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Abstract
Approximately 41% of adults aged 18–24 years in the United States are enrolled in
a college or university (
1
). Wearing a face mask can reduce transmission of SARS-CoV-2, the virus that causes
coronavirus disease 2019 (COVID-19) (
2
), and many colleges and universities mandate mask use in public locations and outdoors
when within six feet of others. Studies based on self-report have described mask use
ranging from 69.1% to 86.1% among adults aged 18–29 years (
3
); however, more objective measures are needed. Direct observation by trained observers
is the accepted standard for monitoring behaviors such as hand hygiene (
4
). In this investigation, direct observation was used to estimate the proportion of
persons wearing masks and the proportion of persons wearing masks correctly (i.e.,
covering the nose and mouth and secured under the chin*) on campus and at nearby off-campus
locations at six rural and suburban universities with mask mandates in the southern
and western United States. Trained student observers recorded mask use for up to 8
weeks from fixed sites on campus and nearby. Among 17,200 observed persons, 85.5%
wore masks, with 89.7% of those persons wearing the mask correctly (overall correct
mask use: 76.7%). Among persons observed indoors, 91.7% wore masks correctly. The
proportion correctly wearing masks indoors varied by mask type, from 96.8% for N95-type
masks and 92.2% for cloth masks to 78.9% for bandanas, scarves, and similar face coverings.
Observed indoor mask use was high at these six universities with mask mandates. Colleges
and universities can use direct observation findings to tailor training and messaging
toward increasing correct mask use.
Direct in-person observation is used in health care settings to measure adherence
to infection prevention and control recommendations, such as hand hygiene and the
correct use of personal protective equipment (
4
). A similar approach was used to directly observe mask use at universities, using
a protocol and sampling methodology based on one from Resolve to Save Lives, an initiative
promoting the measuring and adoption of face mask use to reduce transmission of COVID-19
(
5
). CDC staff members discussed the direct observation protocol with 12 universities,
six of which chose to participate in this investigation. The participating universities
included five public universities with student populations ranging from 29,000 to
52,000 and one private university with a student population of 2,300; five universities
were in the South U.S. Census region (two in East South Central and three in South
Atlantic), and one was in the West. Approximately 10 student observers per university
were trained by one CDC staff member who conducted training for all participating
universities using a standard protocol.
†
Universities selected approximately 10 observation locations where mask use was mandated.
§
Indoor mask use was mandated by all selected universities and their surrounding communities.
Outdoor mask use was mandated when other physical distancing measures were difficult
to maintain.
¶
Observation locations could be either indoors or outdoors; however, because determining
whether persons observed outdoors should have been wearing a mask was not always possible,
the analyses focused on indoor mask use. For up to 8 weeks (range: 2 to 8 weeks across
universities), observers tracked mask use on varying days and times from fixed sites
on campus (e.g., libraries, classroom buildings, dining facility entrances, student
centers, and lobbies of recreation centers and workout facilities) and, at five universities,
at nearby off-campus, public locations frequented by students (e.g., grocery stores,
pharmacies, and cafes). Observers modeled correct mask wearing, remained inconspicuous,
and refrained from interacting with the persons they were observing. Each observer
was instructed to record 40 observations at a single location or to observe for 1
hour, whichever came first, for a total of approximately 400 observations per week
per university by the 10 observers. Correct mask use was recorded if the mask completely
covered the nose and mouth and was secured under the chin. Observers were advised
to record only what they could see; for example, if a person’s face could not be observed
but mask straps were visible behind the person’s head or ears, mask use was recorded
as “unknown.” Observers were asked to remain stationary and record 1) whether a mask
was worn, 2) whether the mask was worn correctly, and 3) the type of mask worn (cloth,
surgical, gaiter, masks that appeared to be N95 respirators [referred to as N95 type],
or other) for every third person passing a prespecified location, such as a building
entrance. If foot traffic was too high to observe every third person, observers were
asked to select every tenth person for the entire observation period (
5
). Observation times varied during the mornings and afternoons and at night and occurred
on weekdays and weekends. Because social groups might exhibit more similar mask use
behaviors, only one person from a social group (e.g., an easily identifiable family
unit, group of friends, or sports team) was sampled to avoid the effects of clustering.
Observers were instructed to observe the first person in the group who corresponded
to the third person following the preceding observation and then skip remaining group
members and resume counting every third person after the group passed. Observations
were restricted to persons who appeared to be aged ≥12 years and were not limited
to students. One participating university released weekly media reports highlighting
their data from this assessment to encourage mask use in their community. A second
university released a single media report after 3 weeks of data collection. The remaining
four universities did not publicize this investigation.
