Association Between Covid 19 Outcomes And Mask Mandates Adherence And Attitudes
Summary
Association between COVID-19 outcomes and mask mandates, adherence, and attitudes, a research article by Dhaval Adjodah and co-authors published in PLoS ONE 16(6): e0252315 on June 23, 2021. The study uses an event study framework covering all 50 states, the District of Columbia and 857 counties from Feb 2, 2020 to September 27, 2020. It reports that mask mandates are associated with decreases of 3.55 cases per 100,000 people, 0.13 deaths per 100,000 people and 2.38 percentage points in the proportion of hospital admissions up to 40 days after introduction. It also reports a 23.4 percentage point increase in mask adherence in four states and, using 847 thousand survey responses in 69 countries, finds community mask adherence and attitudes associated with fewer cases and deaths. The article closes with its numbered reference list.
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“Association Between Covid 19 Outcomes And Mask Mandates Adherence And Attitudes.”
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PLOS ONE
RESEARCH ARTICLE
Association between COVID-19 outcomes and
mask mandates, adherence, and attitudes
Dhaval Adjodah1,2, Karthik Dinakar3, Matteo Chinazzi ID4, Samuel P. Fraiberger1,5,6,
Alex Pentland3, Samantha Bates2, Kyle Staller7,9, Alessandro Vespignani4, Deepak
L. Bhatt ID8,9*
1 Connection Science, MIT, Cambridge, MA, United States of America, 2 Center of Complex Interventions,
Wellesley, MA, United States of America, 3 Media Lab, MIT, Cambridge, MA, United States of America,
4 Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston,
a1111111111 MA, United States of America, 5 Development Data Group, World Bank, Washington, DC, United States of
a1111111111 America, 6 Department of Computer Science, New York University, New York, NY, United States of America,
7 Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, United States of America,
a1111111111 8 Heart & Vascular Center, Brigham and Women’s Hospital, Boston, MA, United States of America, 9 Harvard
a1111111111 Medical School, Harvard University Boston, MA, United States of America
a1111111111
* dlbhattmd@post.harvard.edu
OPEN ACCESS
Abstract
Citation: Adjodah D, Dinakar K, Chinazzi M, We extend previous studies on the impact of masks on COVID-19 outcomes by investigat-
Fraiberger SP, Pentland A, Bates S, et al. (2021) ing an unprecedented breadth and depth of health outcomes, geographical resolutions,
Association between COVID-19 outcomes and
types of mask mandates, early versus later waves and controlling for other government
mask mandates, adherence, and attitudes. PLoS
ONE 16(6): e0252315. https://doi.org/10.1371/ interventions, mobility testing rate and weather. We show that mask mandates are associ-
journal.pone.0252315 ated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13
Editor: Valerio Capraro, Middlesex University, per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40
UNITED KINGDOM days after the introduction of mask mandates both at the state and county level. These
Received: March 4, 2021 effects are large, corresponding to 14% of the highest recorded number of cases, 13% of
deaths, and 7% of admission proportion. We also find that mask mandates are linked to a
Accepted: May 13, 2021
23.4 percentage point increase in mask adherence in four diverse states. Given the recent
Published: June 23, 2021
lifting of mandates, we estimate that the ending of mask mandates in these states is associ-
Copyright: © 2021 Adjodah et al. This is an open ated with a decrease of -3.19 percentage points in mask adherence and 12 per 100K (13%
access article distributed under the terms of the
of the highest recorded number) of daily new cases with no significant effect on hospitaliza-
Creative Commons Attribution License, which
permits unrestricted use, distribution, and tions and deaths. Lastly, using a large novel survey dataset of 847 thousand responses in
reproduction in any medium, provided the original 69 countries, we introduce the novel results that community mask adherence and commu-
author and source are credited. nity attitudes towards masks are associated with a reduction in COVID-19 cases and
Data Availability Statement: Data and code are deaths. Our results have policy implications for reinforcing the need to maintain and encour-
available on GitHub (https://github.com/d-val/ age mask-wearing by the public, especially in light of some states starting to remove their
Mask_Mandates_Adherence_Attitudes).
mask mandates.
Funding: The author(s) received no specific
funding for this work.
Competing interests: MyoKardia, PhaseBio, PLx
Pharma, Regado Biosciences; Board of Directors: Introduction
Boston VA Research Institute, Society of
Cardiovascular Patient Care, TobeSoft; Chair:
As of December 2020, SARS-CoV-2, the virus responsible for COVID-19 has infected at least
American Heart Association Quality Oversight 66 million people worldwide and caused more than 1.5 million deaths [1]. Numerous studies
Committee; Data Monitoring Committees: Baim have analyzed the role played by masks during the COVID-19 pandemic [2–6]: masks have
PLOS ONE | https://doi.org/10.1371/journal.pone.0252315 June 23, 2021 1 / 26
PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Institute for Clinical Research (formerly Harvard been associated with a reduction in the infection rate among health care workers in a large
Clinical Research Institute, for the PORTICO trial, hospital network [7], mask mandates have helped reduce the number of cases in the United
funded by St. Jude Medical, now Abbott),
States and in Germany [8–10], and simulations have shown that wearing a mask can protect
Cleveland Clinic (including for the ExCEED trial,
funded by Edwards), Contego Medical (Chair, against droplet infection by preventing the spread of viral particles [11–16]. Despite this evi-
PERFORMANCE 2), Duke Clinical Research dence, 6 states recently lifted their mask mandates even though they have some of the lowest
Institute, Mayo Clinic, Mount Sinai School of vaccination rates in the country.
Medicine (for the ENVISAGE trial, funded by Daiichi A large body of literature already exists and is ongoing about the drivers behind mask
Sankyo), Population Health Research Institute;
usage: studies have shown strong relationships between mask usage and different types of mes-
Honoraria: American College of Cardiology (Senior
Associate Editor, Clinical Trials and News, ACC.org;
saging, gender and country mandatoriness [17], masculinity [18], perceptions of masks [19],
Vice-Chair, ACC Accreditation Committee), Baim political ideology and risk attitudes [20], gender, age and location [21, 22], rhetoric [23, 24]
Institute for Clinical Research (formerly Harvard amongst many other individual, community and societal attributes [25]. Similarly, there has
Clinical Research Institute; RE-DUAL PCI clinical been work looking at the effect of masks on the economy [26], at the drivers behind the early
trial steering committee funded by Boehringer mask shortages [27, 28] and, more generally, a large amount of work on how to optimally
Ingelheim; AEGIS-II executive committee funded
cause behavior change in individuals and populations [29–34]. Here, we focus on the down-
by CSL Behring), Belvoir Publications (Editor in
Chief, Harvard Heart Letter), Canadian Medical and stream effect of masks on COVID-19 outcomes and aim to strengthen the case that masks
Surgical Knowledge Translation Research Group have a positive effect on the pandemic.
