Connecting past to present: Examining different approaches to linking historical redlining to present day health inequities

The Child Opportunity Index 2.0 and exacerbation-prone asthma in a cohort of urban children

RATIONALE

Social determinants of health underlie disparities in asthma. However, the effects of individual determinants likely interact, so a summary metric may better capture their impact. The Child Opportunity Index 2.0 (COI) is one such tool, yet its association with exacerbation-prone (EP) asthma is unknown.

OBJECTIVE

To investigate the association between the COI and EP asthma and clinical measures of asthma severity in children.

METHODS

We analyzed data from two prospective observational pediatric asthma cohorts (n = 193). Children were classified as EP (≥1 exacerbation in the past 12 months) or exacerbation-null (no exacerbations in the past 5 years). Spirometry, exhaled nitric oxide, IgE, and Composite Asthma Severity Index (CASI) were obtained. The association between COI and EP status was assessed with logistic regression. We fit linear and logistic regression models to test the association between COI and each clinical measure.

RESULTS

A 20-point COI decrease conferred 40% higher odds of EP asthma (OR 1.4; 95%CI 1.1-1.76). The effect was similar when adjusted for age and sex (OR 1.38, 95%CI 1.1-1.75) but was attenuated with additional adjustment for race and ethnicity (OR 1.19, 95%CI 0.92-1.54). A similar effect was seen for the Social/Economic and Education COI domains but not the Health/Environment Domain. A 20-point COI decrease was associated with an increase in CASI of 0.34. COI was not associated with other clinical measures.

CONCLUSIONS

Lower COI was associated with greater odds of EP asthma. This highlights the potential use of the COI to understand neighborhood-level risk and identify community targets to reduce asthma disparities.

Evaluating Methods for Mapping Historical Redlining to Census Tracts for Health Equity Research

Neighborhood characteristics including housing status can profoundly influence health. Recently, increasing attention has been paid to present-day impacts of "redlining," or historic area classifications that indicated less desirable (redlined) areas subject to decreased investment. Scholarship of redlining and health is emerging; limited guidance exists regarding optimal approaches to measuring historic redlining in studies of present-day health outcomes. We evaluated how different redlining approaches (map alignment methods) influence associations between redlining and health outcomes. We first identified 11 existing redlining map alignment methods and their 37 logical extensions, then merged these 48 map alignment methods with census tract life expectancy data to construct 9696 linear models of each method and life expectancy for all 202 redlined cities. We evaluated each model's statistical significance and R2 values and compared changes between historical and contemporary geographies and populations using Root Mean Squared Error (RMSE). RMSE peaked with a normal distribution at 0.175, indicating persistent difference between historical and contemporary geographies and populations. Continuous methods with low thresholds provided higher neighborhood coverage. Weighting methods had more significant associations, while high threshold methods had higher R2 values. In light of these findings, we recommend continuous methods that consider contemporary population distributions and mapping overlap for studies of redlining and health. We developed an R application {holcmapr} to enable map alignment method comparison and easier method selection.

Child opportunity index is associated with pediatric firearm injury in Philadelphia, Pennsylvania

Introduction

Firearm injury is the leading cause of death in children. This study uses geospatial mapping to illustrate the burden of pediatric firearm injury in Philadelphia and assesses the relationship between Child Opportunity Index (COI) and injury, hypothesizing that lower COI zip codes would have higher injury and mortality rates.

Methods

Pediatric firearm injury data for children aged 0-19 years in Philadelphia, from 2015 to February 2023, was visualized by race/ethnicity, fatal versus non-fatal status, and COI for zip code. COI was then dichotomized as "High" or "Low" based on nationally normed scores and used to compare incidence and odds of mortality. Injury incidence rates by COI were calculated using weighted Poisson regression, to adjust for the total number of children in each COI category. Odds of mortality by COI, adjusted for age, sex and race/ethnicity, were calculated using multivariable logistic regression.

