Application of Self-Efficacy Theory in Adherence to Iron Chelation Therapy: A Single-Center Cross-Sectional Study
Notice bibliographique
Résumé
Abstract 5284 Introduction: Poor adherence to iron chelation therapy (ICT) in beta-Thalassemia Major (TM) is associated with increased risk of cardiac complications and endocrinopathies, and lower survival, with substantial cost to the patient and the health care system. Canada is unique in that several predictors of non-adherence (Financial barriers to medical care, cost of medication and inadequate follow-up) are minimized due to the presence of universal health care, governmental subsidies for medications for patients with chronic disease, and the availability of comprehensive care center for most of the thalassemia patients in the country. Also, the availability of Deferiprone (DFP) via compassionate release program since July 2004 provides an alternative to patients intolerant or having suboptimal response to Deferoxamine (DFO) or Deferasirox (DFX). We hypothesize that the absence of these barriers improve adherence in the Canadian thalassemic population. We also explored self-efficacy as a concept of adherence behavior in our patient population, defined as “individuals' personal beliefs regarding their capabilities to carry out a specific task to achieve a desired outcome” (Bandura, 1989). Methods: A cross-sectional survey was conducted in June and July 2011 at a regional comprehensive care center for transfusion-dependent thalassemia patients. We assessed the age, sex, education, employment status, insurance coverage, types and dosage of ICT, self-reported level of adherence, and side effects. We adapted the Medication Adherence Self-Efficacy Scale (MASES) to assess self-efficacy (Ogedegbe, 2003). Results: Survey return rate was 45% (46/103), with each type of ICT proportionally represented (P = 0.6401). Eight surveys were discarded due to incompletion and 38 were analyzed. Thirty-two patients were on single agent ICT (6 on DFO, 23 on DFX, 3 on DFP) and 6 patients were on combination treatment (1 on DFO+DFX; 3 on DFO+DFP; 2 on DFX+DFP). Median duration of iron chelation was more than 10 years. All patients had either government (n = 10) or workplace (n = 28) coverage. Twenty-three patients (61%) were self-described as completely adherent and 15 were not completely adherent. Mean level of adherence is 90% (SD 16%), similar to those reported in the literature (Trachtenberg et al., 2011), with no significant difference between the different types of ICT (P = 0.1085). Half of the non-adherent patients (8/15, 53%) miss 1 prescribed day of medication per week. There was no significant difference between adherent and non-adherent patients in age (P = 0.1484), sex (P = 0.3764), type of insurance coverage (P = 4752), family support (P = 0.7190), type of ICT (P = 0.0611), participation and satisfaction with the Exjade Patient Support Program (P = 1.000 and 0.3012 respectively), duration of chelation (P = 0.3951), rate of side effects (P = 0.4167), or feelings of depression (P = 0.4780). There was a trend towards differences in education level (P = 0.0565) and a higher proportion of professionals in the non-adherent group. The mean self-efficacy score of patients self-described as completely adherent was significantly higher than the non-completely adherent group (2.66 vs 1.93, P<0.0001). Discussion: In this self-reported survey of patients on ICT in a Canadian regional comprehensive care center, age, presence of family support, and feelings of depression were not found to be a significant predictor of poor adherence, unlike previous studies. This could be because previous studies only examined certain types of ICTs whereas the present study examined all forms of chelation. Small sample sizes of patients on DFO and DFP is the main limitation of the study. This is also the first known application of self-efficacy theory in explaining adherence to ICT. Further studies are required to examine the internal consistency and test-retest reliability of MASES in evaluating self-efficacy in adherence to ICT. Disclosures: Kuo: Novartis Canada: Research Funding. Off Label Use: Deferiprone is an unlicensed drug in Canada and USA. It is an oral iron chelator.
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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 | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».