Quantifying the social distancing privilege gap: a longitudinal study of smartphone movement

  1. Nabarun Dasgupta
  2. Michele Jonsson Funk
  3. Allison Lazard
  4. Benjamin Eugene White
  5. Stephen W. Marshall

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Abstract

In response to the coronavirus pandemic, social distancing became a widely deployed countermeasure in March 2020. We examined whether healthier and wealthier places more successfully implemented social distancing.

Methods

Mobile device location data were used to quantify declines in movement by county (n=2,633) in the United States of America, comparing April 15–17 (n=65,544,268 traces) to baseline of February 17 - March 7. Negative binomial regression was used to estimate gradients of privilege across eleven healthcare and economic indicators, adjusting for rurality and stay-at-home mandates. External validation used separate venue-specific data from Google Location Services.

Findings

Counties without stay-at-home orders showed a mobility decline of −52·3% (95% CI: −50·3%, −54·3%), slightly less than the decline in mandated areas (−60·8%; 95% CI: −60·0%, −61·6%). Strong linear gradients in privilege were observed. After adjusting for rurality and stay-at-home orders, counties in the highest quintile of social distancing mobility restriction had: 52% less uninsured, 47% more primary care providers, 29% more exercise space, 27% less food insecurity, 26% less child poverty, 17% higher incomes, 14% less overcrowding, 9·6% more racial segregation, 8·2% less youth, 7·4% more elderly, and 6·2% less influenza vaccination, compared to least social distancing areas.

Interpretation

Healthier and wealthier counties displayed a social distancing privilege gap, measured via smartphone mobility change. Structural inequities in this key countermeasure will influence immunity, and disease incidence and mortality.

Funding

None

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  1. ScreenIT

    SciScore for 10.1101/2020.05.03.20084624: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics Statement: The aggregated public county-level data analyzed in this study were not considered human subjects research and exempted from ethics review per the guidance of the University of North Carolina Office of Human Research Ethics.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data came from aggregated and anonymized GPS traces of devices for which the Location History setting within Google apps had been turned on enabled.
    Google
    suggested: (Google, RRID:SCR_017097)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: We measured change in movement as a proxy for success in implementing social distancing. A limitation is that change in movement does not capture physical proximity to other people. Maintaining two meters of distance from others is a key element of social distancing not reflected in mobility changes measured in kilometers. Our study also has an underrepresentation of extremely rural areas because we required at least ten 8-hour smartphone traces per day to maintain privacy; findings might not be generalizable to these areas. The distributions of jobs that are amenable to remote work-from-home likely also vary by rurality, but we did not have data to test this hypothesis beyond commute times. We intentionally did not analyze positive-among-tested coronavirus counts because of strong variation between states leading to selection bias (e.g., diagnostic suspicion bias for test eligibility).

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.05.03.20084624v1 on medRxiv