Non-healthcare system interventions and COVID-19 daily cases: a multilevel time series analysis.

Abstract

Background: The global COVID-19 pandemic has significantly impacted public health and socio-economic development worldwide. This study aims to investigate the effects of non-healthcare system interventions on the daily new cases of COVID-19 from January 2020 to October 2022.

Methods: With the aid of multilevel approach, we identified income group, region and country as stratification factors that affect the number of COVID-19 daily new cases. Data on COVID-19 cases collected by Johns Hopkins University were used, and policy implementation details were recorded through the Oxford COVID-19 Government Response Tracker dataset. To analyze the effects of national, regional, and income group factors on the number of daily new COVID-19 cases, we implemented three multilevel sequential mixed-effects models and applied restricted maximum likelihood to estimate the variance of random effects.

Results: Our results indicate a correlation between income group and the rise in intercepts of random effects in the multilevel sequential mixed-effects models. High-income countries recorded the highest intercept at 713.26, while low-income countries showed the lowest at -313.79. Under the influence of policies, the implementation of "Canceling public events" and "International travel restrictions" has been shown to significantly reduce the daily number of new COVID-19 cases. In contrast, "Restrictions on gatherings" appear to have the opposite effect, potentially leading to an increase in daily new COVID-19 cases.

Conclusions: In designing epidemic control policies, due consideration should be given to factors such as income group, as well as medical, demographic, and social differences among nations influenced by economic factors. In policy-making, policymakers should pay greater attention to policy implementation and people's responses, in order to maximize the effectiveness and adherence of such policies.