Table 3

Findings from included studies, stratified by study design

StudyMain findingOutcome measureDetailed resultsOther comments
School closures—pooled multiple-area before-after comparison studies (n=22)
Auger et al
14
School closures associated with reduced transmission:
school closures were associated with decreases in the rate of growth of COVID-19 incidence and mortality
Regression coefficient estimating effect of school closures on changes to weekly incidence and mortality ratesAdjusted model:
incidence: 62% (95% CI 49% to 71%) relative reduction
Mortality: 58% (95% CI 46% to 67%) relative reduction
Sensitivity analysis of shorter and longer lag periods did not significantly alter the findings.
Early school closure associated with greater relative reduction in COVID-19 incidence and mortality.
Banholzer et al,
15
School closures not associated with a change in transmission:
school closures not statistically significantly associated with a reduction in the incidence rate
Relative reduction in new cases compared with cumulative incidence rate prior to NPI implementation8% (95% CrI 0% to 23%)Sensitivity analyses for altering n=100 cases start point, and 7-day lag, did not significantly change the findings.
Concede that close temporal proximity of interventions precludes precise estimates, but that NPIs were sufficiently staggered within countries, and sufficiently heterogeneous across countries to have confidence that school closures were less effective than other NPIs.
Brauner et al
18
School closures not associated with a change in transmission:
school closures not statistically significantly associated with a reduction in Rt
% reduction in Rt with 95% Bayesian CrI8.6% (95% CrI −13.3% to 30.5%)Authors report close collinearity with university closures making independent estimates difficult.
Findings robust to variety of sensitivity analyses.
Chernozhukov et al
19
School closures associated with a mixed effect on transmission:
school closures not associated with a change in incidence rate, but statistically significantly associated with a reduction in mortality rate
Regression coefficient estimating the change in weekly incidence rate and weekly mortality rate, measured on the log scaleIncidence rate: 0.019 (SE 0.101)
Mortality rate: −0.234 (SE 0.112)
The authors report more precise estimates for other NPIs due to considerable variation in their timing between states, whereas there was very little variation in the timing of school closures across the country, with 80% of states closing schools within a couple of days of 15 March 2020.
School closures significantly associated with reductions in mobility.
Courtemanche et al
20
School closures not associated with a change in transmission:
school closures not statistically associated with the growth rate of confirmed cases
Regression coefficient estimating effect of school closures on the growth rate of cases (% change)Applying a 10-day lag: 1.71% (95% CI −0.38% to 3.79%)
Applying a 20-day lag: 0.17% (95% CI −1.60% to 1.94%)
Dreher et al
21
School closures associated with a mixed effect on transmission:
school closures associated with a statistically significant reduction in Rt, but no association with doubling time of cases or deaths
Regression coefficients from the linear and cox proportional hazards regressions. The first analysis is stratified into the first 7 days after iimplementation, and the second 7 days
  1. First week: −0.17 (95% CI −0.30 to –0.05). Second week: −0.12 (-0.21 to –0.04)

  2. 0.63 (0.25 to 1.63)

  3. Null effect but numbers not reported

In adjusted models using Google mobility data, a 10% increase in time spent at home was reported in the week following school closures.
Garchitorena et al
24
School closures associated with reduced transmission:
school closures statistically significantly associated with a reduction in COVID-19 transmission
Ratio of transmission rates with and without implementation of the NPI (assessed over the duration of the NPI being in place) Presented as a forest plot so the reported results here are estimatedEY settings: 9% reduction
(95% CI 1% to 16%)
Primary schools: 10% reduction (95% CI 2% to 18%)
Secondary schools: 11% reduction (95% CI 3% to 19%)
Hsiang et al
26
School closures not associated with a change in transmission:
school closures not statistically associated with the growth rate of confirmed cases
Regression coefficient estimating effect of school closures on the continuous growth rate (log scale)Italy: −0.11 (95% CI −0.25 to 0.03)
France: −0.01 (95% CI −0.09 to 0.07)
USA: 0.03 (95% CI −0.03 to 0.09)
Sensitivity analysis applying a lag to NPI measures on data from China did not significantly alter the findings.
