MMWR, Volume 70, Issue 35 — September 3, 2021

Morbidity and Mortality Weekly Report

Weekly / Vol. 70 / No. 35 September 3, 2021

Continuing Education examination available at

https://www.cdc.gov/mmwr/mmwr_continuingEducation.html

National, Regional, State, and Selected Local Area Vaccination Coverage

Among Adolescents Aged 13–17 Years — United States, 2020

Cassandra Pingali, MPH, MS

; David Yankey, PhD

; Laurie D. Elam-Evans, PhD

; Lauri E. Markowitz, MD

; Charnetta L. Williams, MD

;

Benjamin Fredua, MS

1,3

; Lucy A. McNamara, PhD

; Shannon Stokley, DrPH

; James A. Singleton, PhD

The Advisory Committee on Immunization Practices

(ACIP) recommends that adolescents aged 11–12 years

routinely receive tetanus, diphtheria, and acellular pertussis

(Tdap); meningococcal conjugate (MenACWY); and human

papillomavirus (HPV) vaccines. Catch-up vaccination is

recommended for hepatitis B (HepB); hepatitis A (HepA);

measles, mumps, and rubella (MMR); and varicella (VAR)

vaccines for adolescents whose childhood vaccinations are

not current. Adolescents are also recommended to receive a

booster dose of MenACWY vaccine at age 16 years, and shared

clinical decision-making is recommended for the serogroup B

meningococcal vaccine (MenB) for persons aged 16–23 years

(1). To estimate coverage with recommended vaccines, CDC

analyzed data from the 2020 National Immunization Survey–

Teen (NIS-Teen) for 20,163 adolescents aged 13–17 years.*

Coverage with ≥1 dose of HPV vaccine increased from 71.5%

in 2019 to 75.1% in 2020. The percentage of adolescents

* Eligible participants were born during January 2002–January 2008. Tetanus

toxoid, reduced diphtheria toxoid, and acellular pertussis vaccine represents

coverage with ≥1 Tdap dose at age ≥10 years. Meningococcal conjugate vaccine

represents coverage with the quadrivalent meningococcal conjugate vaccine or

meningococcal-unknown type vaccine. HPV vaccination coverage includes

receipt of any HPV vaccine and does not distinguish between nine-valent

(9vHPV), quadrivalent (4vHPV), or bivalent (2vHPV) vaccines. Some

adolescents might have received more than the 2 or 3 recommended HPV

vaccine doses. Hepatitis A, hepatitis B, varicella, and measles, mumps, and

rubella vaccines are considered childhood vaccinations and are recommended

for adolescents who are not up to date with these vaccinations. Estimates in

this report include those who might have received vaccinations on-time or as

catch-up. Except as noted, coverage estimates for ≥1 and ≥2 varicella vaccine

doses were obtained among adolescents with no history of varicella disease.

Influenza vaccination coverage data are not included in this report but are

available at https://www.cdc.gov/flu/fluvaxview/index.htm.

INSIDE

1191 Evaluation of Syndromic Surveillance Data

for Studying Harmful Algal Bloom-Associated

Illnesses — United States, 2017–2019

1195 Multiple Variants of SARS-CoV-2 in a University

Outbreak After Spring Break — Chicago, Illinois,

March–May 2021

1201 Screening Programs for SARS-CoV-2 Infections

on a University Campus — Austin, Texas,

September 30–November 30, 2020

1206 COVID-19 Vaccination Coverage Among

Adolescents Aged 12–17 Years — United States,

December 14, 2020–July 31, 2021

1214 Outbreak Associated with SARS-CoV-2 B.1.617.2

(Delta) Variant in an Elementary School — Marin

County, California, May–June 2021

1220 COVID-19 Case Rates in Transitional Kindergarten

Through Grade 12 Schools and in the

Community — Los Angeles County, California,

September 2020–March 2021

1223 Epidemiologically Linked COVID-19 Outbreaks at a

Youth Camp and Men’s Conference — Illinois,

June–July 2021

1228 Association Between COVID-19 and Myocarditis

Using Hospital-Based Administrative Data —

United States, March 2020–January 2021

1233 QuickStats

U.S. Department of Health and Human Services

Centers for Disease Control and Prevention

Morbidity and Mortality Weekly Report

1184 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

The MMWR series of publications is published by the Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention (CDC),

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Centers for Disease Control and Prevention

Rochelle P. Walensky, MD, MPH, Director

Debra Houry, MD, MPH, Acting Principal Deputy Director

Daniel B. Jernigan, MD, MPH, Deputy Director for Public Health Science and Surveillance

Rebecca Bunnell, PhD, MEd, Director, Office of Science

Jennifer Layden, MD, PhD, Deputy Director, Office of Science

Michael F. Iademarco, MD, MPH, Director, Center for Surveillance, Epidemiology, and Laboratory Services

MMWR Editorial and Production Staff (Weekly)

Charlotte K. Kent, PhD, MPH, Editor in Chief

Jacqueline Gindler, MD, Editor

Brian A. King, PhD, MPH, Guest Science Editor

Paul Z. Siegel, MD, MPH, Associate Editor

Mary Dott, MD, MPH, Online Editor

Terisa F. Rutledge, Managing Editor

Teresa M. Hood, MS, Lead Technical Writer-Editor

Leigh Berdon, Glenn Damon, Soumya Dunworth, PhD,

Srila Sen, MA, Stacy Simon, MA,

Jeffrey D. Sokolow, MA, Morgan Thompson,

Technical Writer-Editors

Martha F. Boyd, Lead Visual Information Specialist

Alexander J. Gottardy, Maureen A. Leahy,

Julia C. Martinroe, Stephen R. Spriggs,

Brian Taitt, Tong Yang,

Visual Information Specialists

Quang M. Doan, MBA, Phyllis H. King,

Terraye M. Starr, Moua Yang,

Information Technology Specialists

MMWR Editorial Board

Timothy F. Jones, MD, Chairman

Matthew L. Boulton, MD, MPH

Carolyn Brooks, ScD, MA

Jay C. Butler, MD

Virginia A. Caine, MD

Jonathan E. Fielding, MD, MPH, MBA

David W. Fleming, MD

William E. Halperin, MD, DrPH, MPH

Jewel Mullen, MD, MPH, MPA

Jeff Niederdeppe, PhD

Celeste Philip, MD, MPH

Patricia Quinlisk, MD, MPH

Patrick L. Remington, MD, MPH

Carlos Roig, MS, MA

William Schaffner, MD

Nathaniel Smith, MD, MPH

Morgan Bobb Swanson, BS

Abbigail Tumpey, MPH

Ian Branam, MA,

Acting Lead Health Communication Specialist

Shelton Bartley, MPH,

Lowery Johnson, Amanda Ray,

Jacqueline N. Sanchez, MS,

Health Communication Specialists

Will Yang, MA,

Visual Information Specialist

who were up to date

†

with HPV vaccination (HPV UTD)

increased from 54.2% in 2019 to 58.6% in 2020. Coverage

with ≥1 dose of Tdap, ≥1 dose (and among adolescents

aged 17 years, ≥2 doses) of MenACWY remained similar to

coverage in 2019 (90.1%, 89.3%, and 54.4% respectively).

Coverage increased for ≥2 doses of HepA among adolescents

aged 13–17 years and ≥1 dose of MenB among adolescents

aged 17 years. Adolescents living below the federal poverty

level

had higher HPV vaccination coverage than adolescents

living at or above the poverty level. Adolescents living outside

a metropolitan statistical area (MSA)

had lower coverage

with ≥1 MenACWY and ≥1 HPV dose, and a lower propor-

tion being HPV UTD than adolescents in MSA principal

†

Adolescents were considered to be up to date with HPV vaccination if they

had received ≥3 doses, or if each of the following applied: 1) they had received

2 doses; 2) the first dose was received before their 15th birthday; and 3) the

difference between dates of first and second doses was ≥5 months minus

4 days, the absolute minimum interval between the first and second doses.

https://www.cdc.gov/vaccines/programs/iis/cdsi.html

Adolescents were classified as being below the federal poverty level if their

total family income was less than the level specified for the applicable family

size and number of children aged <18 years. All others were classified as at or

above the poverty level (https://www.census.gov/data/tables/time-series/demo/

income-poverty/historical-poverty-thresholds.html). Poverty status was

unknown for 711 adolescents.

MSA status was determined from household reported city and county of

residence and was grouped into three categories: MSA principal city, MSA

nonprincipal city, and non-MSA. MSA and MSA principal city were as defined

by the U.S. Census Bureau (https://www.census.gov/programs-surveys/metro-

micro.html). Non-MSA areas include urban populations not located within

an MSA and completely rural areas.

cities. In 2020, the COVID-19 pandemic disrupted routine

immunization services. Results from the 2020 NIS-Teen reflect

adolescent vaccination coverage before the COVID-19 pan-

demic. The 2020 NIS-Teen data could be used to assess the

impact of the COVID-19 pandemic on catch-up vaccination

but not on routine adolescent vaccination because adolescents

included in the survey were aged ≥13 years, past the age when

most routine adolescent vaccines are recommended, and most

vaccinations occurred before March 2020. Continued efforts

to reach adolescents whose routine medical care has been

affected by the COVID-19 pandemic are necessary to protect

persons and communities from vaccine-preventable diseases

and outbreaks.

NIS-Teen is an annual random-digit–dialed telephone

survey** that monitors vaccination coverage in adolescents aged

13–17 years in all 50 states, the District of Columbia, selected

local areas, and some U.S. territories.

††

Parents or guardians of

** Persons living in all identified mobile-telephone households were eligible for

interview. Sampling weights were adjusted for single frame (mobile telephone),

nonresponse, noncoverage, and overlapping samples of mixed telephone users. A

description of NIS-Teen single-frame survey methodology and its effect on reported

vaccination estimates is available at https://www.cdc.gov/vaccines/imz-managers/

coverage/teenvaxview/pubs-presentations/dual-to-single-frame-teen.html.

††

Local areas that received federal immunization funds under Section 317 of

the Public Health Service Act were sampled separately. Those included

Chicago, Illinois; New York, New York; Philadelphia County, Pennsylvania;

Bexar County, Texas; and Houston, Texas. Two territories were sampled

separately in 2020: Guam and Puerto Rico.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1185

US Department of Health and Human Services/Centers for Disease Control and Prevention

eligible adolescents are interviewed to gather sociodemographic

information about the household, and consent to contact the

adolescent’s vaccination provider (or providers) is requested; if

permission is granted, a questionnaire is mailed to the provider

(or providers) to obtain the adolescent’s vaccination history.

Vaccination coverage estimates are based on provider-reported

immunization records and include any vaccines administered

before the 2020 NIS-Teen interview date. This report provides

vaccination coverage estimates for 20,163 adolescents aged

13–17 years.

§§

The overall household response rate

¶¶

was

20.7%; 45.2% of adolescents with completed interviews had

adequate provider data. Data were weighted and analyzed

to account for the complex survey design, and T-tests using

Taylor-series variance estimates were used to assess vaccination

coverage differences by survey year (2020 versus 2019) and

between sociodemographic groups.*** P-values <0.05 were

considered statistically significant. Analyses were conducted

using SAS-callable SUDAAN (version 11; RTI International).

This activity was reviewed by CDC and was conducted

consistent with applicable federal law and CDC policy.

†††

National Vaccination Coverage

In 2020, HPV vaccination coverage (≥1 dose) among

adolescents was 75.1%, and 58.6% were HPV UTD (Figure)

(Table 1). Coverage with ≥1 dose of Tdap and MenACWY

remained high and stable (90.1% and 89.3% respectively).

Among adolescents aged 17 years, coverage with ≥2 doses of

MenACWY was 54.4%, similar to 2019 (53.7%); coverage

increased for ≥1 dose of Men B among adolescents aged

17 years and catchup vaccination with ≥2 doses of HepA

among adolescents 13−17 years from 2019. Coverage surpassed

90% for ≥2 doses of MMR, ≥3 doses of HepB, and ≥1 and

§§

The 2020 NIS-Teen sample included 9,576 females and 10,587 males.

Adolescents from Guam (300), and Puerto Rico (169) were excluded from

the national estimates.

¶¶

The Council of American Survey Research Organizations response rate is

the product of three other rates: 1) the resolution rate (the proportion of

telephone numbers that can be identified as either for business or residence),

2) the screening rate (the proportion of qualified households that complete

the screening process), and 3) the cooperation rate (the proportion of

contacted eligible households for which a completed interview is obtained).

*** The NIS-Teen methodology for weighting and synthesizing provider-reported

vaccination histories has been previously described. https://www.cdc.gov/

vaccines/imz-managers/nis/downloads/NIS-TEEN-PUF19-DUG.pdf

†††

45 C.F.R. part 46.102(l)(2); 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d);

5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

§§§

HepA, HepB, VAR, and MMR vaccines are considered childhood

vaccinations and are recommended for adolescents who are not up to date

with these vaccinations. Estimates in this report include those who might

have received vaccinations on-time or as catch-up.

≥2 doses of varicella vaccine among adolescents without a

history of varicella disease.

§§§

Vaccination Coverage by Selected Characteristics

Among adolescents living in non-MSA areas, vaccination

coverage was lower compared with those living in MSA prin-

cipal cities with ≥1 dose MenACWY (85.7% versus 90.2%

[−4.5 percentage points]), ≥1 dose HPV (68.0% versus 77.8%

[−9.8 percentage points]), and ≥2 doses HepA (76.2% versus

83.6% [−7.4 percentage points])], and being HPV UTD

(49.2% versus 60.4% [−11.2 percentage points]) (Table 2).

These MSA disparities persisted among adolescents at or above

the poverty level but were not significant among those below

the poverty level for HPV UTD status and ≥2 dose–HepA

coverage. The coverage disparity in non-MSA areas compared

with MSA principal cities among adolescents living at or above

the poverty level were largest for HPV UTD status (46.0%

versus 59.8% [−13.8 percentage points]), ≥1-dose HPV cov-

erage (64.9% versus 76.2% [−11.3 percentage points], and

≥2-dose HepA coverage (74.4% versus 83.6% [−9.2 percent-

age points]). Coverage varied by jurisdiction (Supplementary

Table, https://stacks.cdc.gov/view/cdc/109214), race and

ethnicity,

¶¶¶

and health insurance status.****

COVID-19 Pandemic Effects on HPV Vaccination

The COVID-19 pandemic was declared a national emer-

gency on March 13, 2020. To evaluate the impact of the

pandemic on HPV vaccination, CDC conducted two analyses

comparing the 2019 and 2020 NIS-Teen samples. Historically,

HPV vaccination coverage has been lower than coverage with

most other routine vaccines, allowing for more catch-up vacci-

nations among adolescents aged 13–17 years. Most adolescents

had initiated HPV vaccination before March 1 in both survey

¶¶¶

Hispanic adolescents had lower coverage for ≥1 Tdap (−3.9 percentage

points), ≥2 MMR (−4.0 percentage points), ≥3 HepB (−4.3 percentage

points), ≥1 Var (−2.6 percentage points), and ≥2 Var (−3.2 percentage

points) than White adolescents. Black adolescents had lower coverage for

≥2 MenACWY (−2.5 percentage points) than White adolescents. Results

showed higher HPV vaccine coverage (≥1 dose) for Black, Hispanic,

American Indian or Alaska Native, Asian, and multiracial adolescents than

White adolescents. Results also showed higher HPV UTD for Black,

Hispanic, and multiracial adolescents. The higher HPV coverage has been

observed for Black and Hispanic adolescents compared with White

adolescents for several years. https://www.cdc.gov/vaccines/imz-managers/

coverage/teenvaxview/pubs-presentations/NIS-teen-vac-coverage-estimates-

2020-tables.html#table-01

**** Adolescents with any Medicaid insurance had lower coverage for ≥1 Tdap

(−3.1 percentage points) but higher coverage with ≥1 HPV (6.1 percentage

points) compared with adolescents with private health insurance.

Adolescents who were uninsured had lower coverage for all routine

vaccinations (Tdap, MenACWY, and HPV vaccines) compared with

adolescents with private health insurance. https://www.cdc.gov/vaccines/

imz-managers/coverage/teenvaxview/pubs-presentations/NIS-teen-vac-

coverage-estimates-2020-tables.html#table-02

Morbidity and Mortality Weekly Report

1186 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE. Estimated vaccination coverage with selected vaccines and doses* among adolescents aged 13–17 years, by survey year

†

— National

Immunization Survey–Teen,

§,¶

United States, 2006–2020

100

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Percentage vaccinated

Year

≥1 Tdap

≥1 MenACWY

≥2 MenACWY

≥1 HPV vaccine

≥3 HPV vaccine

HPV UTD

2011–2015 ACIP

recommendation

2016 ACIP

recommendation

Single mobile telephone

sampling frame estimates

Abbreviations: ACIP = Advisory Committee on Immunization Practices; HPV = human papillomavirus; MenACWY = quadrivalent meningococcal conjugate vaccine;

NIS-teen = National Immunization Survey–Teen; Tdap = tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis vaccine; UTD = up to date.

* ≥1 dose Tdap at age ≥10 years; ≥1 dose MenACWY or meningococcal-unknown type vaccine; ≥2 doses MenACWY or meningococcal-unknown type vaccine,

calculated only among adolescents aged 17 years at time of interview. Does not include adolescents who received their first and only dose of MenACWY at age

≥16 years; HPV vaccine, nine-valent (9vHPV), quadrivalent (4vHPV), or bivalent (2vHPV). The routine ACIP recommendation for HPV vaccination was made for females

in 2006 and for males in 2011. Because HPV vaccination was recommended for males in 2011, coverage for all adolescents was not measured before that year; HPV

UTD includes those with ≥3 doses, and those with 2 doses when the first HPV vaccine dose was initiated at age <15 years and at least 5 months minus 4 days elapsed

between the first and second dose.

†

NIS-Teen implemented a revised adequate provider data definition in 2014 and retrospectively applied the revised definition to 2013 data. Estimates using a revised

definition might not be directly comparable.

NIS-Teen moved in 2018 to a single-sample frame.

ACIP revised the recommended HPV vaccination schedule in late 2016. The schedule changed from a 3-dose to 2-dose series with appropriate spacing between

receipt of the first and second dose for immunocompetent adolescents initiating the series before the 15th birthday. Three doses are still recommended for adolescents

initiating the series at age ≥15 years. Because of the change in definition, the graph includes estimates for ≥3 doses of HPV during 2011–2015 and the HPV UTD

estimate for 2016–2020. Because HPV vaccination was recommended for males in 2011, coverage for all adolescents was not measured before that year.

years (69.1% in 2019 and 73.6% in 2020). An additional 2.4%

and 1.5% of adolescents initiated the series after this date in

2019 and 2020, respectively.

The second analysis evaluated adolescents in the 2020

NIS-Teen sample who had not received HPV vaccine before

March 1 and whose parent or guardian was interviewed on

or after that date. This cohort was compared with a similarly

constructed cohort using 2019 NIS-Teen data. Cumulative

daily HPV vaccination initiation estimates from March

through December for these cohorts were calculated using the

††††

Kaplan-Meier methods were used to calculate cumulative daily vaccination

estimates from March–December 2019 and from March–December 2020.

In 2019, 4,918 adolescents had not received a dose of HPV vaccine and

had not been interviewed as of March 1, 2019; 452 were vaccinated between

March–December 2019. In 2020, 4,527 adolescents had not received a

dose of HPV vaccine and had not been interviewed as of March 1, 2020;

282 were vaccinated between March–December 2020.

Kaplan-Meier method.

††††

Among the 4,918 adolescents who

had not received HPV vaccine as of March 1, 2019 (26.2%

of the total sample), 452 (15.0%) initiated the series by mid-

December 2019. Among the 4,527 adolescents who had not

received HPV vaccine as of March 1, 2020, (22.5% of total

sample), 282 (15.2%) initiated the series by mid-December

2020. HPV vaccination initiation in the 2020 cohort was

lower than that in the 2019 cohort by April. The difference

between the two cohorts was largest in August and September

(4.9 percentage points lower in 2020 in both months) but nar-

rowed in subsequent months and was no longer significant by

end of November (Supplementary Figure, https://stacks.cdc.

gov/view/cdc/109215).

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1187

US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 1. Estimated vaccination coverage with selected vaccines and doses among adolescents aged 13–17* years, by age at interview —

National Immunization Survey–Teen, United States, 2020

Vaccine

Age at interview (yrs), % (95% CI)

†

Total, % (95% CI)

†

13 14 15 16 17 2020 2019

(n = 4,276) (n = 4,173) (n = 3,998) (n = 4,028) (n = 3,688) (N = 20,163) (N = 18,788)

Tdap

≥1 dose 88.9 (87.0–90.6) 89.4 (87.1–91.3) 90.7 (88.7–92.5) 90.4 (88.3–92.1) 91.1 (88.7–93.0) 90.1 (89.2–90.9) 90.2 (89.2–91.1)

MenACWY

≥1 dose 87.5 (85.3–89.4) 87.6 (85.0–89.8) 90.4 (88.6–92.0)** 89.1 (86.9–91.0) 92.3 (90.3–93.9)** 89.3 (88.4–90.2) 88.9 (88.0–89.8)

≥2 doses

††

NA NA NA NA 54.4 (51.2–57.5) 54.4 (51.2–57.5) 53.7 (49.9–57.4)

HPV

§§

vaccine

All adolescents

≥1 dose 69.4 (66.6–72.1) 72.3 (69.4–75.0) 77.6 (75.3–79.8)** 77.2 (74.7–79.6)** 79.0 (76.4–81.4)** 75.1 (73.9–76.2)

¶¶

71.5 (70.1–72.8)

HPV UTD*** 45.6 (42.7–48.5) 56.0 (53.0–58.9)** 61.9 (58.9–64.7)** 65.5 (62.6–68.2)** 64.5 (61.5–67.4)** 58.6 (57.3–60.0)

¶¶

54.2 (52.7–55.8)

Females

≥1 dose 71.3 (67.7–74.7) 72.9 (68.4–77.0) 78.1 (74.6–81.3)** 80.3 (76.3–83.8)** 83.5 (80.8–85.9)** 77.1 (75.4–78.7)

¶¶

73.2 (71.3–75.0)

HPV UTD 48.4 (44.3–52.5) 57.2 (52.6–61.7)** 63.7 (59.4–67.8)** 68.5 (64.0–72.6)** 70.4 (66.6–73.9)** 61.4 (59.5–63.3)

¶¶

56.8 (54.6–59.0)

Males

≥1 dose 67.5 (63.2–71.5) 71.7 (67.9–75.2) 77.1 (73.9–80.1)** 74.5 (71.1–77.6)** 74.8 (70.4–78.6)** 73.1 (71.5–74.8)

¶¶

69.8 (67.9–71.7)

HPV UTD 42.7 (38.6–46.9) 54.8 (50.9–58.6)** 60.0 (56.1–63.9)** 62.8 (58.9–66.4)** 59.0 (54.4–63.5)** 56.0 (54.1–57.8)

¶¶

51.8 (49.7–53.9)

MenB ≥1 dose

†††

NA NA NA NA 28.4 (25.5–31.5) 28.4 (25.5–31.5)

¶¶

21.8 (18.9–24.9)

MMR ≥2 doses 92.5 (90.7–94.0) 92.1 (90.3–93.5) 92.5 (90.4–94.2) 93.2 (91.5–94.7) 91.6 (89.2–93.5) 92.4 (91.6–93.2) 91.9 (90.8–92.8)

Hepatitis A vaccine

≥2 doses

§§§

86.5 (84.1–88.5) 84.9 (82.6–86.9) 81.5 (79.1–83.6)** 79.8 (77.5–81.8)** 77.7 (75.0–80.1)** 82.1 (81.1–83.1)

¶¶

77.1 (75.8–78.4)

Hepatitis B vaccine

≥3 doses

91.8 (89.8–93.4) 93.5 (92.1–94.8) 92.5 (90.7–94.0) 93.6 (92.0–94.8) 91.4 (89.1–93.3) 92.6 (91.8–93.3) 91.6 (90.6–92.6)

Varicella

History of varicella

¶¶¶

6.8 (5.4–8.5) 6.9 (5.7–8.3) 8.7 (7.1–10.6) 7.6 (6.4–9.1) 12.0 (9.7–14.8)** 8.4 (7.6–9.2) 9.1 (8.4–9.9)

No history of varicella disease

≥1 dose vaccine 96.2 (94.8–97.2) 95.9 (94.4–97.0) 95.3 (93.5–96.7) 95.3 (93.3–96.7) 95.2 (93.6–96.5) 95.6 (94.9–96.2) 95.2 (94.3–95.9)

≥2 doses vaccine 93.6 (92.0–95.0) 91.6 (89.6–93.2) 92.8 (90.6–94.5) 90.8 (88.3–92.9)** 90.5 (88.1–92.5)** 91.9 (91.0–92.7) 90.6 (89.5–91.7)

History of varicella or

received ≥2 doses

varicella vaccine

94.1 (92.6–95.3) 92.1 (90.3–93.6) 93.4 (91.4–95.0) 91.5 (89.2–93.4) 91.6 (89.5–93.4)** 92.6 (91.7–93.3) 91.5 (90.4–92.4)

Abbreviations: CI = confidence interval; HPV = human papillomavirus; MenACWY = quadrivalent meningococcal conjugate vaccine; MenB = serogroup B meningococcal

vaccine; MMR = measles, mumps, and rubella vaccine; NA = not applicable; NIS-Teen = National Immunization Survey–Teen; Tdap = tetanus toxoid, reduced diphtheria

toxoid, and acellular pertussis vaccine; UTD = up to date.

* Adolescents (20,163) in the 2020 NIS-Teen were born during January 2002–January 2008.

†

Estimates with 95% CI widths >20 might be unreliable.

Includes percentages receiving Tdap vaccine at age ≥10 years.

Includes percentages receiving MenACWY or meningococcal-unknown type vaccine.

** Statistically significant difference (p<0.05) in estimated vaccination coverage by age: reference group was adolescents aged 13 years.

††

≥2 doses of MenACWY or meningococcal-unknown type vaccine. Calculated only among adolescents who were aged 17 years at interview. Does not include

adolescents who received 1 dose of MenACWY vaccine at age ≥16 years.

§§

HPV vaccine, nine-valent (9vHPV), quadrivalent (4vHPV), or bivalent (2vHPV). For ≥1 HPV dose measure and HPV-UTD measure, percentages are reported among

females and males combined (20,163) and among females only (9,576) and among males only (10,587).

¶¶

Statistically significant difference (p<0.05) compared with 2019 NIS-Teen estimates.

*** HPV UTD includes those with ≥3 doses, and those with 2 doses when the first HPV vaccine dose was initiated before age 15 years and there was at least 5 months

minus 4 days between the first and second dose. This update to the HPV recommendation occurred in December 2016.

†††

≥1 dose of MenB. Calculated only among adolescents who were aged 17 years at interview. Administered based on individual clinical decision.

§§§

In July 2020, ACIP revised recommendations for HepA vaccination to include catch-up vaccination for children and adolescents aged 2–18 years who have not

previously received HepA vaccine at any age. https://www.cdc.gov/mmwr/volumes/69/rr/rr6905a1.htm?s_cid

¶¶¶

By parent or guardian report or provider records.

Discussion

NIS-Teen 2020 data indicate that although ≥1 dose HPV

coverage and HPV UTD status continue to increase, they

remain lower than coverage with most other routinely recom-

mended vaccines. Improvements in HPV vaccination coverage

are crucial to lowering rates of HPV-attributable cancers in the

United States. Coverage with ≥1 dose of Tdap and MenACWY

vaccines remains high and stable, while coverage with ≥2 doses

of MenACWY remains low, indicating the need for increased

awareness of the importance of the booster dose.

