Dataset Information

Long-term sickness absence in a working population: development and validation of a risk prediction model in a large Dutch prospective cohort.

ABSTRACT: BACKGROUND:Societal expenditures on work-disability benefits is high in most Western countries. As a precursor of long-term work restrictions, long-term sickness absence (LTSA) is under continuous attention of policy makers. Different healthcare professionals can play a role in identification of persons at risk of LTSA but are not well trained. A risk prediction model can support risk stratification to initiate preventative interventions. Unfortunately, current models lack generalizability or do not include a comprehensive set of potential predictors for LTSA. This study is set out to develop and validate a multivariable risk prediction model for LTSA in the coming year in a working population aged 45-64?years. METHODS:Data from 11,221 working persons included in the prospective Study on Transitions in Employment, Ability and Motivation (STREAM) conducted in the Netherlands were used to develop a multivariable risk prediction model for LTSA lasting ?28 accumulated working days in the coming year. Missing data were imputed using multiple imputation. A full statistical model including 27 pre-selected predictors was reduced to a practical model using backward stepwise elimination in a logistic regression analysis across all imputed datasets. Predictive performance of the final model was evaluated using the Area Under the Curve (AUC), calibration plots and the Hosmer-Lemeshow (H&L) test. External validation was performed in a second cohort of 5604 newly recruited working persons. RESULTS:Eleven variables in the final model predicted LTSA: older age, female gender, lower level of education, poor self-rated physical health, low weekly physical activity, high self-rated physical job load, knowledge and skills not matching the job, high number of major life events in the previous year, poor self-rated work ability, high number of sickness absence days in the previous year and being self-employed. The model showed good discrimination (AUC 0.76 (interquartile range 0.75-0.76)) and good calibration in the external validation cohort (H&L test: p =?0.41). CONCLUSIONS:This multivariable risk prediction model distinguishes well between older workers with high- and low-risk for LTSA in the coming year. Being easy to administer, it can support healthcare professionals in determining which persons should be targeted for tailored preventative interventions.

SUBMITTER: van der Burg LRA

PROVIDER: S-EPMC7227258 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Long-term sickness absence in a working population: development and validation of a risk prediction model in a large Dutch prospective cohort.

van der Burg Lennart R A LRA van Kuijk Sander M J SMJ Ter Wee Marieke M MM Heymans Martijn W MW de Rijk Angelique E AE Geuskens Goedele A GA Ottenheijm Ramon P G RPG Dinant Geert-Jan GJ Boonen Annelies A

BMC public health 20200515 1

<h4>Background</h4>Societal expenditures on work-disability benefits is high in most Western countries. As a precursor of long-term work restrictions, long-term sickness absence (LTSA) is under continuous attention of policy makers. Different healthcare professionals can play a role in identification of persons at risk of LTSA but are not well trained. A risk prediction model can support risk stratification to initiate preventative interventions. Unfortunately, current models lack generalizabili ...[more]

PMID: 32414410

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Project description:BackgroundLong-term sickness absence results in increased risks of permanent disability and a compromised quality of life. Return to work is an important factor in reducing these risks. Little is known about return to work factors for long-term sick-listed workers with subjective health complaints. The aim of this study was to evaluate prognostic factors for partial or full return to a paid job for at least 28 days for long-term sick-listed workers with subjective health complaints, and to compare these factors with those of workers with other disorders.MethodsData from a prospective cohort study of 213 participants with subjective health complaints and 1.037 reference participants were used. The participants answered a questionnaire after 84 weeks of sickness absence. Return to work was measured after one and two years. Univariable logistic regression analyses were performed (P ≤ 0.157) for variables per domain with return to work (i.e. demographic, socio-economic and work-related, health-related, and self-perceived ability). Subsequently, multivariable logistic regression analyses with backward selection (P ≤ 0.157) were performed. Remaining factors were combined in a multivariable and final model (P ≤ 0.05).ResultsBoth for workers with subjective health complaints and for the reference group, non-health-related factors remained statistically significant in the final model. This included receiving a partial or complete work disability benefit (partial: OR 0.62, 95% CI 0.26-1.47 and OR 0.69, 95% CI 0.43-1.12; complete: OR 0.24, 95% CI 0.10-0.58 and OR 0.12, 95% CI 0.07-0.20) and having a positive self-perceived possibility for return to work (OR 1.06, 95% CI 1.01-1.11 and OR 1.08, 95% CI 1.05-1.11).ConclusionsNon-health-related factors seem to be more important than health-related factors in predicting return to work after long-term sickness absence. Receiving a work disability benefit and having negative expectations for return to work seem to complicate return to work most for workers with subjective health complaints. With respect to return to work predictors, workers with subjective health complaints do not differ from the reference group.

