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Community-based active case finding (ACF) for tuberculosis (TB) involves an offer of screening to populations at risk of TB, oftentimes with additional health promotion, community engagement and health service strengthening. Recently updated World Health Organization TB screening guidelines conditionally recommend expanded offer of ACF for communities where the prevalence of undiagnosed pulmonary TB is greater than 0.5% among adults, or with other structural risk factors for TB. Subclinical TB is thought to be a major contributor to TB transmission, and ACF, particularly with chest X-ray screening, could lead to earlier diagnosis. However, the evidence base for the population-level impact of ACF is mixed, with effectiveness likely highly dependent on the screening approach used, the intensity with which ACF is delivered, and the success of community- and health-system participation. With recent changes in TB epidemiology due to the effective scale-up of treatment for HIV in Africa, the impacts of the COVID-19 pandemic, and the importance of subclinical TB, researchers and public health practitioners planning to implement ACF programmes must carefully and repeatedly consider the potential population and individual benefits and harms from these programmes. Here we synthesise evidence and experience from implementing ACF programmes to provide practical guidance, focusing on the selection of populations, screening algorithms, selecting outcomes, and monitoring and evaluation. With careful planning and substantial investment, community-based ACF for TB can be an impactful approach to accelerating progress towards elimination of TB in high-burden countries. However, ACF cannot and should not be a substitute for equitable access to responsive, affordable, accessible primary care services for all. [ABSTRACT FROM AUTHOR]

Background Tuberculosis case-finding interventions are critical to meeting World Health Organization End TB strategy goals. We investigated the impact of community-wide tuberculosis active case finding (ACF) alongside scale-up of human immunodeficiency virus (HIV) testing and care on trends in adult tuberculosis case notification rates (CNRs) in Blantyre, Malawi. Methods Five rounds of ACF for tuberculosis (1–2 weeks of leafleting, door-to-door enquiry for cough and sputum microscopy) were delivered to neighborhoods ("ACF areas") in North-West Blantyre between April 2011 and August 2014. Many of these neighborhoods also had concurrent HIV testing interventions. The remaining neighborhoods in Blantyre City ("non-ACF areas") provided a non-randomized comparator. We analyzed TB CNRs from January 2009 until December 2018. We used interrupted time series analysis to compare tuberculosis CNRs before ACF and after ACF, and between ACF and non-ACF areas. Results Tuberculosis CNRs increased in Blantyre concurrently with start of ACF for tuberculosis in both ACF and non-ACF areas, with a larger magnitude in ACF areas. Compared to a counterfactual where pre-ACF CNR trends continued during ACF period, we estimated there were an additional 101 (95% confidence interval [CI] 42 to 160) microbiologically confirmed (Bac+) tuberculosis diagnoses per 100 000 person-years in the ACF areas in 3 and a half years of ACF. Compared to a counterfactual where trends in ACF area were the same as trends in non-ACF areas, we estimated an additional 63 (95% CI 38 to 90) Bac + diagnoses per 100 000 person-years in the same period. Conclusions Tuberculosis ACF was associated with a rapid increase in people diagnosed with tuberculosis in Blantyre. [ABSTRACT FROM AUTHOR]

Background: The prevalence of diseases other than tuberculosis (TB) detected during chest X‐ray screening is poorly described in sub‐Saharan Africa. Computer‐assisted digital chest X‐ray technology is available for TB screening and has the potential to be a screening tool for non‐communicable diseases as well. Low‐ and middle‐income countries are in a transition period where the burden of non‐communicable diseases is increasing, but health systems are mainly focused on addressing infectious diseases. Methods: Participants were adults undergoing computer‐assisted chest X‐ray screening for tuberculosis in a community‐wide tuberculosis prevalence survey in Blantyre, Malawi. Adults with abnormal radiographs by field radiographer interpretation were evaluated by a physician in a community‐based clinic. X‐ray classifications were compared to classifications of a random sample of normal chest X‐rays by radiographer interpretation. Radiographic features were classified using WHO Integrated Management for Adult Illnesses (IMAI) guidelines. All radiographs taken at the screening tent were analysed by the Qure.ai qXR v2.0 software. Results: 5% (648/13,490) of adults who underwent chest radiography were identified to have an abnormal chest X‐ray by the radiographer. 387 (59.7%) of the participants attended the X‐ray clinic, and another 387 randomly sampled normal X‐rays were available for comparison. Participants who were referred to the community clinic had a significantly higher HIV prevalence than those who had been identified to have a normal CXR by the field radiographer (90 [23.3%] vs. 43 [11.1%] p‐value < 0.001). The commonest radiographic finding was cardiomegaly (20.7%, 95% CI 18.0–23.7). One in five (81/387) chest X‐rays were misclassified by the radiographer. The overall mean Qure.ai qXR v2.0 score for all reviewed X‐rays was 0.23 (SD 0.20). There was a high concordance of cardiomegaly classification between the physician and the computer‐assisted software (109/118, 92.4%). Conclusion: There is a high burden of cardiomegaly on a chest X‐ray at a community level, much of which is in patients with diabetes, heart disease and high blood pressure. Cardiomegaly on chest X‐ray may be a potential tool for screening for cardiovascular NCDs at the primary care level as well as in the community. [ABSTRACT FROM AUTHOR]

We investigated whether household to clinic distance was a risk factor for death on tuberculosis (TB) treatment in Malawi. Using enhanced TB surveillance data, we recorded all TB treatment initiations and outcomes between 2015 and 2018. Household locations were geolocated, and distances were measured by a straight line or shortest road network. We constructed Bayesian multi-level logistic regression models to investigate associations between distance and case fatality. A total of 479/4397 (10.9%) TB patients died. Greater distance was associated with higher (odds ratio (OR) 1.07 per kilometre (km) increase, 95% credible interval (CI) 0.99–1.16) odds of death in TB patients registered at the referral hospital, but not among TB patients registered at primary clinics (OR 0.98 per km increase, 95% CI 0.92–1.03). Age (OR 1.02 per year increase, 95% CI 1.01–1.02) and HIV-positive status (OR 2.21, 95% CI 1.73–2.85) were also associated with higher odds of death. Model estimates were similar for both distance measures. Distance was a risk factor for death among patients at the main referral hospital, likely due to delayed diagnosis and suboptimal healthcare access. To reduce mortality, targeted community TB screening interventions for TB disease and HIV, and expansion of novel sensitive diagnostic tests are required. [ABSTRACT FROM AUTHOR]