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A Demonstration of Modified Treatment Policies to Evaluate Shifts in Mobility and COVID-19 Case Rates in US Counties.
Am J Epidemiol ; 192(5): 762-771, 2023 05 05.
Article in English | MEDLINE | ID: covidwho-2188225
ABSTRACT
Mixed evidence exists of associations between mobility data and coronavirus disease 2019 (COVID-19) case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall of 2020 in the United States and to demonstrate modified treatment policies to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indexes on the number of newly reported cases per 100,000 residents 2 weeks ahead. Primary analyses used targeted minimum loss-based estimation 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 indexes and subsequent new case rates. However, after confounder adjustment, none of the indexes 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Health Policy / Local Government Type of study: Experimental Studies / Observational study Limits: Humans Country/Region as subject: North America Language: English Journal: Am J Epidemiol Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Health Policy / Local Government Type of study: Experimental Studies / Observational study Limits: Humans Country/Region as subject: North America Language: English Journal: Am J Epidemiol Year: 2023 Document Type: Article