(clinical trial steering committees), Duke Clinical To do so, we extend previous studies by investigating an unprecedented breadth and depth
Research Institute (clinical trial steering of outcomes, geographical resolutions, types of mask mandates, control variables, early versus
committees, including for the PRONOUNCE trial,
later waves and controlling for other government interventions (such as stay-at-home orders).
funded by Ferring Pharmaceuticals), HMP Global
(Editor in Chief, Journal of Invasive Cardiology),
For example, it has been shown that state-level mask mandates have a positive effect on cases
Journal of the American College of Cardiology [8] using the same methodology as ours, the event study framework. Building on this work, we
(Guest Editor; Associate Editor), K2P (Co-Chair, investigate the effect of mandates not only on cases, but also on hospitalizations and deaths,
interdisciplinary curriculum), Level Ex, adding new controls for individual mobility, temperature and precipitation, COVID-19 test
Medtelligence/ReachMD (CME steering rates, and also investigating the effect of county-level mask mandates. Similarly, mask man-
committees), MJH Life Sciences, Population Health
dates have been found to be beneficial in a study of cases in Germany [10], but without looking
Research Institute (for the COMPASS operations
committee, publications committee, steering at hospitalization and deaths, and not controlling for critically important temporally evolving
committee, and USA national co-leader, funded by variables such as mobility and testing rate (additionally, we expand our investigation to 69
Bayer), Slack Publications (Chief Medical Editor, countries). The work closest to ours investigates causal impact of mask policies [35] but does
Cardiology Today’s Intervention), Society of not look at hospitalizations, county-level mandates and outcomes, different types of mandates,
Cardiovascular Patient Care (Secretary/Treasurer),
earlier vs. later affected states, etc. However, we base our approach of how to control for volun-
WebMD (CME steering committees); Other: Clinical
Cardiology (Deputy Editor), NCDRACTION Registry
tary individual behavioral responses to information on their causal graphical model.
Steering Committee (Chair), VA CART Research Overall, our work makes the following novel contributions relative to recent studies: we
and Publications Committee (Chair); Research look at a much larger number of counties (covering 77% of the US population) and all 50 states
Funding: Abbott, Afimmune, Amarin, Amgen, and the District of Columbia controlling for a number of temporally evolving and geography-
AstraZeneca, Bayer, Boehringer Ingelheim, Bristol- specific variables compared to [9]; we investigate longer time horizon treatment effects and
Myers Squibb, Cardax, Chiesi, CSL Behring, Eisai,
expand beyond hospital populations compared to [7]; we include states beyond New York and
Ethicon, Ferring Pharmaceuticals, Forest
Laboratories, Fractyl, HLS Therapeutics, Idorsia, Washington compared to [11]; we include outcomes beyond hospitalization, look at all states
Ironwood, Ischemix, Lexicon, Lilly, Medtronic, and investigate county-level effects compared to [36]; we investigate the temporal evolution of
MyoKardia, Owkin, Pfizer, PhaseBio, PLx Pharma, mask mandates before and after their introduction compared to [6]; and we analyze much
Regeneron, Roche, Sanofi, Synaptic, The larger populations and include the temporal evolution and geographical heterogeneity of treat-
Medicines Company; Royalties: Elsevier (Editor,
ment effects compared to [37].
Cardiovascular Intervention: A Companion to
Braunwald’s Heart Disease); Site Co-Investigator:
In addition to strengthening evidence of the effect of mask mandates on COVID-19 out-
Biotronik, Boston Scientific, CSI, St. Jude Medical comes, this work introduces two novel contributions to the research of masks: we investigate
(now Abbott), Svelte; Trustee: American College of the effect of mask mandates on mask adherence (the percentage of people who wear masks)
Cardiology; Unfunded Research: FlowCo, Merck, and the effect of mask adherence on COVID-19 cases and deaths. Secondly, using a novel data-
Novo Nordisk, Takeda. This does not alter our set of survey responses in 69 countries around the world across months-long waves of surveys
adherence to PLOS ONE policies on sharing data
containing 847 thousand responses in 51 languages, we investigate the effect of community
and materials.
mask adherence (how many people in a community wear masks) and community mask atti-
tudes (how many people in a community believe that masks are an important for COVID-19)
PLOS ONE | https://doi.org/10.1371/journal.pone.0252315 June 23, 2021 2 / 26
PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
on country-level cases and deaths. This is especially important as similar community and
adherence drivers behind mask avoidance [38] might impact the on-going deployment of
vaccines.
This paper is structured as follows: in the next section, we describe our results: the main
result is that mask mandates are associated with a significant improvement in COVID-19 out-
comes (corresponding to 14% of the highest recorded number of daily new cases, 13% of
deaths, and 7% of hospital admissions). As described in the Materials and Methods section,
our analysis covers all 50 states (including the District of Columbia) and 857 counties (repre-
senting 77% of the US population) from Feb 2, 2020 to September 27, 2020, and we include a
large number of robustness tests.
In support of our main result, we also observe that mask mandates are associated with a
23.4 percentage point increase in mask adherence in four geographically, culturally and politi-
cally diverse states—Hawaii, Iowa, North Dakota and New Hampshire—and that mask adher-
ence is itself associated with a significant decrease in COVID-19 cases and deaths in all 50
states and D.C. We also observe that the recent lifting of state mask mandates is associated
with decreases in adherence and increases in cases. Finally, using a large novel dataset [39], we
observe that community mask adherence and attitudes towards masks are associated with
fewer cases and deaths in 69 countries, controlling for population density, human develop-
ment and mobility. We end by discussing the limitations and implications of this work.
Taken together, these results strongly suggest the positive effect of mask mandates, mask
adherence and mask attitudes on COVID-19 cases, hospitalizations and deaths.
Results
Mask mandates and COVID-19 outcomes
As show in Fig 1, mask mandates were introduced at day zero, shown by the red vertical line.
Because we are using an event study model, the y-axis represents the treatment effect associ-
ated with mask mandates on the COVID-19 outcome for each day before or after the introduc-
tion of a mask mandate relative to the day before the introduction of a mask mandate.
For all three outcomes, we observe the treatment effect (y-axis) to be negative which means
that mask mandates are associated with a decrease in cases, hospitalizations and deaths. Specif-
ically, we observe this treatment effect to start near zero when mask mandates are introduced
and to gradually increase (i.e. the treatment effect becomes more negative) reaching, 40 days
after the introduction of mask mandates, -0.49 standard deviations (95% confidence interval is
[-0.61,-0.37], adjusted R2 is 0.632 and p < 0.001) for new cases, -2.38 percentage points
([-2.99,-1.78], adjusted R2 is 0.698 and p < 0.001) for the proportion of daily hospitalization
admissions due to COVID-19, and 0.52 standard deviations (95% confidence interval is
[-0.69,-0.35], adjusted R2 is 0.537 and p < 0.001) for deaths.
In real terms, these treatment effects are large, corresponding to an associated decrease of
3.55 cases per 100,000 people (or 14% of the highest recorded number of new cases per
100,000 people during our observation period), 0.13 deaths per 100,000 people (or 13% of the
highest recorded number of new deaths), and a 7% decrease compared to the highest recorded
proportion (34%) of COVID-19 related hospitalization admissions during our observation
period. As detailed in the specification (Eq 1) of the model in the Methods section, we control
for many confounders such as other NPIs (stay-at-home and non-essential business closures),
mobility, weather, new test rates and various aspects of behavior change.
