Cycling Through Migraine Preventive Treatments: Implications for All-Cause Total Direct Costs and Disease-Specific Costs
Notice bibliographique
Résumé
BACKGROUND: Migraine is a common and disabling neurological disease associated with substantial economic burden. Among patients with migraine, it is unknown if cost differences exist when preventive migraine medication (PMM) switches occur. OBJECTIVE: To understand the cost burden and health care resource utilization of patients who discontinue or cycle through 1 (PMM1), 2 (PMM2), or ≥ 3 (PMM3) unique PMM drug classes over a 12-month period versus patients who adhere persistently to their initial PMM class. METHODS: This retrospective observational study used the Truven Health Analytics MarketScan databases to identify adult patients with migraine initiating their first PMM class (antidepressants, antiepileptics, beta blockers, or neurotoxins) from 2011-2013 (index date = first PMM claim). Patients were required to have ≥ 2 outpatient (1 if inpatient) migraine diagnosis codes (ICD-9-CM 346.xx) from 1 year pre-index to 1 year post-index with ≥ 1 code occurring pre-index. Inclusion criteria also required 12 months of pre- and post-index continuous medical and prescription enrollment. All-cause and migraine-specific total direct costs (outpatient, inpatient, emergency department, and prescriptions), based on the 2014 Consumer Price Index, were estimated for each PMM versus a persistent subgroup during the 12-month post-index period. Propensity score bin bootstrapping, controlling for patient baseline characteristics, was used to adjust separate cost comparisons between each PMM subgroup and the persistent subgroup; bootstrap simulations yielded propensity score-adjusted P values. RESULTS: The study population included 55,402 patients who received a PMM. The study population was mainly female (85%) with a mean age of 39.2 years and mean Charlson Comorbidity Index of 0.31. Antiepileptics were the most common drug class chosen at index across all subgroups; however, lower use of antiepileptics was observed in PMM2 and PMM3 subgroups, which were more likely to be prescribed either antidepressants or beta blockers at index. Mean all-cause total direct costs, including prescription costs, were significantly higher in PMM2 ($13,429) and PMM3 ($18,394) subgroups versus the persistent subgroup ($11,941; each adjusted pairwise comparison, P < 0.001). Mean migraine-specific total direct costs were significantly lower for the persistent subgroup ($2,420) versus PMM2 and PMM3 subgroups and escalated with increasing numbers of drug class discontinuations or switches, from a mean of $2,997 to $5,004 (both adjusted pairwise comparisons, P < 0.001). Subgroup differences in all-cause and migraine-specific direct costs were primarily due to variations in outpatient and emergency department services. CONCLUSIONS: All-cause total direct costs rose with increasing number of PMM switches over the 12-month post-index period, and were significantly higher than in the persistent subgroup, with the exception of PMM1. Additional analyses indicated that the lack of increase between PMM-persistent and PMM1 costs was due to higher pharmacy costs that were likely related to continuous use of medication in the PMM-persistent subgroup. These data suggest an increased cost burden among patients with migraine who cycle through ≥ 2 PMMs versus those who continue to receive their initial medication class. DISCLOSURES: Eli Lilly and Company was the sole sponsor and funder for this study and was responsible for the study design, data collection, data analysis, interpretation of data, and decision to publish the findings. All authors are employees and minor stockholders of Eli Lilly and Company. Nyhuis was employed by Eli Lilly and Company at the time of this study. The findings of this study were presented in part at the 18th Congress of the International Headache Society; September 7-10, 2017; Vancouver, Canada.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».