JAMA | OriginalInvestigation … · 2 days ago · T henovelsevereacuterespiratorysyndromecoronavi-rus2(SARS-CoV-2)causingcoronavirusdisease2019 (COVID-19)wasfirstidentifiedintheUSinJanuary2020

Association Between Statewide School Closure and COVID-19 Incidenceand Mortality in the USKatherine A. Auger, MD, MSc; Samir S. Shah, MD, MSCE; Troy Richardson, PhD; David Hartley, PhD, MPH;Matthew Hall, PhD; Amanda Warniment, MD; Kristen Timmons, MS; Dianna Bosse, BA; Sarah A. Ferris, BA;Patrick W. Brady, MD, MSc; Amanda C. Schondelmeyer, MD, MSc; Joanna E. Thomson, MD, MPH

IMPORTANCE In the US, states enacted nonpharmaceutical interventions, including schoolclosure, to reduce the spread of coronavirus disease 2019 (COVID-19). All 50 states closedschools in March 2020 despite uncertainty if school closure would be effective.

OBJECTIVE To determine if school closure and its timing were associated with decreasedCOVID-19 incidence and mortality.

DESIGN, SETTING, AND PARTICIPANTS US population–based observational study conductedbetween March 9, 2020, and May 7, 2020, using interrupted time series analysesincorporating a lag period to allow for potential policy-associated changes to occur. To isolatethe association of school closure with outcomes, state-level nonpharmaceutical interventionsand attributes were included in negative binomial regression models. States were examinedin quartiles based on state-level COVID-19 cumulative incidence per 100 000 residents at thetime of school closure. Models were used to derive the estimated absolute differencesbetween schools that closed and schools that remained open as well as the number of casesand deaths if states had closed schools when the cumulative incidence of COVID-19 was in thelowest quartile compared with the highest quartile.

EXPOSURES Closure of primary and secondary schools.

MAIN OUTCOMES AND MEASURES COVID-19 daily incidence and mortality per 100 000 residents.

RESULTS COVID-19 cumulative incidence in states at the time of school closure ranged from 0to 14.75 cases per 100 000 population. School closure was associated with a significantdecline in the incidence of COVID-19 (adjusted relative change per week, −62% [95% CI,−71% to −49%]) and mortality (adjusted relative change per week, −58% [95% CI, −68% to−46%]). Both of these associations were largest in states with low cumulative incidence ofCOVID-19 at the time of school closure. For example, states with the lowest incidence ofCOVID-19 had a −72% (95% CI, −79% to −62%) relative change in incidence compared with−49% (95% CI, −62% to −33%) for those states with the highest cumulative incidence. In amodel derived from this analysis, it was estimated that closing schools when the cumulativeincidence of COVID-19 was in the lowest quartile compared with the highest quartile wasassociated with 128.7 fewer cases per 100 000 population over 26 days and with 1.5 fewerdeaths per 100 000 population over 16 days.

CONCLUSIONS AND RELEVANCE Between March 9, 2020, and May 7, 2020, school closure inthe US was temporally associated with decreased COVID-19 incidence and mortality; statesthat closed schools earlier, when cumulative incidence of COVID-19 was low, had the largestrelative reduction in incidence and mortality. However, it remains possible that some of thereduction may have been related to other concurrent nonpharmaceutical interventions.

JAMA. 2020;324(9):859-870. doi:10.1001/jama.2020.14348Published online July 29, 2020.

Viewpoint page 833 andEditorial page 845

Author Audio Interview

Video and Supplementalcontent

Author Affiliations: Authoraffiliations are listed at the end of thisarticle.

Corresponding Author: Katherine A.Auger, MD, MSc, Division of HospitalMedicine, Cincinnati Children’sHospital Medical Center, 3333 BurnetAve, ML 9016, Cincinnati, OH 45229([email protected]).

Research

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T he novel severe acute respiratory syndrome coronavi-rus 2 (SARS-CoV-2) causing coronavirus disease 2019(COVID-19) was first identified in the US in January 2020,

with subsequent spread throughout the country. In the ab-sence of effective treatments, governors and state health of-ficials enacted policies aimed at reducing infections throughnonpharmaceutical interventions.1 The nonpharmaceutical in-terventions included: school closure, nonessential business clo-sure, restaurant and bar closure, and prohibition of gather-ings with more than 10 people. With limited precedent and apaucity of evidence on the effectiveness of nonpharmaceuti-cal interventions, policies varied markedly state to state inscope and timing.

Children infected with SARS-CoV-2 may be asymptom-atic or have mild symptoms indistinguishable from other com-mon upper respiratory tract infections,2,3 allowing them tospread the virus when they feel well. Children are often keytransmitters in viral epidemics like influenza4 because ofspending prolonged periods in close proximity to other chil-dren at school and during physical activities. Prior studies havedemonstrated an association between school closure and re-duced transmission of viral respiratory illnesses.5-8 Given con-cerns that children represented a significant vector for SARS-CoV-2, all states closed schools despite a lack of evidencesupporting the effectiveness of school closure in curbing thespread of this virus.

Schools promote child education, growth, development,and overall well-being.9 Knowing whether school closure is ef-fective in reducing infections is critical to reduce the nega-tive effects of continued school closure on child health if schoolclosure is ineffective. This national study assessed the asso-ciation between school closure and its timing with subse-quent COVID-19 incidence and mortality, with the hypoth-esis that any association between school closure and incidenceand mortality would be strongest in states that closed schoolsearly when the cumulative incidence of disease was low.

