This study seeks to describe the rollout and current state of South Africa’s GeneXpert molecular diagnostic program for tuberculosis (TB). Xpert MTB/RIF was introduced in 2011 with a subsequent expansion to include extra-pulmonary and paediatric testing, followed by Xpert MTB/RIF Ultra in 2017. Through a centralised laboratory information system and the use of a standardised platform for more than a decade, over 23 million tests were analysed, describing the numbers tested, Mycobacterium tuberculosis complex detection, rifampin resistance, and the unsuccessful test rates. The stratification by province, specimen type, age, and sex identified significant heterogeneity across the program and highlighted testing gaps for men, low detection yield for paediatric pulmonary TB, and the effects of inadequate specimen quality on the detection rate. The insights gained from these data can aid in the monitoring of interventions in support of the national TB program beyond laboratory operational aspects.

An investigation was carried out to examine the use of national Xpert MTB/RIF data (2013–2017) and GIS technology for MTB/RIF surveillance in South Africa. The aim was to exhibit the potential of using molecular diagnostics for TB surveillance across the country. The variables analysed include Mycobacterium tuberculosis (Mtb) positivity, the mycobacterial proportion of rifampicin-resistant Mtb (RIF), and probe frequency. The summary statistics of these variables were generated and aggregated at the facility and municipal level. The spatial distribution patterns of the indicators across municipalities were determined using the Moran’s I and Getis Ord (Gi) statistics. A case-control study was conducted to investigate factors associated with a high mycobacterial load. Logistic regression was used to analyse this study’s results. There was striking spatial heterogeneity in the distribution of Mtb and RIF across South Africa. The median patient age, urban setting classification, and number of health care workers were found to be associated with the mycobacterial load. This study illustrates the potential of using data generated from molecular diagnostics in combination with GIS technology for Mtb surveillance in South Africa. Spatially targeted interventions can be implemented in areas where high-burden Mtb persists.

The National Health Laboratory Service (NHLS) collects all public health laboratory test results in South Africa, providing a cohort from which to identify groups, by age, sex, HIV, and viral suppression status, that would benefit from increased tuberculosis (TB) testing. Using NHLS data (2012–2016), we assessed levels and trends over time in TB diagnostic tests performed (count and per capita) and TB test positivity. Estimates were stratified by HIV status, viral suppression, age, sex, and province. We used logistic regression to estimate the odds of testing positive for TB by viral suppression status. Nineteen million TB diagnostic tests were conducted during period 2012–2016. Testing per capita was lower among PLHIV with viral suppression than those with unsuppressed HIV (0.08 vs 0.32) but lowest among people without HIV (0.03). Test positivity was highest among young adults (aged 15–35 years), males of all age groups, and people with unsuppressed HIV. Test positivity was higher for males without laboratory evidence of HIV than those with HIV viral suppression, despite similar individual odds of TB. Our results are an important national baseline characterizing who received TB testing in South Africa. People without evidence of HIV, young adults, and males would benefit from increased TB screening given their lower testing rates and higher test positivity. These high-test positivity groups can be used to guide future expansions of TB screening.

SUMMARY: BACKGROUND: We assessed the impact of the COVID-19 pandemic on TB notifications in Ukraine, stratified by multiple subgroups. DESIGN/METHODS: We analyzed data from Ukraine’s National TB Program from January 2015 to December 2020 using interrupted time series models. We compared observed cases to counterfactual estimated cases had the pandemic not occurred and estimated trends through December 2020 nationally and by various demographics. We compared the proportions of individuals who underwent drug susceptibility testing (DST) in February 2020 and April 2020 to assess the pandemic impact on drug resistance testing. RESULTS: In April 2020, there were 39% (95% CI 36–42) fewer TB notifications than the estimated counterfactual (3,060 estimated; 95% CI 2,918–3,202; 1,872 observed). We observed a greater decrease in notifications among refugees/migrants compared with non-refugees/migrants (64%, 95% CI 60–67 vs. 39%, 95% CI 36–42), and individuals aged <15 years compared with those aged ≥15 years (60%, 95% CI 57–64 vs. 38%, 95% CI 36–41). We also observed a decrease in the proportion of individuals receiving DST for several drugs. CONCLUSIONS: These findings underscore the challenges to TB prevention and care during disruption and may be generalizable to the current wartime situation, especially considering the substantial increase in refugees within and leaving Ukraine.

Background: Despite being the leading cause of death, the global tuberculosis (TB) burden is ill-defined. Existing methods to estimate incidence are time and/or resource-intensive and often inaccurate. Back-calculation was developed to estimate HIV incidence by considering reported cases to be a convolution of the disease duration and the incidence of new cases. New estimates of TB natural history parameters allow us to develop Bayesian back-calculation methods for TB to assign case notification data to the time point of onset of disease. Methods: Recorded counts of TB cases are underestimates of the true burden of disease, so we include a multiplier derived from prevalence to notification ratios to account for underreporting. We assume a Poisson distribution for notifications and incidence and use a penalized-likelihood before smooth estimates. We estimate sex-stratified TB incidence for Vietnam, Cambodia, and the Philippines via Markov chain Monte Carlo. Results: Annual estimated TB incidence was, on average 19% greater than recorded notifications. TB incidence among males was on average 3.8% higher than females in Vietnam, 1.3% in Cambodia, and 2.5% higher in the Philippines. Conclusions: These estimates account for the delay between bacteriologically positive subclinical disease and notification and, as such, may be more temporally accurate than existing methods.

Identifying transmission events is important in understanding infectious disease dynamics. Such events are typically unobservable, particularly in respiratory diseases such as tuberculosis (TB). We apply network techniques to identify transmission clusters and features shared within clusters. We estimate directed pairwise transmission probabilities via an existing iterative algorithm that employs a modified Naïve Bayes classifier and use these probabilities to create a network. We explore noise reduction techniques to trim low-probability edges. We group individuals with TB based on edges informed by transmission probabilities via network clustering algorithms. We apply our framework to simulated data and assess clustering algorithm performance. We then apply this approach to data from a cohort study in Lima, Peru, and examine homogeneity of the clusters using a binary entropy measure. We find cluster performance to be consistent across all edge-trimming scenarios and clustering methods. We find high levels of entropy, implying heterogeneity for age, sex, socioeconomic status, individuals who work outside the home, and people using public transit. We analyze estimated directed pairwise transmission probabilities with network techniques. The approach is consistent across network construction and clustering methods and can be applied to any disease outbreak to understand its dynamics. This article is part of a Special Collection on Latino Health.