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Content analysis of antiretroviral adherence enhancing interview reports
Susan Kamal1, Paul Nulty2, Olivier Bugnon1
1Community Pharmacy, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Community Pharmacy, Department of Ambulatory Care & Community Medicine, University of Lausanne, Geneva, Switzerland.
Patient Education and Counseling
|June 7, 2018
Summary
Computational text analysis of interviews identified key factors influencing antiretroviral (ARV) adherence. Understanding these themes helps healthcare providers support patients in managing their treatment.
Area of Science:
- Computational linguistics
- Health services research
- Pharmacovigilance
Background:
- Antiretroviral (ARV) adherence is crucial for effective HIV management.
- Understanding factors influencing adherence is vital for treatment success.
- Previous research has identified several adherence barriers and facilitators.
Purpose of the Study:
- To identify factors associated with low or high antiretroviral (ARV) adherence.
- To utilize computational text analysis of adherence enhancing programme interview reports.
- To uncover both known and emerging themes impacting ARV adherence.
Main Methods:
- Analysis of 8428 interviews with 522 patients.
- Construction of term-frequency matrices for each patient.
- Application of regularized logistic regression to identify associations with adherence thresholds (above/below 90%).
Main Results:
- 7608 terms were associated with low or high adherence.
- Low adherence factors included: disrupted schedules, side effects, socio-economic issues, stigma, cognitive factors, and smoking.
- High adherence factors included: fixed medication timing, absence of side effects, and positive psychological state.
Conclusions:
- Computational text analysis is effective for analyzing large interview datasets.
- The study confirmed known themes and identified new factors affecting ARV adherence.
- Healthcare providers should leverage this knowledge to support patients and improve adherence.
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