Jamison et al
30
School closures not associated with transmission:
school closures not statistically significantly associated with relative changes in the 5-day rolling average of COVID-19 mortality
Percentage point change to the 5-day rolling average of COVID-19 mortality−2.8 (95% CI −6.7 to 1.0), p=0.150
Kilmek-Tulwin and Tulwin32School closures associated with reduced transmission:
earlier school closures associated with lower incidence rates in the follow-up period
Change in incidence rate on the 16th, 30th and 60th day post 100th cases between countries ranked by the cases/million population at school closure16th day: r=0.647, p=0.004
30th day: r=0.657, p=0.002
60th day: r=0.510, p=0.031
Krishnamachari et al
33
School closures associated with a mixed effect on transmission:
school closures not statistically significantly associated with cumulative incidence rate in most analyses, but associated with a significant reduction in some analyses
Rate ratio of cumulative incidence between areas that below the median time from state-of-emergency declaration to closure and those above the median time, at days 14, 21, 28, 35 and 42 following the area’s 50th caseUS states:
14 days: 2.27 (95% CI 0.80, 1.70) p=0.42
21 days: 1.38 (95% CI 0.91, 2.10) p=0.13
28 days: 1.52 (95% CI 0.98 to 2.33), p=0.06
35 days: 1.59 (95% CI 1.03 to 2.44), p=0.04
42 days: 1.64 (95% CI 1.07 to 2.52), p=0.02
US 25 most populous cities:
14 days: 1.08 (95% CI 0.75 to 1.55), p=0.68
21 days: 1.22 (95% CI 0.81 to 1.83), p=0.34
28 days: 1.24 (95% CI 0.78 to 1.98), p=0.35
35 days: 1.24 (95% CI 0.75 to 2.05), p=0.40
42 days: 1.16 (95% CI 0.67 to 2.02), p=0.59
Secondary analysis comparing results in cities of low and high population density at 35 days post-50th case in the state. In low-density cities, they report a non-significant trend towards early school closures reducing cumulative incidence rate, in high-density cities they report the opposite—a non-significant trend towards late school closures reducing cumulative incidence rate.
Li et al34School closures associated with reduced transmission:
school closures were associated with a reduction in the COVID-19 incidence rate
Reported the additional benefit of every day that school closures were added to travel and work restrictions, and mass gathering bans17.3 (SD 6.6) percentage point reduction in infection rate
Travel and work restriction and mass gathering bans alone: 59.0 (SD 5.2) residual infection rate ovserved compared with DELPHI predicted no intervention
Travel and work restriction and mass gatherings bans with school closures: 41.7 (SD 4.3)
Li et al35School closures associated with reduced transmission:
school closures associated with a reduction in Rt across the 28 days following closures
Ratio between R while NPI in place, and R on the last day of the previous time period. Reported at 7, 14 and 28 days (as well as visual representation of each individual day to demonstrate trend)Day 7: 0.89 (95% CI 0.82 to 0.97)
Day 14: 0.86 (95% CI 0.72 to 1.02)
Day 28: 0.85 (95% CI 0.66 to 1.10)
Liu et al
36
School closures associated with reduced transmission:
school closures associated with a statistically significant reduction in Rt across analyses
‘Strong’ evidence for NPI effectiveness if statistically significant across multiple parsimonious models varying the follow-up period, the lag time and the classification of the NPI. 'Moderate' evidence if significant in some models; ‘weak' if not
Effect sizes from individual models are a regression coefficient on change in R
‘Strong' evidence of effectiveness for school closures. Effect sizes in individual models between 0.0 and −0.1
Papadopoulos et al
39
School closures not associated with a change in transmission:
school closures not statistically significantly associated with a reduction in the total number of log cases or deaths
Regression coefficient estimating the effect of school closures, and timing of school closures relative to first death, on log total cases and log total deathsUnivariate analysis of school closure policy showed no statistically significant association with log total cases (−0.03 (95% CI −0.256 to 0.218) or log total deaths (−0.025 (95% CI −0.246 to 0.211), p=0.776)
Univariate analysis of timing of school closure was significantly associated with reductions in outcomes, so was considered in multivariate analysis. Multivariate analysis showed found no statistically significant association with log total cases (coefficient −0.006, CIs not reported) or deaths (−0.012 (95% CI −0.024 to 0.00), p=0.050)
Piovani et al
40
School closures associated with reduced transmission:
earlier school closures associated with lower cumulative COVID-19 mortality
Regression coefficient estimating % change in cumulative mortality for every day school closures delayedEvery 1 day delay in school closures was associated with an increase of 4.37% (95% CI 1.58 to 7.17), p=0.002 in cumulative COVID-19 mortality over the study period
Rauscher
42
School closures associated with reduced transmission:
school closures were associated with fewer cases and fewer deaths
Percentage point increase in the number of new cases and deaths for every day school closures were delayed (not clear over what period the outcome measure represents, assumed until end of study period on 27 April 2020Each day a state delayed school closures was associated with 0.3% higher cases (p<0.01) and 1.3% higher mortality (p<0.01)Sensitivity analysis removing the seven states that only recommended school closures, but did not mandate them, did not significantly alter the findings.
