Rapid Assessment of Opioid Exposure and Treatment in Cities Through Robotic Collection and Chemical Analysis of Wastewater
Rapid Assessment of Opioid Exposure and Treatment in Cities Through Robotic Collection and Chemical Analysis of Wastewater
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DOI：
10.1007/s13181-019-00756-5
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发表时间：
2020-04-01
影响因子：
2.9
通讯作者：
Chai, Peter R.
中科院分区：
文献类型：
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作者：
Endo, Norkio;Ghaeli, Newsha;Chai, Peter R.
关于阿片类药物使用，过量和治疗的准确数据对于指导社区打击阿片类药物流行的努力非常重要。基于水的流行病学（WBE）是一种潜在的方法，可以在过量数据之外量化社区一级的阿片类药物暴露趋势，这是大多数现有应对工作的基础。然而，大多数WBE努力收集母体阿片类化合物（例如，在废水处理设施中使用吗啡，测量通常代表整个城市的大型集水区的阿片类药物浓度。我们试图在城市内的目标沙井部署机器人采样设备，以半定量地检测阿片类代谢物（例如，吗啡葡糖苷酸）在一个次城市社区决议。方法我们在北卡罗来纳州的一个城市的10个居民沙井，占总城市人口的44.5%，部署了一个机器人污水采样平台。取样装置包括一个带有原位固相萃取的机器人取样臂，在24小时内每小时收集一次样品。我们使用靶向质谱法检测阿片类药物、纳洛酮和丁丙诺啡的定制组的存在。结果综合污水管网和人口地理信息系统数据，选取10个具有代表性的采样点作为全市的调查点。在程序期间检测到所有11种靶向代谢物。9种非法和处方阿片类药物的平均吗啡毫克当量（MME）在废水中排泄和检测，为49.1（标准差为31.9）MME/天/1000人。最常检测到可待因（检测率为100%），最不常检测到丁丙诺啡（12%）。纳洛酮的存在与在院前环境中由紧急医疗服务逆转的已知过量的城市数据相关。结论智能下水道选择和机器人废水收集的水为基础的流行病学是可行的，以检测特定的阿片类药物，纳洛酮，美沙酮，丁丙诺啡在城市内的存在。这些结果表明，废水流行病学可用于检测阿片类药物暴露的模式，并可能最终为阿片类药物使用障碍（OUD）治疗和减少危害计划提供信息。
Introduction Accurate data regarding opioid use, overdose, and treatment is important in guiding community efforts at combating the opioid epidemic. Wastewater-based epidemiology (WBE) is a potential method to quantify community-level trends of opioid exposure beyond overdose data, which is the basis of most existing response efforts. However, most WBE efforts collect parent opioid compounds (e.g., morphine) at wastewater treatment facilities, measuring opioid concentrations across large catchment zones which typically represent an entire municipality. We sought to deploy a robotic sampling device at targeted manholes within a city to semi-quantitatively detect opioid metabolites (e.g., morphine glucuronide) at a sub-city community resolution. Methods We deployed a robotic wastewater sampling platform at ten residential manholes in an urban municipality in North Carolina, accounting for 44.5% of the total municipal population. Sampling devices comprised a robotic sampling arm with in situ solid phase extraction, and collected hourly samples over 24-hour periods. We used targeted mass spectrometry to detect the presence of a custom panel of opioids, naloxone, and buprenorphine. Results Ten sampling sites were selected to be a representative survey of the entire municipality by integrating sewer network and demographic GIS data. All eleven metabolites targeted were detected during the program. The average morphine milligram equivalent (MME) across the nine illicit and prescription opioids, as excreted and detected in wastewater, was 49.1 (standard deviation of 31.9) MME/day/1000-people. Codeine was detected most frequently (detection rate of 100%), and buprenorphine was detected least frequently (12%). The presence of naloxone correlated with city data of known overdoses reversed by emergency medical services in the prehospital setting. Conclusion Wastewater-based epidemiology with smart sewer selection and robotic wastewater collection is feasible to detect the presence of specific opioids, naloxone, methadone, and buprenorphine within a city. These results suggest that wastewater epidemiology could be used to detect patterns of opioid exposure and may ultimately provide information for opioid use disorder (OUD) treatment and harm reduction programs.