Content analysis of antiretroviral adherence enhancing interview reports. Issue 9 (September 2018)

• Record Type: Journal Article
• Authors: Kamal, Susan; Nulty, Paul; Bugnon, Olivier; Cavassini, Matthias; Schneider, Marie P.
• Is Part Of: Patient education and counseling. Volume 101:Issue 9(2018)
• Page Start: 1676
• Page End: 1682
• Publication Date: 2018-09
• Subjects: Medication adherence -- HIV -- Antiretrovirals -- Inter-professional collaboration -- Medication adherence program -- Electronic monitoring -- Motivational interviewing -- Mixed-method -- Pharmacist -- Physician; Patient education -- Periodicals; Health counseling -- Periodicals; Health education -- Periodicals; Counseling -- Periodicals; Patient Education -- Periodicals; Éducation des patients -- Périodiques; Counseling -- Périodiques; Éducation sanitaire -- Périodiques; 615.5071
• DOI: 10.1016/j.pec.2018.05.013
• Languages: English
• ISSNs: 0738-3991

Abstract

Highlights: Computational text analysis help analyze big data of adherence enhancing interviews. Adherence is associated with patient, health care system and HCP related factors. Suboptimal adherence moderators: psycho-social and economic factors, co-morbidity. Optimal adherence moderators: high QOL, stable housing, fixed dose timing. Communication between patients and HCP is key to successful treatment. Abstract: Objective: To identify factors associated with low or high antiretroviral (ARV) adherence through computational text analysis of an adherence enhancing programme interview reports. Methods: Using text from 8428 interviews with 522 patients, we constructed a term-frequency matrix for each patient, retaining words that occurred at least ten times overall and used in at least six interviews with six different patients. The text included both the pharmacist's and the patient's verbalizations. We investigated their association with an adherence threshold (above or below 90%) using a regularized logistic regression model. In addition to this data-driven approach, we studied the contexts of words with a focus group. Results: Analysis resulted in 7608 terms associated with low or high adherence. Terms associated with low adherence included disruption in daily schedule, side effects, socio-economic factors, stigma, cognitive factors and smoking. Terms associated with high adherence included fixed medication intake timing, no side effects and positive psychological state. Conclusion: Computational text analysis helps to analyze a large corpus of adherence enhancing interviews. It confirms main known themes affecting ARV adherence and sheds light on new emerging themes. Practice implications: Health care providers should be aware of factors that are associated with low or high adherence. This knowledge should reinforce the supporting factors and try to resolve the barriers together with the patient.