These findings indicate that interventions for these patients should target fear of returning to work and illness perceptions about subjective health complaints.
BackgroundMulti-state models, as an extension of traditional models in survival analysis, have proved to be a flexible framework for analysing the transitions between various states of sickness absence and work over time. In this paper we study a cohort of work rehabilitation participants and analyse their subsequent sickness absence using Norwegian registry data on sickness benefits. Our aim is to study how detailed individual covariate information from questionnaires explain differences in sickness absence and work, and to use methods from causal inference to assess the effect of interventions to reduce sickness absence. Examples of the latter are to evaluate the use of partial versus full time sick leave and to estimate the effect of a cooperation agreement on a more inclusive working life.MethodsCovariate adjusted transition intensities are estimated using Cox proportional hazards and Aalen additive hazards models, while the effect of interventions are assessed using methods of inverse probability weighting and G-computation.ResultsResults from covariate adjusted analyses show great differences in sickness absence and work for patients with assumed high risk and low risk covariate characteristics, for example based on age, type of work, income, health score and type of diagnosis. Causal analyses show small effects of partial versus full time sick leave and a positive effect of having a cooperation agreement, with about 5 percent points higher probability of returning to work.ConclusionsDetailed covariate information is important for explaining transitions between different states of sickness absence and work, also for patient specific cohorts. Methods for causal inference can provide the needed tools for going from covariate specific estimates to population average effects in multi-state models, and identify causal parameters with a straightforward interpretation based on interventions.
BackgroundReturn to work (RTW) after long-term sick leave can be a long-lasting process where the individual may shift between work and receiving different social security benefits, as well as between part-time and full-time work. This is a challenge in the assessment of RTW outcomes after rehabilitation interventions. The aim of this study was to analyse the probability for RTW, and the probabilities of transitions between different benefits during a 4-year follow-up, after participating in a work-related rehabilitation program.MethodsThe sample consisted of 584 patients (66% females), mean age 44 years (sd = 9.3). Mean duration on various types of sick leave benefits at entry to the rehabilitation program was 9.3 months (sd = 3.4)]. The patients had mental (47%), musculoskeletal (46%), or other diagnoses (7%). Official national register data over a 4-year follow-up period was analysed. Extended statistical tools for multistate models were used to calculate transition probabilities between the following eight states; working, partial sick leave, full-time sick leave, medical rehabilitation, vocational rehabilitation, and disability pension; (partial, permanent and time-limited).ResultsDuring the follow-up there was an increased probability for working, a decreased probability for being on sick leave, and an increased probability for being on disability pension. The probability of RTW was not related to the work and benefit status at departure from the rehabilitation clinic. The patients had an average of 3.7 (range 0–18) transitions between work and the different benefits.ConclusionsThe process of RTW or of receiving disability pension was complex, and may take several years, with multiple transitions between work and different benefits. Access to reliable register data and the use of a multistate RTW model, makes it possible to describe the developmental nature and the different levels of the recovery and disability process.
Purpose The aim of this study was to examine if age, gender, medical diagnosis, occupation, and previous sick leave predicted different probabilities for being at work and for registered sickness benefits, and differences in the transitions between any of these states, for individuals that had participated in an interdisciplinary work-related rehabilitation program. Methods 584 individuals on long-term sickness benefits (mean 9.3 months, SD = 3.4) were followed with official register data over a 4-year period after a rehabilitation program. 66 % were female, and mean age was 44 years (SD = 9.3). The majority had a mental (47 %) or a musculoskeletal (46 %) diagnosis. 7 % had other diagnoses. Proportional hazards regression models were used to analyze prognostic factors for the probability of being on, and the intensity of transitions between, any of the following seven states during follow-up; working, partial sick leave, full sick leave, medical rehabilitation, vocational rehabilitation, partial disability pension (DP), and full DP. Results In a fully adjusted model; women, those with diagnoses other than mental and musculoskeletal, blue-collar workers, and those with previous long-term sick leave, had a lower probability for being at work and a higher probability for full DP during follow-up. DP was also associated with high age. Mental diagnoses gave higher probability for being on full sick leave, but not for transitions to full sick leave. Regression models based on transition intensities showed that risk factors for entering a given state (work or receiving sickness benefits) were slightly different from risk factors for leaving the same state. Conclusions The probabilities for working and for receiving sickness benefits and DP were dependent on gender, diagnoses, type of work and previous history of sick leave, as expected. The use of novel statistical methods to analyze factors predicting transition intensities have improved our understanding of how the processes to and from work, and to and from sickness benefits may differ between groups. Further research is required to understand more about differences in prognosis for return to work after intensive work-related rehabilitation efforts.
Background Systems for monitoring effectiveness and quality of rehabilitation services across health care levels are needed. The purpose of this study was to develop and pilot test a quality indicator set for rehabilitation of rheumatic and musculoskeletal diseases. Methods The set was developed according to the Rand/UCLA Appropriateness Method, which integrates evidence review, in-person multidisciplinary expert panel meetings and repeated anonymous ratings for consensus building. The quality indicators were pilot-tested for overall face validity and feasibility in 15 specialist and 14 primary care rehabilitation units. Pass rates (percentages of “yes”) of the indicators were recorded in telephone interviews with 29 unit managers (structure indicators), and 164 patients (process and outcome indicators). Time use and participants’ numeric rating of face validity (0–10, 10 = high validity) were recorded. Results Nineteen structure, 12 process and five outcome indicators were developed and piloted. Mean (range) sum pass rates for the structure, process and outcome indicators were 59%(84%), 66%(100%) and 84%(100%), respectively. Mean (range) face validity score for managers/patients was 8.3 (8)/7.9 (9), and mean answering time was 6.0/5.5 min. The final indicator set consists of 19 structure, 11 process and three outcome indicators. Conclusion To our knowledge this is the first quality indicator set developed for rehabilitation of rheumatic and musculoskeletal diseases. Good overall face validity and a feasible format indicate a set suitable for monitoring quality in rehabilitation. The variation in pass rates between centers indicates a potential for quality improvement in rheumatic and musculoskeletal rehabilitation in Norway. Electronic supplementary material The online version of this article (10.1186/s12913-019-4091-4) contains supplementary material, which is available to authorized users.
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