MethodsThe study was a population-based time series analysis of all50 US states conducted between March 9, 2020, and May 7,2020. This period allowed for at least 6 weeks of data collec-tion after school closures in each state. The institutional re-view board at Cincinnati Children’s Hospital Medical Centerdeemed this study was not subject to oversight given the useof publicly available data.

Independent VariablesAssociations of primary and secondary school closure (kin-dergarten-grade 12) and its timing with outcomes of interestwere examined. Because school closure timing varied rela-tive to disease progression in the state, we examined the cu-mulative incidence of COVID-19 (defined as total number ofcases per 100 000 residents) grouped in quartiles by the datethe school closure policy went into effect.

The analysis was performed by quartiles of cumulative in-cidence of COVID-19 instead of as a continuous variable be-

cause the relationships between baseline cumulative inci-dence and outcomes were not assumed to be linear.

Outcome MeasuresDaily COVID-19 incidence and daily mortality per 100 000 resi-dents in each state were estimated using publicly available datafrom the Johns Hopkins University School of Public Health.10

This source aggregates data from the US Centers for DiseaseControl and Prevention (CDC) as well as from state and localpublic health departments. In accordance with CDC guide-lines, confirmed COVID-19 cases include presumptive posi-tive cases and probable cases, and death totals include con-firmed and probable cases. The denominator for the outcomemeasures was the state population from the 2018 AmericanCommunity Survey.11

CovariatesState characteristics were included as covariates in the mod-els to assess the independent associations with school clo-sure. For each state, the following non–school-related non-pharmaceutical intervention covariates were considered: stay-at-home or shelter-in-place order, nonessential businessclosure, restaurant and bar closure, and prohibition of gath-erings with more than 10 people. These nonpharmaceutical in-terventions were included based on the policy effective date(eTable 1 in the Supplement) plus a lag period to allow for anypotential policy-related effects on daily COVID-19 incidenceand mortality (eFigure in the Supplement). Potential effectsassociated with subsequent lifting of nonpharmaceutical in-terventions occurred outside the study period and thus are notincluded (eMethods in the Supplement).

SARS-CoV-2 testing rates varied by state and throughout thestudy period. To account for this variation, state-level COVID-19testing12 (calculated daily as the cumulative number of tests per1000 residents) was modeled as a categorical variable. Statemeasures of urban population density (a measure of the state’spopulation density combined with the percentage of residentsliving in urban areas),11 percentage of the state’s population with

Key PointsQuestion Was statewide school closure associated withdecreased incidence and mortality for coronavirus disease 2019(COVID-19)?

Findings In this US population–based time series analysisconducted between March 9, 2020, and May 7, 2020, schoolclosure was associated with a significant decline in both incidenceof COVID-19 (adjusted relative change per week, −62%) andmortality (adjusted relative change per week, −58%). In a modelderived from this analysis, it was estimated that closing schoolswhen the cumulative incidence of COVID-19 was in the lowestquartile compared with the highest quartile was associated with128.7 fewer cases per 100 000 population over 26 days and with1.5 fewer deaths per 100 000 population over 16 days.

Meaning There was a temporal association between statewideschool closure and lower COVID-19 incidence and mortality,although some of the reductions may have been related to otherconcurrent nonpharmaceutical interventions.

Research Original Investigation Association Between US Statewide School Closure and COVID-19 Incidence and Mortality in the US

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obesity,13 percentage of the state’s population aged 15 years oryounger,11 percentage of the state’s population aged 65 years orolder,11 and the number of nursing home residents per 1000people were included.14 The CDC social vulnerability index alsowas included; this index accounts for multiple factors, includ-ing socioeconomic status, household composition, disability sta-tus, race and ethnicity composition, English-language profi-ciency, housing type, and transportation access, to assess acommunity’s preparedness for a natural disaster or illnessoutbreak.15 Many of these factors have been associated withCOVID-19 disease, mortality, or both. All covariates are de-scribed in detail in the eMethods in the Supplement.

Statistical AnalysisInterrupted time series analyses were used to compare the dailychange in outcomes (daily COVID-19 incidence and mortal-ity) before and after school closure. Acknowledging that schoolclosure and other nonpharmaceutical interventions would nothave immediate effects on COVID-19 incidence and mortal-ity, estimates were used to determine when school-based ex-posure could be expected to lead to changes in COVID-19 in-cidence and associated mortality (eFigure in the Supplement).A time from exposure to symptom onset of 5 days was as-sumed per Lauer et al.16 Given the early emphasis (and somestate restrictions17-25) on limiting testing to hospitalized pa-tients, time to diagnosis was defined as time between symp-tom onset and hospitalization (7 days).26 For school closure,given the low documented prevalence of COVID-19 in chil-dren, an additional period was included for a child to infect anadult, assuming a child exposed at school could expose an adultprior to symptom onset and within 4 days. The analyses forthe mortality outcome assumed 17 days from symptom onsetto death.27 For details on lag period calculations, see theeMethods in the Supplement.

Daily COVID-19 incidence and mortality were modeledusing negative binomial regression. Interactions betweenschool closure and all included covariates were explored be-cause school closure may affect at-risk communitiesdifferently.9 Given the large number of covariates and inter-actions considered, a single parsimonious model for each out-come was created selecting covariates from the primary modelusing a stepwise regression approach, with entry and re-moval criteria specified as a P value of <.20. Because factorsassociated with COVID-19 incidence and mortality may varywith school closure, covariate selection was completed inde-pendently during and after the lag period (eMethods in theSupplement).

