Global Health Policy Institute, San Diego, USA.
Department of Healthcare Research and Policy, University of California, San Diego, USA.
Drug Alcohol Rev. 2020 Nov;39(7):908-913. doi: 10.1111/dar.13091. Epub 2020 May 13.
Infoveillance approaches (i.e. surveillance methods using online content) that leverage big data can provide new insights about infectious disease outbreaks and substance use disorder topics. We assessed social media messages about HIV, opioid use and injection drug use in order to understand how unstructured data can prepare public health practitioners for response to future outbreaks.
We conducted an retrospective analysis of Twitter messages during the 2015 HIV Indiana outbreak using machine learning, statistical and geospatial analysis to examine the transition between opioid prescription drug abuse to heroin injection use and finally HIV transmission risk, and to test possible associations with disease burden and demographic variables in Indiana and Marion County. Tweets from October 2014 to June 2015 were compared to disease burden at the county level for Indiana, and classification of census blocks by presence of relevant messages was done at the census block level for Marion County. Marion County was used as it exhibited the highest total count of Tweets.
257 messages about substance abuse and HIV were significantly related to HIV rates (P < 0.001) and opioid-related hospitalisations (P = 0.037). Using 157 characteristics from the American Community Survey, a linear classifier was computed with an appreciable correlation (r = 0.49) to risk-related social media messages from Marion County.
Communities appear to communicate online in response to disease burden. Classification produced an accurate equation to model census block risk based on census data, allowing for high-dimensional estimation of risk for blocks with sparse populations.
利用大数据的信息监测方法（即利用在线内容进行监测的方法）可以为传染病爆发和药物使用障碍问题提供新的见解。我们评估了有关 HIV、阿片类药物使用和注射药物使用的社交媒体信息，以便了解非结构化数据如何为公共卫生从业人员应对未来的爆发做好准备。
我们使用机器学习、统计和地理空间分析，对 2015 年印第安纳州 HIV 爆发期间的 Twitter 消息进行了回顾性分析，以检查阿片类处方药物滥用向海洛因注射使用的转变，最终是 HIV 传播风险，并测试与印第安纳州和马里恩县疾病负担和人口统计学变量的可能关联。2014 年 10 月至 2015 年 6 月的推文与印第安纳州的县一级疾病负担进行了比较，对马里恩县的相关消息出现的普查块进行了普查块分类。选择马里恩县是因为它显示出最高的推文总数。
257 条关于药物滥用和 HIV 的信息与 HIV 发病率（P<0.001）和与阿片类药物相关的住院率（P=0.037）显著相关。使用美国社区调查的 157 个特征，计算出一个与马里恩县与风险相关的社交媒体信息具有可观相关性（r=0.49）的线性分类器。
社区似乎在网上对疾病负担做出回应。分类根据普查数据生成了一个准确的方程来模拟普查块的风险，允许对人口稀少的普查块进行高维风险估计。
