Neighborhood Socioeconomic Deprivation and Health Care Costs in Older Community-Dwelling Adults: Importance of Functional Impairment and Frailty.

BACKGROUND: Low neighborhood socioeconomic status is associated with adverse health outcomes, but its association with health care costs in older adults is uncertain. OBJECTIVES: To estimate the association of neighborhood Area Deprivation Index (ADI) with total, inpatient, outpatient, skilled nursing facility (SNF), and home health care (HHC) costs among older community-dwelling Medicare beneficiaries, and determine whether these associations are explained by multimorbidity, phenotypic frailty, or functional impairments. DESIGN: Four prospective cohort studies linked with each other and with Medicare claims. PARTICIPANTS: In total, 8165 community-dwelling fee-for-service beneficiaries (mean age 79.2 years, 52.9% female). MAIN MEASURES: ADI of participant residence census tract, Hierarchical Conditions Category multimorbidity score, self-reported functional impairments (difficulty performing four activities of daily living), and frailty phenotype. Total, inpatient, outpatient, post-acute SNF, and HHC costs (US 2020 dollars) for 36 months after the index examination. KEY RESULTS: Mean incremental annualized total health care costs adjusted for age, race/ethnicity, and sex increased with ADI ($3317 [95% CI 1274 to 5360] for the most deprived vs least deprived ADI quintile, and overall p-value for ADI variable 0.009). The incremental cost for the most deprived vs least deprived ADI quintile was increasingly attenuated after separate adjustment for multimorbidity ($2407 [95% CI 416 to 4398], overall ADI p-value 0.066), frailty phenotype ($1962 [95% CI 11 to 3913], overall ADI p-value 0.22), or functional impairments ($1246 [95% CI -706 to 3198], overall ADI p-value 0.29). CONCLUSIONS: Total health care costs are higher for older community-dwelling Medicare beneficiaries residing in the most socioeconomically deprived areas compared to the least deprived areas. This association was not significant after accounting for the higher prevalence of phenotypic frailty and functional impairments among residents of socioeconomically deprived neighborhoods.

Optimized spatial information for 1990, 2000, and 2010 U.S. census microdata.

We report on the successful completion of a project to upgrade the positional accuracy of every response to the 1990, 2000, and 2010 U.S. decennial censuses. The resulting data set, called Optimized Spatial Census Information Linked Across Time (OSCILAT), resides within the restricted-access data warehouse of the Federal Statistical Research Data Center (FSRDC) system where it is available for use with approval from the U.S. Census Bureau. OSCILAT greatly improves the accuracy and completeness of spatial information for older censuses conducted prior to major quality improvements undertaken by the Bureau. Our work enables more precise spatial and longitudinal analysis of census data and supports exact tabulations of census responses for arbitrary spatial units, including tabulating responses from 1990, 2000, and 2010 within 2020 block boundaries for precise measures of change over time for small geographic areas.

Travel-time barriers to specialized cancer care for adolescents and young adults with acute lymphoblastic leukemia.

BACKGROUND: Prior studies demonstrate that 20%-50% of adolescents and young adults (age 15-39 years) with acute lymphoblastic leukemia (ALL) receive care at specialty cancer centers, yet a survival benefit has been observed for patients at these sites. Our objective was to identify patients at risk of severe geographic barriers to specialty cancer center-level care. METHODS: We used data from the North American Association of Central Cancer Registries Cancer in North America database to identify adolescent and young adult ALL patients diagnosed between 2004 and 2016 across 43 US states. We calculated driving distance and travel time from counties where participants lived to the closest specialty cancer center sites. We then used multivariable logistic regression models to examine the relationship between sociodemographic characteristics of counties where adolescent and young adult ALL patients resided and the need to travel more than 1 hour to obtain care at a specialty cancer center. RESULTS: Among 11 813 adolescent and young adult ALL patients, 43.4% were aged 25-39 years, 65.5% were male, 32.9% were Hispanic, and 28.7% had public insurance. We found 23.6% of adolescent and young adult ALL patients from 60.8% of included US counties would be required to travel more than 1 hour one way to access a specialty cancer center. Multivariable models demonstrate that patients living in counties that are nonmetropolitan, with lower levels of educational attainment, with higher income inequality, with lower internet access, located in primary care physician shortage areas, and with fewer hospitals providing chemotherapy services are more likely to travel more than 1 hour to access a specialty cancer center. CONCLUSIONS: Substantial travel-related barriers exist to accessing care at specialty cancer centers across the United States, particularly for patients living in areas with greater concentrations of historically marginalized communities.