Data collection was standardized through common training materials and data collection
forms to provide comparable data across the six universities. Data were collected
using a paper form and entered into REDCap (version 9.7; Vanderbilt University) electronic
data capture and management tools hosted at CDC or collected directly using the REDCap
tools. Each week, data for each university were compiled and returned to the university,
including the proportion of persons observed wearing masks, the proportion of those
persons wearing masks correctly, and the most common type of mask worn. Staff members
at universities performed quality control processes weekly and provided updated, corrected
data to CDC. All analyses were conducted with SAS (version 9.4; SAS Institute). Frequencies
and ranges were calculated for mask use, correct mask use, type of mask worn, and
locations observed. Chi-squared tests were used to compare indoor mask use and indoor
correct mask use for on-campus and nearby off-campus locations. The Tukey honestly
significant difference test was used to compare mask types among the proportion used
correctly indoors; p-values <0.05 were considered statistically significant. This
activity was reviewed by CDC and was conducted consistent with applicable federal
laws and CDC policies.**
A total of 17,200 persons were observed at six universities (ranging from 438 persons
observed during 2 weeks of data collection to 8,580 during 8 weeks of data collection)
(Table 1). Two thirds (66.6%) of the observations took place indoors, and 69% took
place on campus. Most (85.5%) observed persons wore masks, with 89.7% of those wearing
them correctly (overall correctly wearing masks: 76.7% [range: 72.2%–93.6%]). Cloth
masks were most common (68.3%), followed by surgical masks (25.7%). Less common were
gaiters (3.8%) and N95-type masks (1.9%). Other face coverings, such as bandanas and
scarves, were rarely observed (0.3%). Overall, mask use was significantly more common
indoors (94.0%) than outdoors (67.6%) (p<0.001). Among observations conducted indoors,
mask use was more prevalent at on-campus (94.8%) than at nearby off-campus locations
(90.6%) (p<0.001), as was correct mask use among those wearing masks (92.1% versus
90.0%, respectively; p = 0.002) (Table 2). Correct mask use indoors differed by mask
type, with N95-type masks most likely to be worn correctly indoors (96.8%), followed
by cloth masks (92.2%), surgical masks (90.8%), gaiters (86.8%), and other face coverings
(78.9%) (Table 3). These mask types accounted for 1.7%, 68.2%, 26.1%, 3.7%, and 1%,
respectively, of observed masks worn indoors.
TABLE 1
Observed number and percentage of persons wearing face masks on six university campuses*
and at nearby off-campus locations,
†
by selected characteristics — United States, September–November 2020
Characteristic
No. (%) of persons observed
Total
University A (observed 8 wks)
University B (observed 7 wks)
University C (observed 6 wks)
University D (observed 5 wks)
University E (observed 2 wks)
University F (observed 2 wks)
Overall mask use
17,200 (100)
8,580 (49.9)
3,144 (18.3)
2,922 (17.0)
1,460 (8.5)
438 (2.5)
656 (3.8)
Mask worn
14,704 (85.5)
7,018 (81.8)
2,637 (83.9)
2,619 (89.6)
1,384 (94.8)
430 (98.2)
616 (93.9)
Mask worn correctly
13,189 (89.7)
6,434 (91.7)
2,269 (86.0)
2,320 (88.6)
1,171 (84.6)
410 (95.3)
585 (95.0)
Type of mask
Cloth
10,042 (68.3)
5,042 (71.8)
1,645 (62.4)
1,587 (60.6)
1,079 (78.0)
278 (64.7)
411 (66.7)
Surgical
3,774 (25.7)
1,592 (22.7)
804 (30.5)
839 (32.0)
236 (17.1)
134 (31.2)
169 (27.4)
Gaiter
563 (3.8)
200 (2.8)
154 (5.8)
125 (4.8)
56 (4.0)
5 (1.2)
23 (3.7)
N95 type
280 (1.9)
175 (2.5)
29 (1.1)
48 (1.8)
10 (0.7)
10 (2.3)
8 (1.3)
Other
45 (0.3)
9 (0.1)
5 (0.2)
20 (0.8)
3 (0.2)
3 (0.7)
5 (0.8)
Location
Indoors
11,451 (66.6)
4,686 (54.6)
1744 (55.5)
2,758 (94.4)
1,279 (87.6)
438 (100)
546 (83.2)
Outdoors
5,546 (32.2)
3,734 (43.5)
1,400 (44.5)
121 (4.1)
181 (12.4)
—§
110 (16.8)
On bus
203 (1.2)
160 (1.9)
—
43 (1.5)
—
—
—
Campus
On campus
11,875 (69.0)
5,884 (68.6)
2,709 (86.2)
905 (31.0)
1,460 (100)
329 (75.1)
588 (89.6)
Nearby off campus
5,122 (29.8)
2,536 (29.6)
435 (13.8)
1,974 (67.6)
—
109 (24.9)
68 (10.4)
On bus
203 (1.2)
160 (1.9)
—
43 (1.5)
—
—
—
* Includes five public universities with student populations ranging from 29,000 to
52,000 and one private university with a student population of 2,300; five universities
were in the South U.S. Census region (two in East South Central and three in South
Atlantic), and one was in the West.
† Data are from five universities. Nearby, indoor and outdoor off-campus locations
in the surrounding community that were known to be frequented by students (e.g., grocery
stores, pharmacies, and cafes) in counties where mask use was mandated indoors or
outdoors if 6 feet of distance could not be maintained.
§ Data not collected.
TABLE 2
Observed overall number and percentage of persons wearing face masks indoors* and
wearing face masks indoors correctly on six university campuses
†
and at nearby, indoor off-campus locations
§
— United States, September–November 2020
Characteristic
No. (%) of persons observed
Total wearing masks
On campus
Nearby off campus
Mask worn indoors¶
10,760 (94.0)
8,648 (94.8)
2,112 (90.6)
Mask worn indoors correctly**
9,862 (91.7)
7,962 (92.1)
1,900 (90.0)
* Indoor, on-campus locations where mask use was mandated (e.g., libraries, classroom
buildings, dining facility entrances, student centers, and lobbies of recreation centers
and workout facilities).
† Includes five public universities with student populations ranging from 29,000 to
52,000 and one private university with a student population of 2,300; five universities
were in the South U.S. Census region (two in East South Central and three in South
Atlantic), and one was in the West.
§ Data are from five universities. Nearby, indoor off-campus locations in the surrounding
community that were known to be frequented by students (e.g., grocery stores, pharmacies,
and cafes) in counties where mask use was mandated indoors or outdoors if 6 feet of
distance could not be maintained.
