Table 1

Characteristics of included studies, stratified by study design

StudyCountryStudy periodSetting typeUnit of exposureConfounders/Co-interventions adjusted forOther NPI measuresAnalysis type
School closures—pooled multiple-area before-after comparison studies (n=22)
Auger et al
14
USAStudy period: 13 March 2020 to 23 March 2020
Exposure period: 1 January 2020 to 29 April 2020
Lag period: 16 days (incidence), 26 days (mortality)
Primary and secondary schoolsUS stateIncidence: NPIs preschool closure (restaurant closure, stay-at-home orders). NPIs postschool closure (stay-at-home orders). Testing rate preschool and post school closure
Mortality: NPIs preschool closure (restaurant closure, mass gathering ban, stay-at-home orders). NPIs postschool closure (restaurant closures, stay-at-home orders)
Both: cumulative COVID-19 cases preschool closure. % of population under 15, % of population over 65, % of nursing home residents, social vulnerability index and population density
VariableNegative binomial regression to estimate effect of school closures on the changes in incidence and mortality rates, as calculated by interrupted time series analysis.
Banholzer et al
15
USA, Canada, Australia, Norway, Switzerland and EU-15 countriesStudy period: n=100 cases until 15 April 2020
Exposure date: variable
Lag period: 7 days
Primary school closure data used to determine exposure dateCountryBorder closure, event ban, gathering ban, venue closure, lockdown, work ban, day-of-the-week effectsVariableBayesian hierarchical model assuming negative binomial distribution of new cases.
Brauner et al
18
34 European and 7 non-European countriesStudy period: 22 January 2020 to 30 May 2020
Exposure period: variable
Incubation period: 6 days
Infection to death: 22 days
Primary and secondary schoolsRegional data where available, otherwise countryMass gathering bans, business closures, university closures, stay-at-home ordersVariableBayesian hierarchical model to estimate effectiveness of individual NPIs on the reproduction number
Chernozhukov et al
19
USAStudy period: 7 March 2020 to 3 June 2020
Exposure period:
variable, but 80% of states closed within 2 days of 15 March 2020
Lag period: 14 days (incidence), 21 days (mortality)
Primary and secondary schoolsUS stateBusiness closures, stay-at-home orders, hospitality closures, mask mandates, mobility data, national case/mortality trendsVariableRegression model with autoregressive strucutres to allow for dynamic effects of other NPIs and mobility data.
Courtemanche et al
20
USAStudy period:
1 March 2020 to 27 April 2020
Exposure period: variable, generally mid-March
Lag period:
10 and 20 days
Not specifiedUS counties, or county equivalentsOther NPIs (stay-at-home orders, hospitality closure, limiting gathering size), total daily tests done in that stateVariableFixed effects regression to estimate the effect of school closure on the growth rate of cases (% change).
Dreher et al
21
USAStudy period:
500th case until 30 April 2020
Exposure period:
variable
Not specifiedUS stateData collected on: demography (population density, population size, GDP, state-wide health and healthcare capacity) and on NPIs (stay-at-home orders, mass gathering bans and business closures). However, covariables with a p>0.1 in univariate analysis and collinear variables were excluded. Full details are not available of which covariables were includedVariable
  1. Univariate linear regression of NPI implementation and average Rt after the 500th case.

  2. Cox proportional hazards regression of the association between NPI implementation and time for cases to double from 500th to 1000th case.

  3. Cox proportional hazards regression of the association between NPI implementation and time for deaths to double from 50 to 100.

Garchitorena et al
24
32 European countriesStudy period: 1 February 2020 to 16 September 2020
Exposure period: variable
Lag period: no lag applied
Early years settings, primary schools and secondary schoolsCountryStay-at-home orders, university closures, mass gathering bans, mask mandates, work-from-home orders, public space closures, business and retail closuresVariableUsed incidence data, supplemented by a capture-recapture method using mortality data to infer undiagnosed cases. Compared this with a counterfactual age-structured Susceptible-Exposed-Infectious-Removed (SEIR) model coupled with Monte Carlo Markov Chain to estimate effectiveness of NPI combinations—then estimated their disentangled effects (considering each individual NPI over the duration of their implementation).
