Association of country-wide coronavirus mortality with demographics, testing, lockdowns, and public wearing of masks (Update June 15, 2020).

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Abstract
Background. Wide variation between countries has been noted in per-capita mortality from the disease (COVID-19) caused by the SARS-CoV-2 virus. Determinants of this variation are not fully understood. Methods. Potential predictors of per-capita coronavirus-related mortality in 198 countries were examined, including age, sex ratio, obesity prevalence, temperature, urbanization, smoking, duration of infection, lockdowns, viral testing, contact tracing policies, and public mask-wearing norms and policies. Multivariable linear regression analysis was performed. Results. In univariate analyses, the prevalence of smoking, per-capita gross domestic product, urbanization, and colder average country temperature were positively associated with coronavirus-related mortality. In a multivariable analysis of 194 countries, the duration of infection in the country, and the proportion of the population 60 years of age or older were positively associated with per-capita mortality, while duration of mask-wearing by the public was negatively associated with mortality (all p<0.001). The prevalence of obesity was independently associated with mortality in models which controlled for testing levels or policy. International travel restrictions were independently associated with lower per-capita mortality, but other containment measures and viral testing and tracing policies were not. In countries with cultural norms or government policies supporting public mask-wearing, per-capita coronavirus mortality increased on average by just 8.0% each week, as compared with 54% each week in remaining countries. On multivariable analysis, lockdowns tended to be associated with less mortality (p=0.43), and increased per-capita testing with higher reported mortality (p=0.70), though neither association was statistically significant. Conclusions. Societal norms and government policies supporting the wearing of masks by the public, as well as international travel controls, are independently associated with lower per-capita mortality from COVID-19.
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1
Association of country-wide coronavirus mortality with demographics, testing,
lockdowns, and public wearing of masks (Update July 2, 2020).
Christopher T. Leffler, MD, MPH.1,2 *
Edsel Ing MD, MPH, CPH, MIAD.3
Joseph D. Lykins V, MD.4,5
Matthew C. Hogan, MS, MPH.6
Craig A. McKeown, MD.7
Andrzej Grzybowski, MD, PhD, MBA.8,9
1. Department of Ophthalmology. Virginia Commonwealth University. Richmond, VA 23298.
chrislefflermd@gmail.com
2. Department of Ophthalmology. Hunter Holmes McGuire VA Medical Center, Richmond, VA.
3. Department of Ophthalmology & Vision Sciences, University of Toronto.
4. Department of Internal Medicine, Virginia Commonwealth University. Richmond, VA 23298.
5. Department of Emergency Medicine. Virginia Commonwealth University. Richmond, VA 23298.
6. School of Medicine, Virginia Commonwealth University, Richmond, VA 23298.
7. Bascom Palmer Eye Institute, University of Miami, Miller School of Medicine.
8. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.
9. Institute for Research in Ophthalmology, Poznan, Poland.
*Corresponding author: Christopher T. Leffler, MD, MPH.
Department of Ophthalmology. Virginia Commonwealth University. 401 N. 11th St., Box 980209,
Richmond, VA 23298. chrislefflermd@gmail.com.
July 2, 2020.
None of the authors has any conflicts of interest to disclose.
2
Abstract.
Background. There is wide variation between countries in per-capita mortality from
COVID-19 (caused by the SARS-CoV-2 virus). Determinants of this variation are not
fully understood.
Methods. Potential predictors of per-capita coronavirus-related mortality in 198
countries were examined, including age, sex ratio, obesity prevalence, temperature,
urbanization, smoking, duration of infection, lockdowns, viral testing, contact tracing
policies, and public mask-wearing norms and policies. Multivariable linear regression
analysis was performed.
Results. In univariate analyses, the prevalence of smoking, per-capita gross domestic
product, urbanization, and colder average country temperature were positively
associated with coronavirus-related mortality. In a multivariable analysis of 194
countries, the duration of infection in the country, and the proportion of the population
60 years of age or older were positively associated with per-capita mortality, while
duration of mask-wearing by the public was negatively associated with mortality (all
p<0.001). The prevalence of obesity was independently associated with mortality in
models which controlled for testing levels or policy. International travel restrictions were
independently associated with lower per-capita mortality, but other containment
measures and viral testing and tracing policies were not. In countries with cultural
norms or government policies supporting public mask-wearing, per-capita coronavirus
mortality increased on average by just 7.2% each week, as compared with 55.0% each
week in remaining countries. On multivariable analysis, lockdowns tended to be
associated with less mortality (p=0.41), and increased per-capita testing with higher
reported mortality (p=0.55), though neither association was statistically significant.
Conclusions. Societal norms and government policies supporting the wearing of masks
by the public, as well as international travel controls, are independently associated with
lower per-capita mortality from COVID-19.
3
Introduction.
The COVID-19 global pandemic caused by infection with severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented a major public health
challenge. For reasons that are not completely understood, the per-capita mortality
from COVID-19 varies by several orders of magnitude between countries.1 Numerous
sources of heterogeneity have been hypothesized. Higher mortality has been observed
in older populations and in men.2,3 Patient-level behaviors, such as smoking, might also
have an impact.3 Other potentially relevant factors include economic activity, and
environmental variation, such as temperature.4 More urban settings and increased
population density would be expected to enhance viral transmission.5
In addition, public health responses to the COVID-19 pandemic may influence
per-capita mortality. Various strategies have been implemented, ranging from robust
testing programs to lockdown or stay-at-home orders, to mandates regarding social
distancing and face mask usage. Practices with theoretical benefit, such as social
distancing, stay-at-home orders, and implementation of mandates regarding use of
masks in public spaces, must be assessed quickly, as implementation has the potential
to reduce morbidity and mortality.
Mask usage by the public is postulated to decrease infection by blocking the
spread of respiratory droplets,1 and was successfully implemented during other
coronavirus outbreaks (i.e. SARS and MERS).6 In the context of the ongoing pandemic,
we assessed the impact of masks on per-capita COVID-19-related mortality, controlling
for the aforementioned factors. We hypothesized that in countries where mask use was
either an accepted cultural norm or favored by government policies on a national level,
the per-capita mortality might be reduced, as compared with countries which did not
advocate masks.
Methods.
Data acquisition.
Country-wide coronavirus mortality data was retrieved from the publicly available
Worldometers Database on May 9, 2020.7 Countries were included if either: 1)
coronavirus testing data were available by May 9, 2020,7 or if: 2) testing and lockdown
policies had been graded by the University of Oxford Coronavirus Government
Response Tracker.8-9
Oxford University defined and scored several composite government response
indices. The stringency index was defined in terms of containment policy and public
information.8 The government response index incorporated containment, economic
measures, public information, and testing and tracing policies.8 The containment and
health index was defined in terms of containment measures, public information, and
testing and tracing policies.8
Archived viral testing data for April 2020 were also downloaded.10 The date of
the country’s first reported infection and first death were obtained from the European
Centre for Disease Prevention and Control (which did tabulate worldwide data).11
Mean temperature in each country during the pandemic was estimated using the
average monthly temperature in the country’s largest city from public sources.12,13
4
Online news reports and government statements, including those cited by a
previous review14 and a public database,15 were searched to identify countries in which
the public wore masks early in the outbreak based on tradition, as well as countries in
which the national government mandated or recommended mask-wearing by the public
before April 16, 2020.
For each country, the population,16 fraction of the population age 60 years and
over, and age 14 and under, male: female ratio per country,17 surface area,16,17 gross
domestic product per capita,18 percent urbanization,16,19 adult smoking prevalence20-23
and prevalence of adult obesity24-43 were tabulated. Whether a nation was an isolated
political entity on an island was also recorded.
Statistical analysis.
The prevalence of an infectious process undergoing exponential growth (or
decay) appears linear over time when graphed on a logarithmic scale.1 Therefore, we
postulated that the logarithm of the country-wide infection prevalence would be linearly
related with the duration of the infection in each country. In addition, our analysis
postulated that deaths from coronavirus would follow infections with some delay.
On average, the time from infection with the coronavirus to onset of symptoms is
5.1 days,44 and the time from symptom onset to death is on average 17.8 days.45
Therefore, the time from infection to death is expected to be 23 days.1,46 These
incubation and mortality times were prespecified.1,46 Therefore, the date of each
country’s initial infection was estimated as the earlier of: 5 days before the first reported
infection, or 23 days before the first death.10,11,47 Deaths by May 9, 2020 would typically
reflect infections beginning 23 days previously (by April 16). Therefore, we recorded the
time from the first infection in a country until April 16. We also recorded the period of
the outbreak: 1) from the mandating of activity restrictions until April 16, and 2) from
when public mask-wearing was recommended until April 16. In addition, we calculated
the mean time-weighted score for each lockdown and testing policy as graded by the
University of Oxford for the duration of the country’s outbreak, from beginning through
April 16.8 For instance, if the school closure score was 1 for half the outbreak and 2 for
the other half, then the mean score was 1.5.
Per-capita mortality can be analyzed as a binary outcome (low or high), or as a
continuous variable. Each approach has strengths and weaknesses.
Analysis of a binary outcome is not unduly influenced by outliers. Countries with
extremely low or high mortality are included in the appropriate group, but the exact
mortality value does not change the results. Moreover, analysis of a binary outcome
facilities clear communication, because one can describe the characteristics of low and
high mortality countries.
On the other hand, per-capita mortality is in fact a continuous variable, and the
separation of countries just below or just above a threshold value is somewhat arbitrary,
or susceptible to chance variation. Analysis of mortality as a continuous variable uses
all the information available, and can appropriately model the exponential growth of an
infection. We view the binary and continuous analyses as complementary. When one
sees that a univariate association is found with both types of analysis, one gains
5
confidence that the association is not an artifact of the analytic method selected.
Therefore, we used both methods for initial univariate selection of variables.
In univariate analysis, characteristics of countries with above-median per-capita
mortality were compared with the remaining (lower mortality) countries by the two-
sample t-test using groups. The odds ratio for being in the high-mortality group was
calculated by logistic regression. In addition, logarithm (base 10) of per-capita
coronavirus-related mortality was predicted by linear regression.
Significant predictors of per-capita coronavirus mortality in the univariate analysis
were analyzed by stepwise backwards multivariable linear regression analysis. The
dependent variable was the logarithm (base 10) of per-capita coronavirus-related
mortality. Because of the importance relative to public health, the weeks the country
spent in lockdown and using masks, and per-capita testing levels, were retained in the
model. In addition, because of their biological plausibility and presumed importance,
urbanization, prevalence of obesity, and average ambient temperature were retained in
most of the multivariable models presented below. Statistical analysis was performed
with xlstat 2020.1 (Addinsoft, New York). An alpha (p value) of 0.05 was deemed to be
statistically significant. The study was approved by the Virginia Commonwealth
University Office of Research Subjects Protection.