¶ p<0.001. Total number observed = 11,451, on-campus indoor observed = 9,119, and
nearby off-campus observed = 2,332. The chi-squared test was used to assess the difference
between masks worn indoors on campus and at nearby off-campus locations in the surrounding
community.
** p = 0.002. Total number observed indoors = 10,758, excluding 693 observations (no
mask use or unknown mask use) and missing data for two observations. The chi-squared
test was used to assess the difference between correct mask use indoors on campus
and at nearby off-campus locations in the surrounding community.
TABLE 3
Observed number and percentage of persons wearing face masks indoors correctly among
all persons wearing face masks on six university campuses* and at nearby, indoor off-campus
locations,
†
by mask type — United States, September–November 2020
Type of mask§
Mask worn indoors
Mask worn indoors correctly
No.
No. (%)
Total
10,760¶
9,862 (91.7)
Cloth
7,334
6,760 (92.2)
Surgical
2,807
2,549 (90.8)
Gaiter
394
342 (86.8)
N95 type
187
181 (96.8)
Other**
38
30 (78.9)
* Includes five public universities with student populations ranging from 29,000 to
52,000 and one private university with a student population of 2,300; five universities
were in the South U.S. Census region (two in East South Central and three in South
Atlantic), and one was in the West.
† Nearby, indoor off-campus locations in the surrounding community that were known
to be frequented by students (e.g., grocery stores, pharmacies, and cafes) in counties
where mask use was mandated indoors or outdoors if 6 feet of distance could not be
maintained.
§ p<0.05. Post hoc comparisons using the Tukey honestly significant difference test
indicated differences between mask type and the proportion used correctly indoors.
Significant differences were observed between all mask types, except cloth and surgical
(p = 0.24), cloth and N95 type (p = 0.18), and gaiter and other (p = 0.32).
¶ Total observed indoors = 11,451, excluding 691 observations (no mask use or unknown
mask use).
** Other face coverings include bandanas and scarves.
Discussion
Mask mandates have been shown to decrease SARS-CoV-2 case transmission,
††
and widespread mask use is a core intervention for curbing the COVID-19 pandemic (
6
,
7
). Direct observation at six universities indicated that mask use was high on campuses
in locations where masks were mandated. Mask use was similarly high at nearby, indoor
off-campus locations where masks were mandated. Mask use was lower outdoors in areas
where use was mandated only when physical distancing could not be maintained. These
data provide evidence that adherence to university mask mandates is high (
5
). However, correct mask use varied by mask type.
Universities have several opportunities to enforce policies such as mask mandates.
For example, universities could impose sanctions for noncompliance with university
policy. Universities also could use multimodal education and messaging to reinforce
mask use, as well as messaging specific to mask type and that is focused on correct
use. One university found that having students sign a compact agreeing to mask use,
physical distancing, and testing might also be effective in promoting these behaviors
(
8
).
Observational investigations can provide rapid feedback to universities on the prevalence
and type of mask use in their population. Using trained student volunteers, participating
universities can quickly organize and collect substantial amounts of data weekly at
low to no cost and review the data quickly to assess and report on mask use. Universities
and their communities can use these data to tailor and evaluate the effectiveness
of messages and education to reinforce and increase mask use and to identify locations
with lower adherence for policy enforcement.
The findings in this report are subject to at least three limitations. First, because
the period of observation ranged from 2 to 8 weeks among universities, overall percentages
are influenced by the universities with more data. However, all six universities are
continuing to collect data during the 2021 spring semester. Second, observations were
sampled without recording information about the persons observed and were not limited
to university students, staff members, or faculty members. Off-campus locations likely
included more persons not affiliated with the university, and off-campus percentages
should be considered a measure of community mask use. Finally, none of the universities
mandated outdoor mask use, unless physical distancing could not be maintained. Observers
did not record whether physical distancing was or was not maintained.
Compliance with CDC’s recommended COVID-19 mitigation strategy of mask wearing exceeded
80% at six U.S. universities. Mask use is likely to remain a critical COVID-19 mitigation
strategy, and CDC has made the training materials used in this study available for
universities that would like to monitor mask use on their campuses. However, in addition
to mask mandates, universities have implemented multicomponent strategies that included
reduced residential density; surveillance and entry testing; educational campaigns;
and other campus and community mitigation strategies. Monitoring mask use, tailoring
messages to promote healthy behaviors (e.g., mask use, handwashing, and physical distancing)
on and off campus, and developing measures to enforce or ensure compliance with healthy
behaviors have the potential to improve implementation and effectiveness of public
health strategies to protect persons on campus and in the surrounding communities
by preventing the spread of SARS-CoV-2.
Summary
What is already known about this topic?
Correct use of face masks limits COVID-19 transmission. Many institutions of higher
education mandate masks in public indoor locations and outdoors when within six feet
of others.
What is added by this report?
During September–November 2020, mask use was directly observed at six universities
with mask mandates. Among persons observed indoors, 91.7% wore masks correctly, varying
by mask type, from 96.8% for N95-type masks and 92.2% for cloth masks to 78.9% for
bandanas, scarves, and similar face coverings.
What are the implications for public health practice?
Direct observation provides rapid feedback on mask use prevalence. Institutions of
higher education can use this feedback to tailor training and messaging for correct
mask use.
State policies mandating public or community use of face masks or covers in mitigating the spread of coronavirus disease 2019 (COVID-19) are hotly contested. This study provides evidence from a natural experiment on the effects of state government mandates for face mask use in public issued by fifteen states plus Washington, D.C., between April 8 and May 15, 2020. The research design is an event study examining changes in the daily county-level COVID-19 growth rates between March 31 and May 22, 2020. Mandating face mask use in public is associated with a decline in the daily COVID-19 growth rate by 0.9, 1.1, 1.4, 1.7, and 2.0 percentage points in 1-5, 6-10, 11-15, 16-20, and 21 or more days after state face mask orders were signed, respectively. Estimates suggest that as a result of the implementation of these mandates, more than 200,000 COVID-19 cases were averted by May 22, 2020. The findings suggest that requiring face mask use in public could help in mitigating the spread of COVID-19.
