Adherence to adjuvant endocrine therapy in seniors with breast cancer, predictors and challenges
Notice bibliographique
Résumé
BACKGROUND: Nearly one-third of breast cancers (BC) occur in women 65 years and older. Anti-estrogen therapy (AET) significantly reduces BC recurrence and death in these patients, as they more often have hormone receptor positive tumors. However, prior studies suggest that adherence to AET in older women is a challenge. OBJECTIVE: To characterize AET adherence in seniors with BC and identify factors influencing it. METHODS: Cancer registry data and administrative claims for all non-metastatic BC diagnosed in Quebec between 1998 and 2005 were accessed from the provincial health insurance program. Patients ≥ 65 years who started AET (Tamoxifen, Anastrozole, Exemestane or Letrozole) and had 5 years of follow up were studied. Five-year medication possession ratio (MPR) was calculated and multivariate linear regression was used to assess the association between patient, disease, and physician characteristics and MPR. RESULTS: 4,715 women were included. Mean age was 72.9. 66.77% had no other significant comorbidities and only 4.16% had 3 or more comorbidities. Stage distribution was: 6.43% in situ, 74.13%localized and 19.45% regional disease. Mean MPR was 83.5% (SD 26.8%). 1596 (34%) women had AET interruption at some point during the entire period of follow up. The cumulative probability of therapy interruption was 33.8% and the mean time to interrupt was 833.4 days. Among those who had therapy interruptions, 39.1% reinstituted AET (mean time to reinstitute was 185.6 days), of which, 48.2% re-interrupted AET again. 5-year MPR decreased with increasing age (p=0.05) and hospitalizations not related to BC (0.73% per each hospitalization, p-value=0.009). Compared to women with node positive disease, those with in situ disease had on average an MPR lower by 6.5%(p-value=0.0003). Having more active prescriptions at baseline increased the MPR by 0.6% for each medication, (p-value< 0.0001). However, adding further new medications after the start of AET affected the MPR negatively (0.3% decrease in MPR for each new medication added, p-value< 0.0001). Among psychotropes, antidepressants were the only group that did show a significant effect, resulting in a MPR decrease of 4.7% among those who were known to take antidepressants prior to the diagnosis and treatment of breast cancer (p-value= 0.003). Women on Tamoxifen, compared to those on Anastrozole, had on average a MPR that is lower by 6%, (p-value= 0.002). Compared to those who never switched their AET type, those who switched early in their treatment course, during the first year, had lower MPR by 5.3% (p-value=0.003). On the other hand, those who switched later had on average an MPR higher by 7.4% (p-value<0.0001). CONCLUSION: Most seniors with BC had high adherence to AET. Patients with more advanced age, less advanced disease and more non-BC related health service use, and women treated with antidepressants prior to their breast cancer were at higher risk of suboptimal adherence.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».