Doctors' prediction of certified sickness absence.

BACKGROUND Doctors' ability to predict the duration of their patients' certified sickness absence is important for follow-up efforts aimed at patients with increased probability of long-term absence. OBJECTIVES The aim of this study was to examine the accuracy of doctors' predictions of their patients' sickness absence status 4 weeks ahead, and which factors were associated with it. METHODS A questionnaire survey was carried out in primary health care concerning 796 patients certified sick within 140 days after the start of absence. The episodes of absence were labelled short-standing (up to 2 weeks) and long-standing (from 3 to 20 weeks), at the time of consultation. The doctors' prediction of the patients' absence status 4 weeks ahead, diagnoses, work ability, clinical information sources used and the presence of non-medical factors that could have influenced the doctors' work ability assessments were collected. The predictions were compared with the patients' absence status 4 weeks later by positive predictive values (PPVs) for the statements 'returned to work' and 'still certified sick'. Factors associated with the accuracy of the predictions were analysed by multiple logistic regression analyses. RESULTS The doctors accurately predicted return to work in 84% [95% confidence interval (CI) 79-87] of the cases in short-standing episodes, and in 53% (43-62) in long-standing episodes. The corresponding PPVs for still certified sick were 72% (62-80) and 91% (85-94). In short-standing episodes, the doctors' probability of making accurate predictions was higher for respiratory disorders [odds ratio (OR) 2.84; 95% CI 1.36-5.90], than for the reference category 'all other disorders', and lower for mental disorders (0.46; 0.24-0.89). In long-standing episodes, the probability was lower for musculoskeletal disorders (0.33; 0.12-0.86) and injuries (0.12; 0.03-0.48). Neither the age nor gender of patients or doctors, nor the degree of work ability reduction, nor other factors were associated with the accuracy of the predictions. CONCLUSIONS The doctors' predictions were highly accurate for return to work in short-standing episodes, and for still certified sick in long-standing episodes. Diagnoses were associated with the accuracy; other factors, including the doctors' work ability assessments, were not.