The impact of a combined TB/HIV intervention on the incidence of TB infection among adolescents and young adults in the HPTN 071 (PopART) trial communities in Zambia and South Africa

Comparing patterns of recent and remote Mycobacterium tuberculosis infection determined using the QuantiFERON-TB Gold Plus assay in a high TB burden setting

One quarter of the world's population is estimated to be infected with Mycobacterium tuberculosis. Identifying recent TB infection (TBI) offers an avenue to targeted TB preventative therapy provision, and prevention to disease progression. However, detecting recent TBI remains challenging. The QuantiFERON-TB Gold Plus assay (QFT-Plus) claims to have improved sensitivity in detecting recent TBI, by the addition of the TB2 antigen tube to the TB1 tube used in previous tests. TB2 detects CD8-mediated interferon gamma response, a potential marker of recent infection. We compared QFT-Plus TB1 and TB2 responses in individuals with recent and remote infection in high-burden settings. The Tuberculosis Reduction through Expanded Antiretroviral Treatment and TB Screening (TREATS) Project followed a cohort of adolescents and young people (AYP) aged 15-24 years in Zambia and South Africa to determine TBI incidence measured by QFT-Plus over 24 months. We categorised individuals with QTF-Plus positive result into recent and remote infection. We compared their TB1 and TB2 responses and the antigen tube differential [TB2-TB1], an indicator of CD8-activity, using logistic regression. At baseline, 3876 AYP, 1852/3876 (47.8%) were QFT-Plus positive whilst 2024/3876 (52.2%) QFT-Plus negative. Of the QFT-Plus baseline positives, 1069/1852 (57.7%) tested positive at both 12 and 24 months-remote infection. Of the QFT-Plus baseline negatives, 274/2024(13.3%) converted within a 12-month period- recent infection. TB1 and TB2 responses were higher in remote than recent infection. In recent infection, TB2 responses were greater than TB1 responses. The mean differential was 0.01 IU/ml in recent and -0.22 IU/ml in remote infection, (p = 0.145). The quantitative QFT-Plus results did not appear to reflect a marked distinction between recent and remote infection. Further analysis of the responses of infected individuals who developed disease is required to determine whether any signal in QFT-Plus results may predict progression to disease.

Airborne transmission risks of tuberculosis and COVID-19 in schools in South Africa, Switzerland, and Tanzania: Modeling of environmental data

The COVID-19 pandemic renewed interest in airborne transmission of respiratory infections, particularly in congregate indoor settings, such as schools. We modeled transmission risks of tuberculosis (caused by Mycobacterium tuberculosis, Mtb) and COVID-19 (caused by SARS-CoV-2) in South African, Swiss and Tanzanian secondary schools. We estimated the risks of infection with the Wells-Riley equation, expressed as the median with 2.5% and 97.5% quantiles (credible interval [CrI]), based on the ventilation rate and the duration of exposure to infectious doses (so-called quanta). We computed the air change rate (ventilation) using carbon dioxide (CO2) as a tracer gas and modeled the quanta generation rate based on reported estimates from the literature. The share of infectious students in the classroom is determined by country-specific estimates of pulmonary TB. For SARS-CoV-2, the number of infectious students was estimated based on excess mortality to mitigate the bias from country-specific reporting and testing. Average CO2 concentration (parts per million [ppm]) was 1,610 ppm in South Africa, 1,757 ppm in Switzerland, and 648 ppm in Tanzania. The annual risk of infection for Mtb was 22.1% (interquartile range [IQR] 2.7%-89.5%) in South Africa, 0.7% (IQR 0.1%-6.4%) in Switzerland, and 0.5% (IQR 0.0%-3.9%) in Tanzania. For SARS-CoV-2, the monthly risk of infection was 6.8% (IQR 0.8%-43.8%) in South Africa, 1.2% (IQR 0.1%-8.8%) in Switzerland, and 0.9% (IQR 0.1%-6.6%) in Tanzania. The differences in transmission risks primarily reflect a higher incidence of SARS-CoV-2 and particularly prevalence of TB in South Africa, but also higher air change rates due to better natural ventilation of the classrooms in Tanzania. Global comparisons of the modeled risk of infectious disease transmission in classrooms can provide high-level information for policy-making regarding appropriate infection control strategies.

Assessment of non-tuberculosis abnormalities on digital chest x-rays with high CAD4TB scores from a tuberculosis prevalence survey in Zambia and South Africa

BACKGROUND

Chest X-rays (CXRs) have traditionally been used to aid the diagnosis of TB-suggestive abnormalities. Using Computer-Aided Detection (CAD) algorithms, TB risk is quantified to assist with diagnostics. However, CXRs capture all other structural abnormalities. Identification of non-TB abnormalities in individuals with CXRs that have high CAD scores but don't have bacteriologically confirmed TB is unknown. This presents a missed opportunity of extending novel CAD systems' potential to simultaneously provide information on other non-TB abnormalities alongside TB. This study aimed to characterize and estimate the prevalence of non-TB abnormalities on digital CXRs with high CAD4TB scores from a TB prevalence survey in Zambia and South Africa.

METHODOLOGY

This was a cross-sectional analysis of clinical data of participants from the TREATS TB prevalence survey conducted in 21 communities in Zambia and South Africa. The study included individuals aged ≥ 15 years who had high CAD4TB scores (score ≥ 70), but had no bacteriologically confirmed TB in any of the samples submitted, were not on TB treatment, and had no history of TB. Two consultant radiologists reviewed the images for non-TB abnormalities.

RESULTS

Of the 525 CXRs reviewed, 46.7% (245/525) images were reported to have non-TB abnormalities. About 11.43% (28/245) images had multiple non-TB abnormalities, while 88.67% (217/245) had a single non-TB abnormality. The readers had a fair inter-rater agreement (r = 0.40). Based on anatomical location, non-TB abnormalities in the lung parenchyma (19%) were the most prevalent, followed by Pleura (15.4%), then heart & great vessels (6.1%) abnormalities. Pleural effusion/thickening/calcification (8.8%) and cardiomegaly (5%) were the most prevalent non-TB abnormalities. Prevalence of (2.7%) for pneumonia not typical of pulmonary TB and (2.1%) mass/nodules (benign/ malignant) were also reported.

CONCLUSION

A wide range of non-TB abnormalities can be identified on digital CXRs among individuals with high CAD4TB scores but don't have bacteriologically confirmed TB. Adaptation of AI systems like CAD4TB as a tool to simultaneously identify other causes of abnormal CXRs alongside TB can be interesting and useful in non-faculty-based screening programs to better link cases to appropriate care.