Abstract
As HIV/TB co-infected patients are started to be visited, it is common to measure weight and CD4 repeatedly overtime to determine the health status of patients. Most of the time linear mixed modeling of weight and CD4 count cannot handle the association between the outcomes whereas the joint modeling of multivariate linear mixed model does. Thus, this study was an attempt to model jointly the longitudinal CD4 and weight measurements of HIV/TB co-infected patients. This retrospective study consists of 254 HIV/TB co-infected patients who were 18 years old and above, and on ART followup from 1st February 2009 to 1st July 2014 at Jimma University Specialized Hospital. Firstly, weight and square root of CD4 count were analyzed separately. Based on the separate model, the joint models were built to know the correlation between mean change of weight and CD4 count overtime. Finally, appropriate model was selected based on AIC and BIC values. The fit statistics showed that the joint model fitted the data better than the separate model. From the joint model sex, educational level and functional status were the factors contributing to the prediction of HIV/TB co-infected patients weight at baseline. Beside the linear time effect has a positive effect on the mean change of weight whereas the quadratic time change has negative effect. The baseline CD4 count was differ by patient status and functional status. Further, the linear time effect has a positive sign and found to be statistically significant at 5 % level of significance on the mean change of the square root of CD4 count. Nevertheless, the quadratic time effect has a significant negative effect. The finding of the current study revealed that there was a moderate positive association between the mean change of weight and square root of CD4 count overtime.
Highlights
Background of the StudyTuberculosis and HIV have been closely linked since the emergence of AIDS and TB is the most common infectious disease affecting HIV-sero positive individuals and causing to their death [1,2]
The study deals with linear mixed modeling of weight and CD4 count measurements to know the factors that affect the mean change of each outcome variable overtime
When we look at the improvement of the model with inclusion of random intercept to that of random linear and quadratic time effects of the LMM, there was an improvement of the model and this model have lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values than the remaining six LMMs
Summary
Background of the StudyTuberculosis and HIV have been closely linked since the emergence of AIDS and TB is the most common infectious disease affecting HIV-sero positive individuals and causing to their death [1,2]. SBP and DBP for hypertensive patients and CD4 count, beta2-macroglobulin and weight for HIV infected patients were measured longitudinally at same time Since they are highly related changes in either often affect changes in the other. In such cases the univariate longitudinal analysis does not take into account correlation between observations on different response variables at each time points. Beside this knowing how the evolution of one is related to the evolution of the other, as well as how the association changes or evolves overtime is difficult from univariate longitudinal analysis. The study deals with linear mixed modeling of weight and CD4 count measurements to know the factors that affect the mean change of each outcome variable overtime
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