Identifying areas and risk groups with localised<i>Mycobacterium tuberculosis</i>transmission in northern England from 2010 to 2012: spatiotemporal analysis incorporating highly discriminatory genotyping data

Saavedra-Campos, María; Welfare, William; Cleary, Paul; Sails, Andrew D.; Burkitt, Andy; Hungerford, Daniel; Okereke, Ebere; Acheson, Peter; Petrović, Marko D.

doi:10.1136/thoraxjnl-2014-206416

Cited by 9 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have found sociodemographic factors to be predictors of recent TB transmission, such as younger age, minority race/ethnicity status, male sex, homelessness, incarceration, and drug use. 17,18 In our study, domestic overcrowding and social class were associated with genetic clustering, indicating that poverty and socioeconomic status, which are known factors associated with the risk of developing TB, 19 are also linked to recent transmission of the disease in the indigenous population.…”

Section: Discussionmentioning

confidence: 52%

Genetic Clustering of Tuberculosis in an Indigenous Community of Brazil

Sacchi

Tatara

Lima

et al. 2018

The American Journal of Tropical Medicine and Hygiene

View full text Add to dashboard Cite

We conducted a population-based study of tuberculosis (TB) from 2009 to 2015 in an indigenous community of Brazil, the largest in the country, to investigate risk factors associated with recent TB transmission. The clinical isolates of were genotyped by IS-RFLP (restriction fragment length polymorphism) and spoligotyping analysis. Among 67 isolates typed by RFLP, 69% fell into fifteen clusters, and 91% of TB cases with shared IS-RFLP pattern were diagnosed within 2 years of another case in the cluster. Individual risk factors associated with genetic clustering were domestic overcrowding (odds ratio [OR]: 6.10; 95% confidence interval [CI]: 1.50-24.88) and low social class (OR: 3.72; 95% CI: 1.00-13.98). Most reported contacts (76%) were identified within the household of the index TB case, but most of the genetic clustering of occurred outside of household (79%). Expanded contacts investigation and prophylaxis outside of household should be considered as a priority for TB control programs in this population.

show abstract

Section: Discussionmentioning

confidence: 52%

Genetic Clustering of Tuberculosis in an Indigenous Community of Brazil

Sacchi

Tatara

Lima

et al. 2018

The American Journal of Tropical Medicine and Hygiene

View full text Add to dashboard Cite

show abstract

“…These approaches were combined to quantify the role of recent transmission and determine geographical locations of such transmission in 25 studies. This was done either by determining the spatial distribution of genotypic clusters [ 25 , 28 , 59 – 69 ] or by assessing the genotypic similarity of cases contained within geospatial clusters [ 2 , 57 , 65 , 70 , 71 ].…”

Section: Resultsmentioning

confidence: 99%

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

Shaweno

Karmakar

Alene

et al. 2018

BMC Med

View full text Add to dashboard Cite

BackgroundTuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden.MethodsWe conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017.The protocol for this systematic review was prospectively registered with PROSPERO (CRD42016036655).ResultsWe identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff’s spatial scan statistic followed by local Moran’s I and Getis and Ord’s local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined.ConclusionsA range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1178-4) contains supplementary material, which is available to authorized users.

show abstract

“…These analyses have been used to identify areas of likely TB transmission. [6][7][8][9][10][11][12][13][14] Visualisation of spatial data on maps also provides a powerful means of communicating information about the disease to policy makers and the public.…”

Section: Introductionmentioning

confidence: 99%