On October 27, 2020, this report was posted online as an MMWR Early Release. CDC recommends a number of mitigation behaviors to prevent the spread of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). Those behaviors include 1) covering the nose and mouth with a mask to protect others from possible infection when in public settings and when around persons who live outside of one’s household or around ill household members; 2) maintaining at least 6 feet (2 meters) of distance from persons who live outside one’s household, and keeping oneself distant from persons who are ill; and 3) washing hands often with soap and water for at least 20 seconds, or, if soap and water are not available, using hand sanitizer containing at least 60% alcohol ( 1 ). Age has been positively associated with mask use ( 2 ), although less is known about other recommended mitigation behaviors. Monitoring mitigation behaviors over the course of the pandemic can inform targeted communication and behavior modification strategies to slow the spread of COVID-19. The Data Foundation COVID Impact Survey collected nationally representative data on reported mitigation behaviors during April–June 2020 among adults in the United States aged ≥18 years ( 3 ). Reported use of face masks increased from 78% in April, to 83% in May, and reached 89% in June; however, other reported mitigation behaviors (e.g., hand washing, social distancing, and avoiding public or crowded places) declined marginally or remained unchanged. At each time point, the prevalence of reported mitigation behaviors was lowest among younger adults (aged 18–29 years) and highest among older adults (aged ≥60 years). Lower engagement in mitigation behaviors among younger adults might be one reason for the increased incidence of confirmed COVID-19 cases in this group, which have been shown to precede increases among those >60 years ( 4 ). These findings underscore the need to prioritize clear, targeted messaging and behavior modification interventions, especially for young adults, to encourage uptake and support maintenance of recommended mitigation behaviors to prevent the spread of COVID-19. The COVID Impact Survey collected data to provide national estimates of health, economic, and social well-being of U.S. adults, using a national probability sample covering approximately 97% of the U.S. population of non-institutionalized adults with a home address ( 3 ). Surveys were conducted in three waves (April 20–26, May 4–10, and May 30–June 8), without significant resampling of persons across waves. Analyses included a total of 6,475 online or telephone surveys of adults aged ≥18 years.* The response rate among those invited to participate ranged from 19.7% to 26.1% across the three survey waves. Following data collection, an iterative raking process was used to adjust for nonresponse, noncoverage, and under- and oversampling ( 5 ). Demographic weighting variables provided in the dataset were obtained from the 2020 Current Population Survey; estimates reflect the U.S. household population of adults aged ≥18 years. † No personally identifying information was provided in the data file accessed by CDC.§ Respondents were asked, “Which of the following measures, if any, are you taking in response to the coronavirus?” Of the 19 response options, three mitigation behaviors aligning with CDC recommendations were assessed: 1) “wore a face mask,” 2) “washed or sanitized hands,” and 3) “kept six feet distance from those outside my household.” ¶ Three social mitigation behaviors aligning with CDC considerations and White House guidelines from March and April 2020 also were selected for analysis: 1) “avoided public or crowded places,” 2) “cancelled or postponed social or recreational activities,” and 3) “avoided some or all restaurants.”** ,††,§§ Pearson's Chi-squared test was used to assess differences in reported behaviors (individual and cumulative) by age, within each survey wave and stratified by face mask use, based on a significance level of α = 0.05. Logistic regression models were used to test statistical significance of time trends by assigning calendar week of data collection for each survey wave as a single linear predictor for individual and cumulative behavioral outcomes. All analyses were conducted in Stata ES (version 16.1, StataCorp.) with survey weights applied during analyses for nationally representative estimates. Across survey waves, the majority of the weighted sample (range = 62%–65%) identified as Non-Hispanic or Latino White, and 50% identified as female; 14%–15% of respondents were aged 18–29 years. In April, 78% of adults aged ≥18 years reported wearing