Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden
Abstract 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 osteoarth...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-07-01
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| Series: | BMC Musculoskeletal Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12891-021-04400-8 |
| _version_ | 1818907519144165376 |
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| author | Johanna Holm Paolo Frumento Gino Almondo Katalin Gémes Matteo Bottai Kristina Alexanderson Emilie Friberg Kristin Farrants |
| author_facet | Johanna Holm Paolo Frumento Gino Almondo Katalin Gémes Matteo Bottai Kristina Alexanderson Emilie Friberg Kristin Farrants |
| author_sort | Johanna Holm |
| collection | DOAJ |
| description | Abstract 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 %, nspells =8468) and a validation data set (nspells =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. |
| first_indexed | 2024-12-19T21:56:24Z |
| format | Article |
| id | doaj.art-510643f7313a4cfe8c6e030cb34f2c75 |
| institution | Directory Open Access Journal |
| issn | 1471-2474 |
| language | English |
| last_indexed | 2024-12-19T21:56:24Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Musculoskeletal Disorders |
| spelling | doaj.art-510643f7313a4cfe8c6e030cb34f2c752022-12-21T20:04:15ZengBMCBMC Musculoskeletal Disorders1471-24742021-07-012211910.1186/s12891-021-04400-8Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in SwedenJohanna Holm0Paolo Frumento1Gino Almondo2Katalin Gémes3Matteo Bottai4Kristina Alexanderson5Emilie Friberg6Kristin Farrants7Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska InstitutetDepartment of Political Sciences, University of PisaDivision of Insurance Medicine, Department of Clinical Neuroscience, Karolinska InstitutetDivision of Insurance Medicine, Department of Clinical Neuroscience, Karolinska InstitutetDivision of Biostatistics, Institute of Environmental Medicine, Karolinska InstitutetDivision of Insurance Medicine, Department of Clinical Neuroscience, Karolinska InstitutetDivision of Insurance Medicine, Department of Clinical Neuroscience, Karolinska InstitutetDivision of Insurance Medicine, Department of Clinical Neuroscience, Karolinska InstitutetAbstract 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 %, nspells =8468) and a validation data set (nspells =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.https://doi.org/10.1186/s12891-021-04400-8Knee osteoarthritisSick-leavePredictionSickness absenceDuration |
| spellingShingle | Johanna Holm Paolo Frumento Gino Almondo Katalin Gémes Matteo Bottai Kristina Alexanderson Emilie Friberg Kristin Farrants Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden BMC Musculoskeletal Disorders Knee osteoarthritis Sick-leave Prediction Sickness absence Duration |
| title | Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden |
| title_full | Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden |
| title_fullStr | Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden |
| title_full_unstemmed | Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden |
| title_short | Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden |
| title_sort | predicting the duration of sickness absence due to knee osteoarthritis a prognostic model developed in a population based cohort in sweden |
| topic | Knee osteoarthritis Sick-leave Prediction Sickness absence Duration |
| url | https://doi.org/10.1186/s12891-021-04400-8 |
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