Project description:ObjectivesThe number of sick-listed employees has increased dramatically worldwide. Therefore, many countries aim to stimulate early and sustainable return to work opportunities to obtain better health outcomes and lower costs for disability pensions. To effectively orientate resources to patients with a high risk of not resuming work spontaneously, it is necessary to screen patients early in their sickness absence process. In this study, we validate "Quickscan", a new instrument to assess return-to-work needs and to predict risks of long-term sick leave.MethodsAs part of the Quickscan validation process, we tested and compared the reliability and construct validity of the questionnaire in two different populations. First, we conducted a cross-sectional study in which the screening instrument was sent to sick-listed individuals in healthcare insurance. In a second cross-sectional study, sick-listed workers who consulted the occupational health physician for return-to-work assessment were asked to fill out the questionnaire. We compared both samples for descriptive statistics: frequencies, means and standard deviations. Reliability of the scales was calculated using Cronbach's alpha. Confirmatory factor analysis was performed to evaluate the construct (factorial) validity of the studied scales using software package AMOS 24.ResultsThe screening tool was shown to be an instrument with reliable scales (except for the perfectionism and health perception patient scale) in both populations. The construct validity was satisfactory: we found that the hypothesized measurement models with the theoretical factors fitted the data well in both populations. In the first sample, the model improved for scales concerning stressful life events and showed worse fit for person-related factors. Work-related factors and functioning factors both showed similar fit indices across samples. We found small differences in descriptive statistics, which we could explain by the differences in characteristics of both populations.ConclusionsWe can conclude that the instrument has considerable potential to function as a screening tool for disability management and follow-up of sick-leave, provided that some adaptations and validation tests are executed.

Project description:BackgroundMusculoskeletal disorders are the leading cause of sickness absence and disability pension in Norway. There is strong evidence that long-term sickness absence due to musculoskeletal disorders are associated with a reduced probability of return to work (RTW). A way to meet the economic and resource-demanding challenges related to individual follow-up of this group is to identify and treat those individuals with a high risk of prolonged sickness. The overall purposes of this project are 1) to determine the most accurate screening tool to identify people at a high risk of prolonged sickness absence due to an musculoskeletal disorder, and 2) to investigate severity of musculoskeletal health, health-related quality-of-life, health care utilization, and costs across different risk profiles in people on sick leave due to a musculoskeletal disorder.MethodsPeople older than 18 years of age on sick leave for at least 4 weeks due to a musculoskeletal disorder will be invited to participate in this prospective observational cohort study conducted within the Norwegian Welfare and Labor Administration (NAV) system in collaboration with OsloMet - Oslo Metropolitan University. The main outcome is sickness absence, obtained from the NAV registry. Data on sickness absence will be retrieved prospectively in the period from study inclusion to 12 months follow-up, and retrospectively 12 months before inclusion in the study. Possible risk factors will be self-reported by the participants at inclusion while health care utilization will be retrieved from registry data. To conduct analyses including 15 to 20 predictor variables, we aim at including 500-600 people on sick leave due to musculoskeletal disorders.DiscussionThis study may provide tools that can be used to identify individuals with high risk of prolonged sickness absence and may thus be important from both a socioeconomic and individual perspective. Further, the study may give valuable insight into identification of sickness absence profiles and the associations between these profiles and musculoskeletal health status, health-related quality of life and costs.Trial registrationRetrospectively registered in ClinicalTrials.gov (NCT04196634, 27.11.2019).

Dataset Information

Long-term sickness absence in a working population: development and validation of a risk prediction model in a large Dutch prospective cohort.

Publications

Long-term sickness absence in a working population: development and validation of a risk prediction model in a large Dutch prospective cohort.

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