a mask; this increased to 83% in May and 89% in June (Table 1) (p 40% of all adults aged ≥18 years reported all six assessed mitigation behaviors (Table 2). Across all survey waves, reported prevalences of mitigation behaviors were highest among adults aged ≥60 years and lowest among those aged 18–29 years (Table 1) (Supplementary Figure 1: https://stacks.cdc.gov/view/cdc/95944). Age was also significantly associated with the cumulative number of reported mitigation behaviors across all survey waves, with young adults reporting engaging in fewer mitigation behaviors compared with older adults overall and at all time points (Table 2) (Figure). TABLE 1 Self-reported mitigation behaviors,* by adult age group — COVID Impact Survey, United States, April–June 2020 † Behavior/Characteristic Wave 1 Apr (N = 2,190) Wave 2 May (N = 2,238) Wave 3 Jun (N = 2,047) Yes (No.) Weighted % (95% CI) Yes (No.) Weighted % (95% CI) Yes (No.) Weighted % (95% CI) Wore a face mask § Total 1,713 78.1 (76.1–80.1) 1,855 82.9 (81.3–84.4) 1,815 88.7 (87.2–90.0) Age group (yrs) 18–29 195 69.6 (63.3–75.3)† 261 81.8 (77.2–85.7) 273 86.1 (81.9–89.5)† 30–44 506 74.7 (70.7–78.4) 542 83.1 (80.1–85.8) 538 86.4 (83.4–88.8) 45–59 419 79.8 (75.6–83.65) 431 80.7 (77.1–83.8) 406 88.3 (85.0–90.9) ≥60 593 83.7 (80.3–86.6) 621 84.7 (81.9–87.2) 598 92.4 (90.1–94.2) Washed or sanitized hands § Total 2,037 93.1 (91.8–94.2) 2,043 91.3 (90.1–92.4) 1,828 89.3 (87.9–90.6) Age (yrs) 18–29 236 83.5 (77.9–87.8)† 281 88.1 (84.1–91.2)† 259 81.7 (77.1–85.6)† 30–44 615 91.6 (88.9–93.7) 577 88.5 (85.8–90.7) 540 86.7 (83.8–89.1) 45–59 499 95.0 (92.4–96.8) 497 93.1 (90.6–94.9) 429 93.3 (90.6–95.2) ≥60 687 96.5 (94.6–97.7) 688 93.9 (91.9–95.4) 600 92.7 (90.5–94.5) Kept 6 feet distance § Total 1,913 87.4 (85.7–88.9) 1,924 86.0 (84.5–87.4) 1,683 82.2 (80.5–83.8) Age group (yrs) 18–29 202 71.7 (65.5–77.2)† 245 76.8 (71.9–81.1)† 225 71.0 (65.7–75.7)† 30–44 565 84.6 (81.2–87.5) 541 83.0 (79.9–85.7) 490 78.7 (75.3–81.7) 45–59 486 93.1 (90.3–95.1) 468 87.6 (84.6–90.2) 386 83.9 (80.3–87.0) ≥60 660 92.6 (90.0–94.5) 670 91.4 (89.2–93.2) 582 90.0 (87.4–92.0) Cancelled/postponed pleasure, social, or recreational activities § Total 1,554 69.8 (67.5, 71.9) 1,514 67.8 (65.9, 69.7) 1,291 63.1 (61.0, 65.1) Age group (yrs) 18–29 167 60.0 (53.4–66.3)† 208 65.2 (59.8–70.2) 185 58.4 (52.9–63.7)† 30–44 474 68.5 (64.2–72.4) 435 66.7 (63.0–70.2) 377 60.5 (56.6–64.3) 45–59 376 70.3 (65.5–74.6) 363 68.0 (63.9–71.8) 292 63.5 (59.0–67.8) ≥60 537 74.8 (70.9–78.3) 508 69.3 (65.9–72.5) 437 67.5 (63.8–71.0) Avoided public or crowded places ¶ Total 1,762 80.5 (78.5–82.4) 1,724 77.0 (75.2–78.7) 1,542 75.3 (73.4–77.2) Age group (yrs) 18–29 204 74.2 (68.0–79.5)† 238 74.6 (69.5–79.1)† 213 67.2 (61.8–72.1)† 30–44 511 75.5 (71.5–79.1) 494 75.8 (72.3–78.9) 454 72.9 (69.2–76.2) 45–59 432 82.8 (78.7–86.2) 400 74.9 (71.1–78.4) 346 75.2 (70.1–79.0) ≥60 615 86.3 (83.1–89.0) 592 80.8 (77.8–83.5) 529 81.8 (78.6–84.6) Avoided some or all restaurants Total 1,574 71.9 (69.6–74.0) 1,578 70.5 (68.6–72.4) 1,446 70.6 (68.6–72.6) Age group (yrs) 18–29 113 60.4 (53.8–66.6)† 217 68.0 (62.7–72.9) 201 63.4 (58.0–68.5)† 30–44 465 68.2 (64,0–72.2) 470 72.1 (68.5–75.4) 419 67.3 (63.5–70.8) 45–59 148 73.7 (69.2–77.8) 357 66.9 (62.8–70.7) 327 71.1 (66.8–75.1) ≥60 148 78.9 (75.3–82.2) 534 72.9 (69.5–76.0) 499 77.1 (73.7–80.2) Abbreviations: CI = confidence interval; COVID-19 = coronavirus disease 2019. * Wore a face mask, washed or sanitized hands, kept 6 feet of distance, avoided public or crowded places, canceled or postponed pleasure, social, or recreational activities and avoided some or all restaurants. † Chi-square p-value 45% of adults who did not report mask use reported one or fewer other mitigation behaviors (Figure). Overall, a significant positive association between age and the cumulative number of reported mitigation behaviors persisted over time among those who did and those who did not report mask use (Figure). Discussion This report provides four important insights into the practice of mitigation behaviors among U.S. adults to prevent the spread of SARS-CoV-2. First, the majority of U.S. adults reported engaging in most or all of the six mitigation behaviors assessed. Second, age was an important determinant of engagement in mitigation behaviors overall. A smaller percentage of adults aged 60 years. Whereas mask wearing increased over time, other reported mitigation behaviors decreased or remained unchanged. What are the implications for public health practice? Improved communication and policy priorities are needed to promote recommended COVID-19 mitigation behaviors, particularly among young adults.