Stokes et al
46
School closures associated with mixed effect on transmission:
school closures not statistically significantly associated with a reduction in mortality from 0 to 24 days after the first death, but associated with a reduction in the 14–38 days after
Regression coefficient estimating effect of school closure timeliness and stringency on the daily mortality rate per 1 000 000 population0–24 days:
−0.119 (95% CI −1.744 to 0.398)
14–38 days:
−1.238 (95% CI −2.203 to –0.273)
No observable trend by stringency of school closure measure (recommended vs partial closure vs full closure)
Sensitivity analyses for lab-confirmed COVID-19 versus clinical diagnosis; and for using negative binomial regression analayses did not alter the findings.
Wu et al
47
School closures not associated with transmission:
school closures not statistically significantly associated with R
Output from Bayesian mechanistic model in the format: learnt weight (95% CI) Estimating effect of school closures on RSchool closures not statistically significantly associated with Rt in any of the clusters, or when data are aggregated without clustering
No clusters: 0.047 (95% CI –0.118 to 0.212)
Cluster 1: 0.081 (95% CI –0.246 to 0.408)
Cluster 2: 0.060 (95% CI –0.209 to 0.329)
Cluster 3: 0.112 (95% CI –0.292 to 0.516)
Cluster 4: 0.098 (95% CI –0.194 to 0.390)
Cluster 5: 0.038 (95% CI –0.134 to 0.210)
Yang et al
48
School closures associated with reduced transmission:
school closures and early years settings closures statistically significantly associated with reductions in R
% reduction in RSchool closure associated with 37% reduction in R (95% CI 33% to 40%)
Daycare closures associated with 31% reduction (26%–35%)
Sensitivity analysis using mortality data to derive Reff did not significantly alter findings
Secondary analysis using data from google found that 32% (95% CI 28% to 34%) of the effect of school closures was explained by changes in workplace mobility.
Yehya et al
49
School closures associated with reduced transmission:
earlier school closures were associated with reductions in COVID-19 mortality at 28 days
Regression coefficient estimating increase in mortality at 28 days associated with each day school closures were delayed5% (Mortality Rate Ratio 1.05, 95% CI 1.01 to 1.09)Sensitivity analyses for starting exposure from first COVID-19 death, or for excluding New York/New Jersey from analysis, did not significantly change the findings.
Zeilinger et al
50
School closures associated with reduced transmission:
school closures associated with a reduction in growth rate of COVID-19 cases
Growth rate calculated as the ratio of cumulative cases from 1 day to the next, applying a 7-day moving mean to smooth out weekday effectsSchool closures associated with drop in predicted growth rate between 10 and 40 days after implementation, median drop 0.010 (not clear what this value equates to but relatively large compared with other NPIs)
School closures—within-area before-after comparison studies (n=7)
Gandini et al
23
School (re-)closures not associated with a change in transmission:
reclosing schools not associated with a change in the rate of decline of R
Plotting Rt over time with school reclosure timings noted Analysed the effect of reclosing schools on Rt, which was done proactively before national lockdown in two large provincesLombardy and Campania closed schools before the national school closures in November. In both cases, they find that Rt started to decline around 2 weeks before school closures, and the rate of decline did not change after school closuresMitigation measures in place in reopened schools included: temperature checks, hand hygiene, increased cleaning and ventilation, one-way systems, mask mandates, social distancing and bans on school sports/music.
