Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA.
Optum Labs Visiting Scholar, Eden Prairie, MN, USA.
Sci Rep. 2022 May 31;12(1):9031. doi: 10.1038/s41598-022-13168-3.
Emerging research suggests that internet search patterns may provide timely, actionable insights into adverse health impacts from, and behavioral responses to, days of extreme heat, but few studies have evaluated this hypothesis, and none have done so across the United States. We used two-stage distributed lag nonlinear models to quantify the interrelationships between daily maximum ambient temperature, internet search activity as measured by Google Trends, and heat-related emergency department (ED) visits among adults with commercial health insurance in 30 US metropolitan areas during the warm seasons (May to September) from 2016 to 2019. Maximum daily temperature was positively associated with internet searches relevant to heat, and searches were in turn positively associated with heat-related ED visits. Moreover, models combining internet search activity and temperature had better predictive ability for heat-related ED visits compared to models with temperature alone. These results suggest that internet search patterns may be useful as a leading indicator of heat-related illness or stress.
新兴研究表明，互联网搜索模式可能为了解极端高温天气对健康的不利影响以及人们对此的行为反应提供及时、可行的见解，但很少有研究评估这一假设，也没有研究在美国各地进行过此类研究。我们使用两阶段分布式滞后非线性模型，在 2016 年至 2019 年温暖季节（5 月至 9 月）期间，在美国 30 个大都市区，量化了每日最高环境温度、谷歌趋势衡量的互联网搜索活动与有商业健康保险的成年人因热相关的急诊就诊之间的相互关系。最高日温度与与热相关的互联网搜索呈正相关，而搜索反过来又与因热相关的急诊就诊呈正相关。此外，与仅使用温度的模型相比，结合互联网搜索活动和温度的模型对因热相关的急诊就诊具有更好的预测能力。这些结果表明，互联网搜索模式可能是一种有用的热相关疾病或压力的先行指标。
