Sociology, The University of British Columbia, Vancouver, British Columbia, Canada
Sociology, State University of New York, Albany, New York, USA.
J Epidemiol Community Health. 2021 Nov;75(11):1044-1049. doi: 10.1136/jech-2020-215055. Epub 2021 Mar 23.
The coronavirus disease pandemic has disproportionately affected poor and racial/ethnic minority individuals and communities, especially Indigenous Peoples. The object of this study is to understand the spatially varying associations between socioeconomic disadvantages and the number of confirmed COVID-19 cases in New Mexico at the ZIP code level.
We constructed ZIP code-level data (n=372) using the 2014-2018 American Community Survey and COVID-19 data from the New Mexico Department of Health (as of 24 May 2020). The log-linear Poisson and geographically weighted Poisson regression are applied to model the number of confirmed COVID-19 cases (total population as the offset) in a ZIP code.
The number of confirmed COVID-19 cases in a ZIP code is positively associated with socioeconomic disadvantages-specifically, the high levels of concentrated disadvantage and income inequality. It is also positively associated with the percentage of American Indian and Alaskan Native populations, net of other potential confounders at the ZIP code level. Importantly, these associations are spatially varying in that some ZIP codes suffer more from concentrated disadvantage than others.
Additional attention for COVID-19 mitigation effort should focus on areas with higher levels of concentrated disadvantage, income inequality, and higher percentage of American Indian and Alaska Native populations as these areas have higher incidence of COVID-19. The findings also highlight the importance of plumbing in all households for access to clean and safe water, and the dissemination of educational materials aimed at COVID-19 prevention in non-English language including Indigenous languages.
冠状病毒病大流行对贫困和种族/少数民族个人和社区造成了不成比例的影响,尤其是原住民。本研究的目的是了解新墨西哥州在邮政编码层面上社会经济劣势与确诊 COVID-19 病例数量之间的空间变化关联。
我们使用 2014-2018 年美国社区调查和新墨西哥州卫生部门的 COVID-19 数据(截至 2020 年 5 月 24 日)构建了邮政编码层面的数据(n=372)。对数线性泊松和地理加权泊松回归用于对邮政编码中确诊 COVID-19 病例数量(总人口作为偏移量)进行建模。
邮政编码中确诊 COVID-19 病例数量与社会经济劣势呈正相关-特别是高度集中的劣势和收入不平等。它还与美洲印第安人和阿拉斯加原住民人口的百分比呈正相关,这是在邮政编码层面上消除其他潜在混杂因素后的结果。重要的是,这些关联在空间上是变化的,一些邮政编码比其他邮政编码受到更多的集中劣势的影响。
在 COVID-19 缓解工作中,应更多地关注集中劣势、收入不平等程度较高以及美洲印第安人和阿拉斯加原住民人口比例较高的地区,因为这些地区 COVID-19 的发病率较高。这些发现还强调了为所有家庭提供清洁和安全用水以及传播针对 COVID-19 的教育材料(包括原住民语言)的重要性,这些教育材料以非英语语言传播。