Morbidity and Mortality Weekly Report

1188 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 2. Estimated vaccination coverage with selected vaccines and doses among adolescents aged 13–17* years, by metropolitan statistical

area

†

and by poverty level — National Immunization Survey–Teen, United States, 2020

Vaccine

MSA, % (95% CI)

Below poverty level, % (95% CI)

At or above poverty level, % (95% CI)

Non-MSA

MSA

nonprincipal

city

MSA

principal

city Non-MSA

MSA

nonprincipal

city

MSA

principal

city Non-MSA

MSA

nonprincipal

city

MSA

principal

city

(n = 3,678) (n = 8,409) (n = 8,076) (n = 631) (n = 865) (n = 1,352) (n = 2,938) (n = 7,246) (n = 6,420)

Tdap

≥1 dose 90.7

(88.7–92.3)

90.6

(89.3–91.8)

89.3

(87.7–90.7)

93.1

(89.7–95.5)

89.0

(84.7–92.1)

89.6

(86.3–92.2)

89.8

(87.4–91.9)

91.1

(89.7–92.3)

89.2

(87.3–90.9)

MenACWY**

≥1 dose 85.7

(83.7–87.5)

††

89.4

(87.9–90.7)

90.2

(88.7–91.5)

86.1

(81.8–89.5)

††

87.2

(82.6–90.6)

91.6

(88.8–93.7)

85.6

(83.2–87.7)

††

90.2

(88.6–91.5)

89.4

(87.5–91.0)

≥2 doses

§§

50.1

(43.4–56.9)

58.5

(54.0–62.8)

††

50.6

(45.2–56.1)

47.4

(33.5–61.7)

47.6

(33.0–62.7)

48.6

(35.8–61.7)

50.2

(42.3–58.0)

61.2

(56.6–65.6)

††

50.2

(44.1–56.4)

HPV

¶¶

vaccine

All adolescents

≥1 dose 68.0

(65.3–70.6)

††

74.2

(72.5–75.9)

††

77.8

(75.8–79.6)

73.6

(67.8–78.7)

††

83.6

(79.5–87.0)

85.7

(82.0–88.7)

64.9

(61.7–67.9)

††

73.1

(71.3–74.9)

††

76.2

(74.0–78.3)

HPV UTD

***

49.2

(46.3–52.1)

††

59.1

(57.2–61.0)

60.4

(58.2–62.6)

56.7

(50.3–62.9)

63.8

(58.1–69.2)

64.4

(59.2–69.3)

46.0

(42.9–49.3)

††

58.4

(56.4–60.4)

59.8

(57.4–62.2)

Females

≥1 dose

67.8

(63.7–71.7)

††

76.7

(74.5–78.8)

79.8

(76.9–82.4)

75.2

(66.4–82.2)

††

84.4

(78.9–88.6)

87.2

(82.0–91.0)

63.6

(58.6–68.2)

††

75.7

(73.3–78.0)

78.8

(75.7–81.7)

HPV UTD 50.3

(46.0–54.6)

††

62.2

(59.6–64.7)

63.2

(59.9–66.4)

56.9

(47.6–65.8)

65.3

(56.8–72.9)

66.0

(58.2–73.0)

46.8

(42.0–51.6)

††

61.9

(59.1–64.5)

63.5

(60.0–67.0)

Males

≥1 dose 68.1

(64.6–71.5)

††

71.9

(69.3–74.4)

††

75.8

(73.2–78.3)

71.6

(63.7–78.4)

††

82.9

(76.5–87.8)

84.3

(78.8–88.6)

66.1

(61.9–70.0)

††

70.7

(68.0–73.3)

73.7

(70.5–76.6)

HPV UTD 48.1

(44.3–52.0)

††

56.2

(53.4–58.9)

57.8

(54.8–60.7)

56.4

(47.8–64.7)

62.5

(54.5–69.8)

62.9

(55.8–69.4)

45.4

(41.2–49.7)

††

55.2

(52.2–58.1)

56.2

(52.8–59.5)

MMR ≥2 doses

92.8

(91.0–94.2)

92.4

(91.2–93.5)

92.3

(90.9–93.5)

93.6

(89.4–96.2)

89.5

(83.8–93.3)

90.8

(86.2–94.0)

92.3

(90.2–94.0)

92.9

(91.6–93.9)

92.4

(90.9–93.6)

Hepatitis A

vaccine

≥2 doses

†††

76.2

(73.7–78.5)

††

82.0

(80.6–83.4)

83.6

(81.9–85.2)

80.4

(75.0–84.9)

82.1

(77.1–86.3)

83.0

(78.1–87.0)

74.4

(71.5–77.0)

††

81.8

(80.2–83.3)

83.6

(81.7–85.3)

Hepatitis B

vaccine

≥3 doses

92.4

(90.6–93.9)

92.9

(91.7–93.9)

92.3

(90.9–93.5)

92.8

(89.0–95.4)

91.0

(86.3–94.2)

89.9

(85.5–93.2)

92.0

(89.8–93.8)

93.1

(91.9–94.1)

92.9

(91.5–94.1)

See table footnotes on the next page.

Disparities in vaccination coverage by MSA and poverty level

persist. Among adolescents living at or above the poverty level,

those in non-MSAs had lower HPV UTD status and coverage

with ≥2 doses of HepA than adolescents in MSA principal cit-

ies. Further investigation is needed to understand this disparity

and more generally, the relationship between socioeconomic

level, geographic location, barriers to vaccination such as vacci-

nation access, and vaccine confidence. Persons living below the

§§§§

Children aged ≤18 years who are Medicaid-eligible, uninsured, or American

Indian or Alaska Native (as defined by the Indian Health Care Improvement

Act) are eligible to receive vaccines from providers through the Vaccines

for Children (VFC) program. Children categorized as “underinsured”

because their health plans do not include coverage for recommended

vaccinations are eligible to receive VFC vaccines if they are served by a rural

health clinic or federally qualified health center or under an approved

deputization agreement. https://www.cdc.gov/vaccines/programs/vfc/

providers/eligibility.html

poverty level might have better access to the VFC program,

§§§§

which provides vaccines to children whose parents or guard-

ians otherwise might not be able to afford them. Adolescents

living below the poverty level have previously been shown to

have higher HPV vaccine coverage (2–4).

Although HPV vaccination continues to increase in the

United States, and coverage for most other routine vaccinations

remains high and stable, the COVID-19 pandemic threatens

these achievements. An analysis of immunization information

systems data from 10 U.S. jurisdictions during March–May

2020 compared with the same period in 2018 and 2019 iden-

tified a substantial decrease in the number of vaccine doses

administered to children and adolescents in 2020. Increases

in doses administered were noted during June–September

2020 but did not appear sufficient to offset the decline during

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1189

US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 2. (Continued) Estimated vaccination coverage with selected vaccines and doses among adolescents aged 13–17* years, by metropolitan

statistical area

†

and by poverty level — National Immunization Survey–Teen, United States, 2020

Vaccine

MSA, % (95% CI)

Below poverty level, % (95% CI)

At or above poverty level, % (95% CI)

Non-MSA

MSA

nonprincipal

city

MSA

principal

city Non-MSA

MSA

nonprincipal

city

MSA

principal

city Non-MSA

MSA

nonprincipal

city

MSA

principal

city

(n = 3,678) (n = 8,409) (n = 8,076) (n = 631) (n = 865) (n = 1,352) (n = 2,938) (n = 7,246) (n = 6,420)

Varicella

History of

varicella

§§§

10.1

(8.6–11.8)

8.2

(7.2–9.4)

8.0

(6.9–9.4)

9.8

(6.9–13.6)

13.3

(9.4–18.5)

9.3

(6.4–13.3)

10.3

(8.6–12.3)

††

7.2

(6.2–8.4)

7.8

(6.5–9.3)

No history of varicella disease

≥1 dose vaccine 96.1

(94.6–97.1)

95.8

(94.8–96.6)

95.3

(94.1–96.2)

95.9

(91.9–97.9)

96.9

(94.6–98.2)

94.0

(90.0–96.4)

96.1

(94.4–97.3)

95.5

(94.3–96.4)

95.5

(94.2–96.5)

≥2 doses vaccine 92.5

(90.7–94.0)

92.0

(90.6–93.2)

91.6

(90.1–92.9)

94.2

(90.1–96.6)

87.9

(81.2–92.4)

90.7

(85.7–94.0)

91.7

(89.4–93.5)

92.4

(91.0–93.5)

91.6

(90.0–93.0)

History of

varicella or

received

≥2 doses VAR

93.2

(91.6–94.6)

92.7

(91.4–93.8)

92.3

(90.9–93.5)

94.7

(91.0–97.0)

89.5

(83.6–93.4)

91.5

(87.0–94.6)

92.5

(90.5–94.1)

92.9

(91.6–94.0)

92.2

(90.7–93.5)

Abbreviations: CI=confidence interval; HPV=human papillomavirus; MenACWY=quadrivalent meningococcal conjugate vaccine; MMR=measles, mumps, and

rubella vaccine; MSA=metropolitan statistical area; NIS-Teen = National Immunization Survey–Teen; Tdap=tetanus toxoid, reduced diphtheria toxoid, and acellular

pertussis vaccine; UTD=up-to-date; VAR= varicella vaccine.

* Adolescents (20,163) in the 2020 NIS-Teen were born during January 2002–January 2008.

†

MSA status was determined based on household-reported county of residence and was grouped into three categories: MSA principal city, MSA nonprincipal city,

and non-MSA. MSA and principal city were as defined by the U.S. Census Bureau https://www.census.gov/programs-surveys/metro-micro.html). Non-MSA areas

include urban populations not located within an MSA as well as completely rural areas.

Estimates with 95% CI widths >20 might not be reliable.

Includes percentages receiving Tdap vaccine at age ≥10 years.

** Includes percentages receiving MenACWY and meningococcal-unknown type vaccine.

††

Statistically significant difference (p<0.05) in estimated vaccination coverage by metropolitan statistical area; referent group was adolescents living in MSA principal

city areas.

§§

≥2 doses of MenACWY or meningococcal-unknown type vaccine. Calculated only among adolescents who were aged 17 years at interview. Does not include

adolescents who received 1 dose of MenACWY vaccine at age ≥16 years.

¶¶

HPV vaccine, nine-valent (9vHPV), quadrivalent (4vHPV), or bivalent (2vHPV)in females and males combined.

*** HPV UTD includes those with ≥3 doses, and those with 2 doses when the first HPV vaccine dose was initiated before age 15 years and there was at least 5 months

minus 4 days between the first and second dose. This update to the HPV recommendation occurred in December 2016.

†††

In July 2020, ACIP revised recommendations for HepA vaccination to include catch-up vaccination for children and adolescents aged 2–18 years who have not

previously received HepA vaccine at any age. https://www.cdc.gov/mmwr/volumes/69/rr/rr6905a1.htm?s_cid

§§§

By parent or guardian report or provider records.

March–May 2020 (5). Analysis of adolescents in the 2019 and

2020 NIS-Teen data who were aged ≥13 years and had not

initiated HPV vaccination as of March 1 showed lower series

initiation initially from April through the end of October in

2020 compared with 2019; however, initiation of the HPV

series in 2019 and 2020 was similar by November–December.

Although this is encouraging, the NIS-Teen data cannot

yet be used to assess the potential impact of the pandemic

on adolescents who were due to receive vaccinations at age

11–12 years. As adolescents aged 11–12 years who were due

to receive routine vaccinations during the pandemic age into

the NIS-Teen survey sample (13–17 years), the full impact of

the COVID-19 pandemic can be assessed.

The findings in this report are subject to at least three

limitations. First, the household response rate was 20.7%,

and 45.2% of respondents had adequate provider data. Low

survey response rates can increase potential biases if survey

participants differ from nonrespondents (6). Second, bias in

estimates might remain after adjustment for household and

provider nonresponse and phoneless households. A recent

survey error assessment indicated that NIS-Teen estimates

might underestimate true coverage, with the largest under-

estimation for Tdap vaccine (−5.3 percentage points).

¶¶¶¶

Little evidence exists of a change in survey accuracy between

2019 and 2020.***** Finally, opportunity is limited to assess

the effect of the pandemic on routine coverage using 2020

¶¶¶¶

An assessment of validity of the 2019 NIS-Teen estimates has been reported

(https://www.cdc.gov/vaccines/imz-managers/nis/downloads/NIS-TEEN-

PUF19-DUG.pdf). NIS-Teen vaccination coverage estimates tended to be

slightly low compared with true values derived after adjusting for

noncoverage, nonresponse, and vaccination underascertainment, reaching

up to 5.3 percentage points too low for Tdap. This was primarily attributed

to underascertainment of vaccinations by the NIS provider record check.

The validity of estimates did not change from 2018 to 2019.

***** https://www.cdc.gov/vaccines/imz-managers/coverage/teenvaxview/pubs-

presentations/NIS-teen-vac-coverage-estimates-2020-tables.html#table-03

Morbidity and Mortality Weekly Report

1190 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

Summary

What is already known about this topic?

Tetanus, diphtheria, and acellular pertussis (Tdap), meningococ-

cal conjugate (MenACWY), and human papillomavirus (HPV)

vaccines are routinely recommended for adolescents.

What is added by this report?

In 2020, adolescent coverage with Tdap and the first dose of

MenACWY remained high and continued to improve for HPV

vaccines, with some disparities. Adolescents living outside a

metropolitan statistical area (MSA) had lower vaccination coverage

compared with adolescents living in MSA principal cities.

What are the implications for public health?

Results from the 2020 National Immunization Survey–Teen

reflect adolescent vaccination coverage before the COVID-19

pandemic. Efforts to reach adolescents whose routine medical

care has been affected by the pandemic are necessary to

protect adolescents and communities from vaccine-preventable

diseases and outbreaks.

NIS-Teen data; because many vaccines are recommended for

children aged 11–12 years, most adolescents aged 13–17 years

received their routine vaccinations before the pandemic started.

Health care providers should review patient vaccination

records and administer any vaccines or doses that are due.

Children and adolescents aged 12–17 years are also eligible

(those aged 16–17 years as of December 11, 2020 and those

aged 12–15 years as of May 10, 2021) for a COVID-19 vac-

cine, which may be administered with other vaccines at the

same visit (7). Ensuring that routine vaccination is maintained

and that adolescents catch up on any missed doses is essential to

protecting persons and communities from vaccine-preventable

diseases and outbreaks.

Corresponding author: Cassandra Pingali, [email protected]v.

Immunization Services Division, National Center for Immunization and

Respiratory Diseases, CDC;

Division of Viral Diseases, National Center for

Immunization and Respiratory Diseases, CDC;

Leidos Health, Atlanta,

Georgia;

Division of Bacterial Diseases, National Center for Immunization

and Respiratory Diseases, CDC.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. No potential conflicts of interest were disclosed.

References

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Committee on Immunization Practices recommended immunization

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2. Henry KA, Swiecki-Sikora AL, Stroup AM, Warner EL, Kepka D. Area-

based socioeconomic factors and human papillomavirus (HPV) vaccination

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3. Swiecki-Sikora AL, Henry KA, Kepka D. HPV vaccination coverage

among US teens across the rural-urban continuum. J Rural Health

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4. Elam-Evans LD, Yankey D, Singleton JA, et al. National, regional, state,

and selected local area vaccination coverage among adolescents aged

13–17 years—United States, 2019. MMWR Morb Mortal Wkly Rep

2020;69:1109–16. PMID:32817598 https://doi.org/10.15585/mmwr.

mm6933a1

5. Murthy BP, Zell E, Kirtland K, et al. Impact of the COVID-19 pandemic

on administration of selected routine childhood and adolescent

vaccinations—10 U.S. jurisdictions, March–September 2020. MMWR

Morb Mortal Wkly Rep 2021;70:840–5. PMID:34111058 https://doi.

org/10.15585/mmwr.mm7023a2

6. Pew Research Center. What low response rates mean for

telephone surveys. Washington, DC: Pew Research Center;

2017. https://www.pewresearch.org/methods/2017/05/15/

what-low-response-rates-mean-for-telephone-surveys/

7. CDC. COVID-19 vaccines for children and teens. Atlanta, GA: US

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gov/coronavirus/2019-ncov/vaccines/recommendations/adolescents.html

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1191

US Department of Health and Human Services/Centers for Disease Control and Prevention

Evaluation of Syndromic Surveillance Data for Studying Harmful

Algal Bloom-Associated Illnesses — United States, 2017–2019

Amy M. Lavery, PhD

; Lorraine C. Backer, PhD

; Virginia A. Roberts, MSPH

; Jourdan DeVies, MS

; Johnni Daniel, DHSc

Harmful algal and cyanobacterial blooms (harmful algal

blooms) are large colonies of algae or cyanobacteria that can

harm humans, animals, and the environment (1–3). The num-

ber of algal blooms has been increasing in the United States,

augmented by increasing water temperatures and nutrients in

water from industry and agricultural run-off (4,5). The extent

to which harmful algal bloom exposures cause human illness or

long-term health effects is unknown. As the number of blooms

increases annually, the likelihood of negative health outcomes

(e.g., respiratory or gastrointestinal illness) from exposure also

increases (4,5). To explore the utility of syndromic surveillance

data for studying health effects from harmful algal bloom expo-

sures, CDC queried emergency department (ED) visit data

from the National Syndromic Surveillance Program (NSSP)

for harmful algal bloom exposure–associated administrative

discharge diagnosis codes and chief complaint text terms

related to harmful algal bloom exposure (6). A total of 321

harmful algal bloom-associated ED visits were identified during

January 1, 2017–December 31, 2019. An increase in harm-

ful algal bloom–associated ED visits occurred during warmer

months (June–October), consistent with seasonal fluctuations

of blooms and recent publications (6,7). Although syndromic

surveillance data are helpful for understanding harmful algal

bloom–associated ED visits in the United States, exposures

were documented infrequently with discharge diagnosis codes;

67% of harmful algal bloom–associated ED visits were iden-

tified through querying chief complaint text. Improving the

documentation of harmful algal bloom exposures in medical

records would further benefit future health studies.

NSSP is a collaboration among CDC, state, and local

health departments, and academic and private sector partners

which captures data electronically from EDs throughout the

country. As of the end of the study period (December 2019),

the national database represented approximately 70% of all

ED visits in the United States. Data are queried by creating

Boolean search terms of diagnostic codes and chief complaint

text. Chief complaint text terms are also used to categorize

visits into many broad, medically similar syndromes using

prebuilt algorithms.

For the current analysis, a query was created that comprises

main terms from the chief complaint (e.g., red tide, algae)

along with discharge diagnostic codes associated with expo-

sure to harmful algal blooms (International Classification of

Diseases, Tenth Revision, Clinical Modification [ICD-CM-10])

codes and their corresponding Systematized Nomenclature

of Medicine [SNOMED]* Clinical Terms codes). The final

query was reviewed using the NSSP query development tool.

†

Records identified by this query are defined as harmful algal

bloom-associated ED visits. To exclude ED visits associated

with the ingestion of contaminated seafood, relevant keywords

such as “shellfish” or “ciguatera poisoning” and corresponding

ICD-CM-10 codes (e.g., ciguatera poisoning, ICD-CM-10

code T61.0), were omitted from the query. Basic demographic

information for patients with harmful algal bloom–associated

ED visits was summarized by frequency and percentage. The

number of identified harmful algal bloom–associated ED vis-

its during 2017–2019 was described by U.S. Department of

Health and Human Services region and visualized using a time

series graph. Because the number of facilities reporting to NSSP

has increased since 2017, regional and time series comparisons

were shown as a percentage of total ED visits within NSSP.

The frequencies with which various syndrome categories

were

recorded during the harmful algal bloom-associated ED visits

were examined. Variables were created to indicate whether an

ED visit was related to neurologic, gastrointestinal, respiratory,

or dermatologic conditions.

This activity was reviewed by

* http://www.snomed.org/snomed-ct/why-snomed-ct

†

The NSSP Chief Complaint Query Validation data source contains chief

complaint and discharge diagnosis codes only; to help protect anonymity, it

does not include any demographic data. This data subset helps users to iteratively

develop queries with inclusion and exclusion terms to capture only the records

of interest. Some NSSP sites do not contribute data to this data source. The

final query included the following ICD-10-CM codes: T65.82, toxic effect

harmful algae and algae toxins; Z77.121, contact with and suspected exposure

to harmful algae and algae toxins; SNOMED codes: 137512, 240914003,

10076437, 10076441, 402161005, 702986006, and 81034007; and main

terms related to algae and red tide. International Classification of Diseases, Ninth

Edition (ICD-9) codes were not included because ICD-10-CM codes for HAB

exposure were implemented in October 2015 and were similar, but more

descriptive than the ICD-9 codes for HAB exposure. Other terms related to

HAB exposure, such as “cyanobacteria” and “hab,” did not identify additional

records and were not included in the final query. The final query did not include

ICD-10-CM codes for HAB exposures through seafood or shellfish poisonings.

An automated algorithm codes standard symptom categories and subcategories

based on text in the chief complaint.

Neurologic conditions include altered mental status, such as dizziness,

drowsiness, and muscle weakness; gastrointestinal conditions such as abdominal

pain, diarrhea, gastrointestinal bleeding, loss of appetite, nausea, and vomiting;

respiratory conditions such as acute bronchitis, chest congestion, difficulty

breathing, sore throat, influenza-like illness, nasal congestion, otitis media,

shortness of breath, upper respiratory infection, wheezing; and dermatologic

conditions (e.g., rash).

Morbidity and Mortality Weekly Report

1192 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

CDC and was conducted consistent with applicable federal

law and CDC policy.**

A total of 321 harmful algal bloom–associated ED visits were

identified during January 1, 2017–December 31, 2019. Among

these visits, 106 (33%) were identified through ICD-CM-10

codes only; the addition of chief complaint text key terms to

the query identified an additional 215 visits. Harmful algal

bloom–associated ED visits increased in the summer months

(June–October) in all 3 years (Figure). A notable peak occurred

in October 2018, corresponding with a large-scale red tide

event in the Gulf of Mexico during August–November 2018;

of the 197 ED visits occurring during July-November 2018,

73% occurred in Region 4 (southeastern United States).

Harmful algal bloom–associated ED visits occurred primar-

ily among patients aged 18–44 years (37%) and 45–64 years

** 45 C.F.R. part 46.102(l)(2), 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d);

5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

(30%) (Table 1); the majority (59%) occurred among females.

The largest number of harmful algal bloom–associated ED

visits was identified in Region 4 (31.1%). The most frequent

syndrome category was respiratory (41%), followed by gastro-

intestinal (14%), neurologic (10%), and dermatologic (8%)

(Table 2).

Discussion

This analysis identified approximately 300 harmful algal

bloom–associated ED visits during 2017–2019. ED visits

increased during the warmer months, consistent with seasonal

patterns of harmful algal blooms in the environment, with a

notable peak in 2018. Syndrome categories recorded for ED

visits were consistent with harmful algal bloom exposures

through inhalation (e.g., respiratory and neurologic), ingestion

(e.g., gastrointestinal), or skin contact (e.g., dermatologic) (5).

Most ED visits were identified through the chief complaint

text rather than through the use of ICD-10-CM codes. These

FIGURE. Harmful algal bloom exposure–associated emergency department visits among all emergency department visits, by month — National

Syndromic Surveillance Program, United States, 2017–2019*

0.0000

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

0.0008

0.0009

100.0000

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

2017 2018 2019

Percentage of ED visits due to harmful algal blooms

Year

This spike corresponds with

a large-scale red tide event

in the Gulf of Mexico during

August–November 2018.

* Percentage of all emergency department visits in the National Syndromic Surveillance Program was utilized to account for the increasing number of facilities

contributing data to the National Syndromic Surveillance Program.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1193

US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 1. Demographic characteristics of patients with harmful algal

bloom-associated emergency department visits (n = 321) — National

Syndromic Surveillance Program, United States, 2017–2019

Characteristic No. (%)

Age group (yrs)

0–4 19 (5.9)

5–17 35 (10.9)

18–44 118 (36.8)

45–64 96 (29.9)

≥65 50 (15.6)

Unknown 3 (0.9)

Sex

Female 190 (59.2)

Male 131 (40.8)

HHS Region*

,†

1 13 (9.7)

2 13 (4.9)

3 11 (4.1)

4 213 (31.1)

5 29 (6.6)

6 8 (4.1)

7 5 (5.3)

8 7 (12.9)

9 11 (7.6)

10 11 (13.7)

Abbreviation: HHS=U.S. Department of Health and Human Services.

* https://www.hhs.gov/about/agencies/iea/regional-offices/index.html

†

Percentages for HHS regions are adjusted for the total number of emergency

department visits during the time periods to account for the increasing number

of facilities reporting to NSSP since 2017.

results corroborate an earlier analysis using a commercial claims

data set, which identified few records with harmful algal bloom

exposure ICD-10-CM codes (8). Searching the chief complaint

text in NSSP more than doubled the number of harmful algal

bloom–associated visits, compared with the number that would

have been identified by searching on ICD-10-CM codes only.

The peak in ED visits during 2018 occurred primarily within

Region 4, corresponding to a large-scale red tide event in the

Gulf of Mexico that persisted during June 2018–November

2018 (9). The occurrence of this peak at the time of a red tide

event might explain the higher frequency of chief complaints

associated with respiratory symptoms because red tide has

been linked to respiratory health outcomes (2,3). Presumably,

these types of large-scale events might cause providers to ask

patients about recent harmful algal bloom exposures or cause

patients to mention them.

The NSSP query development tool made it possible to review

a sample of the full chief complaint text without linking to

other visit data, which helped to protect patient anonymity.

Several chief complaints (six) used terms such as, “patient

denies red tide exposure.” The final query was adjusted to

exclude these records; however, this finding implies that provid-

ers might have been asking patients if they had been exposed to

red tide, or patients might have mentioned that they had not

been on the beach or exposed to red tide before their ED visit.

TABLE 2. Primary syndrome categories associated with harmful algal

bloom exposure used among 321 harmful algal bloom-associated

emergency department visits

Syndrome type No. (%)*

Respiratory

†

133 (41.4)

Gastrointestinal

44 (13.7)

Neurologic

33 (10.3)

Dermatologic** 27 (8.4)

* Records could contain multiple syndromes. Percentages might not sum to

100% because of missing values or listings of other syndrome types that

were not included for this analysis.

†

Respiratory symptoms consist of acute bronchitis, chest congestion, cough,

difficulty breathing, sore throat, influenza-like illness, nasal congestion, otitis

media, shortness of breath, upper respiratory infection, or wheezing.

Gastrointestinal symptoms consist of abdominal pain, diarrhea,

gastrointestinal bleeding, loss of appetite, nausea, or vomiting.

Neurologic symptoms consist of altered mental status, dizziness, drowsiness,

headache, or muscle weakness.

** Dermatologic symptoms consist only of rash.

Increasing awareness so that more patients know to mention

harmful algal bloom exposure and more physicians know to

ask about harmful algal blooms would enhance understanding

of harmful algal bloom–associated ED visits.

The findings in this report are subject to at least two limi-

tations. First, some records might have been misclassified or

miscoded. For example, the query development tool identified

some records with a chief complaint that seemed unrelated to

harmful algal bloom exposure (e.g., meningitis exposure or

vaginal problems) despite the use of the Z77.121 harmful algal

bloom exposure ICD-10-CM code. In addition, it is unknown

what occurred during the ED visit between when the chief

complaint was assigned at triage and when the final diagnosis

was determined. Some patients might have described a harm-

ful algal bloom exposure, but medical personnel might have

ruled it out as the primary reason for diagnosis. Second, NSSP

undercounts the number of harmful algal bloom–associated

ED visits that resulted from environmental exposures because

1) only 70% of ED visits nationally are included within the

data set, and 2) ICD-10-CM codes are from billing data and

codes for harmful algal bloom exposures might not be included

if they do not affect reimbursement. Despite these limitations,

however, these analyses provide information of how often

exposure to a harmful algal bloom is documented during ED

visits through diagnostic codes and chief complaints.

