Predictors of adherence to public health behaviors for fighting COVID-19 derived from longitudinal data

Abstract

The present paper examines longitudinally how subjective perceptions about COVID-19, one’s community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey ( N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions.

Full text

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Predictors of adherence
to public health behaviors
for ghting COVID‑19 derived
from longitudinal data
Birga M. Schumpe1,74*, Caspar J. Van Lissa2,74, Jocelyn J. Bélanger3, Kai Ruggeri4,
Jochen Mierau5, Claudia F. Nisa3, Erica Molinario6, Michele J. Gelfand7, Wolfgang Stroebe5,
Maximilian Agostini5, Ben Gützkow5, Bertus F. Jeronimus5, Jannis Kreienkamp5,
Maja Kutlaca8, Edward P. Lemay Jr9, Anne Margit Reitsema5, Michelle R. vanDellen10,
Georgios Abakoumkin11, Jamilah Hanum Abdul Khaiyom12, Vjollca Ahmedi13,
Handan Akkas14, Carlos A. Almenara15, Mohsin Atta16, Sabahat Cigdem Bagci17,
Sima Basel3, Edona Berisha Kida13, Allan B. I. Bernardo18, Nicholas R. Buttrick19,
Phatthanakit Chobthamkit20, Hoon‑Seok Choi21, Mioara Cristea22, Sara Csaba23,
Kaja Damnjanović24, Ivan Danyliuk25, Arobindu Dash26, Daniela Di Santo27,
Karen M. Douglas28, Violeta Enea29, Daiane Faller30, Gavan J. Fitzsimons31,
Alexandra Gheorghiu29, Ángel Gómez32, Ali Hamaidia33, Qing Han34, Mai Helmy35,
Joevarian Hudiyana36, Ding‑Yu Jiang37, Veljko Jovanović38, Zeljka Kamenov39,
Anna Kende23, Shian‑Ling Keng40, Tra Thi Thanh Kieu41, Yasin Koc5, Kamila Kovyazina42,
Inna Kozytska25, Joshua Krause43, Arie W. Kruglanski9, Anton Kurapov25,
Nóra Anna Lantos23, Cokorda Bagus J. Lesmana44, Winnifred R. Louis45, Adrian Lueders46,
Najma Iqbal Malik16, Anton P. Martinez47, Kira O. McCabe48, Jasmina Mehulić39,
Mirra Noor Milla36, Idris Mohammed49, Manuel Moyano50, Hayat Muhammad51,
Silvana Mula27, Hamdi Muluk36, Solomiia Myroniuk5, Reza Naja52, Boglárka Nyúl23,
Paul A. O’Keefe53, Jose Javier Olivas Osuna54, Evgeny N. Osin55, Joonha Park56,
Gennaro Pica57, Antonio Pierro27, Jonas H. Rees58, Elena Resta27, Marika Rullo59,
Michelle K. Ryan60, Adil Samekin61, Pekka Santtila62, Edyta Sasin3, Heyla A. Selim63,
Michael Vicente Stanton64, Samiah Sultana5, Robbie M. Sutton28, Eleftheria Tseliou11,
Akira Utsugi65, Jolien A. van Breen66, Kees Van Veen5, Alexandra Vázquez32, Robin Wollast67,
Victoria Wai‑Lan Yeung68, Somayeh Zand69, Iris Lav Žeželj24, Bang Zheng70, Andreas Zick71,
Claudia Zúñiga72 & N. Pontus Leander73
The present paper examines longitudinally how subjective perceptions about COVID‑19, one’s
community, and the government predict adherence to public health measures to reduce the spread
of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust
in the governmental response and communications about COVID‑19, conspiracy beliefs, social
norms on distancing, tightness of culture, and community punishment predict various containment‑
related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal
hygiene behavior was predicted by personal infection risk perception. At social level, social distancing
behaviors such as abstaining from face‑to‑face contact were predicted by perceived social norms.
Support for behavioral mandates was predicted by condence in the government and cultural
tightness, whereas support for anti‑lockdown protests was predicted by (lower) perceived clarity
of communication about the virus. Results are discussed in light of policy implications and creating
eective interventions.
