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.

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.

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.

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.

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.

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.

External Validation and Update of a Prediction Rule for the Duration of Sickness Absence Due to Common Mental Disorders.

Purpose The objective of the present study was to validate an existing prediction rule (including age, education, depressive/anxiety symptoms, and recovery expectations) for predictions of the duration of sickness absence due to common mental disorders (CMDs) and investigate the added value of work-related factors. Methods A prospective cohort study including 596 employees who reported sick with CMDs in the period from September 2013 to April 2014. Work-related factors were measured at baseline with the Questionnaire on the Experience and Evaluation of Work. During 1-year follow-up, sickness absence data were retrieved from an occupational health register. The outcome variables of the study were sickness absence (no = 0, yes = 1) at 3 and 6 months after reporting sick with CMDs. Discrimination between workers with and without sickness absence was investigated at 3 and 6 months with the area under the receiver operating characteristic curve (AUC). Results A total of 220 (37 %) employees agreed to participate and 211 (35 %) had complete data for analysis. Discrimination was poor with AUC = 0.69 and AUC = 0.55 at 3 and 6 months, respectively. When 'variety in work' was added as predictor variable, discrimination between employees with and without CMD sickness absence improved to AUC = 0.74 (at 3 months) and AUC = 0.62 (at 6 months). Conclusions The original prediction rule poorly predicted CMD sickness absence duration. After adding 'variety in work', the prediction rule discriminated between employees with and without CMD sickness absence 3 months after reporting sick. This new prediction rule remains to be validated in other populations.

Shortened version of the work ability index to identify workers at risk of long-term sickness absence.

BACKGROUND: The Work Ability Index (WAI) identifies non-sicklisted workers at risk of future long-term sickness absence (LTSA). The WAI is a complicated instrument and inconvenient for use in large-scale surveys. We investigated whether shortened versions of the WAI identify non-sicklisted workers at risk of LTSA. METHODS: Prospective study including two samples of non-sicklisted workers participating in occupational health checks between 2010 and 2012. A heterogeneous development sample (N= 2899) was used to estimate logistic regression coefficients for the complete WAI, a shortened WAI version without the list of diseases, and single-item Work Ability Score (WAS). These three instruments were calibrated for predictions of different (≥2, ≥4 and ≥6 weeks) LTSA durations in a validation sample of non-sicklisted workers (N= 3049) employed at a steel mill, differentiating between manual (N= 1710) and non-manual (N= 1339) workers. The discriminative ability was investigated by receiver operating characteristic analysis. RESULTS: All three instruments under-predicted the LTSA risks in both manual and non-manual workers. The complete WAI discriminated between individuals at high and low risk of LTSA ≥2, ≥4 and ≥6 weeks in manual and non-manual workers. Risk predictions and discrimination by the shortened WAI without the list of diseases were as good as the complete WAI. The WAS showed poorer discrimination in manual and non-manual workers. CONCLUSIONS: The WAI without the list of diseases is a good alternative to the complete WAI to identify non-sicklisted workers at risk of future LTSA durations ≥2, ≥4 and ≥6 weeks.

Psychological distress screener for risk of future mental sickness absence in non-sicklisted employees.

BACKGROUND: Recently, a three-item screener, derived from the 16-item distress scale of the Four-Dimensional Symptom Checklist (4DSQ), was used to measure psychological distress in sicklisted employees. The aim of the present study was to investigate the ability of the 16-item distress scale and three-item distress screener to identify non-sicklisted employees at risk of sickness absence (SA) due to mental disorders. METHODS: Prospective cohort study including 4877 employees working in distribution and transport. The 4DSQ distress scale was distributed at baseline in November 2010. SA diagnosed within the International Classification of Diseases -10 chapter F was defined as mental SA and retrieved from an occupational health register during 2-year follow-up. The area under the receiver operating characteristic curve (AUC) was used to discriminate between workers with ('cases') and without ('non-cases') mental SA during follow-up. RESULTS: A total of 2782 employees (57%) were included in complete cases analysis; 73 employees had mental SA during 2-year follow-up. Discrimination between cases and non-cases was similar for the 16-item distress scale (AUC = 0.721; 95% CI, 0.622-0.823) and the three-item screener (AUC = 0.715; 95% CI, 0.615-0.815). CONCLUSION: Healthcare providers could use the three-item distress screener to identify non-sicklisted employees at risk of future mental SA.

Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions.

BACKGROUND: Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (≥ 30) SA days and high (≥ 3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic models. The purpose of this study was to investigate fatigue as new predictor in SA prognostic models by using risk reclassification methods and measures. METHODS: This was a prospective cohort study with 1-year follow-up of 1,137 office workers. Fatigue was measured at baseline with the 20-item checklist individual strength and added to the existing SA prognostic models. SA days and episodes during 1-year follow-up were retrieved from an occupational health service register. The added value of fatigue was investigated with Net Reclassification Index (NRI) and integrated discrimination improvement (IDI) measures. RESULTS: In total, 579 (51 %) office workers had complete data for analysis. Fatigue was prospectively associated with both high SA days and episodes. The NRI revealed that adding fatigue to the SA days model correctly reclassified workers with high SA days, but incorrectly reclassified workers without high SA days. The IDI indicated no improvement in risk discrimination by the SA days model. Both NRI and IDI showed that the prognostic model predicting high SA episodes did not improve when fatigue was added as predictor variable. CONCLUSION: In the present study, fatigue increased false-positive rates which may reduce the cost-effectiveness of interventions for preventing SA.

