Assessment of Casual Relations, Direct and Indirect Effects of Latent Covariates On CD4 Cell Count Change Among HIV Positive Adults

Background: The use of structural equation modelling and latent variables remains unusual in epidemiology despite its potential usefulness and assessment of casual relations. Measuring direct and indirect effect of latent variables helps for proper intervention and for the ART program to be effective. The main objective of current investigation was to assess casual relations, direct and indirect effect of latent covariates on CD4 cell count change for HIV positive adults under HAART. Methods: Based on the repeated measures of CD4 cell count change data obtained in ART section at Felege Hiwot Teaching and Specialized hospital, AMOS software was employed parameter estimation. The data was conducted on 792 randomly selected HIV positive adults. The data was collected by the health staff for sake of delivering medication adherence. Results: CD4 cell count change was directly and indirectly affected by the latent variables. The powers of effects of observed variables with and without latent variables were a little bit different from each other. Hence, the power effect of observed variables with latent variables was lower as compared to without latent variables. The direct effect of latent variables on the response variable was a little bit greater than indirect effect. Conclusion: The power of effects of observed variables were stronger than their effects with latent variables. Hence, the latent variables had significant contributions for progress of CD4 cell count change. Health related education about the direct and indirect effect of latent variables should be given for patients under HAART.

Structural Equation Modeling to Detect Predictors of CD4 Cell Count Change due to Long Term Antiretroviral Therapy Administered to HIV-Positive Adults at Felege Hiwot Teaching and Specialized Hospital, Bahir Dar, Ethiopia

Background The relationship between predictors and the variable of interest was estimated using a structural equation model which is used to predict latent variables. The main advantage of the SEM is the ability to estimate the direct and indirect pathways of the effect of the primary independent variable on the outcome, given sufficient sample sizes. Despite not directly modeling the mediated pathways, GLMMs excluding mediating variables performed well with respect to power, bias and coverage probability in modeling the total effect of the primary independent variables on the outcome. In longitudinal studies, data are collected from subjects at several time points. The main purpose of longitudinal analysis is to detecting the trends or trajectories of the variables of interest. Methods A longitudinal study was conducted on 792 adults living with HIV/AIDS who commenced HAART. Structural equation modeling was used to construct a model to detecting predictors of CD4 cell count change. The procedure was illustrated by applying it to longitudinal health-related quality-of-life data on HIV/AIDS patients, collected from September 2008 to August 2012 monthly for the first six months and quarterly for remaining study period. Results The result of current investigation indicates that CD4 cell count change was highly influenced by certain socio-demographic and clinical variables. Out of all the participants, 141 (82%) have been considered 100% adherent to antiretroviral therapy. Structural equation modeling has confirmed the direct effect that personality (decision-making and tolerance of frustration) has on motives to behave, or act accordingly, which was in turn directly related to medication adherence behaviors. In addition, these behaviors have had a direct and significant effect on viral load, as well as an indirect effect on CD4 cell count. The final model demonstrates the congruence between theory and data (x2/df. = 1.480, goodness of fit index = 0.97, adjusted goodness of fit index = 0.94, comparative fit index = 0.98, root mean square error of approximation = 0.05), accounting for 55.7% of the variance. Conclusions The results of this study support our theoretical model as a conceptual framework for the prediction of medication adherence behaviors in persons living with HIV/AIDS. Implications for designing, implementing, and evaluating intervention programs based on the model are to be discussed.

Socio-Demographic and Clinical Predictor Variables on CD4 Cell Count Change among HIV Positive adults; a Structural Equations Modelling

Background: The prevalence of HIV/AIDS among adult individuals has been increasing in Sub-Sahara African countries over the last decade. In Ethiopia , the prevalence of HIV among adults was 1%. Hence, 23, 000 people were newly infected with HIV and 11,000 people were died because of AIDS related illness in 2018. The purpose of this study was to identify the most significant socio-demographic, economic, individual and clinical determinants of CD4 cell count change in HIV positive adults who initiated HAART at Felege Hiwot Teaching and Specialized Hospital, North-West Ethiopia. Methods: A secondary and retrospective study design was conducted on 792 HIV positive adults. A structural equation modeling was employed to identify the socio-demographic and clinical covariates that have a statistically significant effect on the status of CD4 cell count change. Results: Literate patients, patients living with partner, patients living in urban area, patients disclosed the disease to family members, high income , ownership of cell, age and sex (male) were statistically significant variables. Conclusion: There was direct relation between socio-demographic variables with retention of HIV positive individuals in HAART program. There was also a direct and significant effect of clinical variables on adherence competence and adherence on CD4 cell change. Retention of patients in the HAART program had direct and significant effect on CD4 cell count change. This finding will be important for policy makers, health officials and for patients to easier access to healthcare service. Keywords: Socio-demographic, clinical factors, individual characteristics, Structural equation, CD4count change