Results are reported as the relative change in the out-come from week to week. Adjusted changes in both dailyCOVID-19 incidence and daily mortality over time are graphi-cally displayed for all states by quartile of cumulative inci-dence at the time of school closure. To estimate absolute dif-ferences associated with school closure, the projected COVID-19incidence and mortality if schools had remained open werecompared with the modeled incidence and mortality withschool closure. Both linear and exponential assumptions wereused to project COVID-19 incidence and mortality if schoolshad remained open (eMethods in the Supplement). To esti-

mate absolute differences in outcomes based on school clo-sure timing, model parameters were used to estimate the ab-solute differences in the number of COVID-19 cases and deathsfor a state that closed schools when the cumulative incidenceof COVID-19 was in the lowest quartile compared with a statethat closed schools when the cumulative incidence of COVID-19was in the highest quartile (eMethods in the Supplement).

Analyses were performed using SAS version 9.4 (SAS In-stitute Inc) and 2-sided P values of <.05 were considered sta-tistically significant.

Sensitivity Analyses Around the Lag PeriodBecause the COVID-19 incidence and mortality lag period es-timates are based on emerging evidence, the sensitivity analy-ses examined the robustness of findings if the lag period wasshorter (10 days for incidence and 20 days for mortality) or lon-ger (21 days for incidence and 33 days for mortality). Detailedmethods and rationale for the sensitivity ranges appear in theeMethods and eFigure in the Supplement.

ResultsAll 50 states closed schools between March 13, 2020, and March23, 2020. The cumulative incidence of COVID-19 at the time ofschool closure ranged from 0 to 14.75 cases per 100 000 popu-lation. State characteristics by COVID-19 incidence quartile atthe time of school closure appear in Table 1. There was wide vari-ability in the testing rate per 1000 residents and in the numberof nursing home residents per 1000 people. There was less vari-ability in the percentage of the state populations for the num-ber of nonpharmaceutical interventions enacted; 39 states en-acted all 4 nonpharmaceutical interventions examined.

States in the highest quartile of cumulative incidence ofCOVID-19 at the time of school closure enacted multiple non-pharmaceutical interventions over a shorter period. The me-dian time from school closure to the last enacted nonpharma-ceutical intervention was 5 days (interquartile range, 2.5-8days). In comparison, states in the lowest quartile of cumula-tive incidence of COVID-19 at the time of school closure en-acted nonpharmaceutical interventions over a longer period.The median time from school closure to the last enacted non-pharmaceutical intervention was 12 days (interquartile range,8-14 days; Table 1).

COVID-19 IncidenceThe observed case rates of COVID-19 in each state (relative tothe day of school closure by cumulative incidence) and the 16-day lag period are depicted in Figure 1A. In the unadjustedanalyses during the period prior to potential effects of schoolclosure (ie, during the lag period), the overall relative changein COVID-19 incidence per week was 220% (95% CI, 205% to236%). The unadjusted relative change per week associatedwith school closure was −68% (95% CI, −70% to −66%). Theunadjusted effect size associated with school closure variedby cumulative COVID-19 incidence at the time of school clo-sure, with states in the highest quartile of cumulative COVID-19incidence having the smallest relative effect size (Table 2).

Association Between US Statewide School Closure and COVID-19 Incidence and Mortality in the US Original Investigation Research

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55.6

17.5

15.1

5.2

27.6

>150

54.5

45

Colo

rado

12.7

323

16.5

1913

.12.

923

<50

334

3

Was

hing

ton

14.7

517

30.8

18.7

14.5

2.2

28.7

50-1

0041

.94

8

Abbr

evia

tions

:IQ

R,in

terq

uart

ilera

nge;

NPI

s,no

npha

rmac

eutic

alin

terv

entio

ns.

aCa

lcul

ated

daily

bydi

vidi

ngth

ecu

mul

ativ

enu

mbe

roft

ests

perf

orm

edin

ast

ate

byth

est

ate

popu

latio

n;m

odel

edas

aca

tego

rical

varia

ble.

The

min

imum

test

ing

rate

was

less

than

0.1t

ests

per1

00

0re

siden

tsan

doc

curr

edon

the

first

day

ofte

stin

gfo

reve

ryst

ate.

The

max

imum

test

ing

rate

was

the

test

ing

rate

onth

ela

stda

yof

the

stud

ype

riod.

bD

efin

edas

abo

dym

assi

ndex

(cal

cula

ted

asw

eigh

tin

kilo

gram

sdiv

ided

byhe

ight

inm

eter

ssqu

ared

)of3

0or

grea

ter.

cA

mea

sure

ofth

est

ate’

spop

ulat

ion

dens

ityco

mbi

ned

with

the

perc

enta

geof

resid

ents

livin

gin

urba

nar

eas;

exam

ined

asa

cate

goric

alva

riabl

e.

dU

SCe

nter

sfor

Dise

ase

Cont

rola

ndPr

even

tion

mea

sure

ofa

com

mun

ity’s

prep

ared

ness

fora

natu

rald

isast

eror

illne

ssou

tbre

akby

acco

untin

gfo

rsoc

ioec

onom

icst

atus

,hou

seho

ldco

mpo

sitio

n,di

sabi

lity

stat

us,r

ace

and

ethn

icity

com

posit

ion,

Engl

ish-la

ngua

gepr

ofic

ienc

y,ho

usin

gty

pe,a

ndtr

ansp

orta

tion

acce

ss.S

cale

is0

to10

0w

ithlo

wer

num

bers

indi

catin

gbe

tter

prep

ared

ness

fora

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isast

eror

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ak;t

hena

tiona

lmed

ian

is50

.e

Rang

edbe

twee

n1a

nd4.

