Association of Neighborhood Racial and Ethnic Composition and Historical Redlining With Built Environment Indicators Derived From Street View Images in the US.
Association of Neighborhood Racial and Ethnic Composition and Historical Redlining With Built Environment Indicators Derived From Street View Images in the US.
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邻里种族和种族组成以及历史上的红线与美国街景图像得出的建筑环境指标的关联。
DOI：
10.1001/jamanetworkopen.2022.51201
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发表时间：
2023-01-03
影响因子：
13.8
通讯作者：
Nsoesie, Elaine O.
中科院分区：
文献类型：
--
作者：
Yang, Yukun;Cho, Ahyoung;Nguyen, Quynh;Nsoesie, Elaine O.
从在线街道图像中获得的建筑环境指标与社区的种族和民族组成有什么联系？在这项横断面研究中，与多个种族和民族的居民，主要是黑人居民和主要是黑人以外的少数民族或民族居民的社区相比，主要是白色社区的破旧建筑物较少，非单一家庭住宅较少，单车道道路较少，更多的绿色空间。这些研究结果表明，改善大规模数据的建筑环境功能可以提供更好的文件邻里不平等，并提高理解如何通过建筑环境表现出结构性种族主义与健康状况不佳的结果。这项横断面研究使用来自街道级图像的建筑环境指标来量化建筑环境资源中的种族和民族差异，并评估建筑环境如何介导邻里种族和民族组成与不良健康结果之间的关联。种族主义政策（如红线）在建筑环境中造成不平等，产生种族和民族隔离的社区，恶劣的住房条件，无法步行的社区和普遍的劣势。关于建筑环境差异的研究通常仅限于现有来源或可以手动收集的措施和数据。使用从在线街道级图像生成的建筑环境指标来调查美国城市地区的邻里种族和民族组成，建筑环境和健康结果之间的关联。这项横断面研究使用了从2019年11月1日至30日收集的1.64亿张谷歌街景图像中得出的建筑环境指标。种族，民族和社会经济数据来自2019年美国社区调查（ACS）5年估计;健康结果来自疾病控制和预防中心2020年人口水平分析和社区估计（PLACES）数据集。采用多层次建模和中介分析方法。美国共有59231个城市人口普查区被包括在内。在线图像和ACS数据包括所有人口普查区域。PLACES数据包括18岁或以上的调查受访者。数据分析时间为2022年5月23日至11月16日。模型估计图像衍生的建筑环境指标和人口普查区（邻里）种族和民族组成之间的关联，以及建筑环境与邻里种族组成和健康之间的关联。在59231个城市人口普查区，美洲印第安人和阿拉斯加原住民，53 321 345人西班牙裔，462 259人（0.2%）夏威夷土著和其他太平洋岛民，17 166 370人（6.3%）非西班牙裔亚裔居民、35 985 480（13.2%）非西班牙裔黑人居民和158 043 260（57.7%）非西班牙裔白色居民。与其他社区相比，以白色为主的社区有更少的破旧建筑和更多的绿色空间指标，通常与良好的健康相关，以及更少的人行横道（例如，以少数民族或非黑人居民为主的社区比以白色居民为主的社区有6%的破旧建筑）。此外，建筑环境指标部分介导了邻里种族和民族组成与健康结果之间的关联，包括糖尿病，哮喘和睡眠问题。最重要的中介是非单一家庭住宅（与房屋所有权相关的措施），它介导了12.8%的黑人居民以外的主要少数种族或民族社区与睡眠问题之间的关联，以及24.2%的未分类社区与哮喘之间的关联。这项横断面研究的结果表明，如果使用得当，具有地理代表性的大型数据集可能会为种族和民族健康不平等提供新的见解。量化结构性种族主义对健康的社会决定因素的影响是制定政策和干预措施以创造公平的建筑环境资源的一个步骤。
What is the association of built environment indicators derived from online street-level images with racial and ethnic composition of neighborhoods? In this cross-sectional study, predominantly White neighborhoods overall had fewer dilapidated buildings, fewer non–single family homes, fewer single-lane roads, and more green space compared with neighborhoods with residents of multiple races and ethnicities, predominantly Black residents, and predominantly minoritized racial or ethnic group residents other than Black. These findings suggest that improved large-scale data on built environment features may provide better documentation of neighborhood inequalities and improve understanding of how structural racism manifested through the built environment is associated with poor health outcomes. This cross-sectional study uses built environment indicators derived from street-level images to quantify racial and ethnic disparities in built environment resources and assess how the built environment mediates the association between neighborhood racial and ethnic composition and poor health outcomes. 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. 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. 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. 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. 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%. 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.
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DOI：
10.3390/ijerph110303453
发表时间：
2014-03-21
影响因子：
--
作者：
Beyer KM;Kaltenbach A;Szabo A;Bogar S;Nieto FJ;Malecki KM
通讯作者：
Malecki KM
影响因子：
12.7
作者：
Krieger, Nancy;Van Wye, Gretchen;Bassett, Mary T.
通讯作者：
Bassett, Mary T.
影响因子：
12.7
作者：
Farina, Mateo P.;Zajacova, Anna;Hayward, Mark D.
通讯作者：
Hayward, Mark D.
影响因子：
5
作者：
Johnson, Dayna A.;Hirsch, Jana A.;Roux, Ana V. Diez
通讯作者：
Roux, Ana V. Diez
影响因子：
5
作者：
Homan P;Brown TH;King B
通讯作者：
King B