MMWR, Mitigation Policies and COVID-19–Associated Mortality — 37 European Countries, January 23

Morbidity and Mortality Weekly Report

58 MMWR / January 15, 2021 / Vol. 70 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention

Mitigation Policies and COVID-19–Associated Mortality —

37 European Countries, January 23–June 30, 2020

James A. Fuller, PhD

; Avi Hakim, MPH

; Kerton R. Victory, PhD

; Kashmira Date, MD

; Michael Lynch, MD

; Benjamin Dahl, PhD

;

Olga Henao, PhD

; CDC COVID-19 Response Team

On January 12, 2021, this report was posted as an MMWR

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

As cases and deaths from coronavirus disease 2019

(COVID-19) in Europe rose sharply in late March, most

European countries implemented strict mitigation policies,

including closure of nonessential businesses and mandatory

stay-at-home orders. These policies were largely successful at

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

COVID-19 (1), but they came with social and economic costs,

including increases in unemployment, interrupted education,

social isolation, and related psychosocial outcomes (2,3).

A better understanding of when and how these policies were

effective is needed. Using data from 37 European countries, the

impact of the timing of these mitigation policies on mortality

from COVID-19 was evaluated. Linear regression was used

to assess the association between policy stringency at an early

time point and cumulative mortality per 100,000 persons on

June 30. Implementation of policies earlier in the course of

the outbreak was associated with lower COVID-19–associated

mortality during the subsequent months. An increase by one

standard deviation in policy stringency at an early timepoint

was associated with 12.5 cumulative fewer deaths per 100,000

on June 30. Countries that implemented stringent policies

earlier might have saved several thousand lives relative to those

countries that implemented similar policies, but later. Earlier

implementation of mitigation policies, even by just a few

weeks, might be an important strategy to reduce the number

of deaths from COVID-19.

Using data from 37 European countries, the impact of the

timing and stringency of early mitigation policies on cumu-

lative mortality from COVID-19 on June 30 was assessed.

Countries with >250,000 inhabitants and for which relevant

data were available were included. Mortality data were obtained

from the World Health Organization (WHO) Coronavirus

Disease Dashboard (4). Data on mitigation policies were

obtained from the CDC COVID-19 International Taskforce

global mitigation database accessible through WHO* (5) and

the University of Oxford’s Coronavirus Government Response

Tracker (6), specifically the Oxford Stringency Index (OSI)

* Mitigation policies implemented by government authorities during

January 1–June 30, 2020 were abstracted from media reports and government

and United Nations websites and compiled by WHO. The CDC COVID-19

International Taskforce global mitigation database is a sub-set of the WHO

public health and social measures database.

(6), which is a composite index based on nine mitigation

policies. These include cancellation of public events, school

closures, gathering restrictions, workplace closures, border

closures, internal movement restrictions, public transport

closure, recommendations to stay at home, and stay-at-home

orders; mask requirements are not included. The OSI ranges

from 0 to 100 and increases over time if more stringent

mitigation policies are implemented or decreases if policies

are rescinded (Supplementary Figure, https://stacks.cdc.gov/

view/cdc/100148); however, this index is also weighted on the

strictness of each policy, which can vary among countries (6).

For each country, the value of the OSI was extracted on the

date that the country first reached a defined threshold of daily

mortality from COVID-19 (mortality threshold). This report

uses a threshold of a daily rate of 0.02 new COVID-19 deaths

per 100,000 population (based on a 7-day moving average);

several thresholds were explored,

†

all of which produced similar

results. The mortality threshold is used to identify a common

epidemiologic point early in the pandemic in each country to

align countries by the progression of their epidemic, rather

than by calendar date.

