Piotr Hippner

Mobile health (mHealth) technologies are increasingly used to support community-based healthcare. However, their real-world impact often remains unclear. Understanding implementation factors is essential for advancing their use and achieving meaningful health outcomes. We evaluated an mHealth tool (AitaHealth) after customising its modules and workflows for household Tuberculosis (TB) contact tracing and other community-based data collection by community health workers (CHWs). We describe the acceptability, feasibility, and implementation fidelity of this approach. We conducted a mixed-methods evaluation in two South African districts: uMkhanyakude and Ekurhuleni. We collected qualitative data through focus group discussions (FGDs) and in-depth interviews (IDIs) with CHWs, team leaders, and key stakeholders. We used deductive thematic analysis grounded in the Technology Acceptance Model (TAM) to assess the acceptability and implementation feasibility of the mHealth tool. We used quantitative data from the AitaHealth metadata to assess implementation fidelity. CHWs appreciated AitaHealth’s efficiency, data security, and credibility. Across the two districts, 103 CHWs recorded data for 2,452 households and 10,649 household members. However, they reported challenges in ease of use, with unreliable devices, weak support, and safety concerns hindering data collection. These issues led to inconsistent engagement, with 48.5% of CHWs logging in fewer than 15 times during implementation. Despite these challenges, when used, AitaHealth ensured high-quality data collection and household coverage, with TB-related fields completed in over 94% of households, demonstrating its potential under better conditions. AitaHealth`s limitations stemmed from system constraints rather than user resistance. To achieve full impact, mHealth tools require reliable infrastructure and supportive environments for both the tools and their implementers.

Background: Inadequate adherence to tuberculosis (TB) treatment increases the risk of treatment failure and recurrence. Identifying factors contributing to poor adherence could refine targeting strategies and optimize resource distribution. We examined specific individual-level factors for TB treatment adherence among adults with drug-sensitive TB, including HIV status, antiretroviral therapy, time to access clinical care, and perceived stigma. Methods: Data are from the “TB Mate” cluster-randomized trial, which evaluated a TB treatment adherence intervention across 18 public health clinics in South Africa (PACTR201902681157721). Treatment adherence was measured using smart pillboxes, with pillbox opening recorded as a proxy for the dose taken. Adults in the control group, utilizing the pillbox in silent mode, were included in this analysis. We used logistic regression to model poor adherence (< 80% pillbox engagement), and negative binomial regression to model adherence as a count of pillbox engagement. Directed acyclic graphs informed confounder selection in the models. Results: Among 1,213 participants (nine clinics) in the control group, 61.2% (742) were male, the median age was 36 years, 63% (769) were living with HIV, with 66% (507/769) on antiretroviral therapy. The median time to access clinical care was 127 min and 95% (1151/1213) reported no perceived stigmatization upon starting TB treatment. Overall 51% (614) exhibited adherence below 80%, with a geometric mean pillbox engagement of 59.6%. Living with HIV was associated with poor adherence to TB treatment, with an adjusted odds ratio of 1.68 (95% confidence interval [CI] 1.27–2.22) for < 80% adherence and an adjusted rate ratio of 0.90 (0.83–0.97) for pillbox engagement, compared to being HIV-negative. Antiretroviral therapy, time to clinical care access, and perceived stigma showed no association with either measure of adherence. Conclusions: The low adherence underscores the necessity for TB treatment support interventions, particularly among those living with HIV.

Evidence on the relative costs and effects of interventions that do not consider ‘real-world’ constraints on implementation may be misleading. However, in many low- and middle-income countries, time and data scarcity mean that incorporating health system constraints in priority setting can be challenging. We developed a ‘proof of concept’ method to empirically estimate health system constraints for inclusion in model-based economic evaluations, using intensified case-finding strategies (ICF) for tuberculosis (TB) in South Africa as an example. As part of a strategic planning process, we quantified the resources (fiscal and human) needed to scale up different ICF strategies (cough triage and WHO symptom screening). We identified and characterised three constraints through discussions with local stakeholders: (1) financial constraint: potential maximum increase in public TB financing available for new TB interventions; (2) human resource constraint: maximum current and future capacity among public sector nurses that could be dedicated to TB services; and (3) diagnostic supplies constraint: maximum ratio of Xpert MTB/RIF tests to TB notifications. We assessed the impact of these constraints on the costs of different ICF strategies. It would not be possible to reach the target coverage of ICF (as defined by policy makers) without addressing financial, human resource and diagnostic supplies constraints. The costs of addressing human resource constraints is substantial, increasing total TB programme costs during the period 2016–2035 by between 7% and 37% compared to assuming the expansion of ICF is unconstrained, depending on the ICF strategy chosen. Failure to include the costs of relaxing constraints may provide misleading estimates of costs, and therefore cost-effectiveness. In turn, these could impact the local relevance and credibility of analyses, thereby increasing the risk of sub-optimal investments.