On November 17, 2020, this report was posted online as an MMWR Early Release. On university campuses and in similar congregate environments, surveillance testing of asymptomatic persons is a critical strategy ( 1 , 2 ) for preventing transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). All students at Duke University, a private research university in Durham, North Carolina, signed the Duke Compact ( 3 ), agreeing to observe mandatory masking, social distancing, and participation in entry and surveillance testing. The university implemented a five-to-one pooled testing program for SARS-CoV-2 using a quantitative, in-house, laboratory-developed, real-time reverse transcription–polymerase chain reaction (RT-PCR) test ( 4 , 5 ). Pooling of specimens to enable large-scale testing while minimizing use of reagents was pioneered during the human immunodeficiency virus pandemic ( 6 ). A similar methodology was adapted for Duke University’s asymptomatic testing program. The baseline SARS-CoV-2 testing plan was to distribute tests geospatially and temporally across on- and off-campus student populations. By September 20, 2020, asymptomatic testing was scaled up to testing targets, which include testing for residential undergraduates twice weekly, off-campus undergraduates one to two times per week, and graduate students approximately once weekly. In addition, in response to newly identified positive test results, testing was focused in locations or within cohorts where data suggested an increased risk for transmission. Scale-up over 4 weeks entailed redeploying staff members to prepare 15 campus testing sites for specimen collection, developing information management tools, and repurposing laboratory automation to establish an asymptomatic surveillance system. During August 2–October 11, 68,913 specimens from 10,265 graduate and undergraduate students were tested. Eighty-four specimens were positive for SARS-CoV-2, and 51% were among persons with no symptoms. Testing as a result of contact tracing identified 27.4% of infections. A combination of risk-reduction strategies and frequent surveillance testing likely contributed to a prolonged period of low transmission on campus. These findings highlight the importance of combined testing and contact tracing strategies beyond symptomatic testing, in association with other preventive measures. Pooled testing balances resource availability with supply-chain disruptions, high throughput with high sensitivity, and rapid turnaround with an acceptable workload. Duke’s SARS-CoV-2 surveillance program commenced when the campus reopened for fall 2020 classes. As advised by the Atlantic Coast Conference Medical Advisory Group, a total of 781 student-athletes and student athletic assistants have been participating in a separate surveillance program, in which teams are categorized as high-, medium-, or low-risk. Results described here focus on testing of students who are not student-athletes. The pooled testing program was aimed at students, but was also available to faculty and staff members. Not included are the results of specimens tested from 8,012 faculty and staff members (including pooled tests of specimens from asymptomatic persons and individual testing of specimens from symptomatic persons) by mid-September. Students self-quarantined at home for 14 days before arriving at the reopened campus in scheduled windows during August 11–15. The surveillance program includes entry testing for all incoming students, surveillance of asymptomatic persons using pooled testing, and individual testing for symptomatic persons. Upon arrival, all students underwent entry SARS-CoV-2 screening that included collection of nasopharyngeal swabs that were tested using standard protocols in a CAP/CLIA-certified* laboratory; students were sequestered in prearranged housing (dormitories or off-campus housing) pending results ( 7 ). The students who were already in residence on campus or in the local community did not participate in entry testing. Mitigation strategies included converting all dormitory rooms to single-occupancy, modifying classrooms and common areas to accommodate social distancing, and distributing packaged meals. All students signed the Duke Compact ( 3 ), agreeing to observe mandatory masking, social distancing, and participation in entry and surveillance testing. Students who missed scheduled surveillance tests lost access to campus facilities and services. Compliance for testing among students on the date requested was approximately 95% ( 8 ). In addition, contact tracing was performed for all positive cases. Exposed contacts were quarantined for 14 days, and students, whether asymptomatic or symptomatic, submitted specimens for testing upon initiating quarantine and again if they became symptomatic during quarantine. Students also installed the custom-built SymMon (symptom monitoring) smartphone app, † which administers a daily symptom survey ( 7 ). The app facilitates testing for symptomatic users and for asymptomatic persons undergoing pooled testing. The app’s barcode scanner enables linking of specimens to persons and creation of labels for electronic health record system orders. In addition to students, all faculty and staff members were required to complete the same SymMon symptom survey before arrival on each day they entered the campus. Duke’s SARS-CoV-2 surveillance program is ongoing. Testing of nasal swabs collected from symptomatic persons is conducted in a CAP/CLIA–certified laboratory using a platform approved under the Food and Drug Administration (FDA) Emergency Use Authorization (EUA). Testing sites for asymptomatic persons receive prelabeled tubes, swabs, and specimen bags. Supervised self-collected nasal swabs are obtained, § and unique barcodes are scanned using the SymMon app to record date and time and establish the link between person and specimen. Specimens are placed in secondary containers and driven to the processing laboratory. Testing of asymptomatic persons reached full capacity on September 20; since then, residential undergraduates are tested twice weekly, off-campus undergraduates one to two times per week, and graduate students approximately once weekly. At full capacity during weeks 6–9, an average of 11,390 samples were pooled per week (2,278 samples per day, 5 days per week). Laboratory automation was rapidly repurposed to provide a high-throughput, rapid platform for pooling specimens for RT-PCR testing. An automated five-to-one pooling run transfers 120 primary samples into 24 2-mL tubes in 13 minutes, 9 seconds (33 seconds per pool). After pooling, specimens are held at 39.2°F (4°C) pending final disposition. Pooled samples are tested using an automated QIAsymphony (Qiagen LLC) laboratory-developed two-step RT-PCR and the World Health Organization E_Sarbeco primer-probe set (Charité/Berlin). ¶ For pooled assays, viral load calibration standards are run on each plate, and positive pool viral loads are extrapolated from the calibration