Postgraduate Institute of Medical Education & Research, Drug Deaddiction and Treatment Center & Department of Psychiatry, Chandigarh, India.
Department of Psychiatry Columbia University Irving Medical Center, New York, New York, USA.
Eur Addict Res. 2022;28(1):33-40. doi: 10.1159/000517302. Epub 2021 Jul 15.
There is a need to strengthen the standard surveillance of the opioid overdose crisis in the USA. The role of Google Trends (GT) was explored in this context.
In this study, a systemic GT search was done for a period from January 2004 to December 2018. "Naloxone" and "drug overdose" were chosen as search inputs. By using locally weighted scatterplot smoothing, we locally regressed and smoothed the relative search data generated by the GT search. We conducted a changepoint analysis (CPA) to detect significant statistical changes in the "naloxone" trend from 2004 to 2018. Cross-correlation function analyses were done to examine the correlation between 2 time series: year-wise relative search volume (RSV) for "naloxone" and "drug overdose" with the age-adjusted drug overdose mortality rate. Pearson's correlation was performed for the state-wise age-adjusted mortality rate due to drug overdose and RSV for "naloxone" and "drug overdose."
Smoothed and regressed GT of "naloxone" were similar to the "opioid overdose" trend published by the National Center for Health Statistics. The CPA showed 2 statistically significant points in 2011 and 2015. CPA of year-wise RSV for "naloxone" and "drug overdose" showed significantly positive correlation with the age-adjusted drug overdose mortality at lag zero. State-wise RSV for "naloxone" and "drug overdose" too showed a strong and significant positive correlation with the state-wise mortality data.
DISCUSSION/CONCLUSION: Inexpensive, publicly accessible, real-time GT data could supplement and strengthen the monitoring of opioid overdose epidemic if used in conjunction with the existing official data sources.
美国需要加强对阿片类药物过量危机的标准监测。本研究探讨了谷歌趋势（GT）在这方面的作用。
本研究进行了系统的 GT 搜索，时间范围为 2004 年 1 月至 2018 年 12 月。选择“纳洛酮”和“药物过量”作为搜索输入。通过局部加权散点平滑法，对 GT 搜索生成的相对搜索数据进行局部回归和平滑处理。我们进行了变点分析（CPA），以检测 2004 年至 2018 年“纳洛酮”趋势的显著统计学变化。交叉相关函数分析用于检查两个时间序列之间的相关性：逐年相对搜索量（RSV）“纳洛酮”和“药物过量”与年龄调整后药物过量死亡率。对州一级由于药物过量导致的年龄调整后死亡率和“纳洛酮”和“药物过量”的 RSV 进行了 Pearson 相关性分析。
“纳洛酮”的平滑和回归 GT 与国家卫生统计中心发布的“阿片类药物过量”趋势相似。CPA 显示 2011 年和 2015 年有 2 个统计学上显著的点。“纳洛酮”和“药物过量”的逐年 RSV 的 CPA 与年龄调整后药物过量死亡率呈显著正相关，滞后为零。州一级的 RSV“纳洛酮”和“药物过量”也与州一级死亡率数据呈强烈且显著的正相关。
讨论/结论：如果与现有官方数据源结合使用，廉价、公众可获取、实时的 GT 数据可以补充和加强对阿片类药物过量流行的监测。
