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Enregistrement W4411431504 · doi:10.1016/j.ard.2025.05.052

OP0386 PREDICTORS OF PRESENTEEISM OVER TIME IN INDIVIDUALS WITH INFLAMMATORY AND NON-INFLAMMATORY ARTHRITIS

2025· article· en· W4411431504 sur OpenAlexaffabout
Vanessa G. Macintyre, Annelies Boonen, Diane Lacaille, S. Wilkinson, Mark F. Lunt, S. Shoop-Worrall, J. Canas da Silva, G. Crepaldi, Sabrina Dadoun, Sofia Hagel, Carina Mihai, Sofía Ramiro, Garifallia Sakellariou, S. Meisalu, Johan K. Wallman, S. Verstappen

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueRheumatoid Arthritis Research and Therapies
Établissements canadiensResearch CanadaUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineInflammatory arthritisArthritisPresenteeismInternal medicineInflammationImmunologyPhysical therapyAbsenteeism

Résumé

récupéré en direct d'OpenAlex

Background: Work productivity is an important health outcome for people with rheumatic diseases and has economic consequences for the wider society. Several different outcome measurement instruments have been developed to measure work productivity loss (presenteeism). However, few studies have investigated predictors of presenteeism or included multiple presenteeism measures, so data on the impact of contextual factors on presenteeism is limited. Objectives: In this international study we aimed to identify possible factors associated with different measures of presenteeism over time in individuals with inflammatory and non-inflammatory arthritis in Europe and Canada. Methods: This three-month, longitudinal, international (UK, Estonia, Romania, Italy, France, Sweden, Portugal, Netherlands, and Canada) observational study (EULAR-PRO) investigated presenteeism. Adults with inflammatory arthritis (rheumatoid arthritis, axial spondyloarthritis, or psoriatic arthritis) or osteoarthritis in paid employment were included. Data was collected on demographic characteristics (e.g. age), disease characteristics (e.g. pain), and work-related variables (e.g. job type). Presenteeism was assessed at multiple time points following recruitment from clinics (0, 1, 2, 3, 4, 8 and 12 weeks), applying four outcome measures: Work Productivity and Activity Impairment Questionnaire (WPAI), Worker Productivity Scale-Arthritis (WPS-RA), Work Ability Index (WAI), and Quality and Quantity (QQ) questionnaire. WAI and WPS-RA scores range from 0-10, whereas WPAI and QQ scores range from 0-100. Multivariable, non-parametric maximum likelihood estimation was applied to estimate the association between baseline variables and presenteeism over time in separate models for each presenteeism measure, adjusting for country and arthritis type. For each measure variables were selected based on univariable results and previous research. Results: The total population (N = 554) comprised 62% female participants with a mean age of 48 (SD = 10.1) and disease duration of 11 years (SD = 9.5). Of these participants, 510 (92%) had inflammatory arthritis and 101 (18%) had two or more comorbid conditions. The mean percentage of time missed from work due to ill health was 7% (SD = 19.9). Table 1 shows the results for each presenteeism measure. The following variables significantly predicted more presenteeism over time: Higher Health Assessment Questionnaire (HAQ) and pain scores (all four outcome measures); higher Rheumatology Attitudes Index (RAI) scores (WPAI, WPS-RA, and WAI); very/extremely demanding work (WPAI and WPS-RA); technical (QQ) and routine job types (WPAI and QQ); never being able to organise one's work (QQ). Conversely, feeling satisfied about one's current condition predicted less presenteeism (WAI). Conclusion: Numerous disease-related and work-related factors impact presenteeism. Functional ability, health-related quality of life and pain predict more presenteeism over time, regardless of how presenteeism