Results.
We studied coronavirus mortality in 198 countries, of which 183 had testing
data,7 161 had government policies scored by Oxford University,8 and 146 fell into both
categories.
The 99 lower-mortality countries had 1.0 deaths per million population, in
contrast with an average of 94.2 deaths per million population in the 99 higher-mortality
countries (p<0.001, Table 1, Appendix Table A1). The median value was 3.7 deaths
per million population. The same independent variables were found to be statistically
significant on univariate analysis regardless of whether per-capita mortality was
considered a binary or continuous variable, as outlined below (Table 1, Appendix Table
A2). We assumed that island nations might find it less challenging to isolate and
protect their populations. However, 20 of 99 low-mortality countries were isolated on
islands, compared with 27 of 99 high-mortality countries (p=0.32). Country surface area
and population were not associated with coronavirus mortality (Table 1).
Population characteristics.
Countries with older populations suffered higher coronavirus mortality. Countries
with low mortality had on average 8.9% of their population over age 60, as compared
with 18.9% in the high-mortality countries (Table 1). The proportion of the population
which was male was not associated with country-wide mortality (p=0.98, Table 1).
Smoking prevalence was on average 13.8% in low mortality countries and 18.5% in
high-mortality countries (p<0.001, Table 1). The prevalence of obesity was on average
14.7% in low-mortality countries and 24.0% in high-mortality countries (p<0.001, Table
1).
6
Temperature.
Colder countries were associated with higher coronavirus mortality in univariate
analysis. The mean temperature was 22.3 C (SD 7.6 C) in the low-mortality countries,
and 14.0 C (SD 9.1 C) in the high-mortality countries (p<0.001, Table 1).
Economics.
Urbanization was associated with coronavirus mortality in univariate analysis. In
low-mortality countries, on average 53% of the population was urban, as compared with
71% of the population in the high-mortality countries (p<0.001, Table 1). Richer
countries suffered a higher coronavirus related mortality. The mean GDP per capita
was $9,350 in the low-mortality countries, and was $27,380 in the high-mortality
countries (Table 1, p<0.001).
Table 1. Characteristics of countries with low and high per-capita coronavirus mortality
by May 9, 2020 in 198 countries.
Mean (SD)
p value
Odds ratio (95% CI)
Low Mortality
High Mortalilty
Deaths (per million)
--
1.02 (1.18)
94.2 (183.4)
<0.001
Deaths (per capita, log)
--
-6.46 (0.76)
-4.54 (0.64)
<0.001
Duration infection (weeks)
1.23 (1.09-1.38)
6.51 (2.87)
7.84 (2.31)
<0.001
Duration infection without
masks (weeks)
1.43 (1.24-1.66)
5.01 (2.25)
6.81 (2.31)
<0.001
Duration infection without
lockdown (weeks)
1.08 (0.97-1.20)
2.83 (2.93)
3.37 (2.39)
0.16
Temperature, mean (C)
0.90 (0.86-0.93)
22.3 (7.6)
14.0 (9.1)
<0.001
Urban population (%)
1.04 (1.02-1.05)
53.2 (22.4)
70.6 (19.9)
<0.001
GDP per capita ($1000)
1.05 (1.03-1.07)
9.350 (17.140)
27.380 (27.530)
<0.001
Age 14 & under (% of pop.)
0.85 (0.81-0.89)
32.2 (9.7)
20.1 (6.6)
<0.001
Age 60 & over (% of pop.)
1.20 (1.14-1.27)
8.9 (5.3)
18.4 (7.9)
<0.001
Surface area (million km2)
1.08 (0.92-1.26)
0.557 (1.12)
0.804 (2.455)
0.37
Population (million)
0.998 (0.995-1.001)
53.1 (20.1)
24.9 (48.2)
0.18
Prevalence males (%)
1.001 (0.921-1.088)
50.1 (2.1)
50.2 (4.2)
0.98
Smoking prevalence, adult (%)
1.09 (1.04-1.13)
13.8 (7.8)
18.5 (7.7)
<0.001
Obesity prevalence, adult (%)
1.14 (1.10-1.19)
14.7 (9.0)
24.0 (7.3)
<0.001
Tests per cap. (log) by Apr 4
2.99 (1.92-4.68)
-3.73 (1.20)
-2.65 (0.76)
<0.001
Tests per cap. (log) by Apr 16
3.60 (2.31-5.80)
-3.09 (0.87)
-2.31 (0.67)
<0.001
Tests per cap. (log) by May 9
4.69 (2.85-7.73)
-2.76 (0.85)
-1.91 (0.61)
<0.001
Durations run from the estimated date of first infection in the country until 23 days before May 9,
2020 (i.e. April 16), or the stated event (mask recommendation or lockdown). Obesity data available for
194 countries. Testing data available for 135 countries by April 4, 162 countries by April 16, and 183
countries by May 9.
7
Masks: Early Adoption.
The World Health Organization initially advised against widespread mask
wearing by the public, as did the United States CDC.1,48 The WHO reversed course and
recommended masks in public on June 5, 2020.49
Despite these initial recommendations, a number of countries did favor mask
wear by the public early in their outbreak, and such countries experienced low
coronavirus-related mortality (Table 2, Figure 1).50-132 It is likely that in Mongolia50 and
Laos,52 both of which reported no coronavirus-related mortality by May 9, the public
began wearing masks before any cases were confirmed in their countries (Table 2). We
identified 20 countries with recommendations or cultural norms favoring mask-wearing
by the public within 21 days of the estimated onset of the country’s outbreak:1 including
(beginning with those favoring masks earliest in the course of their outbreak):
Japan,53,54 Venezuela, the Philippines,62 Macau,63 Hong Kong,53,64,65 Sierra Leone,
Cambodia,71,72 Vietnam,74 Malaysia,77 Bhutan,79 Taiwan,82 Slovakia, South Korea,53,89
Grenada, Mozambique, Uzbekistan, Thailand,98 and Malawi (Table 2). The average
mortality by May 9 for the 20 early mask-wearing countries was 1.5 per million (SD 2.1).
Sixteen of the 20 were lower-mortality countries (p=0.008).
An additional 9 countries had recommended that the public wear masks by 31
days into their outbreak: Czechia, Zambia, Benin, Sudan, Antigua and Barbuda, Bosnia,
Côte d'Ivoire, Kenya, and El Salvador (Table 2). The average mortality by May 9 for this
group was 10.4 per million (SD 14.2).
8
Table 2. Countries in which masks were widely used by the public or recommended by
the government within 31 days of the estimated local onset of the outbreak, by
timeliness of mask-wearing.
Mask
Delay
(days)
First Case
Date
Mortality
(per
mil.), by
May 9.
Comment.
0
Mar. 10
0.0
Mongolians began wearing masks in January.50
0
Mar. 24
0.0
Health officials in Laos advised mask-wearing by
March 6,51 and the public began wearing masks
even before any cases were reported in the
country.52
5
Jan. 16
4.8
Public use of masks is traditional.48 Surveys
indicate that 64% of adults habitually wore a mask
in Winter.53 Public masking was manifest by Jan.
16 when the first domestic case was announced.54-
56 The government initially recommended masks
when in “confined, badly ventilated spaces”.48 One
survey documented mask wear prevalence over
60% by March 14, increasing to over 75% by Aprail
12.57 In another poll, 62% indicated wearing a
mask in public by March 17, and 76% by April 13,
2020.58
5
Mar. 1360
0.4
President Maduro demonstrated wearing of masks
on live television on March 13 (the day the first
case was confirmed), and required masks on
public transport.59-60 Masks were required in any
public space by March 20.61
5
Jan. 30
6.4
Masks were used extensively as early as Jan. 30.62
In a poll, 60% indicated wearing a mask in public
on Feb. 24, and 82% by March 30.58 Masks were
mandated on April 2.
6
Jan. 22
0.0
Mask use is traditional. By Jan. 23, the
government had implemented a mask distribution
program for the public.63
6
Jan. 2365
0.5
Surgical masks were traditionally used, and also
were recommended on public transport and in
crowded places, on January 24, 2020.48,64 Surveys
indicated that masks were worn by about 73% in
the week of Jan. 21, and by 98% of the public by
mid-February, which persisted into May.66 In
February 2020, 94.8% of pedestrians were
observed to wear masks, and 94.1% believed
mass masking reduces the chance of community
outbreak.67 A poll consistently found that 85% or
more wore masks in public between Feb. 25 and
Apr. 21, 2020.58
6
Mar. 3168
2.3
Masks were recommended in public on April 1.69
Compliance has been incomplete.70
6
Jan. 2771
0.0
Masks were widely used by the public by January
28.72,173
9
Jan. 23
0.0
Masks were widely used by the public by January
27,74,75 and were mandated by the government on
March 16. One survey found the prevalence of
mask wear consistently from 85-90% from March
9
12 to April 14.57 A poll reported 59% wore a mask
on March 23, and over 80% from March 30 to Apr.
20.58 From March 31 to April 6, 2020, 99.5% of
respondents reported using a mask when
outside.76
10
Jan. 25
3.3
Masks were used by the public by January 30.77 A
poll reported 55% wore a mask in public on Feb.
24, 69% on Mar. 23, and 82% on Apr 6.58
10
Mar. 678
0.0
On Mar. 11, the Ministry of Health advised wearing
of masks in “a crowded place”.79
11
Jan. 21
0.3
Use of masks is traditional. By January 24, Taiwan
banned the export of surgical masks.80,81 By
January 27, the government had to limit mask
exports and limit sales from pharmacies to those
needed for personal use.82 On January 28, the
government began releasing 6 million masks daily,
with each resident able to purchase 3 masks
weekly at a set price.80 A poll consistently found
over 80% wore a mask from Feb. 25 to Apr 21,
2020.58
13
Mar. 7
4.8
Masks were mandated in shops and transit on
March 15,83 and more broadly in public on March
25.84
15
Jan. 20
5.0
Use of masks is traditional.48 The alert level was
raised from yellow to orange on Jan. 27.85
Children were advised to wear masks at school by
January 30.86 By Feb. 2, mask sales increased
373 times year-over-year.85 Stores were selling
out of masks by February 3.87 A superspreader
event in mid-February was associated with a
religious group which did not use masks at their
gatherings.88 South Korea initially had trouble
obtaining enough masks, but at the end of
February the government began to control the
distribution of masks to the public.91 On Feb. 22,
the government instructed the wearing of masks in
the epidemic area.85
18
Mar. 2190
0.0
On April 3, the Ministry of Health recommended all
wear a mask, which could be purchased at a
pharmacy, to “prevent asymptomatic people from
transmitting the disease unknowingly”.91 Masks
were mandated outside the home on April 6.92
18
Mar 2293
0.0
Masks were recommended by health authorities on
April 4,94 and were required on public transport or
in gatherings on April 8.95
19
Mar. 1596
0.3
The first coronavirus death was on March 29.
