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.
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.
Authors: Michael Von Korff; Paul Crane; Michael Lane; Diana L Miglioretti; Greg Simon; Kathleen Saunders; Paul Stang; Nancy Brandenburg; Ronald Kessler Journal: Pain Date: 2005-02 Impact factor: 6.961
Authors: Jon Michael Gran; Stein Atle Lie; Irene Øyeflaten; Ørnulf Borgan; Odd O Aalen Journal: BMC Public Health Date: 2015-10-23 Impact factor: 3.295