The

NPI

sexa

min

edin

clud

edst

ay-a

t-ho

me

orsh

elte

r-in-

plac

eor

der,

none

ssen

tial

busin

essc

losu

re,r

esta

uran

tand

barc

losu

re,a

ndpr

ohib

ition

ofga

ther

ings

with

mor

eth

an10

peop

le.A

llsta

tes

enac

ted

atle

ast1

non–

scho

ol-r

elat

edN

PI.






Figure 1. Observed Daily COVID-19 Incidence and Mortality by Cumulative Incidence Quartile at the Time of School Closure

Daily incidence by quartile of cumulative incidence at the time of school closureA

40

30

20

10

Daily

CO

VID-

19 in

cide

nce

per 1

00 0

00

Time from school closure, d

End of lag period

–10 0 10 20 30 40 50

Lowest quartile

40

30

20

10

Daily

CO

VID-

19 in

cide

nce

per 1

00 0

00


End of lag period

–10 0 10 20 30 40 50

Second lowest quartile

40

30

20

10

Daily

CO

VID-

19 in

cide

nce

per 1

00 0

00


End of lag period

–10 0 10 20 30 40 50

Second highest quartile

40

30

20

10

0 0

0 0

Daily

CO

VID-

19 in

cide

nce

per 1

00 0

00


End of lag period

–10 0 10 20 30 40 50

Highest quartile

Daily mortality rate by quartile of cumulative incidence at the time of school closureB

5

4

2

1Daily

CO

VID-

19 d

eath

spe

r 100

000


End of lag period

–10 0 10 20 30 40 50

Lowest quartile

5

4

2

1Daily

CO

VID-

19 d

eath

spe

r 100

000


End of lag period

–10 0 10 20 30 40 50

Second lowest quartile

5

4

3

2

Daily

CO

VID-

19 d

eath

spe

r 100

000


End of lag period

–10 0 10 20 30 40 50

Second highest quartile

5

4

3

2

3 3

1 1

0 0

0 0

Daily

CO

VID-

19 d

eath

spe

r 100

000


End of lag period

–10 0 10 20 30 40 50

Highest quartile

COVID-19 indicates coronavirus disease 2019.






Tabl

e2.

COVI

D-19

Inci

denc

ean

dM

orta

lity

and

Effe

ctSi

zeAs

soci

ated

With

Scho

olCl

osur

e

Mod

elAl

lSta

tes

COVI

D-19

cum

ulat

ive

inci

denc

equ

artil

eat

the

time

ofsc

hool

clos

ure

Low

est

Seco

ndlo

wes

tSe

cond

high

est

Hig

hest

COVI

D-19

inci

denc

e

Perio

dbe

fore

scho

olcl

osur

e(d

urin

gla

gpe

riod:

days

1-16

)

Ove

rall

new

case

sper

100

000

resi

dent

s,N

o.71

2829

3915

1

New

case

sper

100

000

resi

dent

sper

stat

e,m

edia

n(I

QR)

19(1

7to

52)

16(1

5to

19)

21(1

8to

34)

30(1

7to

45)

73(5

1to

107)

Unad

just

edre

lativ

ech

ange

perw

eek,

%(9

5%CI

)22

0(2

05to

236)

324

(284

to36

9)25

9(2

25to

296)

195

(170

to22

3)13

5(1

14to

157)

Com

posi

tea

adju

sted

bre

lativ

ech

ange

perw

eek,

%(9

5%CI

)26

5(2

31to

303)

387

(327

to45

6)33

9(2

85to

402)

240

(200

to28

6)14

3(1

17to

173)

Perio

daf

ters

choo

lclo

sure

(aft

erla

gpe

riod:

days

17-4

2)

Ove

rall

new

case

sper

100

000

resi

dent

s,N

o.23

815

716

018

338

7

New

case

sper

100

000

resi

dent

sper

stat

e,m

edia

n(I

QR)

116

(79

to21

7)79

(69

to12

2)12

4(8

4to

257)

126

(110

to19

8)12

1(9

0to

764)

Com

posi

tec

adju

sted

bre

lativ

ech

ange

perw

eek,

%(9

5%CI

)10

(1to

18)

7(–

2to

18)

16(5

to28

)20

(9to

32)

–4(–

12to

5)

Rela

tive

chan

gepe

rwee

kas

soci

ated

with

scho

olcl

osur

e,%

(95%

CI)

Unad

just

ed–6

8(–

70to

–66)

–77

(–80

to–7

3)–6

9(–

74to

–65)

–62

(–67

to–5

6)–6

1(–

66to

–55)

Adju

sted

b–6

2(–

71to

–49)

–72

(–79

to–6

2)–6

6(–

75to

–54)

–55

(–66

to–3

9)–4

9(–

62to

–33)

COVI

D-19

mor

talit

y

Perio

dbe

fore

scho

olcl

osur

e(d

urin

gla

gpe

riod:

days

1-26

)

Ove

rall

new

deat

hspe

r100

000

resi

dent

s,N

o.7

33

314

New

deat

hspe

r100

000

resi

dent

sper

stat

e,m

edia

n(I

QR)

2(1

to4)

1(1

to2)

2(1

to3)

2(1

to3)

6(2

to10

)

Unad

just

edre

lativ

ech

ange

perw

eek,

%(9

5%CI

)17

1(1

60to

184)

212

(184

to24

3)18

2(1

57to

210)

169

(148

to19

2)12

9(1

13to

146)

Com

posi

tea

adju

sted

dre

lativ

ech

ange

perw

eek,

%(9

5%CI

)18

6(1

75to

197)

243

(212

to27

7)22

4(1

95to

258)

172

(152

to19

5)12

0(1

05to

136)

Perio

daf

ters

choo

lclo

sure

time

(aft

erla

gpe

riod:

days

27-4

2)

