When Do Published Cost-Effectiveness Analyses Include Societal Costs? An Empirical Analysis, 2013–2023
Notice bibliographique
Résumé
ObjectiveTo examine trends in the inclusion of societal costs in published cost-effectiveness analyses (CEAs), factors associated with their inclusion, and the impact of societal costs on incremental costs and incremental cost-effectiveness ratios (ICERs).MethodsWe analyzed 7,800 CEAs from 2013 to 2023 using the Tufts Medical Center CEA registry. The inclusion of societal costs in CEAs was evaluated across study characteristics. Associations between study characteristics and the inclusion of societal costs were analyzed using multivariate logistic regression. For studies reporting health care and societal perspectives, we assessed the impact of including societal costs on incremental costs and ICERs.ResultsFrom 2013 to 2023, CEAs including societal costs increased from 19% to 28%. Productivity was the most frequently reported component (12%), followed by transportation (8%), caregiver time (6%), patient time (5%), and consumption costs (1%). Compared with US-based analyses, studies from Scandinavian countries (adjusted odds ratio [OR]: 3.6) and the Netherlands (5.6) had higher odds of including societal costs, whereas studies from Canada (0.7), Australia (0.6), and the United Kingdom (0.4) had lower odds. Studies on mental health disorders (6.2) and immunization (4.1) had the highest odds of including societal costs. Compared with CEAs focused on adults, CEAs targeting pediatric populations had higher odds (OR: 1.6), while those targeting the elderly had lower odds (OR: 0.7). Upon inclusion of societal costs, incremental costs decreased in 72% and increased in 28% of studies; the ICER decreased in 74% and increased in 26% of studies.ConclusionDespite the increase in recent years, societal costs are infrequently included in CEAs, with substantial variation by country, disease, and population. Including societal costs can meaningfully improve value assessments and should be guided by relevance, evidence, and decision context.HighlightsBuilding on prior work by Kim et al. (2020), which analyzed approximately 6,900 cost-effectiveness analyses (CEAs), this study examined a larger and more recent sample of 7,800 CEAs from 2013 to 2023. In addition to updating the evidence base, we conducted new analyses to assess trends, associated factors, and the effect of including societal costs on incremental cost-effectiveness ratios (ICERs), thus providing insights that were not explored in prior work and addressing a key evidence gap in health economics.The inclusion of societal costs in CEAs rose modestly from 19% to 28% from 2013 to 2023, with substantial variation across countries, diseases, and intervention types. In some cases, the inclusion of societal costs affected incremental costs and ICERs enough to cross commonly used cost-effectiveness thresholds.The inclusion of societal costs can help improve value assessments in health care interventions, but it should be guided by relevance, available evidence, and the potential to influence decision making. Identifying when and where societal costs meaningfully affect outcomes can support more consistent and appropriate use.
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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 | 0,174 | 0,521 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,009 |
| Bibliométrie | 0,011 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,009 | 0,011 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».