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

Abstract

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.

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

“…Prediction models for risk of LTSA among non-sick-listed workers show adequate calibration but poor-to-moderate discrimination with AUCs varying between 0.68 (4) and 0.76 (6). Among sick-listed unemployed workers, Louwerse et al (8) reported discrimination by a three-predictor (educational level, expected sickness absence duration, and help-seeking ability) model, with an over-optimism adjusted AUC of 0.76. The Long-term sickness absence predictions present study confirms that workers with an increased risk of LTSA can be identified early after sick-listing.…”

“…LTSA is more prevalent and may be better predicted among workers who report sick. Recently, Louwerse et al ( 8 ) developed an LTSA prediction model for sick-listed individuals without an employment contract. In The Netherlands, unemployed individuals report sick to UWV, the Dutch social security agency.…”

Social Provision of Disabled People in Welfare States: the Scope of Social Benefits and Anti-Crisis Regulation. Part 1

“…Вероятность зачисления возрастает среди менее образованных, неженатых, вдовых, бездетных, представителей не европеоидной расы [17,28,6]. У лиц с высшим образованием и заявителей молодого возраста (от 20 до 34 лет) всё чаще встречаются расстройства нервной системы и психики 1 [22,24]. В целом лица моложе 45 лет, с лучшими профессиональными навыками для трудовой деятельности, реже зачисляются в программы [18].…”

Use of a Decision Support Tool on Prognosis of Work Ability in Work Disability Assessments: An Experimental Study Among Insurance Physicians

“…However, the longer individuals are absent from work, the less likely they are to return [42][43][44]. A prediction model for workers who have just recently been sick-listed can help occupational health professionals to target individuals at risk of long-term sickness absence and identify effective early interventions [45,46]. From a rehabilitation point of view, such a tool could have more impact.…”