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1.
Environ Res ; 250: 118521, 2024 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-38382663

RESUMO

Structural racism in the United States has resulted in neighborhoods with higher proportions of non-Hispanic Black (Black) or Hispanic/Latine residents having more features that intensify, and less that cool, the local-heat environment. This study identifies areas of New York City (NYC) where racial/ethnic heat exposure disparities are concentrated. We analyzed data from the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data on summer surface temperatures, and NYC Land Cover Dataset at the census tract-level (n = 2098). Four cross-sectional regression modeling strategies were used to estimate the overall City-wide association, and associations across smaller intra-city areas, between tract-level percent of Black and percent Hispanic/Latine residents and summer day surface temperature, adjusting for altitude, shoreline, and nature-cover: overall NYC linear, borough-specific linear, Community District-specific linear, and geographically weighted regression models. All three linear regressions identified associations between neighborhood racial and ethnic composition and summer day surface temperatures. The geographically weighted regression models, which address the issue of spatial autocorrelation, identified specific locations (such as northwest Bronx, central Brooklyn, and uptown Manhattan) within which racial and ethnic disparities for heat exposures are concentrated. Through examining the overall effects and geographic effect measure modification across spatial scales, the results of this study identify specific geographic areas for intervention to mitigate heat exposure disparities experienced by Black and Hispanic/Latine NYC residents.


Assuntos
Temperatura Alta , Cidade de Nova Iorque , Humanos , Hispânico ou Latino/estatística & dados numéricos , Estudos Transversais , Negro ou Afro-Americano/estatística & dados numéricos , Disparidades nos Níveis de Saúde , Etnicidade/estatística & dados numéricos
2.
Artigo em Inglês | MEDLINE | ID: mdl-39179752

RESUMO

BACKGROUND: Lower socioeconomic (SES) communities are more likely to be situated in urban heat islands and have higher heat exposures than their higher SES counterparts, and this inequality is expected to intensify due to climate change. OBJECTIVES: To examine the relationship between surface temperatures and SES in New York City (NYC) by employing a novel analytical approach. Through incorporating modifiable features, this study aims to identify potential locations where mitigation interventions can be implemented to reduce heat disparities associated with SES. METHODS: Using the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data surface temperatures (measured on 8/12/2016), and the NYC Land Cover Dataset at the census tract level (2098 tracts), this study examines the association between two components of tract-level SES (percentage of individuals living below the poverty line and the percentage of individuals without a high school degree) and summer day surface temperature in NYC. First, we examine this association with an unrestricted NYC linear regression, examining the city-wide association between the two SES facets and summer surface temperature, with additional models adjusting for altitude, shoreline, and nature-cover. Then, we assess geographic effect measure modification by employing the same models to three supplemental regression model strategies (borough-restricted and community district-restricted linear regressions, and geographically weighted regression (GWR)) that examined associations within smaller intra-city areas. RESULTS: All regression strategies identified areas where lower neighborhood SES composition is associated with higher summer day surface temperatures. The unrestricted NYC regressions revealed widespread disparities, while the borough-restricted and community district-restricted regressions identified specific political boundaries within which these disparities existed. The GWR, addressing spatial autocorrelation, identified significant socioeconomic heat disparities in locations such as northwest Bronx, central Brooklyn, and uptown Manhattan. These findings underscore the need for targeted policies and community interventions, including equitable urban planning and cooling strategies, to mitigate heat exposure in vulnerable neighborhoods. IMPACT STATEMENT: This study redefines previous research on urban socioeconomic disparities in heat exposure by investigating both modifiable (nature cover) and non-modifiable (altitude and shoreline) built environment factors affecting local temperatures at the census tract level in New York City. Through a novel analytical approach, the research aims to highlight intervention opportunities to mitigate heat disparities related to socioeconomic status. By examining the association between surface temperatures and socioeconomic status, as well as investigating different geographic and governmental scales, this study offers actionable insights for policymakers and community members to address heat exposure inequalities effectively across different administrative boundaries. The objective is to pinpoint potential sites for reducing socioeconomic heat exposure disparities at various geographic and political levels.

3.
Curr Epidemiol Rep ; 9(4): 316-325, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-37961046

RESUMO

Purpose of review: Violence is a leading cause of death, disability, and health inequity in the United States. This review summarizes the scientific literature on place-based interventions and violence, describes study design challenges, and suggests future directions for this group of interventions. Recent findings: Violence prevention strategies commonly target high-risk individuals, but recent research has found that place-based interventions are practical, sustainable, and high-impact opportunities that benefit communities at large. This body of work has largely consisted of quasi-experimental studies of land and building place-based interventions and interpersonal violence. Summary: Current epidemiological evidence suggests that place-based interventions are cost-effective solutions for violence prevention. Future work is needed using mixed methods to better understand their mechanisms of action and to inform implementation efforts. There are opportunities for the broader development of implementation science to bring promising and established place-based interventions to scale and to extend these interventions to other types of violence.

4.
Artigo em Inglês | MEDLINE | ID: mdl-35564502

RESUMO

In the 1980s, activists' concerns about the disproportionate placements of landfills in low-income communities ignited the environmental justice movement. Today, similar issues of environmental injustice-the limited availability of litter bins across New York City (NYC) neighborhoods-remain unresolved. This study examines the association between NYC neighborhood income and litter bin availability. The NYC Department of Sanitation 2020 Litter Bin Inventory and archival measures of neighborhood composition and socioeconomic status were aggregated within NYC census tract neighborhoods. Multilevel Bayesian conditional autoregressive Poisson models estimated the prevalence rate ratio for counts of litter bins according to median household income in each census tract, accounting for spatial autocorrelation. Bivariate associations identified that census tracts with higher median household income had a greater prevalence of litter bins than census tracts with lower median household income; however, spatial autocorrelation attenuated the relationship between median household income and availability of litter bins. Further research is necessary to identify the spatially structured condition that accounted for the observed effect. The results warrant further investigation of both perceived and actual disparities in litter bin availability.


Assuntos
Renda , Características de Residência , Teorema de Bayes , Cidade de Nova Iorque/epidemiologia , Classe Social , Fatores Socioeconômicos
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