Padellini Tullia, Jersakova Radka, Diggle Peter J, Holmes Chris, King Ruairidh E, Lehmann Brieuc C L, Mallon Ann-Marie, Nicholson George, Richardson Sylvia, Blangiardo Marta
MRC Centre for Environment and Health, Dept of Epidemiology and Biostatistics, Imperial College London.
The Alan Turing Institute, London, UK.
medRxiv. 2021 Nov 9:2021.11.09.21266054. doi: 10.1101/2021.11.09.21266054.
Ethnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK.
Using a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate (number of positive tests over the total number of tests) and estimated unbiased prevalence (proportion of individuals in the population who would test positive) at the Lower Tier Local Authority (LTLA) level. The model also adjusts for age, urbanicity, vaccine uptake and spatio-temporal correlation structure.
Comparing the least deprived and predominantly White areas with most deprived and predominantly non-White areas over the whole study period, the weekly positivity rate increases by 13% from 297% to 335%. Similarly, prevalence increases by 10% from 037% to 041%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes slightly more pronounced at the peak of the second wave and then again in May-June 2021. Not all BAME groups were equally affected: in the second wave of the pandemic, LTLAs with large South Asian populations were the most affected, whereas areas with large Black populations did not show increased values for either outcome during the entire period under analysis.
At the area level, IMD and BAME% are both associated with an increased COVID-19 burden in terms of prevalence (disease spread) and test positivity (disease monitoring), and the strength of association varies over the course of the pandemic. The consistency of results across the two outcome measures suggests that community level characteristics such as deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits.
EPSRC, MRC, The Alan Turing Institute, NIH, UKHSA, DHSC, NIHR.
在英国，不同种族和社会经济贫困社区受到新冠疫情的影响存在差异。
我们使用多层次回归模型评估严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染与地区层面的贫困和种族之间随时间变化的关联。我们分别在较低层级地方当局（LTLA）层面考虑每周检测阳性率（阳性检测数占总检测数的比例）和估计的无偏患病率（人群中检测呈阳性的个体比例）。该模型还对年龄、城市化程度、疫苗接种率和时空相关结构进行了调整。
在整个研究期间，将最不贫困且主要为白人的地区与最贫困且主要为非白人的地区进行比较，每周阳性率从2.97%上升13%至3.35%。同样，患病率从0.37%上升10%至0.41%。在2020年10月之前，贫困的影响更强，而种族的影响在第二波疫情高峰期以及2021年5月至6月期间变得更为明显。并非所有黑人和少数族裔群体受到的影响都相同：在疫情的第二波中，南亚人口众多的较低层级地方当局受影响最大，而在整个分析期间，黑人人口众多的地区在这两个结果指标上均未显示出数值增加。
在地区层面，多重贫困指数（IMD）和黑人和少数族裔比例（BAME%）在患病率（疾病传播）和检测阳性率（疾病监测）方面均与新冠负担增加相关，且关联强度在疫情过程中有所变化。两种结果指标的结果一致性表明，贫困和种族等社区层面特征对疾病暴露或易感性有不同影响，而非对检测机会和习惯有影响。
工程和物理科学研究委员会（EPSRC）、医学研究委员会（MRC）、艾伦·图灵研究所、美国国立卫生研究院（NIH）、英国卫生安全局（UKHSA）、英国卫生和社会保健部（DHSC）、英国国家卫生与临床优化研究所（NIHR）。
