Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States
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SciScore for 10.1101/2020.06.25.20139915: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: This observational study is subject to several limitations. Although several research teams have used smartphone mobility data, including SafeGraph data, to study mobility trends, these data are novel and have not …
SciScore for 10.1101/2020.06.25.20139915: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: This observational study is subject to several limitations. Although several research teams have used smartphone mobility data, including SafeGraph data, to study mobility trends, these data are novel and have not been validated against traditional data sources. Moreover, we lacked individual-level information on smartphone users, and therefore imputed user characteristics from BG data. Our sample was likely not representative of the overall population, since smartphone ownership varies, e.g., by age and income.18 We believe SafeGraph data track mobility trends more accurately than the absolute levels of the behaviors they measure. Trends in SafeGraph data appear to align with trends in data from similar smartphone location aggregation companies,29 and weekly trends in these data align with Gallup survey data on physical distancing practices.30 However, SafeGraph data could systematically over- or undercount the number of smartphone users staying home or going to work, in part because SafeGraph does not obtain data from every device at regular intervals through the day. Instead, the data represent locations from an irregularly timed sample of timepoints for each device throughout the day. As a result, there are periods in which a device is assumed to be at its last known location. We do not believe these errors would be correlated with socioeconomic position, supporting their use for comparing time trends across income levels. In our analysis of state policy effe...
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.
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