is assessed. This study highlights the importance of tailored interventions to reduce presenteeism in the workplace. REFERENCES: NIL . Table 1Predictors of presenteeism assessed by the WPAI, WPS-RA, WAI, and QQ using multivariable, non-parametric maximum likelihood estimationOutcome measureWPAIWPS-RAWAIQQPredictorβ (95% CI)β (95% CI)β (95% CI)β (95% CI)Age (years)0.0 (-0.2, 0.2)0.0 (-0.0, 0.0)-0.0 (-0.0, 0.1)-0.1 (-0.1, 0.0)Male gender1.3 (-2.1, 4.6)-0.2 (-0.6, 0.2)-0.1 (-0.4, 0.2)-0.0 (-2.0, 1.9)HAQ12.6 (7.4, 17.3)1.4 (0.9, 2.0)-0.8 (-1.3, -0.4)-5.8 (-8.5, -3.0)RAI1.0 (0.5, 1.4)0.1 (0.0, 0.1)-0.1 (-0.1, -0.4)-0.0 (-0.3, 0.3)One vs no comorbid conditions ≥ 2 vs no comorbid conditions-1.3 (-5.0, 2.4) -1.2 (-5.9, 3.6)-0.1 (-0.4, 0.3) -0.3 (-0.9, 0.2)0.1 (-0.2, 0.4) -0.2 (-0.6, 0.2)1.1 (-1.1, 3.3) 0.2 (-2.3, 2.7)Pain VAS4.4 (3.4, 5.4)0.5 (0.3, 0.6)-0.1 (-0.2, -0.1)-0.6 (-1.1, -0.1)Considers vs does not consider current condition satisfactory-1.3 (-5.9, 3.3)0.2 (-0.4, 0.8)0.6 (0.2, 1.0)0.4 (-2.6, 3.3)Technical vs managerial/professional jobs Routine vs managerial/professional jobs1.5 (-2.3, 5.2) 5.7 (0.7, 10.7)-0.0 (-0.5, 0.5) 0.4 (-0.1, 0.9)-0.2 (-0.5, 0.1) -0.2 (-0.6, 0.2)-2.4 (-4.7, -0.1)-3.8 (-6.3, -1.4)Demanding vs undemanding work Extremely demanding vs undemanding work4.4 (-0.0, 8.7) 5.5 (0.7, 10.2)0.3 (-0.2, 0.8) 0.6 (0.1, 1.0)NANANeither satisfied nor dissatisfied about work vs very satisfied Very unsatisfied vs very satisfied about work-0.2 (-4.9, 4.60) -1.00 (-6.0, 4.0)0.0 (-0.5, 0.6) 0.2 (-0.4, 0.7)-0.2 (-0.6, 0.2) -0.1 (-0.6, 0.3)2.3 (-0.2, 4.7) -1.2 (-4.1, 1.7)Sometimes vs often able to postpone work tasks Never vs often able to postpone work tasks-1.8 (-6.4, 2.9) -4.0 (-9.8, 1.8)-0.3 (-0.8, 0.1) -0.2 (-0.8, 0.4)NANASometimes vs often able to organise own work Never vs often able to organise own work1.6 (-2.3, 5.6) 1.9 (-4.7, 8.6)0.1 (-0.4, 0.5) 0.3 (-0.4, 1.0)-0.3 (-0.6, 0.1) 0.2 (-0.4, 0.8)-1.1 (-3.2, 1.1) -3.9 (-6.9, -0.9)WPAI: Work Productivity and Activity Impairment Questionnaire; WPS-RA: Work Productivity Scale–Rheumatoid Arthritis; WAI: Work Ability Index; QQ: Quality and Quantity questionnaire; HAQ: Health Assessment Questionnaire; RAI: Rheumatology Attitudes Index; VAS: Visual analogue scale; β: Coefficient; CI: Confidence interval; NA: Not applicable – not included in multivariable model due to being nonsignificant in univariable model. Higher scores indicate worse presenteeism on the WPAI and WPS-RA and less presenteeism on the QQ and WAI. N=488 in each model due to listwise deletion of cases with missing data. Significant results are highlighted in bold. Acknowledgements: The EULAR-PRO study was funded by EULAR. Disclosure of Interests: Vanessa G Macintyre: None declared, Annelies Boonen Pfizer, Novartis, UCB, Galapagos, Eli-Lilly, Abbvie, Diane Lacaille: None declared, Sarah Wilkinson: None declared, Mark Lunt: None declared, Stephanie Shoop-Worrall: None declared, José Canas da Silva: None declared, Gloria Crepaldi: None declared, Sabrina Dadoun: None declared, Sofia Hagel: None declared, Carina Mihai Speaker fees from MED Talks Switzerland, Medbase, Mepha, MedTrix, Novartis, PlayToKnow, Consultancy relationship with Boehringer Ingelheim and Janssen, Support from Boehringer Ingelheim, Sofia Ramiro Eli Lilly, Novartis and UCB, AbbVie, Eli Lilly, Galapagos/Alfasigma, Janssen, MSD, Pfizer, UCB, Sanofi, AbbVie, Galapagos/Alfasigma, MSD, Novartis, Pfizer, UCB, Garifallia Sakellariou Abbvie, Alfasigma, Sandra Meisalu Abbvie, Sandoz, AstraZeneca, Fresenius Kabi, Johan K Wallman AbbVie, Amgen, AbbVie, Amgen, Eli Lilly, Novartis, Pfizer, S. Verstappen BMS and AbbVie. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,045
Score d'incertitude au seuil0,090

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,260
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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