Treatment satisfaction and medication adherence and predictors among patients with heart failure in Ethiopia: a hospital-based cross-sectional study.

BACKGROUND

Globally, about 18 million people died from cardiovascular diseases (CVDs) in 2019, over three-quarters in developing countries. Non-adherence to medication in CVD patients causes hospitalization, worsened symptoms, higher healthcare costs, and more emergency visits. Hence, this study aimed to assess treatment satisfaction and medication adherence and predictors in heart failure (HF) patients attending Debre Berhan Comprehensive Specialized Hospital (DBCSH), Ethiopia.

METHODS

A hospital-based cross-sectional study was undertaken at the medical referral clinic of DBCSH. A total of 344 ambulatory HF patients who visited the medical care of the DBCSH medical referral clinic during the study period were included. Treatment satisfaction was assessed using a self-administered Medicine Questionnaire (SATMED-Q). Relationships between predictor variables and treatment satisfaction were determined using one-way analysis of variance (ANOVA) and an independent -test. Medication adherence was determined using the Morisky Green Levin Medication Adherence Scale (MGLS).

RESULTS

Participants with drug-drug interactions (DDIs) were approximately 38% less likely to adhere to medication compared to their counterparts (AOR = 0.62, 95% CI: 0.54-0.71). Additionally, participants who had taken five or more drugs were approximately 68% less likely to adhere to medication compared to those who had taken only one drug (AOR = 0.32, 95% CI: 0.2-0.51). The correlation between medication adherence and drug-drug interactions remains a possible pseudo-correlation via the number of medications taken. There was a noteworthy positive correlation (rs = 0.34, = 0.027) between participants' treatment adherence and treatment satisfaction.

CONCLUSION

The rate of treatment satisfaction and treatment adherence among HF patients was 67.6% and 60.9%, respectively. The presence of DDI and the number of drugs were identified as predictors to medication adherence.