Mask mandate scope: Business employees vs. the public. We conduct a number of
checks to test the robustness of our results. First, we investigate changing how mask mandates
PLOS ONE | https://doi.org/10.1371/journal.pone.0252315 June 23, 2021 3 / 26
PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Fig 1. Effect of state-level mask mandates on COVID-19 outcomes. Shaded area represents 95% confidence interval, with
standard errors clustered at the state level. Red vertical line represents the start of the mask mandate, and the red horizontal line
represents the line of zero treatment effect. We observe the treatment effect to start near zero when mask mandates are introduced
(red vertical line) and to gradually increase (i.e. the treatment effect becomes more negative) reaching, 40 days after the introduction
of mask mandates, -0.49 standard deviations (95% confidence interval is [-0.61,-0.37], adjusted R2 is 0.632 and p < 0.001) for new
cases (a), -2.38 percentage points ([-2.99,-1.78], adjusted R2 is 0.698 and p < 0.001) for the proportion of daily hospitalization
admissions (b) due to COVID-19, and 0.52 standard deviations (95% confidence interval is [-0.69,-0.35], adjusted R2 is 0.537 and
p < 0.001) for deaths (c). These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and
7% of admission proportion.
https://doi.org/10.1371/journal.pone.0252315.g001
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Fig 2. Effect of different types of mask mandates. Robustness check of main event study estimation where, here, we only consider
mask mandates that require business employees (right) vs. the public (left) to wear masks. The trajectory and magnitude of the
treatment effect is consistent with our previous main result, and do not differ significantly between each other, which demonstrates
that our main result is not sensitive to our definition of mask mandate start dates. Full regression results are shown in S5 and S6
Tables in S1 File.
https://doi.org/10.1371/journal.pone.0252315.g002
are defined. As noted earlier, so far, we have defined the start of a mask mandate as the earlier
date between mandates for all employees or mandates for all members of the public.
Here, we consider two alternative specifications in which we only consider mask mandates
requiring everyone to wear a mask outside, and another specification where we only consider
mandates requiring only business employees to wear masks. The former specification is more
stringent than our original specification (which used the earlier of the two types of mandates)
as these public mandates generally came later in the year than those focusing on business
employees only, and more states still do not have the public mandates.
As shown in Fig 2, the trajectory and magnitude of the treatment effect associated with
mask mandates under either types of mandates is consistent with our earlier estimates (shown
in Fig 1) which suggests that our treatment effects are not sensitive to our definition of mask
mandates.
Early wave vs. later wave states. So far, we have always looked at all 50 states and the Dis-
trict of Columbia all together. Here we investigate the heterogeneity of treatment effect
between earlier and later-wave states as it is possible that the effect of mask mandates on states
that were part of the COVID-19 pandemic’s early wave might be different from states that
were affected later. For example, mask shortages were present during the early stages of the
pandemic [27, 40, 41] which might affect the proportion of people who wear masks (mask
adherence) following a mask mandate for early states compared to later states.
Therefore, we look separately at the first 15 states to have the highest number of cases per
100K during the month of April (namely, NY, NJ, MA, CT, MI, DC, RI, IL, WA, PA, GA, VT,
PLOS ONE | https://doi.org/10.1371/journal.pone.0252315 June 23, 2021 5 / 26
PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Fig 3. Effect of mask mandates on earlier-hit or later-hit states. Robustness check of main event study results (section Fig 1) where
we compare the effect of mask mandates on cases, deaths and hospitalization for earlier wave states only (i.e. states who had early
spikes of COVID-19 peaks in the pandemic, namely, NY, NJ, MA, CT, MI, DC, RI, IL, WA, PA, GA, VT, MD, FL and LA) vs later
wave states. Masks mandates took more time to have an effect on earlier states whereas they had a faster effect on later states. Full
regression results are shown in S9 Table in S1 File.
https://doi.org/10.1371/journal.pone.0252315.g003
MD, FL, LA) compared to the rest of the states (i.e. later wave states). As shown in Fig 3, the
effect of masks mandates was more gradual and noisier in earlier states whereas they had a
faster effect on later states. This is consistent with the literature of behavior change whereby
asking people to change their habits (here, mask-wearing) is much harder when the behavior
is still very novel [29, 31]. Because the trajectory and magnitude of the treatment effect overall
is consistent with our previous main result, this suggests that our specification is not sensitive
to subsets of earlier wave, later wave or all states.
Testing rate exogeneity. We also investigate if the decreases in COVID-19 outcomes we
observe might be because the number of COVID-19 tests each day done might be decreasing
following the introduction of mask mandates leading us to observe, erroneously, a lower rate
of COVID-19 outcomes. Although we already control for the testing rate in our event study
specification, here, we also investigate if testing rates were themselves exogenously decreasing
during the period of observation. We do so in two ways: first, we look at the per-state and
mean (over all states) testing rate (daily new tests per 100K) every day. As shown in S2 Fig in
S1 File (left), it does not decrease but instead increases over our period of investigation. As a
further test, we conduct an event study estimation (Eq 2) to investigate a confounding treat-
ment effect associated with mask mandates on testing rate. As shown in S2 Fig in S1 File
(right), there is no associated treatment effect of mask mandates on testing rate. Therefore, it is
unlikely that our estimates of decreasing COVID-19 outcomes after the introduction of a
mask mandate are due to decreased detection of COVID-19 outcomes.
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Controlling for confirmed cases. We can estimate our treatment effect of mandates on
the number of deaths and the proportion of hospitalizations when controlling for the number
of cases. This specification allows us to investigate whether when we keep the number of cases
constant (by controlling for cases), we observe that hospitalizations and deaths still decrease
following the introduction of mandates. As shown in S6 Fig in S1 File, controlling for the num-
ber of cases has little effect on the trajectory of the effect of mask mandates on deaths and hos-
pitalization which suggest that the treatment effect of mask mandates is strong even when
controlling for cases.
Political affiliation, demographics and trends through time and unit fixed effects.
Because we control for time and unit (state or county) fixed effects, we are implicitly control-
ling for a range of factors that did not change geographically and temporally. For example,
including unit fixed effects allows us to control for political affiliation and including the 2018
election vote proportion by political party for each state (or county) correctly did not alter our
estimation. The same is true for demographic factors such as age distributions and economic
inequality. Similarly, including time fixed effects allows the model to disentangle the particular
trajectory in outcome numbers that each state (or county) had over time.
Behavioral change endogeneity and pre-trends. A potential issue with the event study
framework is that policy adoption might be endogenous which here might mean that people
might be changing their behavior before the policy was introduced, or that they changed their
exposure to the virus for other endogenous reasons unrelated to but coinciding with the timing
of the introduction of mask mandates. For example, people might be going out less around the
time that the mandate was introduced due to fear of infection, had started wearing masks
before the mandate was introduced, or that the mandate did not increase mask adherence
(how much people wear masks).
We first focus on the issue of pre-trends, which is the fact that people might have anticipa-
torily started wearing masks even before the introduction of mask mandates. For all three out-
comes, the pre-treatment effect is statistically indistinguishable from zero (because 95%
confidence interval still contains the zero-effect horizontal zerp line). We do observe a slight
upward trend but further investigation using the standard methods for pre-trend robustness
checks [42] shows that this pre-trend is still minimal: as show in S5 Fig in S1 File, omitting
time and unit fixed effects and using a longer pre-treatment window of 20 days (we cannot use
values earlier than -20 as it leads to some state no longer having enough data as many states
implemented mandates very soon after COVID-19 outcome data was available) shows that the
anticipatory behavior is indistinguishable from zero for cases and hospitalization, and small
for deaths.
