Regional transport and its association with tuberculosis in the Shandong province of China, 2009–2011

Ge, Erjia; Lai, PC; Zhang, Xiulei; Yang, Xiaomei; Li, Xuezheng; Wang, Haiying; Wei, Xiaolin

doi:10.1016/j.jtrangeo.2015.06.021

Cited by 8 publications

(11 citation statements)

References 58 publications

(54 reference statements)

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“…A number of 147, 941 confirmed TB cases were geocoded in terms of their residential addresses through matching each nominal address (completed with five to six hierarchical administrative district names) against the gazetter records and determining its longitude and latitude coordinates through the Google geocoding service and the toolbox of Geoprocessing in ArcGIS v.10.1 (ESRI Inc, Redlands, CA, USA). This process of geocoding was similar to previous studies applied in previous studies [ 14 , 15 ]. We aggregated the TB cases by county in terms of locations of their homes.…”

Section: Methodssupporting

confidence: 75%

“…The actual rate of TB notification in migrants, however, might be underestimated, because migrants usually do not seek medical cares or seeking care in unqualified clinics due to the lack of health insurance and other protections [ 39 , 40 ]. In addition, well-connected traffic networks and infrastructures could also be a factor in association with people’s migration and thus facilitating the transmission of TB in distance [ 15 ]. From this study, we may notice that solid socio-economic status could be associated with the reduction of TB notification rates, but a thorough healthcare and prevention system is crucial to be established for the control of the disease.…”

Section: Discussionmentioning

confidence: 99%

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Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009-2012

Zhang

Wang

et al. 2016

Infect Dis Poverty

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BackgroundTuberculosis (TB) is an infectious disease of major public health concern. The disease has demonstrated large space-time variations. This study aims to explore the space-time dynamics of TB cases in an economically and geographically dynamic province in China with specific references of TB control for policy makers.MethodsData on all reported TB cases from 2009 to 2012 were collected from the TB program at the Zhejiang Provincial Center for Disease Control and Prevention. We employed time series and exploratory spatial data analyses, including Moran’s I, Local Getis’s Gi*, and Kulldorff’s space-time scan statistics, to identify the temporal trends and spatial patterns of TB at a county level.ResultsA total of 147,941 TB cases were reported during 2009–2012 in Zhejiang. A higher proportion of TB cases were younger, male, and registered permanent residents among all TB cases notified in the province. TB cases were reported most frequently in April with small peaks in June, July, and October. This disease was spatially clustering with Moran’s I values ranged from 0.29 to 0.32 (p < 0.001). A most likely cluster and ten secondary clusters were identified, mainly concentrated in the southeast and west counties of the province.ConclusionsThis study identified seasonal patterns and significant space-time clusters of TB cases in Zhejiang, China. Poverty, migration, and seasonal effects may play important roles in potential clusters.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-016-0104-2) contains supplementary material, which is available to authorized users.

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Section: Methodssupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009-2012

Zhang

Wang

et al. 2016

Infect Dis Poverty

Self Cite

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“…During the last decade, rapid improvements in public transportation and related infrastructure have increased population movement and promoted cross-regional economic activities. At the same time, it has potential risks for public health, for example, the spread of communicable diseases [5,21]. Consistent with economic development, both the absolute and relative health disparities in TB across provinces increased from 1990 to 2016 in China [22].…”

Section: Introductionmentioning

confidence: 98%

Association of High-Speed Rail and Tuberculosis Transmission in Newly Integrated Regions: Quasi-Experimental Evidence from China

Liu

Tang

et al. 2021

Int J Public Health

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Objectives: The spread of tuberculosis (TB) is related to changes in the social network among the population and people’s social interactions. High-speed railway (HSR) fundamentally changed the integrated market across cities in China. This paper aims to examine the impact of HSR on TB transmission in newly integrated areas.Methods: By exploiting the opening and operation of the first HSR in Sichuan province as a quasi-natural experiment, we have collected and used the economic, social, and demographic data of 183 counties in Sichuan province from 2013 to 2016.Results: The new HSR line is associated with a 4.790 increase in newly diagnosed smear-positive TB cases per 100,000 people among newly integrated areas. On average, an additional increase of 34.178 newly diagnosed smear-positive TB cases occur every year in counties (or districts) covered by the new HSR.Conclusion: HSR development has significantly contributed to the transmission of TB. The public health system in China needs to pay more attention to the influences of new, mass public transportation.

