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Background: Pulmonary tuberculosis (PTB) diagnosis relies on sputum examination, a challenge in sputum-scarce patients. Alternative non-invasive sampling methods such as face mask sampling (FMS) have been proposed., Objective: To evaluate the value of FMS for PTB diagnosis by assessing its agreement with sputum samples processed by GeneXpert MTB/RIF (Ultra)(Xpert) testing, and describe FMS sensitivity and specificity., Methods: This was a prospective study conducted at the Carrière TB clinic in Guinea. Presumptive TB patients willing to participate were asked to wear a surgical mask containing a polyvinyl alcohol (PVA) strip for thirty minutes. Subsequently, two spot sputum samples were collected, of which one was processed by microscopy on site and the other by Xpert in Guinea's National Reference Laboratory of Mycobacteriology (LNRM). The first 30 FMS were processed at the Supranational Reference Laboratory in Antwerp, Belgium, and the following 118 FMS in the LNRM., Results: One hundred fifty patients participated, of whom 148 had valid results for both mask and sputum. Sputum smear microscopy was positive for 47 (31.8%) patients while sputum-Xpert detected MTB in 54 (36.5%) patients. Among the 54 patients testing sputum-Xpert positive, 26 (48.1%) yielded a positive FMS-Xpert result, while four sputum-Xpert negative patients tested positive for FMS and 90 patients were Xpert-negative for both sputum and mask samples, suggesting a moderate level of agreement (k-value of 0.47). The overall mask sensitivity was 48.1%, with 95.7% specificity., Conclusion: In our setting, Xpert testing on FMS did not yield a high level of agreement to sputum sample., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Most countries in Subsaharan Africa have well-established National Tuberculosis Control Programs with relatively stable routine performances. However, major epidemiological events may result in significant disruptions. In March 2014, the World Health Organization announced the outbreak of Ebola virus disease in Guinea, a country with a high incidence of TB and HIV. Our study aimed to assess the impact of the Ebola virus disease outbreak on TB notification, treatment, and surveillance, using main indicators., Methods: This is a retrospective cohort study that compared TB trends using surveillance data from the periods before (2011-2013), during (2014-2016), and after (2017-2018) Ebola virus disease outbreak. A time-series analysis was conducted to investigate the linkages between the decline in TB notification and the Ebola virus disease outbreak through cross-correlation. The lag in the cross-correlation test was evaluated using ANCOVA type II delayed variable dependent model. The surveillance system was assessed using TB surveillance standards and benchmarks and vital registration systems recommended by WHO, compared with those of 2015 during the Ebola virus disease., Results: The rate of reporting of TB declined from 120 cases per 100,000 in 2011 to 100 cases per 100,000 in 2014, at the peak of the Ebola virus disease outbreak. The time-series cross-correlation test of all notified cases of TB and Ebola showed a significant lag of - 0.4 (40%), reflecting a drop in the rate of notification (F-value = 5.7 [95% CI: 0.2-21.3]). The Ebola virus disease had no negative impact on patient treatment outcomes (F-value = 1.3 [95% CI: 0.0-8.8]). Regarding the surveillance system, five out of 13 WHO standards and benchmarks were met following their evaluation in 2019, after the Ebola virus disease outbreak, compared to three in 2015., Conclusion: Major epidemics such as the Ebola virus disease outbreak may have a significant impact on well-established TB control programs as shown in the example of Guinea. Sudden disruptions of routine performance may lead programs to improve their surveillance system. The experience acquired in the fight against EVD and the investments made should make it possible to prepare the health system in a coherent manner for the other probable episodes.

Background: Despite the predictive role of body weight variation in treatment outcome in multidrug-resistant tuberculosis (MDR-TB), few corroborating data are available. We studied weight variation in patients with MDR-TB to identify groups of weight change and to determine factors that influence these changes., Methods: We analyzed patients with rifampicin resistance who were treated with an MDR-TB treatment regimen between June 07, 2016 and June 22, 2018 at three major drug-resistant TB centers in Guinea. Patients were seen monthly until the end of treatment. Clinical outcome was the body mass index (BMI). We used a linear mixed model to analyze trajectories of BMI and a latent class mixed model to identify groups of BMI trajectories., Results: Of 232 patients treated for MDR-TB during the study period, 165 were analyzed. These patients had a total of 1387 visits, with a median of 5 visits (interquartile range, 3-8 visits). Monthly BMI increase was 0.24 (SE 0.02) per kg/m 2 . Factors associated with faster BMI progression were success of MDR-TB treatment (0.24 [SE 0.09] per kg/m 2 ; p = 0.0205) and absence of lung cavities on X-ray (0.18 [0.06] per kg/m 2 ; p = 0.0068). Two groups of BMI change were identified: rapid BMI increase (n = 121; 85%) and slow BMI increase (n = 22; 15%). Patients in the slow BMI increase group were mostly female (68%) had no history of TB treatment (41%), had a positive HIV infection (59%), and had a more severe clinical condition at baseline, characterized by a higher frequency of symptoms including depression (18%), dyspnea (68%), poor adherence to MDR-TB treatment (64%), lower platelet count, and higher SGOT. These patients also had a longer time to initial culture conversion (log-rank test: p = 0.0218)., Conclusion: Quantitative BMI data on patients with MDR-TB treated with a short regimen allowed the identification of subgroups of patients with different trajectories of BMI and emphasized the usefulness of BMI as a biomarker for the monitoring of MDR-TB treatment outcome.