Factors Affecting Patient Adherence to Highly Active Antiretroviral Therapy

Escobar, Ismael; Campo, Mercedes; Martín, Jesús Carbajo San; Fernández-Shaw, Clara; Pulido, Federico; Rubio, Rafael

doi:10.1345/aph.1c427

Cited by 33 publications

(23 citation statements)

References 18 publications

(36 reference statements)

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“…The result of this study also indicates that female patients had more probability to adhere the prescribed medication than males, and through time they had achieved high rate of change of CD4 cell count. This result, however, is inconsistent with previous studies [38]. …”

Section: Discussioncontrasting

confidence: 99%

Joint longitudinal data analysis in detecting determinants of CD4 cell count change and adherence to highly active antiretroviral therapy at Felege Hiwot Teaching and Specialized Hospital, North-west Ethiopia (Amhara Region)

2017

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BackgroundAdherence and CD4 cell count change measure the progression of the disease in HIV patients after the commencement of HAART. Lack of information about associated factors on adherence to HAART and CD4 cell count reduction is a challenge for the improvement of cells in HIV positive adults. The main objective of adopting joint modeling was to compare separate and joint models of longitudinal repeated measures in identifying long-term predictors of the two longitudinal outcomes: CD4 cell count and adherence to HAART.MethodsA longitudinal retrospective cohort study was conducted to examine the joint predictors of CD4 cell count change and adherence to HAART among HIV adult patients enrolled in the first 10 months of the year 2008 and followed-up to June 2012. Joint model was employed to determine joint predictors of two longitudinal response variables over time. Furthermore, the generalized linear mixed effect model had been used for specification of the marginal distribution, conditional to correlated random effect.ResultsA total of 792 adult HIV patients were studied to analyze the longitudinal joint model study. The result from this investigation revealed that age, weight, baseline CD4 cell count, ownership of cell phone, visiting times, marital status, residence area and level of disclosure of the disease to family members had significantly affected both outcomes. From the two-way interactions, time * owner of cell phone, time * sex, age * sex, age * level of education as well as time * level of education were significant for CD4 cell count change in the longitudinal data analysis. The multivariate joint model with linear predictor indicates that CD4 cell count change was positively correlated (p ≤ 0.0001) with adherence to HAART. Hence, as adherence to HAART increased, CD4 cell count also increased; and those patients who had significant CD4 cell count change at each visiting time had been encouraged to be good adherents.ConclusionJoint model analysis was more parsimonious as compared to separate analysis, as it reduces type I error and subject-specific analysis improved its model fit. The joint model operates multivariate analysis simultaneously; and it has great power in parameter estimation. Developing joint model helps validate the observed correlation between the outcomes that have emerged from the association of intercepts. There should be a special attention and intervention for HIV positive adults, especially for those who had poor adherence and with low CD4 cell count change. The intervention may be important for pre-treatment counseling and awareness creation. The study also identified a group of patients who were with maximum risk of CD4 cell count change. It is suggested that this group of patients needs high intervention for counseling.

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Section: Discussioncontrasting

confidence: 99%

Joint longitudinal data analysis in detecting determinants of CD4 cell count change and adherence to highly active antiretroviral therapy at Felege Hiwot Teaching and Specialized Hospital, North-west Ethiopia (Amhara Region)

2017

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“…The reasons for these associations are not clear. One possible explanation is that these groups had lower levels of adherence [33][34][35][36][37]. However, because all individuals in the present study had achieved at least 1 viral load !50 copies/mL, one might expect some of this effect to be removed.…”

Section: Discussionmentioning

confidence: 72%

The Rate of Viral Rebound after Attainment of an HIV Load <50 Copies/mL According to Specific Antiretroviral Drugs in Use: Results from a Multicenter Cohort Study

Smith¹,

Phillips²,

Hill³

et al. 2005

J INFECT DIS

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“…27,28 Moreover, other factors that often accompany injection drug use, such as low income, low education level, and unemployment, may create additional barriers to adherence. 29,30 Interventions to improve adherence among IDUs include drug maintenance therapy, 31,32 in which the addiction itself is treated and may result in better adherence to HAART, and directly observed therapy, 33,34 in which patients are observed taking their antiretroviral medications. Such interventions should be made widely available for IDUs and presented in a gender-sensitive manner.…”

Section: Discussionmentioning

confidence: 99%

Gender Differences in HIV-1 RNA Rebound Attributed to Incomplete Antiretroviral Adherence Among HIV-Infected Patients in a Population-Based Cohort

Kuyper¹,

Wood²,

Montaner³

et al. 2004

JAIDS Journal of Acquired Immune Deficiency Syndromes

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We found that higher rates of HIV-1 RNA rebound among women were primarily explained by incomplete adherence, which was more prevalent among women in this cohort. Our findings suggest that psychosocial factors such as drug use and incomplete adherence predict HIV-1 RNA rebound and that gender differences in time to rebound can be largely attributed to a disproportionate prevalence of these factors among women in this population.

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Factors Affecting Patient Adherence to Highly Active Antiretroviral Therapy

Abstract: We recommended special intervention to reinforce adherence for younger patients, patients taking a high number of antiretroviral drugs, those who have a history of intravenous drug use, and those with high anxiety status.

Cited by 33 publications

References 18 publications

Joint longitudinal data analysis in detecting determinants of CD4 cell count change and adherence to highly active antiretroviral therapy at Felege Hiwot Teaching and Specialized Hospital, North-west Ethiopia (Amhara Region)

Joint longitudinal data analysis in detecting determinants of CD4 cell count change and adherence to highly active antiretroviral therapy at Felege Hiwot Teaching and Specialized Hospital, North-west Ethiopia (Amhara Region)

The Rate of Viral Rebound after Attainment of an HIV Load <50 Copies/mL According to Specific Antiretroviral Drugs in Use: Results from a Multicenter Cohort Study

Gender Differences in HIV-1 RNA Rebound Attributed to Incomplete Antiretroviral Adherence Among HIV-Infected Patients in a Population-Based Cohort

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