Factors associated with early nonpersistence among patients experiencing side effects from a new medication
Notice bibliographique
Résumé
BACKGROUND: Drug discontinuation (i.e., nonpersistence) is often attributed to the emergence of adverse effects. However, it is not known whether other factors increase the risk of nonpersistence when adverse effects occur. OBJECTIVES: To identify factors associated with early nonpersistence among patients experiencing adverse effects from newly prescribed medications. METHODS: A questionnaire was mailed to new users of antihypertensive, antihyperglycemic, and lipid-lowering medications in Saskatchewan, Canada, between 2019 and 2020. Only respondents experiencing adverse effects were included. Responses were compared between the nonpersistent group (i.e., people who had discontinued their medication) and the persistent group (i.e., those who were taking their medication at the time of the survey). Statistically significant factors were tested in multivariable logistic regression models. Odds ratios (ORs) and 95% CIs were reported. RESULTS: Of the 3973 returned questionnaires, 813 respondents experienced adverse -effects from their new medication and were included in the study. Of these, 143 respondents (17.5%) had stopped their medication at the time of survey completion; most discontinuations (72.1%) occurred within 1 month of the first dose. Nonpersistent patients were older, had lower income, and were less likely to be taking an antihyperglycemic medication. After covariate adjustment, 6 factors were independently associated with nonpersistence: age less than 65 years (OR 1.56 [95% CI 1.01-2.41]), female sex (1.67 [1.08-2.59]), health condition not considered dangerous (2.09 [1.25-3.51]), medication not considered important for health (6.90 [4.40-10.84]), failure to expect adverse effects before starting medication (2.67 [1.74-4.10]), and taking 2 or more medications (0.45 [0.27-0.73]). CONCLUSION: Despite the strong link between the emergence of adverse effects and early nonpersistence, our findings confirm that this association is highly influenced by several factors external to the physical experiences caused by the new medication.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».