Virologic failure and second-line antiretroviral therapy in children in South Africa--the IeDEA Southern Africa collaboration

Article approval pendingWith expanding pediatric antiretroviral therapy (ART) access, children will begin to experience treatment failure and require second-line therapy. We evaluated the probability and determinants of virologic failure and switching in children in South Africa

Switching to second-line antiretroviral therapy in resource-limited settings: comparison of programmes with and without viral load monitoring.

In high-income countries, viral load is routinely measured to detect failure of antiretroviral therapy (ART) and guide switching to second-line ART. Viral load monitoring is not generally available in resource-limited settings. We examined switching from nonnucleoside reverse transcriptase inhibitor (NNRTI)-based first-line regimens to protease inhibitor-based regimens in Africa, South America and Asia

Growth of HIV-exposed uninfected infants in the first 6 months of life in South Africa: The IeDEA-SA collaboration

BACKGROUND: HIV-exposed uninfected (HEU) infants are a growing population in sub-Saharan Africa especially with the increasing coverage of more effective prevention of mother-to-child transmission (PMTCT) antiretroviral therapy regimens. This study describes the characteristics of South African HEU infants, investigates factors impacting birth weight and assesses their growth within the first 28 weeks of life. METHODS: This is a retrospective cohort based on routine clinical data from two South African PMTCT programmes. Data were collected between 2007 and 2013. Linear regression assessed factors affecting birth weight-for-age z-scores (WAZ) while growth (longitudinal WAZ) was assessed using mixed effects models. RESULTS: We assessed the growth of 2621 HEU infants (median birth WAZ was -0.65 (IQR -1.46; 0.0) and 51% were male). The feeding modalities practised were as follows: 0.5% exclusive breastfeeding, 7.9% breastfeeding with unknown exclusivity, 0.08% mixed breastfeeding and 89.2% formula feeding. Mothers with CD4 <200 cells/μl delivered infants with a lower birth WAZ (adjusted ß -0.253 [95% CI -0.043; -0.072], p = 0.006) compared to mothers with aCD4 ≥500 cells/μl. Similarly, mothers who did not receive antiretroviral drugs delivered infants with a lower birth WAZ (adjusted ß -0.39 [95% CI -0.67; -0.11], p = 0.007) compared to mothers who received antenatal antiretrovirals. Infants with a birth weight <2 500g (ß 0.070 [95% CI 0.061; 0.078], p <0.0001) experienced faster growth within the first 28 weeks of life compared to infants with a birth weight ≥2 500g. Infants with any breastfeeding exposure experienced slower longitudinal growth compared to formula fed infants (adjusted ß -0.012 [95% CI 0.021; -0.003], p = 0.011). CONCLUSION: Less severe maternal disease and the use of antiretrovirals positively impacts birth weight in this cohort of South African HEU infants. Formula feeding was common with breastfed infants experiencing marginally slower longitudinal growth

Identifying groups of people with similar sociobehavioural characteristics in Malawi to inform HIV interventions:a latent class analysis

Within many sub-Saharan African countries including Malawi, HIV prevalence varies widely between regions.This variability may be related to the distribution of population groups with specific sociobehavioural characteristics that influ-ence the transmission of HIV and the uptake of prevention. In this study, we intended to identify groups of people in Malawiwith similar risk profiles

Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.

INTRODUCTION High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics. METHODS We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies. RESULTS Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV. CONCLUSIONS We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints

Socio-behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub-Saharan African countries

