Evaluation of two staging systems for HIV infection for use in developing countries

Assessment of the prognostic value of the World Health Organization clinical staging system for HIV/AIDS in HIV-infected children and adolescents in a cohort in Belo Horizonte, Brazil

This study aimed to evaluate the prognostic value of WHO clinical staging system in HIV-infected children and adolescents in Belo Horizonte, Brazil. WHO clinical stages were evaluated for risk of disease progression in 335 patients admitted from 1989 to 2003. In Kaplan-Meier analysis, age, clinical stage, CD4% <15% and viral load >5 log(10) were statistically significant. In Cox proportional hazards model, the relative risk of disease progression for Stage 4 at admission and in the worst moment were 3.47 [confidence interval (CI) 95% CI = 1.92-6.26] and 2.89 (95% CI = 1.44-5.79). Stages 2 and 3 were neutral as predictors of risk either of disease progression. CD4% <15% and viral load > 5 log(10) remained statistically significant in multivariate analysis. WHO clinical Stage 4 was a good predictor of risk of progression in this cohort. The findings support WHO proposition to start antiretroviral treatment for patients at a more advanced clinical stage.

Calculating stage duration statistics in multistage diseases

Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed.

Late-disease stage at presentation to an HIV clinic in the era of free antiretroviral therapy in Sub-Saharan Africa

BACKGROUND

Access to free antiretroviral therapy in sub-Saharan Africa has been steadily increasing, and the success of large-scale antiretroviral therapy programs depends on early initiation of HIV care. However, little is known about the stage at which those infected with HIV present for treatment in sub-Saharan Africa.

METHODS

We conducted a cross-sectional analysis of initial visits to the Immune Suppression Syndrome Clinic of the Mbarara University Teaching Hospital, including patients who had their initial visit between February 2007 and February 2008 (N = 2311).

RESULTS

The median age of the patients was 33 years (range 16-81 years), and 64% were female. More than one third (40%) were categorized as late presenters, that is, World Health Organization disease stage 3 or 4. Male gender, age 46-60 years (vs. younger), lower education level, being unemployed, living in a household with others, being unmarried, and lack of spousal HIV status disclosure were independently associated with late presentation, whereas being pregnant, having young children, and consuming alcohol in the prior year were associated with early presentation.

CONCLUSIONS

Targeted public health interventions to facilitate earlier entry into HIV care are needed, as well as additional study to determine whether late presentation is due to delays in testing vs. delays in accessing care.