scholarly journals No causal effect of school closures in Japan on the spread of COVID-19 in spring 2020

2021 ◽  
Author(s):  
Kentaro Fukumoto ◽  
Charles T. McClean ◽  
Kuninori Nakagawa
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Causal Inference ◽  
Causal Effect ◽  
Negative Consequences ◽  
Learning Loss ◽  
School Closures ◽  
Null Results ◽  
Children And Parents ◽  
Community Transmission ◽  
Pharmaceutical Interventions

AbstractAmong tool kits to combat the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, school closures are one of the most frequent non-pharmaceutical interventions. However, school closures bring about substantial costs, such as learning loss. To date, studies have not reached a consensus about the effectiveness of these policies at mitigating community transmission, partly because they lack rigorous causal inference. Here we assess the causal effect of school closures in Japan on reducing the spread of COVID-19 in spring 2020. By matching each municipality with open schools to a municipality with closed schools that is the most similar in terms of potential confounders, we can estimate how many cases the municipality with open schools would have had if it had closed its schools. We do not find any evidence that school closures in Japan reduced the spread of COVID-19. Our null results suggest that policies on school closures should be reexamined given the potential negative consequences for children and parents.

scholarly journals Shut Down Schools, Knock Down the Virus? No Causal Effect of School Closures on the Spread of COVID-19

2021 ◽  
Author(s):  
Kentaro Fukumoto ◽  
Charles T. McClean ◽  
Kuninori Nakagawa
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Causal Effects ◽  
Negative Consequences ◽  
School Closures ◽  
Knock Down ◽  
Level Data ◽  
Children And Parents ◽  
Municipal Level ◽  
Do So

AbstractAs COVID-19 spread in 2020, most countries shut down schools in the hopes of slowing the pandemic. Yet, studies have not reached a consensus about the effectiveness of these policies partly because they lack rigorous causal inference. Our study aims to estimate the causal effects of school closures on the number of confirmed cases. To do so, we apply matching methods to municipal-level data in Japan. We do not find that school closures caused a reduction in the spread of the coronavirus. Our results suggest that policies on school closures should be reexamined given the potential negative consequences for children and parents.

scholarly journals Do school closures reduce community transmission of COVID-19? A systematic review of observational studies

2021 ◽  
Author(s):  
Sebastian Walsh ◽  
Avirup Chowdhury ◽  
Simon Russell ◽  
Vickie Braithwaite ◽  
Joseph Ward ◽  
...  
Keyword(s):  
Systematic Review ◽  
At Risk ◽  
Empirical Evidence ◽  
Observational Studies ◽  
Risk Of Bias ◽  
Research Database ◽  
Negative Consequences ◽  
School Closures ◽  
Community Transmission ◽  
Harmful Effects

AbstractIntroductionSchool closures are associated with significant negative consequences and may exacerbate inequalities. They were implemented worldwide to control SARS-CoV-2 in the first half of 2020, but their effectiveness remains uncertain. This review summarises the empirical evidence of their effect on SARS-CoV-2 community transmission.MethodsThe study protocol was registered on Prospero (ID:CRD42020213699). On 12 October 2020 we searched PubMed, Web of Science, Scopus, CINAHL, the WHO Global COVID-19 Research Database, ERIC, the British Education Index, and the Australian Education Index. We included empirical studies with quantitative estimates of the effect of school closures/reopenings on SARS-CoV-2 community transmission. We excluded prospective modelling studies and intra-school transmission studies. We performed a narrative synthesis due to data heterogeneity.ResultsWe identified 3,318 articles, of which ten were included, with data from 146 countries. All studies assessed school closures, and one additionally examined re-openings. There was substantial heterogeneity between studies. Three studies, including the two at lowest risk of bias, reported no impact of school closures on SARS-CoV-2 transmission; whilst the other seven reported protective effects. Effect sizes ranged from no association to substantial and important reductions in community transmission.DiscussionStudies were at risk of confounding and collinearity from other non-pharmacological interventions implemented close to school closures. Our results are consistent with school closures being ineffective to very effective. This variation may be attributable to differences in study design or real differences. With such varied evidence on effectiveness, and the harmful effects, policymakers should take a measured approach before implementing school closures.Panel: ‘Research in context’Evidence before this studyA previous systematic review, published by some of us in April 2020, found good evidence that school closures are effective for the control of influenza, but limited evidence of effectiveness for coronavirus outbreaks. At the time there was no available empirial evidence from the COVID-19 pandemic.Added value of this studyThis study is the first systematic review of the empirical evidence from observational studies of the effect of school closures and reopenings on community transmission of SARS-CoV-2. We include 10 studies, covering 146 countries. There was significant heterogeneity between studies. Some studies reported large reductions in incidence and mortality associated with school closures, however, studies were at risk of confounding and collinearity, and studies at lower risk of bias reported no association.Implications of all the available evidenceThe evidence is consistent with either no effect, or a protective effect of school closures. With such varied evidence on effectiveness, and the harmful effects, policymakers should take a measured approach before implementing school closures.

