An Interrupted Time Series Analysis of COVID-19 Positivity before, during and after Lockdown in Four States of India

• 作者: Shailaja Tetali Guru Rajesh Jammy Edwin Sam Asirvatham Bogam Ranjeeth Kumar Lincoln Priyadarshi Choudhury
• 来源期刊: 流行病学期刊(英文)
• 学科: 数学
• 关键词: CAUSALITY Interrupted Time Series COVID-19 Impact Evaluation
• 年，卷（期）: 2021,（1）
• 页码范围: 47-55
• 页数: 9页
• 分类号: O17

摘要

<strong>Objectives:</strong> The objective of this study was to examine the impact of large scale non-pharmaceutical interventions on COVID-19 pandemic. <strong>Methods:</strong> We used interrupted time series analysis (ITS), a quasi-experimental model to evaluate the effect of interventions in four states of India by comparing the COVID-19 positivity before lockdown, during lockdown and opening-up period. <strong>Results:</strong> The positivity in all the four states declined during lockdown and the trends reversed soon after the lockdown measures were relaxed as the states opened-up. The rate of reduction of positivity was significantly different between states. Between the lockdown and opening-up period, an increase in positivity was recorded in all the states with significant variation between states. <strong>Conclusion:</strong> The analysis provides conclusive evidence that the lockdown measures had a positive effect in reducing the burden of COVID-19 and establishes a causal relationship.