The Forgotten Costs of SLE: Estimating Indirect Costs in a National SLE Cohort
Notice bibliographique
Résumé
Objectives Economic analyses of SLE often include only direct healthcare costs. Indirect costs, particularly from lost productivity in unpaid labor, are often overlooked, especially relevant for a disease disproportionately affecting women. We assessed indirect costs due to lost productivity in both paid and unpaid labor, stratified by gender, in a national multicenter SLE cohort. Methods Patients fulfilling ACR or SLICC SLE Classification Criteria completed a validated questionnaire on lost productivity. Total indirect costs included: 1) absenteeism (time lost from paid labor because of illness), 2) presenteeism (degree of productivity impairment in paid/unpaid labor), 3) opportunity costs (additional time patients would be working in paid/unpaid labor if not ill). Opportunity costs were calculated as the difference between the time patients worked versus an age, sex, and geographic-matched general population in paid and unpaid labor. Indirect costs from paid and unpaid labor were valued using age-and-sex-specific wages from Statistics Canada. The association of gender with annual indirect costs was assessed (adjusted for race/ethnicity, age, disease duration, and the SLICC/ACR Damage Index [SDI]) using random effects linear regression modeling. Results 1804 patients participated, 90.8% female, 66.9% white, mean age at diagnosis 33.7 (SD 13.9) years, mean SLE duration 14.7 (11.7) years, and mean SDI 1.3 (range 0-12.0). Patients were followed a mean of 4.9 (range 1.0-9.6) years with 48.9% employed (49.5% among females, 44.0% among males) at the initial and 37.0% (37.1% among females, 36.7% among males) at the final observation. Overall, total annual indirect costs were $36 405 (absenteeism: $829; presenteeism in paid labor: $4624; presenteeism in unpaid labor: $8922; opportunity costs in paid labor: $8512; opportunity costs in unpaid labor: $13 519). Among women, opportunity costs from unpaid labor were 38.8% ($14 175/$36 538) and from paid labor 21.3% ($7802/$36 538) of total indirect costs; among men, opportunity costs from unpaid labor were 19.3% ($6765/$35 064) and from paid labor 44.7% ($15 685/$35 064) of total indirect costs (Figure 1). Regression modeling showed that women incurred higher opportunity costs from unpaid labor (coefficient $7309, 95% CI $3514, $11 105), and lower opportunity costs from paid labor (coefficient −$8336, 95% CI −$12 524, −$4147). Figure 1a: Components of Annual Indirect Costs: Female Total: $36 538 (2023 Canadian Dollars) Figure 1b: Components of Annual Indirect Costs: Male Total: $35 064 (2023 Canadian Dollars) Conclusion Indirect costs, particularly from unpaid labor, are substantial, especially in women, where they represent 38.8% of total indirect costs versus 19.3% in men. Hence, economic analyses weighing costs and benefits of novel/emerging therapies should incorporate costs resulting from lost productivity, particularly important for a disease that disproportionately affects women.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».