Iwata et al
29
School closures not associated with a change in transmission:
school closures not statistically associated with the incidence rate of new cases
Time series analysis coefficient estimating effect of school closures on the change in daily incidence rate0.08 (95% CI −0.36 to 0.65)Sensitivity analysis for different lag times did not change the general finding of null effect.
Matzinger and Skinner
37
School closures associated with reduced transmission:
school closures were associated with reductions in the doubling time of new COVID-19 cases, hospitalisations and deaths
Changes to the doubling time of the epidemic in each state, following school closuresGeorgia: 7 days after school closures the doubling time slowed from 2.1 to 3.4 days
Tennessee: 8 days after school closures the doubling time slowed from 2 to 4.2 days
Mississippi: 10–14 days after school closures the doubling time slowed from 1.4 to 3.5 days
Only included Georgia, Tennessee and Mississippi in their explicit analysis of school closure effect because these were the only states where the authors felt there was a long enough gap between implementation of school closures and other NPI measures. However, they show several figures of other states that initiated school closures at the same time as other lockdown measures. In these states (Arizona, Florida, Ilinois, Maryland, Massachussetts, New Jersey, New York and Texas), a similar pattern is observed for doubling time of cases, with time lags varying between 1 and 2 weeks. Patterns appeared to be similar for hospitalisations and deaths, although these data were not always reported, and more difficult to interpret.
Neidhofer and Neidhofer
38
School closures associated with reduced transmission:
school closures were associated with reductions in COVID-19 mortality
% Reduction in deaths in the 18 days postschool closure, compared with synthetic control unitArgentina: 63%–90% reduction, Italy: 21%–35% reduction, South Korea: 72%–96% reduction in daily average COVID-19 deaths over the 18 days following school closures, compared with the counterfactualSensitivity analysis using only excess mortality in Italy reached similar conclusion
Selected Argentina, Italy and South Korea because they closed schools at a different time to enacting national lockdown. Supplementary analysis of: Switzerland, Germany, the Netherlands, Indonesia, Canada, Brazil, France, UK, Spain, where school closure was implemented relatively later, and alongside other NPIs:
  • large (protective) effect in Switzerland, the Netherlands, Indonesia and Canada;

  • no effect of closures in Germany, Brazil, France and Spain;

  • large (harmful) effect in the UK.

Shah et al
53
School closures associated with mixed effect on transmission:
in Italy, school closures were associate with a reduction in mortality. In the other four countries no aassociation was found between school closures and mortality
Regression coefficient for effect of school closures on mortality (not explained in any greater detail)Italy 0.81 (95% CI 0.68 to 0.97)
Reported only as ‘no association’ for other countries
Sruthi et al
43
School closures associated with reduced transmission:
secondary school closure was associated with a reduction in Rt
Changes to time-varying reproductive number R, estimated from data on new cases. Assumed to be in an infectious state for 14 days from diagnosisSecondary school closures associated with an average reduction of Rt around 1.0
Stage et al
44
School closures associated with reduced transmission:
school closures associated with reductions in the growth rate of new cases
% reduction in growth rate of new cases (Germany only—in Denmark and Norway the graph is drawn without formal statistical analysis)26%–65% reduction in growth rate of cases across the different states of Germany. No quantitative estimate for Norway or Denmark but authors report a ‘clear drop’ in new cases after school closures
School closures—pooled multiple-area comparisons of interventions in place at a fixed time point (n=3)
Juni et al
31
School closures associated with reduced transmission:
school closures were statistically significantly associated with a relative reduction in the incidence rate of COVID-19
Regression coefficient estimating effect of school closures on changes to the incidence rateAdjusted model:
0.77 (95% CI 0.63 to 0.93), p=0.009
Sensitivity analyses of seperating out high income countries did not significantly effect the results.
Walach and Hockertz
52
School closures associated with increased transmission:
school closures associated with an increase in COVID-19 mortality
Regression coefficient estimating effect of school closures on the COVID-19 mortality rateCases: school closures not associated with cases in univariate analysis so not considered for modelling
Mortality: 2.54 (95% 1.24 to 3.85), p<0.0001
Wong et al
51
School closures associated with reduced transmission:
school closures were associated with a smaller rate of increase in cumulative incidence of COVID-19
Regression coefficient estimating effect of school closures on the rate of increase in cumulative incidence−0.53 (95% CI −1.00 to –0.06), p=0.027Report no collinearity or interactions between different covariables in the model.
