Katalin Gémes1, Paolo Frumento2, Gino Almondo1, Matteo Bottai2, Johanna Holm1, Kristina Alexanderson3, Emilie Friberg4. 1. Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden. 2. Unit of Biostatistics, Department of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden. 3. Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden. Electronic address: kristina.alexanderson@ki.se. 4. Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden. Electronic address: emilie.friberg@ki.se.
Abstract
BACKGROUND: Stress-related disorders are leading causes of long-term sickness absence (SA) and there is a great need for decision support tools to identify patients with a high risk for long-term SA due to them. AIMS: To develop a clinically implementable prediction model for the duration of SA due to stress-related disorders. METHODS: All new SA spells with F43 diagnosis code lasting >14 days and initiated between 2010-01-01 and 2012-06-30 were identified through data from the Social Insurance Agency. Information on baseline predictors was linked on individual level from other nationwide registers. Piecewise-constant hazard regression was used to predict the duration of the SA. Split-sample validation was used to develop and validate the model, and c-statistics and calibration plots to evaluate it. RESULTS: Overall 83,443 SA spells, belonging to 77,173 individuals were identified. The median SA duration was 55 days (10% were >365 days). Age, sex, geographical region, employment status, educational level, extent of SA at start and SA days, outpatient healthcare visits, and multi-morbidity in the preceding 365 days were selected to the final model. The model was well calibrated. The overall c-statistics was 0.54 (95% confidence intervals: 0.53-0.54) and 0.70 (95% confidence intervals: 0.69-0.71) for predicting SA spells >365 days. LIMITATIONS: The heterogeneity of the F43-diagnosis and the exclusive use of register-based predictors limited our possibility to increase the discriminatory accuracy of the prediction. CONCLUSION: The final model could be implementable in clinical settings to predict duration of SA due to stress-related disorders and could satisfyingly discriminate long-term SA.
BACKGROUND: Stress-related disorders are leading causes of long-term sickness absence (SA) and there is a great need for decision support tools to identify patients with a high risk for long-term SA due to them. AIMS: To develop a clinically implementable prediction model for the duration of SA due to stress-related disorders. METHODS: All new SA spells with F43 diagnosis code lasting >14 days and initiated between 2010-01-01 and 2012-06-30 were identified through data from the Social Insurance Agency. Information on baseline predictors was linked on individual level from other nationwide registers. Piecewise-constant hazard regression was used to predict the duration of the SA. Split-sample validation was used to develop and validate the model, and c-statistics and calibration plots to evaluate it. RESULTS: Overall 83,443 SA spells, belonging to 77,173 individuals were identified. The median SA duration was 55 days (10% were >365 days). Age, sex, geographical region, employment status, educational level, extent of SA at start and SA days, outpatient healthcare visits, and multi-morbidity in the preceding 365 days were selected to the final model. The model was well calibrated. The overall c-statistics was 0.54 (95% confidence intervals: 0.53-0.54) and 0.70 (95% confidence intervals: 0.69-0.71) for predicting SA spells >365 days. LIMITATIONS: The heterogeneity of the F43-diagnosis and the exclusive use of register-based predictors limited our possibility to increase the discriminatory accuracy of the prediction. CONCLUSION: The final model could be implementable in clinical settings to predict duration of SA due to stress-related disorders and could satisfyingly discriminate long-term SA.
Authors: Annina Ropponen; Mo Wang; Jurgita Narusyte; Sanna Kärkkäinen; Victoria Blom; Pia Svedberg Journal: BMC Health Serv Res Date: 2021-04-07 Impact factor: 2.655
Authors: Annina Ropponen; Jurgita Narusyte; Mo Wang; Sanna Kärkkäinen; Lisa Mather; Victoria Blom; Gunnar Bergström; Pia Svedberg Journal: Int Arch Occup Environ Health Date: 2021-12-28 Impact factor: 2.851