PDF(Pubmed)
Abstract：
BACKGROUND: Medical narcotics must be administered under medical supervision because of their potential for misuse and abuse, leading to more dangerous and addictive substances. The control of medical narcotics requires close monitoring to ensure that they remain safe and effective. This study proposes a methodology that can effectively identify the overprescription of medical narcotics in hospitals and patients.
METHODS: Social network analysis (SNA) was applied to prescription networks for medical narcotics. Prescription data were obtained from the Narcotics Information Management System in South Korea, which contains all data on narcotic usage nationwide. Two-mode networks comprising hospitals and patients were constructed based on prescription data from 2019 to 2021 for the three most significant narcotics: appetite suppressants, zolpidem, and propofol. Two-mode networks were then converted into one-mode networks for hospitals. Network structures and characteristics were analyzed to identify hospitals suspected of overprescribing.
RESULTS: The SNA identified hospitals that overprescribed medical narcotics. Patients suspected of experiencing narcotic addiction seek treatment in such hospitals. The structure of the network was different for the three narcotics. While appetite suppressants and propofol networks had a more centralized structure, zolpidem networks showed a less centralized but more fragmented structure. During the analysis, two types of hospitals caught our attention: one with a high degree, meaning that potential abusers have frequently visited the hospital, and the other with a high weighted degree, meaning that the hospital may overprescribe. For appetite suppressants, these two types of hospitals matched 84.6%, compared with 30.0% for propofol. In all three narcotics, clinics accounted for the largest share of the network. Patients using appetite suppressants were most likely to visit multiple locations, whereas those using zolpidem and propofol tended to form communities around their neighborhoods.
CONCLUSIONS: The significance of this study lies in its analysis of nationwide narcotic use reports and the differences observed across different types of narcotics. The social network structure between hospitals and patients varies depending on the composition of the medical narcotics. Therefore, these characteristics should be considered when controlling medication with narcotics. The results of this study provide guidelines for controlling narcotic use in other countries.
METHODS: Social network analysis (SNA) was applied to prescription networks for medical narcotics. Prescription data were obtained from the Narcotics Information Management System in South Korea, which contains all data on narcotic usage nationwide. Two-mode networks comprising hospitals and patients were constructed based on prescription data from 2019 to 2021 for the three most significant narcotics: appetite suppressants, zolpidem, and propofol. Two-mode networks were then converted into one-mode networks for hospitals. Network structures and characteristics were analyzed to identify hospitals suspected of overprescribing.
RESULTS: The SNA identified hospitals that overprescribed medical narcotics. Patients suspected of experiencing narcotic addiction seek treatment in such hospitals. The structure of the network was different for the three narcotics. While appetite suppressants and propofol networks had a more centralized structure, zolpidem networks showed a less centralized but more fragmented structure. During the analysis, two types of hospitals caught our attention: one with a high degree, meaning that potential abusers have frequently visited the hospital, and the other with a high weighted degree, meaning that the hospital may overprescribe. For appetite suppressants, these two types of hospitals matched 84.6%, compared with 30.0% for propofol. In all three narcotics, clinics accounted for the largest share of the network. Patients using appetite suppressants were most likely to visit multiple locations, whereas those using zolpidem and propofol tended to form communities around their neighborhoods.
CONCLUSIONS: The significance of this study lies in its analysis of nationwide narcotic use reports and the differences observed across different types of narcotics. The social network structure between hospitals and patients varies depending on the composition of the medical narcotics. Therefore, these characteristics should be considered when controlling medication with narcotics. The results of this study provide guidelines for controlling narcotic use in other countries.
摘要：
背景：医疗麻醉品必须在医疗监督下管理，因为它们可能被误用和滥用，导致更危险和上瘾的物质。对医疗麻醉品的控制需要密切监测，以确保它们保持安全和有效。本研究提出了一种方法，可以有效地识别医院和患者中医疗麻醉品的过量处方。
方法：将社会网络分析（SNA）应用于医疗麻醉品处方网络。处方数据来自韩国的毒品信息管理系统，其中包含全国麻醉品使用的所有数据。基于2019年至2021年三种最重要的麻醉品的处方数据，构建了由医院和患者组成的双模式网络：食欲抑制剂，唑吡坦,还有异丙酚.然后将双模式网络转换为医院的单模式网络。分析了网络结构和特征，以识别怀疑处方过多的医院。
结果：SNA确定了过度开具医疗麻醉品的医院。怀疑有麻醉成瘾的患者在这些医院寻求治疗。三种麻醉品的网络结构不同。虽然食欲抑制剂和异丙酚网络具有更集中的结构，唑吡坦网络显示出不那么集中但更分散的结构。在分析过程中,两种类型的医院引起了我们的注意:一种是学位高的医院，这意味着潜在的虐待者经常去医院，另一个权重很高，这意味着医院可能开得过多。对于食欲抑制剂，这两类医院的比例为84.6%，丙泊酚为30.0%。在所有三种毒品中，诊所占网络的最大份额。使用食欲抑制剂的患者最有可能去多个地点，而那些使用唑吡坦和异丙酚的人倾向于在他们的社区周围形成社区。
结论：本研究的意义在于对全国麻醉品使用报告的分析以及在不同类型麻醉品之间观察到的差异。医院和患者之间的社会网络结构根据医疗麻醉品的组成而变化。因此,用麻醉品控制药物治疗时应考虑这些特征。这项研究的结果为控制其他国家的麻醉品使用提供了指南。
方法：将社会网络分析（SNA）应用于医疗麻醉品处方网络。处方数据来自韩国的毒品信息管理系统，其中包含全国麻醉品使用的所有数据。基于2019年至2021年三种最重要的麻醉品的处方数据，构建了由医院和患者组成的双模式网络：食欲抑制剂，唑吡坦,还有异丙酚.然后将双模式网络转换为医院的单模式网络。分析了网络结构和特征，以识别怀疑处方过多的医院。
结果：SNA确定了过度开具医疗麻醉品的医院。怀疑有麻醉成瘾的患者在这些医院寻求治疗。三种麻醉品的网络结构不同。虽然食欲抑制剂和异丙酚网络具有更集中的结构，唑吡坦网络显示出不那么集中但更分散的结构。在分析过程中,两种类型的医院引起了我们的注意:一种是学位高的医院，这意味着潜在的虐待者经常去医院，另一个权重很高，这意味着医院可能开得过多。对于食欲抑制剂，这两类医院的比例为84.6%，丙泊酚为30.0%。在所有三种毒品中，诊所占网络的最大份额。使用食欲抑制剂的患者最有可能去多个地点，而那些使用唑吡坦和异丙酚的人倾向于在他们的社区周围形成社区。
结论：本研究的意义在于对全国麻醉品使用报告的分析以及在不同类型麻醉品之间观察到的差异。医院和患者之间的社会网络结构根据医疗麻醉品的组成而变化。因此,用麻醉品控制药物治疗时应考虑这些特征。这项研究的结果为控制其他国家的麻醉品使用提供了指南。