These findings provide information about how harmful

algal bloom exposure can be identified through syndromic

surveillance ED visit data and potentially used to identify

the extent of illness from harmful algal bloom exposure in

the United States. As the frequency and geographic extent

of harmful algal blooms increase, it is important for health

care providers to discuss and document harmful algal bloom

exposures and health effects during medical visits to ensure

proper patient treatment and help patients understand how

Morbidity and Mortality Weekly Report

1194 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

Summary

What is already known about this topic?

Harmful algal and cyanobacterial blooms are large colonies of

algae or cyanobacteria that can harm humans, animals, and

the environment.

What is added by this report?

National syndromic surveillance data identified 321 emergency

department visits related to harmful algal bloom exposure

during 2017–2019. Frequency of these visits was highest during

warmer months.

What are the implications for public health practice?

Syndromic surveillance data are useful for studying the extent

of harmful algal bloom–associated illness. Increasing awareness

so that more patients know to mention harmful algal bloom

exposures and more physicians know to ask about them could

improve documentation of health effects and enable further

use of health records for health studies.

to prevent exposure in the future. As access to information

from electronic medical records for research improves, better

documentation of harmful bloom exposures and illnesses can

help support a more accurate assessment of their acute public

health impact. With better documentation, electronic health

record systems with longitudinal data could potentially pro-

vide data for monitoring long-term health effects from these

exposures, the extent of which are largely unknown.

Acknowledgments

Kathleen Hartnett, Aaron Kite-Powell, Praveena Jayanthi, Division

of Health Informatics and Surveillance, Center for Surveillance,

Epidemiology, and Laboratory Services, CDC; Andrea Winquist,

Division of Environmental Health Science and Practice, National

Center for Environmental Health, CDC.

Corresponding author: Amy M. Lavery, alaver[email protected].

Division of Environmental Health Science and Practice, National Center for

Environmental Health, CDC;

Division of Foodborne, Waterborne, and

Environmental Diseases, National Center for Emerging and Zoonotic Infectious

Diseases, CDC;

Division of Health Informatics and Surveillance, Center for

Surveillance, Epidemiology, and Laboratory Services, Centers for Disease

Control and Prevention.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. No potential conflicts of interest were disclosed.

References

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Department of Commerce, National Oceanic and Atmospheric

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gov/hazards/hab/

2. Backer LC, Fleming LE, Rowan A, et al. Recreational exposure to

aerosolized brevetoxins during Florida red tide events. Harmful Algae

2003;2:19–28. https://doi.org/10.1016/S1568-9883(03)00005-2

3. Kirkpatrick B, Fleming LE, Backer LC, et al. Environmental exposures

to Florida red tides: effects on emergency room respiratory diagnoses

admissions. Harmful Algae 2006;5:526–33. PMID:20357898 https://

doi.org/10.1016/j.hal.2005.09.004

4. Environmental Protection Agency. Causes of CyanoHABs. Washington,

DC: Environmental Protection Agency; 2021. https://www.epa.gov/

cyanohabs/causes-cyanohabs

5. CDC. Harmful algal bloom (HAB)-associated illness: causes and

ecosystem impacts. Atlanta, GA: US Department of Health and Human

Services, CDC; 2021. https://www.cdc.gov/habs/environment.html

6. CDC. National Syndromic Surveillance Program (NSSP): syndromic

data critical to COVID-19. Atlanta, GA: US Department of Health and

Human Services, CDC; 2021. https://www.cdc.gov/nssp/index.html

7. Roberts VA, Vigar M, Backer L, et al. Surveillance for harmful algal bloom

events and associated human and animal illnesses—one health harmful

algal bloom system, United States, 2016–2018. MMWR Morb Mortal

Wkly Rep 2020;69:1889–94. PMID:33332289 https://doi.org/10.15585/

mmwr.mm6950a2

8. Lavery AM, Backer LC, Daniel J. Evaluation of electronic health records

to monitor illness from harmful algal bloom exposure in the United States.

J Environ Health 2021;83.9:8-14. https://www.neha.org/sites/default/

files/jeh/JEH5.21-Feature-Evaluation-Electronic-Health-Records.pdf

9. National Ocean Service. Fall 2018 red tide event that affected Florida and

the Gulf Coast. Silver Spring, MD; US Department of Commerce, National

Oceanic and Atmospheric Administration, National Ocean Service; 2021.

https://oceanservice.noaa.gov/hazards/hab/florida-2018.html

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1195

US Department of Health and Human Services/Centers for Disease Control and Prevention

Multiple Variants of SARS-CoV-2 in a University Outbreak After Spring Break —

Chicago, Illinois, March–May 2021

Kate Doyle, MPH

; Richard A. Teran, PhD

1,2

; Jennita Reefhuis, PhD

; Janna L. Kerins, VMD

; Xueting Qiu, PhD

; Stefan J. Green, PhD

;

Hyeree Choi, MPH

; Sabrina A. Madni, MPH

3,5

; Nazia Kamal, PhD

; Emily Landon, MD

; Reynald Christopher Albert, MHA

; Massimo Pacilli, MPH

;

Laura E. Furtado, MS

; Mary K. Hayden, MD

; Kevin J. Kunstman

; Cindy Bethel

; Lauren Megger

; Marielle J. Fricchione, MD

*; Isaac Ghinai, MBBS

1,2,

To prevent transmission of SARS-CoV-2, the virus that

causes COVID-19, colleges and universities have implemented

multiple strategies including testing, isolation, quarantine,

contact tracing, masking, and vaccination. In April 2021,

the Chicago Department of Public Health (CDPH) was

notified of a large cluster of students with COVID-19 at an

urban university after spring break. A total of 158 cases of

COVID-19 were diagnosed among undergraduate students

during March 15–May 3, 2021; the majority (114; 72.2%)

lived in on-campus dormitories. CDPH evaluated the role of

travel and social connections, as well as the potential impact

of SARS-CoV-2 variants, on transmission. Among 140

infected students who were interviewed, 89 (63.6%) reported

recent travel outside Chicago during spring break, and 57

(40.7%) reported indoor social exposures. At the time of the

outbreak, undergraduate-aged persons were largely ineligible

for vaccination in Chicago; only three of the students with

COVID-19 (1.9%) were fully vaccinated. Whole genome

sequencing (WGS) of 104 specimens revealed multiple distinct

SARS-CoV-2 lineages, suggesting several nearly simultaneous

introductions. Most specimens (66; 63.5%) were B.1.1.222, a

lineage not widely detected in Chicago before or after this out-

break. These results demonstrate the potential for COVID-19

outbreaks on university campuses after widespread student

travel during breaks, at the beginning of new school terms,

and when students participate in indoor social gatherings. To

prevent SARS-CoV-2 transmission, colleges and universities

should encourage COVID-19 vaccination; discourage unvacci-

nated students from travel, including during university breaks;

implement serial COVID-19 screening among unvaccinated

persons after university breaks; encourage masking; and imple-

ment universal serial testing for students based on community

transmission levels.

University Prevention Measures

In spring 2021, approximately 2,100 students were living

on the campus of an urban university in Chicago, Illinois. In

* These authors contributed equally to this report.

response to the COVID-19 pandemic, the university imple-

mented numerous prevention strategies.

†

Students living on-

and off-campus were required to report positive SARS-CoV-2

test results to the university. Students living in the dormitories

were required to receive testing for SARS-CoV-2 every week

(serial screening); testing was offered for free by the university.

During March 20–29, 2021, university activities, including

classes, paused for spring break, and the university recom-

mended that students avoid all travel during this period;

dormitories remained open. After the break, students who

lived on campus were advised to stay in their dormitories for

1 week, and all classes were held remotely.

In addition to

regular serial screening, students who lived in dormitories were

required to receive testing for SARS-CoV-2 before resuming

in-person learning.

Investigation and Response

On April 7, 2021, the university notified CDPH of 37 stu-

dents with positive SARS-CoV-2 test results detected through

serial screening conducted during March 29–April 5, 2021.

In response to this cluster of COVID-19 cases, the university

implemented a stay-at-home order for students living on cam-

pus (requiring students to stay in their dormitories), held all

classes remotely, and prohibited gatherings. During the stay-

at-home order, the university modified the screening schedule

to require testing for students living on campus twice during

the first 10 days of the order. In consultation with CDPH,

after additional testing found few cases, the university lifted

the order after 14 days.

A case was defined as receipt of a positive SARS-CoV-2

test result by an undergraduate student living on or near the

†

For the 2020–21 academic year, dormitory capacities were reduced, bedrooms

were single-occupancy, and numerous changes to physical infrastructure were

made. All students were required to sign a health agreement and pledge to avoid

social gatherings. The university maintained separate housing space for students

with positive SARS-CoV-2 test results; these students were required to follow

isolation procedures.

Real-time reverse transcription–polymerase chain reaction testing was performed

on anterior nasal swab specimens collected by trained medical assistants.

Students were permitted to leave their dormitories to take walks, buy food, or

pick up meals from the dining halls.

Morbidity and Mortality Weekly Report

1196 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

university campus during March 15–May 18, 2021.** For all

students with COVID-19, the university provided information

on residence (on-campus dormitory or off-campus), age, gen-

der, and positive specimen collection date. CDPH conducted

interviews to collect information on demographic characteris-

tics, clinical signs or symptoms, travel history, social activities,

attendance at social gatherings, and close contacts. Diagnostic

testing history and results were extracted from state surveillance

and vaccination records from immunization registry systems.

Available specimens were sequenced and assigned a lineage.

††

Similar sequences (differing by fewer than five nucleotides)

were assumed to represent a single viral introduction.

§§

** The investigation period began 2 weeks before the start of spring break and

ended 2 weeks after the last positive SARS-CoV-2 test result for an

undergraduate student. Any positive test result among undergraduate students

who lived on campus or in Chicago was evaluated for inclusion. Graduate

students and staff members with COVID-19 were not included in this

outbreak case definition. Assessment of cases among these populations

indicated very limited or no interaction with undergraduate students who

lived on or very near campus.

††

Available SARS-CoV-2 positive specimens were submitted to CDPH’s

Regional Innovative Public Health Laboratory for whole genome sequencing

using the Swift Amplicon SARS-CoV-2 Panel (Swift Biosciences) and deep

sequencing on an Illumina instrument. Viral lineages were assigned using the

Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN)

tool (version v3.0.2; Rambaut Laboratory). Nucleotide differences were

assessed with IQ-TREE.

§§

Among the specimens of B.1.1.222 lineage, the majority of sequences were

genetically similar (within two nucleotides). Although there is no standard

genomic definition of sequences linked by transmission, this level of similarity,

together with the lack of detection of similar contemporaneous specimens in

Chicago, is likely consistent with one viral introduction.

identify possible geographic sources of importations, outbreak

lineages were compared with all contemporaneous sequences

of the same lineage available on the Global Initiative on

Sharing All Influenza Data (GISAID) platform. Descriptive

and social network analyses were completed using R (ver-

sion 4.1.0; R Foundation) and MicrobeTrace (version 0.7.0;

CDC), respectively. This activity was reviewed by CDC and

was conducted consistent with applicable federal law and

CDC policy.

¶¶

A total of 158 COVID-19 cases were identified among

undergraduate students (Figure 1), including 76 (48.1%) in

women; the median age of students with COVID-19 was

19.4 years (interquartile range=18.9–20.3 years) (Table). A

total of 114 (72.2%) students with COVID-19 lived in dor-

mitories (Supplementary Table, https://stacks.cdc.gov/view/

cdc/109260); the rest lived off-campus but near the university.

Among the 158 students with COVID-19, 140 (88.6%)

were interviewed, among whom 127 (90.7%) reported at least

one COVID-19 symptom (Table). Two were evaluated in an

emergency department after diagnosis; no infected student was

hospitalized or died. One student with COVID-19 had a pre-

vious laboratory-confirmed diagnosis of COVID-19 >90 days

before the infection was identified during the investigation

period. Among all interviewed students with COVID-19, 93

(66.4%) were unvaccinated, and 43 (30.7%) were partially

vaccinated (i.e., received 1 dose of a 2-dose COVID-19 vaccine

¶¶

45 C.F.R. part 46.102(l)(2), 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d);

5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

FIGURE 1. Date of onset* and viral lineage among undergraduate students with COVID-19 (n = 158) — Chicago, Illinois, March–April 2021

No. of COVID-19 cases

Date

B.1.1.222

B.1.1.7

Not sequenced

Other lineage

Spring break period

CDPH begins investigation

15 16 17 18 19 20 21 22

Mar Apr

23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Post–spring break

stay-at-home

advisory

Stay-at-home order in place

Stay-at-home

order rescinded

Abbreviation: CDPH = Chicago Department of Public Health.

* Or date of specimen collection for asymptomatic and presymptomatic persons. One specimen was collected and tested in May, but the date of symptom onset for

the student was in April.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1197

US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE. Characteristics of undergraduate students with COVID-19

(n = 158) — Chicago, Illinois, March–May 2021

Characteristic

(no. with available information) No. (%)

Demographics (158)

Female 76 (48.1)

Median age, yrs (IQR) 19.4 (18.9–20.2)

Residence (158)

Dormitory A 35 (22.2)

Dormitory B 32 (20.3)

Dormitory C 31 (19.6)

Dormitory D 7 (4.4)

Dormitory E 5 (3.2)

Dormitory F 4 (2.5)

Off-campus 44 (27.8)

Previous COVID-19 diagnosis

>90 days before test date*

1 (0.6)

Interviewed (140)

Symptomatic 127 (90.7)

Provided at least one contact name 88 (62.9)

Reported indoor social exposure 57 (40.7)

Reported party exposure 3 (2.1)

Reported travel 89 (63.6)

Vaccination (140)

Reported not vaccinated 93 (66.4)

Reported partially vaccinated 43 (30.7)

Reported fully vaccinated

†

3 (2.1)

Travel destinations (89)

Florida 20 (22.5)

California 11 (12.4)

New York 11 (12.4)

Colorado 5 (5.6)

Within Illinois 3 (3.4)

Other U.S. states 32 (36.0)

International 6 (6.7)

Purpose of travel (89)

Vacation away from home 43 (48.3)

Visiting home 23 (25.8)

Moving to campus 3 (3.4)

Unknown 20 (22.5)

Lineage (104)

B.1.1.222 66 (63.5)

B.1.1.7 22 (21.2)

P. 1 9 (8.7)

B.1.526 3 (2.9)

B.1.526.1 1 (1.0)

B.1.526.2 1 (1.0)

B.1.1 1 (1.0)

B.1.429 1 (1.0)

Abbreviation: IQR=interquartile range.

* Previous diagnosis of COVID-19 was laboratory-confirmed.

†

Vaccination information was collected by self-report and verified, when

possible, with the state immunization registry. The three persons who reported

full vaccination could not be verified because vaccinations were administered

out of state (two) and as part of a clinical trial (one).

series or completed a vaccine series <14 days before diagnosis).

Three (1.9%) students with COVID-19 reported being fully

vaccinated; two of these students experienced symptoms.

The majority (88; 62.9%) of students with COVID-19 pro-

vided the name of at least one other student with COVID-19

with whom they had had contact in the 2 weeks preceding

Summary

What is already known about this topic?

SARS-CoV-2 transmission on college and university campuses

can occur when unvaccinated students return to campus after

travel or attend social gatherings.

What is added by this report?

After spring break 2021, COVID-19 cases increased rapidly at a

Chicago university despite mitigation measures. Interviews

indicated that the majority of cases occurred in unvaccinated

persons with a history of recent travel. Sequencing corrobo-

rated multiple introductions to campus and demonstrated that

even a single importation can result in many cases.

What are the implications for public health practice?

To mitigate SARS-CoV-2 transmission, colleges and universities

can encourage COVID-19 vaccination; discourage unvaccinated

students from traveling, including during university breaks;

implement serial screening after university breaks; test based

on community transmission; and encourage masking.

symptom onset or test date. Fifty-seven (40.7%) students

with COVID-19 described unmasked indoor exposures to

other students at small gatherings, meals, or while studying.

Although the university was aware of several large gatherings,

only three infected students (2.1%) reported having attended a

party. A network diagram was constructed to show social con-

nections, residence, travel, and viral lineage (Figure 2). Based

on interview data, 25 groups of socially connected students

with COVID-19 (clusters) were identified; the median cluster

size was two, and the maximum was 45. Several social groups

included multiple dormitories.

Overall, 89 (63.6%) interviewed students with COVID-19

reported travel outside Chicago during spring break. Fourteen

students traveled with at least one other infected student in five

different travel groups. Destinations included seven different

countries and 23 U.S. states; the most commonly visited states

were California, Colorado, Florida, and New York (Table).

The most commonly reported reason for travel was vacation

(43; 48.3%).

Residual specimens were available for 120 (75.9%)

infected students, 104 (86.7%) of which were successfully

sequenced. Sequences were assigned nine different lineages,

mostly B.1.1.222 (66; 63.5%), followed by B.1.1.7 (Alpha)

(22; 21.2%) (Table) (Supplementary Figure, https://stacks.cdc.

gov/view/cdc/109259). All B.1.1.222 sequences differed by

fewer than five nucleotides and likely represent a single source

introduction. When compared with sequences in GISAID, the

B.1.1.222 sequences in this outbreak were most closely related

to specimens from California. Eight of the 66 students (12.1%)

infected with the SARS-CoV-2 B.1.1.222 lineage had a his-

tory of travel to California. In Dormitory A, 25 of 35 (71.4%)

Morbidity and Mortality Weekly Report

1198 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE 2. Social networks among undergraduate students with COVID-19 (n = 158), by residence and viral lineage — Chicago, Illinois,

March–May 2021

Dormitory A

O campus

Dormitory B

Dormitory C

Dormitory D

Dormitory E

Dormitory F

Reported travel Housing link

Social linkNo reported travel

Unknown travel

B.1.1.222

B.1.1.7

Not sequenced

Other lineage

specimens from infected students were sequenced, and 24 of 25

(96.0%) were confirmed as the B.1.1.222 lineage (Figure 2).

Among B.1.1.7 (Alpha) specimens, 10 groups of sequences dif-

fered by five or more nucleotides, indicating multiple separate

importations; groups ranged in size from one to five students.

B.1.1.7 was circulating widely in Chicago and elsewhere in the

United States at the time of this outbreak. Among the students

who traveled together, some travel groups had the same lineage

(though students reported additional close contact on campus),

while other groups included several lineages. Specimens from

all three fully vaccinated students were available; however, only

one (from a symptomatic student) was successfully sequenced

as a B.1.1.222 lineage.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1199

US Department of Health and Human Services/Centers for Disease Control and Prevention

Discussion

Previous reports have described outbreaks of COVID-19

among university students with complex social networks

and social exposures (1–5). In this outbreak, 158 cases of

COVID-19 were identified after many unvaccinated students

traveled during a university break, despite university policies

advising against travel. Subsequent on-campus gatherings led to

further transmission within and across social networks, includ-

ing between dormitories. Notably, this outbreak occurred

immediately before expansion of eligibility for vaccination in

Chicago; undergraduate-aged persons were largely ineligible

for vaccination before April 19, 2021.***

WGS identified several lineages and multiple distinct

introductions of SARS-CoV-2 that were possibly driven by

student travel. Phylogenetic analyses illustrated gaps in the

social network; for example, several students with no reported

social connections were infected with nearly identical strains

of B.1.1.222, a lineage not widely identified in Chicago before

or after this outbreak.

†††

Transmission likely occurred among

students without known social connections or through unde-

tected cases associated with the outbreak, although these links

cannot be confirmed with available case interview data.

The findings in this report are subject to at least four limita-

tions. First, some students with COVID-19 refused interviews,

omitted critical details, or provided false and conflicting infor-

mation, such as denying travel when other students indicated

that they had traveled together. This reticence limited the

ability to thoroughly assess social networks and transmission

chains. Second, serial screening was mandatory only for stu-

dents living on-campus; students living off-campus might have

had COVID-19 but did not receive testing during the outbreak

period. Given potentially undiagnosed infections, the magni-

tude of the outbreak might have been greater than described.

Third, not all SARS-CoV-2 specimens could be sequenced;

additional viral introductions or transmission chains might

have been missed. Finally, because publicly available sequence

data include only a subset of all viruses, the source of viral

introductions could not be definitively identified.

These findings support existing CDC recommendations for

the control of COVID-19 in colleges and universities; these

recommendations are especially important given the rapid

spread of the B.1.617.2 (Delta) variant of concern.

§§§

Serial

*** https://www.chicago.gov/city/en/sites/covid19-vaccine/home/vaccine-

distribution-phases.html

†††

Only one other B.1.1.222 sequence from a specimen collected in 2021 in

Chicago was available on GISAID (out of 674 Chicago sequences from 2021

not associated with this outbreak). This sequence, from a specimen collected

3 months before this outbreak, differed by more than five nucleotides from

the largely homogenous sequences observed in this outbreak.

§§§

https://www.cdc.gov/coronavirus/2019-ncov/community/colleges-

universities/considerations.html

testing successfully detected an outbreak among university

undergraduates; isolation of students with COVID-19, contact

tracing, and university-wide prevention measures contributed

to reductions in transmission. Nevertheless, unvaccinated

persons traveling during a university break and subsequent

socializing among students resulted in multiple clusters of

COVID-19 before vaccines were widely offered to undergradu-

ate-aged persons in Chicago. Vaccination is the leading preven-

tion strategy to protect persons from COVID-19, and colleges

and universities can benefit from encouraging vaccination for

all students, faculty, and staff members. In settings where not

everyone is fully vaccinated or where students have contact with

community members who are not fully vaccinated, colleges

and universities can encourage unvaccinated students to refrain

from travel; implement serial screening testing for unvaccinated

students, faculty, and staff members after university breaks; test

for SARS-CoV-2 based on community transmission levels;

encourage masking indoors; and make free, voluntary testing

readily available, including for fully vaccinated persons who

are experiencing COVID-19 symptoms.

¶¶¶

Acknowledgments

Francis Ambrosio, Mario Arias, Kelly Arndt, Carla Barrios, Fatima

Saifi, Chicago Department of Public Health case investigators;

university COVID response team; CDC MicrobeTrace team;

undergraduate students who participated in the investigation.

Corresponding author: Richard A. Teran, [email protected].

Chicago Department of Public Health, Chicago, Illinois;

Epidemic

Intelligence Service, CDC;

Division of Birth Defects and Infant Disorders,

National Center on Birth Defects and Developmental Disabilities, CDC;

Rush

University Medical Center, Chicago, Illinois;

4ES Corporation, San Antonio,

Texas;

Division of Preparedness and Emerging Infections, National Center

for Emerging and Zoonotic Infectious Diseases, CDC;

University of Chicago,

Chicago, Illinois.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. Mary K. Hayden reports participation on a clinical

adjudication panel for Sanofi related to investigational COVID-19

vaccines. No other potential conflicts of interest were disclosed.

References

1. Lewis M, Sanchez R, Auerbach S, et al. COVID-19 outbreak among

college students after a spring break trip to Mexico—Austin, Texas,

March 26–April 5, 2020. MMWR Morb Mortal Wkly Rep 2020;69:830–5.

PMID:32614814 https://doi.org/10.15585/mmwr.mm6926e1

2. Wilson E, Donovan CV, Campbell M, et al. Multiple COVID-19 clusters

on a university campus—North Carolina, August 2020. MMWR Morb

Mortal Wkly Rep 2020;69:1416–8. PMID:33001871 https://doi.

org/10.15585/mmwr.mm6939e3

¶¶¶

https://www.cdc.gov/coronavirus/2019-ncov/community/colleges-

universities/considerations.html#section3

Morbidity and Mortality Weekly Report

1200 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

3. Teran R, Ghinai I, Gretsch S, et al. COVID-19 outbreak among a

university’s men’s and women’s soccer teams—Chicago, Illinois, July–

August 2020. MMWR Morb Mortal Wkly Rep 2020;69:1591–4. https://

doi.org/10.15585/mmwr.mm6943e5

4. Vang KE, Krow-Lucal ER, James AE, et al. Participation in fraternity and

sorority activities and the spread of COVID-19 among residential

university communities—Arkansas, August 21–September 5, 2020.

MMWR Morb Mortal Wkly Rep 2021;70:20–3. PMID:33411698

https://doi.org/10.15585/mmwr.mm7001a5

5. Fox MD, Bailey DC, Seamon MD, Miranda M. Response to a COVID-19

outbreak on a university campus—Indiana, August 2020. MMWR Morb

Mortal Wkly Rep 2021;70:118–22. PMID:33507894 https://doi.

org/10.15585/mmwr.mm7004a3

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1201

US Department of Health and Human Services/Centers for Disease Control and Prevention

Screening Programs for SARS-CoV-2 Infections on a University Campus —

Austin, Texas, September 30–November 30, 2020

Kayleigh J. Nerhood

; Emily R. James

; Allen Hardin, MS

; James E. Bray, MD

; Terrance S. Hines, MD

; Amy E. Young, MD

; Darlene Bhavnani, PhD

Colleges and universities in the United States have relied

on various measures during the COVID-19 pandemic to

prevent transmission of SARS-CoV-2, the virus that causes

COVID-19, including implementing testing programs (1–3).

These programs have permitted a safer return to campus for

students by identifying infected persons and temporarily isolat-

ing them from the campus population (2,3). The University

of Texas at Austin (UT Austin) implemented COVID-19

prevention measures in Fall 2020* including the following

testing programs: clinic-based diagnostic testing, voluntary

community screening, and targeted screening (testing of spe-

cific student populations in situations of increased transmission

risk). During September 30–November 30, 2020, UT Austin

students participated in tests for SARS-CoV-2, which resulted

in the detection of 401 unique student cases of COVID-19

from among 32,401 tests conducted.

†

Among students who

participated in one targeted screening program for students

attending campus events, 18 (37.5%) of 48 infected students

were asymptomatic at the time of their positive test result

compared with 45 (23%) of 195 students identified through

community testing and nine (5.8%) of 158 students identified

through clinic-based testing. Targeted screening also identified

a different population of students than did clinic-based and

community testing programs. Infected students tested through

targeted screening were more likely to be non-Hispanic White

persons (chi square=20.42; p<0.03), less likely to engage in

public health measures, and more likely to have had interac-

tions in settings where the risk for SARS-CoV-2 transmis-

sion is higher, such as restaurants, gyms, and residence halls.

In addition to clinic-based SARS-CoV-2 testing at colleges

and universities, complementary testing programs such as

community and targeted screening might enhance efforts to

identify and control SARS-CoV-2 transmission, especially

among asymptomatic persons and disproportionately affected

populations that might not otherwise be reached.

During September 30–November 30, 2020, UT Austin

employed the following SARS-CoV-2 testing programs:

1) clinic-based diagnostic testing administered by University

Health Services for persons who were symptomatic or reported

* https://protect.utexas.edu/

†

A COVID-19 case was defined as a positive SARS-CoV-2 nucleic acid

amplification test or antigen test result.

exposure to SARS-CoV-2 (clinic-based testing); 2) Proactive

Community Testing, which involved voluntary screening of

asymptomatic persons offered at several fixed or rotating sites

on-and-off campus (community testing); and 3) targeted

screening of specific student populations in situations of

increased transmission risk. One targeted screening program

focused on Big Ticket holders, students with season tickets to

athletic events. These events are large gatherings that might

involve several SARS-CoV-2 infection risk factors such as

several hours of possible exposure, the potential for crowding,

and behaviors such as singing and shouting.

