Projections of Public Spending on Pharmaceuticals: A Review of Methods
Notice bibliographique
Résumé
BACKGROUND: Forecasting future public pharmaceutical expenditure is a challenge for healthcare payers, particularly owing to the unpredictability of new market introductions and their economic impact. No best-practice forecasting methods have been established so far. The literature distinguishes between the top-down approach, based on historical trends, and the bottom-up approach, using a combination of historical and horizon scanning data. The objective of this review is to describe the methods for projections of pharmaceutical expenditure that apply the "bottom-up" approach and to synthesize the knowledge of their predictive accuracy. METHODS: Projections of public pharmaceutical expenditure applicable to Western economies including a comprehensive method description and published 2000-2024 were searched in scientific databases (MEDLINE, EMBASE, and EconLit) and in gray literature (websites of international health organizations and national healthcare authorities). The data sources, assumptions about the future market dynamics, analytical approaches, and the projection results are summarized. RESULTS: Twenty-four out of 3492 screened publications were included, associated with nine expenditure projection models. Four models were developed for all reimbursable drugs in the USA, the UK, the Stockholm region (Sweden), and seven European Union (EU) countries: France, Germany, Greece, Hungary, Poland, Portugal, and the UK, respectively. The other five models concerned specific groups of medicines: orphan drugs in Belgium, the Eurozone plus the UK, and Canada, respectively; psychotropic medications in the USA; and outpatient intravenous cancer medicines in the Province of Ontario (Canada). For trend analysis, drug coverage claims or sales data were used, applying linear and/or nonlinear models. The budget impact of new launches and patent expirations was estimated through (a form of) horizon scanning, i.e., a systematic monitoring of the pharmaceutical pipeline, with engagement of clinical expert judgment. Projections with a predictive time window greater than 3 years largely relied on previously observed trends to model new market introductions. Four models were validated through an ex post comparison of projected and observed expenditure. The absolute difference between the forecasted and actual percentual change in pharmaceutical expenditure was: 0.3% ("UK model"), 1.9% ("Stockholm model"), and 2% (nonfederal hospitals, "US model"). The "Ontario cancer drug model" overestimated the actual expenditure by 1%. Overall, the largest errors were attributable to new market launches and unforeseen policy reforms. Prediction accuracy decreased substantially for forecasts beyond 1 year in the future. For two not validated projections, a face validity check was feasible. One of the models forecasted a decrease in pharmaceutical expenditure from 2012 to 2016 in six European countries, contrasting with the currently available statistics. A 10-year projection of orphan drug expenditure underestimated the number of rare diseases treated in Europe by over 100%. CONCLUSIONS: Published projections of national pharmaceutical expenditure are scarce and marked by significant methodological variability. Short-term forecasts based on high-quality historical data and rigorous horizon scanning tend to be more accurate than long-term forecasts built on theoretical assumptions. The combination of mathematical algorithms and expert judgment should be further explored, to increase the accuracy and efficiency of pharmaceutical expenditure projections.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 tête enseignante, 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 ».