Ove

rall

new

deat

hspe

r100

000

resi

dent

s,N

o.9

76

715

New

deat

hspe

r100

000

resi

dent

sper

stat

e,m

edia

n(I

QR)

3(2

to9)

3(2

to4)

3(2

to11

)4

(2to

5)4

(2to

27)

Com

posi

tec

adju

sted

dre

lativ

ech

ange

perw

eek,

%(9

5%CI

)2

(–8

to14

)6

(–7

to20

)10

(–4

to25

)7

(–6

to22

)–1

2(–

21to

–1)

Rela

tive

chan

gepe

rwee

kas

soci

ated

with

scho

olcl

osur

e,%

(95%

CI)

Unad

just

ed–6

4(–

67to

–61)

–69

(–73

to–6

4)–6

3(–

68to

–57)

–63

(–68

to–5

7)–6

1(–

66to

–56)

Adju

sted

d–5

8(–

67to

–46)

–64

(–73

to–5

2)–6

1(–

71to

–47)

–54

(–65

to–3

9)–5

3(–

63to

–40)

Abbr

evia

tions

:CO

VID

-19,c

oron

aviru

sdise

ase

2019

;IQ

R,in

terq

uart

ilera

nge.

aIn

dica

test

heov

eral

lincr

ease

.The

com

pone

ntso

feac

hco

varia

teat

trib

utin

gto

the

over

allc

hang

ebe

fore

scho

olcl

osur

ear

epr

esen

ted

ineT

able

2(in

cide

nce)

and

eTab

le5

(mor

talit

y)in

the

Supp

lem

ent.

bAd

just

edfo

rallm

odel

com

pone

ntsr

etai

ned

inin

cide

nce

mod

el(in

terc

ept:

perc

enta

geof

stat

e’sp

opul

atio

nag

ed�

15ye

ars,

perc

enta

geof

stat

e’sp

opul

atio

nag

ed�

65ye

ars,

and

US

Cent

ersf

orD

iseas

eCo

ntro

land

Prev

entio

n(C

DC)

soci

alvu

lner

abili

tyin

dex;

befo

resc

hool

clos

ure:

stay

-at-

hom

eor

shel

ter-i

n-pl

ace

orde

r,re

stau

rant

and

barc

losu

re,t

estin

gra

tepe

r10

00

resid

ents

,and

urba

nde

nsity

;aft

ersc

hool

clos

ure:

test

ing

rate

per1

00

0re

siden

ts,s

tay-

at-h

ome

orsh

elte

r-in-

plac

eor

der,

perc

enta

geof

stat

e’sp

opul

atio

nag

ed�

65ye

ars,

num

bero

fnur

sing

hom

ere

siden

tspe

r10

00

peop

le,a

ndur

ban

dens

ity).

cBa

sed

onth

elin

earc

ombi

natio

nof

the

follo

win

gm

odel

para

met

eres

timat

es:r

elat

ive

chan

gebe

fore

scho

olcl

osur

e,th

eef

fect

asso

ciat

edw

ithsc

hool

clos

ure,

and

allo

ther

non–

scho

olcl

osur

eef

fect

sove

rtim

e.d

Adju

sted

fora

llmod

elco

mpo

nent

sret

aine

din

mor

talit

ym

odel

(inte

rcep

t:pe

rcen

tage

ofst

ate’

spop

ulat

ion

aged

�15

year

s,pe

rcen

tage

ofst

ate’

spop

ulat

ion

aged

�65

year

s,an

dCD

Cso

cial

vuln

erab

ility

inde

x;be

fore

scho

olcl

osur

e:st

ay-a

t-ho

me

orsh

elte

r-in-

plac

eor

der,

proh

ibiti

onof

gath

erin

gsw

ith>1

0pe

ople

,res

taur

anta

ndba

rcl

osur

e,pe

rcen

tage

ofst

ate’

spop

ulat

ion

aged

�15

year

s,pe

rcen

tage

ofst

ate’

spop

ulat

ion

aged

�65

year

s,nu

mbe

rofn

ursin

gho

me

resid

ents

per1

00

0pe

ople

,and

urba

nde

nsity

;aft

ersc

hool

clos

ure:

rest

aura

ntan

dba

rcl

osur

e,nu

mbe

rofn

ursin

gho

me

resid

ents

per1

00

0pe

ople

,and

urba

nde

nsity

).






In the adjusted analyses during the period prior to poten-tial effects of school closure (ie, during the lag period), the rela-tive change in COVID-19 incidence per week was 265% (95%CI, 231% to 303%; Table 2). The overall combined compositerelative weekly change in COVID-19 incidence after school clo-sure was 10% (95% CI, 1% to 18%). This composite change af-ter school closure is a combination of the changes associatedwith school closure and other non–school-related changes dur-ing the period after school closure and is visually depicted asthe change after school closure in Figure 2. When examiningonly school closure, it was associated with a relative changein COVID-19 incidence per week of −62% (95% CI, −71% to−49%; Table 2).

The states that closed early, when the cumulative inci-dence of COVID-19 was lowest, had the greatest relative changeper week associated with school closure (−72% [95% CI, −79%to −62%]). States that were slowest to close schools and hadthe highest cumulative incidence of COVID-19 had a relative

change per week associated with school closure of −49% (95%CI, −62% to −33%; Table 2 and Figure 2). The full model withall covariate estimates appears in eTable 2 in the Supple-ment. The relative change associated with school closure forCOVID-19 incidence varied significantly by the testing rate per1000 residents, by the percentage of the state’s population aged65 years or older, by the number of nursing home residents per1000 people, and by urban density. Information on interpret-ing relative weekly changes appears in the eMethods in theSupplement.

