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

Abstract

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.

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

“…Therefore, the high-risk periods in spring and early summer complied with the disease characteristics. Such seasonal patterns were consistent with the previous studies done in Yunnan province, where is the registration peak of TB cases during spring [34]. Additionally, spatial scanning results displayed statistically significant 5-7 cluster areas for TB diseases in Qinghai each year, which were similar to the results of our previous study [7].…”

Spatial-temporal analysis of Tuberculosis in Chongqing, China 2011-2018

“…The reason for the decrease in patients reported in February is that the Chinese Spring Festival usually falls in February, in which people are busy celebrating the lunar New Year, and avoiding to seek medical care. This seasonal pattern is the same as that of a national study [12,13], and studies in Guangxi province [36], Xinjiang Uygur Autonomous Region [37], Zhaotong city of Yunnan province [24,32] and other western regions, but a little different from that of studies in Zhejiang province [23] , Hong Kong [38] and Taiwan [39], which are located in the eastern coastal area.…”

“…The coordinate information was used for spatial-temporal analysis. This geocoding process has been widely applied in previous studies [7,19,23,24], and we used a similar approach.…”

Spatio-temporal variations of licensed doctor distribution in China: measuring and mapping disparities

“…More specifically, it identifies those units in which the rates of change of licensed doctor density are significantly higher or lower than the others. Kulldorff's space-time scan statistic is defined by a circular window with a geographic base [26]. The first step of space-time scan analysis is to impose the window on the map that is then moved in space and time.…”