Drug Marketing and Prescribing Patterns: Challenges Facing Physicians Entering the New Trump Era of Health Care: Are There Parallels with the Canadian Experience?
Notice bibliographique
Résumé
Chronic pain exacts an enormous socioeconomic toll throughout the world. It is the leading cause of years lost to disability in the world, with the estimated annual cost exceeding a half trillion dollars per year in the United States. One major driving force for the surge in health care expenditures is the rising use of expensive interventions and medications that have not resulted in a concomitant reduction in pain prevalence or disability rates. This surge in costs has resulted in increased scrutiny from third-party payers, lawmakers, and medical watchdog groups, as well as a growing emphasis on comparative- and cost-effective analyses. With a new administration entering the White House, will there be a new approach to curbing these costs? The Canadian experience that is documented in this month’s Journal article by Kwok and colleagues—“Impact of unrestricted access to pregabalin on the use of opioids and other CNS-active medications: A cross-sectional time series analysis” perhaps opens a window on the challenges facing legislators and physicians as we move forward into uncharted territory. Pregabalin, produced by Pfizer Pharmaceuticals under the trade name Lyrica, was released for sale in 2004 as a follow-up drug to the soon-to-be off-patent gabapentin (i.e., Neurontin). The release of Lyrica was promoted extensively by Pfizer at the time and remains heavily marketed to US physicians. The benefits of pregabalin over its close relative gabapentin include the relative ease of dosing (e.g., BID vs TID, and faster titration), more favorable pharmacokinetics (e.g., more rapid absorption, higher bioavailability with less interindividual variability, and increased binding affinity), and a marginally different side effect profile. In addition to postherpetic neuralgia, which both drugs are approved for by the US Food and Drug Administration, pregabalin has added pain indications for treatment of diabetic neuropathy, fibromyalgia, and neuropathic pain associated with spinal cord injury [1,2]. There is significant regional price variability across geographic boundaries, but in most countries an equianalgesic dose of pregabalin is more expensive than its predecessor. Although ostensibly labeled as an “anticonvulsant,” the treatment of seizures comprises only a small portion of sales of pregabalin. The real allure of the drug has always been its on- and off-label pain-related uses. It has been widely recognized as a treatment for all types of neuropathic pain and fibromyalgia, being advocated as a firstline medication for these conditions. Whereas the marketing of off-label uses is expressly prohibited, deviations from these restrictions resulted in Pfizer paying a substantial fine to the US government in 2009 [3,4]. Yet, a fine cannot erase past actions, beliefs, or prescribing habits, and the off-label indications are now touted throughout the medical community. The success of their marketing strategy is evidenced by Pfizer’s balance sheet. In 2015, Lyrica was the company’s most popular product, ranking 13th in total sales volume in the United States, with annual revenue estimated at $4.83 billion USD [5]. Despite a lawsuit in favor of Pfizer retaining exclusive rights to the sale of pregabalin, it is due to come off patent in the United States in 2018 [6]. The drug has already come off patent in Canada and the United Kingdom, with numerous generic versions now available in both countries. The release of patent restrictions in Canada and the subsequent alterations in prescribing limitations is the subject of this month’s article by Kwok and colleagues [7], which seeks to shed light on three very topical issues. The first question the authors try to answer is: “How effective is the placement of prior authorization requirements in deterring prescribing?” The second is: “What effect does the addition of pregabalin have on reducing the need for other pain medications?” The third question is: “What is the prevalence of “off-label” usage of this frequently prescribed pain medication?” It turns out that placing prior authorization requirements on physicians has a profound effect on prescribing patterns. Prior to April 2013, for a Canadian patient to have pregabalin authorized by a third-party payer, they had to meet specific diagnostic criteria (i.e., failure or intolerable side effects from a trial of tricyclic antidepressants [TCA] or gabapentin). Anyone on the front line in clinical practice is familiar with this requirement; we have all dealt with this headache. If a typical pain patient arrives at a clinic complaining of persistent pain despite an adequate trial with gabapentin or a TCA, pregabalin is a viable follow-up treatment option. However, the routine use of this algorithm is not borne out by the findings of Kwok et al. Before lifting the prior authorization requirements, pregabalin was prescribed to only one out of every 1,000 individuals. Within two years after restrictions were lifted, it was being prescribed to 22 of every 1,000 individuals. This 22-fold increase represents a striking upsurge in utilization. The reluctance to previously prescribe pregabalin may charitably be attributed to a sense of fiduciary responsibility by physicians but is more likely a reflection of our reluctance to take on additional nonreimbursable tasks in a cost-cutting environment characterized by ever-growing demands on physician time that have little to do with clinical decision-making. The second question the authors sought to answer is: “What effect does the addition of pregabalin have on reducing the need for other pain medications?” Based on the results of this study, it seems to have very little impact. Among patients who filled pregabalin prescriptions, the utilization rate for opioids was 58.9%, for antidepressants it was 52.9%, for NSAIDS it was 39.6%, and for benzodiazepines it was 30.2%. The addition of pregabalin to patients’ existing analgesic regimens resulted in no meaningful diminution in drug usage except for NSAIDs, which declined by 20%. With regard to opioids, neither the number of prescriptions dispensed nor the average dose administered decreased. The significance of these findings is beyond the scope of this editorial but signifies the challenges faced by doctors who manage chronic pain. The third question addressed in the article was about the prevalence of "off-label" usage. Whereas the authors assert that the off-label utilization of pregabalin was low (20.1% carried a diagnosis of musculoskeletal pain without a neurological diagnosis), it is likely that a significant proportion of patients diagnosed with a “neurological” diagnosis actually had a mixed pain condition expected to be less responsive to a membrane stabilizer [8,9] or suffered from a neurological condition for which pregabalin has been found to be ineffective for, or not been rigorously studied in [10]. It is also probable that if there had been further stratification of diagnoses, the off-label utilization rate of pregabalin would have even been higher. This finding has been noted in a previous study examining gabapentin utilization [11]. As evidenced by the recent American presidential election, the effects of marketing in the current environment can be profound. Every time we open our e-mail accounts, sit down to a free lunch, and turn on our computers or television sets, we are faced with forces that promote these high-cost pharmaceuticals and interventions. As doctors are simultaneously bombarded with clinical information and overwhelmed by administrative tasks that have nothing to do with patient care, perceptions often “trump” reality. A prime example of this disparity is the ongoing opioid crisis, which our health care system played a substantial role in creating. Pregabalin is an effective drug that has helped millions of pain patients. Yet, the inconsistency between its efficacy, which is less than that for TCAs [12], and its market share, which dwarfs that of the generic drugs, is striking. As health care costs continue to explode, the administrative commitments of physicians to address these rising costs (e.g., prior authorizations) grows in tandem. This study makes it clear that the depth of our belief in many of these drugs is shallow, with rather minor roadblocks resulting in major changes in our prescribing habits. It appears from the findings of the current study that we have lost some of our clinical autonomy, ceded to insurance companies who burden us with extra steps and pharmaceutical companies that bombard us with one-sided promotions of what are essentially minimally altered old drugs. We as providers have to find better ways to consider issues of comparative- and cost-effectiveness when making health care decisions. Perhaps moving forward, physicians can take the lead in elucidating these challenges to legislators and work with the framers of the next iteration of the Affordable Care Act. We as doctors have the best insight and breadth of knowledge to help create a more affordable and accessible health care system.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,011 | 0,006 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,023 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».