School closures during COVID-19: an overview of systematic reviews

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

Background

The use of Non-Pharmaceutical Interventions (NPIs), such as lockdowns, social distancing and school closures, against the COVID-19 epidemic is debated, particularly for the possible negative effects on vulnerable populations, including children and adolescents. This study therefore aimed to quantify the impact of NPIs on the trend of pediatric hospitalizations during 2 years of pandemic compared to the previous 3 years, also considering two pandemic phases according to the type of adopted NPIs.

Methods

This is a multicenter, quasi-experimental before-after study conducted in 12 hospitals of the Emilia-Romagna Region, Northern Italy, with NPI implementation as the intervention event. The 3 years preceding the beginning of NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (up to September 2020) and a subsequent mitigation measures phase with less stringent restrictions. School closure was chosen as delimitation as it particularly concerns young people. Interrupted Time Series (ITS) regression analysis was applied to calculate Hospitalization Rate Ratios (HRR) on the diagnostic categories exhibiting the greatest variation. ITS allows the estimation of changes attributable to an intervention, both in terms of immediate (level change) and sustained (slope change) effects, while accounting for pre-intervention secular trends.

Results

Overall, in the 60 months of the study there were 84,368 cases. Compared to the pre-pandemic years, statistically significant 35 and 19% decreases in hospitalizations were observed during school closure and in the following mitigation measures phase, respectively. The greatest reduction was recorded for "Respiratory Diseases," whereas the "Mental Disorders" category exhibited a significant increase during mitigation measures. ITS analysis confirms a high reduction of level change during school closure for Respiratory Diseases (HRR 0.19, 95%CI 0.08-0.47) and a similar but smaller significant reduction when mitigation measures were enacted. Level change for Mental Disorders significantly decreased during school closure (HRR 0.50, 95%CI 0.30-0.82) but increased during mitigation measures by 28% (HRR 1.28, 95%CI 0.98-1.69).

Conclusion

Our findings provide information on the impact of COVID-19 NPIs which may inform public health policies in future health crises, plan effective control and preventative interventions and target resources where needed.

The Ethical Obligation for Research During Public Health Emergencies: Insights From the COVID-19 Pandemic

In times of crises, public health leaders may claim that trials of public health interventions are unethical. One reason for this claim can be that equipoise-i.e. a situation of uncertainty and/or disagreement among experts about the evidence regarding an intervention-has been disturbed by a change of collective expert views. Some might claim that equipoise is disturbed if the majority of experts believe that emergency public health interventions are likely to be more beneficial than harmful. However, such beliefs are not always justified: where high quality research has not been conducted, there is often considerable residual uncertainty about whether interventions offer net benefits. In this essay we argue that high-quality research, namely by means of well-designed randomized trials, is ethically obligatory before, during, and after implementing policies in public health emergencies (PHEs). We contend that this standard applies to both pharmaceutical and non-pharmaceutical interventions, and we elaborate an account of equipoise that captures key features of debates in the recent pandemic. We build our case by analyzing research strategies employed during the COVID-19 pandemic regarding drugs, vaccines, and non-pharmaceutical interventions; and by providing responses to possible objections. Finally, we propose a public health policy reform: whenever a policy implemented during a PHE is not grounded in high-quality evidence that expected benefits outweigh harms, there should be a planned approach to generate high-quality evidence, with review of emerging data at preset time points. These preset timepoints guarantee that policymakers pause to review emerging evidence and consider ceasing ineffective or even harmful policies, thereby improving transparency and accountability, as well as permitting the redirection of resources to more effective or beneficial interventions.

Evaluating the Effectiveness of School Closure in COVID-19-Related Syndromes From Community-Based Syndromic Surveillance: Longitudinal Observational Study

BACKGROUND

During the COVID-19 pandemic, a school closure policy was adopted to prevent cluster transmission in schools and subsequent household transmission. However, the effectiveness of school closure is not consistent in studies conducted in different countries.

OBJECTIVE

This study aimed to explore the association between school closure and the daily standardized incidence of COVID-19-related syndromes in an outpatient syndromic surveillance system.

METHODS

We calculated the incidence of COVID-19-related syndromes derived from a community-based syndromic surveillance system between the first week of January and the second or fourth weeks after school closure in 2021 and 2022 in Taipei City, Taiwan. The effect of school closure on the standardized incidence of COVID-19-related syndromes was evaluated by interrupted time series analysis using an autoregressive integrated moving average with a distributed lag function. The exogenous variables were changes in human mobility measured by Google COVID-19 community mobility reports. Furthermore, the models quantified the influence of different age groups and the hierarchy of medical facilities, such as clinics or community hospitals.

RESULTS

School closure was only negatively and significantly associated with the overall standardized incidence of COVID-19-related syndromes in 2021 for 2 weeks after the intervention (coefficient -1.24, 95% CI -2.40 to -0.08). However, in different age groups, school closure had a significantly negative association with the standardized incidence among people aged 13-18 years and ≥65 years for 2 weeks after the intervention in clinics in 2021. In community hospitals, school closure was significantly positively associated with the standardized incidence among people aged 19-24 years in 2021. In 2022, 2 weeks after the intervention, school closure had a significantly negative association with the standardized incidence among people aged 0-6, 7-12, and 19-24 years in community hospitals and aged >45 years in clinics. Furthermore, the standardized incidence was positively associated with movement change toward grocery and pharmacy stores in all age groups in 2022. In addition, movement changes toward residences were significantly positively associated with the standardized incidence among all age groups.

CONCLUSIONS

Overall, school closure effectively suppresses COVID-19-related syndromes in students owing to the reduction of physical contact. In addition, school closure has a spillover effect on elderly people who stay at home.

The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling

BACKGROUND

Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources.

MATERIALS AND METHODS

We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation.

RESULTS

Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26-59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%.

CONCLUSION

The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.