OPEN
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On March 11th, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Since then, the
novel coronavirus SARS-CoV-2, which leads to the illness referred to as COVID-19, has put society to the test.
From washing hands to getting vaccinated, individual health behavior is the most important defense to curb the
spread of the virus 1,2, behavioral sciences need to be leveraged to build trust that encourages individuals to fully
utilize public health recommendations 3.
Predicting adherence to public health measures
Based on existing evidence regarding health behaviors, we focus this work on personal risk perception (rst-order
eects), subjective social and community norms (second-order eects), and broader political and governmental
perceptions (third-order eects).
First‑order eects. Individuals’ perceived personal risk of infection is oen studied in the context of com-
municable diseases such as H1N1 (‘Swine Flu’) or the original SARS outbreaks in 2002–2003. A greater per-
ception of personal risk was strongly related to increased willingness to take precautionary measures against
infection 4–6. Protection Motivation eory 7 proposes that the severity of a threatening event and the perceived
probability of its occurrence determines whether people engage in healthy behaviors. Recent ndings indicate
that perceived economic risk is also positively related to mitigation behavior and policy support 8.
Given these insights, we hypothesize that personal risk perception regarding COVID-19 predicts willingness
to engage in protective health behaviors and support for pandemic response related policies. Importantly, this
hypothesis has not previously been tested longitudinally, hence, claims of causality are not possible.
Second‑order eects. Social psychology has a long history of observing that social norms—the subjec-
tive perception of what others are doing and approving of—are a reliable predictor for people’s behavior across
contexts 9–11. Likewise, social norms play an important role in predicting health behaviors 12,13. Specically, for
the COVID-19 pandemic, social distancing norms might predict compliance with health measures. We predict
that injunctive social norms, that is, norms of “ought” 14 would predict more compliance over time with public
health measures.
Injunctive norms may be manifested in what community perceive they and others ought to do as well as
through subjective perceptions that norm violators are punished. at is, people may perceive their community
would impose punishments (e.g., scorn or nes) for not following public health recommendations enforced by
government authorities. Examples of these behaviors include breaking quarantine or not wearing face masks.
But does the perception of a punitive community encourage compliance in individuals? On the one hand,
increasing the intensity or duration of punishments can lead to greater suppression of targeted (unwanted)
behaviors 15. However, individuals are more likely to resist when they perceive their freedom to engage in a
1University of Amsterdam, Amsterdam, The Netherlands. 2Utrecht University, Utrecht, Netherlands. 3New York
University Abu Dhabi, Abu Dhabi, United Arab Emirates. 4Columbia University, New York, USA. 5University of
Groningen, Groningen, Netherlands. 6Florida Gulf Coast University, Fort Myers, USA. 7Stanford University, Palo
Alto, USA. 8Durham University, Durham, UK. 9University of Maryland, College Park, USA. 10University of Georgia,
Athens, USA. 11University of Thessaly, Volos, Greece. 12International Islamic University Malaysia, Kuala Lumpur,
Malaysia. 13Pristine University, Pristine, Kosovo. 14Ankara Science University, Ankara, Turkey. 15Universidad
Peruana de Ciencias Aplicadas, Lima, Peru. 16University of Sargodha, Sargodha, Pakistan. 17Sabanci University,
Istanbul, Turkey. 18De La Salle University, Manila, Philippines. 19University of Virginia, Charlottesville,
USA. 20Thammasat University, Pathumthani, Thailand. 21Sungkyunkwan University, Seoul, Korea. 22Heriot
Watt University, Edinburgh, UK. 23ELTE Eötvös Loránd University, Budapest, Hungary. 24University of Belgrade,
Belgarde, Serbia. 25Taras Shevchenko National University of Kyiv, Kyiv, Ukraine. 26Leuphana University of Lüneburg,
