Department of Plastic and Oral Surgery, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA.
Associates in Process Improvement, 115 East Fifth Street, Suite 300, Austin, TX 78701, USA.
Int J Qual Health Care. 2021 Dec 4;33(4). doi: 10.1093/intqhc/mzab151.
As the globe endures the coronavirus disease 2019 (COVID-19) pandemic, we developed a hybrid Shewhart chart to visualize and learn from day-to-day variation in a variety of epidemic measures over time.
Countries and localities have reported daily data representing the progression of COVID-19 conditions and measures, with trajectories mapping along the classic epidemiological curve. Settings have experienced different patterns over time within the epidemic: pre-exponential growth, exponential growth, plateau or descent and/ or low counts after descent. Decision-makers need a reliable method for rapidly detecting transitions in epidemic measures, informing curtailment strategies and learning from actions taken.
We designed a hybrid Shewhart chart describing four 'epochs' ((i) pre-exponential growth, (ii) exponential growth, (iii) plateau or descent and (iv) stability after descent) of the COVID-19 epidemic that emerged by incorporating a C-chart and I-chart with a log-regression slope. We developed and tested the hybrid chart using international data at the country, regional and local levels with measures including cases, hospitalizations and deaths with guidance from local subject-matter experts.
The hybrid chart effectively and rapidly signaled the occurrence of each of the four epochs. In the UK, a signal that COVID-19 deaths moved into exponential growth occurred on 17 September, 44 days prior to the announcement of a large-scale lockdown. In California, USA, signals detecting increases in COVID-19 cases at the county level were detected in December 2020 prior to statewide stay-at-home orders, with declines detected in the weeks following. In Ireland, in December 2020, the hybrid chart detected increases in COVID-19 cases, followed by hospitalizations, intensive care unit admissions and deaths. Following national restrictions in late December, a similar sequence of reductions in the measures was detected in January and February 2021.
The Shewhart hybrid chart is a valuable tool for rapidly generating learning from data in close to real time. When used by subject-matter experts, the chart can guide actionable policy and local decision-making earlier than when action is likely to be taken without it.
在全球应对 2019 年冠状病毒病（COVID-19）大流行之际，我们开发了一种混合休哈特图，以便随着时间的推移，直观地了解和学习各种流行措施的日常变化。
各国和地方报告了代表 COVID-19 病情和措施进展的每日数据，轨迹沿着经典的流行病学曲线映射。在流行期间，各个地方的情况随时间呈现出不同的模式：指数增长前、指数增长、平台或下降以及/或下降后计数较低。决策者需要一种可靠的方法来快速检测流行措施的转变，为控制策略提供信息，并从采取的行动中吸取经验。
我们设计了一种混合休哈特图，通过结合 C 图和 I 图以及对数回归斜率，描述 COVID-19 流行的四个“阶段”((i) 指数增长前、(ii) 指数增长、(iii) 平台或下降以及 (iv) 下降后的稳定)。我们结合地方专题专家的指导，使用国家、地区和地方各级的国际数据开发和测试了混合图，其中包括病例、住院和死亡等措施。
混合图有效地、快速地发出了四个阶段中的每一个阶段的信号。在英国，COVID-19 死亡人数进入指数增长的信号于 9 月 17 日发出，这比大规模封锁宣布提前了 44 天。在美国加利福尼亚州，2020 年 12 月，在全州范围内实行居家令之前，县一级的 COVID-19 病例增加的信号被检测到，随后几周呈下降趋势。在爱尔兰，2020 年 12 月，混合图检测到 COVID-19 病例增加，随后是住院、重症监护病房入院和死亡。12 月底全国实施限制措施后，2021 年 1 月和 2 月检测到这些措施类似的减少序列。
休哈特混合图是一种从近实时数据中快速获取知识的有价值的工具。当由主题专家使用时，该图可以在没有该图的情况下采取行动之前，更早地为可操作的政策和地方决策提供指导。
