uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predicting return to work among sickness-certified patients in general practice: Properties of two assessment tools
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centrum för klinisk forskning i Sörmland (CKFD). (Centrum för klinisk forskning i Sörmland (CKFD))
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centrum för klinisk forskning i Sörmland (CKFD). Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine.
2014 (English)In: Upsala Journal of Medical Sciences, ISSN 0300-9734, E-ISSN 2000-1967, Vol. 119, no 3, 268-277 p.Article in journal (Refereed) Published
Abstract [en]

Abstract Aim. The purpose was to analyse the properties of two models for the assessment of return to work after sickness certification, a manual one based on clinical judgement including non-measurable information ('gut feeling'), and a computer-based one. Study population. All subjects aged 18 to 63 years, sickness-certified at a primary health care centre in Sweden during 8 months (n = 943), and followed up for 3 years. Methods. Baseline information included age, sex, occupational status, sickness certification diagnosis, full-time or part-time current sick-leave, and sick-leave days during the past year. Follow-up information included first and last day of each occurring sick spell. In the manual model all subjects were classified, based on baseline information and gut feeling, into a high-risk (n = 447) or a low-risk group (n = 496) regarding not returning to work when the present certificate expired. It was evaluated with a Cox's analysis, including time and return to work as dependent variables and risk group assignment as the independent variable, while in the computer-based model the baseline variables were entered as independent variables. Results. Concordance between actual return to work and return to work predicted by the analysis model was 73%-76% during the first 28-180 days in the manual model, and approximately 10% units higher in the computer-based model. Based on the latter, three nomograms were constructed providing detailed information on the probability of return to work. Conclusion. The computer-based model had a higher precision and gave more detailed information than the manual model.

Place, publisher, year, edition, pages
2014. Vol. 119, no 3, 268-277 p.
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:uu:diva-226087DOI: 10.3109/03009734.2014.922143ISI: 000340110800008PubMedID: 24873686OAI: oai:DiVA.org:uu-226087DiVA: diva2:723799
Available from: 2014-06-11 Created: 2014-06-11 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Early risk assessment of long-term sick leave among patients in primary health care: risk factors, assessment tools, multidisciplinary intervention, and patients’ views on sick leave conclusion
Open this publication in new window or tab >>Early risk assessment of long-term sick leave among patients in primary health care: risk factors, assessment tools, multidisciplinary intervention, and patients’ views on sick leave conclusion
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background. Long-term sick leave is one of the main risk factors for permanent exit out of the labour market. The longer the duration of sickness absence, the less likely sick leave conclusion.

Objectives and Methods. The aims were to analyse possible determinants of sick leave conclusion and their relative impacts, to analyse the properties of two models for the assessment of sick leave conclusion, to study the impact of a multidisciplinary vocational intervention for sick leave conclusion in a high-risk group for long-term sick leave compared to a matched-control group, and to compare the patients’ own assessment on chance to sick leave conclusion within 6 months with the assessment of a team of rehabilitation professionals. A prospective cohort study of 943 patients aged 18 to 63 years, sickness certified at a Primary Health Care Centre in Sweden during 8 months in 2004, and follow-up for three years.

Results. Significant determinants increasing time to sick leave conclusion were number of sick leave days the year before baseline, age and a psychiatric diagnosis (F in ICD-10). Concordance between actual sick leave conclusion and that predicted by a computer-based model was 73-76% during the first 28-180 days in a manual model, and approximately 10% units higher in a computer based model. Three nomograms provided detailed information on the probability on sick leave conclusion. Before intervention started, the rehabilitation group had a 73% higher sick leave conclusion rate than the control group but during the rehabilitation programme period, a 51% lower conclusion rate, and after there were no significant differences between the groups. The patients’ and the rehabilitation teams’ assessment scores were highly correlated (r=0.49).  

Conclusions. Previous sick leave was the most influential variable associated with sick leave conclusion. A computer- based assessment model gave more detailed information on sick leave conclusion than a manual model. A multidisciplinary intervention declined sick leave in a high-risk group for long-term sick leave but after intervention there was no difference between groups. Patients’ own view on sick leave conclusion was highly correlated to the assessment of professionals’.

 

Place, publisher, year, edition, pages
Uppsala: Avhandlingsproduktion, 2016. 87 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1194
Keyword
sick leave, sick leave conclusion, return to work, risk factors, assessment tool, multidisciplinary intervention, risk assessment, gut feeling
National Category
Medical and Health Sciences
Research subject
Medical Science
Identifiers
urn:nbn:se:uu:diva-280414 (URN)978-91-554-9508-4 (ISBN)
Public defence
2016-04-29, A1:107a, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (Swedish)
Opponent
Supervisors
Available from: 2016-04-07 Created: 2016-03-10 Last updated: 2016-04-12

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Authority records BETA

von Celsing, Anna-SophiaSvärdsudd, KurtWallman, Thorne

Search in DiVA

By author/editor
von Celsing, Anna-SophiaSvärdsudd, KurtWallman, Thorne
By organisation
Family Medicine and Preventive MedicineCentrum för klinisk forskning i Sörmland (CKFD)
In the same journal
Upsala Journal of Medical Sciences
Public Health, Global Health, Social Medicine and Epidemiology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 485 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf