Brazil is on the list of affected countries in which, prevention, diagnosis, and treatment of TB are challenging. Treatment success among those diagnosed is slightly more than 70%. COVID-19 has turned the situation even more critical by dismantling policies aimed at confronting the disease, the stress generated within health services, and changes in the epidemiology of TB in the Brazilian context. Essential strategies such as DOTS, which were successful in controlling the disease have had their activities suspended. The inclusion of TB in the political agenda has been questionable by Social Movements, and their actions may have had a portentous effect in protecting families affected by TB. Technological innovations are needed by TB health services, to monitor people affected by the disease remotely. However, many professionals found themselves without this resource. Thus, this study aims to evaluate the impact of COVID-19 on TB prevention and control. The project is structured in three domains: Domain 1: Policy approach: interviews will be conducted with key informants from institutions, management of social movements, and collection of official documents, with subsequent triangulation. In Domain 2: Epidemiologic evaluation: A survey of TB data and COVID-19 to conform the cohorts and thus evaluate the real impact of the pandemic impact on TB. In Domain 3: digital technology impact assessment: users with TB (using the VDOT system) in some municipalities of the five macro-regions will be selected based on Domain 2. The vDOT will be verified and the impact of vDOT on adherence and care will be assessed. This project is being sponsored by CNPQ – The National Council for Scientific and Technological Development) in Brazil.
Adherence to HIV and tuberculosis (TB) treatment is the most effective means to improve patient outcomes. Stigma, which undermines adherence and reinforces healthcare inequities, remains a major barrier to achieving TB eradication and HIV prevention goals. Despite this, we know little about if and how stigma changes over time, or in response to hallmark events in infectious disease treatment. As individuals move from pre-diagnosis to diagnosis of one or more infectious diseases, and towards TB cure and/or HIV viral suppression, they move in and out of illness identities. This research will use mixed-methods to explore stigma through the TB/HIV care continuum to determine if individuals experience higher levels of stigma at specified time points, and whether illness identity, mediated by the hallmark events of HIV viral suppression as well as TB smear and/or culture conversion, impact a person’s stigma score.
A nested prospective cohort within LEAP-TB-SA will undergo serial stigma measurements to determine if mean level of stigma changes through the care continuum. This data will be triangulated against serial qualitative interviews to highlight if and how stigma changes over time and across hallmark events.
Mycobacterium tuberculosis (TB) is the leading cause of death for persons living with HIV (PLWH) in South Africa (SA). Estimates suggest that if factoring in immediate lost to follow-up, a mere 52% of TB/HIV co-infected individuals have successful treatment outcomes.
mHealth solutions designed to support affordable human resources for health, such as community health workers (CHWs), offer the opportunity to reimagine a patient-centered, system-level solution that may radically change care models in low resource settings. The ‘leap’ of mHealth is most potent and practical in settings where desktop-based infrastructure is lacking, and hard-wired internet connectivity is unavailable. This study combines individual cascade steps through TB and HIV smartphone and tablet-based mHealth applications implemented by a CHW with an innovative TB/HIV cascade intervention.
Hypothesis: The intervention will have fewer composite negative TB outcomes (i.e. treatment failure, loss to follow-up, and death) compared to attention controls.
South Africa has a high burden of persons co-infected with multi-drug resistant tuberculosis (MDR-TB) and human immunodeficiency virus (HIV). MDR-TB/HIV co-infection is difficult to treat due to drug-drug interactions which lead to antiretroviral treatment (ART) substitutions, overlapping side-effect profiles, and high pill burden. While worldwide only about 55% of MDR-TB patients are successfully treated, South Africa has shown a recent improvement in MDR-TB treatment outcomes. Although many patients with MDR-TB/HIV co-infection will be cured of MDR-TB, they must continue on daily ART for the rest of their lives. Data suggest that some patients who successfully complete MDR-TB treatment fail to achieve HIV viral suppression by the time they complete MDR-TB treatment. As new TB treatment options are introduced and more people survive MDR-TB, understanding the effects of MDR-TB treatment on HIV viral suppression will only increase in importance.
The purpose of this study is to investigate predictors of HIV viral suppression among people living with HIV/AIDS (PLWHA) who have successfully completed MDR-TB treatment.
This portfolio of work seeks to optimize treatment opportunities by integrating DR-TB and HIV treatment into primary care settings, where nurses provide the vast majority of care in South Africa.
The World Health Organization estimates that 16% of all multi-drug resistant tuberculosis (MDR-TB) patients are lost to follow up (LTFU), placing them at increased risk for the development of additional resistance to antituberculosis medications and early death. Despite mounting knowledge about the risk factors for LTFU from MDR-TB treatment and the End TB Strategy directive that patients at-risk for suboptimal treatment success be given priority attention, there is currently no evidence-based method that allows for the early identification of patients at-risk for being lost from care. This study will develop a model for predicting LTFU from MDR-TB treatment that can ultimately be used to guide MDR-TB providers in identifying patients at high-risk for LTFU and prioritizing their receipt of support services that promote care engagement and retention.
Primary Aim: To develop a prediction model for LTFU from MDR-TB care based on the patient characteristics available at treatment initiation utilizing LASSO regression and k-fold cross-validation.