School reopening studies (n=11)
Beesley
16
School reopenings associated with a mixed effect on transmission:
school reopening was associated with increases in the 7-day rolling average of new cases in most countries, but not all
Change in 7-day rolling average of new casesChina saw no change. Austria, Canada, France, Germany, Israel, Japan, the Netherlands, Singapore, Spain, Switzerland and the UK saw increases after 24–47 days; with longer lag times attributed to these countries opening schools in a limited to staggered wayPrimary versus secondary: in the Netherlands, it was noted that the rise in cases 24 days after primary schools opened was much smaller than the rise 40 days after secondary schools reopened.
Ehrhardt et al
22
School reopenings not associated with a change in transmission:
school reopenings not associated with any change in the rate of new cases
Presentation of an epidemic curve showing daily confirmed new cases, with school reopening date labelledDaily new cases peaked at 1400/day and dropped to around 100/day at the time of staggered school reopening. Daily new cases remained at, or generally below, this level throughout the following 3 months until after schools broke up for summer holidaysRange of comprehensive infection prevention and control measures were in place in schools at the time of school reopening.
Gandini et al
23
School reopenings not associated with a change in transmission:
timing of school reopenings not consistently associated with onset of increases in R
Plotting R over time with school reopening timings noted. Pairing geographically neighbouring and socioeconomically similar provinces who reopened schools at different times. Comparing time between school reopening and subsequent increases in R—measured as the start of 3 consecutive weeks of increasing RBolzano opened schools a week earlier than Trento, but Trento saw a sustained rise in R 1 week ealier than Bolzano. In Abruzzo and Marche; Sicily and Calabria; and Veneto and Apulia; one province reopened schools a week before the other, but Rt increases occured at the same timeMitigation measures in place in reopened schools included: temperature checks, hand hygiene, increased cleaning and ventilation, one-way systems, mask mandates, social distancing and bans on school sports/music.
Garchitorena et al
24
School reopenings not associated with a change in transmission:
partial relaxations of school closure measures associated with a null effect on COVID-19 transmission
Ratio of transmission rates with and without implementation of the NPI (assessed over the duration of the NPI being in place) Presented as a forest plot so the reported results here are estimatedEY settings: 0%
(95% CI −8% to 8%)
Primary schools: 2%
(95% CI −7% to 10%)
Secondary schools: 1%
(95% CI −7% to 9%)
Harris et al
25
School reopenings not associated with a change in transmission:
school reopenings not statistically significantly associated with an increase in COVID-19 hospitalisation rate
Regression coefficient reported for both hospitalisations per 100 000 population, and log total hospitalisationsHospitalisations per 100 000 population:
0.295 (95% CI −0.072 to 0.662)
Log total hospitalisations:
−0.019 (95% CI −0.074 to 0.036)
Post hoc stratified analysis showed a statistically significant increase in hospitalisations for those counties in the top 25% of hospitalisation preschool reopenings, but no effects for those <75th centile.
Ingelbeen et al
27
School reopenings associated with increased transmission:
R increased after schools were reopened
Plotted R compared against the changes to the NPIs in place during the study periodR started to increase from approximately 1 week before schools reopened (from 0.9 to 1 at reopening), and then increase more sharply to 1.5 over the next fortnightAlso used the national contact tracing data to examine age-specific trends in number of contacts per case, and number of transmission events between age groups. The increase in Rt after school reopening did not appear to be driven by school-aged children, but by general increases in social mixing across all age groups.
Isphording et al
28
School reopenings not associated with a change in transmission:
school reopenings not statistically significantly associated with a change in rate of new COVID-19 cases
Regression coefficient estimating change in number of new cases per 100 000 in the 3 weeks postschool reopeningsReduction of 0.55 cases per 100 000 associated with first 3 weeks of reopening schools. CIs reported only graphically, but upper estimate just crosses 0 (ie, reopening schools led to non-sginificant reduction in transmission of COVID-19)Sensitivity analysis showed this to be true for all age groups. West German counties drove the non-significant reduction in transmission associated with reopening of schools, while in East Germany the rate of new cases remained constant.