Hsiang et al
26
Italy, France, USAStudy period:
25 February 2020 to 6 April 2020
Exposure date:
varied by country
Lag period:
no lag applied
Not specifiedProvincial/Regional level (Italy and France), state level (USA)Other NPIs (travel ban and quarantine, work-from-home order, no social gatherings, social distancing rules, business and religious closures, home isolation), test regimesVariableReduced-form econometric (regression) analysis to estimate the effect of school closures on the continuous growth rate (log scale).
Jamison et al
30
13 European countriesStudy period: until 16 May 2020
Exposure period: variable
Lag period: 18 days
Not specifiedCountryWorkplace closures, public event cancellations, restricting gathering sizes, closing public transport, stay-at-home orders, internal movement restrictions and international travel, mobility data, population >65 years, population density, number of acute care beds per population, starting date of epidemic, day of the epidemicVariableLinear regression model reporting the percentage point reduction in the daily change of deaths measured as a 5-day rolling average.
Kilmek-Tulwin and Tulwin3215 European countries; Argentina, Brazil and JapanStudy period: not specified
Exposure period: variable
Not specifiedCountryNoneNot specifiedWilcoxon signed rank test to determinethe significance of differences between pairs of incidence rates from different time points. Time points considered: 16th day, 30th day, 60th day since 100th case. Cases/million population compared following implementation of school closures.
Krishnamachari et al
33
USAStudy period: not specified
Exposure period: variable
Not specifiedUS state
US city
State analysis: days for preparation, population density, % urban, % black, % aged >65 years, % female
City analysis: use of public transport for work, use of carpool for work, population density and % black
Both analyses: days from state-level emergency declaration to gathering size restrictions, non-essential business closures, stay-at-home orders, gathering restrictions, restaurant closures
VariableNegative binomial regression comparing states/cities above and below median value for days to implement school closures, on rate ratio of cumulative incidence on days 14, 21, 28, 35 and 42 following the area’s 50th case. All variables in analysis classified a 1 if above median value for dataset, and 0 if below.
Li et al34Worldwide (167 geopolitical areas)Study period: 1 January 2020 to 19 May 2020
Exposure period: variable
Not specifiedCountry, province or stateNone specifiedSchool closures only considered in the context of travel and work restrictions, and mass gathering bans already being in placeValidate a novel SEIR model ('DELPHI') in the 167 countries between 28 April 2020 and 12 May 2020. Then elicit the effect of each day an NPI was in place on the DELPHI-derived changes to the infection rate at each time point.
Li et al3Worldwide (131 countries)Study period: 1 January 2020 to 20 July 2020
Exposure period: variable
Not specifiedCountryOther NPIs (international travel bans, internal travel bans, stay-at-home requirements, public transport closures, mass gathering bans, public event bans, workplace closures)VariableDefined a time period as a period in which the NPIs in a given country were the same. Calculated the R ratio as the ratio between the daily R of each period and the R from the last day of the previous period. Pooled countries using log-linear regression with the introduction and relaxation of each NPI as independent variables for the first 28 days after introduction/relaxation of the NPI.
Liu et al
36
Worldwide (130 countries)Study period: 1 January 2020 to 22 June 2020
Exposure period: variable
Lag periods: 1, 5 and 10 days
Not specifiedMostly country, although lags were examined at the World Region levelVarious parsimonious models. Variables considered: workplace closure, cancellation of public events, gathering size restrictions, public transport closures, stay-at-home requirements, internal movement restrictions, international travel restrictions, income support for households, public information campaigns, testing policy and contact tracing policyVariableParsimonious linear fixed effects panel regression, using stepwise backwards variable selection. Accounted for collinearity of interventions by conducting hierarchical cluster analysis with multiscale bootstrapping to test the statistical significance of identified clusters.
Papadopoulos et al
39
Worldwide (150 countries)Study period: 1 January 2020 to 29 April 2020
Exposure period: variable
Lag period: no lag applied
Not specifiedCountryNPIs (workplace closure, public event cancellations, gathering size restrictions, public transport closures, stay-at-home restrictions, internal travel restrictions, international travel restrictions, public information campaigns, testing systems and contact tracing systems), timing of each NPI in days since first case, overall stringency index and sociodemographics (population, life expectancy, purchasing power, longitude, date of first death, average household size)VariableUnivariate regression model for effect of school closures on total log cases and total log deaths. Multivariate regression model for effect of timing of school closures (relative to first case) on log total cases and log total deaths.
