对 2020 年和 2021 年的新冠病毒感染确诊病例和感染天数的 95%置信区间控制线进行了对比，使用 hT 指数来评估对公共卫生的影响。
The 95% control lines on both confirmed cases and days of infection with COVID-19 were applied to compare the impact on public health between 2020 and 2021 using the hT-index.
机构信息
Department of Internal Medicine, Chi Mei Medical Center, Chiali District, Tainan, Taiwan.
Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan, Taiwan.
出版信息
Medicine (Baltimore). 2023 May 19;102(20):e33570. doi: 10.1097/MD.0000000000033570.
BACKGROUND
COVID-19, the disease caused by the novel coronavirus, is now a worldwide pandemic. The number of infected people has continually increased, and currently, this pandemic continues to present challenges to public health. Scatter plots are frequently used to interpret the impact in relation to confirmed cases. However, the 95% confidence intervals are rarely given to the scatter plot. The objective of this study was to; Develop 95% control lines on daily confirmed cases and infected days for countries/regions in COVID-19 (DCCIDC) and; Examine their impacts on public health (IPH) using the hT-index.
METHODS
All relevant COVID-19 data were downloaded from GitHub. The hT-index, taking all DCCIDCs into account, was applied to measure the IPHs for counties/regions. The 95% control lines were proposed to highlight the outliers of entities in COVID-19. The hT-based IPHs were compared among counties/regions between 2020 and 2021 using the choropleth map and the forest plot. The features of the hT-index were explained using the line chart and the box plot.
RESULTS
The top 2 countries measured by hT-based IPHs were India and Brazil in 2020 and 2021. The outliers beyond the 95% confidence intervals were Hubei (China), with a lower hT-index favoring 2021 ( = 6.4 in 2021 vs 15.55 in 2020) and higher hT indices favoring 2021 in Thailand (28.34 vs 14,77) and Vietnam (27.05 vs 10.88). Only 3 continents of Africa, Asia, and Europe had statistically and significantly fewer DCCIDCs (denoted by the hT-index) in 2021. The hT-index generalizes the h-index and overcomes the disadvantage without taking all elements (e.g., DCCIDCs) into account in features.
CONCLUSIONS
The scatter plot combined with the 95% control lines was applied to compare the IPHs hit by COVID-19 and suggested for use with the hT-index in future studies, not limited to the field of public health as we did in this research.
背景
由新型冠状病毒引起的 COVID-19 现已在全球范围内流行。感染人数不断增加，目前，这一流行病继续对公共卫生构成挑战。散点图常用于解释与确诊病例相关的影响。然而，散点图很少给出 95%置信区间。本研究的目的是：为 COVID-19（DCCIDC）国家/地区的每日确诊病例和感染日制定 95%控制线；并使用 hT-指数评估其对公共卫生的影响（IPH）。
方法
从 GitHub 下载所有相关的 COVID-19 数据。应用 hT-指数，考虑所有 DCCIDCs，来衡量各县/地区的 IPH。提出 95%控制线，以突出 COVID-19 实体的异常值。使用专题地图和森林图比较 2020 年和 2021 年各县/地区之间基于 hT 的 IPH。使用折线图和箱线图解释 hT-指数的特征。
结果
按基于 hT 的 IPH 衡量，排名前两位的国家是印度和巴西，在 2020 年和 2021 年。超出 95%置信区间的异常值是中国湖北省，2021 年 hT 指数较低（2021 年为 6.4，2020 年为 15.55），而泰国（28.34 比 14.77）和越南（27.05 比 10.88）的 hT 指数在 2021 年较高。只有非洲、亚洲和欧洲三个大洲在 2021 年的 DCCIDCs（用 hT-指数表示）数量统计上显著减少。hT-指数推广了 h-指数，并克服了不考虑特征中的所有元素（例如 DCCIDCs）的缺点。
结论
将散点图与 95%控制线相结合，用于比较 COVID-19 对公共卫生的影响，并建议在未来的研究中使用 hT-指数，而不仅仅局限于我们在这项研究中所涉及的公共卫生领域。
Zotero 插件