Masks were mandated on March 25.97
20
Jan. 13
0.8
Masks, including N95 masks, were already worn
outdoors in early January to combat smog. The
Thai government was handing out masks and
advising wearing of masks in public to prevent
coronavirus by January 28, 2020.98-101 The
recommendation of cloth masks for the public was
reaffirmed by the Ministry of Public Health on
March 3, 2020.102 Enforcement of a mask
10
mandate on public transport began on March 26.102
One survey reported high mask-wearing: 73% by
Feb. 24, 80% by March 23, and 89% by March
30.58 During March 2020, another survey found
masks were worn “all the time” by 14% of COVID-
19 cases and 24% of controls, and “some of the
time” by 38% of cases and 15% of controls.102
20
Apr. 2
0.2
The first death was on April 7.103 The public was
required to wear masks on April 4.104,105 A survey
in Karonga from April 25 to May 23 found that 22%
of urban residents and 5% of rural residents wore a
mask.106
23
Mar. 1107
25.8
Masks were required in public on March 19.108
24
Mar. 18
0.4
The first death was recorded on April 2. On April 4,
masks were recommended for the public “at all
times” by the Zambian Minister of Health.109 This
spurred the manufacture of cloth masks.110 On
April 16, masks were mandated for the public.111
26
Mar. 16112
0.2
Masks were recommended in public on April 6,113
mandated on April 7,114 and enforced by police
beginning April 8.115
27
Mar. 12
1.5
The first death occurred on March 12. Masks were
dispensed by pharmacists for free in Sudan by
March 16.116.117 A survey from March 25 to April 4
of 2336 adults found that 703 (30.1%) had been to
a crowded area, and 1153 (49.4%) had worn a
mask outside the home in the previous few days.118
28
Mar. 13119
30.6
Masks were required in all public spaces on April
5.120
29
Mar. 5121
31.1
Masks were required in public by March 29.122-123
29
Mar. 11124
0.8
On April 4, senior health officials recommended
masks when in public.125
30
Mar. 12126
0.6
The March 12 case had arrived from the U.S. on
March 5.126 The first death was on March 26, of a
man who arrived in Kenya on March 13.127 Masks
were mandated in Kenya on public transport on
April 2,128 and more broadly in public on April
4.128,129 A survey in Nairobi published on May 5,
2020 found that 89% had worn a face mask in the
previous week, and 73% said they always did so
outside the home.130
31
Mar. 18131
2.6
The first death was reported March 31. Masks
were mandated in public on April 8.132
The delay was the number of days from the start of the outbreak until masks were
recommended by the government or became widespread due to cultural norms. The estimated
start of the outbreak was 5 days before the first infection was reported, or 23 days before the
first death (whichever was first).
11
Masks in Asia.
Throughout much of East, South, and Southeast Asia, masks were worn by the
public as a preventive measure, rather than a policy implemented after evidence
emerged of health system overload (Table 2). The public sometimes implemented
masks before government recommendations were issued.
As the country where the pandemic started, China is a noteworthy case of a
nation which traditionally has favored mask-wearing by the public for respiratory
illnesses, but which did not deploy masks immediately. The first cases in China had
begun by December 1, 2019.133 By the time human-to-human transmission was
confirmed on Jan. 20, 2020, many in Beijing were already wearing masks.134 The
government required masks in public in Wuhan on Jan. 22.135 From Jan. 23-25, thirty
regions in China mandated masks in public.85,136 Masks were ordered throughout
China when around others in public on Jan. 31.137 China suffered a very significant
outbreak in Wuhan, but appears not to have experienced the same level of infection in
other regions. Surveys indicate that the prevalence of public mask wear in China
remained between 82% and 89% between February 24 and April 20.58 Another survey
confirmed mask wear from 80-90% from March 12 to April 14.57 The reported country-
wide per-capita mortality by May 9, 2020 was 3.2 per million population.
For several countries in South or Southeast Asia with mortality lower than in the
West, we did not score the country as mask-wearing in the primary analysis until their
governments issued recommendations to do so. Nonetheless, there is evidence of
significant mask wear by the public before the recommendations: Nepal, India,
Indonesia, Bangladesh, Myanmar, and Sri Lanka.
In Nepal, facemasks are commonly seen in urban centers due to air pollution.138
The first case of COVID-19 in the country was reported on January 13, in a student
returning from Wuhan for winter break.139 However, no subsequent cases were
reported in Nepal until the second week of March.139 By January 29, all students at
some schools were wearing masks.140 By February 3, pharmacies were selling out of
masks due to increased demand.141 With the outbreak, tailors began sewing cloth
masks.107 By February 8, 2020, “a majority” of the public was wearing masks.142 The
recommendation to wear masks in public became more formalized on March 25.143 The
Ministry of Health distributed masks to children and elderly in shelters by March 25.144
A survey in Nepal at the end of March found that 83% of respondents agreed that
asymptomatic people should wear masks to prevent COVID-19 infection.139 As of May
9, Nepal reported no coronavirus-related mortality. We used the March 25
recommendation as the date in the mask analysis, but earlier mask use might have
forestalled the epidemic in Nepal.
In Indonesia, the public scrambled to buy face masks in February before any
cases had been identified in the country.145 The first cases of coronavirus in Indonesia
were announced on March 2,146 and the first death occurred on March 3.147 A poll
reported that the proportion of Indonesian adults wearing a mask in public was 54% on
Feb. 24, 2020, 47% on March 29, 71% on March 30, and 79% on April 13.58 In
Indonesia, masks were mandated in public on April 5.148 By May 9, the per-capita
mortality was 3.5 per million.
12
In India, the first case of coronavirus was diagnosed on January 30.149 The
Health Ministry recommended homemade face masks on April 4, 2020.150 However,
mask wear was high both before and after the recommendation. According to one poll,
masks were worn by 60% of the public from March 12-14, 67% from March 19-21, and
then from 73% to 76% between March 26 through April 12.57 According to another poll,
masks were worn by 43% of the public on March 16, 46% on March 20, 65% on March
27, 71% on April 3, 79% on April 10, and 84% on April 17.58 By May 9, the per-capita
mortality was 1.5 per million.
In Sri Lanka, the public immediatedly bought masks at the end of January when
the first cases were identified.151 Masks were mandated in public on April 11.152 The
per-capita mortality by May 9 was 0.4 per million.
In Myanmar, the first cases of COVID-19 were reported on March 23,153 and the
first death on March 31.154 A study from March 3-20, 2020 found that 72% of adults
reported that they were confident they would wear a surgical mask whenever visiting a
crowded area.155 This study concluded just 12 days after the likely onset of the
outbreak. By May 9, the per-capita mortality in Myanmar was 0.1 per million.
In Bangladesh, from March 11-19, 2020, when students age 17 to 28 were asked
if they were wearing a surgical face mask in public, 53.8% responded “yes” and 6.6%
responded “occasionally”.156 A survey from March 29 to April 29 found that 98.7%
reported wearing a face mask in crowded places.157 By May 9, the per-capita mortality
was 1.3 per million.
Singapore was slower than its Asian neighbors to embrace masks, but when the
government shifted course, the public was ready to respond. On March 27, only 27% of
respondents indicated that they wore a mask.58 On April 3, when the government
announced that they would no longer discourage mask-wearing by the public, and
would instead distribute masks,158-160 37% indicated that they wore a mask.58 By April
10, just one week later, 73% of the public wore a mask.58
Early in the pandemic, masks were noted to be “somewhat common” in
Afghanistan.161 By March 29, 2020, the Taliban had begun distributing masks to the
public in areas under their control.162
In March 2020, 78% of Pakistanis in Sargodha were in favor of wearing a mask
to prevent coronavirus.163 Another survey conducted from April 1-12 indicated that 80%
of Pakistanis believed the government should mandate mask wearing for adults outside
the home.164
Masks in the Middle East.
In parts of the Middle East, masks were embraced by the public even before
government requirements. In the United Arab Emirates, the first cases were reported
on January 29.165 By February 29, mask usage had become “more prominent”, but the
Ministry of Health and Community Protection advised that N95 masks should be
reserved for medical personnel treating coronavirus patients, and could cause
“respiratory illness” if worn by the public.166 Despite this warning, a poll of UAE
residents found that masks were worn by 39% of the public on March 18, and 44% on
March 25.58 On March 27, the government followed the people’s lead, and mandated
13
masks when indoors.167 Subsequently, masks were worn by 63% on April 1 and 79%
on April 14.58 By May 9, the per-capita mortality was 18.7 per million.
In Saudi Arabia, the first case was announced on March 2.168 A poll of Saudi
residents found that 35% wore a mask on March 18, 54% on April 1, and 59% on April
14,58 despite the lack of any official guidance to do so. By May 9, the per-capita
mortality was 6.9 per million.
In Lebanon, the first case was reported on February 21.169 Masks were popular
among the public from mid-March to early April.170,171 By May 9, the per-capita mortality
was 3.8 per million.
In March 2020 in Egypt, 76.4% of adults expressed an understanding of the
value of wearing a mask in public, but only 36.4% agreed that they actually did so.172 At
this time, the government was not mandating masks, but by March 20, prices of masks
had soared, and volunteer organizations were advocating public masking in Egypt.173
In Iran, no infections were announced until February 19, when two deaths were
reported.174 By March 12, satellite imagery demonstrated the digging of mass graves in
Qom.175 In accord with WHO guidelines, the guidance of the Iranian Health Ministry
available on March 24, 2020 advised that the public wear a mask only if symptomatic or
caring for the sick.14 However, a new guidance which recommended universal masking
in gyms, parks, and public transit was issued by the Ministry by March 29,14 an
estimated 62 days after the start of the outbreak (assuming the reported deaths were
really the first). A survey conducted from February 25 to April 25 found that 64% of the
public reported wearing a mask and gloves in crowded places.176 By May 9, the
reported per-capita mortality in Iran was 78.4 per million, though many, even those
within the Iranian government, have questioned the official figures.177-179
In Yemen, 90% of women wear the niqab, which local doctors believe might
reduce transmission of the virus by functioning as a mask.180 By May 9, the per-capita
mortality in Yemen was 0.2 per million.
