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      Neighborhood socioeconomic inequality based on everyday mobility predicts COVID-19 infection in San Francisco, Seattle, and Wisconsin

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          Abstract

          Race and class disparities in COVID-19 cases are well documented, but pathways of possible transmission by neighborhood inequality are not. This study uses administrative data on COVID-19 cases for roughly 2000 census tracts in Wisconsin, Seattle/King County, and San Francisco to analyze how neighborhood socioeconomic (dis)advantage predicts cumulative caseloads through February 2021. Unlike past research, we measure a neighborhood’s disadvantage level using both its residents’ demographics and the demographics of neighborhoods its residents visit and are visited by, leveraging daily mobility data from 45 million mobile devices. In all three jurisdictions, we find sizable disparities in COVID-19 caseloads. Disadvantage in a neighborhood’s mobility network has greater impact than its residents’ socioeconomic characteristics. We also find disparities by neighborhood racial/ethnic composition, which can be explained, in part, by residential and mobility-based disadvantage. Neighborhood conditions measured before a pandemic offer substantial predictive power for subsequent incidence, with mobility-based disadvantage playing an important role.

          Abstract

          Abstract

          Disadvantage in a neighborhood’s mobility network has a larger impact than its own economic status on COVID-19 incidence.

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          The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

          Reuben M. Baron, David A Kenny (1986)
          In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.
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            Sensitivity Analysis in Observational Research: Introducing the E-Value.

            Peng Ding, Tyler J. VanderWeele (2017)
            Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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              Mobility network models of COVID-19 explain inequities and inform reopening

              Serina Chang, Emma Pierson, Pang Koh … (2020)
              The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.
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                Author and article information

                Contributors
                Brian L. Levy:
                ORCID: https://orcid.org/0000-0002-5784-2204
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Karl Vachuska:
                ORCID: https://orcid.org/0000-0003-1686-0112
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                S. V. Subramanian: Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing - review & editing
                Robert J. Sampson:
                ORCID: https://orcid.org/0000-0003-4259-8146
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing - original draftRole: Writing - review & editing
                Journal
                Journal ID (nlm-ta): Sci Adv
                Journal ID (iso-abbrev): Sci Adv
                Journal ID (publisher-id): sciadv
                Journal ID (hwp): advances
                Title: Science Advances
                Publisher: American Association for the Advancement of Science
                ISSN (Electronic): 2375-2548
                Publication date Collection: February 2022
                Publication date (Electronic, pub): 18 February 2022
                Volume: 8
                Issue: 7
                Electronic Location Identifier: eabl3825
                Affiliations
                [1 ]Department of Sociology and Anthropology, George Mason University, Fairfax, VA 22030, USA.
                [2 ]Center for Social Science Research, George Mason University, Fairfax, VA 22030, USA.
                [3 ]Department of Sociology, University of Wisconsin-Madison, Madison, WI 53706, USA.
                [4 ]Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
                [5 ]Department of Sociology, Harvard University, Cambridge, MA 02138, USA.
                [6 ]Harvard Center for Population and Development Studies, Cambridge, MA 02138, USA.
                Author notes
                [* ]Corresponding author. Email: blevy4@ 123456gmu.edu
                Author information
                https://orcid.org/0000-0002-5784-2204
                https://orcid.org/0000-0003-1686-0112
                https://orcid.org/0000-0003-4259-8146
                Article
                Publisher ID: abl3825
                DOI: 10.1126/sciadv.abl3825
                PMC ID: 8856620
                PubMed ID: 35179963
                SO-VID: 116ab686-dac8-4398-8125-eacf29f560fa
                Copyright © Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 09 July 2021
                Date accepted : 23 December 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: SES-1735505
                Categories
                Subject: Research Article
                Subject: Social and Interdisciplinary Sciences
                Subject: SciAdv r-articles
                Subject: Epidemiology
                Subject: Social Sciences
                Subject: Coronavirus
                Custom metadata
                Copyeditor Nicole Falcasantos

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