Piovani et al,4037 OECD Member CountriesStudy period: 1 January 2020 to 30 June 2020
Exposure period: variable
Lag period: 26 days
Not specifiedCountryTiming of mass gathering bans, time from first death to peak mortality, cumulative incidence at first death, log population size, hospital beds per population, % population aged 15–64 years, % urban, annual air passengers and population densityVariableMultivariable negative binomial regression with panel data.
Rauscher
42
USAStudy period: until 27 April 2020
Exposure period: state’s 100th death until time of school closures
Lag period: not specified
Not specifiedUS statePopulation density, number of schools, public school enrolment, stay-at-home order date, whether school closures were mandated or recommendedVariableRegression analyses of time between the state’s 100th cases and day of school closures and the daily cumulative cases and deaths, measured on the log scale per 100 000 residents.
Stokes et al
46
Worldwide (130 countries)Exposure: time before first death; and first 14 days after first death
Lag period: up to 24 days
Not specifiedCountryAn overall average strictness and timeliness of NPI measures (as a whole) derived from data on school closures, workplace closures, public event bans, gathering bans, public transport closures, stay-at-home orders, internal movement restrictions, international travel restrictions and public information campaigns. Also adjusted for days since NPI implementation, population density, % over 65, % male, life expectancy, hospital beds, GDP, health expenditure, international tourism, governance, region, testing policy, contact tracing policyVariableMultivariable linear regression to estimate the effect of NPIs (including school closures) as lagged variables on the daily mortality rate per 1 million 0–24 days after the first death, 14–38 days after the first death.
Wu et al
47
USAStudy period: until 28 May 2020
Exposure period: variable
Not specifiedUS countiesStay-at-home orders, mass gathering bans, restaurant closures, hospitality and gym closures, federal guidelines, foreign travel banVariableGrouped together demographically and socioeconomically similar counties into five clusters, then developed a model of R for each cluster applying a Bayesian mechanistic model to excess mortality data.
Yang et al
48
USAStudy period: 21 January 2020 to 5 June 2020
Exposure period: variable
Early years, and ‘schools’ (presumed primary and secondary)US countiesCounty-level demographic characteristics, NPIs (school closures, leisure activity closure, stay-at-home orders, face mask mandates, daycare closures, nursing home visiting bans, medical service suspension) and previous week log RVariable, but school closures generally implemented before other measuresMechanistic transmission models fitted to lab-confirmed cases, applying lag times from the literature. Used generalised estimating equations with autoregression of confounders.
Yehya et al
49
USAStudy period: 21 January 2020 to 29 April 2020
Exposure measure: time (days) between 10th COVID-19 death and school closure
Lag (exposure to mortality): up to 28 days
Primary and secondary schoolsUS statePopulation size, population density, % aged <18 years, % aged >65 years, % black, % Hispanic, % in poverty, geographical regionVariableMultivariable negative binomial regression to estimate mortality rate ratios associated with each day of delaying school closure.
Zeilinger et al
50
Worldwide (176 countries)Study period: until 17 August 2020
Exposure period: variable
Not specifiedCountryNPIs (mass gathering bans, social distancing rules, business closures, curfews, declaration of emergencies, border restrictions, lockdown); % population >65, % population urban, GDP, % exposed to high PM2.5 air pollution; day of the year, and days since 25th cumulative caseVariableNon-parametric machine learning model applied to each country, before pooling the estimated NPI effects across countries. Including only the 90 days after the 25th cumulative case.
School closures—within-area before-after comparison studies (n=7)
Gandini et al23 2021
No evidence of association between schools and SARS-CoV-2 second wave in Italy
ItalyStudy period: 7 August 2020 to 2 December 2020
Exposure period: variable. School reopenings during September. Closures in October and Nobermber
Lag: under investigation
Early years, primary and secondary schoolsItalian provinceNone specifiedVariableCreated a model of R from data on new cases, parameters estimated using data from the first wave in Italy (serial interval 6.6) and Bayesian methodology to account for the epidemiological uncertainty. Reported as the median for the 7-day posterior moment. Compared neighbouring provinces that reopened or reclosed schools at different times.
Iwata et al
29
JapanStudy period: 27 January 2020 to 31 March 2020
Exposure date: 29 February 2020
Lag period: 9 days
Primary and secondary schoolsCountryNone specifiedNot specifiedTime series analysis using Bayesian inference to estimate effect of school closures on the incidence rate of COVID-19.
Matzinger and Skinner
37
USAStudy period:
6 March 2020 to 1 May 2020
Exposure date: 14 March 2020 (Georgia, Tennessee), 6 March 2020 (Mississippi)
Lag period:
under investigation
Primary and secondary schoolsUS stateNone specifiedNot specifiedCalculated changes to the doubling time of new cases, hospitalisations and deaths by plotting log2 of cases, hospitalisations and deaths against time, and using segmented regression to analyse changes in the trends in response to NPI implementation.
Neidhofer and Neidhofer
38
Argentina, Italy, South KoreaStudy period: not specified
Exposure date:
Italy 4 March 2020
Argentina 16 March 2020
South Korea not specified
Lag period: analysis up to 18 days postschool closure
Not specifiedCountryIndirectly adjusted for in derivation of counterfactual, based on most comparable countries for: population size and density, median age, % aged >65 years, GDP per capita, hospital beds per 100 000 inhabitants, public health expenditures, average number of reported COVID-19 deaths before day zero, growth rate of reported COVID-19 cases with respect to the day before and mobility patterns retrieved from Google Mobility ReportsAll three countries: banning of public events, restriction of international flights, contact tracing, public information campaign. Other unspecified interventions in place in each countryDifference-in-differences comparison to a synthetic control unit (derived from the weighted average of the epidemic curves from comparable countries that closed schools later), to estimate the % reduction in deaths in the 18 days postschool closure.
Shah et al
53
Australia, Belgium, Italy, UK, USAStudy period: 1 February 2020 to 30 June 2020
Exposure period: variable
Lag period: 6 weeks
Not specifiedCountryOther NPIs (workplace closures, public event cancellations, restrictions on mass gatherings, public transport closure, stay-at-home orders, internal movement restrictions) and mobility data from AppleNot specifiedPoisson regression to estimate the effect of NPIs on mortality (outcome measure not fully explained).
Sruthi et al
43
SwitzerlandStudy period: 9 March 2020 to 13 September 2020Secondary schools used as exposure dateSwiss Canton (region)Closures of hairdressers, bars, nightclubs, restaurants and retail. Travel restrictions. Mask mandates. Number of hotel rooms within the Canton. Results stratified by Cantons with and without mask mandates in place within secondary schoolsVariableArtificial intelligence model to disentangle the effect of individual NPIs on Rt. R estimated exclusively from incidence data.
Stage et al
44
Denmark, Germany, NorwayStudy period: March–June 2020
Closure dates: Around 16 March 2020
Reopening dates: staggered, from late April to mid-May
Lag period: under study
Early years, primary and secondary schoolsCountryNone specified but timing of other NPIs, and changes to testing capacity outlined within analysisVariableClosures: observed data compared against counterfactual unmitigated simulation using an epidemic model fitted by Approximate Bayesian Computation, with a Poisson Gaussian process regression model. Response dates measured as a change in growth rate occurring at least 5 days after the intervention, exceeding the 75th centile of the modelled data, and where the deviation persists for at least 5 days.
Reopening: growth rate change for each loosening of restrictions, estimating an instantaneous growth rate via a General Additive Model using a quasi-Poisson family with canonical link and default thin plate regression splines.
School closures—pooled multiple-area comparisons of interventions in place at a fixed time point (n=3)
Juni et al
31
Worldwide (144 countries)Study period:
Until 28 March 2020
Exposure date: 11 March 2020
Lag period:
10 days
Not specifiedCountryCountry-specific factors (GDP per capita, health expenditure as % of GDP, life expectancy, % aged ≥65 years, Infectious Disease Vulnerability Index, urban population density), geography factors (flight passengers per capita, closest distance to a geopolitical area with an already established epidemic, geogrpahical region) and climatic factors (temperature, humidity)VariableWeighted random-effects regression analysis to estimate the effect of school closures on the changes to the incidence rate (measured as the ratio of rate ratios, dividing cumulative cases up to 28 March 2020, by cumulative cases until 21 March 2020, for each area).
Walach and Hockertz
52
34 European countries, Brazil, Canada, China, India, Iran, Japan and USAStudy period: until 15 May 2020
Exposure period: cut-off 15 May 2020
Lag period: no lag applied
Not specifiedCountryDays of pandemic, life expectancy, smoking prevalenceVariableFirst examined correlations between multiple individual variables and cases/deaths in non-parametric analysis. Then incorporated those with an r>0.3 into generalised linear models, starting with the best correlated variables and adding in only those that improved model fit.