Results

Of 2,339 total pediatric firearm injuries, 366 (16%) were fatal. Males (89%), adolescents (95%) and Black children (88%) were predominately affected. Geospatial mapping showed highest burden in North and West Philadelphia, which corresponded with areas of low COI. The incidence rate ratio (IRR) of injury in low COI zip codes was 2.5 times greater than high COI (IRR 2.5 [1.93-3.22]; p < 0.01). After adjusting for age, sex, and race/ethnicity, odds of mortality in low COI zip codes was nearly twice that of high COI zip codes (aOR 1.95 [0.77-4.92]), though did not demonstrate statistical significance (p = 0.16).

Conclusion

Child opportunity index is associated with pediatric firearm injury in Philadelphia, Pennsylvania.

Census Tracts Are Not Neighborhoods: Addressing Spatial Misalignment in Studies Examining the Impact of Historical Redlining on Present-day Health Outcomes

BACKGROUND

Research examining the effects of historical redlining on present-day health outcomes is often complicated by the misalignment of contemporary census boundaries with the neighborhood boundaries drawn by the US Home Owners' Loan Corporation (HOLC) in the 1930s. Previous studies have used different approaches to assign historical HOLC grades to contemporary geographies, but how well they capture redlining exposure is unknown.

METHODS

Our analysis included 7711 residences identified in the Multiple Listing Service database in Atlanta, Georgia (2017-2022). We evaluated the classification of HOLC grade assignment (A, B, C, D, or ungraded) when assigning exposure under four area-level approaches (centroid, majority land area, weighted score, and highest HOLC) compared with using complete address data (gold standard). We additionally compared approaches across three 2020 census geographies (tract, block group, and block).

RESULTS

When comparing the use of census tracts to complete address data, sensitivity was highest for the weighted score approach, which correctly identified 77% of residences in truly A-D graded neighborhoods as compared with the majority land area (44%), centroid (54%), and highest HOLC (59%) approaches. Regarding specificity, the majority land area approach best-classified residences in truly ungraded neighborhoods (93%) as compared with the weighted score (65%), centroid (81%), and highest HOLC (54%) approaches. Classification improved regardless of approach when using census block compared with the census tract.

CONCLUSIONS

Misclassification of historical redlining exposure is inevitable when using contemporary census geographies rather than complete address data. This study provides a framework for assessing spatial misalignment and selecting an approach for classification.

Structural Racism and Pedestrian Safety: Measuring the Association Between Historical Redlining and Contemporary Pedestrian Fatalities Across the United States, 2010‒2019

Objectives. To examine the association between historical redlining and contemporary pedestrian fatalities across the United States. Methods. We analyzed 2010-2019 traffic fatality data, obtained from the Fatality Analysis Reporting System, for all US pedestrian fatalities linked by location of crash to 1930s Home Owners' Loan Corporation (HOLC) grades and current sociodemographic factors at the census tract level. We applied generalized estimating equation models to assess the relationship between the count of pedestrian fatalities and redlining. Results. In an adjusted multivariable analysis, tracts graded D ("Hazardous") had a 2.60 (95% confidence interval = 2.26, 2.99) incidence rate ratio (per residential population) of pedestrian fatalities compared with tracts graded A ("Best"). We found a significant dose‒response relationship: as grades worsened from A to D, rates of pedestrian fatalities increased. Conclusions. Historical redlining policy, initiated in the 1930s, has an impact on present-day transportation inequities in the United States. Public Health Implications. To reduce transportation inequities, understanding how structurally racist policies, past and present, have an impact on community-level investments in transportation and health is crucial. (Am J Public Health. 2023;113(4):420-428. https://doi.org/10.2105/AJPH.2022.307192).

Association of Neighborhood Racial and Ethnic Composition and Historical Redlining With Built Environment Indicators Derived From Street View Images in the US

Importance

Racist policies (such as redlining) create inequities in the built environment, producing racially and ethnically segregated communities, poor housing conditions, unwalkable neighborhoods, and general disadvantage. Studies on built environment disparities are usually limited to measures and data that are available from existing sources or can be manually collected.

Objective

To use built environment indicators generated from online street-level images to investigate the association among neighborhood racial and ethnic composition, the built environment, and health outcomes across urban areas in the US.

