PREDICTORS OF NON-ADHERENCE TO HAART AND TIME TO DEFAULT FROM TREATMENT ADMINISTERED TO HIV‑POSITIVE ADULTS AT FELEGE HIWOT  TEACHING AND SPECIALIZED HOSPITAL: A JOINT MODELING DATA ANALYSIS:

KOYACHEW, BITEW

dc.contributor.author	KOYACHEW, BITEW
dc.date.accessioned	2018-07-18T10:00:49Z
dc.date.available	2018-07-18T10:00:49Z
dc.date.issued	2018-07-18
dc.identifier.uri	http://hdl.handle.net/123456789/8904
dc.description.abstract	Back ground: East and Southern Africa is the hardest region hit by the HIV as compared to countries in other parts of the world. Ethiopia is one of the few countries with the highest number of people living with HIV/AIDS. Amhara region is one of the worst regions significantly affected by the disease in the country. The main objective of this research was to identifying the predictors of non adherence of HIV/AIDS patients and time to default from HAART in FelegeHiwot Teaching and Specialized Hospital by using joint modeling approach. Methods: Longitudinal data was obtained from a simple of 220 HIV/AIDS adult patients at Felege-Hiwot Teaching and Specialized Hospital in north-west, Ethiopia. Two methods of modeling approaches were used: separate and joint modeling. Joint modeling was conducted for an analysis of non-adherence and the time to default from HAART. In the joint model, a GLMM and Cox PH sub-models were employed together for non-adherence and time to default respectively. The two models were linked through their shared unobserved random effects using a shared parameter model. Results: Both separate and joint modeling approach depicted consistent results for significant predictors. However, the joint modeling approach as compared to separate models revealed a reduction in the standard errors which indicates that more adequate and efficient inferences can be made by using joint model estimates. About 51% of the patients were categorized under well nourished and over 70% of them disclosed their disease status. The estimated hazard for the association parameter (𝜃 ) in the survival sub model under joint model analysis was 6.359 which is statistically significantly [with95% CI: 2.440-16.574, p-value=0.0002]. This indicates that there was strong evidence the effect of the longitudinal biomarker to the risk of defaulting. Patient’s sex, religion, disclosure of the disease, WHO stages, functional status and nutritional status were significant predictors for both non adherence and the time-to default from HAART. 0 Conclusions: Under this investigation, some groups of HIV/AIDS patients were with high nonadherence rate and defaulting rate from HAART, therefore, such patients need high intervention to adhere the prescribed medication and stay on HAART. Keywords: Non-adherence, Separate Model, Joint Model, Time to default, HAART	en_US
dc.language.iso	en_US	en_US
dc.subject	STATISTICS	en_US
dc.title	PREDICTORS OF NON-ADHERENCE TO HAART AND TIME TO DEFAULT FROM TREATMENT ADMINISTERED TO HIV‑POSITIVE ADULTS AT FELEGE HIWOT TEACHING AND SPECIALIZED HOSPITAL: A JOINT MODELING DATA ANALYSIS:	en_US
dc.type	Thesis	en_US