Susan Kamal1, Paul Nulty2, Olivier Bugnon1, Matthias Cavassini3, Marie P Schneider4. 1. Community 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. 2. Centre for Research in Arts, Social Science, and Humanities, University of Cambridge, Cambridge, UK. 3. Infectious Disease Service, Lausanne University Hospital and University of Lausanne, Geneva, Switzerland. 4. Community 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. Electronic address: marie-paule.schneider@hospvd.ch.
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.
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.
Authors: Sarah J Iribarren; Hannah Milligan; Cristina Chirico; Kyle Goodwin; Rebecca Schnall; Hugo Telles; Alejandra Iannizzotto; Myrian Sanjurjo; Barry R Lutz; Kenneth Pike; Fernando Rubinstein; Marcus Rhodehamel; Daniel Leon; Jesse Keyes; George Demiris Journal: Lancet Reg Health Am Date: 2022-06-10
Authors: Susan Kamal; Tracy R Glass; Thanh Doco-Lecompte; Sophie Locher; Olivier Bugnon; Jean-Jacques Parienti; Matthias Cavassini; Marie P Schneider Journal: Open Forum Infect Dis Date: 2020-08-13 Impact factor: 3.835