Investigation of space-time clusters and geospatial hot spots for the occurrence of tuberculosis in Beijing

Liu, Yingjie; Li, X; Wang, Wei; Li, Z; Hou, Miao Miao; He, Yi; Wu, Wenchao; Wang, H; Liang, Hui; Guo, Xiuhua

doi:10.5588/ijtld.11.0255

Cited by 52 publications

(59 citation statements)

References 20 publications

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“…These results correspond with findings from Liu et al, 2012, who also detected higher TB incidence rate in the urban district of Beijing/China (1997-2001) than in other districts with lower urbanization 31 .…”

Section: Discussionsupporting

confidence: 82%

Distribution of pulmonary tuberculosis in Rio de Janeiro (Brazil): a spatial analysis

Rodrigues

Andrade

O’Dwyer

et al. 2017

Ciênc. saúde coletiva

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The aim of this study was to analyze the spatial distribution of the tuberculosis endemic in Rio de Janeiro State from 2002 to 2011. A retrospective study was conducted in the state of Rio de Janeiro from 2002 to 2011. Spatial analysis techniques were used to describe the distribution of tuberculosis incidence in the state. Multilevel Poisson regression model was used to access the relationship of tuberculosis and the following factors: “sex”, “age-group” and “diagnostic year” (individual-level factors). Demographic density and municipality were also included in the model as contextual-level factors. A reduction in endemic tuberculosis was observed over the years. The highest incidence rates were concentrated on the south coast of the state, covering Rio de Janeiro City (capital) and neighboring cities. We detected a significant clustering of high TB incidence rates on the south coast of the state and a cluster of low incidence in the northeastern region of state. The risk of tuberculosis was higher in early 2000s, in males and in 40-59 age group. Metropolitan regions are important risk areas for the spread of tuberculosis. These findings could be used to plan control measures according to the characteristics of each region.

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Section: Discussionsupporting

confidence: 82%

Distribution of pulmonary tuberculosis in Rio de Janeiro (Brazil): a spatial analysis

Rodrigues

Andrade

O’Dwyer

et al. 2017

Ciênc. saúde coletiva

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“…It is difficult to incorporate the spatial heterogeneity of the effects of ecological factors (meteorological and air pollutant factors) with those traditional methods [23,24]. In this regard, the geographic information system (GIS) is the preferred method and technique for the underlying epidemic characteristics of an infectious disease [25], which allow one to attain more easily and more efficiently data visualization and manipulation, as well as to analyse and display the geographic data [26,27,28]. However, such studies have not been conducted yet to investigate the geospatial distribution of scarlet fever in the Beijing region using GIS technology.…”

Section: Introductionmentioning

confidence: 99%

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

Mahara

Wang

Yang

et al. 2016

IJERPH

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(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.

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“…TB is a reemerging infectious disease and a substantial public health problem in Beijing [2], [3], the capital of the People’s Republic of China, a megacity containing many districts with large populations and high population mobility. It has been reported that the prevalence and incidence of TB is gradually becoming higher in Beijing [4], thus the prevention and control of TB is a great challenge for this city.…”

Section: Introductionmentioning

confidence: 99%

Genotypic Diversity Analysis of Mycobacterium tuberculosis Strains Collected from Beijing in 2009, Using Spoligotyping and VNTR Typing

et al. 2014

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BackgroundTuberculosis (TB) is a serious problem in China. While there have been some studies on the nationwide genotyping of Mycobacterium tuberculosis (M. tuberculosis), there has been little detailed research in Beijing, the capital of China, which has a huge population. Here, M. tuberculosis clinical strains collected in Beijing during 2009 were genotyped by classical methods.Methodology/Principal FindingsOur aim was to analyze the genetic diversity of M. tuberculosis strains within the Beijing metropolitan area. We characterized these strains using two standard methods, spoligotyping (n = 1585) and variable number of tandem repeat (VNTR) typing (n = 1053). We found that the most prominent genotype was Beijing family genotype. Other genotypes included the MANU, T and H families etc. Spoligotyping resulted in 137 type patterns, included 101 unclustered strains and 1484 strains clustered into 36 clusters. In VNTR typing analysis, we selected 12-locus (QUB-11b, MIRU10, Mtub21, MIRU 23, MIRU39, MIRU16, MIRU40, MIRU31, Mtub24, Mtub04, MIRU20, and QUB-4156c) and named it 12-locus (BJ) VNTR. VNTR resulted in 869 type patterns, included 796 unclustered strains and 257 strains clustered into 73 clusters. It has almost equal discriminatory power to the 24-locus VNTR.Conclusions/SignificanceOur study provides a detailed characterization of the genotypic diversity of M. tuberculosis in Beijing. Combining spoligotyping and VNTR typing to study the genotyping of M. tuberculosis gave superior results than when these techniques were used separately. Our results indicated that Beijing family strains were still the most prevalent M. tuberculosis in Beijing. Moreover, VNTR typing analyzing of M. tuberculosis strains in Beijing was successfully accomplished using 12-locus (BJ) VNTR. This method used for strains genotyping from the Beijing metropolitan area was comparable. This study will not only provide TB researchers with valuable information for related studies, but also provides guidance for the prevention and control of TB in Beijing.

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Investigation of space-time clusters and geospatial hot spots for the occurrence of tuberculosis in Beijing

Cited by 52 publications

References 20 publications

Distribution of pulmonary tuberculosis in Rio de Janeiro (Brazil): a spatial analysis

Distribution of pulmonary tuberculosis in Rio de Janeiro (Brazil): a spatial analysis

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

Genotypic Diversity Analysis of Mycobacterium tuberculosis Strains Collected from Beijing in 2009, Using Spoligotyping and VNTR Typing

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