curve (Table 1). Clinical viral loads are reported from a similar calibration process. TABLE 1 Validation data* for the SARS-CoV-2 quantitative viral load assay indicating 100% target detection at 62 copies/mL and 74% at 15 copies/mL — Duke University, Durham, North Carolina, August–October 2020 Sample ID† Target viral load (RNA copies/mL) % Detection (95% CI) Both replicates detected Single replicate detected Validation panel A 5,000,000 100 (94.9–NE) 100 (94.9–NE) Validation panel B 500,000 100 (94.9–NE) 100 (94.9–NE) Validation panel C 50,000 100 (94.9–NE) 100 (94.9–NE) Validation panel D 5,000 100 (94.9–NE) 100 (94.9–NE) Validation panel E 500 100 (94.9–NE) 100 (94.9–NE) Validation panel F 250 100 (94.9–NE) 100 (94.9–NE) Validation panel G 125 99 (92.3–99.9) 100 (94.9–NE) Validation panel H 62 83 (72.0–91.0) 100 (94.9–NE) Validation panel I 31 56 (43.3–68.6) 94 (86.0–98.4) Validation panel J 15 27 (17.2–39.1) 74 (62.0–84.0) Abbreviations: CI = confidence interval; NE = not able to estimate. * Validation panels were tested 70 times to determine limit of detection with 95% CIs. † Genomic viral RNA was used to establish the validation panels. Positive pools are flagged for follow-up by deconvolution (individual testing of specimens in positive pools). For each pool, the five component specimens are retrieved, aliquoted, and labeled with unique barcodes. SymMon data are used to generate clinical orders, and specimens are tested in the CLIA-certified Duke Clinical Microbiology Laboratory using standard protocols. Results are entered into electronic health records and reported to the University’s Student Health. Clinical assays (Xpert-Xpress SARS-COV-2 [Cepheid], Abbott Alinity mSARS-COv-2 [Abbott Diagnostics] or Roche cobas SARS-COV-2 [Roche Diagnostics]) were authorized for emergency use by the FDA. The two-stage testing strategy described here was designed so that the first stage used a sensitive test in a low-prevalence population, and the second stage used EUA clinical tests in the identified subset of samples where the pretest probability was higher. Additional details regarding clinical assays, sample pooling, and testing are available online.** During August 2–October 11, a total of 10,265 undergraduate and graduate students, representing all students residing on campus or in the Durham community, but excluding athletes (781) and students attending class remotely outside of Durham (4,452), participated in pooled testing. Overall, 68,913 tests were performed for students, including 8,873 entry tests (1,392 students were already in residence on campus or in the Durham community), 59,476 pooled tests, 379 contact-traced tests, and 185 tests for symptomatic students (Table 2). TABLE 2 Number of tests* positive for SARS-CoV-2 among students, by test category — Duke University, Durham, North Carolina, August 2–October 11, 2020 Test category No. of tests performed No. of positive tests No. (%) of persons† asymptomatic at testing Entry testing 8,873 17 9 (53) Pooled testing§ 59,476 29 29 (100) Contact tracing¶ 379 23 5 (22) Symptom monitoring¶ 185 15 0 (0) Total 68,913 84 43 (51) * Testing was performed on specimens from a total population of 10,265 undergraduate and graduate students residing on Duke University campus or in the surrounding Durham community. † Who received positive test results. § Total number of positive pools = 158, which upon deconvolution yielded 29 individual positive specimens among students. ¶ Because numbers for total tests in contact tracing and symptom monitoring were encoded together, classifications of tests as resulting from contact tracing or symptom monitoring in this table represent an estimate. Duke’s comprehensive strategy includes multiple categories of tests to identify COVID-19 infections (Table 2). During August 2–October 11, a total of 84 cases among students were identified. Across testing categories, 17 cases (20.2%) were detected by entry testing (nine asymptomatic and eight symptomatic), 29 cases (34.5%) by pooled testing (all asymptomatic), 23 cases (27.4%) by contact tracing (five asymptomatic and 18 symptomatic at time of testing), and 15 (17.9%) by symptom monitoring. Overall, among 84 total students who received positive test results, 43 (51%) did not report symptoms at the time of testing (Table 2). Contact tracing was activated for each case detected. Among 379 students quarantined as a result of contact tracing, 23 (6.1%) received positive test results while in quarantine. Thus, the combined number of cases in asymptomatic students identified by testing (entry and pooled) and cases in all students identified by contact tracing accounted for 61 (73%) of the 84 COVID-19 cases that might not have been detected as rapidly or completely through symptomatic testing alone. Because of high testing frequency, an accurate weekly per-capita infection incidence was calculated, averaging 0.08% during the measurement period. Pooled testing for asymptomatic students comprises two steps: pooled screening and deconvolution. The pooled screening resulted in 158 positive pools that, upon deconvolution, identified 29 (18.4%) confirmed cases. Estimated viral load was reported for pooled tests and clinical deconvolution tests. Specimens that tested positive upon deconvolution indicated good concordance with viral load estimates for positive pools (Figure). Viral load estimates for multiple asymptomatic students reached levels >10,000,000 copies/mL (geometric mean = 2,590 copies/mL [range = 3–32,360,000 copies/mL]). For pooled testing, the time between sampling, return of a positive pool, subsequent deconvolution, and return of clinical results was 18–30 hours. In addition, pooled testing permitted a nearly 80% savings in use of reagents and laboratory resources compared with testing each individual specimen. FIGURE Cumulative number of nasal swab specimens processed for pooled SARS-CoV-2 real-time reverse transcription–polymerase chain reaction testing, August 18–October 11, 2020 (A) and viral load estimates for pooled (n = 158) and confirmatory specimens (n = 30), August–October 2020 (B)* — Duke University, Durham, North Carolina Abbreviation: VL = viral load. * In addition to data for students, plot includes data for one faculty member with a positive test result. The figure consists of two line graphs showing 1) the cumulative number of nasal swab specimens processed for pooled SARS-CoV-2 real-time reverse transcription–polymerase chain reaction testing during August 18–October 11, 2020 and 2) viral load estimates for 158 pooled and 30 confirmatory specimens during August–October 2020 at Duke University, Durham, North Carolina. Discussion For the fall 2020 semester at Duke University, COVID-19 mitigation strategies included mandatory mask wearing, social distancing, emphasis of hand hygiene, daily symptom self-monitoring/reporting, and a multipronged testing strategy that comprised entry testing of all students, frequent testing of pooled student specimens, contact tracing with