The absolute effects associated with school closure dur-ing the 26-day period after school closure (days 17-42), whichwere calculated using model estimates with the assumptionof linear growth, yielded 638.7 cases per 100 000 that wouldhave occurred if schools had remained open (Table 3). Com-pared with the 214.8 cases per 100 000 estimated from theschool closure model, the absolute difference associated withschool closure was 423.9 (95% CI, 375.0 to 463.7) cases per

Figure 2. Modeled Association of School Closure With Coronavirus Disease 2019 (COVID-19) Incidence and Mortality

Daily incidence for all statesA

30

20

10

Daily

CO

VID-

19 in

cide

nce

per 1

00 0

00


End of lag period

–10 0 10 20 30 40 50

30

20

10

Daily

CO

VID-

19 in

cide

nce

per 1

00 0

00


End of lag period

–10 0 10 20 30 40 50

2.0

1.5

1.0

0.5

Daily

CO

VID-

19 d

eath

spe

r 100

000


End of lag period

–10 0 10 20 30 40 50

2.0

1.5

1.0

0.5

0 0

0 0

Daily

CO

VID-

19 d

eath

spe

r 100

000


End of lag period

–10 0 10 20 30 40 50

Mortality for all statesC

Daily incidence by cumulative incidence quartile at the timeof school closure

B

Mortality by cumulative incidence quartile at the time of school closureD

Highest quartileSecond highest quartileSecond lowest quartileLowest quartile

Highest quartileSecond highest quartileSecond lowest quartileLowest quartile

The data markers indicate the national unadjusted daily rates. The lines depictaggregated national daily rates adjusted for each state’s unique set of testingand demographic characteristics on each day of the study period with 95% CIsdepicted by gray lines. Six weeks after school closure, states in the lowestquartile had fewer new cases and fewer deaths compared with the states in thehighest quartile. Panels A and B were adjusted for all model componentsretained in the incidence model (intercept: percentage of state’s populationaged �15 years, percentage of state’s population aged �65 years, and USCenters for Disease Control and Prevention [CDC] social vulnerability index;before school closure: stay-at-home or shelter-in-place order, restaurant andbar closure, testing rate per 1000 residents, and urban density; after schoolclosure: testing rate per 1000 residents, stay-at-home or shelter-in-place order,

percentage of state’s population aged �65 years, number of nursing homeresidents per 1000 people, and urban density). Panels C and D were adjustedfor all model components retained in the mortality model (intercept:percentage of state’s population aged �15 years, percentage of state’spopulation aged �65 years, and CDC social vulnerability index; before schoolclosure: stay-at-home or shelter-in-place order, prohibition of gatherings with>10 people, restaurant and bar closure, percentage of state’s population aged�15 years, percentage of state’s population aged �65 years, number of nursinghome residents per 1000 people, and urban density; after school closure:restaurant and bar closure, number of nursing home residents per 1000 people,and urban density).









100 000. States that closed schools late (in the highest quar-tile of cumulative incidence of COVID-19) had the largest ab-solute reduction in cases (621.7 [95% CI, 535.4 to 742.6] per100 000). However, states that closed schools earlier (in thelowest quartile) had fewer total cases (−128.7 [95% CI, −168.7to −74.2] per 100 000) during the period after school closure(Table 3). The absolute difference in COVID-19 incidence as-suming continued exponential growth appears in eTable 3 inthe Supplement.

Sensitivity Analyses Around the Lag PeriodThe point estimates ranged from −61% to −63% for the rela-tive change per week associated with school closure as the lagperiod varied. The point estimates for the relative change foreach quartile varied slightly across the lag period (eTable 4 inthe Supplement).

COVID-19 MortalityThe observed death rates in each state by quartile of cumula-tive incidence of COVID-19 at the time of school closure ap-pears in Figure 1B. In the unadjusted analyses during the pe-riod prior to potential effects of school closure (ie, during thelag period), the overall relative change in mortality per weekwas 171% (95% CI, 160% to 184%). In the unadjusted analy-ses, the relative mortality change per week associated withschool closure was −64% (95% CI, −67% to −61%). The unad-justed effect size associated with school closure varied byCOVID-19 cumulative incidence at the time of school closure,with states in the lowest quartile having the largest associ-ated effect size (Table 2).

In the adjusted analyses during the period prior to poten-tial effects of school closure (ie, during the lag period),COVID-19 mortality increased by 186% (95% CI, 175% to197%) per week (Table 2). The overall combined compositerelative weekly change in mortality after school closure was2% (95% CI, −8% to 14%; Table 2). This composite change inmortality after school closure is visually depicted in Figure 2.When examining only school closure, it was associated with arelative change per week in COVID-19 mortality of −58% (95%CI, −67% to −46%). This association was greatest in stateswith the lowest cumulative COVID-19 incidence at the time ofschool closure (relative change per week of −64% [95% CI,−73% to −52%]). In comparison, states that closed schoolslater when cumulative COVID-19 incidence was in the highestquartile had the smallest associated relative decline in mor-tality (−53% [95% CI, −63% to −40%]; Table 2 and Figure 2).The full model with all covariates appears in eTable 5 in theSupplement. The relative change in mortality associated withschool closure varied significantly by restaurant and bar clo-sure and urban density.