Secondly, because we control for six types of mobility (i.e. mobility at the residential, work-
place, transit, parks, grocery and pharmacy and retail and recreation) and include the squared
values of each mobility indicator as a proxy for social contacts [43, 44], our estimate of the
effect of mask mandates has precluded (theoretically, they have been expected out) the effect of
people’s endogenous mobility and contact behavior. Similarly, we include controls for the two
broadest and far-reaching NPIs, stay-at-home and non-essential business closures, which
again precludes the potential confounding effect of other NPIs. Another potential source of
endogeneity is the fact that people might be wearing masks (or not wearing them) irrespective
of the mask mandates, i.e. the mask mandates themselves did not cause behavior changes. We
investigate this in the next section where we show that mask mandates lead to a 23.4 percent-
age point increase in mask adherence (how many people wear masks) following a mask man-
date, and that mask adherence itself are directly associated with improved case and death
outcomes in the US, and in 67 other countries.
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
As another control for behavioral changes, we leverage the causal graphical model of [35]
which shows that declines COVID-19 outcomes are not only due to the introduction of poli-
cies but are also a result of private behavioral responses to observations of increased outcome
numbers. A consequence of their very general causal model is that conditioning on the delayed
outcome and the growth rate of the outcome with an approximately 2 week lag enables the
confounding path due to behavior change on the outcome to be blocked (see [45] for a review
of causal D-separation). As seen in S1 Fig in S1 File, not controlling for the delayed outcome
and growth rate has little effect on the overall trajectory and magnitude of the treatment effect,
suggesting that our main model estimation is robust to behavioral endogeneity.
County-level heterogeneity. So far, our analysis has been at the state level. There is signif-
icant heterogeneity at the county and city level as to when these smaller units introduced mask
mandates [9]. We therefore use a dataset of county-level mask mandates [46] on county-level
outcomes and controls to investigate the effect of mask mandates at the county-level. We make
sure to only use county-level mask mandates (as opposed to county and state-level mandates)
for location which had both, and we do not include data that happened after a state-mandate
was also introduced. As shown in S4 Fig in S1 File, we again observe that mask mandates are
associated with a helpful effect on cases, hospitalizations and deaths. However, the treatment
effect is noisier as is expected due to a number of noise signals when using data at the county
level such as peer-effects of neighboring counties’ mandates due to the underlying interdepen-
dence between county mobility patterns [47], as well as the fact that people living in one
county often have to travel to a different county to get medical care, resulting in inconsistent
accounting of COVID-19 health outcomes at the county level [48].
Overall, our analysis shows that the introduction of mask mandates at the state and county
level is associated with a statistically significant and large decrease of cases, hospitalizations,
and deaths.
Mask mandate and mask adherence
Here, as a supporting result, we investigate whether the introduction of mask mandates is asso-
ciated with a significant increase in mask adherence i.e., do mask mandates lead to more peo-
ple wearing masks? Although there are significant on-going efforts to estimate mask
adherence at a population level such as by leveraging computer vision on social media [49–52],
data that tracks on-the-ground mask usage is not currently available. On the other hand, there
are a large number of studies that have created instruments to measure mask adherence based
on online [53, 54] and telephone [2] surveys, and numerous studies have shown that self-
reported COVID-19 attributes (e.g. symptoms [55, 56], social distancing [57]) correlate
strongly with ground truth. We therefore use survey-based self-reported mask adherence as a
proxy for on-the-ground mask usage in this work.
Here, we use the CMU Delphi project’s survey [58] which, as of September 8, 2020, esti-
mates the percentage of people who wore a mask most or all of the time while in public in the
past 5 days by asking the question “In the past 5 days, how often did you wear a mask when in
public?” in an on-going Facebook online survey ran on millions of people in the US. We there-
fore use their weighted state-level estimates of mask adherence as our dependent variable.
Their weighting strategy to obtain representative samples is described briefly in the Data sec-
tion, and comprehensively described in their technical report [59].
During the period starting September 8 2020 (when adherence data started being available),
only 4 states have enacted new state level requirements: Hawaii and Iowa on November 16
2020, North Dakota on November 14 2020 and New Hampshire on November 20 2020.
Although some of these states had county-level mask mandates, had memos encouraging
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Fig 4. Effect of mask mandates on mask-adherence. Treatment effect of mask mandates on mask adherence in the only four states (Hawaii, Iowa,
North Dakota and New Hampshire) which had late mask mandates (Nov 14, 16, and 20) during the period we have daily state-level mask adherence
data. We find that there is a flat pre-treatment trend up to 13 days before the introduction of a mask mandate followed by a 23.4 [12.0,34.8] percentage
points increase in mask adherence following introduction (adjusted R2 is 0.925 and p < 0.001).
https://doi.org/10.1371/journal.pone.0252315.g004
mandates at county or state-level, or had mask requirements in certain businesses, they did
not have a state-wide requirement with implementation until the above dates. We run a similar
event study estimation as earlier but this time using mask adherence as the outcome variable
and for 13 days after mandate introduction (because this is the longest time horizon we have
simultaneously for all four states).
As shown in Fig 4, there is a flat pre-treatment trend up to 8 days before the introduction of
a mask mandate followed by an uptick in mask adherence after the introduction of mask man-
dates with a maximum increase in adherence of 23.4 [12.0,34.8] percentage points (adjusted R2
is 0.925 and p < 0.001) 13 days after mask mandate introduction. We observe a delay of about
4 days before we observe an upward trend in mask adherence which is consistent with behav-
ior change literature [29, 31].
As a robustness test, we investigate if our estimation changes significantly if we remove test-
ing rate, and cases and deaths as controls which we included as a confounders because, per-
haps, the more case and deaths people see, the more they might be likely to wear masks. As
seen in S7 Fig in S1 File, our estimation is robust to these controls. Unfortunately, we cannot
replicate this result at the county level because no county mask mandate datasets currently
exist with county-level mask mandates starting during the period that mask adherence data is
available. Our results are consistent with previous work [53] although they do not control for
time and unit fixed effects which prevents the disentanglement of people already increasing
their mask wearing irrespective of the mask mandate.
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Although we only estimate the effect of mandates on adherence in these 4 states due to the
fact that they are the only ones that implemented mask mandates late enough that we have
adherence data for them, we expect this result to support our main result that mask mandates
are associated with a significant decrease in COVID-19 outcomes, especially due to the fact
that these four states (Hawaii, Iowa, North Dakota and New Hampshire) are very geographi-
cally, culturally and politically diverse.
As an additional result, because we have a limited window of time where we have both
COVID-19 outcomes (only cases and deaths, no hospitalization data as access ended in Septem-
ber 2020) and mask adherence data, we can use it to investigate if mask adherence is more
directly associated with a positive effect on COVID-19 outcomes. To do so, we implement a
multi-linear regression of COVID-19 outcomes on compliance, controlled, as before, by a num-
ber of factors as specified in Eq 3. Note that we use all states (including D.C.) here, not just the
four states that had mandates after Sep. 8th. As shown in S16 Table in S1 File, an increase of 1%
in mask adherence leads to a decrease of -1.63 [-1.76, -1.50] new confirmed cases per 100K
(adjusted R2 is 0.502 and p < 0.001), and -0.016 [-0.015, -0.018] new deaths per 100K (adjusted
R2 is 0.343 and p < 0.001), controlling for test rates, weather and mobility. For reference, the rate
of COVID-19 outcomes was 25.4 new daily cases per 100K and 0.95 daily new deaths per 100K,
suggesting that even a 1% increase in masking adherence can have a significant positive effect.