Students were

tested up to 3 days before each event. Either a negative test

result or proof of previous SARS-CoV-2 infection 14–90 days

before the event was required for entry. Community testing and

targeted screening programs were provided to students at no

cost; clinic-based tests were billed to students’ insurance. Cases

were identified through clinic-based testing using SARS-CoV-2

nucleic acid amplification tests (NAATs), including reverse

transcription–polymerase chain reaction (RT-PCR) or isother-

mal NAAT (ID NOW [Abbott] or Aptima SARS-CoV-2 Assay

[Hologic]). Community testing used a Clinical Laboratory

Improvement Amendments (CLIA)-certified RT-PCR test

performed at a UT laboratory, and testing for Big Ticket

holders used an antigen test (Sofia SARS Antigen Fluorescent

Immunoassay [Quidel Corporation])

or UT’s CLIA-certified

RT-PCR test. Test results were reported to Dell Medical School

at UT Austin, which was delegated by Austin Public Health to

conduct contact tracing. Contact tracers interviewed infected

persons to identify close contacts** during their infectious

period,

††

and collected exposure details, including dates,

proximity, location, duration of exposure, and mask use.

https://www.cdc.gov/coronavirus/2019-ncov/community/large-events/

considerations-for-events-gatherings.html

https://www.fda.gov/media/137884/download

** Close contact was defined as being within 6 ft of a person with laboratory-

confirmed or probable COVID-19 infection for a cumulative total of ≥15 minutes

during a 24-hour period; or having physical contact with; or sharing living spaces

such as bedrooms, bathrooms, or kitchens. https://www.cdc.gov/coronavirus/2019-

ncov/php/contact-tracing/contact-tracing-plan/appendix.html

††

The infectious period was estimated to begin 2 days before symptom onset

and end ≥10 days after symptom onset or positive test result, as long as other

symptoms (except loss of taste or smell) were improving and the patient had

been fever-free for 24 hours without fever-reducing medication, according to

CDC guidance. https://www.cdc.gov/coronavirus/2019-ncov/php/contact-

tracing/contact-tracing-plan/investigating-covid-19-case.html

Morbidity and Mortality Weekly Report

1202 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

Characteristics, symptom status, isolation practices, and case

investigation outcomes among students with COVID-19 were

assessed; statistical comparisons among cases identified by the

different testing programs were performed using chi square

tests or one-way ANOVA in Python (version 3.7.9; Python

Software Foundation) using the SciPy statistical package (ver-

sion 1.5.4; Python Software Foundation); p values <0.05 were

considered statistically significant. This study was reviewed

by a UT Institutional Review Board and deemed to not be

human subjects research. This activity was reviewed by CDC

and conducted consistent with applicable federal law and

CDC policy.

§§

Among 32,401 tests of UT Austin students, 401 unique

COVID-19 cases were identified (Table 1); 3,044 tests were

§§

45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C.

Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

done through clinic-based testing, 25,042 through community

testing, and 4,314 through testing of Big Ticket holders.

Among one targeted screening program for Big Ticket holders,

75% of infected students self-identified as non-Hispanic

White persons, compared with 48.7% of infected students

detected by community testing and 58.9% of infected students

detected by clinic-based testing (chi square=20.42; p<0.03).

The proportion of non-Hispanic White students identified

by each of the three testing programs was higher than that

reported for the overall UT Austin student population

¶¶

(38.9%; chi square= 177; p<0.001). UT contact tracers

interviewed 85.5% of all infected persons. Among Big Ticket

holders, 75% of infected persons were interviewed, 20.8% were

unreachable by phone, and 4.2% stated they were unwilling to

¶¶

https://www.utexas.edu/about/facts-and-figures

TABLE 1. Demographic characteristics, symptom status, isolation practices, and case investigation outcomes among students with COVID-19,

by testing program — University of Texas at Austin, September 30–November 30, 2020

Characteristic

(no. with available information)

No. (%)

Total

Testing program

Big Ticket holder* Community Clinic-based

Students in testing programs 401 (100) 48 (12.0) 195 (48.6) 158 (39.4)

Age, yrs, median (range) 20 (18–29) 19.5 (18–22) 20 (18–28) 21 (18–29)

Sex (401)

Male 187 (46.6) 19 (39.6) 86 (44.1) 82 (51.9)

Female 213 (53.1) 29 (60.4) 108 (55.4) 76 (48.1)

Unknown 1 (0.2) 0 (—) 1 (0.5) 0 (—)

Race/Ethnicity (401)

White, non-Hispanic 224 (55.9) 36 (75.0) 95 (48.7) 93 (58.9)

Black, non-Hispanic 14 (3.5) 0 (—) 7 (3.6) 7 (4.4)

Asian, non-Hispanic 37 (9.2) 2 (4.2) 21 (10.8) 14 (8.9)

White, Hispanic 89 (22.2) 5 (10.4) 56 (28.7) 28 (17.7)

Multiracial 8 (2.0) 0 (—) 3 (1.5) 5 (3.2)

Unknown 29 (7.2) 5 (10.4) 13 (6.7) 11 (6.9)

Outcomes of COVID-19 case investigations (401)

Interviewed 343 (85.5) 36 (75.0) 171 (87.7) 136 (86.1)

Unable to interview 53 (13.2) 10 (20.8) 22 (11.3) 21 (13.3)

Unwilling to participate 5 (1.2) 2 (4.2) 2 (1.0) 1 (0.6)

Symptom status

Symptomatic 284 (70.8) 22 (45.8) 129 (66.2) 133 (84.2)

Asymptomatic 72 (18.0) 18 (37.5) 45 (23.1) 9 (5.7)

Unknown 45 (11.2) 8 (16.7) 21 (10.7) 16 (10.1)

Patient isolation (343)

†

Yes 317 (92.4) 29 (80.6) 156 (91.2) 132 (97.1)

No 23 (6.7) 5 (13.9) 14 (8.2) 4 (2.9)

Unknown 3 (0.9) 2 (5.6) 1 (0.6) 0 (—)

Specimen collection relative to symptom onset

(274)

Before symptom onset 28 (10.2) 3 (15.0) 18 (14.2) 7 (5.5)

On or after symptom onset 246 (89.8) 17 (85.0) 109 (85.8) 120 (94.5)

Start of isolation relative to symptom onset

(274)

Before symptom onset 42 (15.3) 0 (—) 15 (11.8) 27 (21.3)

On or after symptom onset 203 (74.1) 13 (65.0) 98 (77.2) 92 (72.4)

Unknown 29 (10.6) 7 (35.0) 14 (11.0) 8 (6.3)

* Screening targeted to students who held season tickets to athletic events.

†

Population limited to persons who were interviewed.

Population limited to persons who were interviewed and symptomatic.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1203

US Department of Health and Human Services/Centers for Disease Control and Prevention

participate in the interview, a larger proportion of refusals than

for community testing (1.0%) and clinic-based testing (0.6%).

Approximately 38% of cases among Big Ticket holders

occurred in persons who were asymptomatic at the time of their

positive test results, compared with 23% identified through

community testing and 6% through clinic-based testing

(chi square=35; p<0.001). Higher proportions of infected

students from the Big Ticket and community testing programs

were tested before symptom onset (15.0% and 14.2%, respec-

tively) compared with clinic-based testing (5.5%); however,

these differences were not statistically significant. Infected

persons detected through testing of Big Ticket holders were

less likely to have isolated after receiving a positive result (80%)

than were those identified through community (91.2%) and

clinic-based testing (97.1%).

Among 195 cases detected through community testing and

48 through testing of Big Ticket holders, 120 (61.5%) and

35 (72.9%) persons, respectively had no previous engagement

with community testing (Table 2). Among 40 asymptomatic

infected persons who had no previous community testing his-

tory, the testing program for Big Ticket holders identified a

higher proportion of asymptomatic cases than did community

testing (31.4% versus 24.2%; chi square=7.53; p = 0.02).

A similar average number of close contacts was reported by

infected persons identified from testing of Big Ticket holders

(2.6 per person), community testing (3.1), and clinic-based

testing (2.7) (p = 0.5). The most frequently reported exposure

location among all testing programs was household (44%),

defined as a shared living space (including a shared room or

suite in a residence hall) (Table 3). The second most common

exposure location identified through community and clinic-

based testing was private residence or apartment visits (24%

and 29%, respectively). In contrast, restaurants (22%) and

residence halls (16%) were the next most common exposure

locations among infected persons identified through testing

for Big Ticket holders. These persons also reported a higher

proportion of exposures in fitness or recreational facilities

(6%) than did persons identified through community testing

(3%) and clinic-based testing (1%), and a lower proportion

of exposures outdoors (2% versus 13% and 6%, respectively;

chi square=145; p<0.001). Across all programs, most expo-

sures were characterized by one or both students not wearing

TABLE 2. Symptom status* of student COVID-19 cases detected by community testing and testing for Big Ticket holders,

†

stratified by previous

history with community testing — University of Texas at Austin, September 30–November 30, 2020

Symptom status

No. (%)

Total

N = 243

History of community testing

n = 155

Yes

n = 88

Community

n = 120

Big Ticket holder

n = 35

Community

n = 75

Big Ticket holder

n = 13

Asymptomatic 63 (25.9) 29 (24.2) 11 (31.4) 16 (21.3) 7 (53.8)

Symptomatic 151 (62.1) 76 (63.3) 17 (48.6) 53 (70.7) 5 (38.5)

Unknown 29 (11.9) 15 (12.5) 7 (20.0) 6 (8.0) 1 (7.7)

* Symptom status reported at time of case investigation.

†

Excluding cases detected by the University Health Services clinic-based testing.

Infected persons had at least one COVID-19 test via community testing at any time before their positive result and during the study period.

Students who held season tickets to athletic events.

TABLE 3. Location of exposure* among persons with COVID-19

†

and their contacts, by testing program — University of Texas at Austin,

September 30–November 30, 2020

Location

Total

N = 1,147

Testing program, no. (%)

Big Ticket holder

n = 123

Community

n = 603

Clinic-based

n = 421

Household 502 (44) 42 (34) 250 (41) 210 (50)

Restaurant 74 (6) 27 (22) 34 (6) 13 (3)

Residence hall visit 53 (5) 20 (16) 25 (4) 8 (2)

Private residence visit 292 (25) 17 (14) 145 (24) 130 (31)

Fitness or recreational facility 32 (3) 7 (6) 20 (3) 5 (1)

Outdoor 105 (9) 2 (2) 77 (13) 26 (6)

Other 89 (8) 8 (7) 52 (9) 29 (7)

* If an infected person and a close contact interacted in multiple locations, contact tracers chose the most likely transmission site based on duration, proximity,

ventilation, and mask use.

†

Population limited to persons who were interviewed and named close contacts.

Students who held season tickets to athletic events.

Morbidity and Mortality Weekly Report

1204 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

a mask (91.4% of Big Ticket holders and 87.9% of those who

received community and clinic-based testing) (chi square=1.1;

p = 0.3). Contact tracers provided counseling to both infected

persons and close contacts on appropriate mask use to prevent

future exposures or reinfection.

Discussion

Clinic-based diagnostic testing is a valuable tool to detect

SARS-CoV-2 infection, particularly among symptomatic

persons; however, complementary testing programs might

enhance case detection (4). At UT Austin, one targeted screen-

ing program (conducted before vaccine availability) that tested

Big Ticket holders identified a significantly higher proportion

of asymptomatic persons than did clinic-based diagnostic test-

ing at University Health Services (as expected), and voluntary

screening through Proactive Community Testing. This targeted

testing program resulted in the identification of potential

asymptomatic spreaders, who might not have been detected

through clinic-based or community testing (5).

Targeted screening of Big Ticket holders identified a different

population from those identified by community and clinic-

based testing: students who were predominantly non-Hispanic

White and less likely to participate in voluntary public health

prevention strategies including community testing, early

isolation, and contact tracing. These Big Ticket holders also

had more exposures in restaurants, a documented risk factor

for SARS-CoV-2 infection (6), and in fitness or recreational

facilities, locations of several large outbreaks (7). They also

interacted more within residence halls, which include shared

facilities and social areas; risks for transmission in these set-

tings might be similar to those experienced in long-term care

facilities (1,8,9).

The findings of this study are subject to at least six limi-

tations. First, this study analyzed only one targeted testing

program among students aged 18–29 years. Assessment of

other targeted programs to include a broader age range might

alter these findings. Second, both antigen tests and NAATs

were used in testing of Big Ticket holders with different

turnaround times for results (<2 hours for antigen tests and

24–48 hours for NAATs), which might have affected infected

persons’ isolation timing and number of close contacts during

their infectious period. Differences in NAAT and antigen test

sensitivity might have also affected case ascertainment, with

antigen tests potentially missing contagious persons and NAAT

potentially detecting persons no longer infectious (10). Antigen

tests were not confirmed with NAATs, because rapid results

were required to exclude potentially infectious persons from

next-day events. Third, symptom status was self-reported and

recorded at the time of the interview; therefore, the number of

Summary

What is already known about this topic?

University testing programs have permitted a safer return of

students to campus by identifying persons with COVID-19 and

temporarily isolating them from the campus population.

What is added by this report?

Targeted screening identified 48 cases of COVID-19 during

September–November 2020, 18 (38%) of which were in

asymptomatic persons. This population of infected students

was demographically different from those identified through

other testing programs, more risk-tolerant, and less willing to

participate in public health prevention activities.

What are the implications for public health practice?

In addition to clinic-based diagnostic SARS-CoV-2 testing at

colleges and universities, a complementary strategy of commu-

nity and targeted screening programs might enhance efforts to

identify and control transmission of COVID-19.

asymptomatic cases could have been overestimated. However,

targeted screening would have still succeeded in identifying

presymptomatic cases. Fourth, symptoms caused by allergies,

stress, or other infectious diseases might have been incorrectly

attributed to COVID-19, inflating the number of symptomatic

cases, particularly among those from clinic-based testing. Fifth,

whether symptoms that started the day of the test began before

or after the test is not known, which might underestimate the

proportion of students who were tested before symptom onset.

Finally, the higher proportion of infected Big Ticket holders

who were unavailable or unwilling to participate in contact

tracing compared with the other testing program groups, might

have affected comparisons of symptom status, isolation, and

exposures to close contacts.

Screening tests are an important part of risk-reduction strate-

gies on college and university campuses and in other congregate

settings. Targeted testing in this university effort facilitated

reaching and identifying infected persons who might not

have been detected through other testing measures. Therefore,

targeted testing might be used as a complement to diagnostic

and voluntary community screening measures on college and

university campuses, particularly in high-risk or large gatherings

such as university athletic events or graduation ceremonies.

However, if antigen tests are used for asymptomatic screening,

confirmatory NAATs of positive results should be considered

if the likelihood of SARS-CoV-2 infection is low, such as if the

person has no known exposure (10). Further research on targeted

testing in other potential high-risk settings such as residence halls

is warranted, especially if a large proportion of these persons are

unvaccinated, or as variants of SARS-CoV-2 emerge.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1205

US Department of Health and Human Services/Centers for Disease Control and Prevention

Acknowledgments

Daniel Iken, Jessica Klima, Luke Klima, Jennifer Sarchet-Morgan,

Ta-Shina Williams, Carol Xia, contact tracing staff members and

volunteers, UT Health Austin and University Health Services,

University of Texas at Austin; Dell Medical School, University of

Texas at Austin and UT Health Austin leadership; staff members

of University Health Services, University of Texas at Austin; staff

members of University of Texas at Austin Athletics.

Corresponding author: Kayleigh Nerhood, [email protected].

Dell Medical School, University of Texas at Austin;

University of Texas at

Austin Athletics;

University Health Services, University of Texas at Austin.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of

potential conflicts of interest. Amy E. Young reports receipt of

honoraria from the American College of Obstetrics and Gynecology,

and travel support from the American Board of Obstetrics and

Gynecology. No other potential conflicts of interest were disclosed.

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1,2

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; Neil Murthy, MD

1,2

; Lu Meng, PhD

; Seth Meador, MPH

1,2

;

Kirsten Reed, MPH

; Lauren Shaw, MS

1,2

; Lynn Gibbs-Scharf, MPH

1,2

; A.D. McNaghten, PhD

; Anita Patel, PharmD

; Shannon Stokley, DrPH

1,2

;

Stephen Flores, PhD

; Jonathan S. Yoder, MPH

; Carla L. Black, PhD

1,2

; LaTreace Q. Harris, MPH

1,2

On August 27, 2021, this report was posted as an MMWR Early

Release on the MMWR website (https://www.cdc.gov/mmwr).

Although severe COVID-19 illness and hospitalization are

more common among adults, these outcomes can occur in ado-

lescents (1). Nearly one third of adolescents aged 12–17 years

hospitalized with COVID-19 during March 2020–April 2021

required intensive care, and 5% of those hospitalized required

endotracheal intubation and mechanical ventilation (2). On

December 11, 2020, the Food and Drug Administration

(FDA) issued Emergency Use Authorization (EUA) of the

Pfizer-BioNTech COVID-19 vaccine for adolescents aged

16–17 years; on May 10, 2021, the EUA was expanded to

include adolescents aged 12–15 years; and on August 23,

2021, FDA granted approval of the vaccine for persons aged

≥16 years. To assess progress in adolescent COVID-19 vac-

cination in the United States, CDC assessed coverage with

≥1 dose* and completion of the 2-dose vaccination series

†

among adolescents aged 12–17 years using vaccine administra-

tion data for 49 U.S. states (all except Idaho) and the District

of Columbia (DC) during December 14, 2020–July 31,

2021. As of July 31, 2021, COVID-19 vaccination coverage

among U.S. adolescents aged 12–17 years was 42.4% for

≥1 dose and 31.9% for series completion. Vaccination coverage

with ≥1 dose varied by state (range=20.2% [Mississippi] to

70.1% [Vermont]) and for series completion (range=10.7%

* Receipt of ≥1 COVID-19 vaccine dose is defined as having received either ≥1

of the 2 Pfizer-BioNTech or Moderna vaccine doses, or a single dose of the

Janssen (Johnson & Johnson) vaccine. As of August 17, 2021, only the Pfizer-

BioNTech vaccine had been authorized for use among adolescents aged

12–17 years. Moderna and Janssen COVID-19 vaccines were not authorized

under emergency use for this age group during December 14, 2020–July 31,

2021. However, doses of these vaccines administered to persons aged 12–17 years

were included in this analysis. During February 27, 2021–July 31, 2021, a total

of 21,919 adolescents aged 12–17 years were reported to have received 1 dose

of the Janssen COVID-19 vaccine. During December 14, 2021–July 31, 2021,

a total of 27,226 adolescents aged 12–17 years were reported to have received

only the first dose of the Moderna COVID-19 vaccine; 66,032 adolescents

aged 12–17 years were reported to have received both doses of the Moderna

COVID-19 vaccine; 2,190 were reported to have received Pfizer-BioNTech for

the first dose but Moderna for the second dose; and 5,726 were reported to

receive Moderna for the first dose but Pfizer-BioNTech for the second dose.

†

Series completion was defined as receipt of either both doses of the Pfizer-

BioNTech or Moderna vaccines, including those that might have received

mismatched products between the first and second dose (i.e., Pfizer-BioNTech

for the first dose and Moderna for the second dose or vice versa) or a single

dose of the Janssen vaccine.

[Mississippi] to 60.3% [Vermont]). By age group, 36.0%,

40.9%, and 50.6% of adolescents aged 12–13, 14–15, and

16–17 years, respectively, received ≥1 dose; 25.4%, 30.5%, and

40.3%, respectively, completed the vaccine series. Improving

vaccination coverage and implementing COVID-19 preven-

tion strategies are crucial to reduce COVID-19–associated

morbidity and mortality among adolescents and to facilitate

safer reopening of schools for in-person learning.

Data on COVID-19 vaccine administration in the

United States are reported to CDC by jurisdictions, pharma-

cies, and federal entities through immunization information

systems (IISs),

the Vaccine Administration Management

System (VAMS),

or direct data submission.** Adolescents

aged 12–17 years with valid residence in one of 49 states or

DC who received ≥1 dose of a COVID-19 vaccine during

December 14, 2020–July 31, 2021, and whose data were

reported to CDC by August 11, 2021, were included in this

analysis.

††

COVID-19 vaccine doses administered to persons

residing in Idaho were excluded because the state has data-

sharing restrictions on information reported to CDC.

Receipt of ≥1 COVID-19 vaccine dose and series completion

among adolescents aged 12–17 years was calculated overall

and stratified by age (12–13, 14–15, and 16–17 years), sex,

and jurisdiction (49 states and DC). As of August 17, 2021,

only the Pfizer-BioNTech vaccine had been authorized for

use among adolescents aged 12–17 years in the United States.

Moderna and Janssen (Johnson & Johnson) COVID-19 vac-

cines were not authorized under emergency use for this age

group during the analysis period; however, for reasons that are

IISs are confidential, computerized, population-based systems that collect and

consolidate vaccination data from providers in 64 public health jurisdictions

and can be used to track administered vaccines and measure vaccination

coverage. The 64 IIS jurisdictions comprise the 50 U.S. states, eight U.S.

territories and freely associated states (Puerto Rico, U.S. Virgin Islands,

American Samoa, Commonwealth of the Northern Mariana Islands, Guam,

Marshall Islands, Palau, and the Federated States of Micronesia), and six local

jurisdictions (Chicago, IL; Houston, TX; San Antonio, TX; Philadelphia, PA;

New York City, NY; and Washington, DC).

https://www.cdc.gov/vaccines/covid-19/reporting/vams/program-information.html

** https://www.cdc.gov/vaccines/covid-19/reporting/overview/IT-systems.html

††

Providers are required to document vaccination in their medical records within

24 hours of administration and submit these data to their jurisdiction’s IIS

within 72 hours of administration.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1207

US Department of Health and Human Services/Centers for Disease Control and Prevention

not known, many adolescents were reported to have received

these vaccines, and doses administered to adolescents were

included in this analysis. Vaccination coverage by race and

ethnicity was not calculated because of high rates of missing

data. Population size by age group and sex was obtained from

the U.S. Census Bureau’s 2019 Population Estimates Program

(3). Second dose completion was calculated among adolescents

who received ≥1 dose of a 2-dose COVID-19 vaccination

series and for whom sufficient time to receive a second dose

during the analysis period had elapsed.

§§

Among adolescents

who received the first dose of a 2-dose COVID-19 vaccination

series, the proportions of adolescents who had already received

the second dose, of those who had not received the second

dose but were still within the recommended time interval to

receive the second dose, and of those who had not received

and were overdue for the second dose were calculated. Tests

for statistical significance were not conducted because these

data are reflective of the U.S. population (excluding Idaho)

and were not based on population samples. All analyses were

conducted using SAS software (version 9.4; SAS Institute). This

activity was reviewed by CDC and was conducted consistent

with applicable federal law and CDC policy.

¶¶

As of July 31, 2021, 42.4% of adolescents aged 12–17 years

had received ≥1 dose of a COVID-19 vaccine (Table 1),

and 31.9% had completed the vaccination series (Table 2).

Adolescent COVID-19 vaccination coverage with ≥1 dose varied

by state (range=20.2% [Mississippi] to 70.1% [Vermont]), as it

did for series completion (range=10.7% [Mississippi] to 60.3%

[Vermont]), with higher vaccination coverage in the Northeast

and on the West Coast and lower vaccination coverage in the

South (Figure). Coverage was higher among adolescents aged

16–17 years (50.6% for ≥1 dose; 40.3% for series completion)

than among those aged 12–13 years (36.0% for ≥1 dose; 25.4%

for series completion) and 14–15 years (40.9% for ≥1 dose;

30.5% for series completion). Vaccination coverage was similar

among males and females across all age groups.

Overall, 86.8% of adolescents aged 12–17 years who

received the first dose of a 2-dose COVID-19 vaccination

§§

Although the recommended interval between doses is 21 days for the Pfizer-

BioNTech vaccine, adolescents whose second doses were administered as early

as 17 days after the first dose or >21 days after the first dose were considered

to have completed the vaccination series. As of August 17, 2021, the Moderna

COVID-19 vaccine had not been authorized for use among adolescents.

However, the interval between the 2 Moderna COVID-19 vaccine doses was

assessed in the analysis. Although the recommended interval between doses

is 28 days for the Moderna vaccine, second doses received as early as 24 days

after the first dose or >28 days after the first dose were considered to complete

the vaccine series.

¶¶

45 C.F.R. part 46.102(l)(2); 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d);

5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

series*** received the second dose within the recommended

interval. A total of 2.4% had not received the second dose but

were within the allowable interval, and 10.8% were overdue

for the second dose (i.e., >42 days since receipt of the first

dose) (Supplementary Table, https://stacks.cdc.gov/view/

cdc/109000).

Discussion

Among all U.S. adolescents aged 12–17 years who received

the first dose of a 2-dose COVID-19 vaccine series, the vast

majority received the second dose, indicating high adherence

to completing the COVID-19 vaccine series. However, as of

July 31, 2021, only 42.4% of adolescents had received ≥1 dose

of a COVID-19 vaccine, and fewer than one third (31.9%) had

completed the vaccination series. Further, vaccination cover-

age varied widely by state, with those in the Northeast and on

the West Coast reporting the highest COVID-19 vaccination

coverage among adolescents. Vaccination coverage also varied

widely by age group, with reported coverage higher among

those aged 16–17 years compared with those aged 12–15 years.

This is likely because the older age group has been vaccine-

eligible for a longer period (i.e., since December 2020).

After the start of the COVID-19 pandemic, many schools

shifted to virtual or hybrid learning. Because in-person

learning fosters social and emotional development,

†††

safely

returning to schools for in-person learning remains a goal.

However, given the rapid emergence and spread of the highly

transmissible B.1.617.2 (Delta) variant of SARS-CoV-2, the

virus that causes COVID-19, and the increase in cases and

hospitalizations among children and adolescents (1), ensur-

ing high adolescent vaccination coverage is crucial to a safer

return to the classroom. Unvaccinated or undervaccinated

adolescents can become ill with COVID-19 and spread the

SARS-CoV-2 virus in schools, and by extension, in local com-

munities, placing other populations at risk. School systems can

consider implementing layered prevention strategies consistent

with CDC’s guidance for COVID-19 prevention in schools,

including universal indoor masking regardless of vaccination

status, improving ventilation, screening testing, physical dis-

tancing where feasible, and contact tracing in combination

with quarantine and isolation. As the 2021–22 school year

begins, concerted public health efforts are needed to increase

*** Among persons who received their first dose on or before July 6, 2021, for

Pfizer-BioNTech (i.e., >25 days between the first dose and July 31, 2021)

or June 29, 2021, for Moderna (i.e., >32 days between the first dose and

July 31, 2021). Percentages might not sum to 100% because persons who

were not yet due for the second dose were excluded from this analysis.

