Differential COVID‐19 case positivity in New York City neighborhoods: Socioeconomic factors and mobility
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Abstract
Background
New York City (NYC) has been one of the hotspots of the COVID‐19 pandemic in the United States. By the end of April 2020, close to 165 000 cases and 13 000 deaths were reported in the city with considerable variability across the city's ZIP codes.
Objectives
In this study, we examine: (a) the extent to which the variability in ZIP code‐level case positivity can be explained by aggregate markers of socioeconomic status (SES) and daily change in mobility; and (b) the extent to which daily change in mobility independently predicts case positivity.
Methods
COVID‐19 case positivity by ZIP code was modeled using multivariable linear regression with generalized estimating equations to account for within‐ZIP clustering. Daily case positivity was obtained from NYC Department of Health and Mental Hygiene and measures of SES were based on data from the American Community Survey. Changes in human mobility were estimated using anonymized aggregated mobile phone location systems.
Results
Our analysis indicates that the socioeconomic markers considered together explained 56% of the variability in case positivity through April 1 and their explanatory power decreased to 18% by April 30. Changes in mobility during this time period are not likely to be acting as a mediator of the relationship between ZIP‐level SES and case positivity. During the middle of April, increases in mobility were independently associated with decreased case positivity.
Conclusions
Together, these findings present evidence that heterogeneity in COVID‐19 case positivity during NYC’s spring outbreak was largely driven by residents’ SES.
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SciScore for 10.1101/2020.07.01.20144188: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources POIs are defined by SafeGraph as “a specific physical location which someone might find interesting” and includes businesses, workplaces, educational institutions, and transit centers. SafeGraphsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. First, this is an ecologic analysis and thus inference is limited to the ZIP code-level, not the individual level. ZIP code is not a perfect measure of neighborhood and can mask …
SciScore for 10.1101/2020.07.01.20144188: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources POIs are defined by SafeGraph as “a specific physical location which someone might find interesting” and includes businesses, workplaces, educational institutions, and transit centers. SafeGraphsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. First, this is an ecologic analysis and thus inference is limited to the ZIP code-level, not the individual level. ZIP code is not a perfect measure of neighborhood and can mask some important heterogeneity in both exposure and outcome measures in this study. Second, we are unable to adjust for differential changes in population density by ZIP code as individuals with the means to leave NYC during this time period were likely to be disproportionately those of higher SES (16). As such, our measure of mobility cannot distinguish between ZIP codes having fewer visitors who individually have not reduced their visitation frequency, from a ZIP code with a constant volume of visitors who on average reduced their visit frequency. Third, limitations in the availability of COVID-19 positivity by ZIP code at the beginning of the pandemic limits our ability to fully understand the relationship between ZIP code-level mobility, SES, and positivity. We observed that the majority of the reduction in mobility occurred before public positivity data were available. Availability of ZIP level COVID-19 positivity in March 2020 would greatly strengthen our understanding. Finally, COVID-19 positivity is an imprecise outcome measure as it is heavily influenced both by the overall COVID-19 prevalence in a given ZIP code and access to diagnostic tests. Furthermore, the daily case and test counts were calculated as the difference between two successive cumulative c...
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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