Creating a replicable, valid cross-platform buffering technique: the sausage network buffer for measuring food and physical activity built environments.

BACKGROUND: Obesity 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. METHODS: In 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. RESULTS: Results 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. CONCLUSIONS: Findings 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.

The Role of Chance in the Census Bureau Database Reconstruction Experiment.

The Census Bureau plans a new approach to disclosure control for the 2020 census that will add noise to every statistic the agency produces for places below the state level. The Bureau argues the new approach is needed because the confidentiality of census responses is threatened by "database reconstruction," a technique for inferring individual-level responses from tabular data. The Census Bureau constructed hypothetical individual-level census responses from public 2010 tabular data and matched them to internal census records and to outside sources. The Census Bureau did not compare these results to a null model to demonstrate that their success in matching would not be expected by chance. This is analogous to conducting a clinical trial without a control group. We implement a simple simulation to assess how many matches would be expected by chance. We demonstrate that most matches reported by the Census Bureau experiment would be expected randomly. To extend the metaphor of the clinical trial, the treatment and the placebo produced similar outcomes. The database reconstruction experiment therefore fails to demonstrate a credible threat to confidentiality.

Multidimensional structural racism predicts birth outcomes for Black and White Minnesotans.

OBJECTIVE: The objective of this study is to determine the linkage between multidimensional structural racism typologies and preterm birth (PTB), low birthweight (LBW), and small-for-gestational-age (SGA) birth among infants of White, US-born Black, and foreign-born Black pregnant people in Minnesota. DATA SOURCES: The measures of structural racism were based on the 2017 American Community Survey 5-year estimates and the 2017 jail incarceration data from the Vera Institute of Justice. Birth outcomes of infants born in 2018 were based on birth records from the Minnesota Department of Health. STUDY DESIGN: We conducted a latent class analysis to identify multidimensional structural racism typologies in 2017 and related these typologies to birth outcomes of pregnant people who gave birth in Minnesota in 2018 using Vermunt's 3-step approach. Racial group-specific age-adjusted risks of PTB, LBW, and SGA by structural racism typologies were estimated. DATA COLLECTION: Study data were from public sources. PRINCIPAL FINDINGS: Our analysis identified three multidimensional structural racism typologies in Minnesota in 2017. These typologies can have high structural racism in some dimensions but low in others. The interactive patterns among various dimensions cannot simply be classified as "high" (i.e., high structural racism in all dimensions), "medium," or "low." The risks of PTB, LBW, and SGA for US-born Black pregnant Minnesotans were always higher than for their White counterparts regardless of the typologies in which they lived during pregnancy. Furthermore, these excess risks among US-born Black pregnant people did not vary significantly across the typologies. We did not find clear patterns when comparing the predicted risks for infants of US- and foreign-born Black pregnant people. CONCLUSION: Multidimensional structural racism increases the risks of adverse birth outcomes for US-born Black Minnesotans. Policy interventions to dismantle structural racism and eliminate birth inequities must be multi-sectoral as changes in one or a few dimensions, but not all, will unlikely reduce birth inequities.

Where are the labor markets?: Examining the association between structural racism in labor markets and infant birth weight.

Racist policies and practices that restrict Black, as compared to white workers, from employment may drive racial inequities in birth outcomes among workers. This study examined the association between structural racism in labor markets, measured at a commuting zone where workers live and commute to work, and low-birthweight birth. We found the deleterious effect of structural racism in labor markets among US-born Southern Black pregnant people of working age, but not among African- or Caribbean-born counterparts in any US region. Our analysis highlights the intersections of structural racism, culture, migration, and history of racial oppression that vary across regions and birth outcomes of Black workers.