†††

https://www.cdc.gov/coronavirus/2019-ncov/community/schools-childcare/

k-12-guidance.html#anchor_1625661937509

Morbidity and Mortality Weekly Report

1208 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 1. Receipt of ≥1 COVID-19 vaccine dose by adolescents aged 12–17 years,* by age group and sex

†

— United States,

December 14, 2020–July 31, 2021

Jurisdiction

Age group and sex, no. (%)

12–17 yrs 12–13 yrs 14–15 yrs 16–17 yrs

Total Female Male Total Female Male Total Female Male Total Female Male

United States 10,677,934

(42.4)

5,425,265

(44.1)

5,216,450

(40.5)

3,094,245

(36.0)

1,543,152

(36.8)

1,541,710

(35.0)

3,454,771

(40.9)

1,750,329

(42.2)

1,693,216

(39.5)

4,128,918

(50.6)

2,131,784

(53.9)

1,981,524

(47.1)

Alabama 77,773

(20.6)

40,050

(22.4)

37,692

(19.0)

127,065

(17.5)

11,189

(18.7)

11,094

(16.5)

25,257

(19.6)

12,996

(20.3)

12,256

(18.9)

30,221

(24.8)

15,865

(28.7)

14,342

(21.6)

Alaska 23,706

(46.4)

11,621

(50.6)

11,788

(41.9)

14,859

(46.0)

3,279

(38.2)

3,480

(55.5)

7,627

(37.0)

3,755

(42.5)

3,789

(32.1)

9,241

(59.2)

4,587

(82.9)

4,519

(44.8)

Arizona 224,638

(38.9)

114,136

(40.9)

109,744

(36.8)

201,971

(32.3)

32,501

(32.7)

32,543

(31.7)

72,338

(37.3)

36,750

(38.9)

35,297

(35.5)

87,023

(48.1)

44,885

(52.7)

41,904

(43.7)

Arkansas 73,861

(30.3)

37,256

(31.6)

35,813

(28.6)

80,882

(25.0)

9,905

(24.7)

10,097

(24.8)

24,873

(31.2)

12,407

(30.4)

12,209

(31.5)

28,754

(34.7)

14,944

(40.3)

13,507

(29.5)

California 1,642,427

(53.2)

836,970

(55.5)

801,906

(50.8)

1,054,889

(44.3)

233,673

(45.3)

232,862

(43.2)

541,389

(52.3)

275,356

(54.7)

264,914

(49.9)

633,560

(63.6)

327,941

(67.2)

304,130

(59.8)

Colorado 222,780

(50.3)

113,015

(53.3)

109,520

(47.6)

147,908

(45.0)

33,118

(48.3)

33,343

(42.0)

73,879

(48.7)

37,316

(51.5)

36,481

(46.1)

82,383

(57.6)

42,581

(59.8)

39,696

(55.3)

Connecticut 166,941

(62.3)

84,333

(64.6)

82,242

(59.9)

87,364

(55.1)

23,935

(55.1)

24,047

(54.7)

53,242

(58.7)

27,015

(62.4)

26,116

(55.1)

65,592

(72.9)

33,383

(76.0)

32,079

(69.7)

Delaware 32,169

(45.2)

16,559

(49.0)

15,560

(41.6)

21,190

(44.0)

4,614

(50.5)

4,698

(39.0)

10,526

(37.8)

5,428

(34.9)

5,080

(41.2)

12,319

(55.7)

6,517

(71.3)

5,782

(44.6)

District of

Columbia

17,256

(52.3)

8,872

(53.3)

8,325

(50.9)

11,514

(49.8)

2,965

(56.6)

2,741

(43.7)

5,356

(46.0)

2,700

(38.3)

2,637

(57.5)

6,168

(62.6)

3,207

(73.4)

2,947

(53.7)

Florida 558,957

(37.6)

286,050

(39.4)

272,548

(35.9)

514,351

(31.0)

80,517

(32.9)

78,894

(29.3)

183,765

(37.2)

93,750

(37.6)

89,911

(36.8)

215,683

(45.3)

111,783

(48.4)

103,743

(42.3)

Georgia 271,600

(30.7)

138,608

(32.6)

132,222

(28.8)

307,972

(25.5)

39,194

(26.1)

39,086

(24.8)

87,107

(29.1)

44,308

(30.9)

42,597

(27.3)

105,965

(38.3)

55,106

(41.8)

50,539

(34.9)

Hawaii 60,457

(63.7)

30,251

(67.7)

30,035

(59.8)

33,044

(52.3)

8,501

(54.1)

8,725

(50.4)

19,774

(64.0)

9,869

(74.0)

9,857

(56.0)

23,409

(75.7)

11,881

(76.0)

11,453

(74.8)

Illinois 527,953

(53.2)

268,107

(54.1)

257,707

(52.0)

331,413

(45.4)

75,084

(44.8)

74,889

(45.7)

175,184

(52.1)

88,684

(52.1)

85,790

(51.7)

202,272

(62.4)

104,339

(65.9)

97,028

(58.5)

Indiana 164,717

(29.8)

84,039

(31.5)

79,638

(28.0)

194,055

(24.6)

23,834

(25.1)

23,786

(24.0)

52,778

(29.8)

26,775

(31.6)

25,704

(27.9)

64,144

(35.4)

33,430

(38.2)

30,148

(32.2)

Iowa 88,317

(36.7)

45,436

(38.6)

42,643

(34.8)

83,053

(31.8)

13,341

(32.8)

13,058

(30.8)

28,451

(36.9)

14,440

(36.2)

13,970

(37.5)

33,421

(41.6)

17,655

(47.3)

15,615

(36.4)

Kansas 88,601

(36.4)

45,509

(38.0)

42,995

(34.9)

84,150

(31.6)

13,211

(30.7)

13,352

(32.5)

27,907

(34.8)

14,328

(40.8)

13,557

(30.1)

34,100

(43.3)

17,970

(43.1)

16,086

(43.3)

Kentucky 115,204

(32.7)

59,363

(34.5)

55,723

(31.0)

122,071

(27.8)

17,009

(27.7)

16,927

(27.9)

37,571

(33.0)

19,163

(35.5)

18,375

(30.6)

43,680

(37.6)

23,191

(40.7)

20,421

(34.4)

Louisiana 81,272

(21.9)

41,478

(23.4)

39,560

(20.3)

131,531

(17.7)

11,736

(19.3)

11,536

(16.3)

26,369

(21.6)

13,273

(22.2)

13,019

(20.9)

31,616

(26.7)

16,469

(29.1)

15,005

(24.4)

Maine 48,729

(55.1)

24,474

(59.0)

23,874

(50.9)

27,699

(53.3)

7,247

(64.3)

7,370

(44.9)

16,031

(52.4)

8,004

(54.5)

7,858

(49.4)

17,937

(59.6)

9,223

(59.6)

8,646

(59.2)

Maryland 263,433

(56.3)

132,880

(57.8)

130,206

(54.7)

163,386

(49.1)

39,948

(51.2)

40,174

(47.1)

84,806

(53.6)

42,484

(52.7)

42,206

(54.4)

98,420

(67.4)

50,448

(70.9)

47,826

(63.8)

Massachusetts 319,741

(65.7)

161,726

(68.5)

157,494

(62.9)

158,110

(59.9)

47,185

(62.7)

47,336

(57.1)

105,067

(65.2)

53,176

(64.4)

51,711

(65.7)

120,042

(71.7)

61,365

(78.3)

58,447

(65.7)

Michigan 273,071

(36.0)

139,194

(38.1)

133,776

(34.1)

254,314

(30.6)

39,045

(31.8)

38,746

(29.5)

86,078

(34.7)

43,910

(36.8)

42,132

(32.7)

109,164

(42.7)

56,239

(45.5)

52,898

(40.0)

Minnesota 198,287

(44.3)

101,571

(45.8)

95,698

(42.4)

149,301

(40.8)

30,696

(39.3)

30,025

(42.1)

60,068

(38.7)

30,610

(40.9)

29,257

(36.4)

77,289

(54.1)

40,265

(58.4)

36,416

(49.3)

Mississippi 49,940

(20.2)

25,444

(21.0)

24,454

(19.3)

86,695

(16.7)

7,162

(17.7)

7,272

(15.7)

16,559

(21.4)

8,298

(20.3)

8,245

(22.7)

18,931

(22.6)

9,984

(25.0)

8,937

(20.4)

Missouri 152,486

(32.4)

77,515

(33.0)

74,807

(31.7)

158,781

(28.9)

22,844

(29.3)

23,025

(28.5)

49,384

(31.0)

25,006

(31.7)

24,325

(30.2)

57,185

(37.5)

29,665

(38.0)

27,457

(37.0)

Montana 23,962

(30.3)

12,105

(31.5)

11,683

(28.8)

25,348

(28.9)

3,579

(28.1)

3,669

(29.1)

7,428

(28.9)

3,730

(28.5)

3,638

(28.9)

9,209

(32.9)

4,796

(38.0)

4,376

(28.5)

Nebraska 62,131

(39.2)

31,723

(39.8)

30,292

(38.5)

56,881

(33.1)

9,447

(31.5)

9,343

(34.7)

19,599

(37.1)

9,928

(40.0)

9,649

(34.5)

23,719

(48.7)

12,348

(49.6)

11,300

(47.5)

Nevada 89,835

(37.2)

46,021

(37.7)

43,775

(36.6)

85,434

(29.8)

12,717

(30.8)

12,723

(28.8)

29,148

(36.3)

14,933

(34.9)

14,202

(37.8)

35,241

(46.5)

18,371

(48.4)

16,850

(44.6)

New Hampshire 48,188

(49.5)

24,264

(49.9)

23,250

(47.7)

34,943

(38.1)

6,575

(38.3)

6,609

(37.2)

15,129

(51.1)

7,567

(47.1)

7,332

(54.2)

19,747

(60.1)

10,122

(65.7)

9,309

(53.3)

New Jersey 357,267

(52.5)

180,504

(54.7)

175,521

(50.0)

232,003

(43.8)

50,304

(42.3)

51,137

(45.2)

113,832

(50.9)

57,509

(54.3)

55,960

(47.5)

141,702

(62.9)

72,691

(69.2)

68,424

(56.9)

New Mexico 92,891

(55.1)

46,824

(55.5)

44,864

(53.1)

57,115

(49.5)

13,992

(49.8)

13,800

(47.6)

29,505

(50.9)

14,745

(49.8)

14,367

(50.7)

35,137

(65.5)

18,087

(68.0)

16,697

(61.6)

New York 651,562

(46.6)

328,743

(48.5)

319,985

(44.5)

471,237

(39.3)

91,375

(39.4)

93,069

(38.9)

205,664

(44.3)

103,529

(46.5)

101,489

(42.0)

260,904

(56.6)

133,839

(60.1)

125,427

(52.6)

North Carolina 288,722

(35.4)

147,723

(35.7)

139,514

(34.8)

280,592

(29.4)

41,310

(28.5)

40,939

(30.1)

95,543

(35.3)

48,666

(35.0)

46,473

(35.3)

110,577

(42.0)

57,747

(44.3)

52,102

(39.1)

See table footnotes on the next page.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1209

US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 1. (Continued) Receipt of ≥1 COVID-19 vaccine dose by adolescents aged 12–17 years,* by age group and sex

†

— United States,

December 14, 2020–July 31, 2021

Jurisdiction

Age group and sex, no. (%)

12–17 yrs 12–13 yrs 14–15 yrs 16–17 yrs

Total Female Male Total Female Male Total Female Male Total Female Male

North Dakota 13,910

(26.3)

7,084

(25.4)

6,613

(26.5)

18,993

(20.2)

1,888

(20.4)

1,894

(19.5)

4,533

(27.4)

2,290

(24.1)

2,175

(30.8)

5,535

(32.0)

2,906

(31.8)

2,544

(31.3)

Ohio 284,374

(31.9)

145,410

(33.8)

138,167

(29.9)

300,214

(27.1)

40,975

(28.2)

40,302

(26.0)

89,895

(29.9)

45,960

(31.6)

43,719

(28.2)

113,035

(38.8)

58,475

(42.1)

54,146

(35.5)

Oklahoma 92,409

(29.1)

47,313

(31.4)

44,973

(27.1)

113,915

(23.4)

13,242

(25.7)

13,346

(21.4)

29,283

(29.1)

15,020

(30.0)

14,228

(28.2)

36,505

(35.5)

19,051

(38.7)

17,399

(32.5)

Oregon 147,476

(49.3)

74,896

(49.7)

72,231

(48.8)

100,819

(43.5)

21,971

(44.0)

21,828

(42.9)

48,739

(48.1)

24,714

(47.6)

23,927

(48.3)

54,859

(56.8)

28,211

(57.6)

26,476

(55.5)

Pennsylvania 437,303

(47.7)

219,211

(48.7)

209,686

(44.8)

308,332

(41.3)

62,448

(41.8)

62,623

(39.4)

140,842

(45.2)

70,283

(46.6)

67,547

(42.1)

168,972

(56.7)

86,480

(57.7)

79,516

(53.7)

Rhode Island 42,660

(55.4)

21,683

(60.8)

20,919

(50.6)

25,863

(48.2)

6,167

(45.7)

6,290

(50.9)

13,645

(51.2)

7,024

(61.9)

6,595

(43.1)

16,544

(67.4)

8,492

(78.4)

8,034

(58.6)

South Carolina 100,830

(25.8)

51,820

(26.7)

48,946

(24.9)

135,830

(19.9)

13,591

(20.5)

13,384

(19.2)

33,001

(24.6)

17,005

(25.3)

15,977

(23.9)

40,842

(33.7)

21,224

(34.9)

19,585

(32.5)

South Dakota 24,848

(34.4)

12,468

(34.6)

11,989

(33.1)

24,483

(30.1)

3,612

(32.6)

3,661

(27.3)

8,051

(30.9)

4,073

(29.1)

3,850

(31.9)

9,439

(43.5)

4,783

(43.5)

4,478

(41.8)

Tennessee 126,159

(24.3)

65,267

(26.1)

60,591

(22.6)

185,246

(19.6)

18,164

(20.1)

18,156

(19.2)

40,295

(24.0)

20,848

(24.2)

19,407

(23.8)

49,495

(29.9)

26,255

(35.8)

23,028

(24.9)

Texas 1,028,789

(40.6)

521,461

(42.2)

506,643

(39.0)

854,580

(34.6)

147,957

(35.6)

147,505

(33.6)

330,444

(38.5)

167,302

(39.9)

162,971

(37.1)

402,745

(49.1)

206,202

(51.6)

196,167

(46.6)

Utah 129,559

(41.9)

65,495

(43.8)

63,818

(40.0)

106,783

(34.6)

18,393

(34.5)

18,549

(34.7)

39,977

(38.8)

20,029

(41.4)

19,925

(36.5)

52,615

(53.1)

27,073

(56.6)

25,344

(49.3)

Vermont 28,904

(70.1)

14,332

(74.4)

14,474

(65.9)

11,732

(75.0)

4,306

(83.5)

4,464

(67.9)

9,454

(70.3)

4,790

(91.7)

4,627

(56.3)

10,649

(66.3)

5,236

(58.9)

5,383

(75.1)

Virginia 342,958

(53.7)

173,904

(56.1)

168,793

(51.3)

222,929

(45.3)

50,509

(46.5)

50,461

(44.1)

113,259

(53.0)

57,040

(54.8)

56,153

(51.2)

128,655

(63.6)

66,355

(68.2)

62,179

(59.2)

Washington 296,782

(53.1)

149,501

(53.6)

145,592

(52.0)

192,800

(48.7)

46,631

(49.0)

46,691

(47.8)

95,740

(50.0)

47,895

(48.3)

47,255

(51.2)

107,187

(61.3)

54,975

(64.9)

51,646

(57.3)

West Virginia 38,159

(30.2)

19,127

(31.5)

18,459

(28.2)

44,298

(23.8)

5,126

(24.7)

5,263

(22.3)

12,061

(30.2)

6,090

(32.4)

5,782

(27.3)

15,564

(37.1)

7,911

(37.2)

7,414

(35.8)

Wisconsin 174,211

(39.9)

88,947

(41.5)

84,996

(38.2)

142,836

(35.8)

25,720

(38.3)

25,427

(33.6)

55,260

(36.8)

28,010

(37.0)

27,178

(36.4)

67,750

(47.1)

35,217

(49.2)

32,391

(44.9)

Wyoming 9,729

(20.4)

4,982

(21.7)

4,706

(19.0)

18,337

(15.8)

1,420

(16.5)

1,471

(15.2)

3,058

(20.9)

1,548

(19.9)

1,497

(21.8)

3,772

(25.7)

2,014

(30.8)

1,738

(21.4)

* Receipt of ≥1 COVID-19 vaccine dose is defined either as receiving at least one of the 2 doses of the Pfizer-BioNTech or Moderna vaccines or a single dose of the Janssen (Johnson & Johnson)

vaccine. As of August 17, 2021, only the Pfizer-BioNTech vaccine had been authorized for use among adolescents aged 12–17 years. Moderna and Janssen COVID-19 vaccines were not

authorized under emergency use for this age group during the analysis period; however, these vaccinations were included in this analysis.

†

Fewer than 0.5% of the records were missing information on sex.

COVID-19 vaccine doses administered to adolescents residing in Idaho were excluded because the state has data-sharing restrictions on information reported to CDC.

COVID-19 vaccination coverage among adolescents in addi-

tion to implementing COVID-19 prevention strategies based

on community transmission.

Public health practitioners can use various measures to

increase adolescent COVID-19 vaccination coverage. Building

on lessons from the public-private partnership between

CDC and retail pharmacies in the Federal Retail Pharmacy

Partnership

§§§

regarding vaccination clinics offered for selected

population groups at different times throughout the response

(4), local public health agencies and pharmacies could partner

with school districts and school systems to provide COVID-19

vaccinations to students at schools. Vaccine administration on

site at schools is an effective, evidence-based intervention that

improves childhood and adolescent vaccination rates for rou-

tinely recommended vaccines (5). State and local governments,

§§§

https://www.cdc.gov/vaccines/covid-19/retail-pharmacy-program/index.html

school administrators, community leaders, health care pro-

fessionals, and public health practitioners can facilitate safer

return to schools and improve equity among sociodemographic

groups by prioritizing COVID-19 vaccination among adoles-

cents and incorporating on-site school vaccinations for eligible

students (6,7). In addition, on-site vaccination clinics might

also be planned in coordination with other school-based vac-

cination programs, such as those for seasonal influenza and

routine adolescent vaccination.

Concerted outreach can help inform adolescents and their

parents about the importance of COVID-19 vaccination.

Effective outreach with tailored communication could help

improve vaccine confidence, acceptance, and coverage among

adolescents and their parents. In a recent report, only 56%

of parents of unvaccinated adolescents aged 12–17 years

expressed intent for their adolescent to receive a COVID-19

vaccine (8). Given that parental vaccination status is a marker

Morbidity and Mortality Weekly Report

1210 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 2. COVID-19 vaccination coverage among adolescents aged 12–17 years who completed the vaccine series,* by age group and sex

†

—

United States,

December 14, 2020–July 31, 2021

Jurisdiction

Age group and sex, no. (%)

12–17 yrs 12–13 yrs 14–15 yrs 16–17 yrs

Total Female Male Total Female Male Total Female Male Total Female Male

United States 8,045,685

(31.9)

4,117,404

(33.5)

3,905,344

(30.3)

2,183,597

(25.4)

1,093,057

(26.0)

1,085,039

(24.7)

2,570,498

(30.5)

1,311,724

(31.6)

1,251,765

(29.2)

3,291,590

(40.3)

1,712,623

(43.3)

1,568,540

(37.3)

Alabama 40,925

(10.8)

21,303

(11.9)

19,606

(9.9)

10,360

(8.2)

5,234

(8.7)

5,118

(7.6)

12,421

(9.6)

6,452

(10.1)

5,969

(9.2)

18,144

(14.9)

9,617

(17.4)

8,519

(12.8)

Alaska 18,394

(36.0)

9,066

(39.5)

9,148

(32.5)

4,947

(33.3)

2,384

(27.7)

2,522

(40.2)

5,678

(27.5)

2,778

(31.4)

2,847

(24.1)

7,769

(49.7)

3,904

(70.6)

3,779

(37.4)

Arizona 167,297

(29.0)

85,471

(30.6)

81,203

(27.3)

44,661

(22.1)

22,273

(22.4)

22,209

(21.7)

52,639

(27.1)

26,854

(28.4)

25,546

(25.7)

69,997

(38.7)

36,344

(42.6)

33,448

(34.9)

Arkansas 41,891

(17.2)

21,742

(18.4)

19,956

(15.9)

10,494

(13.0)

5,259

(13.1)

5,194

(12.7)

13,552

(17.0)

6,945

(17.0)

6,551

(16.9)

17,845

(21.5)

9,538

(25.7)

8,211

(17.9)

California 1,271,593

(41.2)

652,802

(43.3)

616,318

(39.0)

344,509

(32.7)

172,803

(33.5)

171,083

(31.7)

416,508

(40.3)

213,322

(42.4)

202,396

(38.1)

510,576

(51.2)

266,677

(54.6)

242,839

(47.7)

Colorado 185,447

(41.9)

94,420

(44.5)

90,901

(39.5)

52,056

(35.2)

25,885

(37.8)

26,150

(32.9)

61,301

(40.4)

31,074

(42.9)

30,191

(38.2)

72,090

(50.4)

37,461

(52.6)

34,560

(48.1)

Connecticut 136,730

(51.0)

69,481

(53.2)

66,983

(48.8)

36,973

(42.3)

18,513

(42.6)

18,368

(41.8)

43,625

(48.1)

22,287

(51.5)

21,253

(44.8)

56,132

(62.4)

28,681

(65.3)

27,362

(59.5)

Delaware 25,675

(36.1)

13,313

(39.4)

12,334

(33.0)

7,027

(33.2)

3,496

(38.2)

3,524

(29.3)

8,378

(30.1)

4,378

(28.2)

3,990

(32.3)

10,270

(46.4)

5,439

(59.5)

4,820

(37.2)

District of

Columbia

11,239

(34.1)

5,818

(34.9)

5,393

(33.0)

3,574

(31.0)

1,847

(35.2)

1,716

(27.4)

3,607

(31.0)

1,849

(26.2)

1,748

(38.1)

4,058

(41.2)

2,122

(48.6)

1,929

(35.1)

Florida 377,443

(25.4)

194,735

(26.8)

182,570

(24.0)

98,344

(19.1)

49,892

(20.4)

48,418

(18.0)

120,847

(24.5)

62,121

(24.9)

58,694

(24.0)

158,252

(33.2)

82,722

(35.8)

75,458

(30.7)

Georgia 166,329

(18.8)

85,830

(20.2)

80,219

(17.5)

41,215

(13.4)

20,691

(13.8)

20,437

(13.0)

48,426

(16.2)

24,922

(17.4)

23,455

(15.0)

76,688

(27.7)

40,217

(30.5)

36,327

(25.1)

Hawaii 35,203

(37.1)

17,549

(39.3)

17,546

(34.9)

9,931

(30.1)

4,831

(30.7)

5,072

(29.3)

11,450

(37.0)

5,705

(42.8)

5,715

(32.5)

13,822

(44.7)

7,013

(44.9)

6,759

(44.2)

Illinois 348,478

(35.1)

179,085

(36.1)

168,328

(33.9)

95,818

(28.9)

48,301

(28.8)

47,255

(28.8)

113,863

(33.9)

58,356

(34.3)

55,143

(33.2)

138,797

(42.8)

72,428

(45.8)

65,930

(39.7)

Indiana 131,406

(23.8)

67,329

(25.2)

63,257

(22.2)

35,025

(18.0)

17,450

(18.4)

17,450

(17.6)

41,394

(23.4)

21,124

(24.9)

20,030

(21.7)

54,987

(30.4)

28,755

(32.8)

25,777

(27.6)

Iowa 70,809

(29.4)

36,654

(31.1)

34,002

(27.7)

19,670

(23.7)

9,953

(24.5)

9,692

(22.8)

22,623

(29.3)

11,540

(28.9)

11,059

(29.7)

28,516

(35.5)

15,161

(40.6)

13,251

(30.9)

Kansas 61,300

(25.2)

31,698

(26.4)

29,559

(24.0)

16,594

(19.7)

8,240

(19.2)

8,339

(20.3)

18,868

(23.5)

9,778

(27.8)

9,082

(20.2)

25,838

(32.8)

13,680

(32.8)

12,138

(32.7)

Kentucky 81,664

(23.2)

42,709

(24.8)

38,895

(21.6)

22,107

(18.1)

11,199

(18.2)

10,903

(18.0)

26,034

(22.8)

13,521

(25.1)

12,500

(20.8)

33,523

(28.8)

17,989

(31.6)

15,492

(26.1)

Louisiana 46,411

(12.5)

24,126

(13.6)

22,181

(11.4)

11,607

(8.8)

5,905

(9.7)

5,695

(8.0)

13,932

(11.4)

7,128

(11.9)

6,772

(10.9)

20,872

(17.6)

11,093

(19.6)

9,714

(15.8)

Maine 42,857

(48.5)

21,496

(51.9)

21,044

(44.8)

12,259

(44.3)

5,993

(53.1)

6,149

(37.4)

14,157

(46.2)

7,069

(48.1)

6,953

(43.7)

16,441

(54.7)

8,434

(54.5)

7,942

(54.4)

Maryland 218,233

(46.7)

110,698

(48.2)

107,376

(45.1)

62,420

(38.2)

31,169

(39.9)

31,214

(36.6)

70,372

(44.5)

35,469

(44.0)

34,851

(44.9)

85,441

(58.5)

44,060

(61.9)

41,311

(55.1)

Massachusetts 263,919

(54.2)

134,332

(56.9)

129,099

(51.5)

74,471

(47.1)

37,267

(49.5)

37,081

(44.7)

86,063

(53.4)

43,839

(53.1)

42,066

(53.5)

103,385

(61.8)

53,226

(67.9)

49,952

(56.2)

Michigan 229,551

(30.3)

117,541

(32.1)

111,939

(28.5)

61,506

(24.2)

30,932

(25.2)

30,548

(23.3)

72,163

(29.1)

36,968

(31.0)

35,175

(27.3)

95,882

(37.5)

49,641

(40.2)

46,216

(34.9)

Minnesota 174,700

(39.0)

89,821

(40.5)

84,347

(37.4)

50,776

(34.0)

25,668

(32.9)

25,006

(35.1)

56,104

(36.1)

28,844

(38.5)

27,156

(33.8)

67,820

(47.4)

35,309

(51.2)

32,185

(43.5)

Mississippi 26,576

(10.7)

13,709

(11.3)

12,846

(10.2)

6,393

(7.4)

3,182

(7.9)

3,204

(6.9)

8,134

(10.5)

4,094

(10.0)

4,033

(11.1)

12,049

(14.4)

6,433

(16.1)

5,609

(12.8)

Missouri 104,029

(22.1)

53,410

(22.7)

50,568

(21.4)

28,825

(18.2)

14,385

(18.5)

14,432

(17.8)

32,843

(20.6)

16,854

(21.4)

15,979

(19.8)

42,361

(27.8)

22,171

(28.4)

20,157

(27.1)

Montana 18,046

(22.8)

9,197

(23.9)

8,794

(21.7)

5,167

(20.4)

2,551

(20.0)

2,598

(20.6)

5,430

(21.2)

2,784

(21.3)

2,626

(20.9)

7,449

(26.6)

3,862

(30.6)

3,570

(23.2)

Nebraska 48,472

(30.6)

25,035

(31.4)

23,394

(29.8)

13,509

(23.7)

6,854

(22.9)

6,647

(24.7)

15,152

(28.7)

7,759

(31.3)

7,384

(26.4)

19,811

(40.7)

10,422

(41.9)

9,363

(39.4)

Nevada 55,558

(23.0)

28,686

(23.5)

26,854

(22.5)

14,043

(16.4)

7,011

(17.0)

7,030

(15.9)

17,412

(21.7)

8,995

(21.0)

8,411

(22.4)

24,103

(31.8)

12,680

(33.4)

11,413

(30.2)

New Hampshire 39,480

(40.5)

19,952

(41.0)

18,996

(38.9)

10,267

(29.4)

5,083

(29.6)

5,094

(28.7)

12,290

(41.5)

6,173

(38.4)

5,945

(43.9)

16,923

(51.5)

8,696

(56.4)

7,957

(45.5)

New Jersey 289,682

(42.5)

146,961

(44.6)

141,715

(40.3)

77,253

(33.3)

38,290

(32.2)

38,734

(34.3)

92,001

(41.1)

46,666

(44.1)

45,052

(38.2)

120,428

(53.4)

62,005

(59.0)

57,929

(48.2)

New Mexico 72,669

(43.1)

37,085

(44.0)

35,178

(41.7)

20,417

(35.7)

10,265

(36.5)

10,012

(34.5)

22,917

(39.6)

11,613

(39.2)

11,184

(39.5)

29,335

(54.6)

15,207

(57.2)

13,982

(51.6)

New York 537,956

(38.5)

272,326

(40.2)

263,665

(36.6)

143,966

(30.6)

71,259

(30.7)

72,385

(30.2)

169,430

(36.5)

85,566

(38.4)

83,440

(34.5)

224,560

(48.7)

115,501

(51.9)

107,840

(45.2)

See table footnotes on the next page.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1211

US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE 2. (Continued) COVID-19 vaccination coverage among adolescents aged 12–17 years who completed the vaccine series,* by age group

and sex

†

— United States,

December 14, 2020–July 31, 2021

Jurisdiction

Age group and sex, no. (%)