Lifting mask mandates
Previously, we estimated the positive effect of the introduction of mask mandates on COVID-
19 outcomes. In the last 3 months, 6 states have lifted their state-level mask mandates: North
Dakota on January 18 2021, Iowa on February 7 2021, Montana on February 12 2021, Texas
on March 10 2021 and Wyoming on March 16 2021, Arkansas on March 30 2021. In this sec-
tion, we estimate the associated effect of lifting mask mandates on, first, mask adherence, and,
second, on COVID-19 outcomes.
An additional control of this study will be daily vaccination rates for each state normalized
by population. This control is included both to control for its effect on COVID-19 outcomes—
as we want to estimate the marginal effect of masks on outcomes controlling for the benefit
due to vaccines—and to account for behavior changes: perhaps people wear masks less follow-
ing getting vaccinated. Since vaccination data are only available since relatively recently, we
consider the period between January 1 2021 and March 30 2021 for this section. Vaccine data
were obtained from the Our World in Data initiative [60].
Mandate lifting on mask adherence. Using the same public data source (CMU Delphi
project [58]) as in the previous section for daily state-level mask adherence values, here we esti-
mate the associated effect of the lifting of mask mandates on mask adherence.
As shown in Fig 5 below, we observe a generally flat pre-treatment period during the 25
days prior to the lifting of the mask mandate indicating that mask adherence did not signifi-
cantly decrease before the lifting of mask mandate. After the lifting, we observe mask adher-
ence to be stable up to almost 30 days, but then a statistically significant decrease of up to -3.19
[-5.85,-0.53] percentage points 40 days after the lifting.
Although noisy due to limited data from the very recent states mandate lifting, this result
suggests that the lifting of mask mandates resulted in a decrease in mask adherence in these 6
states. Note that this result includes controlling for previous controls (e.g. cases and deaths,
testing rate, mobility, weather) and a new control for vaccination. A robustness test without
these controls is included in S8 Fig in S1 File.
Mandate lifting on COVID-19 outcomes. Here, we reproduce the analysis from the first
section, but this time examining the effect of lifting mask mandates on COVID-19 outcomes
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Fig 5. Effect of lifting mask mandates on adherence. We observe a flat pre-trend before the lifting of state-level mask mandates (red vertical line at
zero) followed by a statistically significant decrease. Full regression results in S15 Table in S1 File.
https://doi.org/10.1371/journal.pone.0252315.g005
(e.g. other NPIs, mobility, test rate, mobility and delayed outcomes and growth), and introduc-
ing vaccinations as a control.
As can be seen from Fig 6, the lifting of mask mandates has a statistically significant effect
on daily new cases: we observe a flat pre-treatment up to 45 days before the lifting, followed by
an increase in cases after a delay of about 9–15 days, which is consistent with the behavior
change literature [29, 31]. We observe a maximum increase in cases of 0.55 [0.41,0.71] stan-
dard deviations 45 days after the lifting. In real terms, this corresponds to an increase of 12
cases per 100K, which amounts to 13% of the highest recorded number of daily new cases (91
per 100K) in our observation period. Although noisy due to limited data from the very recent
states mandate lifting, this result suggest that the lifting of mandates is associated with an
increase in cases.
On the other hand, we do not observe a statistically significant effect associated with mask
mandate lifting on hospitalization proportions and deaths. This could be due to a number of
factors: noise due to limited data from the very recent states which mandated lifting, positive
effect of vaccines on the more severe, downstream hospitalization and death effects of the virus
[61, 62], or symptom management and relief practices [63–65].
International 69-country mask adherence and attitudes
We expand the scope of our analysis and look at the effect of mask adherence and attitudes
towards mask wearing internationally. We do so by using the novel survey-based dataset
‘COVID-19 Beliefs, Behaviors & Norms Survey’ which asks more than 150 questions (see [39]
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Fig 6. Effect of lifting mask mandates on COVID-19 outcomes. We observe a significant increase in daily new cases, but no effect on
hospitalizations and deaths. Full regression results in S12 Table in S1 File.
https://doi.org/10.1371/journal.pone.0252315.g006
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
for the complete survey instrument) about COVID-19 to 847 thousand online Facebook sur-
vey respondents in 51 languages from 69 countries. Because the 69 countries we include in our
analysis are from different parts of the world and were in various stages of the pandemic (first
wave, second waves, before mandates, after mandates, etc.) during the 18 waves over 9 months
of observation, we expect that our estimates are unbiased by such confounders.
We focus on the two questions most relevant to the effect of masks on COVID-19 out-
comes: a question about mask adherence: “Out of 100 people in your community, how many
do you think wear a face mask or covering when they go out in public?”; and a question about
mask norms: “Out of 100 people in your community, how many do you think believe the fol-
lowing because of COVID-19: People should wear a face mask or covering when out in pub-
lic?”. The first question is a self-reported sample estimate of the percentage of a respondent’s
community that wears masks in public, and the second question provides a sample estimate of
the percentage of the community that believes masks to be important to wear in public.
We use a weighted survey regression approach to regress each country’s new daily deaths
per 100K and cases per 100K (both smoothed) against the self-reported mask adherence and
mask attitude with countries as survey strata and individual anonymized survey responses as
survey clusters. Samples are expected to be representative because we use a unique weight for
each sample which correct for a variety of biases including demographics (age bracket and sex)
of the respondents compared to the census data in each country and compared to Facebook’s
online population (through post-stratification), and for response and non-response drivers
(the estimated design effects was below four as detailed in [39]). The dataset’s weighting strat-
egy to obtain representative samples is described more in the Data section, and comprehen-
sively described in their technical report [39]. We also control for mobility, new test rate,
population density and the country’s human development index. The regression specification
is described in Eq 4.
As shown in Fig 7, the associated effect coefficients suggest that a 1% increase in mask
adherence is associated with a decrease of -0.45 [-0.70,-0.29] cases per 100K and a decrease of
-0.042 [-0.046,-0.037] deaths per 100K. Similarly, we find that a 1% increase in attitude about
the importance of wearing masks leads to a decrease of -0.53 [-0.64,-0.42] cases per 100K and a
decrease of -0.035 [-0.038,-0.032] deaths per 100K. For reference, the current statistics in the
world as of December 14, 2020 is 10 daily new cases per 100K and 0.17 daily deaths per 100K,
which suggest that even a 1% increase in mask adherence and attitudes is associated with a
strong positive impact on COVID-19 outcomes worldwide.
As a robustness check, we perform the same weighted survey regression but this time dis-
aggregating by survey waves. As shown in S12 Fig in S1 File, the regression coefficients of
mask adherence and attitudes on deaths and cases are negative or indistinguishable from zero,
which supports our results that mask adherence and attitudes have a positive impact on
COVID-19 cases and deaths across waves.