Government mandates or recommendations for mask wearing by the public were
issued in Kuwait for gatherings on March 23,177 in Israel on April 1,181 and in Bahrain on
April 9.182
Masks in Africa.
As noted above, eight African countries recommended masks within 31 days of
the onset of their outbreak: Sierra Leone, Mozambique, Malawi, Zambia, Benin, Sudan,
Côte d'Ivoire, and Kenya (Table 2). The public widely sought masks to wear early in the
outbreak in Gambia.183,184
In Ethiopia, 75.7% of chronic disease patients surveyed from March 2-April 10,
2020 agreed that it was important to wear a mask outside the home to prevent infection
with coronavirus.185 A survey from March 20-24 found that 87% of the public believed
wearing a mask could prevent spread of the virus, but only 14% had done so in the few
days before the survey.186 Masks were mandated in public on April 11.187 In a survey
in that country from April 15-22, 84% believed a mask could provide protection from
coronavirus, 137 people (40%) had gone to a crowded place after the onset of the
14
pandemic, and 82 people (24%) had worn a mask outside the home.188 By May 9,
Ethiopia had reported no deaths from coronavirus.
In Cameroon, the first cases of coronavirus were identified on March 6.189 From
March 10-18, a study found that 93.5% of the public viewed the wearing of face masks
as protective, and 21.7% had already purchased them.189 A study in Northern
Cameroon conducted from March 1-28 found that only 13% wore a mask outside the
home.190 A survey in Cameroon conducted from April 1 to 25 found that 83.6% reported
wearing a mask at gatherings.191 Masks were mandated in public in Cameroon on April
13.192 By May 9, the per-capita mortality was 4.1 per million.
In a city in the Democratic Republic of the Congo not yet affected by the
pandemic at the time of a survey conducted from April 17 to May 11, 61% of
respondents were aware of the value of wearing a face mask, 27% reported wearing a
face mask since the pandemic began, and 65% felt that wearing a face mask was
difficult.193
In Ghana, a study from March 27 to 29 of 43 public transport stations found that
masks were worn by many people at one station, worn by a few people at 27 stations,
and not worn at the remainder.194 Ghana required masks to be worn in public on April
25.195
Masks were required in public in Nigeria on April 14.196,197 A study in Nigeria
from May 7 to 18 found that 65% of respondents had worn a mask outside the home in
recent days.198
In South Africa from April 8-24, 2020, 85.6% of the public agreed that wearing a
mask could help to prevent coronavirus infection.199 South African health officials
recommended mask wear in public on April 10.200
In addition, government mandates or recommendations for mask wearing by the
public were issued in: Mauritius on March 31;200 Tunisia202 and Morocco203 on April 6;
Gabon on April 10;204 Equatorial Guinea on April 14;205 and Libya on April 16.206
Masks in Europe.
Most countries in Europe and North America failed to embrace masks early in
their outbreaks, and only adopted mask policies after signs of health system overload
became apparent. Only two countries in Europe appear to have had government
recommendations for the public to wear masks within 31 days of the onset of their
outbreak: Slovakia and Czechia (Table 2).
The first country in Europe to be strongly affected by the outbreak was Italy,
which reported its first cases on January 31, among a family who arrived from China on
January 23.207 By March 10, doctors in Lombardy indicated that all intensive care beds
were taken, and the system did not have enough respirators for the affected.208 A poll
found that only 26% of Italians wore a mask in public on March 11, but, with the rising
health system overload, 59% did so on March 1958at least 60 days from the local
onset of the outbreak. Another poll confirms that the prevalence of mask wear
exceeded 50% for the first time from March 19-21.57 Lombardy (April 5) and Tuscany
(April 6) required the public to wear masks in early April.209 A poll found that 85% of
15
Italians wore a mask in public on April 16.58 By May 9, the per-capita mortality in Italy
was 502.7 per million.
The next country to suffer was Spain, which reported its first case on January
31,210 and experienced its first death from the virus on February 13.211 The prevalence
of mask wear among the Spanish public was 5% on March 12, 25% on March 19, 42%
on March 25, and 56% on April 858potentially 72 days after the entry of the virus into
the country. Masks were mandated when in transit beginning April 11.212 Mask wearing
in public had climbed to 65% by April 16.58 According to another survey, the prevalence
of mask wear was 50% by March 21, 53% by April 4, and 61% by April 12.57 The per-
capita mortality by May 9 was 566.3 per million.
In France, the first case of coronavirus was reported on January 24,213 and the
first death on February 14, of a man who arrived from China on January 16.214 A poll
found on March 10 that only 5% of those in France wore a mask in public.58 This
number increased to 22% on March 27 and 25% on April 3,58 the day that the Académie
Nationale de Médecine announced that masks should be compulsory in public215at
least 72 days into their outbreak. Polls indicated that mask wear among the public
climbed to 38% on April 10, and 43% on April 17.58 Mask wear below 50% in early April
was confirmed in another survey.57 By May 9, the per-capita mortality in the country
was 403.1 per million.
In Germany, the first case of COVID-19 was reported on January 27. The patient
had contact with a colleague visiting from China beginning January 19.216 By March 30,
only 7% of the public reported wearing a mask in public.58 On March 31, the city of
Jena mandated use of masks by the public.217 The Robert Koch Institute recommended
that the public wear masks on April 1218at least 70 days from the onset of the
outbreak. Masks were worn by 14% of the public on April 6, 17% on April 13, and 24%
on April 20.58 Another survey confirms mask wear at or below 20% in March and early
April.59 By May 9, the per-capita mortality was 90.1 per million.
In the United Kingdom, the first cases of coronavirus were reported on January
31.219 Here, 2% of the population wore a mask by March 20, and 11% by April 17.58
Another survey confirms mask wear below 20% from March 12 to April 12.59 By May 9,
the per-capita mortality was 465.3 per million.
In the Scandinavian countries of Sweden, Norway, Denmark, and Finland, polls
repeatedly showed masks to be worn by 10% or less of the population from March 16
through April 19.58 Finland began recommending that the public wear masks on April
14,220 and therefore was scored as falling under a mask recommendation for just 3 days
in this study (April 14 to 16).
In Poland, the health minister announced on April 9 that a public mask mandate
would go into effect on April 16, and mask vending machines began to be installed.221
In Poland, from April 12-14, 2020, 60.4% of Polish students age 18 to 27 wore a face
mask in the previous 7 days.222 By May 9, the per-capita mortality was 20.7 per million.
The first cases of coronavirus in Russia were reported on January 31, 2020.223
In Russia, the prevalence of mask wear among the public was 11% by March 14, 19%
by March 21, 36% by March 28, and 57% by April 45769 days after the estimated start
of the outbreak. Mask wearing prevalence had increased to 59% by April 12.57 By May
9, the per-capita mortality was 12.5 per million.
16
In Serbia, in April 2020, 60% of the public agreed they were willing to wear a
mask during a pandemic, and respondents on average answered 3.25 (SD 1.6) on a 1
to 5 scale when asked if they wore masks, where 4 represented “agree” and 5
represented “strongly agree”.224
Some additional Western governments mandated or recommended mask-
wearing in public in March 2020. By March 29, masks were mandated in indoor public
spaces in Slovenia.225 In Austria, a mandate to wear masks in shops was announced
on March 30, with the expectation that masks would be available by April 1.226 In
addition, the requirement to wear masks on public transit was announced there on April
6.227 Masks were recommended for the public in Bulgaria on March 30.228 Government
mandates or recommendations for mask wearing by the public were issued by April 16
in: Turkey,229 Cyprus,230 and Ukraine231 on April 3; and Estonia on April 5.232 In
Lithuania, masks were recommended for the public on March 26,233 and mandated on
April 8.234
Masks in the United States and Canada.
The earliest case of COVID-19 in the United States was a 35-year-old man who
returned from China to Washington state on January 15, 2020, and presented at an
urgent care clinic on January 19.235 In the United States, the prevalence of mask wear
in public was 7% on March 2, 5% on March 17, and 17% on March 30. The U.S. C.D.C.
began recommending that asymptomatic people wear a mask in public on the evening
of April 3236at least 79 days after the virus had entered the country. Subsequently,
the prevalence of mask wear was 29% on April 6, 49% on April 13, and 58% on April
20.58 Another survey found that the prevalence of mask wear was 32% from April 2-4,
and 50% from April 9-12.57 According to another survey, from April 14-20, 36% of U.S.
adults always wore a mask outside the home, 32% did so sometimes, and 31% never
did.237 By May 9, the per-capita coronavirus-related mortality was 241.8 per million.
In Canada, the prevalence of mask wear was 6% on March 17, and 18% on April
6,58 when the government announced that masks were now recommended in public.238
Uptake was slow, with mask wearing still just 16% on April 13, and 31% on April 20.58
Another survey confirms mask wear below 30% in March and early April.57 By May 9,
the per-capita coronavirus-related mortality was 124.3 per million.
Masks in Australia.
In Australia, surveys of the public indicated that 10% wore a mask by March 15,
which gradually increased to 27% by April 19.58 Another survey confirms mask wear
below 25% in March and early April.57
17
Masks in Latin America and the Caribbean.
Masks were an accepted preventive measure in some parts of Latin America and
the Caribbean (though not all). Four countries in the region recommended masks by 31
days into their outbreak: Venezuela, Grenada, Antigua and Barbuda, and El Salvador
(Table 2).
Government mandates or recommendations for mask wearing by the public were
issued by April 16 in multiple countries, including: Cuba239 on April 2; Peru on April 3;240
Honduras on April 6;241 Paraguay242 and Panama243 on April 7; Guatemala on April
9;244 Sint Maarten on April 14;245 and the Dominican Republic on April 16.246
In Trinidad and Tobago, masks were recommended early in the outbreak by the
Health Minister and Chief Medical Officer.247 The recommendation was made official on
April 5.248 Compliance was almost immediate, as many people were already wearing
masks, and shops would not provide service without them (Vijay Naraynsingh, personal
communication, June 30, 2020).
On April 3, a reporter in Bogotá noted that 90% of the people on the street were
wearing face masks.249 On April 4, the government of Colombia mandated masks on
public transport and shops.132,250-251
On April 6, the Minister of Health in Chile announced that masks would be
mandatory on public transport starting April 8.252 Due to the shortage of medical masks,
the public was invited to make their own out of cloth.252
Surveys indicate that in Mexico, the prevalence of public mask wear increased
steadily from 17% on March 17 to 37% on April 6, 46% on April 13, and 60% on April
20.58 According to another survey, the prevalence was 31% by March 14, 36% by
March 21, 46% by April 4, and 58% by April 9.57 By May 9, the per-capita mortality was
26.0 per million.