scholarly journals Family Food Environment during the COVID-19 Pandemic: A Qualitative Study

Children ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 354
Author(s):  
Amber J. Hammons ◽  
Ryan Robart
Keyword(s):  
Food Environment ◽  
Eating Habits ◽  
School Closures ◽  
Family Connectedness ◽  
Healthy Habits ◽  
Children And Parents ◽  
The Family ◽  
Virtual Focus Groups ◽  
Public Health Officials ◽  
Family Food Environment

Background: The COVID-19 pandemic, with its cyclical lockdown restrictions and school closures, has influenced family life. The home, work, and school environments have collided and merged to form a new normal for many families. This merging extends into the family food environment, and little is known about how families are currently navigating this landscape. The objective of the present study was to describe families’ adaptations in the family food environment during the COVID-19 pandemic. Methods: Parents participated in one of 14 virtual focus groups (conducted in English and Spanish between December 2020 and February 2021). Reflexive thematic analysis was used to analyze the transcripts. Results: Forty-eight parents (81% Hispanic and SES diverse) participated. Five themes and one subtheme were identified around changes in eating habits and mealtime frequency, increases in snacking, family connectedness at mealtimes, and use of screens at meals. Conclusions: The COVID-19 pandemic has influenced the family food environment. Families shared how their eating habits have changed and that device usage increased at mealtimes. Some changes (e.g., weight gain) may have lasting health implications for both children and parents. Public health officials, pediatricians, and schools should work with families to resume healthy habits post pandemic.

scholarly journals The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatima Khadadah ◽  
Abdullah A. Al-Shammari ◽  
Ahmad Alhashemi ◽  
Dari Alhuwail ◽  
Bader Al-Saif ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Model ◽  
Socioeconomic Groups ◽  
Before And After ◽  
Cross Transmission ◽  
Community Transmission ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Abstract Background Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. Methods We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ($$ {\mathcal{R}}_e $$ R e ). We estimated the basic reproduction number ($$ {\mathcal{R}}_0 $$ R 0 ) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated $$ {\mathcal{R}}_e $$ R e values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. Results We estimate $$ {\mathcal{R}}_e $$ R e at 1·08 (95% CI: 1·00–1·26) for P1 and 2·36 (2·03–2·71) for P2. On March 22nd, $$ {\mathcal{R}}_e $$ R e for P1 and P2 are estimated at 1·19 (1·04–1·34) and 1·75 (1·26–2·11) respectively. After the partial curfew had taken effect, $$ {\mathcal{R}}_e $$ R e for P1 dropped modestly to 1·05 (0·82–1·26) but almost doubled for P2 to 2·89 (2·30–3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. Conclusion Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

A Survey on Causal Inference

2021 ◽  
Vol 15 (5) ◽  
pp. 1-46
Author(s):  
Liuyi Yao ◽  
Zhixuan Chu ◽  
Sheng Li ◽  
Yaliang Li ◽  
Jing Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Research Direction ◽  
Estimation Methods ◽  
Potential Outcome ◽  
Outcome Framework ◽  
Benchmark Datasets ◽  
Inference Methods

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

scholarly journals Reopening schools safely in the face of COVID-19: Can cluster randomized trials help?