Linear regression was used to assess the association between

the OSI on the day the country reached the mortality thresh-

old and cumulative mortality per 100,000 at the end of June

2020. June 30, 2020 was chosen because at that time, the

rate of new COVID-19 deaths per 100,000 had dropped to

relatively low levels for nearly all 37 countries. The regression

model controls for several covariates: the calendar date the

mortality threshold was reached, because countries affected

later might have had more time to prepare and less time before

the fixed endpoint of June 30; hospital beds in the country per

1,000 population as a measure of baseline health care capacity;

median age of the population, because age is an important

risk factor for death from COVID-19; population density,

because urbanization might lead to higher rates of contact; and

gross domestic product per capita to account for differences

in wealth. Controlling for other OSI metrics (e.g., the mean,

median, and maximum OSI from January 1 to June 30) was

explored, but none had a meaningful effect on the results. The

†

The following potential mortality thresholds were explored: number of

cumulative deaths (all values between one and 50 deaths), number of cumulative

deaths per 100,000 population (all values between 0.01 and 0.5 deaths per 100,000),

and the number of daily deaths per 100,000 population (all values between

0.001 and 0.05 deaths per 100,000).

Morbidity and Mortality Weekly Report

MMWR / January 15, 2021 / Vol. 70 / No. 2 59

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

Summary

What is already known about this topic?

Mitigation policies, including closure of nonessential

businesses, restrictions on gatherings and movement, and

stay-at-home orders, have been critical to controlling the

COVID-19 pandemic in many countries, but they come with

high social and economic costs.

What is added by this report?

European countries that implemented more stringent

mitigation policies earlier in their outbreak response tended to

report fewer COVID-19 deaths through the end of June 2020.

These countries might have saved several thousand lives

relative to countries that implemented similar policies, but later.

What are the implications for public health practice?

Earlier implementation of stringent mitigation policies,

even by just a few weeks, appears to be important to prevent

widespread COVID-19 transmission and reduce the

number of deaths.

number of lives lost attributable to a lower OSI on the day the

country reached the mortality threshold was calculated using

the results from the linear regression. For each country whose

OSI was <80 when reaching the mortality threshold, a coun-

terfactual scenario was estimated by calculating the expected

reduction in mortality had their OSI been 80.

Among 37 European countries, the date the mortality

threshold was reached ranged from March 2 (Italy) to April 18

(Ukraine), and the OSI on the date the mortality threshold was

reached ranged from 16.7 (United Kingdom) to 100.0 (Serbia)

(Table). The most common policies implemented in these

countries by the time they reached the mortality threshold

were cancellation of public events (35 countries; 95%), fol-

lowed by school closures (33; 89%), restrictions on gatherings

(31; 84%), workplace closures (31; 84%), border closures

(27; 73%), restrictions on internal movement (25; 68%), and

recommendations to stay at home (14; 38%). Several coun-

tries implemented more stringent policies including closure

of public transportation (18; 49%) and stay-at-home orders

(11; 30%). Countries with more policies in place generally had

a higher OSI; however, several countries had a higher index

with fewer policies in place. For example, Serbia (index=100)

and Hungary (index=76.9) had similar types of policies in

place, but Serbia had stricter policies such as restrictions on

gatherings of ≥10 persons, compared with Hungary, which

had restrictions on gatherings of >1,000 persons.

The expected reduction in mortality was calculated as the product of three

values: 1) the difference between the observed OSI when reaching the mortality

threshold and 80, 2) the linear regression coefficient (−0.55), and 3) the

population size (measured in 100,000 increments to account for the units of

the regression coefficient). A value of 80 for the OSI was selected because it

was the average maximum OSI values that countries reached before June 30, 2020.

Cumulative COVID-19–associated mortality on June 30

was lower in countries that had a higher OSI when reaching

the mortality threshold (Figure). This association persisted

after controlling for the calendar date the mortality threshold

was reached, hospital beds per 1,000 population, median

age of the population, population density, and gross domes-

tic product per capita. For each 1-unit increase in the OSI

when the mortality threshold was reached, the cumulative

mortality as of June 30 decreased by 0.55 deaths per 100,000

(95% confidence interval [CI] = −0.82 to −0.27 deaths

per 100,000). A 1-unit increase in the OSI standard deviation

(22.9 unit increase in the OSI) was associated with a decrease

of 12.5 deaths per 100,000.

Overall, the OSI was <80 when the mortality threshold was

reached in 26 (70%) of 37 countries (Table). On the basis of

the regression model, it was determined that if the OSI in each

of those countries had been 80 when reaching the mortality

threshold, 74,139 fewer deaths would have been expected

across those 26 countries. Most of these potentially averted

deaths would have been in the United Kingdom (22,776;

31% of all averted deaths), France (13,365; 18%), and Spain

(9,346; 13%).