Discussion
We extend previous studies by investigating an unprecedented breadth and depth of out-
comes, geographical resolutions, types of mask mandates, early versus later waves, and control
variables such as mobility, weather, test rate, and other government interventions (such as
stay-at-home orders). We find that, overwhelmingly, mask mandates are associated with a
decrease in cases, hospitalizations and deaths. Additionally, we observe that mask mandates
are associated with an increase in mask adherence in the 4 states where adherence data is pres-
ent before and after the introduction of a mask mandate. Using a novel dataset of surveys from
69 countries across a variety of pandemic phases and pre/post mask mandate stages, we also
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Fig 7. Impact of community mask adherence and attitudes. Using a novel survey-based dataset of 847 thousand online Facebook survey respondents
in 51 languages from 69 countries, we estimate that a 1% increase in community mask adherence is associated with a decrease of -0.61 [-0.82,-0.39]
cases per 100K and a decrease of -0.035 [-0.040,-0.029] deaths per 100K. Similarly, we find that a 1% increase in community attitude about the
importance of wearing masks leads to a decrease of -0.56 [-0.80,-0.32] cases per 100K and a decrease of -0.036 [-0.042,-0.030] deaths per 100K. For
reference, the current COVID-19 outcomes in the world as of December 14, 2020 is 10 daily new cases per 100K and 0.17 daily deaths per 100K, which
suggest that mask adherence and attitudes are associated with a strong positive impact on COVID-19 outcomes worldwide. Samples are expected to be
representative because we use a unique weight for each sample which corrects for a variety of biases.
https://doi.org/10.1371/journal.pone.0252315.g007
observe that community mask adherence (how many people in a community wear masks) and
community mask attitudes (how many people in a community believe that masks are an
important for COVID-19) are associated with a decrease in cases and deaths.
Although different studies report different metrics and use different methodologies over
different timescales, our results are comparable to previous work on the effect of masks. One
influential study which also uses an event study design observes a 2.0 percentage points
decrease [8] in cases following state mask mandate introductions. Their methodology is differ-
ent in that they calculate changes in cases relative to a reference period while we first Z-nor-
malize and population-scale our outcomes and then compare them relative to a reference
period to prevent some states such as New York that had a much higher COVID-19 incidence
than other states to dominate estimates. Over the same time period as them, we observe a
decrease of -0.31 standard deviations [-.54,-0.07] equivalent to 8% [2,15%] of the highest
recorded numbers. The range of our estimate is higher perhaps due to the fact that we analyze
a longer time period which had far higher cases: they examine cases between March and May
2020 while we examine cases between February and the end of September 2020. Two other
influential studies estimated the changes in slope before and after mask mandates and observe
a net slope change of approximately 0.19 cases per 100K per day between counties with and
those without mask mandates [9] and 1.65% before and after implementation of universal
masking [7]. In comparison, we observe a corresponding decrease of 0.09% [0.07,0.11%] cases
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
per day. Our estimates might be lower due to the fact that they do not control for a range of
variables that almost certainly affected their observed decreases (and perhaps led to overesti-
mates) such as decreases in mobility and implementation of other NPIs. Finally, one study
observes mask mandates to have led to a decrease in cases between 15% and 75% in Germany
[10]. We observe mask mandates in the USA to lead to a decrease of 14% of highest recorded
number. Their higher estimates might be explained by the much higher and faster adherence
of masks in Germany that the authors observed.
Even though our results, taken together, strongly suggest the positive effect of mask man-
dates, mask adherence and mask attitudes on COVID-19 cases, hospitalizations and deaths, an
important open question is the investigation of the optimal incentives and regulatory mecha-
nisms to increase mask-adherence [22, 66–70] especially in a climate where conspiracy theo-
ries are emerging to discourage mask-adherence [71]. More research is also needed to probe
improvements in public health communication strategies to encourage mask-adherence,
including harm-reduction frameworks [72] and policies tailored to help disadvantaged com-
munities’ lived experiences [73]. Lessons can be taken from successful approaches in other
public health crises such as HIV [74–77].
Such research into incentives, regulation, and messaging is especially important in light of
our observation that the lifting of mandates is associated with a decrease in mask adherence
and in increasing cases. An on-going debate is whether people would have voluntarily started
wearing masks without requiring masks [78] and if mask wearing would voluntarily continue
even in the absence of mask mandates. Our results suggest that mask mandates were effective
and should be maintained.
Our results suffer from the usual limitations of econometrics-based studies: although we
control for many confounders such as other NPIs (stay-at-home and non-essential business
closures), mobility, weather and test rate, it is possible that unexplained exogeneity and
endogeneity persists. For example, it is possible that our treatment effects are underesti-
mates due to the peer effects caused by neighboring states’ mask mandates affecting other
states [47]. Once outcome data disaggregated by demographics (e.g. age, race, gender) is
available at the state or county level, an interesting question for future work is to investigate
the differential impact of mask mandates over these demographic groups. In our study of
mask mandates on mask adherence, ongoing efforts using computer vision to estimate
mask adherence from social media images and videos [49–52] should soon provide more
detailed data on mask adherence, hopefully starting from the beginning of the pandemic.
Another limitation is that although our survey-based sample estimates of mask adherence
and mask attitudes in the 69-country dataset include weights to correct for a number of
biases, it is possible that there is some bias remains although this is unlikely due to their
small estimated instrument design effect [39].
We leave to future work the important design and estimation around these limitations but
expect that our findings will still provide new insights into the long-term effect of mask man-
dates on mask adherence, COVID-19 cases, hospitalizations and deaths, and introduces the
importance of community mask adherence and community mask attitudes.
Our findings have policy implications for reinforcing the need to maintain and encourage
mask-wearing by the public. These results are especially significant in light of some states start-
ing to remove their mask mandates and attempts by state governments to prevent local gov-
ernments from implementing mask orders, even though they are associated with a minimal
economic impact [26]. Generally, our results are relevant to the ongoing effort to determine
how to best design, implement and sustain the adoption of non-pharmaceutical interventions
(NPIs) to curb the spread of the COVID-19 pandemic [79–84].
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Materials and methods
We obtain data from a large variety of publicly available sources which we detail in the Data
section below. We collected no data for this study. The study was approved under MIT’s
Committee on the Use of Humans as Experimental Subjects (IRB) protocols E-2578 and E-
2859.
The Analysis section next describes how we produced our three main results. To ensure the
reproducibility of our results, we have shared the code and data used to perform data process-
ing, analysis and plotting at https://github.com/d-val/Mask_Mandates_Adherence_Attitudes.
Data
Mask mandate start and end dates and types. To identify the start and, if any, the end of
state-level mask mandates, we used two publicly available state-level mask mandate datasets
[85, 86] and followed their references to official state government memos to perform accuracy
checks. We found the datasets to be highly accurate even though the specifics of how and
when mandates were instituted varies strongly between states. We investigated three specifica-
tions of mandates to use as our treatment: mandates requiring only business employees to
wear masks, mandates for the general public, or whichever came first between mandates for
business employees and the public. For the state-level analysis, we select only mandates that
are at the state-level that require mask use as opposed to county-level mask mandates or using
mandates that merely encouraging people to wear masks. The table of mask mandate start and
end dates is shown in S3 Table in S1 File.
In order to investigate county-level mandate effect heterogeneity within a state, we investi-
gate the effect of county-level mask mandates. We use a publicly available dataset [46] which
we similarly verify for accuracy.
Control NPIs: Stay-at-home and non-essential business closures intervention. Mask
mandates were often introduced around the same time that other non-pharmaceutical inter-
ventions (NPI) such as stay-at-home orders and non-essential business closures were imple-
mented. In order to estimate the effect of mask mandates without the bias of other NPI’s, we
use the same public datasets [85, 86] to obtain the start and end dates of stay-at-home and
non-essential business closures (the two broadest and far-reaching NPIs), which we also verify
for accuracy.
COVID-19 cases, hospitalizations and deaths data. State and county-level COVID-19
outcome data was obtained from the CMU Delphi project [58]. Specifically, we use as out-
comes the daily new confirmed cases per 100K population, new confirmed deaths per 100K
population, percentage of new hospital admissions with COVID-associated diagnoses, and the
percentage of outpatient doctor visits primarily about COVID-related symptoms.