Design, Setting, and Participants

This cross-sectional study was conducted using built environment indicators derived from 164 million Google Street View images collected from November 1 to 30, 2019. Race, ethnicity, and socioeconomic data were obtained from the 2019 American Community Survey (ACS) 5-year estimates; health outcomes were obtained from the Centers for Disease Control and Prevention 2020 Population Level Analysis and Community Estimates (PLACES) data set. Multilevel modeling and mediation analysis were applied. A total of 59 231 urban census tracts in the US were included. The online images and the ACS data included all census tracts. The PLACES data comprised survey respondents 18 years or older. Data were analyzed from May 23 to November 16, 2022.

Main Outcomes and Measures

Model-estimated association between image-derived built environment indicators and census tract (neighborhood) racial and ethnic composition, and the association of the built environment with neighborhood racial composition and health.

Results

The racial and ethnic composition in the 59 231 urban census tracts was 1 160 595 (0.4%) American Indian and Alaska Native, 53 321 345 (19.5%) Hispanic, 462 259 (0.2%) Native Hawaiian and other Pacific Islander, 17 166 370 (6.3%) non-Hispanic Asian, 35 985 480 (13.2%) non-Hispanic Black, and 158 043 260 (57.7%) non-Hispanic White residents. Compared with other neighborhoods, predominantly White neighborhoods had fewer dilapidated buildings and more green space indicators, usually associated with good health, and fewer crosswalks (eg, neighborhoods with predominantly minoritized racial or ethnic groups other than Black residents had 6% more dilapidated buildings than neighborhoods with predominantly White residents). Moreover, the built environment indicators partially mediated the association between neighborhood racial and ethnic composition and health outcomes, including diabetes, asthma, and sleeping problems. The most significant mediator was non-single family homes (a measure associated with homeownership), which mediated the association between neighborhoods with predominantly minority racial or ethnic groups other than Black residents and sleeping problems by 12.8% and the association between unclassified neighborhoods and asthma by 24.2%.

Conclusions and Relevance

The findings in this cross-sectional study suggest that large geographically representative data sets, if used appropriately, may provide novel insights on racial and ethnic health inequities. Quantifying the impact of structural racism on social determinants of health is one step toward developing policies and interventions to create equitable built environment resources.

Access to quality health resources and environmental toxins affect the relationship between brain structure and BMI in a sample of pre and early adolescents

Background

Environmental resources are related to childhood obesity risk and altered brain development, but whether these relationships are stable or if they have sustained impact is unknown. Here, we utilized a multidimensional index of childhood neighborhood conditions to compare the influence of various social and environmental disparities (SED) on body mass index (BMI)-brain relationships over a 2-year period in early adolescence.

Methods

Data were gathered the Adolescent Brain Cognitive Development Study® (n = 2,970, 49.8% female, 69.1% White, no siblings). Structure magnetic resonance imaging (sMRI), anthropometrics, and demographic information were collected at baseline (9/10-years-old) and the 2-year-follow-up (11/12-years-old). Region of interest (ROIs; 68 cortical, 18 subcortical) estimates of cortical thickness and subcortical volume were extracted from sMRI T1w images using the Desikan atlas. Residential addresses at baseline were used to obtain geocoded estimates of SEDs from 3 domains of childhood opportunity index (COI): healthy environment (COIHE), social/economic (COISE), and education (COIED). Nested, random-effects mixed models were conducted to evaluate relationships of BMI with (1) ROI * COI[domain] and (2) ROI * COI[domain] * Time. Models controlled for sex, race, ethnicity, puberty, and the other two COI domains of non-interest, allowing us to estimate the unique variance explained by each domain and its interaction with ROI and time.

Results

Youth living in areas with lower COISE and COIED scores were heavier at the 2-year follow-up than baseline and exhibited greater thinning in the bilateral occipital cortex between visits. Lower COISE scores corresponded with larger volume of the bilateral caudate and greater BMI at the 2-year follow-up. COIHE scores showed the greatest associations (n = 20 ROIs) with brain-BMI relationships: youth living in areas with lower COIHE had thinner cortices in prefrontal regions and larger volumes of the left pallidum and Ventral DC. Time did not moderate the COIHE x ROI interaction for any brain region during the examined 2-year period. Findings were independent of family income (i.e., income-to-needs).

Conclusion

Collectively our findings demonstrate that neighborhood SEDs for health-promoting resources play a particularly important role in moderating relationships between brain and BMI in early adolescence regardless of family-level financial resources.