12–17 yrs 12–13 yrs 14–15 yrs 16–17 yrs

Total Female Male Total Female Male Total Female Male Total Female Male

North Carolina 210,162

(25.8)

108,311

(26.2)

100,839

(25.2)

55,824

(19.9)

28,001

(19.3)

27,612

(20.3)

68,736

(25.4)

35,229

(25.3)

33,228

(25.2)

85,602

(32.5)

45,081

(34.6)

39,999

(30.0)

North Dakota 10,254

(19.4)

5,257

(18.9)

4,842

(19.4)

2,516

(13.2)

1,259

(13.6)

1,219

(12.5)

3,234

(19.5)

1,628

(17.2)

1,556

(22.1)

4,504

(26.1)

2,370

(26.0)

2,067

(25.4)

Ohio 239,023

(26.8)

122,890

(28.6)

115,636

(25.0)

63,374

(21.1)

32,046

(22.0)

31,238

(20.2)

74,684

(24.8)

38,484

(26.4)

36,083

(23.3)

100,965

(34.7)

52,360

(37.7)

48,315

(31.7)

Oklahoma 61,250

(19.3)

31,546

(20.9)

29,633

(17.8)

15,691

(13.8)

7,764

(15.0)

7,913

(12.7)

18,709

(18.6)

9,633

(19.2)

9,056

(18.0)

26,850

(26.1)

14,149

(28.8)

12,664

(23.7)

Oregon 126,346

(42.3)

64,593

(42.8)

61,618

(41.6)

36,145

(35.9)

18,188

(36.4)

17,937

(35.3)

41,459

(40.9)

21,114

(40.7)

20,319

(41.0)

48,742

(50.4)

25,291

(51.7)

23,362

(49.0)

Pennsylvania 303,836

(33.1)

153,011

(34.0)

145,168

(31.0)

84,516

(27.4)

41,529

(27.8)

41,414

(26.0)

98,297

(31.6)

49,226

(32.6)

47,005

(29.3)

121,023

(40.6)

62,256

(41.5)

56,749

(38.3)

Rhode Island 35,520

(46.1)

18,100

(50.7)

17,380

(42.0)

9,733

(37.6)

4,786

(35.4)

4,938

(40.0)

11,386

(42.7)

5,862

(51.6)

5,508

(36.0)

14,401

(58.7)

7,452

(68.8)

6,934

(50.6)

South Carolina 72,130

(18.4)

37,476

(19.3)

34,621

(17.6)

17,802

(13.1)

8,967

(13.5)

8,831

(12.7)

22,947

(17.1)

11,939

(17.7)

10,996

(16.4)

31,381

(25.9)

16,570

(27.3)

14,794

(24.5)

South Dakota 16,383

(22.7)

8,318

(23.1)

7,813

(21.6)

4,264

(17.4)

2,113

(19.1)

2,108

(15.7)

5,037

(19.3)

2,585

(18.5)

2,374

(19.7)

7,082

(32.6)

3,620

(32.9)

3,331

(31.1)

Tennessee 87,019

(16.8)

45,491

(18.2)

41,307

(15.4)

22,260

(12.0)

11,200

(12.4)

11,035

(11.6)

26,342

(15.7)

13,724

(15.9)

12,597

(15.4)

38,417

(23.2)

20,567

(28.0)

17,675

(19.1)

Texas 718,918

(28.4)

369,600

(29.9)

348,945

(26.9)

193,523

(22.6)

97,354

(23.4)

96,096

(21.9)

225,520

(26.2)

115,724

(27.6)

109,695

(24.9)

299,875

(36.5)

156,522

(39.1)

143,154

(34.0)

Utah 96,759

(31.3)

49,212

(32.9)

47,466

(29.8)

25,119

(23.5)

12,578

(23.6)

12,530

(23.4)

29,095

(28.3)

14,641

(30.3)

14,449

(26.4)

42,545

(42.9)

21,993

(46.0)

20,487

(39.9)

Vermont 24,881

(60.3)

12,395

(64.3)

12,437

(56.6)

7,388

(63.0)

3,657

(70.9)

3,720

(56.6)

8,118

(60.4)

4,095

(78.4)

4,006

(48.8)

9,375

(58.4)

4,643

(52.2)

4,711

(65.7)

Virginia 283,385

(44.3)

144,360

(46.6)

138,878

(42.2)

79,268

(35.6)

39,685

(36.5)

39,546

(34.6)

93,389

(43.7)

47,282

(45.5)

46,077

(42.0)

110,728

(54.7)

57,393

(59.0)

53,255

(50.7)

Washington 245,243

(43.9)

124,122

(44.5)

119,901

(42.8)

73,427

(38.1)

36,514

(38.4)

36,573

(37.4)

79,630

(41.6)

40,075

(40.4)

39,149

(42.5)

92,186

(52.7)

47,533

(56.1)

44,179

(49.0)

West Virginia 27,203

(21.6)

13,567

(22.3)

13,174

(20.1)

6,953

(15.7)

3,372

(16.3)

3,453

(14.6)

8,505

(21.3)

4,299

(22.9)

4,066

(19.2)

11,745

(28.0)

5,896

(27.7)

5,655

(27.3)

Wisconsin 140,545

(32.2)

72,235

(33.7)

68,167

(30.6)

37,736

(26.4)

19,067

(28.4)

18,641

(24.6)

43,634

(29.0)

22,269

(29.4)

21,335

(28.6)

59,175

(41.2)

30,899

(43.1)

28,191

(39.1)

Wyoming 6,866

(14.4)

3,540

(15.4)

3,305

(13.4)

1,874

(10.2)

912

(10.6)

956

(9.8)

2,162

(14.8)

1,088

(14.0)

1,070

(15.6)

2,830

(19.3)

1,540

(23.6)

1,279

(15.7)

* Vaccine series completion was defined as receiving either both doses of the Pfizer-BioNTech or Moderna vaccines, including mismatched products between the first and second dose (i.e.,

Pfizer-BioNTech for the first dose and Moderna for the second dose or vice versa) or a single dose for the Janssen (Johnson & Johnson) vaccine. As of August 17, 2021, only the Pfizer-

BioNTech vaccine had been authorized for use among adolescents aged 12–17 years. Moderna and Janssen COVID-19 vaccines were not authorized under emergency use for this age

group during the analysis period; however, these vaccinations were included in this analysis.

†

Fewer than 0.5% of the records were missing information on sex.

COVID-19 vaccine doses administered to adolescents residing in Idaho were excluded because the state has data-sharing restrictions on information reported to CDC.

for adolescent vaccination status,

¶¶¶

vaccine hesitancy or anti-

vaccination sentiments among parents might directly lead to

missed opportunities to vaccinate adolescents (9). Among ado-

lescents and their parents who were surveyed about their intent

to receive a COVID-19 vaccine, many reported that having

more information about the safety and efficacy of COVID-19

vaccines would increase their likelihood of receiving a vaccine

(8). Public health practitioners can use multimodal outreach

efforts involving a variety of traditional and social media

platforms to engage adolescents and their parents to improve

vaccination acceptance and coverage. Further, state and local

governments can consider strategies that encourage receipt

by adolescents of all vaccines recommended by the Advisory

¶¶¶

https://www.kff.org/coronavirus-covid-19/poll-finding/

kff-covid-19-vaccine-monitor-parents-and-the-pandemic

Committee on Immunization Practices, especially given the

declines in routine childhood and adolescent vaccinations

during the pandemic (10).

The findings in this report are subject to at least five limita-

tions. First, vaccination coverage rates were aggregated and

analyzed only at the state level. Calculating coverage at more

specific levels (e.g., by county or urban-rural classification)

could potentially identify geographic areas with low vaccina-

tion coverage rates. Second, because Idaho was excluded from

the analysis, the findings are not representative of the entire

United States. Third, adolescents who received COVID-19

vaccines from different entities that used different methods

for submitting data (e.g., if the first dose was administered at

a pharmacy and the second dose was given at a mass vaccina-

tion site) might not have their first and second doses linked,

Morbidity and Mortality Weekly Report

1212 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE. Percentage of adolescents aged 12–17 years who

completed the COVID-19 vaccination series*

,†

— United States,

December 14, 2020–July 31, 2021

>50.0

40.1–50.0

30.1–40.0

20.1–30.0

≤20.0

Not available

Abbreviation: DC = District of Columbia.

* As of August 17, 2021, only the Pfizer-BioNTech vaccine had been authorized

for use among adolescents aged 12–17 years. Moderna and Janssen (Johnson &

Johnson) COVID-19 vaccines were not authorized under emergency use for this

age group during the analysis period; however, many adolescents had

documentation of receipt of these vaccines. Thus, these vaccine doses were

included in this analysis if they were administered to adolescents aged 12–17 years.

†

Series completion was defined as receipt of either both doses of the Pfizer-

BioNTech or Moderna vaccines, including those who might have received

mismatched products between the first and second dose (i.e., Pfizer-BioNTech

for the first dose and Moderna for the second dose or vice versa) or a single dose

of the Janssen vaccine.

COVID-19 vaccine doses administered to adolescents residing in Idaho were

excluded because the state has data-sharing restrictions on information reported

to CDC.

which could have led to underestimation of the percentage of

adolescents who completed the vaccination series. Fourth, if

an adolescent had inadvertently received a different recipient

ID when receiving their second dose, first and second doses

could not be linked. Finally, vaccination coverage could not

be calculated on the basis of race and ethnicity because of

incomplete reporting.

An estimated 2 million COVID-19 cases and approximately

300 associated deaths have been reported among children aged

5–17 years since the start of the COVID-19 pandemic (1). As

persons in younger age groups become eligible for COVID-19

vaccination, public health practitioners, health care profession-

als, school administrators, and state and local governments can

use evidence-based practices to decrease barriers to vaccination

and increase confidence in COVID-19 vaccines, which can help

facilitate the safer return to in-person learning at schools and ulti-

mately reduce COVID-19–associated morbidity and mortality.

Summary

What is already known about this topic?

Although more common among adults, severe COVID-19 illness

and hospitalization occur among adolescents.

What is added by this report?

As of July 31, 2021, coverage with ≥1 dose of COVID-19 vaccine

among adolescents aged 12–17 years was 42%, and 32% had

completed the series. Series completion rates varied widely by

state, ranging from 11% to 60%, and was 25% for adolescents

aged 12–13 years, 30% for those aged 14–15 years, and 40% for

those aged 16–17 years.

What are the implications for public health practice?

Improving adolescent COVID-19 vaccination coverage is crucial

to reduce COVID-19–associated morbidity and mortality among

adolescents and can help facilitate safer reopening of schools

for in-person learning.

Acknowledgments

COVID-19 Vaccine Task Force; U.S. Department of Defense;

immunization program managers, immunization information system

managers, and other staff members of the immunization programs in

the 64 jurisdictions and five federal entities who provided these data.

Corresponding author: Bhavini Patel Murthy, [email protected].

Immunization Services Division, National Center for Immunization and

Respiratory Diseases, CDC;

CDC COVID-19 Response Team;

Stat-Epi

Associates, Inc., Ponte Vedra Beach, Florida.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. No potential conflicts of interest were disclosed.

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Morbidity and Mortality Weekly Report

1214 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

Outbreak Associated with SARS-CoV-2 B.1.617.2 (Delta) Variant in an

Elementary School — Marin County, California, May–June 2021

Tracy Lam-Hine, MBA

1,2

; Stephen A. McCurdy, MD

1,3

; Lisa Santora, MD

; Lael Duncan, MD

; Russell Corbett-Detig, PhD

;

Beatrix Kapusinszky, PhD

; Matthew Willis, MD

On August 27, 2021, this report was posted as an MMWR Early

Release on the MMWR website (https://www.cdc.gov/mmwr).

On May 25, 2021, the Marin County Department of Public

Health (MCPH) was notified by an elementary school that

on May 23, an unvaccinated teacher had reported receiving

a positive test result for SARS-CoV-2, the virus that causes

COVID-19. The teacher reported becoming symptomatic on

May 19, but continued to work for 2 days before receiving

a test on May 21. On occasion during this time, the teacher

read aloud unmasked to the class despite school requirements

to mask while indoors. Beginning May 23, additional cases of

COVID-19 were reported among other staff members, students,

parents, and siblings connected to the school. To characterize

the outbreak, on May 26, MCPH initiated case investigation

and contact tracing that included whole genome sequencing

(WGS) of available specimens. A total of 27 cases were identified,

including that of the teacher. During May 23–26, among the

teacher’s 24 students, 22 students, all ineligible for vaccination

because of age, received testing for SARS-CoV-2; 12 received

positive test results. The attack rate in the two rows seated clos-

est to the teacher’s desk was 80% (eight of 10) and was 28%

(four of 14) in the three back rows (Fisher’s exact test; p = 0.036).

During May 24–June 1, six of 18 students in a separate grade at

the school, all also too young for vaccination, received positive

SARS-CoV-2 test results. Eight additional cases were also identi-

fied, all in parents and siblings of students in these two grades.

Among these additional cases, three were in persons fully vac-

cinated in accordance with CDC recommendations (1). Among

the 27 total cases, 22 (81%) persons reported symptoms; the most

frequently reported symptoms were fever (41%), cough (33%),

headache (26%), and sore throat (26%). WGS of all 18 available

specimens identified the B.1.617.2 (Delta) variant. Vaccines are

effective against the Delta variant (2), but risk of transmission

remains elevated among unvaccinated persons in schools without

strict adherence to prevention strategies. In addition to vaccina-

tion for eligible persons, strict adherence to nonpharmaceutical

prevention strategies, including masking, routine testing, facility

ventilation, and staying home when symptomatic, are important

to ensure safe in-person learning in schools (3).

Investigation and Findings

The outbreak location was an elementary school in Marin

County, California, which serves 205 students in prekindergarten

through eighth grade and has 24 staff members. Each grade

includes 20 to 25 students in single classrooms. Other than two

teachers, one of whom was the index patient, all school staff

members were vaccinated (verified in California’s Immunization

Registry). The index patient became symptomatic on May 19

with nasal congestion and fatigue. This teacher reported attending

social events during May 13–16 but did not report any known

COVID-19 exposures and attributed symptoms to allergies. The

teacher continued working during May 17–21, subsequently

experiencing cough, subjective fever, and headache. The school

required teachers and students to mask while indoors; interviews

with parents of infected students suggested that students’ adher-

ence to masking and distancing guidelines in line with CDC

recommendations (3) was high in class. However, the teacher was

reportedly unmasked on occasions when reading aloud in class.

On May 23, the teacher notified the school that they received a

positive result for a SARS-CoV-2 test performed on May 21 and

self-isolated until May 30. The teacher did not receive a second

COVID-19 test, but reported fully recovering during isolation.

The index patient’s students began experiencing symptoms

on May 22. During May 23–26, among 24 students in this

grade, 22 were tested. A COVID-19 case was defined as a

positive SARS-CoV-2 reverse transcription–polymerase chain

reaction (RT-PCR) or antigen test result.* Twelve (55%) of the

22 students received a positive test result, including eight who

experienced symptom onset during May 22–26. Throughout

this period, all desks were separated by 6 ft. Students were

seated in five rows; the attack rate in the two rows seated

closest to the teacher’s desk was 80% (eight of 10) and was

28% (four of 14) in the three back rows (Fisher’s exact test;

p = 0.036) (Figure 1).

On May 22, students in a another classroom, who dif-

fered in age by 3 years from the students in the class with the

index case and who were also ineligible for vaccination began

to experience symptoms. The two classrooms were separated by

a large outdoor courtyard with lunch tables that were blocked

off from use with yellow tape. All classrooms had portable

* https://cdn.ymaws.com/www.cste.org/resource/resmgr/ps/

positionstatement2020/Interim-20-ID-02_COVID-19.pdf

Please note: This report has been corrected.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1215

US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE 1. Classroom layout and seating chart for 24 students in index patient’s class, by SARS-CoV-2 testing date, result or status, and

symptoms — Marin County, California, May–June 2021

Teacher’s desk

Symptomatic May 19

Positive May 21

(index patient)

Symptomatic May 23

Positive May 26

Symptomatic May 24

Positive May 25

Symptomatic May 24

Positive May 25

Symptomatic May 22

Positive May 23

Symptomatic May 24

Positive May 24

Symptomatic May 23

Positive May 24

Symptomatic May 26

Positive May 26

Positive May 24

Positive May 26

Symptomatic

(date unknown)

Positive May 26

Negative May 26

Symptomatic case Asymptomatic case Negative test result No testing

Negative May 26

Negative May 26 Negative May 26

Negative

May

Negative May 26

No testing

(Empty seat)

No testing

Air lter

Classroom door

(left open)

Board in front of classroom

Windows (left open)

Negative May 26

6 ft 6 ft 6 ft 6 ft

6 ft

high-efficiency particulate air filters and doors and windows

were left open. Fourteen of 18 students in this separate grade

received testing; six tests had positive results. Investigation

revealed that one student in this grade hosted a sleepover on

May 21 with two classmates from the same grade. All three of

these students experienced symptoms after the sleepover and

received positive SARS-CoV-2 test results. Among infected

students in this class, test dates ranged from May 24 to June 1;

symptom onset occurred during May 22–31.

In addition to the documented infections in the two initial

grades, cases were identified in one student each from four

other grades. Three patients were symptomatic; dates for test-

ing were May 30 or June 2. These four students were siblings

of three students with cases in the index patient’s class, and

exposure was assumed to have occurred in their respective

homes. In addition to the teacher and 22 infected students,

four parents of students with cases were also infected, for a

total of 27 cases (23 confirmed by RT-PCR and four by anti-

gen testing) (Figure 2). Among the five infected adults, one

Morbidity and Mortality Weekly Report

1216 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE 2. Timeline of SARS-CoV-2 illness onset* after onset in the index patient (A) and presumed transmission

†

pathway (B) among students,

siblings, and parents, relative to onset in the index patient — Marin County, California, May 2021

0 5 10

No. of days since onset in index patient

No. of cases

15 20

10 15 20

Index patient

Case in index patient’s class

Case in other aected class

Sibling of infected student in index patient’s class

Parent of infected student in other aected class

Parent of infected student in index patient’s class

Symptom onset in index patient: day 0

Sleepover event: day 2

Presumed exposure pathway

* Symptom onset date or specimen collection date, if asymptomatic.

†

Presumed transmission based on phylogenetic and epidemiologic analyses.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1217

US Department of Health and Human Services/Centers for Disease Control and Prevention

parent and the teacher were unvaccinated; the others were fully

vaccinated. The vaccinated adults and one unvaccinated adult

were symptomatic with fever, chills, cough, headache, and loss

of smell. No other school staff members reported becoming ill.

No persons infected in this outbreak were hospitalized. This

activity was reviewed by Marin County and was conducted

consistent with applicable law.

Public Health Response

On May 26 and June 2, MCPH held testing events at the

school as part of outbreak control. During these 2 days, 231

persons were tested, including 194 of 205 students, 21 of

24 staff members and teachers, and 16 parents and siblings

of students. The California Department of Public Health

assisted with guidance, application of additional prevention

strategies, and on-site testing. Community contacts and all

students and staff members were encouraged to participate.

Specimens for WGS were collected during May 26–June 12;

all 18 positive specimens with detectable virus (cycle threshold

value <32) were sequenced using ClearDx instruments (Clear

Laboratories), Oxford Nanopore MinION sequencing tech-

nology, and SARS-CoV-2 ARTIC V3 protocol for amplicon

sequencing.

†

Consensus genome assembly was performed in

Terra using Titan Clear Laboratories workflow.

All sequences

generated were classified as the Delta variant. A phylogenetic

tree was constructed using the UShER pipeline and visual-

ized using Auspice.us

(4) (Figure 3). Eleven sequences were

genetically indistinguishable from one another; seven sequences

contained additional single nucleotide variations. Among the

indistinguishable specimens, six were from students of the

index patient, four were from students in the separate grade,

and one was from a sibling of a student in the index patient’s

class, suggesting that infections occurring in the two grades

likely were part of the same outbreak. The epidemiologic link

between the two grades remains unknown but is thought to

be interaction at the school. Five additional related sequences

from community cases (in two adults and three children) were

later identified, including three more genetically indistinguish-

able sequences. One was from an adult with specimen collec-

tion 1 day before symptom onset in the index patient. Case

investigation records did not establish an epidemiologic link

between these five community cases and the school outbreak.

Following the outbreak, infected persons were isolated for

10 days after onset of symptoms (or positive test date for

asymptomatic cases). All students with known exposure to an

infected person quarantined at home for 10 days following their

†

https://www.protocols.io/view/ncov-2019-sequencing-protocol-bbmuik6w

https://public-health-viral-genomics-theiagen.readthedocs.io/en/latest/titan_

workflows.html

https://auspice.us

last known contact. Unvaccinated household and community

contacts were directed to quarantine for 10 days following their

last known exposure to an infected person, with the option to

leave quarantine after 7 days if they remained asymptomatic

and received a negative test result from a specimen collected

on day 5 of quarantine or later. The two affected classrooms

were closed and sanitized during May 21–30 and May 24–

June 2, respectively.

Discussion

This outbreak of COVID-19 that originated with an unvac-

cinated teacher highlights the importance of vaccinating

school staff members who are in close indoor contact with

children ineligible for vaccination as schools reopen. The

outbreak’s attack rate highlights the Delta variant’s increased

transmissibility**and potential for rapid spread, especially in

unvaccinated populations such as schoolchildren too young

for vaccination. However, transmission to community contacts

appeared lower than that of some previously reported Delta

variant outbreaks (5). Further transmission might have been

prevented by high levels of community vaccination; at the

time of this outbreak, approximately 72% of eligible persons

in the city where the school is located were fully vaccinated.

††

These findings support evidence that the current COVID-19

vaccines with Food and Drug Administration approval or

Emergency Use Authorization are effective against the Delta

variant; however, transmission risk remains elevated among

unvaccinated persons in schools. In addition to vaccination

of eligible persons, implementation of and strict adherence

to multipronged nonpharmaceutical prevention strategies

including proper masking, routine testing, ventilation, and

staying home while symptomatic are important to ensure safe

school instruction.

The findings in this study are subject to at least three limita-

tions. First, the teacher’s specimen was unavailable for WGS,

which prevented phylogenetic identification of the outbreak’s

index patient. Second, testing for parents and siblings was

self-directed and took place mostly outside the school setting,

which could have led to underascertainment of cases. Finally,

challenges in testing acceptance among possible contacts from

outside the school led to difficulty in characterizing the out-

break’s actual spread into the community, as is evidenced by

later discovery of additional community cases with sequences

indistinguishable from those in the school outbreak.

Ineligibility because of age and lack of vaccination contribute

to persistent elevated risk for outbreaks in schools, especially as

new SARS-CoV-2 variants emerge. However, implementation

** https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-info.html

(accessed August 10, 2021)

††

https://coronavirus.marinhhs.org/ (accessed August 10, 2021)

Morbidity and Mortality Weekly Report

1218 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE 3. Phylogenetic tree*

,†

of SARS-CoV-2 whole genome sequences and specimen collection dates

from a COVID-19 outbreak in an

elementary school

— Marin County, California, May–June 2021

May 18

May 26

May 27

May 29

May 30

Jun 8

C1009T

A29083G

G14428T

C27641T

G23501T

A22034G

A3712G

A28750T

SNVs

34 35

May 26

Jun 1

May 27

May 29

Jun 2

Jun 12

Case in index patient’s class

Case in other aected class

Sibling of infected student in index patient’s class

Parent of infected student in index patient’s class

No identied link

Abbreviations: SNV = single nucleotide variant; WGS = whole genome sequencing.

* Phylogenetic tree was created with UShER, which uses the Fitch–Sankoff algorithm (a maximum parsimony-based phylogenetic placement approach). https://doi.

org/10.1038/s41588-021-00862-7

†

Specimen for the index patient was not available for WGS and is not included on the phylogenetic tree.

Dates in this diagram reflect the collection date for specimens that underwent WGS; thus dates might differ from those reported in the text for persons whose initial

specimens were discarded.

Branches are labeled with SNVs; cases (circles) are color-coded to indicate social relationship within the outbreak and labeled with the collection date for the specimen

that was sequenced. Vertical lines represent genetically identical viruses; horizontal lines represent genetic descendants with additional SNVs. All sequenced

specimens are classified as the SARS-CoV-2 B.1.617.2 (Delta) variant.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1219

US Department of Health and Human Services/Centers for Disease Control and Prevention

Summary

What is already known about this topic?

The SARS-CoV-2 B.1.617.2 (Delta) variant is highly transmissible.

Prevention guidance in schools varies by jurisdiction.

What is added by this report?

During May 23–June 12, 2021, 26 laboratory-confirmed

COVID-19 cases occurred among Marin County, California,

elementary school students and their contacts following

exposure to an unvaccinated infected teacher. The attack rate in

one affected classroom was 50%; risk correlated with seating

proximity to the teacher.

What are the implications for public health practice?

Vaccines are effective against the Delta variant, but transmis-

sion risk remains elevated among unvaccinated persons in

schools. In addition to vaccination, strict adherence to multiple

nonpharmaceutical prevention strategies, including masking,

are important to ensure safe school instruction.

of multiple prevention strategies within schools can mitigate this

risk. The rapid transmission and vaccine breakthrough infections

in this outbreak might have resulted from the schoolchildren’s

vulnerability because of ineligibility for vaccination, coupled

with the high transmissibility of the Delta variant. New evidence

of the Delta variant’s high transmissibility, even among fully vac-

cinated persons (6,7), supports recommendations for universal

masking in schools

§§

(1). Further application of nonpharmaceu-

tical prevention strategies, including routine testing, ventilation,

and staying home while symptomatic, are also important for

protecting the health of schoolchildren ineligible for vaccination

because of their age (3). In addition, phylogenetic analysis can

help to clarify transmission patterns and characterize outbreak

progression. Capacity-building efforts offered by regional and

state laboratories enabled more sophisticated analysis at the local

level; such efforts might be useful as vaccination rates increase,

new variants emerge, and outbreaks become more localized.

§§

https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/K-12-

Guidance-2021-22-School-Year.aspx (accessed August 2, 2021)

Acknowledgments

COVID-19 Case and Contact Investigation Unit, Marin County

Public Health; Naomi Bardach, Kathleen Harriman, Carol Glaser,

Erica Pan, California Department of Public Health; John Oeltmann,

Patrick Moonan, CDC; Teionna Cunningham, Napa-Solano-Yolo-

Marin-Mendocino Public Health Laboratory; Patrick Ayescue,

Allison Black, Chan Zuckerberg BioHub.

Corresponding author: Tracy Lam-Hine, tlam-hine@marincounty.org.

County of Marin Department of Health and Human Services, San Rafael,

California;

UC Berkeley School of Public Health, University of California,

Berkeley, California;

UC Davis School of Medicine, University of California,

Sacramento, California;

Baskin School of Engineering, University of California,

Santa Cruz, California;

Napa-Solano-Yolo-Marin-Mendocino County Public

Health Laboratory, Fairfield, California.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. Lael Duncan reports Pfizer and Moderna stock

ownership. No other potential conflicts of interest were disclosed.

