Dataset Information

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women.

ABSTRACT:

Background

Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements.

Methods

The data is from an ongoing prospective cohort study conducted amongst adult women who are HIV-infected patients in Kwazulu-Natal, South Africa. Participants were enrolled into the acute infection, then into early infection subsequently into established infection and afterward on cART. Generalized linear multilevel models were applied.

Results

Multilevel ordinal non-proportional and proportional-odds growth models were presented and compared. We observed that the effects of covariates can't be assumed identical across the three cumulative logits. Our analyses also revealed that the rate of change of immune recovery of patients increased as the follow-up time increases. Patients with stable sexual partners, middle-aged, cART initiation, and higher educational levels were more likely to have better immunological stages with time. Similarly, patients having high electrolytes component scores, higher red blood cell indices scores, higher physical health scores, higher psychological well-being scores, a higher level of independence scores, and lower viral load more likely to have better immunological stages through the follow-up time.

Conclusion

It can be concluded that the multilevel non-proportional-odds method provides a flexible modeling alternative when the proportional-odds assumption of equal effects of the predictor variables at every stage of the response variable is violated. Having higher clinical parameter scores, higher QoL scores, higher educational levels, and stable sexual partners were found to be the significant factors for trends of CD4 count recovery.

SUBMITTER: Dessie ZG

PROVIDER: S-EPMC7310392 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women.

Dessie Zelalem G ZG Zewotir Temesgen T Mwambi Henry H North Delia D

BMC infectious diseases 20200623 1

<h4>Background</h4>Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements.<h4>Methods</h4>The data is from an ongoing prospective cohort study conducted amongst adult women who are HIV-infected patients in Kwazulu-Natal, ...[more]

PMID: 32576220

Similar Datasets

Project description:IntroductionPast endeavours to deal with the obstacle of expensive Cluster of Difference 4 (CD4+) count diagnostics in resource-limited settings have left a long trail of suggested continuous CD4+ count clinical covariates that turned out to be a potentially important integral part of the human immunodeficiency virus (HIV) treatment process during disease progression. However, an evaluation to determine the strongest candidates among these CD4+ count covariates has not been well documented.MethodsThe Centre for the AIDS Programme of Research in South Africa (CAPRISA) initially enrolled HIV-negative (phase 1) patients into different study cohorts. The patients who seroconverted (237) during follow-up care were enrolled again into a post-HIV infection cohort where they were further followed up with weekly to fortnightly visits up to 3 months (phase 2: acute infection), monthly visits from 3-12 months (phase 3: early infection) and quarterly visits thereafter (phase 4: established infection) until antiretroviral therapy (ART) initiation (phase 5). The CD4+ count and 46 covariates were repeatedly measured at each phase of the HIV disease progression. A multilevel partial least squares approach was applied as a variable reduction technique to determine the strongest CD4+ count covariates.ResultsOnly 18 of the 46 investigated clinical attributes were the strongest CD4+ count covariates and the top 8 were positively and independently associated with the CD4+ count. Besides the confirmatory lymphocytes, these were basophils, albumin, haematocrit, alkaline phosphatase (ALP), mean corpuscular volume (MCV), platelets, potassium and monocytes. Overall, electrolytes, proteins and red blood cells were the dominant categories for the strongest covariates.ConclusionOnly a few of the many previously suggested continuous CD4+ count clinical covariates showed the potential to become an important integral part of the treatment process. Prolonging the pre-treatment period of the HIV disease progression by effectively incorporating and managing the covariates for long-term influence on the CD4+ cell response has the potential to delay challenges associated with ART side effects.

Project description:BackgroundThe CD4 cell count signifies the health of an individual's immune system. The use of data-driven models enables clinicians to accurately interpret potential information, examine the progression of CD4 count, and deal with patient heterogeneity due to patient-specific effects. Quantile-based regression models can be used to illustrate the entire conditional distribution of an outcome and identify various covariates effects at the respective location.MethodsThis study uses the quantile mixed-effects model that assumes an asymmetric Laplace distribution for the error term. The model also incorporated multiple random effects to consider the correlation among observations. The exact maximum likelihood estimation was implemented using the Stochastic Approximation of the Expectation-Maximization algorithm to estimate the parameters. This study used the Centre of the AIDS Programme of Research in South Africa (CAPRISA) 002 Acute Infection Study data. In this study, the response variable is the longitudinal CD4 count from HIV-infected patients who were initiated on Highly Active Antiretroviral Therapy (HAART), and the explanatory variables are relevant baseline characteristics of the patients.ResultsThe analysis obtained robust parameters estimates at various locations of the conditional distribution. For instance, our result showed that baseline BMI (at [Formula: see text] 0.05: [Formula: see text]), baseline viral load (at [Formula: see text] 0.05: [Formula: see text] [Formula: see text]), and post-HAART initiation (at [Formula: see text] 0.05: [Formula: see text]) were major significant factors of CD4 count across fitted quantiles.ConclusionsCD4 cell recovery in response to post-HAART initiation across all fitted quantile levels was observed. Compared to HIV-infected patients with low viral load levels at baseline, HIV-infected patients enrolled in the treatment with a high viral load level at baseline showed a significant negative effect on CD4 cell counts at upper quantiles. HIV-infected patients registered with high BMI at baseline had improved CD4 cell count after treatment, but physicians should not ignore this group of patients clinically. It is also crucial for physicians to closely monitor patients with a low BMI before and after starting HAART.

Project description:BACKGROUND:Despite the success of prevention of mother to child transmission (PMTCT) program in South Africa, the 30% HIV prevalence among women of childbearing age requires the PMTCT program to be maximally efficient to sustain gains in the prevention of vertical HIV transmission. We aimed to determine the immunologic and virologic status at entry into antenatal care (ANC) and at childbirth among HIV positive women who conceived under the CD4<500 cells/?l antiretroviral therapy (ART) eligibility threshold and universal test and treat (UTT) policies in the Gauteng province of South Africa. METHOD:We conducted a retrospective cohort study of 692 HIV positive adult (>18 years) postpartum women who gave birth between September 2016 and December 2017. Demographic, viral load (VL) and CD4 data at ANC start (3-9 months before delivery) and delivery (3 months before/after) were obtained from medical records of consenting women. We compared CD4?500 cell/?l and viral load (VL) suppression (<400 copes/ml) rates at ANC start and delivery among women with a pre-pregnancy ART, women known HIV positive but with in-pregnancy ART and newly diagnosed women with in-pregnancy ART. Predictors of having a high CD4 and suppressed VL were assessed by log-binomial regression. RESULTS:Of the 692 participants, 394 (57.0%) had CD4 data and 326 (47.1%) had VL data. Overall women with a pre-pregnancy ART were more likely to start ANC with CD4 count?500 cell/?l (46.3% vs 24.8%, adjusted risk ratio (aRR) = 1.9; 95% confidence interval (95% CI): 1.4-2.5), compared to newly diagnosed women. This difference was no longer apparent at the time of delivery (aRR 1.2 95% CI: 0.4-3.7). Similarly, viral suppression at delivery was higher among women with pre-pregnancy ART (87.2% vs 69.3%, aRR 1.3, 95% CI: 1.1-1.6) as compared to the newly diagnosed women. Viral suppression rate among newly diagnosed women increased substantially by the time of delivery from 43.5% to 69.3% (p = 0.001). CONCLUSION:These results show that pre-pregnancy ART improves immunologic and virologic control during pregnancy and call for renewed efforts in HIV testing, linkage to ART and viral monitoring.

Dataset Information

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women.

Background

Methods

Results

Conclusion

Publications

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets