Table 1

Characteristics of included studies, stratified by study design

Study	Country	Study period	Setting type	Unit of exposure	Confounders/Co-interventions adjusted for	Other NPI measures	Analysis type
School closures—pooled multiple-area before-after comparison studies (n=22)
Auger et al 14	USA	Study 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 schools	US state	Incidence: 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	Variable	Negative 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 countries	Study period: n=100 cases until 15 April 2020 Exposure date: variable Lag period: 7 days	Primary school closure data used to determine exposure date	Country	Border closure, event ban, gathering ban, venue closure, lockdown, work ban, day-of-the-week effects	Variable	Bayesian hierarchical model assuming negative binomial distribution of new cases.
Brauner et al 18	34 European and 7 non-European countries	Study period: 22 January 2020 to 30 May 2020 Exposure period: variable Incubation period: 6 days Infection to death: 22 days	Primary and secondary schools	Regional data where available, otherwise country	Mass gathering bans, business closures, university closures, stay-at-home orders	Variable	Bayesian hierarchical model to estimate effectiveness of individual NPIs on the reproduction number
Chernozhukov et al 19	USA	Study 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 schools	US state	Business closures, stay-at-home orders, hospitality closures, mask mandates, mobility data, national case/mortality trends	Variable	Regression model with autoregressive strucutres to allow for dynamic effects of other NPIs and mobility data.
Courtemanche et al 20	USA	Study period: 1 March 2020 to 27 April 2020 Exposure period: variable, generally mid-March Lag period: 10 and 20 days	Not specified	US counties, or county equivalents	Other NPIs (stay-at-home orders, hospitality closure, limiting gathering size), total daily tests done in that state	Variable	Fixed effects regression to estimate the effect of school closure on the growth rate of cases (% change).
Dreher et al 21	USA	Study period: 500th case until 30 April 2020 Exposure period: variable	Not specified	US state	Data 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 included	Variable	Univariate linear regression of NPI implementation and average Rt after the 500th case. Cox proportional hazards regression of the association between NPI implementation and time for cases to double from 500th to 1000th case. 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 countries	Study period: 1 February 2020 to 16 September 2020 Exposure period: variable Lag period: no lag applied	Early years settings, primary schools and secondary schools	Country	Stay-at-home orders, university closures, mass gathering bans, mask mandates, work-from-home orders, public space closures, business and retail closures	Variable	Used 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, USA	Study period: 25 February 2020 to 6 April 2020 Exposure date: varied by country Lag period: no lag applied	Not specified	Provincial/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 regimes	Variable	Reduced-form econometric (regression) analysis to estimate the effect of school closures on the continuous growth rate (log scale).
Jamison et al 30	13 European countries	Study period: until 16 May 2020 Exposure period: variable Lag period: 18 days	Not specified	Country	Workplace 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 epidemic	Variable	Linear regression model reporting the percentage point reduction in the daily change of deaths measured as a 5-day rolling average.
Kilmek-Tulwin and Tulwin32	15 European countries; Argentina, Brazil and Japan	Study period: not specified Exposure period: variable	Not specified	Country	None	Not specified	Wilcoxon 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	USA	Study period: not specified Exposure period: variable	Not specified	US 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	Variable	Negative 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 al34	Worldwide (167 geopolitical areas)	Study period: 1 January 2020 to 19 May 2020 Exposure period: variable	Not specified	Country, province or state	None specified	School closures only considered in the context of travel and work restrictions, and mass gathering bans already being in place	Validate 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 al³	Worldwide (131 countries)	Study period: 1 January 2020 to 20 July 2020 Exposure period: variable	Not specified	Country	Other NPIs (international travel bans, internal travel bans, stay-at-home requirements, public transport closures, mass gathering bans, public event bans, workplace closures)	Variable	Defined 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 specified	Mostly country, although lags were examined at the World Region level	Various 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 policy	Variable	Parsimonious 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 specified	Country	NPIs (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)	Variable	Univariate 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,40	37 OECD Member Countries	Study period: 1 January 2020 to 30 June 2020 Exposure period: variable Lag period: 26 days	Not specified	Country	Timing 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 density	Variable	Multivariable negative binomial regression with panel data.