quarantine, and testing for symptomatic and exposed students. The cross-sectional strategy for collecting surveillance/pooled testing specimens involved distributing tests weekly across off- and on-campus student populations. In addition, the frequency of surveillance/pooled testing enabled real-time adaptive sampling, wherein additional individual specimens were focused either geospatially or within identified cohorts of the persons with positive test results. Case identification activated contact tracing for quarantine and testing for exposed asymptomatic contacts. This plan allowed campus to remain open for 10 weeks of classes without substantial outbreaks among residential or off-campus populations. Importantly, no evidence from contact tracing linked transmission with in-person classes. Multiple universities began fall 2020 classes using only symptomatic testing. Among colleges with in-person classes and approximately 5,000 undergraduates, only 6% routinely tested all of their students in the fall semester. †† The finding that 51% of SARS-CoV-2 infections in this analysis were asymptomatic suggests that a substantial proportion of infections would be missed with only symptomatic testing. Entry and pooled testing of asymptomatic students combined with contact tracing allowed identification and isolation of nearly three quarters of students with diagnosed infections. Importantly, despite constrained testing resources, pooled surveillance enabled the data-driven deployment of testing to areas or groups potentially at risk for an outbreak before substantial spread. Frequent testing in addition to asymptomatic entry testing, facilitated isolation of infected students before transmission could occur, keeping baseline incidence low; average weekly per-capita incidence among students was estimated to be 0.08% ( 8 ). By comparison, during October 12–18, weekly per-capita positivity for Durham County was 0.1% ( 9 ). Several asymptomatic students had high viral loads, suggesting substantial potential for transmission ( 1 ). These findings highlight the importance of combined testing and tracing strategies beyond symptomatic testing. Recently, a COVID-19 cluster involving multiple students was identified in off-campus housing. Pooled testing identified the asymptomatic index patient. After contact tracing identified students with potential exposure, eight students linked to the index patient received positive test results. Pooled testing and contact tracing rapidly isolated the cluster, preventing further transmission. In addition, rapid identification of cases among contacts in off-campus locations might have prevented community outbreaks. The high sensitivity of RT-PCR testing could support use of larger pools or more complex two-stage testing strategies than those used in this study. However, deconvolution would also increase turnaround time, reducing capacity for rapid identification and isolation of infections. Using five-to-one pooling balances resource availability with supply-chain disruptions, high throughput with high sensitivity, and rapid turnaround with an acceptable workload for the laboratory conducting confirmatory testing. Further, surveillance testing at this scale in a relatively low-prevalence population will identify more false positives than true positives; thus, the current two-stage approach and pooling size allows rapid identification and confirmation of asymptomatic cases for contact tracing. The findings of this report are subject to at least four limitations. First, the determination of whether students were asymptomatic or symptomatic at the time of testing relied on self-reporting of symptoms, which was unlikely to be fully accurate. Second, some reported positive cases might have included students who were not residing on campus or within the Durham, North Carolina, community at the time of the report. Third, positive pools were deconvoluted in a CLIA-certified clinical laboratory using multiple EUA-certified platforms with different metrics and thresholds for determining positives. Finally, the impact of Duke’s testing program was assessed within the context of an incidence rate specific to the local Durham community and in the context of multiple strategies for mitigations on campus. The precise findings were likely influenced by multiple factors, such as maintaining students in single rooms on campus and by the level of adherence to campus policies on face coverings, social distancing, and symptom monitoring by Duke’s student populations. Before fall 2020, many universities made decisions based on epidemiologic models with scant data for estimating critical parameters ( 2 , 10 ). Among the Duke student body and faculty and staff members, weekly or more frequent mandatory testing led to low infection rates when combined with preventive mitigation strategies such as frequent handwashing, masking, and social distancing. In addition to limiting transmission on campus and within the local community, Duke’s comprehensive COVID-19 mitigation will provide critical data to inform parameters in epidemiologic models and support data-driven approaches on college campuses and in other settings. Summary What is already known about this topic? SARS-CoV-2 can rapidly spread through university settings. Pooling specimens can enable large-scale testing while minimizing needed resources. What is added by this report? In fall 2020, Duke University’s COVID-19 prevention strategy included risk reduction behaviors, frequent testing using pooled SARS-CoV-2 polymerase chain reaction testing, and contact tracing. Among 10,265 students who received testing 68,913 times, 84 had positive results. One half of infections were asymptomatic, and some had high viral loads. What are the implications for public health practice? SARS-CoV-2 transmission was limited in this congregate setting by integration of prevention strategies that included identification of asymptomatic infections through frequent testing. Pooled testing reduced the need for resources while allowing high throughput with high sensitivity and rapid turnaround of results.
Journal ID (iso-abbrev): MMWR Morb Mortal Wkly Rep
Journal ID (publisher-id): WR
Title:
Morbidity and Mortality Weekly Report
Publisher:
Centers for Disease Control and Prevention
ISSN
(Print):
0149-2195
ISSN
(Electronic):
1545-861X
Publication date
(Electronic):
12
February
2021
Publication date Collection: 12
February
2021
Volume: 70
Issue: 6
Pages: 208-211
Affiliations
CDC COVID-19 Emergency Response Team;
Carter Consulting, Inc., Atlanta, Georgia;
4ES Corporation, San Antonio, Texas;
University of Georgia, Athens, Georgia;
West Virginia University, Morgantown, West Virginia;
Auburn University, Auburn, Alabama;
University of Florida, Gainesville, Florida;
University of Pikeville, Pikeville, Kentucky;
Colorado State University, Fort Collins, Colorado.
All material in the MMWR Series is in the public domain and may be used and reprinted
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