The absolute effects associated with school closure dur-ing the 16-day period after school closure (days 27-42), whichwere calculated using model estimates with the assumptionof linear growth, yielded 19.4 deaths per 100 000 that wouldhave occurred if schools had remained open (Table 4). Com-pared with the 6.8 deaths per 100 000 estimated from theschool closure model, the absolute difference associated withschool closure was 12.6 (95% CI, 11.8 to 13.6) deaths per 100 000Ta

ble

3.Es

timat

edAb

solu

teD

iffer

ence

sin

COVI

D-19

Case

saBe

twee

nPe

riod

ofSc

hool

Clos

ure

and

Scho

olsR

emai

ning

Ope

nU

sing

Line

arPr

ojec

tionb

COVI

D-19

case

sper

100

000

inav

erag

est

atec,

d

Estim

ated

abso

lute

diff

eren

ce(9

5%CI

)in

case

sper

100

000

byqu

artil

eof

COVI

D-19

cum

ulat

ive

inci

denc

eat

scho

olcl

osur

ed

Low

est

Seco

ndlo

wes

tSe

cond

high

est

Hig

hest

Scho

olsr

emai

nop

en(p

roje

cted

)

Befo

resc

hool

clos

ure

(dur

ing

lag

perio

d:da

ys1-

16)

37.0

(30.

5to

45.0

)23

.3(1

7.2

to31

.7)

31.8

(23.

1to

43.7

)32

.6(2

4.1

to44

.1)

81.2

(59.

5to

110.

7)

Afte

rlag

perio

d(d

ays1

7-42

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(Table 4). States that closed schools late (in the highest quar-tile of COVID-19 cumulative incidence) had the largest abso-lute reduction in deaths (15.8 [95% CI, 13.9 to 18.1] per 100 000).However, states that closed schools earlier (in the lowest quar-tile) had fewer estimated total deaths (−1.5 [95% CI, −2.7 to −0.1]per 100 000) during the period after school closure (Table 4).The absolute difference in deaths assuming continued expo-nential growth appears in eTable 3 in the Supplement.

Sensitivity Analyses Around the Lag PeriodThe point estimates ranged from −55% to −61% for the rela-tive change associated with school closure as the lag period var-ied. The point estimates for the relative change for each quar-tile varied slightly across the lag period (eTable 4 in theSupplement).

DiscussionBetween March 9, 2020, and May 7, 2020, school closure inthe US was temporally associated with decreased COVID-19 in-cidence and mortality. States that closed schools earlier (whenthe state’s cumulative incidence was lower) had the largest rela-tive reduction in overall incidence and mortality.

In March 2020, states enacted multiple nonpharmaceu-tical interventions, including closing schools, nonessentialbusinesses, and restaurants and bars, and prohibiting largegatherings, to curb SARS-CoV-2 spread and prevent death.Completely isolating the effects of any single nonpharmaceu-tical intervention is impossible because recommendations forincreased handwashing, cleaning, and wearing of masksevolved simultaneously. Measured COVID-19 incidence alsowas affected by testing availability, which was limited early inthe pandemic and varied nationally.

In this study, changes in COVID-19 incidence and mortal-ity associated with school closure were isolated to the extentpossible by adjusting for other state-enacted policies and test-ing rates. In adjusted models, school closure was associatedwith decreased COVID-19 incidence and deaths. These analy-ses do not incorporate the risks of school closure on child edu-cation and development or from a societal perspective. How-ever, the analyses suggest that school closure may be effectivein curbing SARS-CoV-2 spread and preventing deaths duringfuture outbreaks.

These findings complement evolving evidence on the roleof children in the transmission of SARS-CoV-2. Studies havedocumented lower attack rates for children,28 and childrencomprise a small proportion of documented infections.29 Chil-dren may be less susceptible to SARS-CoV-2 infection30; how-ever, studies have documented viral shedding in asymptom-atic children.31 Recent studies suggest school closure may haveonly modest effects on COVID-19 deaths.32-35 School closurein this study was associated with a −62% relative change inCOVID-19 incidence per week. A decline of 62% was equiva-lent to 39% of the projected value with schools open. So, perweek, the incidence was estimated to have been 39% of whatit would have been had schools remained open. Extrapolat-ing the absolute differences of 423.9 cases and 12.6 deaths perTa

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100 000 to 322.2 million residents nationally suggests thatschool closure may have been associated with approximately1.37 million fewer cases of COVID-19 over a 26-day period and40 600 fewer deaths over a 16-day period; however, these fig-ures do not account for uncertainty in the model assump-tions and the resulting estimates.

The analyses presented here suggest that the timing ofschool closure plays a role in the magnitude of changes asso-ciated with school closure. As hypothesized, school closure instates that enacted this intervention early (when the cumula-tive incidence of COVID-19 was low) had greater associated rela-tive decreases in incidence and mortality. Although these rela-tive differences translate into smaller absolute differencesassociated with school closure, states that closed schools later(in the highest quartile of COVID-19 cumulative incidence) hadmore new cases and deaths from COVID-19 during the periodafter school closure. Thus, this study can inform future deci-sions about optimal timing for state and local officials to con-sider school closure to curb SARS-CoV-2 spread in the high like-lihood that the pandemic continues.

The mechanism by which school closure could affectCOVID-19 spread is not only through disrupting spread by oramong children. School closure affects family routines,necessitating alternative childcare and modified work sched-ules. These changes are evident by the number of telework-ers more than doubling.36,37 The disruption in everyday lifelikely influenced how people engaged in group activities,traveled, and conducted business. If the primary effect asso-ciated with school closure is related to altered adult behavior,and not children spreading the virus to adults, the primarylag period considered in these analyses should be adjusted.Eliminating the 4 days for a child to adult transmission wouldresult in a COVID-19 incidence lag period of 12 days and amortality lag period of 22 days. In sensitivity analyses, theeffect sizes associated with school closure at these shorter lagperiods were similar to the primary analysis effect sizes. Thedegree to which the associations with school closure relate todecreased spread of SARS-CoV-2 by children or a combina-tion of child and adult factors is unclear. Because school clo-sure was the first nonpharmaceutical intervention in moststates, the effects associated with school closure may belarger than if school closure had followed other nonpharma-ceutical interventions.

