Population structure of drug-resistant Mycobacterium tuberculosis in Central Asia.

BACKGROUND: Drug-resistant tuberculosis (TB) is a major public health concern threathing the success of TB control efforts, and this is particularily problematic in Central Asia. Here, we present the first analysis of the population structure of Mycobacterium tuberculosis complex isolates in the Central Asian republics Uzbekistan, Tajikistan, and Kyrgyzstan. METHODS: The study set consisted of 607 isolates with 235 from Uzbekistan, 206 from Tajikistan, and 166 from Kyrgyzstan. 24-loci MIRU-VNTR (Mycobacterial Interspersed Repetitive Units - Variable Number of Tandem Repeats) typing and spoligotyping were combined for genotyping. In addition, phenotypic drug suceptibility was performed. RESULTS: The population structure mainly comprises strains of the Beijing lineage (411/607). 349 of the 411 Beijing isolates formed clusters, compared to only 33 of the 196 isolates from other clades. Beijing 94-32 (n = 145) and 100-32 (n = 70) formed the largest clusters. Beijing isolates were more frequently multidrug-resistant, pre-extensively resistant (pre-XDR)- or XDR-TB than other genotypes. CONCLUSIONS: Beijing clusters 94-32 and 100-32 are the dominant MTB genotypes in Central Asia. The relative size of 100-32 compared to previous studies in Kazakhstan and its unequal geographic distribution support the hypothesis of its more recent emergence in Central Asia. The data also demonstrate that clonal spread of resistant TB strains, particularly of the Beijing lineage, is a root of the so far uncontroled MDR-TB epidemic in Central Asia.

Scale-up and impact of digital and molecular diagnostic technologies on TB diagnosis and timely linkage to care in Tajikistan.

INTRODUCTION: Tajikistan is scaling up molecular diagnostic and digital technologies to strengthen its fight against drug-resistant TB (DR-TB). The study aimed to document national scale-up GeneXpert/GxAlert and Open MRS from 2012-2019 and compare time taken from TB diagnosis to treatment and quality of data recording before and after the introduction of GxAlert. METHODOLOGY: This was a longitudinal study that included a comparison of historical cohorts. Continuous variables were compared using Wilcoxon Rank-Sum test and categorical variables using the chi square test. RESULTS: GeneXpert was introduced in 2011 and scaled up to 46 instruments in 43 (51%) diagnostic laboratories by May 2019. GxAlert was introduced in August 2018 and connected with all GeneXpert instruments by February 2019. Open MRS was introduced in 2014 and implemented in all 108 treatment centers by mid-2018. Time from diagnosis to treatment pre-GxAlert (range 0-749, median 3, days) was significantly longer than with GxAlert (range 0-273, median 3, days) (p <0.001). The proportion of patients whose time from diagnosis to treatment was > 2 weeks was 16% (282/1740) pre-GxAlert and 11% (206/1902) with GxAlert (p < 0.001). Between 31%-34% of patients with DR-TB results in Open MRS did not have results available in GeneXpert/GxAlert systems. Where results were present in both systems, there were discrepancies in 8.2% of patients pre-GxAlert and 4.3% with GxAlert (p = 0.25). CONCLUSIONS: The scale-up of GeneXpert and digital technologies in Tajikistan was associated with a reduction in the proportion of patients with delays more than 2 weeks between diagnosis and treatment, but data quality recording improved only slightly.

Tuberculosis in key populations in Tajikistan - a snapshot in 2017.

INTRODUCTION: WHO End TB Strategy aims at achieving targets of 90% mortality reduction and 80% reduction in tuberculosis (TB) incidence by 2030, recommending better addressing TB and multidrug-resistant TB (MDR-TB) issues in key populations. AIM: The study aimed at having a snapshot of the epidemiological characteristics of the key populations among the new TB patients, registered in Tajikistan during 2017. METHODOLOGY: A cross-sectional study was conducted, using official TB registration data for all new TB case notification in Tajikistan in 2017. RESULTS: The key population included 1,029 (19.8%) patients among all 5,182 new TB cases registered in 2017. The following selected sub-populations were identified: migrant workers - 728 (70.7%), diabetics - 162 (15.7%), HIV-positive - 138 (13.4%), heavy drinkers - 74 (7.2%), drug users - 50 (4.8%), ex-prisoners - 50 (4.8%), and homeless - 9 (0.9%). Among the key population, 307 (29.8%) patients were smear-positive, 145 (14.1%) were drug-sensitive and 116 (11.3%) had MonoDR/MDR-TB. Time to treatment initiation for smear-positive cases was ≤ 5 days for 303 (98.7%) patients. Being a key population was inversely related to gender (female) (OR = 0.25, 95% CI (0.21, 0.29)) and population type (rural) (OR = 0.64, 95% CI (0.55, 0.74)). CONCLUSION: Among the key population the identified overlaps of selected sub-populations would enable more efficiently reaching the certain groups. TB case detection at PHC levels needs to be targeted for improved rates for key population detection. In the key population sub-group of migrant workers' special migration destinations are recommended to be explored and find out possible associations with drug resistance.