Association Between Drug Characteristics and Manufacturer Spending on Direct-to-Consumer Advertising
Bibliographic record
Abstract
Importance: Some drugs are heavily marketed through direct-to-consumer advertising. Objective: To identify drug characteristics associated with a greater share of promotional spending on advertising directly to consumers. Design, Setting, and Participants: Exploratory cross-sectional analysis of drug characteristics and promotional spending for the 150 top-selling branded prescription drugs in the US in 2020 as identified from IQVIA National Sales Perspectives data. Promotional spending data were provided by IQVIA ChannelDynamics. Exposures: Drug characteristics (total 2020 sales; total 2020 promotional spending; clinical benefit ratings; number of indications, off-label use; molecule type; nature of condition treated; administration type; generic availability; US Food and Drug Administration [FDA] approval year, World Health Organization anatomical therapeutic chemical classification; Medicare annual mean spending per beneficiary; percent sales attributable to the drug; market size; market competitiveness) assessed from health technology assessment agencies (France's Haute Autorité de Santé and Canada's Patented Medicine Prices Review Board) and drug data sources (Drugs@FDA, the FDA Purple Book, Lexicomp, Merative Marketscan Research Databases, and Medicare Spending by Drug data). Main Outcomes and Measures: Proportion of total promotional spending allocated to direct-to-consumer-advertising for each drug. Results: The 2020 median proportion of promotional spending allocated to direct-to-consumer advertising was 13.5% (IQR, 1.96%-36.6%); median promotional spending, $20.9 million (IQR, $2.72-$131 million); and median total sales, $1.51 billion (IQR, $0.97-$2.26 billion). Of the 150 best-selling drugs, 16 were missing data and key covariates; therefore, the primary study sample comprised 134 drugs. After adjustment for multiple drug characteristics, the mean proportion of total promotional spending allocated to direct-to-consumer advertising for the remaining 134 drugs was an absolute 14.3% (95% CI, 1.43%-27.2%; P = .03) higher for those with low added clinical benefit than for those with high added clinical benefit and an absolute 1.5% (95% CI, 0.44%-2.56%; P = .005) higher for each 10% increase in total sales. Conclusions and Relevance: Among top-selling US drugs in 2020, a rating of lower added benefit and higher total drug sales were associated with a higher proportion of manufacturer total promotional spending allocated to direct-to-consumer advertising. Further research is needed to understand the implications of these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".