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美国各地获得COVID-19临床试验地点的人口统计学差异:一项地理空间分析。

Demographic disparities in access to COVID-19 clinical trial sites across the United States: a geospatial analysis.

作者信息

Cuomo Raphael, McMann Tiana, Xu Qing, Li Zhuoran, Yang Joshua, Hsieh Julie, Lee Christine, Lolic Milena, Araojo Richardae, Mackey Tim

机构信息

School of Medicine, University of California, 8950 Villa La Jolla Drive, A203, San Diego, CA, 92037, USA.

Global Health Policy and Data Institute, San Diego, CA, USA.

出版信息

Int J Equity Health. 2025 Jan 23;24(1):26. doi: 10.1186/s12939-024-02360-8.

Abstract

Throughout the COVID-19 pandemic, underserved populations, such as racial and ethnic minority communities, were disproportionately impacted by illness and death. Ensuring people from diverse backgrounds have the ability to participate in clinical trials is key to advancing health equity. We sought to analyze the spatial variability in locations of COVID-19 trials sites and to test associations with demographic correlates. All available and searchable COVID-19 studies listed on ClinicalTrials.gov until 04/04/2022 and conducted in the United States were extracted at the trial-level, and locations were geocoded using the Microsoft Bing API. Publicly available demographic data were available at the county level for national analysis and the census tract level for local analysis. Independent variables included eight racial and ethnic covariates, both sexes, and twelve age categories, all of which were population-normalized. The county-level, population-normalized count of study site locations, by type, was used as the outcome for national analysis, thereby enabling the determination of demographic associations with geospatial availability to enroll as a participant in a COVID-19 study. Z-scores of the Getis-Ord Gi statistic were used as the outcome for local analysis in order to account for areas close to those with clinical study sites. For both national (p < 0.001) and local analysis (p = 0.006 for Los Angeles, p = 0.030 for New York), areas with greater proportions of men had significantly fewer studies. Sites were more likely to be found in counties with higher proportions of Asian (p < 0.001) and American Indian or Alaska Native residents (p < 0.001). Areas with greater concentrations of Black or African American residents had significantly lower concentrations of observational (p < 0.001) and government-sponsored COVID-19 studies (p = 0.003) in national analysis and significantly fewer concentrations of study sites in both Los Angeles (p < 0.001) and New York (p = 0.007). Though there appear to be a large number of COVID-19 studies that commenced in the US, they are distributed unevenly, both nationally and locally.

摘要

在整个新冠疫情期间,诸如少数族裔社区等医疗服务不足的人群,在疾病和死亡方面受到的影响尤为严重。确保不同背景的人有能力参与临床试验,是促进健康公平的关键。我们试图分析新冠试验地点的空间变异性,并测试其与人口统计学相关因素的关联。截至2022年4月4日,在美国进行的、ClinicalTrials.gov上列出的所有可获取且可搜索的新冠研究,在试验层面被提取出来,地点使用微软必应应用程序编程接口进行地理编码。公开的人口数据在县一级可用于全国分析,在普查区一级可用于本地分析。自变量包括八个种族和族裔协变量、男女两性以及十二个年龄类别,所有这些都进行了人口标准化。按类型划分的、经人口标准化的县级研究地点数量,被用作全国分析的结果,从而能够确定与参与新冠研究的地理空间可及性相关的人口统计学关联。Getis-Ord Gi统计量的Z分数被用作本地分析的结果,以考虑靠近临床研究地点的区域。对于全国分析(p < 0.001)以及本地分析(洛杉矶为p = 0.006,纽约为p = 0.030),男性比例较高的地区研究显著较少。在亚洲居民比例较高(p < 0.001)以及美洲印第安人或阿拉斯加原住民居民比例较高(p < 0.001)的县,更有可能找到试验地点。在全国分析中,黑人或非裔美国居民集中的地区,观察性研究(p < 0.001)和政府资助的新冠研究(p = 0.003)的集中度显著较低,在洛杉矶(p < 0.001)和纽约(p = 0.007),研究地点的集中度也显著较少。尽管美国似乎有大量新冠研究已经启动,但它们在全国和地方的分布都不均衡。

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