Evidence map›Paper›PMID 42261462›Full record

ArticleRisk management and healthcare policy2026

A Tool for Predicting Disability Pension Applications in the Finnish Public Sector: Sickness Absences as Early Indicators of Declining Work Ability.

Petra Sohlman

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Petra SohlmanFaculty of Management and Business Studies, Tampere University, Tampere, Finland.ORCID 0009-0001-2649-0736

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Identifying employees at the greatest risk of work disability is critical to preventing early exit from the labor market. Understanding how different factors influence work disability risk in the years leading to a pension is important in preventing early exits. Our aim in this study is to model the risk factors for an employee applying for a disability pension by using a novel and extensive register dataset and to evaluate the predicted risk for different occupations in the workforce. Methods: We use a logistic model to assess the risk of filing a first-time disability pension application using rich register data. We evaluate the performance of the model and finally present the evaluated risk distribution among current employees. Data are also included for short-term sickness absences, usually not available for register studies. A pension application is used as a follow-up to include employees whose application is rejected but who nonetheless carry a physician's note of diminished work ability, also a new approach to modeling work disability risk. The data cover all public sector occupations in Finland and their diverse disability risk profiles. Results: The constructed risk model accurately and transparently predicts the disability pension application risk in different employee groups. Age, occupation, cumulative sickness days, and number of sickness spells of all lengths, including short ones, were positively associated with the risk; proximity of retirement age and high earnings were negatively associated. Validation measures allow for using the model in providing employers with risk profiles of their employees. Conclusion: The developed model can be used as a tool for predicting the risk status of employee groups and for assessing the economic effects of sickness absences and disability pension risk. The modeling results reported here are already in practical use by public sector employers in Finland, eg, for early intervention.

Indexed as

agingearly interventionhealth impact assessment and monitoringshort term sickness absencesworking life

Identifiers

PMID42261462
PMCPMC13242779

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.