Factors leading to work absenteeism in Tunisian ankylosing spondylitis patients

Incorporating a Machine Learning Model into a Web-Based Administrative Decision Support Tool for Predicting Workplace Absenteeism

Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. In addition, employers typically spend a considerable amount of time managing employees who perform poorly. By using predictive analytics and machine learning algorithms, organizations can make better decisions, thereby increasing organizational productivity, reducing costs, and improving efficiency. Thus, in this paper we propose hybrid optimization methods in order to find the most parsimonious model for absenteeism classification. We utilized data from a Brazilian courier company. In order to categorize absenteeism classes, we preprocessed the data, selected the attributes via multiple methods, balanced the dataset using the synthetic minority over-sampling method, and then employed four methods of machine learning classification: Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), Artificial Neural Network (ANN), and Random Forest (RF). We selected the best model based on several validation scores, and compared its performance against the existing model. Furthermore, project managers may lack experience in machine learning, or may not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool supported by cognitive analytics management (CAM) theory. The web-based decision tool enables managers to make more informed decisions, and can be used without any prior knowledge of machine learning. Understanding absenteeism patterns can assist managers in revising policies or creating new arrangements to reduce absences in the workplace, financial losses, and the probability of economic insolvency.

Impact of biological therapy on work outcomes in patients with axial spondyloarthritis: results from the British Society for Rheumatology Biologics Register (BSRBR-AS) and meta-analysis

OBJECTIVES

To quantify, among patients with axial spondyloarthritis (axSpA), the benefit on work outcomes associated with commencing biologic therapy.

METHODS

The British Society for Rheumatology Biologics Register in Axial Spondyloarthritis (BSRBRAS) recruited patients meeting Assessment of SpondyloArthritis International Society criteria for axSpA naïve to biological therapy across 83 centres in Great Britain. Work outcomes (measured using the Work Productivity and Activity Impairment Index) were compared between those starting biological therapy at the time of recruitment and those not. Differences between treatment groups were adjusted using propensity score matching. Results from BSRBR-AS were combined with other studies in a meta-analysis to calculate pooled estimates.

RESULTS

Of the 577 participants in this analysis who were in employment, 27.9% were starting biological therapy at the time of recruitment. After propensity score adjustment, patients undergoing biological therapy, at 12-month follow-up, experienced significantly greater improvements (relative to non-biological therapy) in presenteeism (-9.4%, 95% CI -15.3% to -3.5%), overall work impairment (-13.9%, 95% CI -21.1% to -6.7%) and overall activity impairment (-19.2%, 95% CI -26.3% to -12.2%). There was no difference in absenteeism (-1.5%, 95% CI -8.0 to 4.9). Despite these improvements, impact on work was still greater in the biological treated cohort at follow-up. In the meta-analysis including 1109 subjects across observational studies and trials, treatment with biological therapy was associated with significantly greater improvements in presenteeism, work impairment and activity impairment, but there was no difference in absenteeism.

CONCLUSIONS

There is consistent evidence that treatment with biological therapy significantly improves work productivity and activity impairment in people with axSpA. However, there remain substantial unmet needs in relation to work.