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Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals

BACKGROUND: The knowledge of factors accurately predicting the long lasting sick leaves is sparse, but information on medical condition is believed to be necessary to identify persons at risk. Based on the current practice, with identifying sick-listed individuals at risk of long-lasting sick leaves...

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Autores principales: Fleten, Nils, Johnsen, Roar, Førde, Olav Helge
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC529266/
https://www.ncbi.nlm.nih.gov/pubmed/15476563
http://dx.doi.org/10.1186/1471-2458-4-46
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author Fleten, Nils
Johnsen, Roar
Førde, Olav Helge
author_facet Fleten, Nils
Johnsen, Roar
Førde, Olav Helge
author_sort Fleten, Nils
collection PubMed
description BACKGROUND: The knowledge of factors accurately predicting the long lasting sick leaves is sparse, but information on medical condition is believed to be necessary to identify persons at risk. Based on the current practice, with identifying sick-listed individuals at risk of long-lasting sick leaves, the objectives of this study were to inquire the diagnostic accuracy of length of sick leaves predicted in the Norwegian National Insurance Offices, and to compare their predictions with the self-predictions of the sick-listed. METHODS: Based on medical certificates, two National Insurance medical consultants and two National Insurance officers predicted, at day 14, the length of sick leave in 993 consecutive cases of sick leave, resulting from musculoskeletal or mental disorders, in this 1-year follow-up study. Two months later they reassessed 322 cases based on extended medical certificates. Self-predictions were obtained in 152 sick-listed subjects when their sick leave passed 14 days. Diagnostic accuracy of the predictions was analysed by ROC area, sensitivity, specificity, likelihood ratio, and positive predictive value was included in the analyses of predictive validity. RESULTS: The sick-listed identified sick leave lasting 12 weeks or longer with an ROC area of 80.9% (95% CI 73.7–86.8), while the corresponding estimates for medical consultants and officers had ROC areas of 55.6% (95% CI 45.6–65.6%) and 56.0% (95% CI 46.6–65.4%), respectively. The predictions of sick-listed males were significantly better than those of female subjects, and older subjects predicted somewhat better than younger subjects. Neither formal medical competence, nor additional medical information, noticeably improved the diagnostic accuracy based on medical certificates. CONCLUSION: This study demonstrates that the accuracy of a prognosis based on medical documentation in sickness absence forms, is lower than that of one based on direct communication with the sick-listed themselves.
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spelling pubmed-5292662004-11-19 Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals Fleten, Nils Johnsen, Roar Førde, Olav Helge BMC Public Health Research Article BACKGROUND: The knowledge of factors accurately predicting the long lasting sick leaves is sparse, but information on medical condition is believed to be necessary to identify persons at risk. Based on the current practice, with identifying sick-listed individuals at risk of long-lasting sick leaves, the objectives of this study were to inquire the diagnostic accuracy of length of sick leaves predicted in the Norwegian National Insurance Offices, and to compare their predictions with the self-predictions of the sick-listed. METHODS: Based on medical certificates, two National Insurance medical consultants and two National Insurance officers predicted, at day 14, the length of sick leave in 993 consecutive cases of sick leave, resulting from musculoskeletal or mental disorders, in this 1-year follow-up study. Two months later they reassessed 322 cases based on extended medical certificates. Self-predictions were obtained in 152 sick-listed subjects when their sick leave passed 14 days. Diagnostic accuracy of the predictions was analysed by ROC area, sensitivity, specificity, likelihood ratio, and positive predictive value was included in the analyses of predictive validity. RESULTS: The sick-listed identified sick leave lasting 12 weeks or longer with an ROC area of 80.9% (95% CI 73.7–86.8), while the corresponding estimates for medical consultants and officers had ROC areas of 55.6% (95% CI 45.6–65.6%) and 56.0% (95% CI 46.6–65.4%), respectively. The predictions of sick-listed males were significantly better than those of female subjects, and older subjects predicted somewhat better than younger subjects. Neither formal medical competence, nor additional medical information, noticeably improved the diagnostic accuracy based on medical certificates. CONCLUSION: This study demonstrates that the accuracy of a prognosis based on medical documentation in sickness absence forms, is lower than that of one based on direct communication with the sick-listed themselves. BioMed Central 2004-10-12 /pmc/articles/PMC529266/ /pubmed/15476563 http://dx.doi.org/10.1186/1471-2458-4-46 Text en Copyright © 2004 Fleten et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Fleten, Nils
Johnsen, Roar
Førde, Olav Helge
Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals
title Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals
title_full Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals
title_fullStr Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals
title_full_unstemmed Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals
title_short Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals
title_sort length of sick leave – why not ask the sick-listed? sick-listed individuals predict their length of sick leave more accurately than professionals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC529266/
https://www.ncbi.nlm.nih.gov/pubmed/15476563
http://dx.doi.org/10.1186/1471-2458-4-46
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