Discussion

European countries that implemented more stringent

mitigation policies by the time they reached an early mor-

tality threshold in spring 2020 tended to report fewer

COVID-19–associated deaths through the end of June.

Countries that implemented stringent policies earlier might

have saved several thousand lives relative to those countries

that implemented similar policies, but later. These findings

suggest that earlier implementation, even by just a few weeks,

might be important to preventing widespread transmission

and large numbers of deaths.

Other research has highlighted the importance of the timing

of control measures in mitigating the COVID-19 pandemic.

One study of the 37 Organization of Economic Cooperation

and Development member countries found that implementing

school closures and gathering bans 1 week earlier could have

reduced mortality by 44% (7). A modeling study highlighted

a “window of opportunity” for implementing social distancing

directives, suggesting that even small delays could lead to much

higher incidence rates (8). An observational study of 43 U.S.

states and 41 countries that implemented stay-at-home orders,

found that jurisdictions that delayed those orders experienced

more prolonged outbreaks (9). Another observational study of

U.S. states and other countries found that several nonpharma-

ceutical interventions, including but not limited to cancelling

small gatherings, airport restrictions, and closure of educational

Morbidity and Mortality Weekly Report

60 MMWR / January 15, 2021 / Vol. 70 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention

TABLE. Mortality threshold date,* stringency index, and COVID-19 mitigation policies implemented, by Oxford Stringency Index (OSI) on date

