU).

Prediction models to identify workers at risk of sick leave due to low-back pain in the Dutch construction industry.

Objective The aim of this study was to develop a prediction model based on variables measured in occupational health checks to identify non-sick listed workers at risk of sick leave due to non-specific low-back pain (LBP). Methods This cohort study comprised manual (N=22 648) and non-manual (N=9735) construction workers who participated in occupational health checks between 2010 and 2013. Occupational health check variables were used as potential predictors and LBP sick leave was recorded during 1-year follow-up. The prediction model was developed with logistic regression analysis among the manual construction workers and validated in non-manual construction workers. The performance of the prediction model was evaluated with explained variances (Nagelkerke's R-square), calibration (Hosmer-Lemeshow test), and discrimination (area under the receiver operating curve, AUC) measures. Results During follow-up, 178 (0.79%) manual and 17 (0.17%) non-manual construction workers reported LBP sick leave. Backward selection resulted in a model with pain/stiffness in the back, physician-diagnosed musculoskeletal disorders/injuries, postural physical demands, feeling healthy, vitality, and organization of work as predictor variables. The Nagelkerke's R-square was 3.6%; calibration was adequate, but discrimination was poor (AUC=0.692; 95% CI 0.568-0.815). Conclusions A prediction model based on occupational health check variables does not identify non-sick listed workers at increased risk of LBP sick leave correctly. The model could be used to exclude the workers at the lowest risk on LBP sick leave from costly preventive interventions.

Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables.

PURPOSE: The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. MATERIALS AND METHODS: Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. RESULTS: The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. CONCLUSIONS: A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for new variables, based on the knowledge and experience of rehabilitation professionals, to improve long-term sickness absence risk profiles.

Psychosocial work environment and mental health-related long-term sickness absence among nurses.

PURPOSE: We investigated which job demands and job resources were predictive of mental health-related long-term sickness absence (LTSA) in nurses. METHODS: The data of 2059 nurses were obtained from the Norwegian survey of Shift work, Sleep and Health. Job demands (psychological demands, role conflict, and harassment at the workplace) and job resources (social support at work, role clarity, and fair leadership) were measured at baseline and linked to mental health-related LTSA during 2-year follow-up. Cox regression models estimated hazard ratios (HR) and related 95% confidence intervals (CI). The c-statistic was used to investigate the discriminative ability of the Cox regression models. RESULTS: A total of 1533 (75%) nurses were included in the analyses; 103 (7%) of them had mental health-related LTSA during 2-year follow-up. Harassment (HR = 1.07; 95% CI 1.01-1.17) and social support (HR = 0.92; 95% CI 0.87-0.98) were associated with mental health-related LTSA. However, the Cox regression model did not discriminate between nurses with and without mental health-related LTSA (c = 0.59; 95% CI 0.53-0.65). CONCLUSIONS: Harassment was positively and social support at the workplace was negatively related to mental health-related LTSA, but both failed to discriminate between nurses with and without mental health-related LTSA during 2-year follow-up.

Work functioning trajectories in cancer patients: Results from the longitudinal Work Life after Cancer (WOLICA) study.

More than 60% of cancer patients are able to work after cancer diagnosis. However, little is known about their functioning at work. Therefore, the aims of this study were to (1) identify work functioning trajectories in the year following return to work (RTW) in cancer patients and (2) examine baseline sociodemographic, health-related and work-related variables associated with work functioning trajectories. This longitudinal cohort study included 384 cancer patients who have returned to work after cancer diagnosis. Work functioning was measured at baseline, 3, 6, 9 and 12 months follow-up. Latent class growth modeling (LCGM) was used to identify work functioning trajectories. Associations of baseline variables with work functioning trajectories were examined using univariate and multivariate analyses. LCGM analyses with cancer patients who completed on at least three time points the Work Role Functioning Questionnaire (n = 324) identified three work functioning trajectories: "persistently high" (16% of the sample), "moderate to high" (54%) and "persistently low" work functioning (32%). Cancer patients with persistently high work functioning had less time between diagnosis and RTW and had less often a changed meaning of work, while cancer patients with persistently low work functioning reported more baseline cognitive symptoms compared to cancer patients in the other trajectories. This knowledge has implications for cancer care and guidance of cancer patients at work.