Ecuador did not require masks early in their outbreak. The first case of COVID-
19 in Ecuador was reported on February 29 in a woman who had arrived from Spain on
February 14.253 The first death was reported on March 13.254 By April 3, it was noted in
Guayaquil that mortuary facilities were overwhelmed, and bodies were being left on the
streets.255 On April 7, the Interior Minister of Ecuador announced that face masks were
mandatory in public256at least 48 days (and possibly 53 days) after the local onset of
the outbreak. By May 9, the reported mortality was 97.3 per million.
The first case of COVID-19 in Brazil was reported on February 26.257 In Brazil,
the prevalence of mask wear in public was 25% by March 14, 28% by March 21, 39%
by April 4, and 56% by April 125750 days after the virus is estimated to have arrived in
the country. By May 9, the per-capita mortality was 50.1 per million.
Graphical Analysis of Mask Effect.
Before the formal statistical analysis, we graphically illustrate the effect of mask
wear (Figures 1, 2). The first figure demonstrates the effect of early mask usage
(Figure 1). In the countries not using masks by April 16, the per-capita mortality by May
9 rises dramatically if the infection has persisted in the country over 60 days (Figure 1,
red line). On the other hand, countries in which a mask was used from 16 to 30 days
18
after infection onset had per-capita mortality several orders of magnitude less by May 9
(Figure 1, orange line). When countries recommended masks within 15 days of the
onset of the outbreak, the mortality was so low that the curve is difficult to distinguish
from the x-axis (Figure 1, blue line).
Figure 1. Per-capita mortality by May 9 versus duration of infection according to
whether early masking was adopted. Data grouped by whether country did not recommend
masks by April 16, 2020 or recommended them more than 60 days after outbreak onset (red
line); recommended masks 16 to 30 days after onset of the country’s outbreak (orange line); or
recommended masks (or traditionally used masks) within 15 days of the outbreak onset (blue
line close to the x-axis). Country mortality was averaged for the following country groups of
infection duration: 0-15 days, 16-30 days, 31-45 days, 46-60 days, 61-75 days, 76-90 days, 91-
105 days. For instance, per-capita mortality for all non-mask or late-masking countries with
infection duration between 61 and 75 days was averaged, and graphed at the x-value 68 days.
Data for graph derived from 198 countries.
For instance, for the early mask-wearing countries in which the infection had
arrived by January (Thailand, Japan, South Korea, Taiwan, Macau, Hong Kong,
Vietnam, Cambodia, Malaysia, the Philippines), the virus was present in the country by
80 or more days by April 16 (Table 2). If masks had no effect, we might have expected
these countries to have a mortality well over 200 deaths per million (Figures 1, 2).
19
Instead, the mortality for these 10 regions was 2.1 per million (SD 2.5, Table 2)
approximately a 100-fold reduction.
On the other hand, the mortality curves for mask and non-mask countries look
reasonably similar if they are compared based on the period of their outbreaks without
masks (Figure 2). The red line in the figure displays the mean per-capita mortality as a
function of the duration of infection in the countries which did not recommend masks by
April 16 (Figure 2). Countries are averaged in two-week (14-day) groups. For instance,
the per-capita mortality data from all countries with an outbreak which had lasted
between 1 and 14 days by April 16 are averaged and displayed together. Not
surprisingly, the longer the outbreak lasts in the country, the higher the mortality.
Beyond 8 weeks (56 days), the per-capita mortality sharply increases. Data from
countries which recommended masks before Apr. 16 are displayed with the thick blue
line (Figure 2). Here, the x-axis is not the total time of the infection, but rather the
period of the infection before masks were recommended. Of note, the curves
demonstrate the same general behavior. The mortality in the mask countries by 11
weeks is lower by a factor of two, but not by a factor of 100. Thus, when compared on
their mask-free periods, mask and non-mask countries appear reasonably similar.
Figure 2. Per-capita mortality by May 9 as a function of the period of the country’s
outbreak without mask recommendations or norms. Data grouped by whether country
recommended (or traditionally used) masks by April 16 (blue line), or not (red line). Data for
graph derived from 198 countries.
In order to provide some graphical idea of the scatter of the data when
exponential growth is assumed, we graphed per-capita mortality by May 9 on a
20
logarithmic scale as a function of the duration of the country’s outbreak not using masks
in all 198 countries (Figure 3). This simple model explained 28.0% of the variation in
per-capita mortality.
Figure 3. Scatter-plot of per-capita mortality by May 9, 2020 as a function of the period
of the country’s outbreak without mask recommendations or norms. The dotted line
represents the best fit using least-squares linear regression. Data for graph derived from 198
countries. Start of outbreak defined as 5 days before first case reported, or 23 days before the
first death (whichever was earlier).
21
Initial multivariable analyses.
An initial multivariable analysis was conducted including all 198 countries. By
multivariable linear regression, significant predictors of the logarithm of each country’s
per-capita coronavirus mortality included: duration of infection in the country, duration of
wearing masks (p<0.001), percentage of the population over age 60, and urbanization
(all p0.009, Appendix Table A3). The model explained 47.2% of the variation in per-
capita mortality (Table A3).
We wanted to determine whether the association of mask use with lower
mortality was simply an artifact of the definition of the start of the outbreak as 5 days
before the first case or 23 days before the first death (whichever was earlier).
Therefore, we ran the multivariable model for all 198 countries with the outbreak start
defined simply as the first case date (Appendix Table A4). In this model, each week of
the outbreak without masks was associated with a 38.5% increase in per-capita
mortality. On the other hand, each week a country wore masks was associated with a
reduction in mortality of 4.6% because 1.3847(0.6887) = 0.954 (Table A4). The model
explained 43.8% of the variation in per-capita mortalityi.e. slightly less than with the
original definition of outbreak start.
We also prepared a multivariable model to predict the logarithm of per-capita
coronavirus mortality in the 194 countries with obesity data. In this model, lockdown,
obesity, temperature, and urbanization were retained due to their plausibility as
important factors (Table 3).
By multivariable linear regression, significant predictors of the logarithm of each
country’s per-capita coronavirus mortality included: duration of infection in the country,
duration of wearing masks, and percentage of the population over age 60 (all p<0.001,
Table 3). The association of obesity with increased mortality approached statistical
significance (p=0.07, Table 3). When controlling for the duration of infection in the
country, there appeared to be a negative association between time in lockdown and
per-capita mortality, but this association was not statistically significant (p=0.41) (Table
3). The model explained 50.3% of the variation in per-capita mortality.
22
Table 3. Predictors of (log) Country-wide Per-capita Coronavirus Mortality by May 9 by
Multivariable Linear Regression in 194 Countries.
10coefficient
Coefficient (SE)
95% CI
P
Duration in country (wks)
1.5499
0.190 (0.033)
0.126 to 0.255
<0.001
Time wearing masks (wks)
0.6917
-0.160 (0.030)
-0.218 to -0.102
<0.001
Time in lockdown (wks)
0.9376
-0.028 (0.034)
-0.094 to 0.038
0.41
Population, age 60 (%)
1.1144
0.047 (0.010)
0.027 to 0.067
<0.001
Urbanization (%)
1.0128
0.00552 (0.004)
-0.002 to 0.013
0.13
Obesity prevalence (%)
1.0378
0.016 (0.009)
-0.001 to 0.034
0.07
Temperature, ambient (C)
0.9825
-0.00767 (0.009)
-0.025 to 0.009
0.38
Constant
--
-7.714 (0.371)
-8.45 to -6.98
<0.001
Duration of infection in country from estimated date of first infection until 23 days before May 9, 2020
(i.e. April 16). Mask and lockdown durations run from the stated event (mask recommendation or
lockdown) or estimated date of first infection in the country (whichever was later) until 23 days before
May 9, 2020 (i.e. April 16). Model r2=0.503.
In countries not recommending masks, the per-capita mortality tended to
increase each week by a factor of 1.550, or 55.0%. In contrast, in countries
recommending masks, the per-capita mortality tended to increase each week by a
factor of 1.5499 * 0.6917 = 1.072, or just 7.2%. Under lockdown (without masks), the
per-capita mortality increased each week by (1.5499)(0.9376) = 1.453, or 45.3%, i.e.
slightly less than the baseline condition (Table 3).
A country with 10% more of its population living in an urban environment than
another country tended to suffer a mortality 13.6% higher (100.0552 = 1.136, Table 3). A
country in which the percentage of the population age 60 or over is 10% higher than in
another country tended to suffer mortality 195% higher (100.47 = 2.95, Table 3). A
country with a prevalence of obesity 10% higher tended to suffer mortality 45% higher
(100.16 = 1.45, Table 3).
Survey-modified Model.
Surveys of mask wearing by the public during the exposure period were available
for 41 countries (see above). To determine the influence that actual mask-wear, as
opposed to mask policies, might have on the model, we scored countries as mask-
wearing if at least 50% of the public wore a mask, and non-mask wearing if less than
50% of the population did so.
Based on surveys, Canada, Finland, France, Germany, and Malawi were not
considered mask-wearing countries at any time during the exposure period (ending April
16). In contrast, Indonesia was scored as mask-wearing beginning February 24,
Bangladesh beginning March 11, Italy beginning March 19,58 Myanmar beginning March
20, Spain58 and India beginning March 21,57 Saudi Arabia beginning April 1,58 Russia
beginning April 4, Singapore beginning April 10,58 and the United States, Brazil and
Mexico beginning April 12.57,58
In this survey-modified model in 198 countries, duration of the outbreak, duration
of mask wear, proportion of the population age 60 or over, and urbanization were all
23
significant predictors of per-capita mortality (all p<=0.01, Appendix Table A5). Each
week that the infection persisted in the country without masks was associated with a
50.6% increase in per-capita mortality. On the other hand, when masks were worn, the
per-capita mortality only increased by 3.1% weekly, (1.5056)(0.6845) = 1.031,
(Appendix Table A5). The model explained 45.9% of the variance in mortality.
Numbers of Viral Tests.
