Application of Self-Efficacy Theory in Adherence to Iron Chelation Therapy: A Single-Center Cross-Sectional Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".