Li et al
35
School reopenings associated with increased transmission:
school reopenings associated with an increase in Rt across the 28 days following reopening
Ratio between R while NPI in place, and R on the last day of the previous time period. Reported at 7, 14 and 28 days (as well as visual representation of each individual day to demonstrate trend)Day 7: 1.05 (95% CI 0.96 to 1.14)
Day 14: 1.18 (95% CI 1.02 to 1.36)
Day 28: 1.24 (95% CI 1.00 to 1.52)
Sruthi et al
43
School reopenings associated with mixed effect on transmission:
secondary school reopening not associated with increase in Rt if mask mandates in place within schools
Changes to time-varying reproductive number R, estimated from data on new cases. Assumed to be in an infectious state for 14 days from diagnosisSecondary schools reopened with mask mandates in place associated with no change in the R, compared with secondary schools being closed
Secondary schools reopened without mask mandates in place associated with an approximate 1.0 increase in R
Stein-Zamir et al
45
School reopenings associated with increased transmission:
school reopenings were associated with an increase in new cases of COVID-19
Presentation of an age-stratified epidemic curve showing confirmed cases of COVID-19 in Jerusalem, by date, and comparing to dates of school closure/reopeningDifficult to elicit exact effect sizes from the epidemic curve, but approximately 2 weeks after schools started to reopen, the number of new cases started to increaseIncreases in cases after school reopening was more pronounced in younger age groups,10–19 but were also seen across all ages to a lesser extent.
Stage et al
44
School reopenings not associated with transmission:
school reopening not associated with increases in the growth rate of hospitalisations or cases
Changes to the incidence rate and changes to instantaneous growth rate in hospitalisations (Denmark) and cases (Denmark, Germany and Norway)In Germany, the growth rate of cases remained stable throughout and after the staggered reopening of schools. In Denmark and Norway, the growth rate of cases (and hospitalisations for Denmark) remained stable and negative, meaning that incidence continued to reduce despite school reopening
School holiday studies (n=3)
Beesley
16
School holidays associated with a mixed effect on transmission:
school holidays were associated with increases in the 7-day rolling average of new cases in most countries, but not all
Change in 7-day rolling average of new casesIn Austria, France, Germany and Switzerland, it was noted that school holidays ‘exacerbated’ the resurgence in incidence rate (not commented on for other countries)
Sweden saw a reduction in the rolling average 23 days after they closed for summer holidays (the rolling average peaked within that 23-day period)
Bjork et al
17
School holidays associated with increased transmission:
timing of a school winter holiday during the exposure period was positively associated with all-cause excess mortality
All-cause weekly excess mortality per million residents, between 30 March 2020 and 7 June 2020 compared with 2015–2019 mortality rates, compared with regions with no winter holiday or a holiday in the week before the exposure periodWinter holiday in weeks 7, 8, 9 and 10 associated with weekly excess mortality of 13.4 (95% CI 9.7 to 17.0), 5.9 (95% CI 2.3 to 9.5), 13.1 (95% CI 9.7 to 16.5) and 6.2 (95% CI 1.0 to 11.4) per million residents, respectivelyThe comparator group included those holidaying in week 6 or not at all, and was itself associated with excess mortality of 8.6 (95% CI 6.9 to 10.3).
Pluemper and Neumayer
41
School holidays associated with increased transmission:
school holidays associated with increases in the incident growth rate
Percentage point increase in the incident growth rate associated with each week of the summer holidayEach week of summer school holidays increased the incident growth rate by an average of 0.72 percentage points (95% 0.41 to 1.03). The effect of individual weeks increased during the holidays, such that the first 3 weeks were not indpendently statistically significant, but the sixth week of holidays was associated with an average 1.91 (95% CI 1.47 to 2.42) percentage points increase, which accounts for 49% of the national average growth rate that weekLarger effect sizes for richer regions, and regions with more foreigners, suggesting these regions had a higher proportion of travellers going abroad (the baseline rate in Germany was low at the start of the summer holidays).
  • CrI, credible interval; NPI, non-pharmaceutical intervention.