Independent Researcher, Liverpool, England.
PLoS One. 2022 Jun 29;17(6):e0270624. doi: 10.1371/journal.pone.0270624. eCollection 2022.
Variable and low uptake of the COVID-19 booster is a recognised problem, associated with individual characteristics including age, gender, ethnicity, and deprivation. Are there other relevant predictors at area level?
Anonymous grouped data was downloaded from the UK Government Coronavirus Dashboard for Middle Super Output Areas (MSOA) in England, along with demographic, employment, and health data from public sources. Mixed models with a random intercept for Upper Tier Local Authority were analysed as quasibinomial Generalized Additive Models. The estimated random effects were then fitted with Bayesian linear mixed models using flu vaccination uptake, change in public health budgets, population proportion of vaccination sites at pharmacies, GP-led, vaccination centres, and hospital hubs, and Region.
Models for the MSOA-level COVID-19 first and second vaccinations and the Third Injection (including the booster), fit well. Index of Multiple Deprivation, proportion Aged 15-24 and 25-44, and ethnicity groupings Other White, Indian-Pakistani-Bangladeshi, and African-Caribbean-Other Black-Other, are highly significant predictors of lower uptake. The estimated random effects vary widely amongst local authorities, with positive impact of flu vaccine uptake and change in public health budgets, and regional impacts which are positive for London and South East (first and second doses only), and negative for North West and North East. The impact of vaccination sites did not reach 90% credibility, in general.
COVID-19 vaccination rates at each stage are very well modelled if local authority random effects are included along with non-linear terms for demographic, employment and health data. Deprivation, younger age, and Other White, South Asian, and African-Caribbean-Other ethnicities are associated with lower uptake. The estimated local effects show strong regional variation and are positively associated with flu vaccination and increasing public health budgets. One simple way to improve COVID-19 vaccine uptake in England would be to increase local public health allocations.
COVID-19 加强针的接种率变化不定且较低，这是一个公认的问题，与个体特征有关，包括年龄、性别、种族和贫困程度。在地区层面是否存在其他相关的预测因素？
从英国政府冠状病毒数据仪表板下载英格兰中超级选区（MSOA）的匿名分组数据，以及公共来源的人口统计、就业和健康数据。使用具有上一级地方当局随机截距的混合模型进行分析，作为拟二项广义加性模型。然后使用流感疫苗接种率、公共卫生预算变化、药店疫苗接种点、由全科医生主导的接种中心、医院中心和地区的比例，拟合估计的随机效应的贝叶斯线性混合模型。
MSOA 层面的 COVID-19 第一剂和第二剂以及第三剂（包括加强针）的模型拟合良好。综合贫困指数、15-24 岁和 25-44 岁人群比例以及其他白人、印度裔-巴基斯坦裔-孟加拉裔和非洲裔-加勒比裔-其他黑人裔等族裔群体，是接种率较低的高度显著预测因素。地方当局之间的估计随机效应差异很大，流感疫苗接种率和公共卫生预算变化呈正相关，而伦敦和东南地区（仅第一和第二剂）的区域影响呈正相关，而西北地区和东北地区的区域影响呈负相关。一般来说，接种点的影响没有达到 90%的可信度。
如果将地方当局的随机效应与人口统计、就业和健康数据的非线性项一起纳入模型，每个阶段的 COVID-19 疫苗接种率都可以得到很好的建模。贫困程度、年龄较小以及其他白人、南亚裔和非洲裔-加勒比裔-其他黑人裔等族裔与接种率较低有关。估计的地方效应显示出强烈的区域差异，与流感疫苗接种和增加公共卫生预算呈正相关。提高英格兰 COVID-19 疫苗接种率的一种简单方法是增加地方公共卫生拨款。
