Harvard Medical School, Boston, Massachusetts; Division of General Pediatrics, Boston Children's Hospital, Boston, Massachusetts; Computational Statistics and Machine Learning Group, Department of Statistics, University of Oxford, Oxford, United Kingdom; Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Harvard Medical School, Boston, Massachusetts.
Am J Prev Med. 2021 Mar;60(3):318-326. doi: 10.1016/j.amepre.2020.10.012. Epub 2020 Nov 13.
Previously estimated effects of social distancing do not account for changes in individual behavior before the implementation of stay-at-home policies or model this behavior in relation to the burden of disease. This study aims to assess the asynchrony between individual behavior and government stay-at-home orders, quantify the true impact of social distancing using mobility data, and explore the sociodemographic variables linked to variation in social distancing practices.
This study was a retrospective investigation that leveraged mobility data to quantify the time to behavioral change in relation to the initial presence of COVID-19 and the implementation of government stay-at-home orders. The impact of social distancing that accounts for both individual behavior and testing data was calculated using generalized mixed models. The role of sociodemographics in accounting for variation in social distancing behavior was modeled using a 10-fold cross-validated elastic net (linear machine learning model). Analysis was conducted in April‒July 2020.
Across all the 1,124 counties included in this analysis, individuals began to socially distance at a median of 5 days (IQR=3-8) after 10 cumulative cases of COVID-19 were confirmed in their state, with state governments taking a median of 15 days (IQR=12-19) to enact stay-at-home orders. Overall, people began social distancing at a median of 12 days (IQR=8-17) before their state enacted stay-at-home orders. Of the 16 studies included in the review, 13 exclusively used government dates as a proxy for social distancing behavior, and none accounted for both testing and mobility. Using government stay-at-home dates as a proxy for social distancing (10.2% decrease in the number of daily cases) accounted for only 55% of the true impact of the intervention when compared with estimates using mobility (18.6% reduction). Using 10-fold cross-validation, 23 of 43 sociodemographic variables were significantly and independently predictive of variation in individual social distancing, with delays corresponding to an increase in a county's proportion of people without a high school diploma and proportion of racial and ethnic minorities.
This retrospective analysis of mobility patterns found that social distancing behavior occurred well before the onset of government stay-at-home dates. This asynchrony leads to the underestimation of the impact of social distancing. Sociodemographic characteristics associated with delays in social distancing can help explain the disproportionate case burden and mortality among vulnerable communities.
之前对社交距离的影响评估没有考虑到在实施居家令之前个人行为的变化，也没有根据疾病负担对这种行为进行建模。本研究旨在评估个人行为与政府居家令之间的不同步，利用移动数据量化社交距离的真实影响，并探索与社交距离实践变化相关的社会人口统计学变量。
本研究是一项回顾性调查，利用移动数据来量化与 COVID-19 首次出现和政府居家令实施相关的行为变化时间。使用广义混合模型计算同时考虑个人行为和检测数据的社交距离影响。使用 10 折交叉验证弹性网络（线性机器学习模型）对社会距离行为变化的社会人口统计学差异进行建模。分析于 2020 年 4 月至 7 月进行。
在本分析包括的 1124 个县中，个体在其所在州累计出现 10 例 COVID-19 确诊病例后中位数 5 天（IQR=3-8）开始社交距离，州政府中位数 15 天（IQR=12-19）后颁布居家令。总体而言，人们在其所在州颁布居家令前中位数 12 天（IQR=8-17）开始社交距离。在纳入的 16 项研究中，有 13 项仅使用政府日期作为社交距离行为的代理，且均未同时考虑检测和移动数据。使用政府居家令日期作为社交距离的代理（每日病例数减少 10.2%）与使用移动数据估计的结果（减少 18.6%）相比，仅能解释干预措施真实影响的 55%。使用 10 折交叉验证，43 个社会人口统计学变量中有 23 个对个体社交距离变化具有显著且独立的预测作用，延迟与县内没有高中文凭的人比例和少数族裔比例的增加相对应。
本项对移动模式的回顾性分析发现，社交距离行为发生在政府居家令开始之前。这种不同步导致对社交距离影响的低估。与社交距离延迟相关的社会人口统计学特征可以帮助解释弱势群体社区的不成比例的病例负担和死亡率。
