Projections of Public Spending on Pharmaceuticals: A Review of Methods
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.027 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".