MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
Population Health Sciences, Bristol Medical School, Bristol, UK.
J Epidemiol Community Health. 2021 Dec;75(12):1165-1171. doi: 10.1136/jech-2021-216666. Epub 2021 Jul 20.
Numerous observational studies have highlighted structural inequalities in COVID-19 mortality in the UK. Such studies often fail to consider the hierarchical, spatial nature of such inequalities in their analysis, leading to the potential for bias and an inability to reach conclusions about the most appropriate structural levels for policy intervention.
We use publicly available population data on COVID-19-related mortality and all-cause mortality between March and July 2020 in England and Wales to investigate the spatial scale of such inequalities. We propose a multiscale approach to simultaneously consider three spatial scales at which processes driving inequality may act and apportion inequality between these.
Adjusting for population age structure and number of local care homes we find highest regional inequality in March and June/July. We find finer grained within region inequality increased steadily from March until July. The importance of spatial context increases over the study period. No analogous pattern is visible for non-COVID-19 mortality. Higher relative deprivation is associated with increased COVID-19 mortality at all stages of the pandemic but does not explain structural inequalities.
Results support initial stochastic viral introduction in the South, with initially high inequality decreasing before the establishment of regional trends by June and July, prior to reported regionality of the 'second-wave'. We outline how this framework can help identify structural factors driving such processes, and offer suggestions for a long-term, locally targeted model of pandemic relief in tandem with regional support to buffer the social context of the area.
许多观察性研究强调了英国 COVID-19 死亡率存在结构性不平等。此类研究在分析中往往未能考虑到这些不平等的分层、空间性质,从而导致潜在的偏差,并无法就政策干预的最合适结构层次得出结论。
我们使用英国和威尔士 2020 年 3 月至 7 月期间与 COVID-19 相关的死亡率和全因死亡率的公开可得的人口数据,来调查这些不平等的空间尺度。我们提出了一种多尺度方法,同时考虑可能导致不平等的三个空间尺度,并在这些尺度之间分配不平等。
在调整人口年龄结构和当地养老院数量后,我们发现 3 月和 6/7 月的区域不平等程度最高。我们发现,3 月至 7 月期间,区域内的不平等程度逐渐增加。在研究期间,空间背景的重要性逐渐增加。非 COVID-19 死亡率没有类似的模式。在整个大流行期间,相对剥夺程度越高,COVID-19 死亡率就越高,但并不能解释结构性不平等。
结果支持最初在南部出现随机病毒传播的假设,最初的高不平等程度在 6 月和 7 月之前逐渐降低,在报告的“第二波”之前,区域趋势已经确立。我们概述了这一框架如何有助于确定推动这些过程的结构性因素,并就长期、有针对性的地区性大流行救济模式提出建议,同时为该地区的社会背景提供区域支持。