Among the 183 countries with viral (PCR) testing data by May 9, per-capita
testing performed at all 3 time points was positively associated with per-capita mortality
in univariate analysis (all p<0.001, Table 1). By May 9, 2020, low-mortality countries
had performed 1 test for every 575 members of the population, while high-mortality
countries had performed 1 test for every 81 members of the population (p<0.001, Table
1). To the multivariable model (Table 3), we added testing by May 9, using data from
179 countries with both testing and obesity data. Duration of infection in the country,
the duration that masks were recommended, and age at least 60 years continued to be
significant predictors of per-capita mortality (all p≤0.001, Appendix Table A6). The
model explained 52.1% of the variation in per-capita mortality. Each week the infection
persisted in a country without masks was associated with a 56.7% increase in per-
capita mortality (Table A6). In contrast, in countries where masks were recommended,
the per-capita mortality tended to increase each week by 10.7% (because
(1.5673)(0.7060) = 1.107, Table A6). In this model, the prevalence of obesity was
significantly associated with country-wide per-capita mortality (p=0.04). If the
prevalence of obesity increased by 10% (e.g. from 10% to 20% of a population), the
per-capita mortality tended to increase by 58% (Appendix Table A6)
In this model, a 10-fold increase (i.e. one logarithm) in per-capita testing tended
to be associated with a 17.0% increase in reported per-capita mortality, though the
trend was not close to reaching statistical significance (p=0.55, Appendix Table A6).
Duration of the infection in the country, time during the outbreak in which masks were
recommended, and the fraction of the population over age 60 were all still significant
predictors of mortality (all p0.001, Appendix Table A3).
If early testing lowers mortality, one might expect negative regression
coefficients. Testing on both April 16 and May 9 were added to the multivariable model
of Table 3, using data from the 158 countries with both obesity and testing data by
these dates. Per-capita testing (log) by April 16 was not negatively associated with per-
capita mortality (log) by May 9 (coefficient 0.330, 95% CI -0.254 to 0.915, p=0.27).
Likewise, testing on both April 4 (the earliest archived data) and May 9 were
added to the multivariable model of Table 3, using data from the 131 countries with both
obesity and testing data by these dates. Per-capita testing (log) by April 4 was not
significantly associated with per-capita mortality (log) by May 9 (coefficient -0.022, 95%
CI -0.343 to 0.299, p=0.89). Given the coefficient, a 10-fold (one log) increase in early
testing would be associated with a (non-significant) decrease in per-capita mortality of
5.0%.
Only 5 countries had performed over 1 test for every 10 people in the country by
May 9, 2020 (in order of most testing to least): the Faeroe Islands, Iceland, the Falkland
24
Islands, the UAE, and Bahrain. The Faeroe and Falkland Islands reported no
coronavirus-related deaths. The remaining 3 countries had per-capita mortality above
the median value. The highest per-capita mortality among this group was 29.0 per
million population (or 1 in 34,480 people), seen in Iceland.
Containment and Testing Policies.
For 161 countries, containment, testing, and health policies were scored by
Oxford University.8 The following countries with mask policies by April 16 were included
in this analysis, but not in the previous multivariable model, for lack of data on numbers
of tests performed: China, Macau, Cameroon, Sierra Leone, and Sudan. In univariate
analysis, scores for school closing, cancelling public events, restrictions on gatherings,
and international travel controls were significantly associated with lower per-capita
mortality (all p<=0.03, Table 4). Policies regarding workplace closing, closing public
transport, stay at home requirements, internal movement restrictions, public information
campaigns, testing, and contact tracing were not significant predictors of mortality (all
p>0.05, Table 4). Likewise, overall indices of stringency, government response, and
containment and health were not associated with mortality (all p>0.05, Table 4).
25
Table 4. Government policies in 161 countries with low and high per-capita coronavirus
mortality by May 9, 2020.
Mean (SD)
p value
Odds ratio (95% CI)
Low Mortality
High Mortalilty
School closing (0-3)
0.46 (0.26 to 0.82)
2.09 (0.66)
1.83 (0.48)
0.006
Workplace closing (0-3)
1.62 (0.98 to 2.66)
1.14 (0.77)
1.33 (0.47)
0.07
Cancel public events (0-2)
0.32 (0.14 to 0.74)
1.38 (0.45)
1.20 (0.34)
0.008
Restrictions on gatherings (0-4)
0.76 (0.53 to 1.09)
1.99 (0.91)
1.78 (0.82)
0.03
Close public transport (0-2)
0.83 (0.43 to 1.59)
0.62 (0.50)
0.58 (0.45)
0.21
Stay at home requirements (0-3)
1.27 (0.73 to 2.21)
0.81 (0.66)
0.88 (0.44)
0.33
Internal movement restrictions (0-2)
0.71 (0.35 to 1.42)
0.92 (0.52)
0.85 (0.36)
0.06
International travel controls (0-4)
0.44 (0.28 to 0.68)
2.87 (0.78)
2.36 (0.82)
<0.001
Income support (0-2)
40.3 (11.1 to 147.0)
0.13 (0.21)
0.50 (0.40)
<0.001
Debt / contract relief (0-2)
4.44 (2.04 to 9.66)
0.28 (0.39)
0.57 (0.49)
<0.001
Public information campaigns (0-2)
0.63 (0.30 to 1.33)
1.68 (0.39)
1.60 (0.45)
0.31
Testing policy (0-3)
0.84 (0.48 to 1.49)
1.10 (0.60)
1.05 (0.49)
0.22
Contact tracing (0-2)
0.89 (0.54 to 1.47)
1.06 (0.63)
1.02 (0.60)
0.17
Stringency Index (0-100)
0.98 (0.96 to 1.01)
52.2 (15.6)
49.0 (12.4)
0.09
Government response index (0-100)
0.99 (0.97 to 1.03)
44.6 (12.3)
44.5 (11.1)
0.42
Containment & health index (0-100)
0.98 (0.96 to 1.01)
50.9 (14.3)
48.1 (11.9)
0.10
Economic support index (0-100)
1.07 (1.04 to 1.10)
9.8 (12.8)
24.7 (17.4)
<0.001
Government policies were scored by Oxford University.8 Characterization as low or high mortality was
defined by the median for all 198 countries.
A multivariable model in 161 countries found that duration of the infection,
duration masks were recommended, prevalence of age at least 60 years, obesity, and
international travel restrictions were independently predictive of per-capita mortality
(Table 5). The model explained 67.3% of the variation in per-capita mortality. At
baseline, each week of the infection in a country was associated with an increase in
per-capita mortality of 29.5% (Table 5). In contrast, for each week that masks were
worn, the per-capita mortality was associated with a decrease of 2.0% each week
(given that 1.2952(0.7567) = 0.980, Table 5).
International travel restrictions were scored by Oxford as: (0) no measures, (1)
screening, (2) quarantine arrivals from high-risk regions; and ban on arrivals from some
(3) or all (4) regions. The regression analysis suggested that as compared with no
border controls, a complete ban on entries from abroad was associated with a change
in mortality of 104*(-0.213) = 0.14, meaning an 86% reduction in per-capita mortality (Table
5). The international travel restrictions were scored as 4 in Greenland, 3.8 in Bermuda,
3.6 in Israel, 3.5 in Czechia and New Zealand, 3.1 in Taiwan, and 2.9 in Australia, and
at the other extreme, were scored as 1.1 in Sweden, and as 0 in Iran, Luxembourg, and
the UK.
26
Table 5. Predictors of (log) Country-wide Per-capita Coronavirus Mortality by May 9 by
Multivariable Linear Regression in 161 Countries.
10coefficient
Coefficient (SE)
95% CI
P
Duration in country (wks)
1.2952
0.1123 (0.032)
0.050 to 0.175
0.001
Time wearing masks (wks)
0.7567
-0.1211 (0.025)
-0.171 to -0.071
<0.001
Time in lockdown (wks)
0.9419
-0.0260 (0.028)
-0.082 to 0.030
0.36
Population, age ≥ 60 (%)
1.1659
0.0667 (0.009)
0.049 to 0.084
<0.001
Urbanization (%)
1.0141
0.00610 (0.004)
-0.001 to 0.013
0.08
Obesity prevalence (%)
1.0594
0.0250 (0.008)
0.008 to 0.042
0.003
Temperature, ambient (C)
1.0141
0.00607 (0.008)
-0.009 to 0.021
0.42
Testing policy (0-3)
1.1602
0.0645 (0.107)
-0.147 to 0.276
0.55
Contact tracing (0-2)
0.7194
-0.143 (0.095)
-0.331 to 0.045
0.14
Internat. travel controls (0-4)
0.6126
-0.213 (0.076)
-0.362 to -0.063
0.006
Constant
--
-7.228 (0.397)
-8.01 to -6.45
<0.001
Duration of infection in country from estimated date of first infection until 23 days before May 9, 2020
(i.e. April 16). Mask and lockdown durations run from the stated event (mask recommendation or
lockdown) or estimated date of first infection in the country (whichever was later) until 23 days before
May 9, 2020 (i.e. April 16). Policies on testing, contact tracing, and international travel controls were
scored by Oxford University. Model r2=0.673.
Per-capita mortality was not significantly associated with policies regarding either
testing policy (p=0.55), or contact tracing (p=0.14, Table 5). Testing policy was scored
as: no policy (0), symptomatic with exposure, travel history, hospitalization, or key
occupation (1), all symptomatic (2), or open to anyone (3). Testing policy tended to be
positively associated with mortality. Contact tracing was scored as: none (0), some
cases (1), or all cases (2), and tended to be inversely related with per-capita mortality
(though not significantly). These countervailing associations meant that as compared
with a country with no testing or tracing policy, a country which opened testing to the
entire public with comprehensive contact tracing might be associated with a reported
change in mortality of 10(3*0.0645+2*(-0.143)) = 0.808, i.e. a 19.2% reduction in per-capita
mortality (though statistical significance was not demonstrated). Thus, testing and
tracing seem unlikely to account for the almost 100-fold variation in per-capita mortality
between low and high mortality countries.
27
Discussion.
These results confirm that over 4 months since the appearance of COVID-19 in
late 2019, there is marked variation between countries in related mortality. Countries in
the lower half of mortality have experienced an average COVID-19-related per-capita
mortality of 1.1 deaths per million population, in contrast with an average of 94.2 deaths
per million in the remaining countries. Depending on the model and dataset evaluated,
statistically significant independent predictors of per-capita mortality included
urbanization, fraction of the population age 60 years or over, prevalence of obesity,
duration of the outbreak in the country, international travel restrictions, and the period of
the outbreak subject to cultural norms or government policies favoring mask-wearing by
the public.
These results support the universal wearing of masks by the public to suppress
the spread of the coronavirus.1 Given the low levels of coronavirus mortality seen in the
Asian countries which adopted widespread public mask usage early in the outbreak, it
seems highly unlikely that masks are harmful.
