Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden

BACKGROUND: Predicting the duration of sickness absence (SA) among sickness absent patients is a task many sickness certifying physicians as well as social insurance officers struggle with. Our aim was to develop a prediction model for prognosticating the duration of SA due to knee osteoarthritis. METHODS: A population-based prospective study of SA spells was conducted using comprehensive microdata linked from five Swedish nationwide registers. All 12,098 new SA spells > 14 days due to knee osteoarthritis in 1/1 2010 through 30/6 2012 were included for individuals 18–64 years. The data was split into a development dataset (70 %, n(spells) =8468) and a validation data set (n(spells) =3690) for internal validation. Piecewise-constant hazards regression was performed to prognosticate the duration of SA (overall duration and duration > 90, >180, or > 365 days). Possible predictors were selected based on the log-likelihood loss when excluding them from the model. RESULTS: Of all SA spells, 53 % were > 90 days and 3 % >365 days. Factors included in the final model were age, sex, geographical region, extent of sickness absence, previous sickness absence, history of specialized outpatient healthcare and/or inpatient healthcare, employment status, and educational level. The model was well calibrated. Overall, discrimination was poor (c = 0.53, 95 % confidence interval (CI) 0.52–0.54). For predicting SA > 90 days, discrimination as measured by AUC was 0.63 (95 % CI 0.61–0.65), for > 180 days, 0.69 (95 % CI 0.65–0.71), and for SA > 365 days, AUC was 0.75 (95 % CI 0.72–0.78). CONCLUSION: It was possible to predict patients at risk of long-term SA (> 180 days) with acceptable precision. However, the prediction of duration of SA spells due to knee osteoarthritis has room for improvement. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-021-04400-8.