Harmonizing Disparate Data across Time and Place: The Integrated Spatio-Temporal Aggregate Data Series.

In this article, the authors describe a new data infrastructure project being developed at the Minnesota Population Center. The Integrated Spatio-Temporal Aggregate Data Series (ISTADS) will make it easier for researchers to use publicly available aggregate data for the United States over a time span that covers virtually the entire life of the nation: 1790-2012. In addition to facilitating access and ease of use, ISTADS will facilitate the use of these various data sets in mapping and spatial analysis.

The intricacy of structural racism measurement: A pilot development of a latent-class multidimensional measure.

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-à-vis using multiple unidimensional measures without capturing their joint effects. FUNDING: This project is funded by the Robert J. Jones Urban Research and Outreach-Engagement Center, University of Minnesota. Additional support is provided by the Minnesota Population Center, which is funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant P2C HD041023).

COVID-19 Mortality At The Neighborhood Level: Racial And Ethnic Inequalities Deepened In Minnesota In 2020.

Substantial racial and ethnic disparities in COVID-19 mortality have been observed at the state and national levels. However, less is known about how race and ethnicity and neighborhood-level disadvantage may intersect to contribute to both COVID-19 mortality and excess mortality during the pandemic. To assess this potential interaction of race and ethnicity with neighborhood disadvantage, we link death certificate data from Minnesota from the period 2017-20 to the Area Deprivation Index to examine hyperlocal disparities in mortality. Black, Indigenous, and people of color (BIPOC) standardized COVID-19 mortality was 459 deaths per 100,000 population in the most disadvantaged neighborhoods compared with 126 per 100,000 in the most advantaged. Total mortality increased in 2020 by 14 percent for non-Hispanic White people and 41 percent for BIPOC. Statistical decompositions show that most of this growth in racial and ethnic disparity is associated with mortality gaps between White people and communities of color within the same levels of area disadvantage, rather than with the fact that White people live in more advantaged areas. Policy interventions to reduce COVID-19 mortality must consider neighborhood context.

Association of Residence in High-Police Contact Neighborhoods With Preterm Birth Among Black and White Individuals in Minneapolis.

Importance: Police 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 January 1 and December 31, 2016. Data were analyzed from March 2019 to October 2020. Exposures: Police contact was measured at the level of the census tract where the pregnant people lived. Police incidents per capita (ie, the number of police incidents divided by the census tract population estimate) were dichotomized into high if the value was in the fourth quartile and low for the remaining three quartiles. Main Outcomes and Measures: Preterm birth status was based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision, Clinical Modification (ICD-10-CM) code. Preterm infants were those with ICD-10-CM codes P07.2 and P07.3 documented in their charts. Results: Of 1059 pregnant people (745 [70.3%] White, 121 [11.4%] US-born Black, 193 [18.2%] Black born outside the US) in the sample, 336 White individuals (45.1%) and 62 Black individuals who were born outside the US (32.1%) gave birth between the ages of 30 and 34 years, while US-born Black individuals gave birth at younger ages, with 49 (40.5%) aged 25 years or younger. The incidence of preterm birth was 6.7% for White individuals (50 pregnant people), 14.0% for US-born Black individuals (17 pregnant people), and 5.7% for Black individuals born outside the US (11 pregnant people). In areas with high police contact vs low police contact, the odds of preterm birth were 90% higher for White individuals (odds ratio [OR], 1.9; 95% CI, 1.9-2.0), 100% higher for US-born Black individuals (OR, 2.0; 95% CI, 1.8-2.2), and 10% higher for Black individuals born outside the US (OR, 1.1; 95% CI, 1.0-1.2). Secondary geospatial analysis further revealed that the proportion of Black residents in Minneapolis census tracts was correlated with the number of police incidents reported between 2012 and 2016 (P = .001). Conclusions and Relevance: In this study, police contact was associated with preterm birth for both Black and White pregnant people. Predominantly Black neighborhoods had greater police contact than predominantly White neighborhoods, indicating that Black pregnant people were more likely to be exposed to police than White pregnant people. These findings suggest that racialized police patterns borne from a history of racism in the United States may contribute to racial disparity in preterm birth.

