Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015

Liu, Meng Yang; Li, Qi Huan; Zhang, Ying Jie; Ma, Yanyun; Liu, Yue; Feng, Wei; Hou, Cheng Bei; Amsalu, Endawoke; Li, Xia; Wang, Wei; Li, Wei Min; Guo, Xiu Hua

doi:10.1186/s40249-018-0490-8

Cited by 70 publications

(62 citation statements)

References 41 publications

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“…Many studies utilizing spatial temporal mapping suggest that the main guideline for selecting an optimal scanning window is reducing the overlapping areas, or that a single cluster should make up no more than 15% of the whole study area [28,29]. Moreover, previous research in China at the prefecture level found that 11% was the optimal parameter for spatial cluster sizes, so we analyzed the notification rate of SS + PTB setting the maximum sizes from 8 to 11% of the total population at risk by increments of 1% [18]. When the maximum size is set at 8 to 11%, there are fewer overlaps, and the biggest cluster covered no more than 15% of all the provinces.…”

Section: Analysis Of Spatial Variation In Temporal Trendsmentioning

confidence: 99%

“…Spatial and spatiotemporal analysis of TB notification cases could provide crucial epidemiological information to guide interventions. In recent years, the geographical information system (GIS) and spatial statistics were used to detect the spatial characteristics of TB in China [14][15][16][17][18]. Several studies have demonstrated that TB is not randomly distributed.…”

Section: Introductionmentioning

confidence: 99%

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The association between internal migration and pulmonary tuberculosis in China, 2005–2015: a spatial analysis

Liao

Gao

et al. 2020

Infect Dis Poverty

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Background: Internal migration places individuals at high risk of contracting tuberculosis (TB). However, there is a scarcity of national-level spatial analyses regarding the association between TB and internal migration in China. In our research, we aimed to explore the spatial variation in cases of sputum smear-positive pulmonary TB (SS + PTB) in China; and the associations between SS + PTB, internal migration, socioeconomic factors, and demographic factors in the country between 2005 and 2015. Methods: Reported cases of SS + PTB were obtained from the national PTB surveillance system database; cases were obtained at the provincial level. Internal migration data were extracted from the national population sampling survey and the census. Spatial autocorrelations were explored using the global Moran's statistic and local indicators of spatial association. The spatial temporal analysis was performed using Kulldorff's scan statistic. Fixed effects regression was used to explore the association between SS + PTB and internal migration. Results: A total of 4 708 563 SS + PTB cases were reported in China between 2005 and 2015, of which 3 376 011 (71.7%) were male and 1 332 552 (28.3%) were female. There was a trend towards decreasing rates of SS + PTB notifications between 2005 and 2015. The result of global spatial autocorrelation indicated that there were significant spatial correlations between SS + PTB rate and internal migration each year (2005-2015). Spatial clustering of SS + PTB cases was mainly located in central and southern China and overlapped with the clusters of emigration. The proportions of emigrants and immigrants were significantly associated with SS + PTB. Per capita GDP and education level were negatively associated with SS + PTB. The internal migration flow maps indicated that migrants preferred neighboring provinces, with most migrating for work or business. Conclusions: This study found a significant spatial autocorrelation between SS + PTB and internal migration. Both emigration and immigration were statistically associated with SS + PTB, and the association with emigration was stronger than that for immigration. Further, we found that SS + PTB clusters overlapped with emigration clusters, and the internal migration flow maps suggested that migrants from SS + PTB clusters may influence the TB epidemic characteristics of neighboring provinces. These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migrants.

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Section: Analysis Of Spatial Variation In Temporal Trendsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The association between internal migration and pulmonary tuberculosis in China, 2005–2015: a spatial analysis

Liao

Gao

et al. 2020

Infect Dis Poverty

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“…Scan statistics were widely applied in study topic related to TB [39][40][41][42][43], whereas, data aggregated into large scales of administrative regions may ignore the disease variation in small size of population, information lose lead to inaccurate and insensitive conclusion [44], these national-level researches could not preciously detect localized cluster on the resolution of province or prefecture [9,10,45]. Meanwhile, due to the stochastic scan statistics sensitive to parameters, the analytical results on high-resolution scan of county-level may not stable.…”

Section: Correlation and Hierarchical Clustering Analysismentioning

confidence: 99%

“…As an airborne disease, PTB epidemics influenced the transmission in geographical neighborhoods, disease hotspots were defined as high-risk clusters. Studies reported hotspots regions or high-risk clusters of TB in China by using spatial-temporal scan analysis [9][10][11][12][13][14], spatial-temporal distribution characteristics were illustrated at the national, provincial, prefectural, county-level or individual level in diverse time frames, all of which have shown the geographical and temporal heterogeneity of TB epidemic.…”

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confidence: 99%

The characteristics of spatial-temporal distribution and cluster of tuberculosis in Yunnan Province, China, 2005-2018.