Mental health symptoms identify workers at risk of long-term sickness absence due to mental disorders: prospective cohort study with 2-year follow-up.

BACKGROUND: Mental health problems are a leading cause of long-term sickness absence (LTSA). Workers at risk of mental LTSA should preferably be identified before they report sick. The objective of this study was to examine mental health symptoms as predictors of future mental LTSA in non-sicklisted workers. METHODS: Prospective cohort study of 4877 non-sicklisted postal workers. Mental health symptoms were measured at baseline in November 2010 with the Four-Dimensional Symptom Questionnaire (distress and depressed mood) and Maslach's Burnout Inventory (fatigue). Mental health symptom scores were analyzed against incident mental LTSA retrieved from an occupational health register in 2011 and 2012. The area under the receiver operating characteristic curve (AUC) represented the ability of mental health symptom scores to discriminate between workers with and without mental LTSA during 2-year follow-up. RESULTS: Complete cases analysis included 2782 (57 %) postal workers of whom 73 had mental LTSA during 2-year follow-up. Distress fairly (AUC = 0.75; 95 % CI 0.67-0.82) and both depressed mood (AUC = 0.64; 95 % CI 0.57-0.72) and fatigue (AUC = 0.61; 95 % CI 0.53-0.69) poorly discriminated between workers with and without mental LTSA during 2-year follow-up. The discriminative ability of distress did not improve by adding depressed mood and fatigue. CONCLUSIONS: Measurement of distress sufficed to identify non-sicklisted postal workers at risk of future mental LTSA. The Four-Dimensional Symptom Questionnaire distress scale is a promising tool to screen working populations for of mental LTSA, which enables secondary preventive strategies.

Health measures in prediction models for high sickness absence: single-item self-rated health versus multi-item SF-12.

BACKGROUND: Self-rated health (SRH) has been found to predict sickness absence (SA). The present study investigated the effect of replacing single-item SRH by a multi-item health measure on SA predictions. METHODS: Longitudinal study of 2059 Norwegian nurses with assessments in three waves each separated by 1 year. Health was measured by single-item SRH and multi-item SF-12 in waves 1 and 2. SA was self-reported in all three waves and high SA was defined as more than or equal to 31 SA days within the last 12 months. Predictions of high SA by a model including age, prior SA and single-item SRH were compared with predictions by a model including age, prior SA and multi-item SF-12. Both models were bootstrapped to correct for over-optimism and prospectively validated for their predictions in a new time frame. RESULTS: 1253 nurses (61%) had complete data for analysis. The SF-12 model predicted the risk of high SA more accurately (χ(2) = 4.294; df = 8) and was more stable over time than the SRH model (model χ(2) = 14.495; df = 8). Both prediction models correctly discriminated between high-risk and low-risk individuals in 73% of the cases at wave 2 and in 71% of the cases at wave 3. CONCLUSIONS: The accuracy of predictions increased when single-item SRH was replaced by multi-item SF-12, but the discriminative ability did not improve. Single-item SRH suffices to identify employees at increased risk of high SA.

Recovery and recurrence of mental sickness absence among production and office workers in the industrial sector.

BACKGROUND: Manual workers in the public sector have previously been found to be at risk of mental sickness absence (SA). As the impact of mental illness differs across economic sectors, this study investigated mental SA in the industrial sector, differentiating between office and production workers. METHODS: Ten-year observational cohort study including 14 369 (8164 production and 6205 office) workers with a total of 101 118 person years. SA data were retrieved from an occupational health register. Mental SA episodes were medically certified as emotional disturbances [10th version of the International Classification of Diseases (ICD-10 R45)] or mental and behavioural disorders (ICD-10 F00-F99). The first mental SA episode since baseline was called index mental SA. Recurrences were defined as any mental SA episode occurring >28 days after recovery from index mental SA. RESULTS: The incidence of mental SA was higher in production workers than in office workers, but office workers needed longer time to recover from mental SA. Mental SA recurred as frequently in production workers as in office workers. The median time to recurrence was 15.9 months and tangibly shorter in office workers (14.9 months) than in production workers (16.7 months). Production and office workers aged >55 years were at increased risk of recurrent mental SA within 12 months of recovery from index mental SA. CONCLUSIONS: The incidence of mental SA was higher in production workers than in office workers, whereas recurrence rates did not differ between them. Occupational health providers should pay special attention to older workers as they are at increased risk of recurrent mental SA.

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

PURPOSE: To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. METHODS: 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). RESULTS: 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. CONCLUSIONS: The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.