Chen Xin, Wang Yu, Yu Xiaxia, Schoenfeld Elinor, Saltz Mary, Saltz Joel, Wang Fusheng
Stony Brook University, Stony Brook, NY.
AMIA Annu Symp Proc. 2018 Apr 16;2017:545-554. eCollection 2017.
Opioid related deaths are increasing dramatically in recent years, and opioid epidemic is worsening in the United States. Combating opioid epidemic becomes a high priority for both the U.S. government and local governments such as New York State. Analyzing patient level opioid related hospital visits provides a data driven approach to discover both spatial and temporal patterns and identity potential causes of opioid related deaths, which provides essential knowledge for governments on decision making. In this paper, we analyzed opioid poisoning related hospital visits using New York State SPARCS data, which provides diagnoses of patients in hospital visits. We identified all patients with primary diagnosis as opioid poisoning from 2010-2014 for our main studies, and from 2003-2014 for temporal trend studies. We performed demographical based studies, and summarized the historical trends of opioid poisoning. We used frequent item mining to find co-occurrences of diagnoses for possible causes of poisoning or effects from poisoning. We provided zip code level spatial analysis to detect local spatial clusters, and studied potential correlations between opioid poisoning and demographic and social-economic factors.
近年来，与阿片类药物相关的死亡人数急剧增加，美国的阿片类药物流行情况正在恶化。对抗阿片类药物流行已成为美国政府和纽约州等地方政府的当务之急。分析患者层面与阿片类药物相关的医院就诊情况，提供了一种数据驱动的方法来发现时空模式，并确定与阿片类药物相关死亡的潜在原因，这为政府决策提供了重要知识。在本文中，我们使用纽约州SPARCS数据（该数据提供医院就诊患者的诊断信息）分析了与阿片类药物中毒相关的医院就诊情况。我们在主要研究中确定了2010年至2014年期间所有主要诊断为阿片类药物中毒的患者，并在时间趋势研究中确定了2003年至2014年期间的此类患者。我们进行了基于人口统计学的研究，并总结了阿片类药物中毒的历史趋势。我们使用频繁项挖掘来找出可能导致中毒或中毒影响的诊断共现情况。我们提供了邮政编码层面的空间分析，以检测局部空间聚集情况，并研究阿片类药物中毒与人口统计学和社会经济因素之间的潜在相关性。
