Perceptions of greenspace and social determinants of health across the life course: The Life Course Sociodemographics and Neighborhood Questionnaire (LSNEQ)

We developed the Life Course Sociodemographics and Neighborhood Questionnaire (LSNEQ) to query older adults about perceived neighborhood greenspaces across the life course (i.e., distance to park, number of neighborhood parks/playgrounds, and neighborhood greenness) and about characteristics hypothesized to confound or moderate/mediate greenspace-health associations. Six perceived life course indices are derived from the LSNEQ: neighborhood socioeconomic status, neighborhood walking/biking, urbanicity, neighborhood amenities, neighborhood park access, and neighborhood greenness. Older adults from St. Louis, Missouri, and Sacramento, California, completed the LSNEQ in 2020-2021. The indices demonstrated borderline acceptable to good internal consistency (alpha = 0.60-0.79) and good to excellent test-retest reliability (ICC = 0.71-0.96) and detected different patterns of park access and neighborhood greenness by racialized group and location. Individuals with index scores indicating more neighborhood walking/biking and greater presence of neighborhood amenities over their life course were more likely to report neighborhood-based walking in older age. Overall, the LSNEQ is a reliable instrument to assess perceptions of life course social determinants of health including neighborhood greenspaces.

Diversity of Studies on Neighborhood Greenspace and Brain Health by Racialized/Ethnic Group and Geographic Region: A Rapid Review

Studies examining associations between greenspace and Alzheimer's disease and related dementia (ADRD) outcomes are rapidly on the rise, yet no known literature reviews have summarized the racialized/ethnic group and geographic variation of those published studies. This is a significant gap given the known disparities in both greenspace access and ADRD risk between racialized/ethnic groups and between developed versus developing countries. In this rapid literature review, we (1) describe the diversity of published greenspace-brain health studies with respect to racialized/ethnic groups and geographic regions; (2) determine the extent to which published studies have investigated racialized/ethnic group differences in associations; and (3) review methodological issues surrounding studies of racialized/ethnic group disparities in greenspace and brain health associations. Of the 57 papers meeting our inclusion criteria as of 4 March 2022, 21% (n = 12) explicitly identified and included individuals who were Black, Hispanic/Latinx, and/or Asian. Twenty-one percent of studies (n = 12) were conducted in developing countries (e.g., China, Dominican Republic, Mexico), and 7% (n = 4) examined racialized/ethnic group differences in greenspace-brain health associations. None of the studies were framed by health disparities, social/structural determinants of health, or related frameworks, despite the known differences in both greenspace availability/quality and dementia risk by racialized/ethnic group and geography. Studies are needed in developing countries and that directly investigate racialized/ethnic group disparities in greenspace-brain health associations to target and promote health equity.

Neighborhood segregation and cognitive change: Multi-Ethnic Study of Atherosclerosis

INTRODUCTION

We investigated associations between neighborhood racial/ethnic segregation and cognitive change.

METHODS

We used data (n = 1712) from the Multi-Ethnic Study of Atherosclerosis. Racial/ethnic segregation was assessed using Getis-Ord (Gi*) z-scores based on American Community Survey Census tract data (higher Gi* = greater spatial clustering of participant's race/ethnicity). Global cognition and processing speed were assessed twice, 6 years apart. Adjusted multilevel linear regression tested associations between Gi* z-scores and cognition. Effect modification by race/ethnicity, income, education, neighborhood socioeconomic status, and neighborhood social support was tested.

RESULTS

Participants were on average 67 years old; 43% were White, 11% Chinese, 29% African American/Black, 17% Hispanic; 40% had high neighborhood segregation (Gi* > 1.96). African American/Black participants with greater neighborhood segregation had greater processing speed decline in stratified analyses, but no interactions were significant.

DISCUSSION

Segregation was associated with greater processing speed declines among African American/Black participants. Additional follow-ups and comprehensive cognitive batteries may further elucidate these findings.

HIGHLIGHTS

A study of neighborhood racial/ethnic segregation and change in cognition. Study was based on a racially and geographically diverse, population-based cohort of older adults. Racial/ethnic segregation (clustering) was measured by the Getis-ord (Gi*) statistic. We saw faster processing speed decline among Black individuals in segregated neighborhoods.

Redlining and Neighborhood Walking in Older Adults: The 2017 National Household Travel Survey

INTRODUCTION

In the 1930s, Black, working-class, and immigrant neighborhoods were color coded on maps (i.e., redlining) indicating investment risk, which negatively impacted mortgage attainment/homeownership for these groups and led to long-standing segregation by race/ethnicity and socioeconomic status. Limited studies have investigated the health impacts of redlining, particularly among older adults who tend to stay closer to their residences. This study examines whether older adults in historically redlined neighborhoods report less neighborhood walking and whether associations vary by race/ethnicity and income.

METHODS

The sample included 4,651 individuals aged ≥65 years from the 2017 U.S. National Household Travel Survey. U.S. Census tract‒based redlining scores were 1=best, 2=still desirable, 3=definitely declining, and 4=hazardous. Multivariable negative binomial regression tested the associations between redlining and neighborhood walking/day in the overall sample and with stratification by poverty status (analyzed in 2022).

