Medication Adherence of People Living with HIV in Japan-A Cross-Sectional Study.
Yoji InoueShinichi OkaSeiji YokoyamaKoichi HasegawaJoerg MahlichUlrike SchaedeNoriyuki HabukaYoko MurataPublished in: Healthcare (Basel, Switzerland) (2023)
Long-term medical care for people living with HIV (PLHIV) is critical for treatment efficacy, and various studies have examined reasons for antiretroviral therapy (ART) non-adherence. In Japan, doctors assume patients maintain high adherence. However, little is known about real-world treatment adherence. We conducted an anonymous self-administered web-based survey asking about adherence for a total of 1030 Japanese PLHIV who were currently on ART. Adherence was determined using the eight-item Morisky Medication Adherence Scale (MMAS-8), for which scoring ranged from 0 to 8 and scores < 6 points were classified as low adherence. Data were analyzed based on patient-related factors; therapy-related factors; condition-related factors, such as a comorbidity with depression (utilizing the Patient Health Questionnaire 9, PHQ-9); and healthcare/system-related factors. Among 821 PLHIV who responded to the survey, 291 responders (35%) were identified as being in the low adherence group. A statistically significant relationship was found between the number of missed anti-HIV drug doses within the previous 2 weeks and long-term adherence, per the MMAS-8 score ( p < 0.001). Risk factors for low adherence included age (younger than 21 years, p = 0.001), moderate to severe depression ( p = 0.002, using the PHQ-9), and drug dependence ( p = 0.043). Adherence was also influenced by a shared decision-making process, including treatment selection, doctor-patient relations, and treatment satisfaction. Adherence was mainly affected by treatment decision factors. Hence, support of care providers should be considered critical for improving adherence.
Keyphrases
- antiretroviral therapy
- hiv infected
- glycemic control
- healthcare
- palliative care
- emergency department
- cross sectional
- public health
- hiv aids
- stem cells
- hiv positive
- hepatitis c virus
- combination therapy
- machine learning
- mental health
- ejection fraction
- end stage renal disease
- chronic kidney disease
- metabolic syndrome
- newly diagnosed
- risk assessment
- bone marrow
- social media
- hiv testing
- quality improvement
- artificial intelligence
- hiv infected patients
- big data
- deep learning
- weight loss