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      Multidrug-resistant tuberculosis

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          Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong

          Superspreading events (SSEs) have characterized previous epidemics of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) infections1-6. For SARS-CoV-2, the degree to which SSEs are involved in transmission remains unclear, but there is growing evidence that SSEs might be a typical feature of COVID-197,8. Using contact tracing data from 1,038 SARS-CoV-2 cases confirmed between 23 January and 28 April 2020 in Hong Kong, we identified and characterized all local clusters of infection. We identified 4-7 SSEs across 51 clusters (n = 309 cases) and estimated that 19% (95% confidence interval, 15-24%) of cases seeded 80% of all local transmission. Transmission in social settings was associated with more secondary cases than households when controlling for age (P = 0.002). Decreasing the delay between symptom onset and case confirmation did not result in fewer secondary cases (P = 0.98), although the odds that an individual being quarantined as a contact interrupted transmission was 14.4 (95% CI, 1.9-107.2). Public health authorities should focus on rapidly tracing and quarantining contacts, along with implementing restrictions targeting social settings to reduce the risk of SSEs and suppress SARS-CoV-2 transmission.
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            National survey of drug-resistant tuberculosis in China.

            The available information on the epidemic of drug-resistant tuberculosis in China is based on local or regional surveys. In 2007, we carried out a national survey of drug-resistant tuberculosis in China. We estimated the proportion of tuberculosis cases in China that were resistant to drugs by means of cluster-randomized sampling of tuberculosis cases in the public health system and testing for resistance to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and streptomycin and the second-line drugs ofloxacin and kanamycin. We used the results from this survey and published estimates of the incidence of tuberculosis to estimate the incidence of drug-resistant tuberculosis. Information from patient interviews was used to identify factors linked to drug resistance. Among 3037 patients with new cases of tuberculosis and 892 with previously treated cases, 5.7% (95% confidence interval [CI], 4.5 to 7.0) and 25.6% (95% CI, 21.5 to 29.8), respectively, had multidrug-resistant (MDR) tuberculosis (defined as disease that was resistant to at least isoniazid and rifampin). Among all patients with tuberculosis, approximately 1 of 4 had disease that was resistant to isoniazid, rifampin, or both, and 1 of 10 had MDR tuberculosis. Approximately 8% of the patients with MDR tuberculosis had extensively drug-resistant (XDR) tuberculosis (defined as disease that was resistant to at least isoniazid, rifampin, ofloxacin, and kanamycin). In 2007, there were 110,000 incident cases (95% CI, 97,000 to 130,000) of MDR tuberculosis and 8200 incident cases (95% CI, 7200 to 9700) of XDR tuberculosis. Most cases of MDR and XDR tuberculosis resulted from primary transmission. Patients with multiple previous treatments who had received their last treatment in a tuberculosis hospital had the highest risk of MDR tuberculosis (adjusted odds ratio, 13.3; 95% CI, 3.9 to 46.0). Among 226 previously treated patients with MDR tuberculosis, 43.8% had not completed their last treatment; most had been treated in the hospital system. Among those who had completed treatment, tuberculosis developed again in most of the patients after their treatment in the public health system. China has a serious epidemic of drug-resistant tuberculosis. MDR tuberculosis is linked to inadequate treatment in both the public health system and the hospital system, especially tuberculosis hospitals; however, primary transmission accounts for most cases. (Funded by the Chinese Ministry of Health.).
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              Bedaquiline, pretomanid and linezolid for treatment of extensively drug resistant, intolerant or non-responsive multidrug resistant pulmonary tuberculosis

              Background Patients with extensively drug resistant tuberculosis (TB) have limited treatment options with historically poor outcomes. We investigated treatment with 3 oral drugs, bedaquiline (B), pretomanid (Pa) and linezolid (L), (B-Pa-L), with TB bactericidal activity and little pre-existing resistance. Methods Nix-TB is an open label single arm study ongoing at three South African sites evaluating the safety and efficacy of B-Pa-L for 26 weeks for extensively drug-resistant TB or treatment intolerant /non-responsive multidrug-resistant TB. We present the efficacy and safety outcomes for all 109 patients enrolled in the trial followed to the predefined primary endpoint, six months after the end of treatment. Results In the intent to treat analysis, 98 patients (90%), (95% CI 82.7-94.9%) had a favourable outcome at 6 months after the end of treatment. Six patients died during the early stages of treatment, one withdrew during treatment, one died during follow-up without evidence of relapse, one relapsed, one relapsed and subsequently died during follow up and one was lost to follow-up. The expected linezolid toxicities of peripheral neuropathy (experienced by 81% of patients) and myelosuppression (48%), while common, were manageable, often requiring reductions of dose and/or interruptions in linezolid. Conclusions These results suggest that B-Pa-L is a viable option for tuberculosis patients with highly resistant forms of TB, provided adequate safety management is available. Trial registration: ClinicalTrials.gov Identifier: NCT02333799 Sponsor: Global Alliance for TB Drug Development (TB Alliance)
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                Author and article information

                Contributors
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                Journal
                Nature Reviews Disease Primers
                Nat Rev Dis Primers
                Springer Science and Business Media LLC
                2056-676X
                December 2024
                March 24 2024
                : 10
                : 1
                Article
                10.1038/s41572-024-00504-2
                38523140
                d9f4fefc-f8e1-49fe-b32b-dc8f4a99dc7f
                © 2024

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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