Department of Modelling and Analytics, Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group, UK National Health Service, UK.
School of Management, Centre for Healthcare Innovation and Improvement, University of Bath, UK.
Int J Health Plann Manage. 2021 Sep;36(5):1936-1942. doi: 10.1002/hpm.3265. Epub 2021 Jul 1.
While it is well established that societal restrictions have been effective in reducing COVID-19 emergency demand, evidence also suggests an impact upon emergency demand not directly related to COVID-19 infection. Hospital planning may benefit from a greater understanding of this association and the ability to reliably forecast future levels of non-COVID-19 demand. Activity data for Accident and Emergency (A&E) attendances and emergency admissions were sourced for all hospitals within the Bristol, North Somerset and South Gloucestershire healthcare system. These were regressed upon publicly available mobility data obtained from Google's Community Mobility Reports for the local area. Seasonal trends were controlled for using time series decomposition. The models were used to predict non-COVID-19 emergency demand under the UK Government's plan to sequentially lift all restrictions by 21 June 2021, in addition to three alternative hypothetical relaxation strategies. Rates of public mobility within the local area were shown to account for 77% and 65% of the variance in non-COVID-19 related A&E attendances and emergency admissions respectively. Modelling supports an increase in emergency demand in line with the level and timing of societal restrictions, with significant increases to be expected upon the ending of all legal limits. This study finds that non-COVID-19 emergency demand associates with the level of societal restrictions, with rates of public mobility representing a key determinant. Through predictive modelling, healthcare systems can improve their demand forecasting in effectively managing hospital capacity.
虽然社会限制已被证实可有效降低 COVID-19 紧急需求，但也有证据表明，紧急需求受到了与 COVID-19 感染无关的影响。医院规划可能受益于对这种关联的更深入理解以及对未来非 COVID-19 需求进行可靠预测的能力。本研究从布里斯托尔、北萨默塞特和南格洛斯特郡医疗保健系统内的所有医院获取了急症室（A&E）就诊和急诊入院的活动数据，并将其回归到从谷歌社区流动性报告中获取的当地公共流动性数据。通过时间序列分解控制季节性趋势。该模型用于预测英国政府计划于 2021 年 6 月 21 日逐步取消所有限制以及三种替代假设放松策略下的非 COVID-19 紧急需求。当地公共流动率分别解释了非 COVID-19 相关 A&E 就诊和急诊入院的 77%和 65%的方差。模型表明，紧急需求将与社会限制的水平和时间保持一致，预计在所有法律限制结束时会出现显著增加。这项研究发现，非 COVID-19 紧急需求与社会限制的水平相关，公共流动率是一个关键决定因素。通过预测性建模，医疗保健系统可以提高需求预测能力，从而有效管理医院容量。
