Predictors of Sickness Absence in a Clinical Population With Chronic Pain

Abstract

Chronic pain-related sickness absence is an enormous socioeconomic burden globally.Optimized interventions are reliant on a lucid understanding of the distribution of social insurance benefits and their predictors. This register-based observational study analyzed data for a 7-year period from a population-based sample of 44,241 chronic pain patients eligible for interdisciplinary treatment (IDT) at specialist clinics. Sequence analysis was used to describe the sickness absence over the complete period and to separate the patients into subgroups based on their social insurance benefits over the final 2 years. The predictive performance of features from various domains was then explored with machine learning-based modeling in a nested cross-validation procedure. Our results showed that patients on sickness absence increased from 17% 5 years before to 48% at the time of the IDT assessment, and then decreased to 38% at the end of follow-up. Patients were divided into 3 classes characterized by low sickness absence, sick leave, and disability pension, with eight predictors of class membership being identified. Sickness absence history was the strongest predictor of future sickness absence, while other predictors included a 2008 policy, age, confidence in recovery, and geographical location. Information on these features could guide personalized intervention in the specialized healthcare.Perspective: This study describes sickness absence in patients who visited a Swedish pain specialist interdisciplinary treatment clinic during the period 2005 to 2016. Predictors of future sickness absence are also identified that should be considered when adapting IDT programs to the patient's needs.

The value of interdisciplinary treatment for sickness absence in chronic pain: A nationwide register‐based cohort study

“…In what follows, we describe the support for the relationship of the confounders to the intervention and the outcome. History of sickness absence is both an indicator of IDT and a strong predictor of future sickness absence, while policy differences result in geographical and temporal variations of the two (Bramberg et al, 2015;Dorner et al, 2015;LoMartire et al, 2021;Ropponen et al, 2020;Wallman et al, 2009). With respect to sociodemographics, both healthcare consumption and sickness absence reportedly increase with age and female sex, while socioeconomic status is inversely associated with both the likelihood to receive adequate healthcare and sickness absence (Adler & Newman, 2002;Cylus et al, 2011;Dorner et al, 2015;Lager et al, 2019;LoMartire et al, 2021;Mastekaasa & Melsom, 2014;Moscelli et al, 2018;Patton & Johns, 2007;Ropponen et al, 2020;Wallman et al, 2009;Wang et al, 2013).…”

“…History of sickness absence is both an indicator of IDT and a strong predictor of future sickness absence, while policy differences result in geographical and temporal variations of the two (Bramberg et al, 2015;Dorner et al, 2015;LoMartire et al, 2021;Ropponen et al, 2020;Wallman et al, 2009). With respect to sociodemographics, both healthcare consumption and sickness absence reportedly increase with age and female sex, while socioeconomic status is inversely associated with both the likelihood to receive adequate healthcare and sickness absence (Adler & Newman, 2002;Cylus et al, 2011;Dorner et al, 2015;Lager et al, 2019;LoMartire et al, 2021;Mastekaasa & Melsom, 2014;Moscelli et al, 2018;Patton & Johns, 2007;Ropponen et al, 2020;Wallman et al, 2009;Wang et al, 2013). As emphasized in ICD-11, emotional distress and pain interference in everyday activities are critical dimensions of chronic pain, and both of these are positively associated with both IDT and sickness absence (Gerdle et al, 2011;Hallman et al, 2019;Svebak & Halvari, 2018;Treede et al, 2019).…”

“…As emphasized in ICD-11, emotional distress and pain interference in everyday activities are critical dimensions of chronic pain, and both of these are positively associated with both IDT and sickness absence (Gerdle et al, 2011;Hallman et al, 2019;Svebak & Halvari, 2018;Treede et al, 2019). Finally, confidence in recovery is an indicator of IDT that reportedly also influences sickness absence (Gerdle et al, 2011;Kuijer et al, 2006;LoMartire et al, 2021;Main et al, 2010;Rotger & Rosholm, 2020).…”

Evidence-based digital support during 1 year after an Interdisciplinary Pain Rehabilitation Programme for persons with chronic musculoskeletal pain to facilitate a sustainable return to work: a study protocol for a registry-based multicentre randomised controlled trial

“…One of the researchers (CT) will enrol and randomise participants who have given informed consent (S3) to participate in the study to either the intervention or control group. As sick leave history is a strong predictor for future sick leave45 participants will be stratified into high or low sick leave history based on self-reported number of sick leave days during the year before IPRP. It has been shown that patients with low sick leave history to a larger extent are younger, have an employment, higher education and are more confident regarding recovery 45.…”

“…As sick leave history is a strong predictor for future sick leave45 participants will be stratified into high or low sick leave history based on self-reported number of sick leave days during the year before IPRP. It has been shown that patients with low sick leave history to a larger extent are younger, have an employment, higher education and are more confident regarding recovery 45. Participants will therefore be divided in high (total number of gross sick leave days ≥70) or low sick leave absence45 and then randomised to intervention or control group.…”