North Carolina A&T State University, Greensboro, NC, United States.
Research Triangle Institute International, Research Triangle Park, NC, United States.
JMIR Public Health Surveill. 2020 Jun 24;6(2):e17574. doi: 10.2196/17574.
Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research.
This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017.
Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs.
The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03).
Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin.
在过去的二十年中，与阿片类药物相关的死亡人数在数量和地域分布上都有所增加，影响了越来越多的个人、家庭和社区。Dasgupta、Beletsky 和 Ciccarone 反思了阿片类药物过量危机的变化性质，提出了一个三阶段框架来解释阿片类药物过量死亡（OOD）从治疗疼痛的处方阿片类药物（始于 2000 年）转移到海洛因（2010 年至 2015 年），然后转移到合成阿片类药物（始于 2013 年）。鉴于 OOD 的快速变化性质，及时的监测数据对于制定应对阿片类药物危机的策略至关重要。利用易于获取的近实时社交媒体数据来改善与阿片类药物危机相关的公共卫生监测工作是一个很有前途的研究领域。
本研究探讨了利用 Twitter 数据监测阿片类药物流行的潜力。具体来说，本研究调查了与阿片类药物相关的推文内容与阿片类药物危机三阶段性质之间的相关性，并与 2009 年至 2017 年北卡罗来纳州的 OOD 相关。
使用 Crimson Hexagon 获取与阿片类药物相关的 Twitter 帖子，并使用自然语言处理将其分类为与处方阿片类药物、海洛因和合成阿片类药物相关。这一过程产生了一个由 100777 条推文、转发、提及和回复组成的语料库。从语料库中随机抽取 10000 条推文，我们通过分析每年的单词频率来确定与阿片类药物相关的术语。OOD 从疾病控制与预防中心广域在线数据进行流行病学研究（CDC WONDER）的死因数据库中获得。最小二乘回归和格兰杰检验比较了与阿片类药物相关的帖子与 OOD 之间的模式。
与处方阿片类药物、海洛因和合成阿片类药物相关的推文模式与 OOD 的三阶段性质相似。对于处方阿片类药物，推文数量与 OOD 无关。提到海洛因和合成阿片类药物的推文与同年的海洛因 OOD 和合成 OOD 显著相关（P=.01 和 P<.001），与次年也显著相关（P=.03 和 P=.01）。此外，某一年的海洛因推文在预测海洛因死亡方面比滞后的海洛因 OOD 更好（P=.03）。
研究结果支持将 Twitter 数据作为阿片类药物过量死亡率的及时指标，尤其是对海洛因而言。