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“…These studies have shown that the survival, suspension, and spread of M. tuberculosis expelled by infected people were often prolonged and promoted by environmental factors (e.g., high temperature, appropriate humidity, and a certain concentration of airborne particulate matter), while the dispersal of its carriers (e.g., saliva and particulate matter) was inhibited by frequent precipitation and favorable vegetation coverage [ 13 – 15 ]. Meanwhile, the exposure and infection probability of susceptible populations, as well as the diagnosis and treatment of TB cases, are heavily influenced by a series of social and economic factors (e.g., higher population density, more frequent population flow, uneven household income, scarce medical resources, and a well-developed public transportation system) [ 9 , 16 , 17 ]. However, possible interactions or combinations among these potential influencing factors, the spaces where they tend to happen, and their relationships with the incidence of TB remain underexplored.…”

Section: Introductionmentioning

confidence: 99%

Specific urban units identified in tuberculosis epidemic using a geographical detector in Guangzhou, China

Ren

et al. 2022

Infect Dis Poverty

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Background A remarkable drop in tuberculosis (TB) incidence has been achieved in China, although in 2019 it was still considered the second most communicable disease. However, TB’s spatial features and risk factors in urban areas remain poorly understood. This study aims to identify the spatial differentiations and potential influencing factors of TB in highly urbanized regions on a fine scale. Methods This study included 18 socioeconomic and environmental variables in the four central districts of Guangzhou, China. TB case data obtained from the Guangzhou Institute of Tuberculosis Control and Prevention. Before using Pearson correlation and a geographical detector (GD) to identify potential influencing factors, we conducted a global spatial autocorrelation analysis to select an appropriate spatial scales. Results Owing to its strong spatial autocorrelation (Moran’s I = 0.33, Z = 4.71), the 2 km × 2 km grid was selected as the spatial scale. At this level, TB incidence was closely associated with most socioeconomic variables (0.31 < r < 0.76, P < 0.01). Of five environmental factors, only the concentration of fine particulate matter displayed significant correlation (r = 0.21, P < 0.05). Similarly, in terms of q values derived from the GD, socioeconomic variables had stronger explanatory abilities (0.08 < q < 0.57) for the spatial differentiation of the 2017 incidence of TB than environmental variables (0.06 < q < 0.27). Moreover, a much larger proportion (0.16 < q < 0.89) of the spatial differentiation was interpreted by pairwise interactions, especially those (0.60 < q < 0.89) related to the 2016 incidence of TB, officially appointed medical institutions, bus stops, and road density. Conclusions The spatial heterogeneity of the 2017 incidence of TB in the study area was considerably influenced by several socioeconomic and environmental factors and their pairwise interactions on a fine scale. We suggest that more attention should be paid to the units with pairwise interacting factors in Guangzhou. Our study provides helpful clues for local authorities implementing more effective intervention measures to reduce TB incidence in China’s municipal areas, which are featured by both a high degree of urbanization and a high incidence of TB. Graphical Abstract

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Regional transport and its association with tuberculosis in the Shandong province of China, 2009–2011

Cited by 8 publications

References 58 publications

Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009-2012

Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009-2012

Association of High-Speed Rail and Tuberculosis Transmission in Newly Integrated Regions: Quasi-Experimental Evidence from China

Specific urban units identified in tuberculosis epidemic using a geographical detector in Guangzhou, China

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