Geography of racial coresidence

Whereas most work on residential race relations in US cities is based on the concept of segregation, our approach studies this issue from a who-lives-with-whom perspective. To this end, we study coresidence profiles – percentages of a given racial subpopulation living in different population zones. Population zones are data-driven divisions of a city based on characteristic racial compositions. We used 1990 and 2010 decennial census block-level data for 61 largest US metropolitan areas to calculate coresidence profiles for four major racial subpopulations in each city at both years. Profiles for each race/year combination were clustered into three archetypes. Cities, where given race profiles belong to the same archetype, have similar coresidence patterns with respect to this race. We present the geographic distributions of co-habitation profiles and show how they changed during the 1990-2010 period. Our results revealed that coresidence profiles depend not only on racial preferences but also on the availability of racial groups; cities in the different geographical regions have different coresidence profiles because they have different shares of White, Black, Hispanic, and Asian subpopulations. Temporal changes in coresidence profiles are linked to the increased share of Hispanic and Asian populations.

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.

Property Risk Assessment of Sinkhole Hazard in Louisiana, U.S.A

Sinkholes (or dolines) are an often-overlooked environmental hazard. The processes that lead to their formation are slow and insidious, which encourage a lack of awareness or concern for the potential danger, until the sudden, climactic formation leads to unexpected property damage and possibly human casualties. This research identifies the risk to residential properties to the sinkhole hazard, using Louisiana, United States as a case study. Risk is defined as the product of the hazard intensity and the loss to structure and contents within the building resulting from the hazard-related disaster. Results suggest that risk is highly scale-dependent. Although the risk due to sinkholes is small on a per capita basis statewide, especially when compared to the per capita risk of other natural hazards, the property risk for census tracts or census blocks partially or completely overlying a salt dome is substantial. At finer scales, Terrebonne Parish, in coastal southeastern Louisiana, has the greatest concentration of salt domes, while Madison Parish, which is east of Monroe, has the highest percentage of area at risk for sinkhole formation, and St. Mary Parish—immediately west of Terrebonne—has the greatest risk of property loss. An Acadia Parish census tract has the maximum annual property losses in 2050 projected at $40,047 (2010$), and the highest projected annual per building ($43) and per capita ($18) property loss are in the same St. Mary Parish census tract. At the census block level, maximum annual property loss ($7,040) is projected for a census block within Cameron Parish, with maximum annual per building loss ($85 within West Baton Rouge Parish), and maximum per capita annual property loss ($120 within Plaquemines Parish). The method presented in this paper is developed generally, allowing application for risk assessment in other locations. The results generated by the methodology are important to local, state, and national emergency management efforts. Further, the general public of Louisiana, and other areas where the developed method is applied, may benefit by considering sinkhole risk when purchasing, remodeling, and insuring a property, including as a basis of comparison to the risk from other types of hazard.

Longitudinal impacts of greenness on cardiovascular disease conditions

Introduction Environmental determinants of health, such as greenness, play an increasing role in our understanding of cardiovascular disease, and can inform how to address geographic cardiovascular disparities. This study assesses the impact of greenness and changes in greenness on cardiovascular disease. Purpose The purpose of this study is to investigate the impact of greenness and changes in greenness on cardiovascular disease diagnoses. Methods The sample for this prospective, longitudinal study was comprised of 243,558 U.S. Medicare beneficiaries 65 years or older, residing on the same Census Block from 2011 through 2016, and who had a Miami-Dade residential 9-digit zip code. Analyses examined the impact of greenness, measured by mean block-level Normalized Difference Vegetation index (NDVI) from satellite imagery to incidence of new cardiovascular conditions including: acute myocardial infarction, atrial fibrillation, heart failure, ischemic heart disease, hypertension, and stroke/ transient ischemic attack. Zero-inflated poisson regression was used to model the odds of developing any new cardiovascular disease and number of new cardiovascular conditions based on block level greenness while adjusting for individual age, gender, race/ethnicity, number of baseline cardiovascular conditions, and neighborhood characteristics including income and walkability. Results Living in a higher greenness Census Block was associated with lower odds of having at least one new cardiovascular condition and having a small number of new conditions, adjusting for individual and neighborhood level characteristics. When compared to individuals living in the lowest tertile of greenness throughout the study, those individuals living in the highest tertile of greenness throughout the study had a 16% lower odds of having any new cardiovascular conditions (OR=0.84, 95% CI: 0.79–0.90, p<0.001) and among individuals developing at least one new cardiovascular disease, 4% fewer new cardiovascular conditions (OR=0.96, 95% CI: 0.92–0.99, p<0.05). When compared to individuals living in the lowest tertile of greenness throughout the study, those individuals living in areas that increased their greenness from the lowest tertile to the highest tertile of greenness had a 15% lower odds of having any new cardiovascular conditions (OR=0.85, 95% CI: 0.75–0.97, p<0.001) and among individuals developing at least one new cardiovascular disease, 9% fewer new cardiovascular conditions (OR=0.91, 95% CI: 0.83–0.99, p<0.05). Conclusions High greenness is associated with lower rates of cardiovascular conditions over time, both when greenness increases and when a Census Block maintains high greenness. It is remarkable that these effects appear in five years, a relatively short amount of time for a positive environmental impact; and that even for those who began in the lowest greenness tertile, an increase to the high greenness tertile had a significant effect. FUNDunding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Robert Wood Johnson Evidence for Action Grant

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.