Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model
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LAN, Yu, KRUDU, V., CIOBANU, Nelly, CODREANU, Alexandru, CHITWOOD, Melanie H., SOBKOWIAK, Benjamin, WARREN, Joshua L., COHEN, Ted. Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model. In: EBioMedicine, 2024, vol. 102, pp. 1-9. ISSN 2353-3964. DOI: https://doi.org/10.1016/j.ebiom.2024.105085
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EBioMedicine
Volumul 102 / 2024 / ISSN 2353-3964 /ISSNe 2352-3964

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model

DOI:https://doi.org/10.1016/j.ebiom.2024.105085

Pag. 1-9

Lan Yu1, Krudu V.2, Ciobanu Nelly2, Codreanu Alexandru2, Chitwood Melanie H.1, Sobkowiak Benjamin1, Warren Joshua L.1, Cohen Ted1
 
1 Yale School of Public Health, New Haven,
2 Institute of Phtysiopneumology „Chiril Draganiuc”
 
 
Disponibil în IBN: 6 aprilie 2024


Rezumat

 Background: Multidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). Methods: We developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. Findings: We found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%–80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. Interpretation: Our results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option. Funding: Funding for this work was provided by the National Institute of Allergy and Infectious Diseases at the US National Institutes of Health.

Cuvinte-cheie
Bayes Theorem, case finding, Data processing, demographics, disease transmission, genotyping, geographic and geological phenomena, geographic distribution, hierarchical clustering, human, incidence, machine learning, mathematical parameters, Moldova, Multidrug Resistant Tuberculosis, multinomial logistic regression, Mycobacterium tuberculosis, phenotype, phylogenetic tree, population density, poverty, public health, residence characteristics, spatial analysis, tuberculosis