Genotypic distribution of multidrug-resistant and extensively drug-resistant tuberculosis in northern Thailand

Risara Jaksuwan,1 Prasit Tharavichikul,2 Jayanton Patumanond,3 Charoen Chuchottaworn,4 Sakarin Chanwong,5 Saijai Smithtikarn,6 Jongkolnee Settakorn7 1Clinical Epidemiology Unit, 2Department of Microbiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 3Division of Clinical Epidemiology, Faculty of Medicine, Thammasat University, Pathum Thani, 4Division of Respiratory Medicine, Chest Disease Institute, Nonthaburi, 5Office of Disease Prevention and Control Region 10, Chiang Mai, 6Bureau of Tuberculosis, Department of Disease Control, Ministry of Public Health, Bangkok, 7Department of Pathology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand Background: Multidrug/extensively drug-resistant tuberculosis (M/XDR-TB) is a major public health problem, and early detection is important for preventing its spread. This study aimed to demonstrate the distribution of genetic site mutation associated with drug resistance in M/XDR-TB in the northern Thai population. Methods: Thirty-four clinical MTB isolates from M/XDR-TB patients in the upper northern region of Thailand, who had been identified for drug susceptibility using the indirect agar proportion method from 2005 to 2012, were examined for genetic site mutations of katG, inhA, and ahpC for isoniazid (INH) drug resistance and rpoB for rifampicin (RIF) drug resistance. The variables included the baseline characteristics of the resistant gene, genetic site mutations, and drug susceptibility test results. Results: All 34 isolates resisted both INH and RIF. Thirty-two isolates (94.1%) showed a mutation of at least 1 codon for katG, inhA, and ahpC genes. Twenty-eight isolates (82.4%) had a mutation of at least 1 codon of rpoB gene. The katG, inhA, ahpC, and rpoB mutations were detected in 20 (58.7%), 27 (79.4%), 13 (38.2%), and 28 (82.3%) of 34 isolates. The 3 most common mutation codons were katG 315 (11/34, 35.3%), inhA 14 (11/34, 32.4%), and inhA 114 (11/34, 32.4%). For this population, the best genetic mutation test panels for INH resistance included 8 codons (katG 310, katG 340, katG 343, inhA 14, inhA 84, inhA 86, inhA 114, and ahpC 75), and for RIF resistance included 6 codons (rpoB 445, rpoB 450, rpoB 464, rpoB 490, rpoB 507, and rpoB 508) with a sensitivity of 94.1% and 82.4%, respectively. Conclusion: The genetic mutation sites for drug resistance in M/XDR-TB are quite variable. The distribution of these mutations in a certain population must be studied before developing the specific mutation test panels for each area. The results of this study can be applied for further molecular M/XDR-TB diagnosis in the upper northern region of Thailand. Keywords: tuberculosis, drug resistance, MDR-TB, XDR-TB, genotype, mutatio

Antimicrobial resistance detection in Southeast Asian hospitals is critically important from both patient and societal perspectives, but what is its cost?

Antimicrobial resistance (AMR) is a major threat to global health. Improving laboratory capacity for AMR detection is critically important for patient health outcomes and population level surveillance. We aimed to estimate the financial cost of setting up and running a microbiology laboratory for organism identification and antimicrobial susceptibility testing as part of an AMR surveillance programme. Financial costs for setting up and running a microbiology laboratory were estimated using a top-down approach based on resource and cost data obtained from three clinical laboratories in the Mahidol Oxford Tropical Medicine Research Unit network. Costs were calculated for twelve scenarios, considering three levels of automation, with equipment sourced from either of the two leading manufacturers, and at low and high specimen throughput. To inform the costs of detection of AMR in existing labs, the unit cost per specimen and per isolate were also calculated using a micro-costing approach. Establishing a laboratory with the capacity to process 10,000 specimens per year ranged from 254,000 dollars to 660,000 dollars while the cost for a laboratory processing 100,000 specimens ranged from 394,000 dollars to 887,000 dollars. Excluding capital costs to set up the laboratory, the cost per specimen ranged from 22–31 dollars (10,000 specimens) and 11–12 dollars (100,000 specimens). The cost per isolate ranged from 215–304 dollars (10,000 specimens) and 105–122 dollars (100,000 specimens). This study provides a conservative estimate of the costs for setting up and running a microbiology laboratory for AMR surveillance from a healthcare provider perspective. In the absence of donor support, these costs may be prohibitive in many low- and middle- income country (LMIC) settings. With the increased focus on AMR detection and surveillance, the high laboratory costs highlight the need for more focus on developing cheaper and cost-effective equipment and reagents so that laboratories in LMICs have the potential to improve laboratory capacity and participate in AMR surveillance