It is important to note that we only observed the proportion of hospitalizations, which is the
number of new hospitalizations due to COVID-19 relative to the number of total admissions.
Unfortunately, this proportion is a possibly noisy proxy for the number of hospitalizations per
100K due to COVID-19 (that we would like to observe) as it is well documented that hospital
capacity was deliberately increased to accommodate surges in the number of hospitalizations
and that fewer people went to the hospital as regular admissions were discouraged and elective
treatments postponed during the pandemic [87–90] which affects the denominator of the hos-
pitalization proportion. However, even though possibly noisy, we investigate the effect of man-
dates on the proportion of hospitalization as it still provides a useful perspective of the effect of
mask mandates on COVID-19 outcomes.
We use the smoothed outcomes measures using 7-day averages and with systematic day-of-
week effects removed. Our outcome data cover all 50 states in addition to the District of
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Columbia and run from Feb 2, 2020 to September 27, 2020 (we cannot obtain data past Sep-
tember for some outcomes such as hospitalization). 43 states and the District of Columbia had
a required state-level mask mandates during this period of data. For the county-level dataset,
we have 857 counties for which we have outcome and control variable data. This is because
county-level datasets are currently not available due to their higher resolution. However, our
857 counties cover a large portion of the country—77% of the US population as shown in S3
Fig in S1 File—and are therefore representative.
We make sure not to use any outcome data that was after a state or county’s mask mandate
was lifted to prevent end-of-treatment effects from biasing our treatment estimates, and we
only use the first mask mandate start and end dates in cases where there were multiple waves
of mandates over the year. Overall, this dataset contains 10,078 data points used for the state-
level event study (described in the next section) and 77,930 data points for the county-level
event study.
Mask adherence data. State-level mask adherence data (how much people are wearing
face masks) became available on September 8, 2020 from the Delphi project [58]. These data
were collected by them via online surveys of more than 1 million U.S. residents and we use
their weighted state-level estimates as our indicator of state-level mask adherence. Their survey
weighing [59] accounts for various factors including adjusting estimates so that they are repre-
sentative of the US population according to a state-by-age-gender stratification, adjusting for
the US Facebook user population, and adjusting for the propensity of a Facebook user to take
the survey. For the regression of the effect of mask mandates on adherence, we have 323 data
points for the four states, and for the regression of adherence on COVID-19 cases and deaths,
we have 3,841 data points for all states (including D.C.). County-level mask mandate data is
only available until August 4, 2020 which is before mask adherence data starts (September 8,
2020), making the use and investigation of county level adherence data not possible.
International mask norm and adherence data. International mask norm and adherence
data was obtained from the COVID-19 Beliefs, Behaviors and Norms Survey [39] which col-
lected 847 thousand online Facebook survey responses in 51 languages, from 69 countries. The
survey was deployed in 18 waves starting on July 7, 2020 and ending in March 2021. Each sur-
vey response comes with a weight that allows for representativeness of estimates at the country
and wave-level. Weights are calculated to account for a variety of biases including demograph-
ics (age bracket and gender) of the respondents compared to the census data in each country
(through post-stratification), and response or non-response behaviors. The reported design
effects due to both the non-response and post-stratification weighting was below four. In our
analysis, we only use survey responses where the full survey was completed (including the
demographic part) and where the mask-related questions we use in our analysis were com-
pleted. Overall, we have 847,398 data points covering 18 waves of survey deployment. The sur-
vey dataset (de-identified) is available to academic and non-profit researchers upon
completion of the Facebook Data Use Agreement at https://dataforgood.fb.com/docs/
preventive-health-survey-request-for-data-access/.
Control variables. We control for a large number of variables in our analyses: we use res-
taurant and recreation, grocery and pharmacy, parks, transit stations, workplace and residen-
tial percentage change from baseline (using the median day-of-the-week value from the first 5
weeks of January and February) mobility data from Google’s COVID-19 Community Mobility
Reports [91]; new tests per 100K population from the COVID Tracking Project [92], and tem-
perature and precipitation data from NOAA’s Weather and Climate Toolkit [93]. For interna-
tional mask norm and adherence, we obtain COVID-19 country-level confirmed cases and
confirmed deaths from the Our World in Data initiative [60].
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Because we also include the squared values of each mobility indicator as a proxy for social
contacts [43, 44], we first re-scale the Google mobility index [91] as such: re-scaled mobility =
(mobility + 100)/100. This is done to prevent the issue of Google’s the square of negative
mobility indicator values to be the same as the positive values.
Analysis
Event study framework. We build on recent work using event study designs to estimate
treatment effects in the context of the COVID-19 pandemic: the effect of NPIs (e.g. business
closures and stay-at-home orders) on the volume of online searches related to unemployment
[94], on social distancing in the US [95], on the airline industry [96], on stock markets world-
wide [97–100], and on COVID-19 cases at the county level [101].
An event study design allows us to estimate the treatment effect associated with mask man-
dates on COVID-19 outcomes on each day following the introduction of the mandate relative
to the day prior to its introduction. Our geographical unit of analysis are states to minimize
peer-effects of neighboring counties’ mandates due to the underlying interdependence
between county mobility patterns [47], as well as the fact that people living in one county often
have to travel to a different county to get medical care, resulting in inconsistent accounting of
COVID-19 health outcomes at the county level [48]. By contrast, patients rarely cross state
borders to get medical help as health insurance in the US is predominantly state-based. We
include a robustness check by running our analysis at the county-level using a dataset of
county-level only mask mandates [46] to investigate county-level heterogeneity.
The event study framework is well-suited for our purposes as it allows for the same state to
be used both as treatment and control based on the timing of a mandate, which is critical
because states introduced mask mandates at different dates over the course of the pandemic
[94]. While the first mandates were implemented in April, the latest ones occurred in Novem-
ber, some states still having had no mandate as of this writing (see S3 Table in S1 File).
Event study specification. Our specification is:
X
40
0
Co;t;s gt 1ðrs;t ¼ tÞ þ as þ at þ bs t þ Xt;s þ Co;t d;s þ Co;t;s þ t;s ð1Þ
g¼ 10
The outcome is Co,t,s (o2 {new deaths due to COVID-19, new confirmed cases of COVID-
19, hospitalization proportion due to COVID-19}) for each day t in state (or county when
investigating county-level mandates) s. We make sure to only use new cases, hospital admis-
sions and deaths each day, as opposed to cumulative numbers. Because some states such as
New York had a much higher COVID-19 incidence than other states, we used population-
scaled (per hundred thousand people) outcomes, and we further Z-normalize the daily value
of each cases and deaths using state-specific means and standard deviations (hospitalization is
already a percentage and therefore does not require normalization). Due to these transforma-
tions, our treatment effect estimates are expressed in units of standard deviations. We also
ensured that all our variables have been smoothed and de-trended (for day of the week varia-
tions). t,s represent the model residuals.
The goal is to estimate treatment effects γτ where τ is the number of days before or after the
start of a mask mandate. 1 is the delta function such that γτ is non-zero only when the number
of days rs,t relative to the start of mask implementation is τ. As is standard in an event study
framework [94], we normalize γτ = −1 = 0 which means that our estimated treatment effects are
to be interpreted relative to the day before the introduction of a mask mandate. We set the
range of days rs,t before or after the introduction of mask mandates to be from -10 to 40
because choosing values earlier than -10 leads to some state no longer having enough data (as
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PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
they implemented mandates very soon after COVID-19 outcome data was available) and 40 to
account for reporting delays and the pathogenesis characteristics of the virus as the interquar-
tile range of the time between symptom onset and death is 8–26 days [102–109]. The clock
time interval for our data is February 1 to September 27, 2020, the time during which data for
all outcomes and controls was available.