Geographically-targeted COVID-19 vaccination is more equitable than age-based thresholds alone.

COVID-19 mortality increases dramatically with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts introduce tradeoffs 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.

Geographically targeted COVID-19 vaccination is more equitable and averts more deaths than age-based thresholds alone.

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 Minnesota­demographically divergent states­we 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.

Building an interdisciplinary framework to advance conceptual and technical aspects of population-environment research focused on women's and children's health.

Great gains have been made in providing researchers geo-spatial data that can be combined with population health data. This development is crucial given concerns over the human health outcomes associated with a changing climate. Merging population and environmental data remains both conceptually and technically challenging because of a large range of temporal and spatial scales. Here we propose a framework that addresses and advances both conceptual and technical aspects of population-environment research. This framework can be useful for considering how any time or space-based environmental occurrence influences population health outcomes and can be used to guide different data aggregation strategies. The primary consideration discussed here is how to properly model the space and time effects of environmental context on individual-level health outcomes, specifically maternal, child and reproductive health outcomes. The influx of geospatial health data and highly detailed environmental data, often at daily scales, provide an opportunity for population-environment researchers to move towards a more theoretically and analytically sound approach for studying environment and health linkages.

Do Irregularly Shaped School Attendance Zones Contribute to Racial Segregation or Integration?

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.

Perceived and Police-Reported Neighborhood Crime: Linkages to Adolescent Activity Behaviors and Weight Status.

PURPOSE: Inadequate physical activity and obesity during adolescence are areas of public health concern. Questions exist about the role of neighborhoods in the etiology of these problems. This research addressed the relationships of perceived and objective reports of neighborhood crime to adolescent physical activity, screen media use, and body mass index (BMI). METHODS: Socioeconomically and racially/ethnically diverse adolescents (N = 2,455, 53.4% female) from 20 urban, public middle and high schools in Minneapolis/St. Paul, Minnesota responded to a classroom survey in the Eating and Activity in Teens 2010 study. BMI was measured by research staff. Participants' mean age was 14.6 (standard deviation = 2.0); 82.7% represented racial/ethnic groups other than non-Hispanic white. Linear regressions examined associations between crime perceived by adolescents and crime reported to police and the outcomes of interest (BMI z-scores, physical activity, and screen time). Models were stratified by gender and adjusted for age, race/ethnicity, socioeconomic status, and school. RESULTS: BMI was positively associated with perceived crime among girls and boys and with reported crime in girls. For girls, there was an association between higher perceived crime and increased screen time; for boys, between higher reported property crime and reduced physical activity. Perceived crime was associated with reported crime, both property and personal, in both genders. CONCLUSIONS: Few prior studies of adolescents have studied the association between both perceived and reported crime and BMI. Community-based programs for youth should consider addressing adolescents' safety concerns along with other perceived barriers to physical activity. Interventions targeting actual crime rates are also important.

VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

Building the School Attendance Boundary Information System (SABINS): Collecting, Processing, and Modeling K to 12 Educational Geography.

The School Attendance Boundary Information System is a social science data infrastructure project that assembles, processes, and distributes spatial data delineating K through 12th grade school attendance boundaries for thousands of school districts in U.S. Although geography is a fundamental organizing feature of K to 12 education, until now school attendance boundary data have not been made readily available on a massive basis and in an easy-to-use format. The School Attendance Boundary Information System removes these barriers by linking spatial data delineating school attendance boundaries with tabular data describing the demographic characteristics of populations living within those boundaries. This paper explains why a comprehensive GIS database of K through 12 school attendance boundaries is valuable, how original spatial information delineating school attendance boundaries is collected from local agencies, and techniques for modeling and storing the data so they provide maximum flexibility to the user community. An important goal of this paper is to share the techniques used to assemble the SABINS database so that local and state agencies apply a standard set of procedures and models as they gather data for their regions.

Because Muncie's Densities Are Not Manhattan's: Using Geographical Weighting in the EM Algorithm for Areal Interpolation.

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.