The Demand-Control-Support model as a predictor of return to work.

The present study investigated work-related determinants of return to work. Our hypothesis was based on the strain hypothesis of the Demand-Control-Support model, which postulates a relation between job demands, job control and support at work on the one hand, and the aetiology of health complaints on the other hand. High demands were hypothesized to obstruct return to work, whereas high control and high support were thought to have a positive effect on return to work. This hypothesis was tested in a population of employees who were sick-listed for 6-8 weeks. Return to work, as operationalized by the categories (i) not working; (ii) return to work with adjustments; and (iii) full return to work, was determined 4 months after the onset of the sick leave. The hypothesis was tested by logistic regression analyses. High job demands were the least predictive of full return to work. However, the likelihood of employees with high job demands returning to work with adjustments was higher than the likelihood of them not working. Therefore, job demands might also work as a pressure to return to work (compare this with Smulders and Nijhuis, 1999). Furthermore, high skill discretion in combination with high job demands predicted working with adjustments in comparison with not working. Finally, high supervisor support was the most predictive of return to work without adjustments, and the least predictive of not working.

Longitudinal assessment of the development of diabetic polyneuropathy and associated risk factors.

AIMS: To longitudinally assess risk factors for diabetic polyneuropathy (DPN) severity, and to longitudinally assess risk factors for the change of DPN severity during 2-4 years of follow-up. METHODS: From 1995 to 1999, 486 Type 2 diabetes patients in general practice were examined annually with regard to DPN severity and its possible risk factors. DPN severity was assessed with a clinical neurological examination (CNE) which included pinprick sense, light touch sense, vibration sense and ankle jerk. Longitudinal (multivariate) linear associations of (change of) CNE score and predicting variables were analysed using multilevel analyses. RESULTS: In this population, 50% of participants were men and had a mean age of 65.4 years, almost one-third (31.7%) of the participants had a CNE score > 4 at baseline and were classified with DPN. CNE score significantly increased during follow-up. Among participants not graded with DPN at baseline, 21.3% progressed towards a CNE score > 4 after 3 years of follow-up. Longitudinal multivariate analyses showed that age, diabetes duration, HbA1c, height, body mass index and ankle-arm index together best predicted CNE score during follow-up. Change of CNE score during follow-up was best predicted by age, diabetes duration and HbA1c, with the latter being the strongest predictor. CONCLUSIONS: Although several factors are longitudinally associated with DPN, HbA1c, age and diabetes duration were the best predictors of CNE change during follow-up. Therefore, improving glycaemia remains an important amenable factor in preventing worsening of diabetic polyneuropathy.