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

Ge, Erjia; Zhang, Xin; Wang, Xiaomeng; Wei, Xiaolin

doi:10.1186/s40249-016-0104-2

Cited by 77 publications

(84 citation statements)

References 41 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The geocoding process was applied, as described in previous studies [13–15]. Demographic information and the administrative code of Zhaotong were obtained from the Annual Statistical Report and integrated into the study database.…”

Section: Methodsmentioning

confidence: 99%

Spatial-temporal analysis of pulmonary tuberculosis in the northeast of the Yunnan province, People’s Republic of China

Huang

Abe

et al. 2017

Infect Dis Poverty

View full text Add to dashboard Cite

BackgroundThe number of pulmonary tuberculosis (PTB) cases in China ranks third in the world. A continuous increase in cases has recently been recorded in Zhaotong prefecture-level city, which is located in the northeastern part of Yunnan province. This study explored the space-time dynamics of PTB cases in Zhaotong to provide useful information that will help guide policymakers to formulate effective regional prevention and control strategies.MethodsThe data on PTB cases were extracted from the nationwide tuberculosis online registration system. Time series and spatial cluster analyses were applied to detect PTB temporal trends and spatial patterns at the town level between 2011 and 2015 in Zhaotong. Three indicators of PTB treatment registration history were used: initial treatment registration rate, re-treatment registration rate, and total PTB registration rate.ResultsSeasonal trends were detected with an apparent symptom onset peak during the winter season and a registration peak during the spring season. A most likely cluster and six secondary clusters were identified for the total PTB registration rate, one most likely cluster and five secondary clusters for the initial treatment registration rate, and one most likely cluster for the re-treatment registration rate. The most likely cluster of the three indicators had a similar spatial distribution and size in Zhenxiong County, which is characterised by a poor socio-economic level and the largest population in Yunnan.ConclusionThis study identified temporal and spatial distribution of PTB in a high PTB burden area using existing health data. The results of the study provide useful information on the prevailing epidemiological situation of PTB in Zhaotong and could be used to develop strategies for more effective PTB control at the town level. The cluster that overlapped the three PTB indicators falls within the geographic areas where PTB control efforts should be prioritised.

show abstract

Section: Methodsmentioning

confidence: 99%

Spatial-temporal analysis of pulmonary tuberculosis in the northeast of the Yunnan province, People’s Republic of China

Huang

Abe

et al. 2017

Infect Dis Poverty

View full text Add to dashboard Cite

show abstract

“…A large number of studies on the spatial and temporal distribution of TB have demonstrated that TB has a highly complex dynamics and is spatially heterogeneous at the provincial, national, and international levels during certain periods of time; however, the variations in small area are always be ignored by using a relatively large scale [3][4][5][6]. In our previous study, the Moran's I spatial autocorrelation analysis method was used to analyze the TB incidence data from 2009 to 2013 in Qinghai and found that the distribution of TB in this province was not random [7].…”

Section: Introductionmentioning

confidence: 99%

Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

Rao

Shi

2017

BMC Infect Dis

View full text Add to dashboard Cite

BackgroundAlthough the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures.MethodsWe extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff’s retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai.ResultsA total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff’s scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016.ConclusionThis study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2643-y) contains supplementary material, which is available to authorized users.

show abstract

“…In 2014, the number of TB infections in China was approximately 930 000, which accounted for nearly 10% of all global infections [2]. A total of 147 941 TB cases were reported from 2009 to 2012 in Zhejiang Province alone [4]. …”

Section: Introductionmentioning

confidence: 99%

Use of low-dose computed tomography to assess pulmonary tuberculosis among healthcare workers in a tuberculosis hospital

Chen

et al. 2017

Infect Dis Poverty

View full text Add to dashboard Cite

BackgroundAccording to the World Health Organization, China is one of 22 countries with serious tuberculosis (TB) infections and one of the 27 countries with serious multidrug-resistant TB strains. Despite the decline of tuberculosis in the overall population, healthcare workers (HCWs) are still at a high risk of infection. Compared with high-income countries, the TB prevalence among HCWs is higher in low- and middle-income countries. Low-dose computed tomography (LDCT) is becoming more popular due to its superior sensitivity and lower radiation dose. However, there have been no reports about active pulmonary tuberculosis (PTB) among HCWs as assessed with LDCT. The purposes of this study were to examine PTB statuses in HCWs in hospitals specializing in TB treatment and explore the significance of the application of LDCT to these workers.MethodsThis study retrospectively analysed the physical examination data of healthcare workers in the Beijing Chest Hospital from September 2012 to December 2015. Low-dose lung CT examinations were performed in all cases. The comparisons between active and inactive PTB according to the CT findings were made using the Pearson chi-square test or the Fisher’s exact test. Comparisons between the incidences of active PTB in high-risk areas and non-high-risk areas were performed using the Pearson chi-square test. Analyses of active PTB were performed according to different ages, numbers of years on the job, and the risks of the working areas. Active PTB as diagnosed by the LDCT examinations alone was compared with the final comprehensive diagnoses, and the sensitivity and positive predictive value were calculated.ResultsA total of 1 012 participants were included in this study. During the 4-year period of medical examinations, active PTB was found in 19 cases, and inactive PTB was found in 109 cases. The prevalence of active PTB in the participants was 1.24%, 0.67%, 0.81%, and 0.53% for years 2012 to 2015. The corresponding incidences of active PTB among the tuberculosis hospital participants were 0.86%, 0.41%, 0.54%, and 0.26%. Most HCWs with active TB (78.9%, 15/19) worked in the high-risk areas of the hospital. There was a significant difference in the incidences of active PTB between the HCWs who worked in the high-risk and non-high-risk areas (odds ratio [OR], 14.415; 95% confidence interval (CI): 4.733 – 43.896). Comparisons of the CT signs between the active and inactive groups via chi-square tests revealed that the tree-in-bud, cavity, fibrous shadow, and calcification signs exhibited significant differences (P = 0.000, 0.021, 0.001, and 0.024, respectively). Tree-in-bud and cavity opacities suggest active pulmonary tuberculosis, whereas fibrous shadow and calcification opacities are the main features of inactive pulmonary tuberculosis. Comparison with the final comprehensive diagnoses revealed that the sensitivity and positive predictive value of the diagnoses of active PTB based on LDCT alone were 100% and 86.4%, respectively.ConclusionsHealthcare workers in tuberculosis hospi...

show abstract

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

Cited by 77 publications

References 41 publications

Spatial-temporal analysis of pulmonary tuberculosis in the northeast of the Yunnan province, People’s Republic of China

Spatial-temporal analysis of pulmonary tuberculosis in the northeast of the Yunnan province, People’s Republic of China

Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

Use of low-dose computed tomography to assess pulmonary tuberculosis among healthcare workers in a tuberculosis hospital

Contact Info

Product

Resources

About