Rauscher 42	USA	Study period: until 27 April 2020 Exposure period: state’s 100th death until time of school closures Lag period: not specified	Not specified	US state	Population density, number of schools, public school enrolment, stay-at-home order date, whether school closures were mandated or recommended	Variable	Regression 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 specified	Country	An 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 policy	Variable	Multivariable 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	USA	Study period: until 28 May 2020 Exposure period: variable	Not specified	US counties	Stay-at-home orders, mass gathering bans, restaurant closures, hospitality and gym closures, federal guidelines, foreign travel ban	Variable	Grouped 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	USA	Study period: 21 January 2020 to 5 June 2020 Exposure period: variable	Early years, and ‘schools’ (presumed primary and secondary)	US counties	County-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 R	Variable, but school closures generally implemented before other measures	Mechanistic 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	USA	Study 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 schools	US state	Population size, population density, % aged <18 years, % aged >65 years, % black, % Hispanic, % in poverty, geographical region	Variable	Multivariable 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 specified	Country	NPIs (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 case	Variable	Non-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	Italy	Study 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 schools	Italian province	None specified	Variable	Created 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	Japan	Study period: 27 January 2020 to 31 March 2020 Exposure date: 29 February 2020 Lag period: 9 days	Primary and secondary schools	Country	None specified	Not specified	Time series analysis using Bayesian inference to estimate effect of school closures on the incidence rate of COVID-19.
Matzinger and Skinner 37	USA	Study 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 schools	US state	None specified	Not specified	Calculated 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 Korea	Study 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 specified	Country	Indirectly 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 Reports	All three countries: banning of public events, restriction of international flights, contact tracing, public information campaign. Other unspecified interventions in place in each country	Difference-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, USA	Study period: 1 February 2020 to 30 June 2020 Exposure period: variable Lag period: 6 weeks	Not specified	Country	Other NPIs (workplace closures, public event cancellations, restrictions on mass gatherings, public transport closure, stay-at-home orders, internal movement restrictions) and mobility data from Apple	Not specified	Poisson regression to estimate the effect of NPIs on mortality (outcome measure not fully explained).
Sruthi et al 43	Switzerland	Study period: 9 March 2020 to 13 September 2020	Secondary schools used as exposure date	Swiss 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 schools	Variable	Artificial intelligence model to disentangle the effect of individual NPIs on Rt. R estimated exclusively from incidence data.
Stage et al 44	Denmark, Germany, Norway	Study 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 schools	Country	None specified but timing of other NPIs, and changes to testing capacity outlined within analysis	Variable	Closures: 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 specified	Country	Country-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)	Variable	Weighted 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 USA	Study period: until 15 May 2020 Exposure period: cut-off 15 May 2020 Lag period: no lag applied	Not specified	Country	Days of pandemic, life expectancy, smoking prevalence	Variable	First 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 specified	Country	Stringency 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 density	Variable	Multivariable 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 first	Country	None	Not specified	Naked eye analysis of 7-day rolling average of new cases.
Ehrhardt et al 22	Germany	Study 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 schools	Baden-Wurttemberg (region of Germany)	None specified	Not specified	Presentation 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 al²³	See description in school closure section above
Garchitorena et al24	See description in school closure section above
Harris et al25	USA	Study period: January–October 2020 Exposure period: variable Lag period: 1–2 weeks	Not specified	US counties	Adjusted 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 effects	Variable	Difference-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	Belgium	Study period: 1 August 2020 to 30 November 2020 Exposure date: 1 September 2020 Lag period: no lag applied	Primary and secondary schools	Brussels, Belgium	None specified	Cafes, restaurants and sports facilities had already been reopened in a limited way from June, and five close contacts were permitted from July	Plotted 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	Germany	Study period: 1 July 2020 to 5 October 2020 Exposure period: variable	Not specified	German counties	Adjusted 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 differences	Not specified	Regression 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 al35	See description in school closure section above
Sruthi et al43	See description in school closure section above
Stein-Zamir et al 45	Germany	Study period: 1 July 2020 to 5 October 2020 Exposure period: variable	Not specified	German counties	Adjusted 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 differences	Not specified	Regression 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 al44	See description in school closure section above
School holiday studies (n=3)
Beesley16	See description in school reopening section above
Bjork et al 17	11 European countries	Study period: 30 March 2020 to 7 June 2020 Exposure period: 10 February 2020 to 8 March 2020 Lag period: n/a	Not specified	Region	Population density, age distribution, country	Variable	Variance-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	Germany	Study period: 10 June 2020 to 23 September 2020 Exposure period: variable	Not specified	School holiday timing: state (n=16) Outcome data: district (n=401)	Average taxable income and proportion of residents who are foreigners	Not specified	Multivariable 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.