INTRODUCTION Socio-behavioural factors may contribute to the wide variance in HIV prevalence between and within sub-Saharan African (SSA) countries. We studied the associations between socio-behavioural variables potentially related to the risk of acquiring HIV. METHODS We used Bayesian network models to study associations between socio-behavioural variables that may be related to HIV. A Bayesian network consists of nodes representing variables, and edges representing the conditional dependencies between variables. We analysed data from Demographic and Health Surveys conducted in 29 SSA countries between 2010 and 2016. We predefined and dichotomized 12 variables, including factors related to age, literacy, HIV knowledge, HIV testing, domestic violence, sexual activity and women's empowerment. We analysed data on men and women for each country separately and then summarized the results across the countries. We conducted a second analysis including also the individual HIV status in a subset of 23 countries where this information was available. We presented summary graphs showing associations that were present in at least six countries (five in the analysis with HIV status). RESULTS We analysed data from 190,273 men (range across countries 2295 to 17,359) and 420,198 women (6621 to 38,948). The two variables with the highest total number of edges in the summary graphs were literacy and rural/urban location. Literacy was negatively associated with false beliefs about AIDS and, for women, early sexual initiation, in most countries. Literacy was also positively associated with ever being tested for HIV and the belief that women have the right to ask their husband to use condoms if he has a sexually transmitted infection. Rural location was positively associated with false beliefs about HIV and the belief that beating one's wife is justified, and negatively associated with having been tested for HIV. In the analysis including HIV status, being HIV positive was associated with female-headed household, older age and rural location among women, and with no variables among men. CONCLUSIONS Literacy and urbanity were strongly associated with several factors that are important for HIV acquisition. Since literacy is one of the few variables that can be improved by interventions, this makes it a promising intervention target

gems: An R Package for Simulating from Disease Progression Models

Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The R package gems is a tool that simulates disease progression in patients and predicts the effect of different interventions on patient outcome. Disease progression is represented by a series of events (e.g., diagnosis, treatment and death), displayed in a directed acyclic graph. The vertices correspond to disease states and the directed edges represent events. The package gems allows simulations based on a generalized multistate model that can be described by a directed acyclic graph with continuous transition-specific hazard functions. The user can specify an arbitrary hazard function and its parameters. The model includes parameter uncertainty, does not need to be a Markov model, and may take the history of previous events into account. Applications are not limited to the medical field and extend to other areas where multistate simulation is of interest. We provide a technical explanation of the multistate models used by gems, explain the functions of gems and their arguments, and show a sample application

Outcomes of Antiretroviral Therapy in the Swiss HIV Cohort Study: Latent Class Analysis

An in-depth understanding of the different groups that make up the HIV-infected population should inform prevention and care. Using latent class analysis (LCA) we identified seven groups with similar socio-demographic and behavioral characteristics at enrolment in the Swiss HIV Cohort Study: older gay men, younger gay men, older heterosexual men, injection drug users, single migrants, migrant women in partnerships and heterosexual men and women. Outcomes of combination antiretroviral therapy (ART) were analyzed in 1,633 patients starting ART. Compared to older gay men, the probability of a virologic response to ART was reduced in single migrants, in older heterosexual men and in IDUs. Loss to follow-up was higher in single migrants and IDUs, and mortality was increased in older heterosexual men and IDUs. Socio-behavioral groups identified by LCA allow insights above what can be gleaned from traditional transmission groups, and may identify patients who could benefit from targeted intervention

"I do all I can but I still fail them": Health system barriers to providing Option B+ to pregnant and lactating women in Malawi.

Malawi's Option B+ program is based on a 'test and treat' strategy that places all HIV-positive pregnant and lactating women on lifelong antiretroviral therapy. The steep increase in patient load placed severe pressure on a health system that has struggled for decades with inadequate supply of health care workers (HCWs) and poor infrastructure. We set out to explore health system barriers to Option B+ by asking HCWs in Malawi about their experiences treating pregnant and lactating women. We observed and conducted semi-structured interviews (SSIs) with 34 HCWs including nine expert clients (ECs) at 14 health facilities across Malawi, then coded and analyzed the data. We found that HCWs implementing Option B+ are so overburdened in Malawi that it reduces their ability to provide quality care to patients, who receive less counseling than they should, wait longer than is reasonable, and have very little privacy. Interventions that increase the number of HCWs and upgrade infrastructure to protect the privacy of HIV-infected pregnant and lactating women and their husbands could increase retention, but facilities will need to be improved to support men who accompany their partners on clinic visits