Scale-Up of HIV Antiretroviral Therapy and Estimation of Averted Infections and HIV-Related Deaths - Uganda, 2004-2022.

On January 28, 2003, the U.S. President's Emergency Plan for AIDS Relief (PEPFAR), the largest commitment by any nation to address a single disease in history, was announced.* In April 2004, the first person in the world to receive PEPFAR-supported antiretroviral therapy (ART) was a man aged 34 years in Uganda. Effective ART reduces morbidity and mortality among persons with HIV infection (1) and prevents both mother-to-child transmission (MTCT) (2) and sexual transmission once viral load is suppressed to undetectable levels (<200 viral copies/mL) (3). By September 2022, more than 1.3 million persons with HIV infection in Uganda were receiving PEPFAR-supported ART, an increase of approximately 5,000% from September 2004. As indicators of the ART program's effectiveness, a proxy MTCT rate decreased 77%, from 6.4% in 2010 to 1.5% in 2022, and the viral load suppression rate (<1,000 viral copies/mL) increased 3%, from 91% in 2016 to 94% in September 2022. During 2004-2022, ART scale-up helped avert nearly 500,000 HIV infections, including more than 230,000 infections among HIV-exposed infants, and approximately 600,000 HIV-related deaths. Going forward, efforts will focus on identifying all persons with HIV infection and rapidly linking them to effective ART. PEPFAR remains committed to continued strong partnership with the Government of Uganda, civil society, and other development partners toward sustainable solutions aligned with the Joint United Nations Programme on HIV/AIDS (UNAIDS) fast-track strategy to ending the global AIDS epidemic by 2030† and safeguarding impact achieved in the long term.

Adaptive network modeling of social distancing interventions.

The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect.

A Low-Dimensional Network Model for an SIS Epidemic: Analysis of the Super Compact Pairwise Model.

Network-based models of epidemic spread have become increasingly popular in recent decades. Despite a rich foundation of such models, few low-dimensional systems for modeling SIS-type diseases have been proposed that manage to capture the complex dynamics induced by the network structure. We analyze one recently introduced model and derive important epidemiological quantities for the system. We derive the epidemic threshold and analyze the bifurcation that occurs, and we use asymptotic techniques to derive an approximation for the endemic equilibrium when it exists. We consider the sensitivity of this approximation to network parameters, and the implications for disease control measures are found to be in line with the results of existing studies.

Triangulating Truth and Reaching Consensus on Population Size, Prevalence, and More: Modeling Study.

BACKGROUND: Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate. OBJECTIVE: This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data. METHODS: In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation. RESULTS: The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation. CONCLUSIONS: The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model's effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges.

Modelling the impact of CD4 testing on mortality from TB and cryptococcal meningitis among patients with advanced HIV disease in nine countries.

INTRODUCTION: Despite antiretroviral therapy (ART) scale-up among people living with HIV (PLHIV), those with advanced HIV disease (AHD) (defined in adults as CD4 count <200 cells/mm3 or clinical stage 3 or 4), remain at high risk of death from opportunistic infections. The shift from routine baseline CD4 testing towards viral load testing in conjunction with "Test and Treat" has limited AHD identification. METHODS: We used official estimates and existing epidemiological data to project deaths from tuberculosis (TB) and cryptococcal meningitis (CM) among PLHIV-initiating ART with CD4 <200 cells/mm3 , in the absence of select World Health Organization recommended diagnostic or therapeutic protocols for patients with AHD. We modelled the reduction in deaths, based on the performance of screening/diagnostic testing and the coverage and efficacy of treatment/preventive therapies for TB and CM. We compared projected TB and CM deaths in the first year of ART from 2019 to 2024, with and without CD4 testing. The analysis was performed for nine countries: South Africa, Kenya, Lesotho, Mozambique, Nigeria, Uganda, Zambia, Zimbabwe and the Democratic Republic of Congo. RESULTS: The effect of CD4 testing comes through increased identification of AHD and consequent eligibility for protocols for AHD prevention, diagnosis and management; algorithms for CD4 testing avert between 31% and 38% of deaths from TB and CM in the first year of ART. The number of CD4 tests required per death averted varies widely by country from approximately 101 for South Africa to 917 for Kenya. CONCLUSIONS: This analysis supports retaining baseline CD4 testing to avert deaths from TB and CM, the two most deadly opportunistic infections among patients with AHD. However, national programmes will need to weigh the cost of increasing CD4 access against other HIV-related priorities and allocate resources accordingly.