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Retention in HIV care plays a major role in the successful management and treatment of HIV (Bofill, Waldrop-Valverde, Metsch, Pereyra, & Kolber, 2011; Burgoyne, 2005; Hightow-Weidman, Smith, Valera, Matthews, & Lyons, 2011). Current research has demonstrated that the effectiveness of HAART is directly related to client adherence to medical appointments (Horstmann, Brown, Islam, Buck, & Agins, 2010). Researchers also reported that psychosocial factors, socioeconomic status, distance traveled to seek HIV care, and gender influences the decision for patients adhering to their HIV medical appointments.
A review of the research evidence indicates that there remains much to be understood about factors that may influence adherence to medical appointments within the context of HIV/AIDS. Interventions effective in promoting medical appointment adherence and subsequently, positive health outcomes, requires an understanding of those factors driving adherence behavior. More importantly, identification of factors influencing adherence to medical appointments amenable to intervention is essential to the development of intervention strategies that are effective in keeping HIV-infected individuals engaged in care and treatment.
Purpose: A majority of the HIV treatment facilities have access to patients social demographic data, viral load, CD4 count, and may have some form of psychometric measurements. The study proposed that a structural equation modeling (SEM) path analysis may be an alternative technique to assess the effects of multiple psychosocial factors with respect to their influence on medical appointment adherence. The study also demonstrates the implementation of SEM in one of the prior medical appointment adherence study.
Methods: SEM is a statistical analysis technique that has the same capabilities as multiple regression analysis, with the interpretation similar to the regression method. In SEM, the relationships among the variables of interests (paths) are tested simultaneously and parse out the direct and indirect effects of variables. The SEM tests the null hypothesis of “no difference” between the hypothesized model and the data; it also tests the hypothesized relationships among study variables. The goal in SEM is to be able to accept rather than reject the null hypothesis. Acceptance of the null hypothesis of “no difference” indicates that the model fits the data and the null hypothesis is confirmed. If the null hypothesis is rejected and the model does not fit the data, the null hypothesis is rejected, and modifications of the model are needed.
Results: From a previous medical appointment adherence study that utilizing SEM, the following proposed relationships among the causal model variable were found to be consistent with the data: (1) distance to treatment facility and depression were found to have direct positive effects on adherence to medical appointments; (2) substance abuse was found to have a direct negative effect on adherence; (3) social network had a direct positive effect on substance abuse; and (4) HIV disease progression had a direct negative effect on substance abuse. The fully trimmed model provided a good fit to the observed data, with a χ2 (21, N = 338) = 22.31, p =.38; GFI = 0.99; RMSEA = 0.03, and CFI = 0.99. Thus the null hypothesis of no differences was supported for the trimmed model. However, the model only accounted for eight percent of the variance in adherence to medical appointments (R2 = 0.08). Longitudinal study of the relationships among causal model variable is recommended for deeper understanding of the pattern of HIV medical appointments adherence over time.
Conclusion: This research will present an overview of the SEM process to the reader(s). It also underscores the capability of utilizing structural equation path analysis in assessing the effects of multiple psychosocial factors with respect to their influence on medical appointment adherence.