mortality threshold was reached — 37 European countries, March–April, 2020

Country

Date

mortality

threshold

reached

OSI when

mortality

threshold

reached

Cancellation

of public

events

School

closures

Gathering

restrictions

Workplace

closures

Border

closures

Internal

movement

restrictions

Public

transport

closure

Recommendations

to stay at home

Stay-at-

home

orders

United Kingdom Mar 16 16.7 N N N Y N N N Y N

Belarus Apr 08 18.5 Y Y N N N N N N N

Luxembourg Mar 11 22.2 Y Y N N N N N N N

Belgium Mar 13 23.2 Y N N N N N N N N

Switzerland Mar 10 25.0 Y N Y N N N N N N

Sweden Mar 12 27.8 Y N Y N N N N N N

France Mar 13 41.2 Y Y Y Y N N N N N

Spain Mar 10 45.8 Y Y Y Y Y Y N N N

Ireland Mar 24 48.2 Y Y Y Y N N N N N

Iceland Mar 17 50.9 Y Y Y Y N N N N N

Cyprus Mar 22 51.9 Y Y N Y Y N N N N

Netherlands Mar 15 54.6 Y Y Y Y N Y N Y N

Norway Mar 23 63.0 N Y Y Y Y Y Y N N

Finland Mar 26 64.8 Y Y Y N Y Y N Y N

Germany Mar 21 68.1 Y Y Y Y Y Y N Y N

Latvia Apr 10 69.4 Y Y Y Y Y N Y Y N

Italy Mar 02 69.9 Y Y Y Y Y Y N Y N

Bulgaria Apr 01 71.3 Y Y Y Y Y Y N Y N

Denmark Mar 18 72.2 Y Y Y Y Y Y Y Y N

Estonia Mar 27 72.2 Y Y Y Y Y Y N N N

Greece Mar 22 74.1 Y Y Y Y Y Y Y N N

Slovakia Apr 16 75.0 Y Y Y Y Y Y Y Y N

Turkey Mar 28 75.9 Y Y N Y Y Y Y Y N

Hungary

†

Mar 31 76.9 Y Y Y Y Y Y Y Y Y

Romania Mar 27 78.7 Y Y Y Y Y Y Y N Y

Slovenia Mar 23 78.7 Y Y Y Y Y N Y Y N

Austria Mar 20 81.5 Y Y Y Y Y Y Y N Y

Lithuania Mar 23 81.5 Y Y Y Y Y Y Y Y N

Poland Apr 01 81.5 Y Y Y Y Y Y N N Y

Czechia Mar 27 82.4 Y Y Y Y Y Y N N Y

Portugal Mar 21 82.4 Y Y Y Y Y Y Y N Y

Albania Mar 24 84.3 Y Y Y Y Y Y Y N Y

Moldova Mar 31 87.0 Y Y Y Y Y Y Y N Y

Ukraine Apr 18 88.9 Y Y Y Y Y Y Y Y N

Bosnia and

Herzegovina

Mar 27 89.8 Y Y Y Y Y Y Y N Y

Croatia Mar 27 96.3 Y Y Y Y Y Y Y N Y

Serbia Mar 27 100.0 Y Y Y Y Y Y Y N Y

Total countries — — 35 33 31 31 27 25 18 14 11

Abbreviations: COVID-19=coronavirus disease 2019; N = no; Y = yes.

* The mortality threshold is the first date that each country reached a daily rate of 0.02 new COVID-19 deaths per 100,000 population based on a 7-day moving average

of the daily death rate. “Yes” indicates that the policy was implemented before the date mortality threshold was reached, and “No” indicates that the policy had not

been implemented. No country rescinded any policy before the mortality threshold was reached. Implementation of more policies in a country could result in a

higher OSI; however, this index is also weighted on the strictness of each policy, which can vary among countries. For example, Serbia (index=100) and Hungary

(index=76.9) had similar types of policies in place, but Serbia had more strict policies such as restrictions on gatherings of ≥10 persons compared with Hungary,

which had restrictions on gatherings of >1,000 persons.

†

Hungary implemented a stay-at-home order with exceptions for persons who commuted or had extraordinary situations; these persons were still under

recommendations (but not requirements) to stay at home.

institutions, could lead to a larger reduction in transmission if

implemented earlier rather than later (10).

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

tions. First, some COVID-19 deaths likely went undetected,

especially during the early stages of the pandemic. This could

impact both the date of reaching the mortality threshold and

the cumulative mortality as of June 30. Second, the OSI does

not capture all mitigation policies that countries might apply.

For example, it does not include requirements for masks,

though such requirements in Europe were rare during the

early stages of the pandemic. Third, adherence to policies or

recommendations was not accounted for and could explain

some of the variability in the impact observed. Finally, many

interventions were implemented simultaneously, making it

difficult to determine which specific policies might have had

the most impact.

Morbidity and Mortality Weekly Report

MMWR / January 15, 2021 / Vol. 70 / No. 2 61

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

FIGURE. Early policy stringency* and cumulative mortality

†

from COVID-19 — 37 European countries, January 23–June 30, 2020

ALB

AUT

BLR

BEL

BIH

BGR

HRV

CYP

CZE

DNK

EST

FIN

FRA

DEU

GRC

HUN

ISL

IRL

ITA

LVA

LTU

LUX

NLD

NOR

POL

PRT

MDA

ROU

SRB

SVK

SVN

ESP

SWE

CHE

GBR

TUR

UKR

100

25 50 75 100

Oxford Stringency Index when mortality threshold was reached

Cumulative mortality through June 30

April 15

April 1

March 15

March 2

Linear t

95% CI

20,000,000 population

40,000,000 population

60,000,000 population

80,000,000 population

Abbreviations: ALB = Albania; AUT = Austria; BEL = Belgium; BGR = Bulgaria; BIH = Bosnia and Herzegovina; BLR = Belarus; CHE = Switzerland; CI = confidence interval;

COVID-19 = coronavirus disease 2019; CYP = Cyprus; CZE = Czechia; DEU = Germany; DNK = Denmark; ESP = Spain; EST = Estonia; FIN = Finland; FRA = France;

GBR = United Kingdom; GRC = Greece; HRV = Croatia; HUN = Hungary; IRL = Ireland; ISL = Iceland; ITA = Italy; LTU = Lithuania; LUX = Luxembourg; LVA = Latvia;

MDA = Moldova; NLD = Netherlands; NOR = Norway; POL = Poland; PRT = Portugal; ROU = Romania; SRB = Serbia; SVK = Slovakia; SVN = Slovenia; SWE = Sweden;

TUR = Turkey; UKR = Ukraine.

