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Spatiotemporal clustering and meteorological drivers of non-occupational carbon monoxide poisoning in Shandong Province from 2019 to 2023
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Zi-xuan WEN1, Qing DUAN2, Yi-xin ZHANG3, Rui-qing MA1, Yu-wei ZHANG2, Wen-gui ZHENG1, Cheng-xi SUN3, Xiao-lin JIANG1, 4
Modern Preventive Medicine | 2025, 52(17) : 3142 - 3148
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Modern Preventive Medicine | 2025, 52(17): 3142-3148
Environmental and Occupational Health
Spatiotemporal clustering and meteorological drivers of non-occupational carbon monoxide poisoning in Shandong Province from 2019 to 2023
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Zi-xuan WEN1, Qing DUAN2, Yi-xin ZHANG3, Rui-qing MA1, Yu-wei ZHANG2, Wen-gui ZHENG1, Cheng-xi SUN3, Xiao-lin JIANG1, 4
Affiliations
  • School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China
Published: 2025-09-10 doi: 10.20043/j.cnki.MPM.202411497
Outline
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Objective

To clarify the spatial and temporal distribution pattern of the incidence of non-occupational carbon monoxide poisoning in Shandong Province, to reveal the meteorological driving factors behind it, and to provide reference for its scientific prevention and control.

Methods

Non-occupational carbon monoxide poisoning incident reports were collected through the China Disease Control and Prevention Information System Public Health Emergency Reporting Management Information System. Descriptive analysis was used to study the prevalence characteristics of non-occupational carbon monoxide poisoning. Spatial autocorrelation analysis and spatio-temporal scanning analysis were performed to explore spatio-temporal aggregation using ArcGIS 10.7 and SaTScan 10.1.2 software, respectively. The influence of meteorological factors on the incidence of non-occupational carbon monoxide poisoning was assessed using an optimal parameter geographical detector.

Results

A total of 12 088 cases of non-occupational carbon monoxide poisoning were reported in Shandong Province from 2019 to 2023, with peak incidence from November to March of the next year each year, and middle-aged and older populations people as the main high-risk groups. Global spatial autocorrelation analysis showed spatial clustering of non-occupational carbon monoxide poisoning incidence rates in both 2020—2022(P<0.05). The southern part of Shandong Province was gradually developing into a new hot spot. Spatio-temporal scanning analysis detected five aggregation zones, of which the primary aggregation zone was mainly concentrated in the northern part of Shandong Province (Nov 2019—Mar 2021, LLR=2 003.71, P<0.001). The results of the optimal parameter geographical detector showed that relative humidity and barometric pressure were important meteorological factors affecting morbidity, with the largest interaction between relative humidity and wind speed.

Conclusion

Non-occupational carbon monoxide poisoning in Shandong Province showed seasonal spatiotemporal clustering. It is recommended to strengthen the winter and spring health education work for middle-aged and older populations and other high-risk groups. At the same time, disease prevention and control organizations at all levels should establish good information communication with meteorological and propaganda departments. Early warning of non-occupational carbon monoxide poisoning accidents in key endemic areas such as northern Shandong Province and southern Shandong Province should be strengthened through these initiatives.

Non-occupational carbon monoxide poisoning  /  Spatiotemporal clustering  /  Optimal parameter geographical detector  /  Meteorological factors
Zi-xuan WEN, Qing DUAN, Yi-xin ZHANG, Rui-qing MA, Yu-wei ZHANG, Wen-gui ZHENG, Cheng-xi SUN, Xiao-lin JIANG. Spatiotemporal clustering and meteorological drivers of non-occupational carbon monoxide poisoning in Shandong Province from 2019 to 2023[J]. Modern Preventive Medicine, 2025 , 52 (17) : 3142 -3148 . DOI: 10.20043/j.cnki.MPM.202411497
Year 2025 volume 52 Issue 17
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doi: 10.20043/j.cnki.MPM.202411497
  • Receive Date:2024-11-30
  • Online Date:2026-03-18
  • Published:2025-09-10
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  • Received:2024-11-30
Affiliations
    School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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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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