Cargando…

Prediction of future labour market outcome in a cohort of long-term sick- listed Danes

BACKGROUND: Targeted interventions for the long-term sick-listed may prevent permanent exclusion from the labour force. We aimed to develop a prediction method for identifying high risk groups for continued or recurrent long-term sickness absence, unemployment, or disability among persons on long-te...

Descripción completa

Detalles Bibliográficos
Autores principales: Pedersen, Jacob, Gerds, Thomas Alexander, Bjorner, Jakob Bue, Christensen, Karl Bang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4055224/
https://www.ncbi.nlm.nih.gov/pubmed/24885866
http://dx.doi.org/10.1186/1471-2458-14-494
_version_ 1782320622599143424
author Pedersen, Jacob
Gerds, Thomas Alexander
Bjorner, Jakob Bue
Christensen, Karl Bang
author_facet Pedersen, Jacob
Gerds, Thomas Alexander
Bjorner, Jakob Bue
Christensen, Karl Bang
author_sort Pedersen, Jacob
collection PubMed
description BACKGROUND: Targeted interventions for the long-term sick-listed may prevent permanent exclusion from the labour force. We aimed to develop a prediction method for identifying high risk groups for continued or recurrent long-term sickness absence, unemployment, or disability among persons on long-term sick leave. METHODS: We obtained individual characteristics and follow-up data from the Danish Register of Sickness Absence Compensation Benefits and Social Transfer Payments (RSS) during 2004 to 2010 for 189,279 Danes who experienced a period of long-term sickness absence (4+ weeks). In a learning data set, statistical prediction methods were built using logistic regression and a discrete event simulation approach for a one year prediction horizon. Personalized risk profiles were obtained for five outcomes: employment, unemployment, recurrent sickness absence, continuous long-term sickness absence, and early retirement from the labour market. Predictor variables included gender, age, socio-economic position, job type, chronic disease status, history of sickness absence, and prior history of unemployment. Separate models were built for times of economic growth (2005–2007) and times of recession (2008–2010). The accuracy of the prediction models was assessed with analyses of Receiver Operating Characteristic (ROC) curves and the Brier score in an independent validation data set. RESULTS: In comparison with a null model which ignored the predictor variables, logistic regression achieved only moderate prediction accuracy for the five outcome states. Results obtained with discrete event simulation were comparable with logistic regression. CONCLUSIONS: Only moderate prediction accuracy could be achieved using the selected information from the Danish register RSS. Other variables need to be included in order to establish a prediction method which provides more accurate risk profiles for long-term sick-listed persons.
format Online
Article
Text
id pubmed-4055224
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-40552242014-06-23 Prediction of future labour market outcome in a cohort of long-term sick- listed Danes Pedersen, Jacob Gerds, Thomas Alexander Bjorner, Jakob Bue Christensen, Karl Bang BMC Public Health Research Article BACKGROUND: Targeted interventions for the long-term sick-listed may prevent permanent exclusion from the labour force. We aimed to develop a prediction method for identifying high risk groups for continued or recurrent long-term sickness absence, unemployment, or disability among persons on long-term sick leave. METHODS: We obtained individual characteristics and follow-up data from the Danish Register of Sickness Absence Compensation Benefits and Social Transfer Payments (RSS) during 2004 to 2010 for 189,279 Danes who experienced a period of long-term sickness absence (4+ weeks). In a learning data set, statistical prediction methods were built using logistic regression and a discrete event simulation approach for a one year prediction horizon. Personalized risk profiles were obtained for five outcomes: employment, unemployment, recurrent sickness absence, continuous long-term sickness absence, and early retirement from the labour market. Predictor variables included gender, age, socio-economic position, job type, chronic disease status, history of sickness absence, and prior history of unemployment. Separate models were built for times of economic growth (2005–2007) and times of recession (2008–2010). The accuracy of the prediction models was assessed with analyses of Receiver Operating Characteristic (ROC) curves and the Brier score in an independent validation data set. RESULTS: In comparison with a null model which ignored the predictor variables, logistic regression achieved only moderate prediction accuracy for the five outcome states. Results obtained with discrete event simulation were comparable with logistic regression. CONCLUSIONS: Only moderate prediction accuracy could be achieved using the selected information from the Danish register RSS. Other variables need to be included in order to establish a prediction method which provides more accurate risk profiles for long-term sick-listed persons. BioMed Central 2014-05-23 /pmc/articles/PMC4055224/ /pubmed/24885866 http://dx.doi.org/10.1186/1471-2458-14-494 Text en Copyright © 2014 Pedersen 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 credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Pedersen, Jacob
Gerds, Thomas Alexander
Bjorner, Jakob Bue
Christensen, Karl Bang
Prediction of future labour market outcome in a cohort of long-term sick- listed Danes
title Prediction of future labour market outcome in a cohort of long-term sick- listed Danes
title_full Prediction of future labour market outcome in a cohort of long-term sick- listed Danes
title_fullStr Prediction of future labour market outcome in a cohort of long-term sick- listed Danes
title_full_unstemmed Prediction of future labour market outcome in a cohort of long-term sick- listed Danes
title_short Prediction of future labour market outcome in a cohort of long-term sick- listed Danes
title_sort prediction of future labour market outcome in a cohort of long-term sick- listed danes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4055224/
https://www.ncbi.nlm.nih.gov/pubmed/24885866
http://dx.doi.org/10.1186/1471-2458-14-494
work_keys_str_mv AT pedersenjacob predictionoffuturelabourmarketoutcomeinacohortoflongtermsicklisteddanes
AT gerdsthomasalexander predictionoffuturelabourmarketoutcomeinacohortoflongtermsicklisteddanes
AT bjornerjakobbue predictionoffuturelabourmarketoutcomeinacohortoflongtermsicklisteddanes
AT christensenkarlbang predictionoffuturelabourmarketoutcomeinacohortoflongtermsicklisteddanes