ArticleBMC public health2025
Non-healthcare system interventions and COVID-19 daily cases: a multilevel time series analysis.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
backgroundThe 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.
methodsWith 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.
resultsOur 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.
conclusionsIn 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.
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