How does direct-to-consumer advertising (DTCA) affect prescribing? A survey in primary care environments with and without legal DTCA.
Bibliographic record
Abstract
BACKGROUND: Direct-to-consumer advertising (DTCA) of prescription drugs has increased rapidly in the United States during the last decade, yet little is known about its effects on prescribing decisions in primary care. We compared prescribing decisions in a US setting with legal DTCA and a Canadian setting where DTCA of prescription drugs is illegal, but some cross-border exposure occurs. METHODS: We recruited primary care physicians working in Sacramento, California, and Vancouver, British Columbia, and their group practice partners to participate in the study. On pre- selected days, patients aged 18 years or more completed a questionnaire before seeing their physician. We asked these patients' physicians to complete a brief questionnaire immediately following the selected patient visit. By pairing individual patient and physician responses, we determined how many patients had been exposed to some form of DTCA, the frequency of patients' requests for prescriptions for advertised medicines and the frequency of prescriptions that were stimulated by the patients' requests. We measured physicians' confidence in treatment choice for each new prescription by asking them whether they would prescribe this drug to a patient with the same condition. RESULTS: Seventy-eight physicians (Sacramento n = 38, Vancouver n = 40) and 1431 adult patients (Sacramento n = 683, Vancouver n = 748), or 61% of patients who consulted participating physicians on pre-set days, participated in the survey. Exposure to DTCA was higher in Sacramento, although 87.4% of Vancouver patients had seen prescription drug advertisements. Of the Sacramento patients, 7.2% requested advertised drugs as opposed to 3.3% in Vancouver (odds ratio [OR] 2.2, 95% confidence interval [CI] 1.2-4.1). Patients with higher self- reported exposure to advertising, conditions that were potentially treatable by advertised drugs, and/or greater reliance on advertising requested more advertised medicines. Physicians fulfilled most requests for DTCA drugs (for 72% of patients in Vancouver and 78% in Sacramento); this difference was not statistically significant. Patients who requested DTCA drugs were much more likely to receive 1 or more new prescriptions (for requested drugs or alternatives) than those who did not request DTCA drugs (OR 16.9, 95% CI 7.5-38.2). Physicians judged 50.0% of new prescriptions for requested DTCA drugs to be only "possible" or "unlikely" choices for other similar patients, as compared with 12.4% of new prescriptions not requested by patients (p < 0.001). INTERPRETATION: Our results suggest that more advertising leads to more requests for advertised medicines, and more prescriptions. If DTCA opens a conversation between patients and physicians, that conversation is highly likely to end with a prescription, often despite physician ambivalence about treatment choice.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".