Ha Nam Xuan, Le-Van Truong, Nam Nguyen Hai, Raut Akshay, Varney Joseph, Huy Nguyen Tien
Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam.
Online Research Club (http://www.onlineresearchclub.org), Nagasaki, Japan.
Health Promot Perspect. 2022 Aug 20;12(2):192-199. doi: 10.34172/hpp.2022.24. eCollection 2022.
The Japanese government advised mild or asymptomatic coronavirus disease-2019 (COVID-19) cases to self-isolate at home, while more severe individuals were treated at health posts. Poor compliance with self-isolation could be a potential reason for the new outbreak. Our study aimed to find out the correlation between the rising new cases of COVID-19 and home-based patients in Japan. A secondary data analysis study was conducted with the data from COVID-19- involved databases collected from Johns Hopkins University, Japanese Ministry of Health, Labour and Welfare, and Community Mobility Reports of Google. New community cases, stringency index, number of tests, and active cases were analyzed. Using a linear regression model, an independent variable was utilized for a given date to predict the future number of community cases. Research results show that outpatient cases, the stringency, and Google Mobility Trend were all significantly associated with the number of COVID-19 community cases from the sixth day to the ninth day. The model predicting community cases on the eighth day (R2=0.8906) was the most appropriate showing outpatients, residential index, grocery and pharmacy index, retail and recreation index, and workplaces index were positively related (β=24.2, 95% CI: 20.3- 26.3, P<0.0001; β=277.7, 95% CI: 171.8-408.2, <0.0001; β=112.4, 95% CI: 79.8-158.3, <0.0001; β=73.1, 95% CI: 53- 04.4, <0.0001; β=57.2, 95% CI: 25.2-96.8, =0.001, respectively). In contrast, inpatients, park index, and adjusted stringency index were negatively related to the number of community cases (β=-2.8, 95% CI: -3.9 - -1.6, <0.0001; β=-33, 95% CI: -43.6 - -27, <0.0001; β=-14.4, 95% CI: -20.1- -12, <0.0001, respectively). Outpatient cases and indexes of Community Mobility Reports were associated with COVID-19 community cases.
日本政府建议轻症或无症状的2019冠状病毒病(COVID-19)患者居家自我隔离,而症状较重的患者则在卫生站接受治疗。自我隔离依从性差可能是新疫情爆发的一个潜在原因。我们的研究旨在找出日本COVID-19新增病例数与居家患者之间的相关性。我们利用从约翰·霍普金斯大学、日本厚生劳动省收集的COVID-19相关数据库以及谷歌社区流动报告中的数据进行了一项二次数据分析研究。分析了新的社区病例、严格指数、检测数量和活跃病例。使用线性回归模型,将给定日期的一个自变量用于预测未来社区病例数。研究结果表明,门诊病例、严格程度和谷歌流动趋势在第6天至第9天均与COVID-19社区病例数显著相关。预测第8天社区病例的模型(R2 = 0.8906)最为合适,该模型显示门诊病例、居住指数、杂货店和药房指数、零售和娱乐指数以及工作场所指数呈正相关(β = 24.2,95%置信区间:20.3 - 26.3,P < 0.0001;β = 277.7,95%置信区间:171.8 - 408.2,< 0.0001;β = 112.4,95%置信区间:79.8 - 158.3,< 0.0001;β = 73.1,95%置信区间:53 - 104.4,< 0.0001;β = 57.2,95%置信区间:25.2 - 96.8,= 0.001)。相比之下,住院病例、公园指数和调整后的严格指数与社区病例数呈负相关(β = -2.8,95%置信区间:-3.9 - -1.6,< 0.0001;β = -33,95%置信区间:-43.6 - -27,< 0.0001;β = -14.4,95%置信区间:-20.1 - -12,< 0.0001)。门诊病例和社区流动报告指数与COVID-19社区病例相关。