Our key finding that the logarithm of per-capita coronavirus mortality is linearly
and positively associated with the duration of the outbreak without mask norms or
mandates was recently confirmed by Goldman Sachs chief economist Jan Hatzius.258
The Goldman Sachs team saw our online preprint dated June 15, and had reached out
to us to discuss our model. The regression analysis performed by Goldman Sachs
confirms that, for prediction of both infection prevalence and mortality, the significance
of the duration of mask mandates or norms in the model persists after controlling for
age of the population, obesity, population density, and testing policy.258
One major limitation is that evidence concerning the actual prevalence of mask-
wearing by the public is unavailable for most countries. Our survey of the literature is
one of the more complete evaluations of the question to date. Available scholarship
and surveys do corroborate reports in the news media that mask wear was common in
public in many Asian countries, including Japan, the Philippines, Hong Kong, Vietnam,
Malaysia, Taiwan, Thailand, China, Indonesia, India, Myanmar and Bangladesh (Table
2). Mask wear was widespread in some low-mortality countries even before, or in the
absence of, a formal government recommendation.
In addition, it is likely that the policies favoring mask-wearing in parts of the
Middle East, Africa, Latin America and the Caribbean were markers of a general cultural
acceptance of masks that helped to limit spread of the virus. Had there been adequate
survey data to fully reflect the early wearing of masks in these regions, it is possible that
the association of masks with lower mortality would be even stronger.
Conversely, in Western countries which had no tradition of mask-wearing, and
which only recommended (rather than mandated) mask-wearing by the public, such as
the United States, the practice has been steadily increasing, but change has not been
immediate.
Much of the randomized controlled data on the effect of mask-wearing on the
spread of respiratory viruses relates to influenza. One recent meta-analysis of 10 trials
in families, students, or religious pilgrims found that the relative risk for influenza with
the use of face masks was 0.78, a 22% reduction, though the findings were not
statistically significant.259 Combining all the trials, there were 29 cases in groups
28
assigned to wear masks, compared with 51 cases in control groups.259 The direct
applicability of these results to mask-wearing at the population level is uncertain. For
instance, there was some heterogeneity in methods of the component trials, with one
trial assigning mask wearing to the person with a respiratory illness, another to his close
contacts, and the remainder to both the ill and their contacts.259 Mask-wearing was
inconsistent. The groups living together could not wear a mask when bathing, sleeping,
eating, or brushing teeth.260-262 In one of the studies reviewed, parents wore a mask
during the day, but not at night when sleeping next to their sick child.262 In a different
trial, students were asked to wear a mask in their residence hall for at least 6 hours
daily (rather than all the time).260 The bottom line is that it is nearly impossible for
people to constantly maintain mask wear around the people with whom they live. In
contrast, wearing a mask when on public transit or shopping is quite feasible. In
addition, as an infection propagates through multiple generations in the population, the
benefits multiply exponentially. Even if one accepts that masks would only reduce
transmissions by 22%, then after 10 cycles of the infection, mask-wearing would reduce
the level of infection in the population by 91.7%, as compared with a non-mask wearing
population, at least during the period of exponential growth (because 0.7810 = 0.083). It
is highly unlikely that entire countries or populations will ever be randomized to either
wear, or not wear, masks. Public policies can only be formulated based on the best
evidence available.
Some countries which used masks were better able to maintain or resume
normal business and educational activities. For instance, in Taiwan, schools reopened
on February 21, 2020, with parents directed to purchase 4 to 5 masks per week for
each child.82
Limits on international travel were significantly associated with lower per-capita
mortality from coronavirus. As compared with no restrictions, complete shutdown of the
border throughout the outbreak was independently associated with 86% lower per-
capita mortality.
Nationwide policies to ban large gatherings and to close schools or businesses,
tended to be associated with lower mortality, though not in a statistically significant
fashion. However, businesses, schools, and individuals made decisions to limit contact,
independent of any government policies. The adoption of numerous public health
policies at the same time can make it difficult to tease out the relative importance of
each. Colder average monthly temperature was associated with higher levels of
COVID-19 mortality in univariate analysis, but not when accounting for other
independent variables. One reason that outdoor temperature might have limited
association with the spread of the virus is that most viral transmission occurs indoors.263
We acknowledge that using the average temperature in the country’s largest city during
the outbreak does not model the outbreak as precisely as modelling mortality and
temperature separately in each of the thousands of cities around the world. However,
to a first approximation, our method did serve to control for whether the country’s
climate was tropical, temperate, or polar, and whether the outbreak began in late Winter
(Northern hemisphere) or late Summer (Southern hemisphere). Environmental factors
which could influence either human behavior or the stability and spread of virus particles
are worthy of further study.
29
Presumably, high levels of testing might identify essentially all coronavirus-
related deaths, and still higher levels of testing, combined with contact tracing, might
lower mortality. However, statistical support for the benefit of testing and tracing on
mortality could not be demonstrated. Policies on testing and tracing were not
significantly associated with mortality. In addition, per-capita testing both early (April 4,
16) and later (May 9) were positively associated with reported coronavirus-related
mortality. It seems likely that countries which test at a low level are missing many
cases. We previously identified just 3 countries (Iceland, the Faeroe Islands, and the
UAE) which had performed over 75,000 tests per million population by April 16, and all
3 had mortality below 1 in 46,000 at that point.264 By May 9, we could add to this “high-
testing” group, the Falkland Islands and Bahrain, as all 5 countries had tested over one
tenth of their population. All 5 countries had a mortality of 29 per million (1 in 34,480
people) or less. The degree to which these results would apply to larger, less isolated,
or less wealthy countries is unknown. Statistical support for benefit of high levels of
testing might be demonstrated if additional and more diverse countries are able to test
at this level.
One limitation of our study is that the ultimate source of mortality data is often
from governments which may not have the resources to provide a full accounting of
their public health crises, or an interest in doing so. It should be noted that the benefit
of wearing masks persisted in a model which excluded data from China (because no
testing data were available, Appendix Table A3). We also acknowledge that country-
wide analyses are subject to the ecologic fallacy.
The source for mortality and testing data we selected is publicly available,7 has
been repeatedly archived,10 contains links to the source government reports for each
country, and agrees with other coronavirus aggregator sites.265 In the interest of
transparency, we presented the per-capita mortality data in Appendix Table A1. One
might question whether any of these data sites or governments provide a complete and
accurate picture of coronavirus mortality. But we must remember that this information
does not exist in a vacuum. Independent sources confirm when mortality has been
high. Social media alerted the world to the outbreaks in Wuhan, Iran, Italy, and New
York. News reports have used aerial photography to confirm the digging of graves in
Iran, New York, and Brazil. Long lines were seen to retrieve remains at crematoria in
Wuhan. Mortuary facilities were inadequate to meet the demand in New York, and
Guayaquil.255 Conversely, signs of health system overload have been noted to be
absent in the countries reporting low mortality. The health systems in Hong Kong,
Taiwan, Japan, and South Korea are believed to be transparent. Reporters in Vietnam
have even called hospitals and funeral homes to confirm the absence of unusual levels
of activity.266 Therefore, while no data source is perfect, we believe that the data used
in the paper are consistent with observations from nongovernmental sources, and are
comparable in reliability to those in other scholarly works.
It is not the case that countries which reported no deaths due to coronavirus
simply were not exposed to the virus. All 198 countries analyzed did report COVID-19
cases. Several countries which traditionally use masks and sustained low mortality (or
none) are close to and have strong travel links to China. Some of these countries
reported cases early in the global pandemic (Table 2). Community transmission has
been described in Vietnam.267
30
In summary, older age of the population, urbanization, obesity, and longer
duration of the outbreak in a country were independently associated with higher
country-wide per-capita coronavirus mortality. International travel restrictions were
associated with lower per-capita mortality. However, other containment measures,
testing and tracing polices, and the amount of viral testing were not statistically
significant predictors of country-wide coronavirus mortality, after controlling for other
predictors. In contrast, societal norms and government policies supporting mask-
wearing by the public were independently associated with lower per-capita mortality
from COVID-19. The use of masks in public is an important and readily modifiable
public health measure.
Funding: None.
Disclosures: The authors have no conflicts of interest.
31
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56
Appendix. Supplemental Tables.
Table A1. Per-capita COVID-19 Mortality by May 9 and Date of Mask
Recommendation or Widespread Use Based on Cultural Norms.
Country.
COVID-19
Mortality
(per mil.
pop.)
Date Masks
Recommended
or Widely
Used by
Cultural
Norms.
Afghanistan
3.0
Albania
10.8
Algeria
11.3
Andorra
621.2
Angola
0.1
Antigua & Barbuda
30.6
4/5/2020
Argentina
6.6
Armenia
14.8
Aruba
28.1
Australia
3.8
Austria
68.3
3/30/2020
Azerbaijan
3.1
Bahamas
28.0
Bahrain
4.7
4/9/2020
Bangladesh
1.3
Barbados
24.4
Belarus
13.3
Belgium
740.4
Belize
5.0
Benin
0.2
4/6/2020
Bermuda
112.4
Bhutan
0.0
3/11/2020
Bolivia
9.8
Bosnia & Herzegov.
31.1
3/29/2020
Botswana
0.4
Brazil
50.1
British Virgin Is.
33.1
Brunei
2.3
Bulgaria
13.0
3/30/2020
Burkina Faso
2.3
Burundi
0.1
Cabo Verde
3.6
Cambodia
0.0
1/28/2020
Cameroon
4.1
4/13/2020
Canada
124.3
4/6/2020
Carib. Netherlands
0.0
57
Cayman Is.
15.2
Central Afric. Rep.
0.0
Chad
1.9
Channel Is.