It is unclear how COVID-19 spread would be affected ifschools remained open while states enacted other policies torestrict movement. It is possible school-related spread may be

mitigated with infection-control interventions recom-mended by the CDC and the American Academy of Pediat-rics, including frequent handwashing, universal mask poli-cies, physical distancing measures, and increased sanitationprocedures.38,39 However, given that school closure also al-ters adult behavior, decreasing COVID-19 spread within schoolsmay be inadequate as a stand-alone intervention and may re-quire continued alteration of adult interactions.

LimitationsThis study has several limitations. First, many states enactedadditional nonpharmaceutical interventions concurrently withor shortly after school closure, making it impossible to fullyisolate potential effects of school closure. Some nonpharma-ceutical interventions, such as increased handwashing, couldnot be included due to lack of available data.

Second, analyses were conducted at the state level. Theanalyses did not account for resident travel leading to viralspread between states. Even though the study modeled state-level policies, some states had more restrictive policies lo-cally (ie, by county). Nevertheless, these analyses are usefulto understand the practical implications of state policy in con-taining spread.

Third, inadequate testing has impeded COVID-19 diagno-sis. Testing variability was accounted for with the use of state-level testing rates as a model covariate; however, testing ratesdo not fully capture a state’s testing capability, infrastruc-ture, and strictness of testing guidelines.

Fourth, the completeness and accuracy of the Johns Hop-kins University database with respect to COVID-19 incidenceand mortality has not been established. This data source ag-gregates publicly available data and accuracy may vary stateto state. As with limitations in testing, inconsistencies in re-porting are unavoidable limitations of all COVID-19 US popu-lation-based studies.

ConclusionsBetween March 9, 2020, and May 7, 2020, school closure inthe US was temporally associated with decreased COVID-19 in-cidence and mortality; states that closed schools earlier, whencumulative incidence of COVID-19 was low, had the largest rela-tive reduction in incidence and mortality. However, it re-mains possible that some of the reduction may have been re-lated to other concurrent nonpharmaceutical interventions.

ARTICLE INFORMATION

Accepted for Publication: July 17, 2020.

Published Online: July 29, 2020.doi:10.1001/jama.2020.14348

Author Affiliations: Division of Hospital Medicine,Cincinnati Children’s Hospital Medical Center,Cincinnati, Ohio (Auger, Shah, Warniment,Timmons, Bosse, Ferris, Brady, Schondelmeyer,Thomson); James M. Anderson Center for HealthSystems Excellence, Cincinnati Children’s HospitalMedical Center, Cincinnati, Ohio (Auger, Shah,Hartley, Brady, Schondelmeyer); Department of

Pediatrics, University of Cincinnati College ofMedicine, Cincinnati, Ohio (Auger, Shah, Hartley,Brady, Schondelmeyer, Thomson); PediatricResearch in Inpatient Settings Network, Cincinnati,Ohio (Auger, Shah, Richardson, Hall, Brady,Thomson); Division of Infectious Diseases,Cincinnati Children’s Hospital Medical Center,Cincinnati, Ohio (Shah); Division of EmergencyMedicine, Cincinnati Children’s Hospital MedicalCenter, Cincinnati, Ohio (Warniment).

Author Contributions: Dr Richardson had fullaccess to all of the data in the study and takes

responsibility for the integrity of the data and theaccuracy of the data analysis. Drs Brady,Schondelmeyer, and Thomson made equalsubstantial contributions to the manuscript and areco-last authors.Concept and design: Auger, Shah, Richardson,Hartley, Hall, Warniment, Bosse, Ferris, Brady,Schondelmeyer, Thomson.Acquisition, analysis, or interpretation of data: Allauthors.Drafting of the manuscript: Auger, Richardson,Timmons, Brady, Schondelmeyer.







Critical revision of the manuscript for importantintellectual content: Shah, Richardson, Hartley, Hall,Warniment, Bosse, Ferris, Brady, Schondelmeyer,Thomson.Statistical analysis: Auger, Richardson, Hartley, Hall.Administrative, technical, or material support:Hartley, Warniment, Timmons, Bosse, Ferris,Thomson.Supervision: Brady, Schondelmeyer, Thomson.

Conflict of Interest Disclosures: None reported.

Funding/Support: Funding for this work wasprovided by Agency for Healthcare Research andQuality awards K08HS024735 (Dr Auger),K08HS023827 (Dr Brady), K08HS026763 (DrSchondelmeyer), and K08HS025138 (Dr Thomson)and award 5UL1TR001425-04 from the NationalCenter for Advancing Translational Sciences,National Institutes of Health.

Role of the Funder/Sponsor: The Agency forHealthcare Research and Quality and the NationalInstitutes of Health had no role in the design andconduct of the study; collection, management,analysis, and interpretation of the data;preparation, review, or approval of the manuscript;and decision to submit the manuscript forpublication.

Disclaimer: The content is solely the responsibilityof the authors and does not represent the officialviews of the Agency for Healthcare Research andQuality, the National Institutes of Health, CincinnatiChildren’s Hospital Medical Center, the Children’sHospital Association, or the Pediatric Research inInpatient Settings Network.

Additional Information: Drs Richardson and Hallare affiliated with the Children’s HospitalAssociation, Lenexa, Kansas.

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https://www.ncbi.nlm.nih.gov/pubmed/32275295















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