Evidence map›Paper›PMID 23144260›Full record

ArticleBMJ open2012

Partial sick leave associated with disability pension: propensity score approach in a register-based cohort study.

Johanna Kausto, Svetlana Solovieva, Lauri J Virta, Eira Viikari-Juntura

Erratum issuedAbstract read
In one paragraph

Article in BMJ open, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Johanna KaustoFinnish Institute of Occupational Health, Helsinki, Finland.
Svetlana Solovieva
Lauri J Virta
Eira Viikari-Juntura

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo support sustainability of the welfare society enhanced work retention is needed among those with impaired work ability. Partial health-related benefits have been introduced for this target. The aim was to estimate the effects of partial sick leave on transition to disability pension applying propensity score methods.

designRegister-based cohort study.

settingSample from the national sickness insurance registers representative of the Finnish working population (full-time workers) with long-term sickness absence due to musculoskeletal disorders, mental disorders, traumas or tumours.

participantsAll recipients of partial or full sickness benefit whose sick leave period had ended between 1 May and 31 December 2007 were included. The sample was limited to four most prevalent diagnostic groups-mental and musculoskeletal disorders, traumas and tumours. The total sample consisted of 1047 subjects on partial sick leave (treatment group) and 28 380 subjects on full sick leave (control group). A subsample (1017 and 25 249 subjects, respectively) was formed to improve the comparability of the two groups. OUTCOME MEASURES: A three-category measure and a binary measure for the occurrence of disability pension on the last day of 2008 were computed.

resultsPartial sickness benefit reduced the risk (change in absolute risk) of full disability pension by 6% and increased the risk of partial disability pension by 8% compared with full sick leave. The effects did not differ markedly for the two main diagnostic groups of musculoskeletal and mental disorders. In men, the use of full disability pension was reduced by 10% with a 5% increase in the use of partial disability pension, while in women the effects were close to those of the total sample.

conclusionsOur findings suggest that combining work with partial sick leave may provide one means to increase work retention at population level. The use of partial sick leave could be encouraged among men.

Identifiers

PMID23144260
PMCPMC3533026

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.