* Based on the Oxford Stringency Index (OSI) on the date the country reached the mortality threshold. The OSI is a composite index ranging from 0–100, based on

the following nine mitigation policies: 1) cancellation of public events, 2) school closures, 3) gathering restrictions, 4) workplace closures, 5) border closures,

6) internal movement restrictions, 7) public transport closure, 8) stay-at-home recommendations, and 9) stay-at-home orders. The mortality threshold is the first

date that each country reached a daily rate of 0.02 new COVID-19 deaths per 100,000 population, based on a 7-day moving average of the daily death rate. The color

gradient represents the calendar date that each country reached the mortality threshold.

†

Deaths per 100,000 population.

This report quantifies the impact of earlier implementation

of mitigation policies on COVID-19 mortality in Europe

during the early stages of the pandemic. Further work should

seek to identify optimal timing and duration of mitigation

policies, evaluate the role of mask policies in relation to other

mitigation policies, and assess which specific interventions are

the most effective.

Corresponding author: James A. Fuller, [email protected].

CDC COVID-19 Response Team.

All authors have completed and submitted the International

Committee of Medical Journal Editors form for disclosure of

potential conflicts of interest. No potential conflicts of interest

were disclosed.

References

1. Flaxman S, Mishra S, Gandy A, et al.; Imperial College COVID-19

Response Team. Estimating the effects of non-pharmaceutical

interventions on COVID-19 in Europe. Nature 2020;584:257–61.

PMID:32512579 https://doi.org/10.1038/s41586-020-2405-7

2. Salari N, Hosseinian-Far A, Jalali R, et al. Prevalence of stress,

anxiety, depression among the general population during the

COVID-19 pandemic: a systematic review and meta-analysis. Global

Health 2020;16:57. PMID:32631403 https://doi.org/10.1186/

s12992-020-00589-w

3. Kuhfeld M, Soland J, Tarasawa B, Johnson A, Ruzek E, Liu J.

Projecting the potential impact of COVID-19 school closures on

academic achievement. Educ Res 2020;49:549–65. https://doi.

org/10.3102/0013189X20965918

Morbidity and Mortality Weekly Report

62 MMWR / January 15, 2021 / Vol. 70 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention

4. World Health Organization. WHO coronavirus disease (COVID-19)

dashboard. Geneva, Switzerland: World Health Organization; 2020.

https://covid19.who.int/

5. World Health Organization. Tracking public health and social measures:

a global dataset. Geneva, Switzerland: World Health Organization; 2020.

https://www.who.int/emergencies/diseases/novel-coronavirus-2019/phsm

6. Thomas Hale SW, Petherick A, Phillips T, Kira B. Coronavirus

government response tracker. Oxford, United Kingdom: University of

Oxford, Blavatnik School of Government; 2020. https://www.bsg.ox.ac.

uk/research/research-projects/coronavirus-government-response-tracker

7. Piovani D, Christodoulou MN, Hadjidemetriou A, et al. Effect of early

application of social distancing interventions on COVID-19 mortality

over the first pandemic wave: an analysis of longitudinal data from 37

countries. J Infect 2020;20:30751–9. Epub December 1, 2020.

PMID:33275956

8. Gevertz JL, Greene JM, Sanchez-Tapia CH, Sontag ED. A novel

COVID-19 epidemiological model with explicit susceptible and

asymptomatic isolation compartments reveals unexpected consequences

of timing social distancing. J Theor Biol 2021;510:110539.

PMID:33242489 https://doi.org/10.1016/j.jtbi.2020.110539

9. Medline A, Hayes L, Valdez K, et al. Evaluating the impact of stay-at-

home orders on the time to reach the peak burden of Covid-19 cases

and deaths: does timing matter? BMC Public Health 2020;20:1750.

PMID:33225945 https://doi.org/10.1186/s12889-020-09817-9

10. Haug N, Geyrhofer L, Londei A, et al. Ranking the effectiveness of

worldwide COVID-19 government interventions. Nat Hum Behav

2020;4:1303–12. PMID:33199859 https://doi.org/10.1038/

s41562-020-01009-0