Wong et al
51
Worldwide (139 countries)Analysis period:
15 April 2020 to 30 April 2020
Exposure cut-off date: 31 March 2020
Lag period: 14 days
Not specifiedCountryStringency index (workplace closure, public event cancellation, restrictions on gathering size, public transport closure, stay-at-home orders, restrictions on internal movement and international travel, public information campaigns), GDP, population densityVariableMultivariable linear regression to estimate the effect of school closures on the rate of increase in cumulative incidence of COVID-19.
School reopening studies (n=11)
Beesley
16
Worldwide (24 countries)Study period: until 1 September 2020
Exposure date: variable
Lag period: under investigation
Mostly all schools, but in the Netherlands noted that primary schools were reopened firstCountryNoneNot specifiedNaked eye analysis of 7-day rolling average of new cases.
Ehrhardt et al
22
GermanyStudy period: 25 February 2020 to 4 August 20202
Exposure period:
school closures 17 March 2020
Staggered school reopening 4 May 2020 to 29 June 2020
Early years settings, primary and secondary schoolsBaden-Wurttemberg (region of Germany)None specifiedNot specifiedPresentation of an epidemic curve showing daily new cases in Baden-Wurttemberg from 25 February 2020 to 7 August 2020 with key school dates labelled.
Gandini et al23See description in school closure section above
Garchitorena et al24See description in school closure section above
Harris et al25USAStudy period: January–October 2020
Exposure period: variable
Lag period: 1–2 weeks
Not specifiedUS countiesAdjusted for NPIs (stay-at-home orders, non-essential business closures, non-essential business reopening, restaurant closures, restaurant reopenings, mask mandates and resumption of religious gatherings), with state, county and calendar week fixed effectsVariableDifference-in-differences event study model with propensity score matching comparing exposure data (codified as: virtual only 0, hybrid model 0.5, in-person teaching only 1) with inpatient hospitalisations with diagnoses of COVID-19 or COVID-19-related symptoms from insurance data.
Ingelbeen et al
27
BelgiumStudy period: 1 August 2020 to 30 November 2020
Exposure date: 1 September 2020
Lag period: no lag applied
Primary and secondary schoolsBrussels, BelgiumNone specifiedCafes, restaurants and sports facilities had already been reopened in a limited way from June, and five close contacts were permitted from JulyPlotted R using data from the national contact tracing system. Also used the contact tracing data to examine age-specific trends in cases/contacts following school reopenings.
Isphording et al
28
GermanyStudy period: 1 July 2020 to 5 October 2020
Exposure period: variable
Not specifiedGerman countiesAdjusted for mobility data from a private company which have data on one-third of German mobile phone users, and Google mobility reports. Fixed effects used to control for demographic differencesNot specifiedRegression model comparing changes in new cases between counties that reopen schools after the summer holidays, with counties that have not yet reopened schools. Considered data from 2 weeks before reopening to 3 weeks after.
Li et al35See description in school closure section above
Sruthi et al43See description in school closure section above
Stein-Zamir et al
45
GermanyStudy period: 1 July 2020 to 5 October 2020
Exposure period: variable
Not specifiedGerman countiesAdjusted for mobility data from a private company which have data on one-third of German mobile phone users, and Google mobility reports. Fixed effects used to control for demographic differencesNot specifiedRegression model comparing changes in new cases between counties that reopen schools after the summer holidays, with counties that have not yet reopened schools. Considered data from 2 weeks before reopening to 3 weeks after.
Stage et al44See description in school closure section above
School holiday studies (n=3)
Beesley16See description in school reopening section above
Bjork et al
17
11 European countriesStudy period: 30 March 2020 to 7 June 2020
Exposure period: 10 February 2020 to 8 March 2020
Lag period: n/a
Not specifiedRegionPopulation density, age distribution, countryVariableVariance-weighted least squares linear regression comparing timing of February/March half-term with excess mortality (compared with 2015–2019 data for each region).
Pluemper and Neumayer
41
GermanyStudy period: 10 June 2020 to 23 September 2020
Exposure period: variable
Not specifiedSchool holiday timing: state (n=16)
Outcome data: district (n=401)
Average taxable income and proportion of residents who are foreignersNot specifiedMultivariable regression model comparing incident growth rate 2 weeks before summer holidays up to 2 weeks afterwards, with fixed effects to account for for interdistrict differences, and a lagged dependent variable to account for background natioinal trends in the data.
  • n/a, not available; NPI, non-pharmaceutical intervention; OECD, Organisation for Economic Co-operation and Development.