References

1. CDC. COVID-19: when you’ve been fully vaccinated: how to protect

yourself and others. Atlanta, GA: US Department of Health and Human

Services, CDC; 2021. Accessed July 27, 2021. https://www.cdc.gov/

coronavirus/2019-ncov/vaccines/fully-vaccinated.html

2. Lopez Bernal J, Andrews N, Gower C, et al. Effectiveness of

Covid-19 vaccines against the B.1.617.2 (Delta) Variant. N Engl J

Med 2021;385:585–94. PMID:34289274 https://doi.org/10.1056/

NEJMoa2108891

3. CDC. COVID-19: guidance for COVID-19 prevention in K-12 schools.

Atlanta, GA: US Department of Health and Human Services, CDC;

2021. Accessed August 5, 2021. https://www.cdc.gov/coronavirus/2019-

ncov/community/schools-childcare/k-12-guidance.html

4. Turakhia Y, Thornlow B, Hinrichs AS, et al. Ultrafast Sample placement

on Existing tRees (UShER) enables real-time phylogenetics for the

SARS-CoV-2 pandemic. Nat Genet 2021;53:809–16. PMID:33972780

https://doi.org/10.1038/s41588-021-00862-7

5. Dougherty K, Mannell M, Naqvi O, Matson D, Stone J. SARS-CoV-2

B.1.617.2 (Delta) variant COVID-19 outbreak associated with a

gymnastics facility—Oklahoma, April–May 2021. MMWR Morb Mortal

Wkly Rep 2021;70:1004–7. PMID:34264910 https://doi.org/10.15585/

mmwr.mm7028e2

6. Brown CM, Vostok J, Johnson H, et al. Outbreak of SARS-CoV-2

infections, including COVID-19 vaccine breakthrough infections,

associated with large public gatherings—Barnstable County, Massachusetts,

July 2021. MMWR Morb Mortal Wkly Rep 2021;70:1059–62.

PMID:34351882 https://doi.org/10.15585/mmwr.mm7031e2

7. Israeli Ministry of Health. Explanation about the effectiveness of the

vaccine for coronavirus in Israel. Jerusalem, Israel: Israel Ministry of

Health; 2021. Accessed July 29, 2021. https://www.gov.il/en/departments/

news/06072021-04

Morbidity and Mortality Weekly Report

1220 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

COVID-19 Case Rates in Transitional Kindergarten Through Grade 12 Schools and in

the Community — Los Angeles County, California, September 2020–March 2021

Sherry Yin, MPH

; Kaitlin Barnes, MBA

; Rebecca Fisher, MPH

; Dawn Terashita, MD

; Andrea A. Kim, PhD

On August 27, 2021, this report was posted as an MMWR Early

Release on the MMWR website (https://www.cdc.gov/mmwr).

In-person instruction during the COVID-19 pandemic con-

cerns educators, unions, parents, students, and public health

officials as they plan to create a safe and supportive learning

environment for children and adolescents (1). Los Angeles

County (LAC), the nation’s largest county, has an estimated

population of 10 million, including 1.7 million children and

adolescents aged 5–17 years (2). LAC school districts moved

to remote learning for some or all students in transitional

kindergarten* through grade 12 (TK–12) schools during the

2020–21 school year (3). Schools that provided in-person

instruction were required by LAC Health Officer orders to

implement prevention measures such as symptom screening,

masking, physical distancing, cohorting, and contact tracing

(4). This analysis compares COVID-19 case rates in TK–12

schools among students and staff members who attended

school in person with LAC case rates during September 2020–

March 2021.

LAC schools are required to report all laboratory-confirmed

COVID-19 cases in persons who were on campus during their

incubation or infectious period to the LAC Department of

Public Health (DPH).

†

School-associated cases were defined

as cases among students and staff members who were on

campus for any length of time from 14 days before symp-

tom onset or testing (whichever was earlier) until isolation.

Cases among students and staff members who participated

exclusively in online learning or worked remotely were not

considered school-associated cases. DPH and the LAC Office

of Education also collected information from the county’s 80

public school districts on the estimated number of students

and staff members routinely on campus each month during

September 2020–March 2021. Monthly attendance was pro-

rated based on when schools opened and ranged from 2,738

* Transitional kindergarten is a public school program serving to bridge preschool

and kindergarten.

†

During the observation period, school districts recommended diagnostic testing

for students and staff members to identify persons with active infection based on

symptoms and exposure. In addition to mandatory laboratory or provider

reporting of positive COVID-19 nucleic acid amplification test (NAAT) and

antigen test results, schools also notified DPH of cases occurring on campus.

Schools relied on staff members, students, parents and guardians, and testing

programs where available to inform them of cases and worked with DPH to

identify and provide testing for close contacts on campus. The Los Angeles Unified

School District (largest of the 80 school districts in the county) implemented a

screening testing program using NAATs for staff members and students on campus

beginning September 2020 and reported all positive results to DPH.

to 62,369 students and 36,862 to 45,757 staff members.

Student and staff member case rates were calculated using the

number of school-associated cases reported to DPH, assigned

to a month by episode date, and divided by monthly atten-

dance. Community case rates among children and adolescents

aged 5–17 years and adults aged 18–79 years were calculated

using the number of reported cases in LAC divided by the

2019 county population.

Standard errors were calculated for

school-associated rates. Analyses were conducted using SAS

(version 9.4; SAS Institute). This public health surveillance

activity was approved by DPH.

During September 1, 2020–March 31, 2021, a total of

463 school-associated cases were reported among students

attending public TK–12 schools in person and 3,927 among

staff members working on-site. During the same period,

105,577 cases among children and adolescents aged 5–17 years

and 771,409 cases among adults aged 18–79 years were

reported in LAC. School-associated case rates remained low

among students, ranging from 110 per 100,000 in September

to 859 in December 2020 (Figure). Case rates among all chil-

dren and adolescents aged 5–17 years in the county were higher

during most of the period, ranging from 167 per 100,000 in

September to 2,938 in December 2020. School-associated case

rates among staff members were lowest in September 2020

(125 per 100,000), peaked in December 2020 (4,109),

and fell sharply through March 2021 (188). These rates

reflected the trend among all adults aged 18–79 years in

the county (319 per 100,000 in September 2020; 4,624 in

December 2020; and 181 in March 2021) but were lower for

most of the period. As total cases fell sharply in February, rates

across the four groups declined to similar levels by March 2021.

The findings in this report are subject to at least three limitations.

First, these findings from one county should be interpreted with cau-

tion and are not necessarily generalizable to other areas. Second, the

analysis did not include the entire school year because estimates of

students and staff members on campus were collected only through

March 2021. Finally, because of limited available information about

the population on campus, rates were unadjusted and did not

examine potential differences in demographic and socioeconomic

characteristics by in-person status. However, sensitivity analysis

showed similar trends across LAC’s eight Service Planning Areas.

Cities of Long Beach and Pasadena were not included because they have their own

health departments separate from LAC DPH. The adult age range reflects the age

range of staff members working at TK–12 schools in LAC during the analysis period.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1221

US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE. COVID-19 case rates* among children, adolescents, and adults

†

in transitional kindergarten through grade 12 schools and in the

community, by month — Los Angeles County, California, September 2020–March 2021

All children and adolescents aged 5–17 yrs in LAC

Students attending public TK–12 schools

All adults aged 18–79 yrs in LAC

Sta members working in public TK–12 schools

Support Width Options

Page wide = 7.5”

QuickStats = 5.0”

1½ columns = 4.65”

1 column = 3.57”

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Sep 2020 Oct 2020 Nov 2020 Dec 2020 Jan 2021 Feb 2021 Mar 2021

Cases per 100,000 population

Month, year

Beginning of surge

(Nov 2020)

Abbreviations: LAC = Los Angeles County; TK–12 = transitional kindergarten through grade 12.

* New cases per month per 100,000 persons; standard error bars shown for school case rates.

†

Adult staff members comprise all school employees and associated workers on campus, including teachers, nurses, public safety officers, administrative staff members,

campus aides, food service workers, custodians, and transportation staff members.

The findings suggest that implementing recommended

prevention measures might protect children, adolescents,

and adults from COVID-19 in TK–12 schools. The level of

protection appears to be higher in children and adolescents

than in adults, which is promising for children aged <12 years

because no COVID-19 vaccine is currently authorized for this

age group. In schools with safety protocols in place for preven-

tion and containment, case rates in children and adolescents

were 3.4 times lower during the winter peak compared with

rates in the community. This analysis reflects transmission pat-

terns before the more transmissible SARS-CoV-2 B.1.617.2

(Delta) variant became predominant in the United States.

A multipronged prevention strategy, including masking,

physical distancing, testing, and most recently vaccination of

children and adolescents aged ≥12 years, will remain critical to

reducing transmission as more students return to the classroom

(5). These findings from a large and diverse county present

preliminary evidence that schools provided a relatively safe

environment during the 2020–21 school year.

Acknowledgments

Sharon Balter, Katie Chun, Barbara Ferrer, Robert Gilchick,

Caitlin Harvey, Mirna Ponce Jewell, Lauren O’Neal, Ellen Paddock,

Alexander Serrano, Nailah Smith, Los Angeles County Department

of Public Health; Division of Acute Communicable Disease Control,

Education Sector Unit, Los Angeles County Department of Public

Health; Debra Duardo, Elizabeth Graswich, Arturo Valdez, Los Angeles

County Office of Education; administrators, staff members, students,

families, and unions in the 80 Los Angeles County school districts.

Corresponding author: Sherry Yin, [email protected].gov.

Los Angeles County Department of Public Health, California;

Los Angeles

County Office of Education, California.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. No potential conflicts of interest were disclosed.

Morbidity and Mortality Weekly Report

1222 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

References

1. Honein MA, Barrios LC, Brooks JT. Data and policy to guide opening

schools safely to limit the spread of SARS-CoV-2 infection. JAMA

2021;325:823–4. PMID:33497433 https://doi.org/10.1001/

jama.2021.0374

2. County of Los Angeles, Internal Services Department, Information

Technology Service, Urban Research-GIS Section. Population and poverty

estimates of Los Angeles County tract-city splits by age, sex, and race-

ethnicity. Los Angeles, CA: County of Los Angeles; 2019.

3. California Safe Schools for All. State of California safe schools for all hub.

Sacramento, CA: State of California; 2021. Accessed June 18, 2021.

https://schools.covid19.ca.gov/

4. County of Los Angeles Department of Public Health. Order of the Health

Officer. Reopening protocols for K–12 schools: Appendix T1. Los Angeles,

CA: County of Los Angeles; 2021. Accessed June 14, 2021. http://

publichealth.lacounty.gov/media/coronavirus/docs/protocols/Reopening_

K12Schools.pdf

5. CDC. COVID-19: guidance for COVID-19 prevention in K–12 schools.

Atlanta, GA: US Department of Health and Human Services, CDC;

2021. Accessed July 19, 2021. https://www.cdc.gov/coronavirus/2019-

ncov/community/schools-childcare/k-12-guidance.html

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1223

US Department of Health and Human Services/Centers for Disease Control and Prevention

Epidemiologically Linked COVID-19 Outbreaks at a Youth Camp and Men’s

Conference — Illinois, June–July 2021

James Matthias, MPH

; Sarah Patrick, PhD

; Ann Wiringa, PhD

; Amanda Pullman, MPH, MT

; Stephanie Hinton, MS, MHS

; Jon Campos

;

Terri Belville

; Mallory Sinner, MPH

; Torrie T. Buchanan, PhD

; Bryan Sim

; Kristin E. Goldesberry, MPH, MLS

On August 31, 2021, this report was posted as an MMWR Early

Release on the MMWR website (https://www.cdc.gov/mmwr).

On June 30, 2021, the Illinois Department of Public Health

(IDPH) contacted CDC concerning COVID-19 outbreaks

at two events sponsored by the same organization: a 5-day

overnight church camp for persons aged 14–18 years and a

2-day men’s conference. Neither COVID-19 vaccination nor

COVID-19 testing was required before either event. As of

August 13, a total of 180 confirmed and probable cases had

been identified among attendees at the two events and their

close contacts. Among the 122 cases associated with the camp

or the conference (primary cases), 18 were in persons who were

fully vaccinated, with 38 close contacts. Eight of these 38 close

contacts subsequently became infected with SARS-CoV-2,

the virus that causes COVID-19 (secondary cases); among

the eight close contacts with secondary cases, one half (four)

were fully vaccinated. Among the 180 total persons with

outbreak-associated cases, five (2.8%) were hospitalized; no

deaths occurred. None of the vaccinated persons with cases

were hospitalized. Approximately 1,000 persons across at least

four states were exposed to SARS-CoV-2 through attendance

at these events or through close contact with a person who had

a primary case. This investigation underscores the impact of

secondary SARS-CoV-2 transmission during large events, such

as camps and conferences, when COVID-19 prevention strate-

gies are not implemented. In Los Angeles County, California,

during July 2021, when the SARS-CoV-2 B.1.617.2 (Delta)

variant was predominant, unvaccinated residents were

five times more likely to be infected and 29 times more likely

to be hospitalized from infection than were vaccinated resi-

dents (1). Implementation of multiple prevention strategies,

including vaccination and nonpharmaceutical interventions

such as masking, physical distancing, and screening testing, are

critical to preventing SARS-CoV-2 transmission and serious

complications from COVID-19.

Investigation and Findings

The camp was held during June 13–17, 2021, and included

persons aged 14–18 years from a church organization with

multiple locations across western Illinois, Iowa, and Missouri.

A total of 294 campers arrived on buses or large passenger vans

and were met by 41 staff members. No proof of COVID-19

vaccination or SARS-CoV-2 pretesting or testing on arrival

was required, and the list of suggested items to bring to camp

did not include masks. Campers were housed in large, shared

boarding facilities of approximately 100 campers each, dined in

a cafeteria together, participated in indoor and outdoor small

group activities in which campers were with the same persons

during program events, and participated in activities with all

campers during all 5 days.

On June 16, the second to last camp day, one camper

departed after becoming ill with a fever and respiratory

symptoms and subsequently received a laboratory-confirmed

diagnosis of COVID-19. Campers and staff members were

notified, encouraged to receive SARS-CoV-2 testing, and

instructed to quarantine per CDC guidance and isolate if they

received a positive test result.*

Six camp staff members who received positive SARS-CoV-2

test results also attended the conference during June 18–19 but

did not receive their results until after the conference ended;

all six staff members had symptom onset during June 17–29.

†

The conference was held at a different location from the camp

and included 500 attendees and 30 staff members, and, as with

the camp, no COVID-19 vaccination, SARS-CoV-2 testing, or

masking was required. The first case in a conference attendee

was diagnosed on June 21, 2 days after the conference. After

conference-associated COVID-19 cases were identified, confer-

ence attendees and staff members were notified, encouraged to

receiving SARS-CoV-2 testing, and instructed to quarantine per

CDC guidance and isolate if they received a positive test result.

A confirmed case was defined as receipt of a positive

SARS-CoV-2 nucleic acid amplification test result in a camp

or conference attendee, and a probable case was defined as

receipt of a positive SARS-CoV-2 antigen test result.

Cases

were identified through case investigation after laboratory

notification of a positive test result. Information on symptom

onset or specimen collection dates (available for 174 [97%] of

* Persons with positive SARS-CoV-2 test results were instructed to isolate in

accordance with CDC guidance at the time (https://www.cdc.gov/

coronavirus/2019-ncov/hcp/duration-isolation.html), and close contacts were

instructed to quarantine in accordance with CDC guidance at the time (https://

www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/quarantine.html).

†

Six staff members (three at the camp and three at the conference) were identified

as having cases on the basis of their symptom onset date. Because the complete

rosters had not been provided to IDPH, persons might have been counted as

attendees at both events.

https://ndc.services.cdc.gov/case-definitions/coronavirus-

disease-2019-2020-08-05/

Morbidity and Mortality Weekly Report

1224 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

180 persons), COVID-19 vaccination status (from the state

immunization registry), county of residence, test results, and

viral sequencing data (available for 31 [17%] persons) was

collected for persons with camp- and conference-associated

cases (primary cases). IDPH’s contact tracing system identified

close contacts of persons with primary cases; close contacts

were defined as unmasked persons who were within 6 ft of a

person with a primary case for >15 minutes during a 24-hour

period while that person was infectious, which was 2 days

before through 10 days after symptom onset (for symptomatic

persons) or after specimen collection date (for asymptomatic

persons). Secondary cases were defined as COVID-19 cases

that occurred in close contacts of persons with a primary case

of confirmed or probable COVID-19.

Persons who had received 2 doses of Pfizer BioNTech or

Moderna COVID-19 mRNA vaccine or 1 dose of Janssen

(Johnson & Johnson) COVID-19 vaccine ≥14 days before

exposure were considered fully vaccinated. IDPH laboratories

performed whole genome sequencing on 25 of 31 available

specimens.

Descriptive analyses were conducted to determine

the distribution of cases by vaccination status, the proportion

of SARS-CoV-2 variants, and the secondary transmission

rate. This activity was reviewed by CDC and was conducted

consistent with applicable federal law and CDC policy.**

As of August 13, a total of 180 outbreak-associated cases

had been identified, including 122 primary cases, 87 (48%) of

which were in camp attendees (among 335 total campers and

staff members; attack rate=26%) and 35 (19%) in confer-

ence attendees (among 530 total conference participants and

staff members; attack rate=7%). Among 262 close contacts

of camp or conference attendees, 58 (22%) secondary cases

were identified, representing 32% of the 180 identified cases

(Figure) (Table). Among the 87 persons with camp-associated

cases, none reported symptom onset before the camp started

on June 13. Among the 35 persons with conference-associated

cases, three reported being symptomatic during the conference

(not including one camp-associated staff member who attended

the conference while symptomatic).

Among the 180 total persons with outbreak-associated cases,

13 (7.2%) required medical care in an emergency department,

and five (2.8%) were hospitalized; no deaths occurred (Table).

Whole genome sequencing was performed on Illumina’s NextSeq 550

instrument using the COVID-Seq workflow. Consensus genome assembly

was performed in Illumina’s DRAGEN analytical pipeline, and variants were

assigned using the most recent Pangolin version.

** 45 C.F.R. part 46.102(l)(2), 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d);

5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

None of the vaccinated persons with cases were hospital-

ized; three sought care at an emergency department. Overall,

29 (16.1%) cases occurred in fully vaccinated persons (camp

cases: 9%; conference cases: 29%). Among the 262 close con-

tacts of persons with a primary case, 52 (20%) were fully vac-

cinated; 11 of these fully vaccinated persons received a positive

SARS-CoV-2 test result, representing 19% of the 58 secondary

cases among close contacts of camp or conference attendees

with primary cases.

Among the 122 cases in camp or conference attendees,

18 were in fully vaccinated persons (eight in camp attendees

and 10 in conference attendees). These 18 fully vaccinated per-

sons reported a total of 38 close contacts; eight (21%) of these

close contacts received positive SARS-CoV-2 test results, four

(50%) of whom were fully vaccinated. Among the 224 reported

close contacts of unvaccinated and partially vaccinated persons

with primary cases, 50 (22%) received positive SARS-CoV-2

test results, including seven fully vaccinated persons. Among

58 persons with secondary cases, 48 (83%) were infected by

household members, four by nonhousehold family members,

three by friends, and one each by a neighbor, at work, or dur-

ing a Bible study group.

Overall, 1,127 persons from at least four states and 18 coun-

ties were exposed to SARS-CoV-2 through attendance at the

camp or conference or through close contact with a person who

had a camp- or conference-associated case. In the 7 days before

the camp (June 6–12), Adams County, Illinois,

††

reported

31 COVID-19 cases, with an averageof4.4 cases per day. In

the 7 days after the last identified secondary case (July 16–22),

the county reported 232 cases, with an averageof33.1 per day,

a 648% increase from the number reported during the week

before the camp (2).

Among samples sequenced from specimens from 31 infected

persons (15 from camp-associated cases, eight from conference-

associated cases, and eight from secondary cases), the B.1.617.2

(Delta) variant was identified in 27 (87%), including two AY.3

(Delta) sequences; the B.1.1.7 (Alpha) variant was identified

in three (10%); and the P.1 (Gamma) variant was identified

in one (3%) (Table). Among eight sequenced samples from

specimens from vaccinated persons, the Delta variant was

identified in seven samples, and Alpha in one.

§§

††

Adams County is used as the reference county because it was the county of

residence for 49% (43) of persons with camp-associated cases, 40% (14) of

persons with conference-associated cases, and 59% (34) of persons with

secondary cases.

§§

Six of these eight vaccinated persons who were infected with SARS-CoV-2

variants of concern were from households with two persons with sequenced

samples; two pairs of the Delta variant and one discordant pair of Delta and

Alpha variants were identified in samples from these households.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1225

US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE. Number of primary COVID-19 cases among attendees of a youth camp (A) and men’s conference (B) and secondary cases among close

contacts* (C), by date of symptom onset or specimen collection

†

— Illinois, June–July 2021

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Jun

Date of symptom onset or specimen collection

Jul

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Jun Jul

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Jun Jul

Start of men's conference:

June 18 (end date June 19)

Start of men's conference:

June 18 (end date June 19)

Start of men's conference:

June 18 (end date June 19)

Start of youth camp:

June 13

(end date June 17)

Start of youth camp:

June 13

(end date June 17)

A. Camp-associated primary cases

B. Conference-associated primary cases

Start of youth camp:

June 13

(end date June 17)

C. Secondary cases among close contacts

No. of cases

* Close contacts were defined as unmasked persons who were within 6 ft of a person with a camp-or conference-associated (primary) case for >15 minutes during a

24-hour period while that person was infectious (i.e., 2 days before through 10 days after symptom onset or specimen collection date). Secondary cases were defined

as COVID-19 cases in close contacts of persons with a primary case of confirmed or probable COVID-19 (confirmed: receipt of a positive SARS-CoV-2 nucleic acid

amplification test result in an attendee; probable: receipt of a positive SARS-CoV-2 antigen test result).

†

Symptom onset date or specimen collection date was missing for six out-of-state persons. Among the remaining 174, onset date was available for 158 (91%), and

specimen collection date was available for 16 (9%).

Morbidity and Mortality Weekly Report

1226 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

Public Health Response

IDPH sent three Epi-X notifications

¶¶

about these outbreaks

to state and local health departments and received case data from

the state health departments in Iowa, Michigan, and Missouri.

On June 30, IDPH requested CDC’s assistance with investigat-

ing these outbreaks. On July 19, a CDC field team arrived in

Illinois to assist with active case finding in several jurisdictions,

collection and analysis of samples, and ascertainment of second-

ary transmission. As of August 13, complete rosters of attendees

and staff members at both events were not available.

Discussion

COVID-19 vaccines currently authorized by the Food and

Drug Administration are safe and highly effective for prevent-

ing COVID-19–related serious illness, hospitalization, and

death.*** In this investigation, most reported COVID-19

cases were identified among unvaccinated persons. However,

transmission of SARS-CoV-2 from vaccinated persons both

to unvaccinated and vaccinated persons likely occurred. These

breakthrough cases among vaccinated persons were identified

among attendees of the camp and the conference and in persons

exposed to the attendees. Consistent with previous studies,

much of the identified secondary transmission occurred within

households, where most prolonged contact occurs (3).

Approximately 1,000 persons in at least four states were

exposed to SARS-CoV-2 through attendance at the camp or

conference or through close contact with a person infected at

the event. The high rate of transmission was likely driven by

the number of persons infected with the SARS-CoV-2 Delta

variant. However, because multiple SARS-CoV-2 variants of

concern were identified from the specimens of camp attendees,

this suggests multiple introductions of SARS-CoV-2 into the

camp, rather than a single introduction event. As of August 7,

COVID-19 outbreaks in at least 21 overnight camps had been

reported in Illinois, reinforcing the importance of COVID-19

prevention measures at these camps, including identifying

infected persons through prearrival and screening testing

programs and consistent implementation of other prevention

efforts, including vaccination, masking, and physical distanc-

ing (4–6). Several camp staff members who were infected with

SARS-CoV-2 (including at least one symptomatic person) or

who had been exposed to the virus attended another large

group event during their infectious period. Therefore, timely

¶¶

Epi-X is a secure, web-based notification system, with approximately 6,000

users from public health agencies, that guides and coordinates public health

professionals during public health threats and investigations. https://

emergency.cdc.gov/epix/index.asp

*** https://www.cdc.gov/coronavirus/2019-ncov/vaccines/vaccine-benefits.html

TABLE. Characteristics of persons with primary COVID-19 cases after

attendance at a youth camp or men’s conference and of close

contacts with secondary COVID-19 cases — Illinois, June–July 2021

Characteristic

No. (%)

Camp

cases

Conference

cases

Secondary

cases Total

Minimum no. of persons

exposed

335 530 262 1,127

Reported cases* 87 (26) 35 (7) 58 (22) 180 (16)

Median age, yrs (range)

†

17 (13–54) 44 (15–68) 38 (3–72) 26 (3–72)

Sex

†

Male 28 (34) 35 (100) 20 (34) 83 (47)

Female 55 (66) 0 (—) 38 (66) 93 (53)

Persons who required

emergency department

care

3 (3) 5 (14) 5 (9) 13 (7)

Persons hospitalized 1 (1) 3 (9) 1 (2) 5 (3)

Fully vaccinated

persons

8 (9) 10 (29) 11 (19) 29 (16)

Vaccine product received by fully vaccinated persons

Pfizer-BioNTech 3 (38) 5 (50) 3 (27) 11 (41)

Moderna 2 (25) 3 (30) 2 (18) 7 (24)

Janssen (Johnson &

Johnson)

3 (38) 2 (20) 6 (55) 11 (38)

Unknown vaccine

product received or

partially vaccinated

79 (91) 25 (71) 47 (81) 151 (84)

No. of viruses

sequenced

15 8 8 31

B.1.617.2 (Delta) 13 (87) 7 (88) 7 (88) 27 (87)

B.1.1.7 (Alpha) 1 (7) 1 (13) 1 (13) 3 (10)

P.1 (Gamma) 1 (7) 0 (—) 0 (—) 1 (3)

Minimum no. of

affected counties**

16 7 8 18

* Percentages might not sum to 100% because of rounding.

†

For 174 of 180 cases in persons with a reported onset or specimen collection

date, sex (176), and date of birth.

Fully vaccinated persons were defined as persons who had received 2 doses

of Pfizer BioNTech or Moderna COVID-19 mRNA vaccine or 1 dose of Janssen

(Johnson & Johnson) COVID-19 vaccine ≥14 days before exposure.

Illinois Department of Public Health laboratories performed whole genome

sequencing on 25 of 31 specimens using Illumina’s NextSeq 550 instrument

with the COVID-Seq workflow. Consensus genome assembly was performed

in Illumina’s DRAGEN analytical pipeline, and variants were assigned using

the most recent Pangolin version.

** Because seven of 16 counties of residence identified among camp and

conference attendees were outside of Illinois, contact tracing data were

extremely limited.

notification of all close contacts and compliance with isolation

and quarantine guidance are also critical.

The findings in this report are subject to at least two limita-

tions. First, the investigation likely underestimates the number

of SARS-CoV-2 primary infections, secondary exposures, and

secondary cases because the case definition required laboratory

confirmation; therefore, infected persons who did not receive

testing or who used at-home SARS-CoV-2 antigen tests (i.e.,

self-collection kits) were not included in the case count. In

addition, not all persons with cases participated in contact

tracing; the close contacts of persons who did participate were

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1227

US Department of Health and Human Services/Centers for Disease Control and Prevention

Summary

What is already known about this topic?

The Illinois Department of Public Health investigated COVID-19

outbreaks at two events sponsored by the same organization: a

5-day overnight church camp for persons aged 14–18 years and

a 2-day men’s conference.

What is added by this report?

Neither COVID-19 vaccination nor COVID-19 testing was

required before either event. Among 122 primary cases, 104

(85%) were in persons who were not fully vaccinated, and 18

(15%) were in fully vaccinated persons. Eight of 38 (21%) close

contacts of the 18 fully vaccinated persons subsequently

became infected with SARS-CoV-2. No vaccinated persons with

COVID-19 were hospitalized.

What are the implications for public health practice?

This investigation underscores the impact of secondary

SARS-CoV-2 transmission during large events such as camps

and conferences when COVID-19 prevention strategies,

including vaccination, masking, physical distancing, and

screening testing, are not implemented.

likely underreported and were biased toward household con-

tacts (7). Second, investigators did not have access to complete

rosters for the camp or conference, which limited case finding

efforts and analyses involving persons who were not infected

(particularly findings related to vaccination status).

