Pharmaceutical company spending on research and development and promotion in Canada, 2013-2016: a cohort analysis
Bibliographic record
Abstract
BACKGROUND: Competing claims are made about the amount of money that pharmaceutical companies spend on research and development (R&D) versus promotion. This study investigates this question in the Canadian context. METHODS: Two methods for determining industry-wide figures for spending on promotion were employed. First, total industry spending on detailing and journal advertising for 2013-2016 was abstracted from reports from QuintilesIMS. Second, the mean total promotion spending for the years 2002-2005 was used to estimate total spending for 2013-2016. Total industry spending on R&D came from the Patented Medicine Prices Review Board (PMPRB). R&D to promotion spending using each method of determining the amount spent on promotion was compared for 2013-2016 inclusive. Data on the 50 top promoted drugs, the amounts spent, the companies marketing these products and their overall sales were abstracted from the QuintilesIMS reports. Spending on R&D and promotion as a percent of sales was compared for these companies. RESULTS: Industry wide, the ratio of R&D to promotion spending went from 1.43 to 2.18 when promotion was defined as the amount spent on detailing and journal advertising for the 50 most promoted drugs. Calculating total promotion spending from the mean of the 2002-2005 figures the ratio was 0.88 to 1.32 for the 50 most promoted drugs. For individual companies marketing one or more of the 50 most promoted drugs, mean R&D spending ranged from 3.7% of sales to 4.1% compared to mean promotion spending that went from 1.7 to 1.9%. The ratio of spending on R&D to promotion varied from 2.11 to 2.32. Eight to 10 companies per year spent more on promotion than on R&D. CONCLUSIONS: Depending on the method used to determine promotion spending, industry-wide the ratio of R&D spending to promotion ranges from 1.45 to 2.18 (sales representatives and journal advertising only) or from 0.88 to 1.32 (total promotion spending estimated based 2003-2005 data.) For the individual companies promoting one or more of the 50 most promoted drugs, 2.11 to 2.32 times more is spent on R&D compared to promotion. However these results should be interpreted cautiously because of data limitations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".