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To provide a basis for formulating precise prevention and control strategies, the temporal and spatial aggregation characteristics of county road traffic injury mortality were analyzed, Shandong, 2012-2021.
The traffic injury mortality rate was calculated by using the data from the death registration and reporting system of Shandong. Geoda 1.18 and SatScan 9.7 were used for spatial autocorrelation analysis and spatiotemporal scanning analysis to explore the characteristics of spatiotemporal aggregation, and principal component regression analysis was used to explore the influencing factors.
Road traffic injury mortality rate in Shandong from 2012 to 2021 showed an overall decreasing trend and was high in the fall. The spatial correlation existed in each year, and the "high-high" aggregation area was mainly located in the central and northwestern parts of Shandong. The spatial scan revealed the existence of four clusters across the entire population. Cluster 1 existed from March 2012 to February 2015, covering 26 districts and counties, primarily located in south-central Shandong. Similar to the total population, urban and rural areas were mainly located in the central and southern part of Shandong, and the main urban agglomeration area existed from January 2012 to December 2014, covering 19 districts and counties; The main rural agglomeration area existed from January 2012 to December 2014, covering 38 districts and counties. Principal component regression analysis showed that the variables reflecting the economic and transportation conditions had a greater impact. Among them, the number of permanent residents and the volume of passenger and freight transportation were positively correlated with the mortality rate, while others were negatively correlated.
From 2012 to 2021, the mortality rate of road traffic injuries in Shandong showed a decreasing trend. The main agglomeration area exists in the central and southern part of Shandong, and the rural agglomeration is more obvious. According to the characteristics of clustering, attention should be paid to the enforcement of laws and regulations, infrastructure construction, population flow and other factors in high-risk areas, and prevention and control strategies should be adopted or adjusted according to local conditions.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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