Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK.
Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK.
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DOI：
10.1136/bmjopen-2021-050574
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发表时间：
2021-10-04
期刊：
影响因子：
2.9
通讯作者：
Lilford RJ
中科院分区：
文献类型：
--
作者：
Watson SI;Diggle PJ;Chipeta MG;Lilford RJ
评估2019冠状病毒病（COVID-19）第一波大流行期间英国伯明翰住院病例的时空分布，以支持公共卫生疾病控制政策的设计。作为实时疾病监测系统的一部分，估计了地理空间统计模型，以预测当地COVID-19的每日发病率。于2020年2月1日至2020年9月30日期间，所有因COVID-19而住院的伯明翰大学医院NHS基金会信托。预测当地COVID-19住院的发病率和累积发病率，其每周变化以及预测协变量的识别。住院高峰发生在2020年4月的第一周和第二周，全市的发病率和发病率比率存在显著差异。人口年龄、种族和社会经济贫困是当地发病率的强预测因素。住院表现出强烈的一周内的影响，周末住院较少（减少10%-20%）。未解释的方差具有较低的时间相关性。到第一个封锁期结束的第50天，最高2.5%的小地区每10000人的病例数是最低2.5%的五倍。当地人口因素是相对发病率水平的强有力预测因素，可用于针对当地地区采取疾病控制措施。实时疾病监测系统通过以精细的空间分辨率制作关键结果的实时、定量和概率摘要，为疾病控制方案提供信息，为其他监测方法提供了有益的补充。
To evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies. A geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19. All hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020. Predictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates. Peak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%–20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%. Local demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.
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影响因子：
1.7
作者：
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