78 Direct-to-consumer advertising: a modifiable driver of overdiagnosis and overtreatment
Bibliographic record
Abstract
The New Zealand government is this year developing a new Therapeutic Products Bill to replace the antiquated Medicines Act 1981. Among the many issues at stake is whether direct-to-consumer advertising (DTCA) of prescription medicines will continue to be permitted. Besides the United States, New Zealand is the only other high-income country that allows unrestricted DTCA. Despite the centre-left Labour Party historically opposing DTCA, the current Labour cabinet has proposed that DTCA should continue, based on 4 key arguments,1 summarised here in relation to the research evidence: the existing combination of government and self-regulation (by industry) of DTCA is adequate. This claim is manifestly false, based on the fact that the present arrangement is unable to ensure that ads contain accurate information on either benefits or harms of medicines, or on how advertised products compare to other available treatment options, including lifestyle modification.2 the increased prescribing (and drug expenditure) triggered by DTCA may be appropriate and useful. While this assertion is doubtless true in some cases, the best overall evidence comes from a randomised controlled trial which showed, as expected, that brand-specific requests stimulate unnecessary prescriptions. Crucially, this trial found that requests from patients with adjustment disorder (pharmacotherapy inappropriate) stimulated prescribing to a greater extent than requests from patients with treatable depression.3 Complementary evidence showing that DTCA-stimulated prescribing can be both inappropriate and harmful comes from a study of patients requesting advertised COX-2 inhibitors.4 people with lower educational status, poorer health, or from an ethnic minority are more likely to seek care as a result of DTCA. While this is presented as a positive attribute, it is taken out of context from a New Zealand study which concludes that DTCA may lead to ‘…the misuse or overuse of medications for diseases that may otherwise be improved by a healthier lifestyle’5 having a more informed society enables better conversations and relationships between patients and prescribers. While a well-informed public is to be encouraged, this argument is undermined by evidence of the poor quality and misleading information typical of DTCA, irrespective of whether it comes from broadcast, print, or online advertising.2 In conclusion, the government’s main arguments for allowing DTCA to continue in New Zealand are both unsustainable and bear remarkable similarity to those advanced by Medicines New Zealand, a body representing the pharmaceutical industry. This coincidence may reflect the virtually unregulated access that lobbyists have to senior government officials in this country.6 In any case, available evidence indicates that banning DTCA would help to promote population health by reducing overdiagnosis, overtreatment, and iatrogenic harm. References Available from: https://www.health.govt.nz/our-work/regulation-health-and-disability-system/therapeutic-products-regulatory-regime New Zealand Medical Journal 2019;132:59-65. JAMA 2005;293:1995-2002. Medical Care Research and Review 2005;62:544-59. Australian and New Zealand Journal of Public Health 2019;43:190-6. Available from: https://www.rnz.co.nz/news/lobbying/486670/lobbyists-in-new-zealand-enjoy-freedoms-unlike-most-other-nations-in-the-developed-world
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".