RESULTS

Participants were on average aged 73 years, and 11% were African/American Black, 75% were White, 8% were Hispanic/Latinx, and 6% were of other race/ethnicity. Participants reported a mean of 7.1 neighborhood walking minutes/day (SD=20.6), and 60% lived in definitely declining or hazardous neighborhoods. Individuals in hazardous neighborhoods (versus those in best neighborhoods) reported less neighborhood walking (prevalence ratio=0.64; 95% CI=0.43, 0.97). Among those living in poverty, living in definitely declining and hazardous neighborhoods was associated with less neighborhood walking (prevalence ratio=0.39 [95% CI=0.20, 0.79] and 0.39 [95% CI=0.18, 0.82], respectively).

CONCLUSIONS

Less neighborhood walking was reported among individuals living in neighborhoods with a historic redlining score of definitely declining or hazardous. Future studies using larger, more diverse cohorts may elucidate whether associations differ by race/ethnicity and geographic location/city.

Neighborhood racial/ethnic segregation and cognitive decline in older adults

Social determinants of health, including neighborhood factors, play a key role in the health of diverse older adults. However, few longitudinal studies have examined the role of neighborhood racial/ethnic segregation on cognitive decline in diverse samples. We examined older non-Hispanic White (NHW), Black, and Latino participants evaluated at an Alzheimer's Disease Research Center. Neighborhood racial/ethnic segregation was measured using the Getis-Ord Gi* statistic, a spatial measure of clustering that was created for Latino and Black clustering separately. Cognitive outcomes included episodic memory, semantic memory, and executive function. We used mixed effects multivariable regression models to evaluate associations between segregation and cognitive function and decline. We had 452 individuals: 46% NHW, 26% Black, and 21% Latino in 309 census tracts with an average of 5.2 years of follow-up data (range 0.6-15.0). In analyses that adjusted for a variety of covariates (including neighborhood SES), individuals in neighborhoods with a higher clustering of Latino residents (higher Gi* statistic) had slower declines over time on semantic memory and those in neighborhoods with a higher clustering of Black residents had slower declines over time on episodic memory. In race/ethnicity-stratified adjusted analyses: for Black participants, the association between clustering and cognition was present for episodic memory and executive function, showing lower baseline scores in highly clustered Black and Latino neighborhoods, respectively. There was no association with cognitive change. Among Latino participants, highly clustered Latino neighborhoods were associated with lower baseline scores in semantic memory, but slower declines in episodic memory; Latinos living in neighborhoods with a greater clustering of Black residents also had slower declines in episodic memory. Among NHWs, residing in neighborhoods with a higher clustering of Latino residents was associated with slower declines over time on semantic memory. Segregated neighborhoods may be differentially associated with cognitive outcomes depending on individual race/ethnicity.

Integrative Interdisciplinary Approaches to Critical Infrastructure Interdependency Analysis

There is a growing understanding that cross-sector risks faced by critical infrastructure assets in natural disasters require a collaborative foresight from multiple disciplines. However, current contributions to infrastructure interdependency analysis remain centered in discipline-specific methodologies often constrained by underlying theories and assumptions. This perspective article contributes to ongoing discussions about the uses, challenges, and opportunities provided by interdisciplinary research in critical infrastructure interdependency analysis. In doing so, several modes of integration of computational modeling with contributions from the social sciences and other disciplines are explored to advance knowledge that can improve the infrastructure system resilience under extreme events. Three basic modes of method integration are identified and discussed: (a) integrating engineering models and social science research, (b) engaging communities in participative and collaborative forms of social learning and problem solving using simulation models to facilitate synthesis, exploration, and evaluation of scenarios, and (c) developing interactive simulations where IT systems and humans act as "peers" leveraging the capacity of distributed networked platforms and human-in-the-loop architectures for improving situational awareness, real-time decision making, and response capabilities in natural disasters. Depending on the conceptualization of the issues under investigation, these broadly defined modes of integration can coalesce to address key issues in promoting interdisciplinary research by outlining potential areas of future inquiry that would be most beneficial to the critical infrastructure protection communities.

Modeling and Mapping High Water Table for a Coastal Region in Florida using Lidar DEM Data

Predicting and mapping high water table elevation in coastal landscapes is critical for both science application projects like inundation risk analysis and engineering projects like pond design and maintenance. Previous studies of water table mapping focused on the application of geostatistical methods, which cannot predict values beyond an observation spatial domain or generate an ideal pattern for regions with sparse measurements. In this study, we evaluated the multiple linear regression (MLR) and support vector machine (SVM) techniques for high water table prediction and mapping using fine spatial resolution lidar-derived Digital Elevation Model (DEM) data, and designed an application protocol of these two techniques for high water table mapping in a coastal landscape where groundwater, tide, and surface water are related. Testing results showed that SVM largely improved the high water table prediction with a mean absolute error (MAE) of 1.22 feet and root mean square error (RMSE) of 2.22 feet compared to the application of the ordinary Kriging method which could not generate a reasonable water table. MLR was also promising with a MAE of around 2 feet and RMSE of around 3 feet. The study suggests that both MLR and SVM are valuable alternatives to estimate high water table elevation in Florida. Fine resolution lidar DEMs are beneficial for high water table prediction and mapping.