αs and αt are state and time fixed effects, βs t is a state-specific trend coefficient multiplying
the time trend variable to allow for the fact that different states had different outcome trajecto-
ries (see [110] for more details). We report standard errors clustered at the state-level.
Xt,s are our controls for a number of confounding factors: (a) indicators of human mobility
[91] (number of visits to recreational areas, grocery stores, pharmacies, parks, transit stations,
workplace and residential areas) to control for variations in the spread of the virus due to vari-
ations in mobility and as a proxy for other NPIs such as stay-at-homes (as they impact mobil-
ity); (b) daily new test rate [92] to control for variations in the number of tests being
administered; (c) the start and end dates of stay-at-home and non-essential business closures
(the two broadest and far-reaching NPIs) to estimate the effect of mask mandates without the
bias of other NPI’s; and (d) temperature and precipitation [93] to control for weather-induced
behaviors.
We also include a time-delayed (δ = two weeks) outcome Co,t−δ,s and a growth rate (over
0
the past two weeks) Co;t;s because conditioning on the delayed outcome and growth rate results
in the confounding path due to behavior change to be blocked based on the general causal
graphical model of [35]. A delay of two weeks was chosen based on both our sensitivity analysis
and that the estimated behavioral lag in the estimation of the causal graphical model of [35].
Additionally, as will be shown in the results section, shorter or longer delays would be at odds
with the pathogenesis characteristics of the virus [102–109]. As a robustness check, we explore
the effect of including or not delays on the event study estimation in S1 Fig in S1 File.
While political affiliation has been shown to correlate the propensity to wear a mask [111],
this effect should captured in the state fixed effect since we do not expect political affiliation to
change significantly during our observational period.
We use the R package’s lfe’s regression function felm to run this specification as it uses
the method of alternating projections to speed up regression on our large datasets [112].
As a robustness check, we repeat employ a similar event study specification to verify that
the decrease in COVID-19 outcomes we observe is not due to a decrease in test rate (per 100K
of population. We use the following specification:
X
20
TRt;s gt 1ðrs;t ¼ tÞ þ as þ at þ a0s t þ Xt;s
0
þ t;s ð2Þ
g¼ 5
0
where TRt,s is the daily new test rate, and all other variables are similar to above, with Xt;s being
our controls without test rate (since it is now the dependent variable).
Mask mandate and mask adherence. Using daily mask adherence data from September 8
to November 30, 2020, we estimate a similar event study specification to the above using mask
adherence As,t as the dependent variable. Additionally, we control for deaths and confirmed
cases because we want to control for the fact that the more case and deaths people see, the
more they might be likely to wear masks (although we do not observe much of a difference
when omitting them). Again, we control for a number of factors: testing rate due to its poten-
tial effect on mask usage as more people might wear masks if they are more regularly tested,
and mobility as we want to control for the fact that perhaps the more people leave their house,
the more they might wear masks.
PLOS ONE | https://doi.org/10.1371/journal.pone.0252315 June 23, 2021 19 / 26
PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
We implement a multi-linear regression of COVID-19 outcomes on compliance, controlled
by a number of factors, as specified below:
00
Co;t;s As;t þ Xt;s þ t;s ð3Þ
00
where Co,t,s are the daily t COVID-19 outcomes o2 {deaths, cases} and Xt;s are the mobility,
new test rate and weather controls.
International mask adherence and norms. Here, we use multi-linear regression to esti-
mate the effect of mask norms or mask adherence on COVID-19 case and death outcomes for
each wave w:
000
Co;t;k Qt;k þ Xt;s þ t;s ð4Þ
where Co,t,k are the daily t COVID-19 outcomes o2 {deaths, cases} for each country k, for ques-
000
tion q, controlling for mobility and new test rate Xt;s . One separate model estimation is run for
each wave w, each outcome o and each question q2 {mask norms, mask adherence}.
More details about survey weights and the dataset can be found in the section. We also ver-
ify that the weighted average mask adherence, weighted average mask attitude and total num-
ber of responses did not change significantly over time during the survey as shown in S9-S11
Figs in S1 File.
We use a complex survey regression (using R’s survey package) with countries as survey
strata and individual anonymized survey responses as clusters. We only select responses that
finished the whole survey (including several attention check questions), and we remove
responses with missing values for the questions under consideration, and also those with miss-
ing weights (<1% of responses).
The 69 countries in our dataset are: United Kingdom, Poland, Italy, Germany, Japan,
Argentina, France, Turkey, Mexico, Colombia, United States, Pakistan, Romania, Indonesia,
Philippines, Egypt, Malaysia, Bangladesh, Nigeria, Brazil, Thailand, Vietnam, India, Nether-
lands, Azerbaijan, Australia, South Africa, Canada, Estonia, Senegal, Afghanistan, Tanzania,
Angola, Ecuador, Georgia, Mongolia, Peru, Algeria, Mozambique, Bolivia, Portugal, Iraq,
Cameroon, Morocco, South Korea, Uruguay, Honduras, Nepal, Sri Lanka, United Arab Emir-
ates, Spain, Cote d’Ivoire, Myanmar, Chile, Venezuela, Guatemala, Trinidad & Tobago, Sudan,
Kenya, Jamaica, Ghana, Uganda, Ukraine, Taiwan, Singapore, Cambodia, and Kazakhstan.
Supporting information
S1 File.
(PDF)
Acknowledgments
The authors would like to thank Seth Benzell, Austin Wright, Avinash Collis, Victor Chernoz-
hukov and Christos Nicolaides for their data and research design help; Ho Yin Au for software
support; Alia Braley for her proofreading help, Sara Mercuro for manuscript submission, and
Alexander James Phillips and Joi Ito for their helpful comments.
Author Contributions
Conceptualization: Dhaval Adjodah, Karthik Dinakar.
Data curation: Dhaval Adjodah.
Formal analysis: Dhaval Adjodah, Matteo Chinazzi, Samuel P. Fraiberger.
PLOS ONE | https://doi.org/10.1371/journal.pone.0252315 June 23, 2021 20 / 26
PLOS ONE Impact of COVID-19 mask mandates, adherence, and attitudes
Funding acquisition: Dhaval Adjodah.
Investigation: Dhaval Adjodah.
Methodology: Dhaval Adjodah.
Project administration: Dhaval Adjodah.
Resources: Dhaval Adjodah.
Software: Dhaval Adjodah.
Supervision: Dhaval Adjodah.
Validation: Dhaval Adjodah.
Visualization: Dhaval Adjodah.
Writing – original draft: Dhaval Adjodah, Karthik Dinakar, Matteo Chinazzi, Samuel P. Frai-
berger, Alex Pentland, Samantha Bates, Kyle Staller, Alessandro Vespignani, Deepak L.
Bhatt.
Writing – review & editing: Dhaval Adjodah, Karthik Dinakar, Matteo Chinazzi, Samuel P.
Fraiberger, Alex Pentland, Samantha Bates, Kyle Staller, Alessandro Vespignani, Deepak L.
Bhatt.
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