U.S. regional differences in physical distancing: Evaluating racial and socioeconomic divides during the COVID-19 pandemic

Health varies by U.S. region of residence. Despite regional heterogeneity in the outbreak of COVID-19, regional differences in physical distancing behaviors over time are relatively unknown. This study examines regional variation in physical distancing trends during the COVID-19 pandemic and investigates variation by race and socioeconomic status (SES) within regions. Data from the 2015–2019 five-year American Community Survey were matched with anonymized location pings data from over 20 million mobile devices (SafeGraph, Inc.) at the Census block group level. We visually present trends in the stay-at-home proportion by Census region, race, and SES throughout 2020 and conduct regression analyses to examine these patterns. From March to December, the stay-at-home proportion was highest in the Northeast (0.25 in March to 0.35 in December) and lowest in the South (0.24 to 0.30). Across all regions, the stay-at-home proportion was higher in block groups with a higher percentage of Blacks, as Blacks disproportionately live in urban areas where stay-at-home rates were higher (0.009 [CI: 0.008, 0.009]). In the South, West, and Midwest, higher-SES block groups stayed home at the lowest rates pre-pandemic; however, this trend reversed throughout March before converging in the months following. In the Northeast, lower-SES block groups stayed home at comparable rates to higher-SES block groups during the height of the pandemic but diverged in the months following. Differences in physical distancing behaviors exist across U.S. regions, with a pronounced Southern and rural disadvantage. Results can be used to guide reopening and COVID-19 mitigation plans.

Percolation of temporal hierarchical mobility networks during COVID-19

Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

1011. Geospatial Analysis of Antibiotic Susceptibility in Wisconsin

Background The global threat of antimicrobial resistance (AMR) varies regionally. Regional differences may be related to socio-economic factors such as the Area Deprivation Index (ADI) score. Our hypothesis is that AMR spatial distribution is not random. Methods Patient level antibiotic susceptibility data was collected from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). Patient addresses were geocoded to coordinates and joined with US Census Block Groups. For each culture source, we included the initial E. coli isolate per patient per year with a patient address in Wisconsin. Percent susceptibility was calculated by block group. Spatial autocorrelation was determined by Global Moran’s I, which quantifies the attribute being analyzed as spatially dispersed, randomly distributed, or clustered by a range of −1 to +1. Linear regression correlated ADI to susceptibility. Hot spot analysis identified blocks with statistically significant higher and lower susceptibility (Figure 1). Figure 1. Geographic example of hot spot analysis and interpretation. Results The UW Health results included more urban areas, more block groups and greater isolate geographic density (n = 44,629 E. coli, 2009-2018), compared to Fort HealthCare (n = 6,065 isolates, 2012-2018) and MCHS (50,405 isolates, 2009-2018). A positive spatially clustered pattern was identified from the UW Health data for ciprofloxacin (Moran’s I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole (TMP/SMX) susceptibility (Moran’s I = 0.180, p < 0.001; Figures 2-3). Fort HealthCare and MCHS distribution was likely random for TMP/SMX and ciprofloxacin by Moran’s I. Linear regression of ADI (scale 1-10, least to most disadvantaged) and susceptibility did not find significance, but susceptibility was lower in more disadvantaged block groups. At the local level, we identified hot and cold spots with 90%, 95%, and 99% confidence, with more hot spots in rural regions. Figure 2. Results from Moran’s Index analysis identifying geographically clustered ciprofloxacin susceptibility results. Figure 3. Results from Moran’s Index analysis identifying geographically clustered sulfamethoxazole/trimethoprim susceptibility results. Conclusion Overall, Moran’s I analysis is more able to identify a clustered pattern in urban versus rural areas. Yet, the local hot spot results indicate that variations in antibiotic susceptibility may be more common in rural areas. The results are limited to data from patients with access to the health systems included. Disclosures Warren Rose, PharmD, MPH, Merck (Grant/Research Support)Paratek (Grant/Research Support, Advisor or Review Panel member)

Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States

Urban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic.

Living in a block group with a higher eviction rate is associated with increased odds of preterm delivery

BackgroundHousing instability is associated with adverse pregnancy outcomes. Recent studies indicate that eviction, which may affect a larger segment of the population than other forms of housing instability, is also associated with adverse pregnancy outcomes. However, these studies evaluate eviction across large areas, such as counties, so it remains unclear whether these patterns extend to individual-level pregnancy outcomes.MethodsWe used data on a cohort of all singleton live births at a single Chicago hospital between March 2008 and March 2018 to investigate the associations between block-group eviction rates and individual adverse pregnancy outcomes. Eviction data were obtained from the Eviction Lab at Princeton University. Generalised estimating equations were used to estimate associations and account for correlations among individuals living in the same block groups.ResultsIndividuals living in block groups in the highest quartile for eviction filing rate were 1.17 times as likely to deliver preterm (95% CI: 1.08 to 1.27) and 1.13 times as likely to deliver a small for gestational age infant (95% CI: 1.03 to 1.25) as compared with individuals living in block groups in the lowest quartile. Further, tests for linear trend indicated that for each quartile increase in eviction filing rate, there was a corresponding increase in odds of adverse outcomes (p<0.05). Results were strongest in magnitude for those with low neighbourhood and individual socioeconomic status, who are most likely to be renters and affected by local eviction policies.ConclusionOur results suggest that individuals living in block groups with higher eviction rates are more likely to deliver preterm. Future research should explore associations of individual experience with eviction on adverse pregnancy outcomes and examine whether policies to improve tenant protections also impact pregnancy outcomes.

