Adherence to Combined Antiretroviral Therapy and Associated Factors Among People Living with HIV Attending Nekemte Specialized Hospital, Oromia, Ethiopia: A Cross-Sectional Study

ART Adherence Among People Living with HIV Seeking Services from Public Health Facilities in Western Ethiopia

Background

The burden of COVID-19 is more severe among the elderly and patients affected with chronic medical conditions like diabetes, cardiovascular diseases, and HIV/AIDS. There is no study regarding the level of adherence to antiretroviral therapy (ART) during the time of COVID-19 in Ethiopia. So, this study aimed to assess the level of HIV positive patient’s adherence to ART treatment and associated factors in Nekemte public health facilities, Western Ethiopia.

Methods

This study was conducted in Nekemte public health facilities, Western Ethiopia from August 1, 2020 to September 30, 2020. A facility-based cross-sectional study was employed among HIV positive patients on ART treatment. A single proportion formula was used to calculate a sample size of 384 . A systematic random sampling technique was employed to select the sample population. Face-to-face interview questionnaires were used during data collection. A binary logistic regression model was fitted to identify factors associated with dependent factor, and an adjusted odds ratio (AOR) was used to estimate the strength of the association between dependent and independent variables.

Results

A total of 361 HIV positive patients have participated in the study, yielding a response rate of 94%. The mean age of the participants was 33.8 (SD ±9.8) years. Seventy-seven (21.3%) HIV-positive patients had not adhered to ART follow-up. The study found that living in rural areas (AOR=3.37, 95% CI=1.80–6.24), age less than 25 years (AOR=3.41, 95% CI=1.26–9.21), and substance use (AOR=5.42, 95% CI=1.8–16.29) were independent predictors of poor adherence to ART.

Conclusion

Generally, non-adherence to antiretroviral treatment among people living with HIV during the pandemic outbreak was high in the study area. A home-based delivery of ART treatment and improving retention mechanism during pandemics is highly recommended for concerned bodies. In addition, counseling on avoidance of substance use should be strengthened to increase retention on treatment.

Designing a Predictive Model for Antiretroviral Regimen at the Antiretroviral Therapy Center in Chiro Hospital, Ethiopia

Nowadays, the huge amount of patient's data significantly increases with respect to the time in repositories and data mining is increasingly used as an emerging research area in medical fields for extracting useful and previously unknown insights/patterns from the repository data. These unknown patterns/hidden insights can help in discovering new knowledge hidden in these data repositories. From the observation, different ARV regimens were ordered for different patients. However, combination of these drugs causes different side effects on the patients. It has been observed that there was a lack of predictive studies and designed models available in hospitals specifically ART Centers that accurately determine or classify the patient's ARV regimen to TDF + 3TC + EFV, TDF + 3TC + NVP, AZT + 3TC + ATV/R, AZT + 3TC + LPV/R, TDF + 3TC + LVP/R, TDF + 3TC + ATV/R, 8888, and ABC + 3TC + LPV/R. In order to solve these kinds of problems, we built an accurate classifier system or model using parameters like Patient Age, Patient Encounter Day, Patient Encounter Month, Patient Encounter Year, Patient Weight, Patient CD4 Count Adult, Patient TB Screen, Patient Following WHO Stage, Patient CD4 Percent Child, Patient Regimen Specify, Patient Regimen, and so on. The general objective of this research was predictive modeling for the patient's ARV regimen class through data mining techniques so as to improve them. The study used the CRIPS-DM methodology to find and interpret patterns in repositories. A decision tree (J48 and Random Forest) algorithm was used for classification. Using all tested classifiers, the investigation of the study shows that the total accuracy was more than 60%. On the other hand, among different classifications, class H (ABC + 3TC + LPV/R) has shown the worst prediction. But it was revealed that the J48 classifier relatively produces higher classification accuracy for the D (AZT-3TC-NVP) regimen. Here, classification depended on the selected parameters, which revealed that prediction accuracy value differed among all classifiers and the selected attributes. Finally, the study concluded that data mining can be used as a significant technique to discover patient regimen based on salient affecting factors with 96.1% precision achieved. Ensemble learning resolves the categorizing models of greater anticipating performance with different learning algorithms. This model aligned with sentimental investigation to magnify the appearances of the dataset either from the social media or from primary data collection. The empirical investigation with different parameters shows the detailed improvement of their learning methods.

Adherence to Antiretroviral Treatment Among People Who Started Treatment on the Same-Day of HIV Diagnosis in Ethiopia: A Multicenter Observational Study

Introduction

Methods

Results

Conclusion