A demonstration of Modified Treatment Policies to evaluate shifts in mobility and COVID-19 case rates in U.S. counties.

A demonstration of Modified Treatment Policies to evaluate shifts in mobility and COVID-19 case rates in U.S. counties.
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演示修改后的治疗政策,以评估美国各县流动性和 COVID-19 病例率的变化。

DOI:
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发表时间:
2021
影响因子:
5
通讯作者:
L. Balzer
L. Balzer
中科院分区:
医学2区
文献类型:
--
作者:
Joshua R Nugent;L. Balzer

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流动性数据与COVID-19病例率之间存在关联的证据不一。我们旨在评估美国2020年夏季/秋季减少流动性对新冠肺炎病例的县级影响,并展示经修改的治疗政策(MTP),以确定持续暴露的因果效应。具体来说,我们调查了改变10个流动性指数的分布对未来两周每10万居民新报告病例数的影响。主要分析使用Super Learner的目标最小损失估计(TMLE),以避免统计估计期间的参数建模假设,并灵活调整各种混杂因素,包括最近的病例发生率。我们还进行了未经调整的分析。在大多数星期,未经调整的分析表明,流动性指数和随后的新病例率之间有很强的关联。然而,混杂因素调整后,没有一个指标显示一致的关联下流动性降低。我们的分析表明,这种新的分布转移方法的效用,定义和估计因果关系的影响,在流行病学和公共卫生的连续曝光。
Mixed evidence exists of associations between mobility data and COVID-19 case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall 2020 in the United States and to demonstrate modified treatment policies (MTPs) to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indices on the number of newly reported cases per 100,000 residents two weeks ahead. Primary analyses used targeted minimum loss-based estimation (TMLE) with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indices and subsequent new case rates. However, after confounder adjustment, none of the indices showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health.
DOI: 10.1371/journal.pone.0241957
发表时间: 2020
期刊: PloS one
影响因子: 3.7
作者:
Huang X;Li Z;Jiang Y;Li X;Porter D
通讯作者: Porter D
DOI: 10.1038/s41586-020-2923-3
发表时间: 2020-11-10
期刊: NATURE
影响因子: 64.8
作者:
Chang, Serina;Pierson, Emma;Leskovec, Jure
通讯作者: Leskovec, Jure