Impact of Neighborhood Socioeconomic Disadvantage on Staffing Hours in US Nursing Homes

Severe socioeconomic disadvantage in neighborhoods where nursing homes (NH) are located may be an important contributor to disparities in resident quality of care. Disadvantaged neighborhoods may have undesirable attributes (e.g., poor public transit) that make it challenging to recruit and retain qualified staff. Lower NH staffing could subsequently leave residents vulnerable to adverse events. Thus, the purpose of this study was to evaluate whether NHs located in socioeconomically disadvantaged neighborhoods had lower healthcare provider staffing levels. We linked publicly available NH data geocoded at the Census block-group level with the Area Deprivation Index, a measure of neighborhood socioeconomic factors including poverty, employment, and housing quality (percentiles: 1-100). Consistent with prior literature on threshold effects of neighborhood poverty on outcomes, we characterized NHs as being located in a disadvantaged neighborhood if the census-block group ADI score was ≥85/100. We used generalized estimating equations clustered at the county level with fixed effects for state and rural location to evaluate relationships between ADI score and staffing. NHs located in socioeconomically disadvantaged neighborhoods had 12.1% lower levels of staffing for registered nurses (mean: 5.8 fewer hours/100 resident-days, 95% CI: 4.4-7.1 hours), 1.2% lower for certified nursing assistants (2.9 fewer hours/100 resident days; 95% CI 0.6-5.1 hours), 20% lower for physical therapists (1.4 fewer hours/100 resident-days; 95% CI 1.1-1.8 hours), and 19% lower for occupational therapists (1.3 fewer hours/100 resident-days; 95% CI 1.0-1.6 hours). These findings highlight disparities that could be targeted with policy interventions focused on recruiting and retaining staff in socioeconomically disadvantaged neighborhoods.

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.

Social disorganization and police arrest trajectories

Prior police decision-making research is limited by (1) its emphasis on individual and organizational predictors and (2) cross-sectional designs, which fail to account for the time-varying aspects of police activities and the factors explaining them. Using group-based trajectory modeling, this study tested the ability of social disorganization theory to explain arrest activity at the Census block group level in Dallas, Texas. Social disorganization variables helped predict certain arrest trajectories, but not all of them. Specifically, socio-economic status was significant in low and medium arrest trajectory groups. An interaction between racial heterogeneity and family disruption was also significant in the medium arrest trajectory group. Theoretical implications are discussed.

Campaign finance vouchers do not reduce donor inequality

Donating to a campaign is inherently costly, and as a result, the composition of campaign donors differs from the composition of the electorate. What happens when the financial barriers to participation in campaign finance are removed? This paper analyzes Seattle's recent campaign finance reforms, where all registered voters receive four $25 vouchers to donate to candidates abiding by stricter campaign finance restrictions. Utilizing individual- and census block group-level data combined with administrative donation records, I find that those most mobilized by the availability of vouchers belong to groups already overrepresented within the donor pool. In many cases, the availability of vouchers appears to pull the donor pool even further from parity within the electorate. This finding is significant across race, income, past political participation, age, and partisanship.

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 statistically 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.

U.S. Regional Disparities in Physical Distancing: Evaluating Racial and Socioeconomic Divides During the COVID-19 Pandemic

ObjectiveTo examine regional variation in physical distancing trends over the course of the COVID-19 pandemic, and to investigate inequalities within regions by race and socioeconomic status (SES).MethodsRace and SES information from the American Community Survey were matched with location data from mobile device location pings at the Census block group level. We present trends in the proportion of residents staying at home by Census region, race, and SES from February-August, 2020.ResultsFrom March-August, the stay-at-home proportion was highest in the Northeast (0.23-0.31) and lowest in the South (0.22-0.28). Nationally, the stay-at-home proportion was higher in block groups with a higher percentage of Blacks, likely because Blacks disproportionately live in urban areas, where stay-at-home rates are higher. Physical distancing was higher among block groups that are wealthier, more educated, or contain the lowest proportion of frontline workers.ConclusionsDisparities in physical distancing behaviors exist across U.S. regions, with a pronounced Southern and rural disadvantage. Results from this study can be used to guide planning and policy recommendations related to COVID-19 mitigation.

Working from a distance: Who can afford to stay home during COVID-19? Evidence from mobile device data

As local and state governments reopen parts of the economy while balancing public health through social distancing, it is important to understand the heterogeneity in how the population has reacted to the COVID-19 pandemic. We match census block group level Safegraph mobile device data with demographic data from the American Community Survey to identify trends amongst different subgroups of the population. We find evidence that people′s ability to work from home is a determinant of time spent at home since the beginning of the pandemic. On April 15th, census block groups classified as being better able to work from home spent 3 more hours at home compared to those who were not. We see supporting trends amongst block groups with differences in income and educational attainment.

The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

Recently, the explosive growth of ridesourcing, or on-demand ridesharing, has attracted a great deal of attention from researchers and planners. Despite its transformative impacts on mobility, limited studies have examined how built environment affects its use. In this study, we investigate the impacts of built environment on ridesourcing demand. We employ structural equation modelling to account for the complex relationships among study variables, and investigate the impacts at census block group level by using RideAustin data in Austin, Texas. Findings reveal strong impacts of built environment on ridesourcing demand and significant temporal heterogeneity. The models show that greater population/employment/service job densities, road density, pavement completeness, land use mix and job accessibility by transit produce more ridesourcing demand. Access to the commuter rail (MetroRail) also leads to greater demand. Furthermore, time-of-day (TOD) models demonstrate that these effects vary significantly according to the time of day. Our research has implications for policy making and for travel demand modelling of ridesourcing.