2021 ◽  
pp. 174077452098486
Author(s):  
Charles Weijer ◽  
Karla Hemming ◽  
Spencer Phillips Hey ◽  
Holly Fernandez Lynch
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trial ◽  
Evidence Based ◽  
School Closures ◽  
School Aged Children ◽  
The Face ◽  
Community Transmission ◽  
Cluster Randomized ◽  
The Impact ◽  
Health Policymaking

The COVID-19 pandemic has highlighted the challenges of evidence-based health policymaking, as critical precautionary decisions, such as school closures, had to be made urgently on the basis of little evidence. As primary and secondary schools once again close in the face of surging infections, there is an opportunity to rigorously study their reopening. School-aged children appear to be less affected by COVID-19 than adults, yet schools may drive community transmission of the virus. Given the impact of school closures on both education and the economy, schools cannot remain closed indefinitely. But when and how can they be reopened safely? We argue that a cluster randomized trial is a rigorous and ethical way to resolve these uncertainties. We discuss key scientific, ethical, and resource considerations both to inform trial design of school reopenings and to prompt discussion of the merits and feasibility of conducting such a trial.

Causal inference via string diagram surgery

2021 ◽  
pp. 1-22
Author(s):  
Bart Jacobs ◽  
Aleks Kissinger ◽  
Fabio Zanasi
Keyword(s):  
Causal Inference ◽  
Probabilistic Reasoning ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Causal Effects ◽  
Stochastic Matrices ◽  
Counterfactual Reasoning ◽  
Special Cases ◽  
String Diagram ◽  
Set Up

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.

scholarly journals The Use of Fabric Masks in Prevention of Community Transmission of COVID-19

2020 ◽  
Vol 8 (2) ◽  
pp. 168-175
Author(s):  
Rudi Saputra
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Respiratory Tract ◽  
Angiotensin Converting Enzyme ◽  
Medical Personnel ◽  
New Disease ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Public Discussion ◽  
The Public ◽  
Community Transmission

Introduction: COVID-19 (Coronavirus Disease 2019) is a new disease due to SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) which can be transmitted through droplets. One effort to prevent transmission of COVID-19 is to use a mask. Medical masks are effective in preventing transmission of COVID-19, but their numbers are very limited and are very much needed by medical personnel when treating COVID-19 patients. Therefore, to prevent the spread of COVID-19 more broadly, alternative medical masks are needed, namely by using cloth masks which have not been discussed much about the purpose of their use to the public. Discussion: SARS-CoV-2 is a cause of COVID-19 and infects the respiratory tract, especially in the lungs (pulmo) through the ACE2 receptor (Angiotensin-Converting Enzyme 2). SARS-CoV-2 has a diameter of around 120 nm. Cloth masks as an alternative to the scarcity of medical masks are recommended for public use. The recommended cloth masks are made of cotton or a cloth towel. A cloth mask is able to hold large droplets (> 5 μm), but not small droplets. Conclusion: Cloth masks can be used by the community in an effort to minimize transmission of COVID-19 by holding large droplets, but it is not effective in preventing transmission of COVID-19 because it can still be passed by SARS-CoV-2. Suggestion: Cloth masks can be optimized using nanoparticles to resist SARS-CoV-2.

scholarly journals Public justification and expert disagreement over non-pharmaceutical interventions for the COVID-19 pandemic

2021 ◽  
pp. medethics-2021-107671
Author(s):  
Marcus Dahlquist ◽  
Henrik D Kugelberg
Keyword(s):  
Empirical Support ◽  
The State ◽  
Alternative View ◽  
Public Justification ◽  
Standard View ◽  
School Closures ◽  
Environmental Cleaning ◽  
Wide Range ◽  
Cleaning And Disinfection ◽  
Pharmaceutical Interventions

A wide range of non-pharmaceutical interventions (NPIs) have been introduced to stop or slow down the COVID-19 pandemic. Examples include school closures, environmental cleaning and disinfection, mask mandates, restrictions on freedom of assembly and lockdowns. These NPIs depend on coercion for their effectiveness, either directly or indirectly. A widely held view is that coercive policies need to be publicly justified—justified to each citizen—to be legitimate. Standardly, this is thought to entail that there is a scientific consensus on the factual propositions that are used to support the policies. In this paper, we argue that such a consensus has been lacking on the factual propositions justifying most NPIs. Consequently, they would on the standard view be illegitimate. This is regrettable since there are good reasons for granting the state the legitimate authority to enact NPIs under conditions of uncertainty. The upshot of our argument is that it is impossible to have both the standard interpretation of the permissibility of empirical claims in public justification and an effective pandemic response. We provide an alternative view that allows the state sufficient room for action while precluding the possibility of it acting without empirical support.

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