235.8
Chile
15.9
4/6/2020
China
3.2
1/20/2020
Colombia
8.7
4/4/2020
Congo
1.8
Costa Rica
1.2
Croatia
21.2
Cuba
6.5
4/2/2020
Curacao
6.1
Cyprus
12.4
4/3/2020
Czechia
25.8
3/19/2020
Dem. Rep. Congo
0.4
Denmark
90.8
Djibouti
3.0
Dominica
0.0
Dominican Republic
35.5
4/16/2020
Ecuador
97.3
4/7/2020
Egypt
5.0
El Salvador
2.6
4/8/2020
Equatorial Guinea
2.9
4/14/2020
Estonia
45.2
4/5/2020
Eswatini
1.7
Ethiopia
0.0
4/11/2020
Faeroe Islands
0.0
Falkland Islands
0.0
Fiji
0.0
Finland
47.8
4/14/2020
France
403.1
4/3/2020
French Polynesia
0.0
Gabon
3.6
4/10/2020
Gambia
0.4
Georgia
2.5
Germany
90.1
4/1/2020
Ghana
0.7
Gibraltar
0.0
Greece
14.5
Greenland
0.0
Grenada
0.0
4/3/2020
Guatemala
1.3
4/9/2020
Guinea-Bissau
1.5
Guyana
12.7
4/9/2020
Haiti
1.1
Honduras
10.8
4/6/2020
Hong Kong
0.5
1/24/2020
Hungary
41.9
Iceland
29.3
58
India
1.5
4/4/2020
Indonesia
3.5
4/5/2020
Iran
78.4
3/29/2020
Iraq
2.7
Ireland
292.8
Isle of Man
270.5
Israel
28.5
4/1/2020
Italy
502.7
Ivory Coast
0.8
4/4/2020
Jamaica
3.0
Japan
4.8
1/16/2020
Jordan
0.9
Kazakhstan
1.7
Kenya
0.6
4/4/2020
Kuwait
11.5
3/23/2020
Krgyzstan
1.8
Laos
0.0
3/6/2020
Latvia
9.5
Lebanon
3.8
Liberia
4.0
Libya
0.4
4/16/2020
Liechtenstein
26.2
Lithuania
18.0
3/26/2020
Luxembourg
161.3
Macao
0.0
1/23/2020
Madagasgar
0.0
Malawi
0.2
4/4/2020
Malaysia
3.3
1/30/2020
Maldives
5.5
Mali
1.8
Malta
11.3
Mauritania
0.2
Mauritius
7.9
3/31/2020
Mayotte
40.3
Mexico
26.0
Moldova
39.9
Mongolia
0.0
1/31/2020
Montenegro
12.7
Montserrat
200.3
Morocco
5.0
4/6/2020
Mozambique
0.0
4/4/2020
Myanmar
0.1
Namibia
0.0
Nepal
0.0
3/25/2020
Netherlands
316.4
New Caledonia
0.0
New Zealand
4.4
Nicaragua
0.8
Niger
1.9
59
Nigeria
0.6
4/14/2020
North Macedonia
43.7
Norway
40.4
Oman
3.3
Pakistan
2.9
Palestine
0.4
Panama
54.9
4/7/2020
Papua New Guinea
0.0
Paraguay
1.4
4/7/2020
Peru
55.0
4/3/2020
Philippines
6.4
1/30/2020
Poland
20.7
4/10/2020
Portugal
110.4
Qatar
4.5
Réunion
0.0
Romania
48.8
Russia
12.5
Rwanda
0.0
Saint Kitts & Nevis
0.0
Saint Lucia
0.0
San Marino
1208.3
Sao Tome & Principe
22.8
Saudi Arabia
6.9
Senegal
1.0
Serbia
24.4
Seychelles
0.0
Sierra Leone
2.3
4/1/2020
Singapore
3.4
4/3/2020
Sint Maarten
349.8
4/14/2020
Slovakia
4.8
3/15/2020
Slovenia
48.6
3/29/2020
Somalia
3.0
South Africa
3.1
4/10/2020
South Korea
5.0
1/30/2020
South Sudan
0.0
Spain
566.3
4/11/2020
Sri Lanka
0.4
4/11/2020
St. Vincent & Gren.
0.0
Sudan
1.5
3/16/2020
Suriname
1.7
Sweden
318.8
Switzerland
211.4
Syria
0.2
Taiwan
0.3
1/27/2020
Tanzania
0.4
Thailand
0.8
1/28/2020
Timor-Leste
0.0
Togo
1.2
Trinidad & Tobago
5.7
4/5/2020
60
Tunisia
3.8
4/7/2020
Turkey
44.3
4/3/2020
Turks and Caicos
25.8
Uganda
0.0
Ukraine
8.6
4/3/2020
United Arab Emir.
18.7
3/27/2020
United Kingdom
465.3
United States
241.8
4/3/2020
Uruguay
5.2
Uzbekistan
0.3
3/25/2020
Venezuela
0.4
3/13/2020
Vietnam
0.0
1/27/2020
Yemen
0.2
Zambia
0.4
4/4/2020
Zimbabwe
0.3
61
Table A2. Predictors of (log) Country-wide Per-capita Coronavirus Mortality by May 9
by Univariate Linear Regression in 198 Countries.
10coefficient
Coefficient (SE)
95% CI
p value
Duration infection (weeks)
1.4182
0.152 (0.030)
0.093 to 0.210
<0.001
Duration infection without
masks (weeks)
1.8092
0.257 (0.029)
0.199 to 0.316
<0.001
Duration infection without
lockdown (weeks)
1.2573
0.099 (0.031)
0.039 to 0.160
0.002
Temperature, mean (C)
0.8819
-0.055 (0.008)
0.071 to -0.038
<0.001
Urban population (%)
1.0477
0.0203 (0.003)
0.014 to 0.027
<0.001
GDP per capita ($1000)
1.0424
0.0180 (0.003)
0.012 to 0.024
<0.001
Age 14 & under (% of pop.)
0.8755
-0.058 (0.007)
-0.072 to -0.044
<0.001
Age 60 & over (% of pop.)
1.1945
0.077 (0.009)
0.060 to 0.094
<0.001
Surface area (million km2)
1.1311
0.053 (0.044)
-0.034 to 0.141
0.23
Population (million)
1.0002
0.00010 (0.0006)
-0.001 to 0.001
0.86
Prevalence males (%)
0.9745
-0.011 (0.025)
-0.061 to 0.039
0.66
Smoking prevalence, adult (%)
1.0489
0.021 (0.010)
0.000 to 0.041
0.047
Obesity prevalence, adult (%)
1.1248
0.051 (0.008)
0.035 to 0.067
<0.001
Tests per cap. (log) by May 9
4.9220
0.692 (0.093)
0.509 to 0.875
<0.001
Durations run from the estimated date of first infection in the country until 23 days before May 9,
2020 (i.e. April 16), or the stated event (mask recommendation or lockdown). Obesity data available for
194 countries. Testing data available for 183 countries by May 9.
62
Table A3. Predictors of (log) Country-wide Per-capita Coronavirus Mortality by May 9
by Multivariable Linear Regression in 198 Countries.
10coefficient
Coefficient (SE)
95% CI
P
Duration in country (weeks)
1.5305
0.185 (0.033)
0.120 to 0.249
<0.001
Time wearing masks (weeks)
0.6604
-0.180 (0.029)
-0.237 to -0.123
<0.001
Time in lockdown (weeks)
0.9749
-0.011 (0.034)
-0.078 to 0.056
0.75
Population, age60 (%)
1.1407
0.057 (0.009)
0.040 to 0.074
<0.001
Urbanization (%)
1.0180
0.00773 (0.003)
0.002 to 0.014
0.01
Constant
--
-7.809 (0.236)
-8.273 to -7.344
<0.001
` Duration of infection in country from estimated date of first infection until 23 days before May 9, 2020
(i.e. April 16). Mask and lockdown durations run from the stated event (mask recommendation or
lockdown) or estimated date of first infection in the country (whichever was later) until 23 days before
May 9, 2020 (i.e. April 16). Model r2=0.472.
63
Table A4. Predictors of (log) Country-wide Per-capita Coronavirus Mortality by May 9
by Multivariable Linear Regression in 198 Countries, with Outbreak Start Defined by
Date of First Case.
10coefficient
Coefficient (SE)
95% CI
P
Duration in country (weeks)
1.3847
0.141 (0.033)
0.076 to 0.207
<0.001
Time wearing masks (weeks)
0.6887
-0.162 (0.030)
-0.221 to -0.103
<0.001
Time in lockdown (weeks)
1.0282
0.012 (0.035)
-0.058 to 0.082
0.73
Population, age60 (%)
1.1434
0.058 (0.009)
0.040 to 0.076
<0.001
Urbanization (%)
1.0197
0.00845 (0.003)
0.002 to 0.015
0.007
Constant
--
-7.506 (0.221)
-7.942 to -7.070
<0.001
` Duration of infection in country from estimated date first case reported until 23 days before May 9,
2020 (i.e. April 16). Mask and lockdown durations run from the stated event (mask recommendation or
lockdown) or date first case reported in the country (whichever was later) until 23 days before May 9,
2020 (i.e. April 16). Model r2=0.438.
64
Table A5. Predictors of (log) Country-wide Per-capita Coronavirus Mortality by May 9
by Multivariable Linear Regression in 198 Countries, with Mask Wear Determined by
Recommendations and Surveys (When Available).
10coefficient
Coefficient (SE)
95% CI
P
Duration in country (weeks)
1.5056
0.178 (0.033)
0.112 to 0.243
<0.001
Time wearing masks (weeks)
0.6845
-0.165 (0.029)
-0.221 to -0.108
<0.001
Time in lockdown (weeks)
0.9669
-0.015 (0.034)
-0.082 to 0.053
0.67
Population, age60 (%)
1.1411
0.057 (0.009)
0.040 to 0.075
<0.001
Urbanization (%)
1.0175
0.00754 (0.003)
0.001 to 0.014
0.01
Constant
--
-7.737 (0.236)
-8.203 to -7.271
<0.001
` Duration of infection in country from estimated date of first infection until 23 days before May 9, 2020
(i.e. April 16). Mask and lockdown durations run from the stated event (mask recommendation or
lockdown) or estimated date of first infection in the country (whichever was later) until 23 days before
May 9, 2020 (i.e. April 16). Model r2=0.459.
65
Table A6. Predictors of (log) Country-wide Per-capita Coronavirus Mortality by May 9
by Multivariable Linear Regression in 179 Countries.
10coefficient
Coefficient (SE)
95% CI
P
Duration in country
(weeks)
1.5673
0.195 (0.034)
0.127 to 0.263
<0.001
Time wearing masks
(weeks)
0.7060
-0.151 (0.032)
-0.214 to -0.088
<0.001
Time in lockdown
(weeks)
0.9591
-0.018 (0.039)
-0.095 to 0.059
0.64
Population, % age 60 or
over
1.0930
0.039 (0.011)
0.016 to 0.061
0.001
Urbanization (%)
1.0119
0.00512 (0.004)
-0.002 to 0.013
0.18
Obesity prevalence (%)
1.0478
0.020 (0.010)
0.001 to 0.039
0.04
Temperature (C)
0.9758
-0.01065 (0.009)
-0.029 to 0.007
0.24
Testing (log per cap.,
by May 9)
1.1698
0.068 (0.114)
-0.156 to 0.293
0.55
Constant
--
-7.539 (0.582)
-8.69 to -6.39
<0.001
Based on 179 countries with both obesity and testing data by May 9. Duration of infection in country
from estimated date of first infection until 23 days before May 9, 2020 (i.e. April 16). Mask and
lockdown durations run from the stated event (mask recommendation or lockdown) or estimated date
of first infection in the country (whichever was later) until 23 days before May 9, 2020 (i.e. April 16).
Model r2=0.521.
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