Examining the Associations Between Neighborhood Socioeconomic Status and the Potential Distribution of Four Urban Ecosystem Services in Rochester, NY

As populations and the total area of impervious surfaces continue to grow in developed areas, planners and policy makers must consider how local ecological resources can be utilized to meet the needs and develop climate resilient and sustainable cities. Urban green spaces (UGS) have been identified as critical resources in improving the climate resiliency of cities and the quality of life for residents through the urban ecosystem services (UES) that they provide. However, certain communities within cities do not have uniform access to these UGS, and this may be due to historical legacies (i.e. redlining) and/or contemporary practices (i.e. urban planning). Therefore, we sought to determine if the supply of UES throughout the city of Rochester, NY is inequitably distributed. We assessed UES using geospatial analysis and literature-based coefficients to measure ecosystem services. We also assessed the distribution of socioeconomic status (SES), including contemporary demographic information (population density, household median income, homeownership percentages, race percentages, and median property value) and historic neighborhood assessment grades assigned by the HomeOwners Loan Corporation (HOLC), throughout the city. By looking at these two sets of data together, we considered the social-ecological conditions and spatial patterns throughout the city to determine if the supply of UES is correlated with SES distribution. We found that there are statistically significant positive correlations between the production of UES in block groups and the SES indicator homeownership percentages, and negative correlations with the percentage of the population that is Black and lower HOLC grades. Furthermore, clusters of block groups with significantly high levels of social need for urban greening projects and a low production of UES were found primarily in the city’s downtown area and the neighborhoods directly surrounding it. This information provides a useful framework for city planners and policy makers to identify where UGS development needs to be prioritized as well how the supply of UES in the city is inequitably distributed.

Post-Hurricane Vegetative Debris Assessment Using Spectral Indices Derived from Satellite Imagery

Transportation systems are vulnerable to hurricanes and yet their recovery plays a critical role in returning a community to its pre-hurricane state. Vegetative debris is among the most significant causes of disruptions on transportation infrastructure. Therefore, identifying the driving factors of hurricane-caused debris generation can help clear roadways faster and improve the recovery time of infrastructure systems. Previous studies on hurricane debris assessment are generally based on field data collection, which is expensive, time consuming, and dangerous. With the availability and convenience of remote sensing powered by the simple yet accurate estimations on the vigor of vegetation or density of manufactured features, spectral indices can change the way that emergency planners prepare for and perform vegetative debris removal operations. Thus, this study proposes a data fusion framework combining multispectral satellite imagery and various vector data to evaluate post-hurricane vegetative debris with an exploratory analysis in small geographical units. Actual debris removal data were obtained from the City of Tallahassee, Florida after Hurricane Michael (2018) and aggregated into U.S. Census Block Groups along with four groups of datasets representing vegetation, storm surge, land use, and socioeconomics. Findings suggest that vegetation and other land characteristics are more determinant factors on debris generation, and Modified Soil-Adjusted Vegetation Index (MSAVI2) outperforms other vegetation indices for hurricane debris assessment. The proposed framework can help better identify equipment stack locations and temporary debris collection centers while providing resilience enhancements with a focus on the transportation infrastructure.

Measuring social equity in urban energy use and interventions using fine-scale data

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities.

The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach

Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors, with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We analyzed geocoded police calls in the 552 census-block groups of the city of Valencia, Spain, related to street-level crime (N = 26,624) and to intimate-partner violence against women (N = 11,673). A Bayesian joint model was run to analyze the spatial overlap. In addition, two Bayesian hierarchical models controlled for different neighborhood characteristics to analyze the relative risks. Results showed that 66.5% of the total between-area variation in risk of reporting street-level crime was captured by a shared spatial component, while for reporting IPVAW the shared component was 91.1%. The log relative risks showed a correlation of 0.53, with 73.6% of the census-block groups having either low or high values in both outcomes, and 26.4% of the areas with mismatched risks. Maps of the shared component and the relative risks are shown to detect spatial differences. These results suggest that although there are some spatial differences between police calls reporting street-level and behind-closed-doors crime, there is also a shared distribution that should be considered to inform better-targeted police interventions.