Tuberculosis (TB) is now the commonest cause of death in many African countries. Several
factors drive this; however, transmission is the mechanism by which these risk factors
translate into active TB. Globally, ~35% (almost 1 in 3) of TB cases are 'missed' (remain
undiagnosed or undetected). In sub-Saharan Africa, 40-50% of the TB case burden remains
undiagnosed within the community and ~30% of such cases are microscopically
smear-positive. These 'missed' TB cases (at primary care level) serve as a reservoir,
which severely undermines TB control. Thus, primary care and community-based case finding
should be a critical component for TB control.
Detecting cases in the community, however, has been restricted by the lack of sensitive
and user-friendly Point-of-Care (POC) diagnostic tools. To address this unmet need, in
2013 the investigators planned a programme of activities (sequential interlinked studies)
with the overarching aim of optimising a model for Xpert-related community-based active
case finding (ACF) for TB (XACT). By 2017, through the EDCTP-funded XACT-I study, the
investigators solved the impasse of rapid POC diagnosis by showing that molecular
Xpert-based community-based screening was effective in identifying missing TB cases in
the peri-urban 'slums' of Cape Town and Harare using a mini-truck with a generator.
However, such an approach was neither broadly affordable nor scalable. The investigators
therefore derived a scalable model using portable battery-operated Xpert Edge installed
within a low-cost (< US$) 15 000 Nissan panel van manned by two health care workers (thus
making the ACF model affordable and scalable). This completed study, XACT-II, screened
over 5 000 participants in the community. The model worked well and was more effective
than smear microscopy. Based on these successes, and to translate the XACT concept into
policy, the Wellcome Trust and UK MRC has funded the XACT-III study. Currently commenced,
XACT-III was initiated as a multi-country demonstration project in four sub-Saharan
African countries.
More recently, there have been rapid advances in the development of triage testing for
TB, which refers to screening tests that are generally applied in a community-based
setting (either at individual community or primary care clinic level). These tests have
very high sensitivity (>95%) but modest specificity (>70%) as defined by TB-specific
target product profiles. A forerunner TB-orientated triage test is computer-assisted
x-ray diagnosis (CAD). This entails using artificial intelligence-enabled software to
read a digital x-ray and produce a probability of TB within seconds. Recent data suggest
that CAD performs on par with experienced radiologists to identify potential TB cases,
hereby reducing the frequency at which Xpert tests are requested and helps to focus
limited resources on the relevant cases. Although these data appear promising, the
feasibility of this strategy in a pragmatic field setting has not been extensively
tested. There are several other unanswered questions. Is the strategy of CAD combined
with Xpert cost-effective and can it reduce Xpert usage without missing an unacceptable
number of TB cases? The investigators will therefore determine the utility of CAD as a
triage tool to further optimise the XACT model.
The COVID-19 pandemic, due to SARS-CoV-2, has ravaged African peri-urban communities
where TB is also common. Symptoms of COVID-19 and TB overlap, and limited affordability,
as well as the stigma associated with both diseases, severely limits testing. Data are
now urgently needed about the feasibility of co-screening and testing for TB and
COVID-19. The utility of such an approach, if any, has not been studied in African
communities. As Xpert POC TB testing and x-rays for CAD will be performed in the proposed
study, it affords a unique and easy opportunity to seamlessly screen for both diseases
when appropriate.
Other nascent screening technologies are rapidly emerging for TB and COVID-19, including
urine- and blood-based triage tests. XACT-19 provides a unique opportunity to collect the
relevant samples and test new technologies in a pragmatic community-based setting.
In summary, the XACT-19 study results will have substantial implications for public
health policy and practice and will likely define a new standard for community-based ACF
for TB, and potentially COVID-19 in tandem.