Chen

Qiu

Yang

et al. 2019

Preprint

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Background: Tuberculosis (TB) makes a big challenge to public health, especially in high TB burden counties of China and Greater Mekong Subregion (GMS). The aim of this study was to identify the spatial-temporal dynamic process and high-risk region of notified pulmonary tuberculosis (PTB), sputum smear-positive tuberculosis (SSP-TB) and sputum smear-negative tuberculosis (SSN-TB) cases in Yunnan, the south-western of China between years of 2005 to 2018. meanwhile, to evaluate the similarity of prevalence pattern for TB among GMS. Methods: Data for notified PTB were extracted from the China Information System for Disease Control and Prevention (CISDCP) correspond to population information in 129 counties of Yunnan between 2005 to 2018. Seasonally adjusted time series defined the trend cycle and seasonality of PTB prevalence. Kulldorff’s space-time scan statistics was applied to identify temporal, spatial and spatial-temporal PTB prevalence clusters at county-level of Yunnan. Pearson correlation coefficient and hierarchical clustering were applied to define the similarity of TB prevalence among borders with GMS. Result: There were a total of 381 855 notified PTB cases in Yunnan, and the average prevalence was 59.1 per 100 000 population between 2005 to 2018. A declined long-term trend with seasonality of a peak in spring and a trough in winter for PTB was observed. Spatial-temporal scan statistics detected the significant clusters of PTB prevalence, the most likely cluster concentrated in the northeastern angle of Yunnan between 2011 to 2015 (RR=2.6, P<0.01), though the most recent cluster for PTB and spatial cluster for SSP-TB was in borders with GMS. There were six potential TB prevalence patterns among GMS. Conclusion: This study detected aggregated time interval and regions for PTB, SSP-TB, and SSN-TB at county-level of Yunnan province. Similarity prevalence pattern was found in borders and GMS. The localized prevention strategy should focus on cross-boundary transmission and SSN-TB control. Keywords: Tuberculosis, Spatial-temporal cluster, Scan statistics, Yunnan, Greater Mekong Subregion

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“…Geospatial analytical methods, such as geographic information systems (GIS) and spatio-temporal scanning analysis, are effective tools for helping to achieve such understanding [12][13][14]. In China, there were some studies intended to reveal the spatio-temporal distribution characteristics of TB under province, or nationwide, however, less discussion on the temporal and spatial distribution of tuberculosis in Xinjiang in recent years [15][16][17][18]. Therefore, the main objectives of this study were to investigate the temporal trends and spatial patterns of the TB surveillance data of Xinjiang from 2013 to 2016 by epidemic characteristics analysis, spatial autocorrelation analysis and spatio-temporal scanning analysis.…”

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confidence: 99%

Spatio-temporal distribution analysis of TB in Xinjiang Uygur Autonomous Region, China

Zhang

Zheng

2019

Preprint

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Background: Tuberculosis (TB) is a major global public health problem, which also affects economic and social development. China has the third largest burden of tuberculosis in the world. TB control made the slowest progress in western China while the highest prevalence of it showed up in Xinjiang. The study was conducted to investigate the spatial epidemiological features of pulmonary tuberculosis in Xinjiang Uygur Autonomous Region (referred to as Xinjiang) and compared the regional differences in the incidence of TB, for the 2013-2016，which can provide scientific reference for TB prevention and control. Methods: Based on the TB monitored data, descriptive statistics was used to analyze the distribution characteristics of TB patients. Spatial correlation analysis and spatio-temporal scanning techniques were used to explore the clustering of TB in Xinjiang. Results: A total of 178,674 TB cases were notified in Xinjiang from 2013 to 2016 with an average annual incidence of 195.32/100,000. The incidence of TB in Xinjiang showed an upward trend. Male and female patients accounted for 52.56％and 47.44％ respectively，with the sex ratio being 1.11:1. The number of cases continuously increased with the increasing age，and the elderly TB patients aged 60 years and above accounted for 46.77％．Most of the patients with TB were farmers and shepherds, accounting for 72.11％．The incidence of TB presented an upward trend from east to west and from north to south. Obvious spatial aggregation was observed in the incidence of TB in 98 countries and districts from 2013 to 2016 and the global Moran’s I was 0.5768 (P<0.001). The reported incidence rate of TB showed remarkable seasonality. The hot spots of TB were mostly concentrated in the southern Xinjiang with Kashgar as the center, while the cold spots were in northern Xinjiang with Urumqi as the center． Conclusion: The TB incidence displayed spatial and temporal aggregation at the levels of district and county in Xinjiang during 2013-2016, with high risk areas relatively concentrating in the southern Xinjiang．It is necessary to conduct targeted TB prevention and control in key areas and allocate health resources reasonably. Keywords: Tuberculosis; Spatial autocorrelation; Spatio-temporal scanning analysis

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Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015

Cited by 70 publications

References 41 publications

The association between internal migration and pulmonary tuberculosis in China, 2005–2015: a spatial analysis

The association between internal migration and pulmonary tuberculosis in China, 2005–2015: a spatial analysis

The characteristics of spatial-temporal distribution and cluster of tuberculosis in Yunnan Province, China, 2005-2018.

Spatio-temporal distribution analysis of TB in Xinjiang Uygur Autonomous Region, China

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