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基于污水中奥司他韦代谢物检测对长江中下游代表性5城市流感流行情况的相关性研究

Correlation study on influenza epidemic in representative 5 cities in the middle and lower reaches of the Yangtze River based on detection of oseltamivir metabolite in wastewater

  • 摘要: 通过选择污水样品中稳定且可检测的药物原型或代谢产物,可以实现对疾病情况的近实时检测。选择一线抗流感病毒药物奥司他韦的主要代谢产物奥司他韦酸作为生物标志物,以污水中奥司他韦酸的浓度,反推计算奥司他韦千人均消耗量和使用率。在长江中下游代表性5城市的46家污水处理厂进行了季度采样,采样时间为2022年11月至2023年12月。污水样本中奥司他韦酸的浓度范围为1.270~1279 ng/L,调查城市的平均奥司他韦千人均消耗量范围为9.560~544.7 mg/d,使用率平均范围为0.06‰~3.63‰。研究结果表明,2023年3月无锡市出现春季流感高峰,蚌埠、铜陵、宿州和常州市5月出现小的夏季流感高峰,2023年11月和12月无锡、常州和蚌埠市出现冬季流感高峰。结果与国家疾控中心和国家流感中心官方统计数据反映的南方城市流感流行情况基本一致。本方法与临床诊断率相结合可以为未来的流感防控工作提供近实时的数据支持。

     

    Abstract: By selecting stable and detectable drug prototypes or metabolites in sewage samples, near real-time detection of disease conditions can be achieved. This study selected oseltamivir carboxylate, the primary metabolite of first-line antiviral oseltamivir, as a biomarker. Based on the concentration of oseltamivir carboxylate in wastewater, the consumption and usage rate of oseltamivir were calculated by reverse engineering. Quarterly sampling was conducted at 46 urban sewage treatment plants in representative 5 cities in the middle and lower reaches of the Yangtze River, from November 2022 to December 2023. The concentration range of oseltamivir acid in sewage samples is 1.270−1 279 ng/L, the daily mass load of oseltamivir per 1 000 inhabitants in the surveyed cities ranged from 9.560 to 544.7 mg/d, and the average utilization rate is 0.06‰−3.63‰. The research results indicate that in March 2023, Wuxi City experienced a spring influenza peak, while Bengbu, Tongling, Suzhou, and Changzhou City experienced a small summer influenza peak in May. In November and December 2023, Wuxi, Changzhou, and Bengbu City experienced a winter influenza peak, the results are consistent with the official statistics of the National Center for Disease Control and Prevention and the National Influenza Center, which reflect the influenza epidemic situation in southern cities. The integration of this methodology with clinical diagnostic rates could provide near real-time data support for future influenza prevention and control strategies.

     

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