Background-Few studies have addressed the potential influence of neighborhood characteristics on adolescent obesity risk and findings have been inconsistent.

Background: Structural racism is a complex system of inequities working in tandem to cause poor health for communities of color, especially for Black people. However, the multidimensional nature of structural racism is not captured by existing measures used by population health scholars to study health inequities. Multidimensional measures can be made using complex analytical techniques. Whether or not the multidimensional measure of structural racism provides more insight than the existing unidimensional measures is unknown. Methods: We derived measures of Black-White residential segregation, inequities in education, employment, income, and homeownership, evaluated for 2,338 Public Use Microdata Areas (PUMAs) in the United States (US), and consolidated them into a multidimensional measure of structural racism using a latent class model. We compared the median COVID-19 vaccination rates observed across 54 New York City (NYC) PUMAs by levels (high/low) of structural racism and the multidimensional class using the Kruskal-Wallis test. This study was conducted in March 2021. Findings: Our latent class model identified three structural racism classes in the US, all of which can be found in NYC. We observed intricate interactions between the five dimensions of structural racism of interest that cannot be simply classified as "high" (i.e., high on all dimensions of structural racism), "medium," or "low." Compared to Class A PUMAs with the median rate of two-dose completion of 6¢9%, significantly lower rates were observed for Class B PUMAs (5¢5%, p = 0¢04) and Class C PUMAs (5¢2%, p = 0¢01). When the vaccination rates were evaluated based on each dimension of structural racism, significant differences were observed between PUMAs with high and low Black-White income inequity only (7¢2% vs. 5¢3%, p = 0¢001). Interpretation: Our analysis suggests that measuring structural racism as a multidimensional determinant of health provides additional insight into the mechanisms underlying population health inequity vis-a-vis using multiple unidimensional measures without capturing their joint effects.

COVID-19 mortality increases markedly with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts can have conflicting implications because BIPOC populations are younger than white populations. In analyses of California and Minnesota-demographically divergent states-we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups. Vaccination schemas directly implicate equitability of access, both domestically and globally.

BackgroundObesity researchers increasingly use geographic information systems to measure exposure and access in neighborhood food and physical activity environments. This paper proposes a network buffering approach, the “sausage” buffer. This method can be consistently and easily replicated across software versions and platforms, avoiding problems with proprietary systems that use different approaches in creating such buffers.MethodsIn this paper, we describe how the sausage buffering approach was developed to be repeatable across platforms and places. We also examine how the sausage buffer compares with existing alternatives in terms of buffer size and shape, measurements of the food and physical activity environments, and associations between environmental features and health-related behaviors. We test the proposed buffering approach using data from EAT 2010 (Eating and Activity in Teens), a study examining multi-level factors associated with eating, physical activity, and weight status in adolescents (n = 2,724) in the Minneapolis/St. Paul metropolitan area of Minnesota.ResultsResults show that the sausage buffer is comparable in area to the classic ArcView 3.3 network buffer particularly for larger buffer sizes. It obtains similar results to other buffering techniques when measuring variables associated with the food and physical activity environments and when measuring the correlations between such variables and outcomes such as physical activity and food purchases.ConclusionsFindings from various tests in the current study show that researchers can obtain results using sausage buffers that are similar to results they would obtain by using other buffering techniques. However, unlike proprietary buffering techniques, the sausage buffer approach can be replicated across software programs and versions, allowing more independence of research from specific software.

This research investigates if and how much the shapes of school attendance zones contribute to racial segregation in schools. We find that the typical school attendance zone is relatively compact and resembles a square-like shape. Compact zones typically draw children from local residential areas, and since local areas are often racially homogeneous, this suggests that high levels of racial segregation in the largest school districts are largely structured by existing residential segregation. Still, this study finds that the United States contains some attendance zones with highly irregular shapes—some of which are as irregular as the most irregular Congressional District. Although relatively rare, attendance zones that are highly irregular in shape almost always contain racially diverse student populations. This racial diversity contributes to racial integration within school districts. These findings contradict recent theoretical and empirical scholarship arguing that irregularly shaped zones contribute to racial segregation in schools. Our findings suggest that most racial segregation in school attendance zones is driven by large-scale segregation across residential areas rather than a widespread practice among school districts to exacerbate racial segregation by delineating irregularly shaped attendance zones.

Areal interpolation transforms data for a variable of interest from a set of source zones to estimate the same variable's distribution over a set of target zones. One common practice has been to guide interpolation by using ancillary control zones that are related to the variable of interest's spatial distribution. This guidance typically involves using source zone data to estimate the density of the variable of interest within each control zone. This article introduces a novel approach to density estimation, the geographically weighted expectation-maximization (GWEM) algorithm, which combines features of two previously used techniques, the expectation-maximization (EM) algorithm and geographically weighted regression. The EM algorithm provides a framework for incorporating proper constraints on data distributions, and using geographical weighting allows estimated control-zone density ratios to vary spatially. We assess the accuracy of GWEM by applying it with land-use/land-cover ancillary data to population counts from a nationwide sample of 1980 United States census tract pairs. We find that GWEM generally is more accurate in this setting than several previously studied methods. Because target-density weighting (TDW)—using 1970 tract densities to guide interpolation—outperforms GWEM in many cases, we also consider two GWEM-TDW hybrid approaches, and find them to improve estimates substantially.

Spatial distance is a critical component of theories across the social, natural, and information sciences, but too often the methods and metrics used to describe spatial distance are implicit or underspecified. How distance is measured may influence model results in unanticipated ways. We examined the differences among distances calculated in three ways: Euclidean distances, vector-based road-network distances, and raster-based cost-weighted distances. We applied these different measures to the case of the economic value of open space, which is frequently derived using hedonic pricing (HP) models. In HP models, distance to open space is used to quantify access for residential properties. Under the assumption that vector-based road distances better match actual travel distance between homes and open spaces, we compared these distances with Euclidean and raster-based cost-weighted distances, finding that the distance values themselves differed significantly. Open-space values estimated using these distances in hedonic models differed greatly and values for Euclidean and cost-weighted distances to open space were much lower than those for road-network distances. We also highlight computational issues that can lead to counterintuitive effects in distance calculations. We recommend the use of road-network distances in valuing open space using HP models and caution against the use of Euclidean and cost-weighted distances unless there are compelling theoretical reasons to do so.

IMPORTANCEPolice contact may have negative psychological effects on pregnant people, and psychological stress has been linked to preterm birth (ie, birth at <37 weeks' gestation). Existing knowledge of racial disparities in policing patterns and their associations with health suggest redesigning public safety policies could contribute to racial health equity. OBJECTIVE To examine the association between community-level police contact and the risk of preterm birth among White pregnant people, US-born Black pregnant people, and Black pregnant people who were born outside the US. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used medical record data of 745 White individuals, 121 US-born Black individuals, and 193 Black individuals born outside the US who were Minneapolis residents and gave birth to a live singleton at a large health system between