Epidemiological characteristics
A total of 5,508 ST cases, including a fatal case, were reported in Jiangxi from 2006 to 2018. The annual incidence ranged from 0.0092 in 2007 to 2.7200 in 2018 per 100,000 individuals (>295-fold change). Male cases were 2,015 (36.58%), and female cases were 3493 (63.42%). During the whole study period, the annual male-to-female ratios were separately 1.25:1, 3:1, 0.69:1, 0.8:1, 0.62:1, 0.77:1, 0.57:1, 0.48:1, 0.57:1, 0.59:1, 0.51:1, 0.50:1, 0.68:1(Fig 2).
During the period between 2006 and 2018, the median age of cases was 55 years old, ranged from 4 months to 94 years old. The median age increased from 44 years old (IQRs= 36.5-51 years old) in 2006 to 55 years old (IQRs= 48-64 years old) in 2018. The median age of the ST cases all kept at a higher level across the whole study period (Fig 3). Overall, the ST cases aged 40 years old and above were the most common (88.2%); the highest proportion was in the age group aged 60 years old and above (36.1%), 50-59 years old (31.1%), and 40-49 years old (20.9%). Additionally, the proportion of older age groups is increasing every year from 2006 to 2018, and the proportion of individuals aged over 60 years old rose from 0.22% in 2006 to 40.27% in 2018. The different age group ratios of the different years were significantly different (χ2 = 227.8, p = 0.000) (Fig 4).
The occupation distribution of the ST cases was diverse, including farmers, students, children, workers, and retirees. Most of the ST cases were made up of farmers, followed by retirees, students, and housework or unemployed. Most ST cases were farmers (92.2%), followed by retirees and students (1.7% and 1.6%, respectively). Different occupational ratios of different years existed significant differences (χ2 = 283.6, p = 0.000) (S1 Table).
Seasonal pattern
A seasonal-trend decomposition of time series analysis was selected as a filtering procedure designed for decomposing a time series into trend, seasonal, and remainder components to explore the various characteristics of periodicity and seasonality of ST. There was a rhythmic vibration in the raw data from 2006 to 2018, and most ST cases occurred yearly between June and October. A bi-peak seasonal pattern was noted, including a major peak in Autumn (between September and October), and a secondary peak in Summer (between June and August). Moreover, there was a rapid increase in the number of reported ST cases during the study period and the variation characteristic of periodicity with the amplitude and the magnitude of the periodical variation increasing (Fig 5).
Spatiotemporal distribution
The cumulative number of ST affected areas in Jiangxi included 9 prefecture cities and 60 counties during the period between 2006 and 2018. The number of affected prefecture cities ranged from 2 to 9, and the number of affected counties ranged from 3 to 60. The numbers of the affected prefecture cities and affected counties showed rapid trends of growing larger (Fig 6). The top five counties with the highest ST incidence rates included Nanfeng county (424.75/100000) of Fuzhou city, and Xunwu county (189.1/100000), Anyuan county (123.55/100000), Longnan county (122.21/100000), and Xinfeng county (77.25/100000) of Ganzhou city, all of which located in Jiangxi eastern mountainous hilly region or Jiangxi southern mountainous region (Fig 7).
Spatial autocorrelation analysis
In the autocorrelation analysis, from 2007 to 2018, the P values of the Global Moran’s I were all less than 0.05, while it was more than 0.05 in 2006 (S2 Table). The global autocorrelation indicated that the spatial distribution of the ST cases was not random except in 2006, but a spatial aggregation distribution. The hotspots (High-High) and outliers of ST transmission in Jiangxi were identified through LISA analysis. High-High hotspots were mainly distributed in southeastern Jiangxi, including Ganzhou city and its neighboring Fuzhou city, however, variations of the location were observed. High-High hotspots were firstly identified in Nankang, Xinfeng, and Longnan counties in 2007, then expanded to surrounding counties and covered the greatest number of counties in 2012, including thirteen counties. Finally, during the entire study period, seven counties of Ganzhou city, including Nankang, Ganxian, Xinfeng, Longnan, Anyuan, Xunwu, and Huichang county, developed into relatively stable High-High hotspots. Furthermore, High-High hotspots also extended to Fuzhou city, from south to east of Jiangxi, three counties, including Nanfeng, Yihuang, and Lichuan, became hotspots successively, and both of Nanfeng and Lichuan county were identified as relatively stable hotspots between 2006 to 2018. It is worth noting that Yifeng and Nanfeng county was High-low outliers in 2006, then Nanfeng county transformed into a High-High hotspot in 2011, 2016, 2017, and 2018. Additionally, the Low-High outliers were also observed in 2008, including Xinfeng, Zhanggong, and Shangyou county (Fig 8).
Spatiotemporal Clusters Analysis
The distribution of annual average ST incidence and the location of spatial clusters identified by using Kulldorff’s spatiotemporal scan statistics for each year from 2006 to 2018 were shown in Fig 5. Apparently, both the number of counties with increased ST incidence expanded persistently from 2006 to 2014, which caused the formation of a large, contiguous geographic area of ST incidence in southeastern Jiangxi. The primary cluster of ST cases was initially located in Yifeng county of Yichun city, after which the area expanded from 2008 to 2018 and shifted to the southeast, where the primary cluster was identified in southeastern Jiangxi that included Ganzhou city and Nanfeng county of Fuzhou city. Secondary clusters of ST cases were also detected in southeastern Jiangxi, with two to five clusters identified each year (Fig 9).
Additionally, spatiotemporal clusters across the whole study period from 2006 to 2018 were identified by using Kulldorff’s spatiotemporal scan statistic. The primary cluster only included a county, Nanfeng county of Fuzhou city, which is in the Jiangxi eastern mountainous hilly region. A total of 287,932 human beings were included with a time frame from September 2015 to November 2016. The expected case number was 3.45, while the observed case number was 567. The relative risk for the analysis was 183.10 (LLR =2359.17, P<0.05). It is worth noting that the primary cluster accounted for only 0.65% of the total population, while included 10.3% of the total cases during that time. Besides, one 1st Secondary cluster was detected in southern Jiangxi, covering six counties in Ganzhou city. Five 2nd Secondary clusters were identified in southeastern Jiangxi, including eleven counties in Ganzhou city, six counties in Fuzhou city, one county in Yingtan city attributing to eastern Jiangxi, and even one county in Ji’an city attributing to central Jiangxi (Fig 10).