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利用跨越空间边界的方法探索伦敦新冠死亡病例的社区级关联因素。

Exploring the neighbourhood-level correlates of Covid-19 deaths in London using a difference across spatial boundaries method.

机构信息

School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK.

出版信息

Health Place. 2020 Nov;66:102446. doi: 10.1016/j.healthplace.2020.102446. Epub 2020 Sep 29.

Abstract

This paper explores neighbourhood-level correlates of the Covid-19 deaths in London during the initial rise and peak of the pandemic within the UK - the period March 1 to April 17, 2020. It asks whether the person-level predictors of Covid-19 that are identified in reports by Public Health England and by the Office of National Statistics also hold at a neighbourhood scale, remaining evident in the differences between neighbours. In examining this, the paper focuses on localised differences in the number of deaths, putting forward an innovative method of analysis that looks at the differences between places that share a border. Specifically, a difference across spatial boundaries method is employed to consider whether a higher number of deaths in one neighbourhood, when compared to its neighbours, is related to other differences between those contiguous locations. It is also used to map localised 'hot spots' and to look for spatial variation in the regression coefficients. The results are compared to those for a later period, April 18 - May 31. The findings show that despite some spatial diffusion of the disease, a greater number of deaths continues to be associated with Asian and Black ethnic groups, socio-economic disadvantage, very large households (likely indicative of residential overcrowding), and fewer from younger age groups. The analysis adds to the evidence showing that age, wealth/deprivation, and ethnicity are key risk factors associated with higher mortality rates from Covid-19.

摘要

本文探讨了英国 2020 年 3 月 1 日至 4 月 17 日期间大流行初期和高峰期伦敦新冠死亡病例的社区层面相关因素。它询问了在英国公共卫生部和国家统计局的报告中确定的新冠个人预测因素是否也在社区层面成立,是否仍然存在于邻居之间的差异中。在研究这一问题时,本文重点关注死亡人数的局部差异,提出了一种创新的分析方法,即关注共享边界的地方之间的差异。具体来说,采用跨越空间边界的方法来考虑一个社区的死亡人数高于其邻居,这是否与这些相邻地点之间的其他差异有关。该方法还用于绘制局部“热点”图,并寻找回归系数的空间变化。将结果与后期(4 月 18 日至 5 月 31 日)进行比较。研究结果表明,尽管疾病存在一定的空间扩散,但更多的死亡仍与亚洲和黑人族群、社会经济劣势、非常大的家庭(可能表明居住拥挤)以及年轻人群体比例较低有关。该分析增加了证据,表明年龄、财富/贫困和种族是与新冠死亡率较高相关的关键风险因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8f6/7539541/49f7b373d658/gr1_lrg.jpg

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