Predicting Long-Term Sickness Absence and Identifying Subgroups Among Individuals Without an Employment Contract.

Purpose Today, decreasing numbers of workers in Europe are employed in standard employment relationships. Temporary contracts and job insecurity have become more common. This study among workers without an employment contract aimed to (i) predict risk of long-term sickness absence and (ii) identify distinct subgroups of sick-listed workers. Methods 437 individuals without an employment contract who were granted a sickness absence benefit for at least two weeks were followed for 1 year. We used registration data and self-reported questionnaires on sociodemographics, work-related, health-related and psychosocial factors. Both were retrieved from the databases of the Dutch Social Security Institute and measured at the time of entry into the benefit. We used logistic regression analysis to identify individuals at risk of long-term sickness absence. Latent class analysis was used to identify homogenous subgroups of individuals. Results Almost one-third of the study population (n = 133; 30%) was still at sickness absence at 1-year follow-up. The final prediction model showed fair discrimination between individuals with and without long-term sickness absence (optimism adjusted AUC to correct for overfitting = 0.761). Four subgroups of individuals were identified based on predicted risk of long-term sickness absence, self-reported expectations about recovery and return to work, reason of sickness absence and coping skills. Conclusion The logistic regression model could be used to identify individuals at risk of long-term sickness absence. Identification of risk groups can aid professionals to offer tailored return to work interventions.

Characteristics of individuals receiving disability benefits in the Netherlands and predictors of leaving the disability benefit scheme: a retrospective cohort study with five-year follow-up.

BACKGROUND: Today, work disability is one of the greatest social and labour market challenges for policy makers in most OECD countries, where on average, about 6% of the working-age population relies on disability benefits. Understanding of factors associated with long-term work disability may be helpful to identify groups of individuals at risk for disability benefit entitlement or continuing eligibility, and to develop effective interventions for these groups. The purpose of this study is to provide insight into the main diagnoses of workers who qualify for disability benefits and how these diagnoses differ in age, gender and education. Using a five-year follow-up, we examined the duration of disability benefits and how durations differ among individuals with various characteristics. METHODS: We performed a cohort study of 31,733 individuals receiving disability benefits from the Dutch Social Security Institute (SSI) with a five-year follow-up. Data were collected from SSI databases. Information about disorders was assessed by an insurance physician upon benefit application. These data were used to test for significant relationships among socio-demographics, main diagnoses and comorbidity, and disability benefit entitlement and continuing eligibility. RESULTS: Mental disorders were the most frequent diagnosis for individuals claiming work disability. Diagnoses differed among age groups and education categories. Mental disorders were the main diagnosis for work disability for younger and more highly educated individuals, and physical disorders (generally musculoskeletal, cardiovascular and cancer) were the main diagnosis for older and less educated individuals. In 82% of the claims, the duration of disability benefit was five years or more after approval. Outflow was lowest for individuals with (multiple) mental disorders and those with comorbidity of mental and physical disorders, and highest for individuals with (multiple) physical disorders. CONCLUSIONS: The main diagnosis for persons entitled to disability benefits was mental health problems, especially for young women. In a five-year follow-up, claim duration for disability benefits was long lasting for most claimants.

Preferences regarding the way of use and design of a work ability prognosis support tool: a focus group study among professionals.

PURPOSE: To explore the preferable way of use and design of a work ability prognosis support tool for insurance physicians (IPs) and labour experts (LEs), based on a prediction model for future changes in work ability among individuals applying for a work disability benefit. METHODS: We conducted three focus groups with professionals of the Dutch Social Security Institute (17 IPs and 7 LEs). Data were audio recorded and qualitatively analysed according to the main principles of thematic analysis. RESULTS: Clarity and ease of use were mentioned as important features of the tool. Most professionals preferred to make their own judgement during the work disability assessment interview with the claimant and afterwards verify their evaluation with the tool. Concerning preferences on the design of the tool, dividing work disability claimants into categories based on the outcome of the prediction model was experienced as the most straightforward and clear way of presenting the results. Professionals expected that this encourages them to use the tool and act accordingly. CONCLUSIONS: The tool should be easy to access and interpret, to increase the chance that professionals will use it. This way it can optimally help professionals making accurate prognoses of future changes in work ability.Implications for rehabilitationA work ability prognosis support tool based on a prediction model for changes in work ability at one-year follow-up can help occupational health professionals in making accurate prognosis of individuals applying for a work disability benefit.To be used in occupational health practice, these tools should have a simple and easy-to-use design.Graphical risk presentation can be used to provide intuitive meaning to numerical information and support users' understanding.Taking professionals' preferences into account when developing these tools encourages professionals to use the tools and act accordingly.