BACKGROUND: National Tuberculosis Programmes (NTPs) require specialist input to support the development of policy and practice informed by evidence, typically against tight deadlines. OBJECTIVE:To describe lessons learned from establishing a dedicated tuberculosis (TB) think tank to advise the South African NTP on TB policy. INTERVENTION AND EVALUATION METHODS: A national TB think tank was established to advise the NTP in support of evidence-informed policy. Support was provided for activities, including meetings, modelling and regular telephone calls, with a wider network of unpaid expert advisers under an executive committee and working groups. Intervention evaluation used desktop analysis of documentary evidence, interviews and direct observation. RESULTS: The TB Think Tank evolved over time to acquire three key roles: an ‘institution', a ‘policy dialogue forum' and an ‘interface'. Although enthusiasm was high, motivating participation among the NTP and external experts proved challenging. Motivation of working groups was most successful when aligned to a specific need for NTP decision making. Despite challenges, the TB Think Tank contributed to South Africa's first ever TB and human immunodeficiency virus (HIV) investment case, and the decision to create South Africa's first ever ring-fenced grant for TB. The TB Think Tank also assisted the NTP in formulating strategy to accelerate progress towards reaching World Health Organization targets. DISCUSSION: With partners, the TB Think Tank achieved major successes in supporting evidence-informed decision making, and garnered increased funding for TB in South Africa. Identifying ways to increase the involvement of NTP staff and other experts, and keeping the scope of the Think Tank well defined, could facilitate greater impact. Think tank initiatives could be replicated in other settings to support evidence-informed policy making.

We evaluated a novel on-site antiretroviral therapy programme in a South African correctional facility using routinely collected programme data, from a retrospective cohort of adult inmates starting antiretroviral therapy between 03/2007 and 03/2009 followed-up to 09/2009. We report (1) mortality (using survival analysis); (2) retention to the programme (to 09/2009); and (3) virological suppression at 6 and 12 months (6 months (95% confidence interval 1.1–7.5)/100 person-years; p < 0.001. On 09/2009, 35.6% (144/404) remained in the correctional facility, with 94.4% (136/144) retained to the programme; 38.4% (155/404) were released; and 20.0% (81/404) transferred to another facility. Among antiretroviral therapy-naïve patients in care six and 12 months after antiretroviral therapy initiation, 94.7% (124/131) and 92.5% (74/80) were virologically suppressed, respectively. High early mortality warrants the early identification and management of HIV-positive inmates. The high mobility of inmates necessitates systems for facilitating the continuity of care. Good virological responses and retention supports decentralising HIV care to correctional facilities.

Tuberculosis (TB) is the leading cause of death from infectious disease worldwide, predominantly affecting low- and middle-income countries (LMICs), where resources are limited. As such, countries need to be able to choose the most efficient interventions for their respective setting. Mathematical models can be valuable tools to inform rational policy decisions and improve resource allocation, but are often unavailable or inaccessible for LMICs, particularly in TB. We developed TIME Impact, a user-friendly TB model that enables local capacity building and strengthens country-specific policy discussions to inform support funding applications at the (sub-)national level (e.g. Ministry of Finance) or to international donors (e.g. the Global Fund to Fight AIDS, Tuberculosis and Malaria). TIME Impact is an epidemiological transmission model nested in TIME, a set of TB modelling tools available for free download within the widely-used Spectrum software. The TIME Impact model reflects key aspects of the natural history of TB, with additional structure for HIV/ART, drug resistance, treatment history and age. TIME Impact enables national TB programmes (NTPs) and other TB policymakers to better understand their own TB epidemic, plan their response, apply for funding and evaluate the implementation of the response. The explicit aim of TIME Impact’s user-friendly interface is to enable training of local and international TB experts towards independent use. During application of TIME Impact, close involvement of the NTPs and other local partners also builds critical understanding of the modelling methods, assumptions and limitations inherent to modelling. This is essential to generate broad country-level ownership of the modelling data inputs and results. In turn, it stimulates discussions and a review of the current evidence and assumptions, strengthening the decision-making process in general. TIME Impact has been effectively applied in a variety of settings. In South Africa, it informed the first South African HIV and TB Investment Cases and successfully leveraged additional resources from the National Treasury at a time of austerity. In Ghana, a long-term TIME model-centred interaction with the NTP provided new insights into the local epidemiology and guided resource allocation decisions to improve impact.