These findings underscore the risk for COVID-19 outbreaks

at camps and large events where prevention strategies are not

implemented and highlight the importance of implement-

ing such strategies to reduce transmission of SARS-CoV-2

in these settings (8). Promoting vaccination, implementing

and encouraging compliance with prompt quarantine and

isolation measures for exposed and infected persons, staying

home when sick, and using nonpharmaceutical interventions

including masking, physical distancing, and screening testing

in large group settings can help reduce secondary infections

in homes and the community and serious complications from

COVID-19 (9).

Acknowledgments

Illinois Department of Public Health and local Illinois Health

Department staff members, health care providers, contact tracers,

and community health representatives; Blessings Hospital, Quincy,

Illinois; Iowa Department of Public Health; Michigan Department

of Health and Human Services; Missouri Department of Health and

Senior Services; Jerrod Welch, Emily Crabtree, Tara Bealor, Ellen

Vonderheide, Katie McConnell, Adams County Health Department;

CDC COVID-19 Response Team; CDC Health Department Task

Force Section.

Corresponding author: James Matthias, [email protected].

CDC COVID-19 Response Team;

Illinois Department of Public Health;

Adams County Health Department, Quincy, Illinois;

Schuyler County Health

Department, Rushville, Illinois;

Illinois Department of Public Health

Laboratory, Springfield, Illinois.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. No potential conflicts of interest were disclosed.

References

1. Griffin JB, Haddix M, Danza P, et al. SARS-CoV-2 infections and

hospitalizations among persons aged ≥16 years, by vaccination status—

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Mortal Wkly Rep 2021;70:1170–6. PMID:34437525 https://doi.

org/10.15585/mmwr.mm7034e5

2. Adams County Health Department. Adams County, Illinois, COVID-19

tracking system. Quincy, IL: Adams County Health Department; 2021.

Accessed August 10, 2021. https://adamscountyilgis.maps.arcgis.com/

apps/dashboards/d8a330c2daa945fca0c8eedeb6e1c409

3. Luo L, Liu D, Liao X, et al. Contact settings and risk for transmission in

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org/10.7326/M20-2671

4. Szablewski CM, Chang KT, Brown MM, et al. SARS-CoV-2 transmission

and infection among attendees of an overnight camp—Georgia, June

2020. MMWR Morb Mortal Wkly Rep 2020;69:1023–5.

PMID:32759921 https://doi.org/10.15585/mmwr.mm6931e1

5. CDC. Guidance for operating youth camps. Atlanta, GA: US Department

of Health and Human Services, CDC; 2021. Accessed August 10, 2021.

https://www.cdc.gov/coronavirus/2019-ncov/community/schools-

childcare/summer-camps.html

6. D’Agostino EM, Armstrong SC, Humphreys L, et al. Symptomatic

SARS-CoV-2 transmission in youth and staff attending day camps.

Pediatrics 2021;147:e2020042416. PMID:33536332 https://doi.

org/10.1542/peds.2020-042416

7. Cope AB, Bernstein K, Matthias J, et al. Unnamed partners from syphilis

partner services interviews, 7 jurisdictions. Sex Transm Dis 2020;47:811–8.

PMID:32890335 https://doi.org/10.1097/OLQ.0000000000001269

8. Carter RJ, Rose DA, Sabo RT, et al.; CDC COVID-19 Response

Team. Widespread severe acute respiratory syndrome coronavirus 2

transmission among attendees at a large motorcycle rally and their

contacts, 30 US jurisdictions, August–September, 2020. Clin Infect Dis

2021;73(Suppl 1):S106–9. PMID:33912907 https://doi.org/10.1093/

cid/ciab321

9. CDC. Science brief: transmission of SARS-CoV-2 in K–12 schools and

early care and education programs. Atlanta, GA: US Department of Health

and Human Services, CDC; 2021. Accessed August 10, 2021. https://

www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/

transmission_k_12_schools.html

Morbidity and Mortality Weekly Report

1228 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

Association Between COVID-19 and Myocarditis Using Hospital-Based

Administrative Data — United States, March 2020–January 2021

Tegan K. Boehmer, PhD

*; Lyudmyla Kompaniyets, PhD

*; Amy M. Lavery, PhD

; Joy Hsu, MD

; Jean Y. Ko, PhD

; Hussain Yusuf, MD

;

Sebastian D. Romano, MPH

; Adi V. Gundlapalli, MD, PhD

; Matthew E. Oster, MD

1,2,3

; Aaron M. Harris, MD

On August 31, 2021, this report was posted as an MMWR Early

Release on the MMWR website (https://www.cdc.gov/mmwr).

Viral infections are a common cause of myocarditis, an

inflammation of the heart muscle (myocardium) that can

result in hospitalization, heart failure, and sudden death (1).

Emerging data suggest an association between COVID-19

and myocarditis (2–5). CDC assessed this association using

a large, U.S. hospital-based administrative database of health

care encounters from >900 hospitals. Myocarditis inpatient

encounters were 42.3% higher in 2020 than in 2019. During

March 2020–January 2021, the period that coincided with the

COVID-19 pandemic, the risk for myocarditis was 0.146%

among patients diagnosed with COVID-19 during an inpa-

tient or hospital-based outpatient encounter and 0.009%

among patients who were not diagnosed with COVID-19.

After adjusting for patient and hospital characteristics, patients

with COVID-19 during March 2020–January 2021 had,

on average, 15.7 times the risk for myocarditis compared

with those without COVID-19 (95% confidence interval

[CI]=14.1–17.2); by age, risk ratios ranged from approxi-

mately 7.0 for patients aged 16–39 years to >30.0 for patients

aged <16 years or ≥75 years. Overall, myocarditis was uncom-

mon among persons with and without COVID-19; however,

COVID-19 was significantly associated with an increased risk

for myocarditis, with risk varying by age group. These findings

underscore the importance of implementing evidence-based

COVID-19 prevention strategies, including vaccination, to

reduce the public health impact of COVID-19 and its associ-

ated complications.

Data for this study were obtained from the Premier

Healthcare Database Special COVID-19 Release (PHD-SR),

a large hospital-based administrative database.

†

The monthly

* These authors contributed equally to this report.

†

PHD-SR, formerly known as the PHD COVID-19 Database, is a large U.S.

hospital-based all-payer database that includes inpatient and hospital-based

outpatient (e.g., emergency department or clinic) health care encounters from

>900 geographically diverse, nonprofit, nongovernmental, community, and

teaching hospitals and health systems from rural and urban areas. PHD-SR

represents approximately 20% of inpatient admissions in the United States.

Updated PHD-SR data are released every 2 weeks; release date August 4, 2021,

access date August 4, 2021. http://offers.premierinc.com/rs/381-NBB-525/

images/PHD_COVID-19_White_Paper.pdf

number of myocarditis

and COVID-19

inpatient encounters

was assessed before and during the COVID-19 pandemic, from

January 2019 through May 2021.

A patient-level cohort was created to assess the association

between COVID-19 and myocarditis. The cohort included all

patients with at least one inpatient or hospital-based outpatient

encounter with discharge during March 2020–January 2021.

To minimize potential bias from vaccine-associated myocarditis

(6), 277,892 patients with a COVID-19 vaccination record

in PHD-SR during December 2020–February 2021 were

excluded. In addition, 37,896 patients for whom information

on sex was missing were excluded. Patients with COVID-19

were defined as those who had their first inpatient or outpatient

encounter with a COVID-19 International Classification of

Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)

code during March 2020–January 2021. Patients with myo-

carditis were defined as those who had their first of at least

one inpatient encounter, at least two outpatient encounters,

or at least one outpatient encounter with a relevant specialist**

with a myocarditis ICD-10-CM code during March 2020–

February 2021.

††

Among patients with COVID-19, the first

myocarditis encounter could have occurred during or after the

first COVID-19 health care encounter.

The risk for myocarditis was defined as the percentage of

patients with myocarditis and was calculated among patients

with and without COVID-19, overall and by sex (male or

Myocarditis was identified by the following International Classification of

Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes: B33.20,

B33.22, B33.24, I40.0, I40.1, I40.8, I40.9, or I51.4.

COVID-19 was identified by ICD-10-CM code B97.29 (other coronavirus

as the cause of diseases classified elsewhere) during March–April 2020 or

ICD-10-CM code U07.1 (COVID-19, virus identified [laboratory-

confirmed]) during or after April 2020. ICD-10-CM code B97.29 was

recommended before the April 1, 2020, release of ICD-10-CM code U07.1.

https://www.cdc.gov/nchs/data/icd/Announcement-New-ICD-code-for-

coronavirus-3-18-2020.pdf

** Attending physician with one of the following specializations: cardiac

electrophysiology, cardiovascular diseases, cardiovascular surgery, infectious

diseases, or rheumatology.

††

Myocarditis was assessed using all encounters for each patient from January

2019 to February 2021. Patients were defined as having new myocarditis

during the study period if their first myocarditis encounter in PHD-SR

occurred during or after March 2020. Myocarditis was assessed through

February 2021 to allow for myocarditis diagnoses to occur after COVID-19

diagnoses in January 2021.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1229

US Department of Health and Human Services/Centers for Disease Control and Prevention

female) and age group (<16, 16–24, 25–39, 40–49, 50–64,

65–74, and ≥75 years). The percentage of myocarditis patients

with a history of COVID-19 was calculated for each age group.

Associations between COVID-19 and myocarditis were

estimated using a multiple logit model with the following

covariates: three-way interaction between COVID-19, sex, and

age group, including lower-order interactions and main effects;

race/ethnicity; payer type; hospital U.S. Census region; and

hospital urbanicity. Adjusted risk differences (aRDs, measure

of absolute risk) were calculated as the difference between

1) the adjusted predicted risk for myocarditis (outcome) among

patients with COVID-19 (exposed group) and 2) adjusted pre-

dicted risk for myocarditis among patients without COVID-19

(unexposed group); adjusted risk ratios (aRRs, measure of

relative risk) were calculated as the ratio of the adjusted

predicted risk among exposed to the adjusted predicted risk

among unexposed

§§

(7,8). All models used standard errors

clustered on a unique hospital identifier. R (version 4.0.2;

R Foundation) and Stata (version 15.1; StataCorp) were used

to conduct all analyses. This activity was reviewed by CDC

and was conducted consistent with applicable federal law and

CDC policy.

¶¶

During 2020, the number of myocarditis inpatient encoun-

ters (4,560) was 42.3% higher than that during 2019 (3,205).

Peaks in myocarditis inpatient encounters during April–

May 2020 and November 2020–January 2021 generally aligned

with peaks in COVID-19 inpatient encounters (Figure 1).

Within the cohort of 36,005,294 patients, 1,452,773

(4.0%) received a diagnosis of COVID-19 during

March 2020–January 2021, and 5,069 (0.01%) received a diag-

nosis of myocarditis during March 2020–February 2021. Overall,

4,339 (85.6%) patients with myocarditis were identified by an

inpatient encounter. Patients with myocarditis were slightly older

than patients without myocarditis (median age=54 years versus

50 years) and were more commonly male (59.3% versus 41.7%)

(Supplementary Table, https://stacks.cdc.gov/view/cdc/109261).

§§

First, a multiple logit model was performed with the following covariates:

three-way interaction between COVID-19, sex, and age group, including

lower-order interactions and main effects; race/ethnicity; payer type; hospital

U.S. Census region; and hospital urbanicity. Then the following average

predicted probabilities (predictive margins) were estimated: 1) P1: the average

predicted probability of myocarditis with COVID-19 set to be present and

all other covariates set to their original values, and 2) P0: the average predicted

probability of myocarditis with COVID-19 set to be absent and all other

covariates set to their original values. aRD represents the difference in predicted

probabilities (P1 minus P0); aRR represents the ratio of the predicted

probabilities (P1 divided by P0). aRRs and aRDs were obtained for the full

sample (where all covariates were set at their original values), for each age

group (where age was set at the specific category of interest and other covariates

were set at their original values), and for each sex (where sex was set at the

specific category of interest and other covariates were set at their original values).

¶¶

45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C.

Sect. 552a; 44 U.S.C. Sect. Sect. 3501 et seq.

Among patients with myocarditis, 2,116 (41.7%) had a

history of COVID-19; this percentage was similar among

males (42.4%) and females (40.9%) and differed by age

group, with the lowest percentages among persons aged

16–24 years (23.7%) and 25–39 years (24.1%) and the high-

est among adults aged ≥75 years (64.6%) (Table). Among

the 2,116 patients with COVID-19 and myocarditis, 1,895

(89.6%) received a diagnosis of COVID-19 and myocarditis

during the same month; the remaining patients received a

myocarditis diagnosis 1 month (139; 6.6%) or ≥2 months

(82; 3.9%) after their COVID-19 diagnosis.

During March 2020–January 2021, the risk for myocarditis

was 0.146% among patients with COVID-19 and 0.009%

among patients without COVID-19. Among patients with

COVID-19, the risk for myocarditis was higher among

males (0.187%) than among females (0.109%) and was

highest among adults aged ≥75 years (0.238%), 65–74 years

(0.186%), and 50–64 years (0.155%) and among children

aged <16 years (0.133%).

In adjusted analyses, patients with COVID-19 had, on

average, 15.7 (95% CI=14.1–17.2) times the risk for myo-

carditis compared with patients without COVID-19; however,

because of the low risk for myocarditis in both groups, the

aRD between patients with and without COVID-19 was

small (aRD

 = 0.126%; 95% CI = 0.112%–0.140%)

(

Table) (Figure 2). The aRR of myocarditis was higher among

females (17.8; 95% CI=15.6–20.0) than among males (13.8;

95% CI=12.3–15.3), whereas the aRD was higher among

males (0.165%; 95% CI= 0.146%–0.183%) than among

females (0.100%; 95% CI=0.087%–0.113%). The aRR and

aRD were lowest for patients aged 25–39 years and were higher

among younger and older age groups. The aRRs ranged from

approximately 7.0 for patients aged 16–24 and 25–39 years

to >30.0 for patients aged <16 years and ≥75 years.

Discussion

In this study, the occurrence of myocarditis inpatient

encounters was 42% higher in 2020 than in 2019. The risk

for myocarditis among patients with COVID-19 during

March 2020–January 2021 was nearly 16 times as high as the

risk among patients without COVID-19, with the association

between COVID-19 and myocarditis being most pronounced

among children and older adults. Further, in this cohort,

approximately 40% of patients with myocarditis had a history

of COVID-19.

These findings suggest an association between COVID-19

and myocarditis, although causality cannot be inferred from

observational data, and are consistent with those from previ-

ous studies (2–5). Before this report, the two largest known

studies, in the United States and in Israel, also found that

Morbidity and Mortality Weekly Report

1230 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE 1. Number of myocarditis and COVID-19 inpatient encounters, by month* — Premier Healthcare Database Special COVID-19 Release,

United States, January 2019–May 2021

No. of COVID-19 inpatient encounters

No. of myocarditis inpatient encounters

20,000

40,000

60,000

80,000

100,000

120,000

100

150

200

250

300

350

400

450

500

No. of myocarditis inpatient encounters

No. of COVID-19 inpatient encounters

550

600

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May

2019 2020 2021

Month and year

* Data from recent months mightbe incomplete.

TABLE. Frequency of and risk for myocarditis among patients with and without COVID-19 and adjusted* myocarditis risk differences and risk ratios

comparing patients with and without COVID-19 — Premier Healthcare Database Special COVID-19 Release, United States, March 2020–January 2021

Characteristic

No. of

patients

with

COVID-19

No. of

patients

without

COVID-19

No. of

patients

with

myocarditis

Myocarditis among

patients

with COVID-19

Myocarditis among

patients

without COVID-19

Adjusted

myocarditis

risk dierence

(95% CI)

Adjusted

myocarditis

risk ratio

(95% CI)

No. (% of

patients with

myocarditis) Risk, %

No. (% of

patients with

myocarditis) Risk, %

Overall 1,452,773 34,552,521 5,069 2,116 (41.7) 0.146 2,953 (58.3) 0.009 0.126 (0.112–0.140) 15.7 (14.1–17.2)

Sex

Male 680,722 14,339,356 3,008 1,274 (42.4) 0.187 1,734 (57.6) 0.012 0.165 (0.146–0.183) 13.8 (12.3–15.3)

Female 772,051 20,213,165 2,061 842 (40.9) 0.109 1,219 (59.1) 0.006 0.100 (0.087–0.113) 17.8 (15.6–20.0)

Age group, yrs

<16 64,898 3,670,762 218 86 (39.4) 0.133 132 (60.6) 0.004 0.122 (0.065–0.179) 36.8 (25.0–48.6)

16–24 123,865 3,067,575 511 121 (23.7) 0.098 390 (76.3) 0.013 0.088 (0.061–0.115) 7.4 (5.5–9.2)

25–39 268,549 6,246,568 862 208 (24.1) 0.077 654 (75.9) 0.010 0.067 (0.052–0.081) 6.7 (5.5–8.0)

40–49 198,561 4,147,909 620 213 (34.4) 0.107 407 (65.6) 0.010 0.093 (0.078–0.109) 10.0 (8.1–11.9)

50–64 356,697 7,965,264 1,226 553 (45.1) 0.155 673 (54.9) 0.008 0.137 (0.121–0.154) 17.0 (14.7–19.3)

65–74 214,331 5,318,474 801 398 (49.7) 0.186 403 (50.3) 0.008 0.160 (0.135–0.184) 23.0 (19.4–26.7)

≥75 225,872 4,135,969 831 537 (64.6) 0.238 294 (35.4) 0.007 0.208 (0.179–0.237) 31.6 (25.9–37.2)

Abbreviation: CI=confidence interval.

* Adjusted risk differences and risk ratios for myocarditis during or after COVID-19 (reference group: no COVID-19), obtained from a single logit model with the

following covariates: a three-way interaction between presence of COVID-19, sex, and age group, including lower-order interactions and main effects; race/ethnicity;

payer type; hospital U.S. Census region; and hospital urbanicity.

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1231

US Department of Health and Human Services/Centers for Disease Control and Prevention

FIGURE 2. Adjusted risk ratio (A) and adjusted risk difference (B) of myocarditis comparing

patients with and without COVID-19,

* overall and by sex and age group — Premier

Healthcare Database Special COVID-19 Release, United States, March 2020–January 2021

0.5 1.0 2.0 8.0 16.0

32.0

4.0

Risk ratio

Overall

Sex

Male

Female

Age group, yrs

<16

16–24

25–39

40–49

50–64

65–74

≥75

Overall

Sex

Male

Female

Age group, yrs

<16

16–24

25–39

40–49

50–64

65–74

≥75

-0.1

0.0 0.2

0.3

0.1

Risk dierence (%)

* The panels show adjusted risk ratios (A) and adjusted risk differences (B) of myocarditis comparing

patients with COVID-19 to patients without COVID-19 (reference), obtained from a single logit model

with the following covariates: a three-way interaction between presence of COVID-19, sex, and age

group, including lower-order interactions and main effects; race/ethnicity; payer type; hospital U.S.

Census region; and hospital urbanicity. 95% confidence intervals indicated by error bars.

COVID-19 was strongly associated with myocarditis (U.S.

study: odds ratio=8.17, 95% CI=3.58–18.62; Israel study:

risk ratio=18.28, 95% CI=3.95–25.12) (3,4).

In this study, the association between COVID-19 and myo-

carditis was lowest for persons aged 25–39 years and higher

among younger (<16 years) and older (≥50 years) age groups, a

pattern that has not been previously described in age-stratified

analyses and that warrants further investigation. This find-

ing might be partially explained by age-related differences in

COVID-19 case ascertainment, because younger adults with

less severe disease might be less likely than older adults to have

a health care encounter with a COVID-19

diagnosis captured within PHD-SR. This age-

related differential misclassification (underas-

certainment) of COVID-19 status might bias

risk differences and risk ratios toward the null

more for younger adults and could partially

explain the observed age-related association.

The risk difference for myocarditis between

persons with and without COVID-19 was

higher among males than among females,

consistent with some earlier studies (2,5). The

finding of a higher risk ratio among females

than among males is novel. However, it likely

reflects the low risk for myocarditis among

female patients without COVID-19 (5).

Although the exact mechanism of

SARS-CoV-2 infection possibly leading to

myocarditis is unknown, the pathophysiol-

ogy is likely similar to that of other viruses

(1). Among persons with COVID-19 and

myocarditis, some myocarditis diagnoses

might represent cases of multisystem inflam-

matory syndrome (MIS), particularly among

children aged <16 years (9). Further study

is warranted to understand how the clinical

course of myocarditis among patients with

COVID-19 might differ by presence or

absence of MIS (10).

Since the introduction of mRNA COVID-19

vaccines in the United States in December

2020, an elevated risk for myocarditis among

mRNA COVID-19 vaccine recipients has

been observed, particularly among males

aged 12–29 years, with 39–47 expected

cases of myocarditis, pericarditis, and myo-

pericarditis per million second mRNA

COVID-19 vaccine doses administered (6).

A recent study from Israel reported that

mRNA COVID-19 vaccination was associ-

ated with an elevated risk for myocarditis

(risk ratio = 3.24; 95% CI = 1.55–12.44); in the same study,

a separate analysis showed that SARS-CoV-2 infection

was a strong risk factor for myocarditis (risk ratio = 18.28,

95% CI = 3.95–25.12) (4). On June 23, 2021, the Advisory

Committee on Immunization Practices concluded that the

benefits of COVID-19 vaccination clearly outweighed the

risks for myocarditis after vaccination (6). The present study

supports this recommendation by providing evidence of an

elevated risk for myocarditis among persons of all ages with

diagnosed COVID-19.

Morbidity and Mortality Weekly Report

1232 MMWR / September 3, 2021 / Vol. 70 / No. 35 US Department of Health and Human Services/Centers for Disease Control and Prevention

The findings in this study are subject to at least six limita-

tions. First, the risk estimates from this study reflect the risk

for myocarditis among persons who received a diagnosis of

COVID-19 during an outpatient or inpatient health care

encounter and do not reflect the risk among all persons who

had COVID-19. Second, misclassification of COVID-19 and

myocarditis is possible because conditions were determined

by ICD-10-CM codes, which were not confirmed by clinical

data (e.g., laboratory tests or cardiac imaging) and could be

improperly coded or coded with a related condition (e.g., peri-

carditis). Third, encounters for COVID-19, myocarditis, and

COVID-19 vaccination occurring outside of hospital systems

that contribute to PHD-SR are not included within this data

set. Fourth, underlying medical conditions and alternative

etiologies for myocarditis (e.g., autoimmune disease) were not

ascertained or excluded. Fifth, the obtained measures of associa-

tion could be biased because of the choice of the comparison

group (all patients without COVID-19) and if physicians were

more likely to suspect or diagnose myocarditis among patients

with COVID-19. Finally, the findings represent a convenience

sample of patients from hospitals reporting to PHD-SR and

might not be generalizable to the U.S. population.

Myocarditis is uncommon among patients with and without

COVID-19; however, COVID-19 is a strong and significant

risk factor for myocarditis, with risk varying by age group. The

findings in this report underscore the importance of imple-

menting evidence-based COVID-19 prevention strategies,

including vaccination, to reduce the public health impact of

COVID-19 and its associated complications.

Acknowledgments

Matthew Ritchey, Paula Yoon, Min Levine, Data, Analytics, and

Visualization Task Force, CDC COVID-19 Response Team; John Su,

Vaccine Task Force, CDC COVID-19 Response Team; Jason Block,

Department of Population Medicine, Harvard Pilgrim Health Care

Institute, Harvard Medical School.

Corresponding author: Tegan Boehmer, [email protected].

CDC COVID-19 Response Team;

Emory University School of Medicine,

Atlanta, Georgia;

Children’s Healthcare of Atlanta, Atlanta, Georgia.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of potential

conflicts of interest. No potential conflicts of interest were disclosed.

Summary

What is already known about this topic?

Viral infections are a common cause of myocarditis. Some

studies have indicated an association between COVID-19

and myocarditis.

What is added by this report?

During March 2020–January 2021, patients with COVID-19 had

nearly 16 times the risk for myocarditis compared with patients

who did not have COVID-19, and risk varied by sex and age.

What are the implications for public health practice?

These findings underscore the importance of implementing

evidence-based COVID-19 prevention strategies, including

vaccination, to reduce the public health impact of COVID-19

and its associated complications.

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nrcardio.2015.108

2. Rathore SS, Rojas GA, Sondhi M, et al. Myocarditis associated with

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3. Murk W, Gierada M, Fralick M, Weckstein A, Klesh R, Rassen JA.

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org/10.1503/cmaj.201686

4. Barda N, Dagan N, Ben-Shlomo Y, et al. Safety of the BNT162b2

mRNA COVID-19 vaccine in a nationwide setting. N Engl J Med 2021.

Epub August 25, 2021. PMID:34432976 https://doi.org/10.1056/

NEJMoa2110475

5. Daugherty SE, Guo Y, Heath K, et al. Risk of clinical sequelae after the

acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ

2021;373:n1098. PMID:34011492 https://doi.org/10.1136/bmj.n1098

6. Gargano JW, Wallace M, Hadler SC, et al. Use of mRNA COVID-19

vaccine after reports of myocarditis among vaccine recipients: update

from the Advisory Committee on Immunization Practices—United

States, June 2021. MMWR Morb Mortal Wkly Rep 2021;70:977–82.

PMID:34237049 https://doi.org/10.15585/mmwr.mm7027e2

7. Kleinman LC, Norton EC. What’s the risk? A simple approach for

estimating adjusted risk measures from nonlinear models including

logistic regression. Health Serv Res 2009;44:288–302. PMID:18793213

https://doi.org/10.1111/j.1475-6773.2008.00900.x

8. Norton EC, Miller MM, Kleinman LC. Computing adjusted risk ratios

and risk differences in Stata. Stata J 2013;13:492–509. https://doi.

org/10.1177/1536867X1301300304

9. Most ZM, Hendren N, Drazner MH, Perl TM. Striking similarities of

multisystem inflammatory syndrome in children and a myocarditis-like

syndrome in adults. Circulation 2021;143:4–6. PMID:32787714

https://doi.org/10.1161/CIRCULATIONAHA.120.050166

10. Vukomanovic VA, Krasic S, Prijic S, et al. Differences between pediatric

acute myocarditis related and unrelated to SARS-CoV-2. Pediatr Infect

Dis J 2021;40:e173–8. PMID:33847291 https://doi.org/10.1097/

INF.0000000000003094

Morbidity and Mortality Weekly Report

MMWR / September 3, 2021 / Vol. 70 / No. 35 1233

US Department of Health and Human Services/Centers for Disease Control and Prevention

QuickStats

FROM THE NATIONAL CENTER FOR HEALTH STATISTICS

Death Rates* from Colorectal Cancer,

†

by Age Group —

United States, 1999–2019

100

120

140

160

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Deaths per 100,000 population

Year

45–54 yrs 55–64 yrs 65–74 yrs 75–84 yrs

* Deaths per 100,000 population in each age group.

†

Deaths from colorectal cancer were identified using International Classification of Diseases, Tenth Revision

underlying cause-of-death codes C18–21.

During 1999–2019, deaths per 100,000 persons from colorectal cancer decreased among persons aged 55–64 years (from 33.5

to 24.4), persons aged 65–74 years (from 77.4 to 41.5), and persons aged 75–84 years (from 146.7 to 77.9). The death rate from

colorectal cancer among persons aged 45–54 years generally increased from 1999 (11.1) to 2019 (12.0). In each year during

1999–2019, the death rate was highest among persons aged 75–84 years and lowest among persons aged 45–54 years.

Source: National Center for Health Statistics, National Vital Statistics System, Mortality Data, 1999–2019. https://www.cdc.gov/nchs/nvss/deaths.htm

Reported by: Sibeso N. Joyner, MPH, [email protected], 301-458-4254; Deepthi Kandi, MS.

Morbidity and Mortality Weekly Report

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