Association between retirement and mortality: working longer, living longer? A systematic review and meta-analysis.

AIM: This study summarised available evidence on the association between early and on-time retirement, compared with continued working, and mortality. Moreover, this study investigated whether and to what extent gender, adjustment for demographics and prior health status influence this association. METHODS: A systematic literature search of longitudinal studies was conducted. A qualitative analysis of the included studies was performed, followed by a meta-regression analysis to assess the influence of gender, prior health and demographics. Random-effects models were used in a meta-analysis to estimate the pooled effects for relevant subgroups identified in the meta-regression. RESULTS: In total, 25 studies were included. Adjustment for prior health and demographics influenced the association between retirement and mortality (p<0.05). The results of the meta-analysis of 12 studies are presented for 'insufficiently adjusted' and 'fully adjusted' subgroups. There was no association between early retirement and mortality compared with working until retirement (fully adjusted subgroup: HR 1.05, 95% CI 0.87 to 1.28). On-time retirement was associated with a higher risk of mortality compared with working beyond retirement (insufficiently adjusted subgroup: HR 1.56, 95% CI 1.41 to 1.73). However, in the subgroup that adjusted for prior health, on-time retirement was not associated with mortality (HR 1.12, 95% CI 0.98 to 1.28). CONCLUSION: Early retirement was not associated with a higher risk of mortality. On-time retirement was associated with a higher risk of mortality, which might reflect the healthy worker effect. It is important to consider information on prior health and demographics when studying the association between retirement and mortality to avoid biased findings.

Predicting future changes in the work ability of individuals receiving a work disability benefit: weighted analysis of longitudinal data.

Objectives Weighted regression procedures can be an efficient solution for cohort studies that involve rare events or diseases, which can be difficult to predict, allowing for more accurate prediction of cases of interest. The aims of this study were to (i) predict changes in work ability at one year after approval of the work disability benefit and (ii) explore whether weighted regression procedures could improve the accuracy of predicting claimants with the highest probability of experiencing a relevant change in work ability. Methods The study population consisted of 944 individuals who were granted a work disability benefit. Self-reported questionnaire data measured at baseline were linked with administrative data from Dutch Social Security Institute databases. Standard and weighted multinomial logit models were fitted to predict changes in the work ability score (WAS) at one-year follow-up. McNemar's test was used to assess the difference between these models. Results A total of 208 (22%) claimants experienced an improvement in WAS. The standard multinomial logit model predicted a relevant improvement in WAS for only 9% of the claimants [positive predictive value (PPV) 62%]. The weighted model predicted significantly more cases, 14% (PPV 63%). Predictive variables were several physical and mental functioning factors, work status, wage loss, and WAS at baseline. Conclusion This study showed that there are indications that weighted regression procedures can correctly identify more individuals who experience a relevant change in WAS compared to standard multinomial logit models. Our findings suggest that weighted analysis could be an effective method in epidemiology when predicting rare events or diseases.

Does self-perceived health correlate with physician-assessed functional limitations in medical work disability assessments?

OBJECTIVE: Our purpose was to obtain information about the correlation between workers' self-perceived health and physician-assessed functional limitations. We also studied whether this correlation differed between workers with subjective health complaints that cannot (SHC) and those that can be explained (non-SHC) by a well-defined medical disease. METHODS: Baseline data of 2040 participants from a prospective cohort study were used for this study. These participants answered a questionnaire on their self-perceived health and received a medical work disability assessment during which physicians reported functional limitations. Pearson correlation analyses were used to calculate correlations between 4 functional limitation factors and 11 self-perceived health factors. For correlations with coefficients ≥0.30, linear regression analyses were performed to assess possible differences between participants with SHC (n = 363) and those with non-SHC (n = 1677). RESULTS: We found correlations ≥0.30 between two functional limitation factors and six self-perceived health factors for all participants. SHC participants showed lower correlations than the non-SHC participants between the physical functional limitation and the SF-36 self-perceived physical health factors (-0.49, 95% CI -0.56 to -0.41 vs. -0.60, 95% CI -0.62 to -0.57) and between the mental functional limitation and the SF-36 self-perceived mental health factors (-0.30, 95% CI -0.39 to -0.20 vs. -0.40, 95% CI -0.44 to -0.36). CONCLUSION: Self-perceived health showed overall low to moderate correlations with physician-assessed functional limitations. Some of these correlations were lower for workers with SHC than for those with non-SHC. This may indicate that physicians rely slightly more on well-defined medical complaints within medical work disability assessments.