Lüneburg, Germany. 27University “La Sapienza”, Rome, Italy. 28University of Kent, Canterbury, UK. 29Alexandru
Ioan Cuza University, Iași, Romania. 30National University of Singapore, Singapore, Singapore. 31Duke University,
Durham, USA. 32Universidad Nacional de Educación a Distancia, Madrid, Spain. 33Setif 2 University, Setif,
Algeria. 34University of Bristol, Bristol, UK. 35Sultan Qaboos University, Muscat, Oman. 36Universitas Indonesia,
Depok, Indonesia. 37National Chung-Cheng University, Minxiong, Taiwan. 38University of Novi Sad, Novi Sad,
Serbia. 39University of Zagreb, Zagreb, Croatia. 40Monash University Malaysia, Subang Jaya, Malaysia. 41HCMC
University of Education, Ho Chi Minh City, Vietnam. 42Nur-Sultan, Kazakhstan. 43University of Groningen,
Kazakhstan, Netherlands. 44Udayana University, Denpasar, Indonesia. 45University of Queensland, Brisbane,
Australia. 46University of Limerick, Limerick, Ireland. 47University of Sheeld, Sheeld, UK. 48Carleton University,
Ottawa, Canada. 49Usmanu Danfodiyo University Sokoto, Sokoto, Nigeria. 50University of Cordoba, Cordoba,
Spain. 51University of Peshawar, Peshawar, Pakistan. 52University of Padova, Padova, Italy. 53Yale-NUS College,
Singapore, Singapore. 54National Distance Education University (UNED), Madrid, Spain. 55HSE University, Moscow,
Russia. 56NUCB Business School, Nagoya, Japan. 57University of Camerino, Camerino, MC, Italy. 58University of
Bielefeld, Bielefeld, Germany. 59University of Siena, Siena, Italy. 60University of Exeter, Exeter, UK. 61M. Narikbayev
KAZGUU University, Nur-Sultan, Kazakhstan. 62New York University Shanghai, Shanghai, China. 63King Saud
University, Riyadh, Saudi Arabia. 64California State University, East Bay, USA. 65Nagoya University, Nagoya,
Japan. 66Leiden University, Leiden, Netherlands. 67Université Clermont-Auvergne, Clermont-Ferrand,
France. 68Lingnan University, Tuen Mun, Hong Kong. 69University of Milano-Bicocca, Milan, Italy. 70Imperial College
London, London, UK. 71Bielefeld University, Bielefeld, Germany. 72Universidad de Chile, Santiago, Chile. 73Wayne
State University, Detroit, USA. 74These authors contributed equally: Birga M. Schumpe and Caspar J. van
Lissa. *email: b.m.schumpe@uva.nl
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specic behavior is threatened 16, especially if they perceive such resistance as the norm among their ingroup 17.
us, we test the predictive power of perceived punishments for adherence to healthy behavior recommenda-
tions from public health ocials over time.
Third‑order eects. On a general level, trust in the government is a predictor for adherence to recom-
mended health behaviors 18,19. In contrast, distrust in the government is known to be related to lower compliance
with, for example, policies aimed at stemming the Ebola outbreak 20. In the COVID-19 context, cross-sectional
studies support this assertion: people in the US who fear the authorities comply less with mitigation measures
to ght COVID-19 21. We predict that, over time, trust in the government to eectively ght COVID-19 would
lead to greater compliance with public health recommendations and greater support for governmental policies
to mitigate the COVID-19 pandemic.
Further, how risks are communicated is a critical consideration 22. Subjective perceptions that one receives
clear and unambiguous messages are deemed central for promoting compliance with public health measures 23.
Taken together, we expect that individuals who perceive to receive clear and unambiguous messages would show
increased adherence to public health recommendations over time.
In contrast to earlier points, the pandemic has fueled numerous misperceptions about the virus and society,
including conspiracy theories. For example, some people believe that the coronavirus was created to be used as
a population-control scheme. False beliefs regarding vaccines (e.g., that they contain chips or lead to genetic
modication) can result in deaths if individuals fail to get protected and avoidable transmission occurs. Indeed,
belief in conspiracy theories predicts resistance to preventive behaviors 24.
People may also perceive that society ought to tighten or loosen its injunctive norms. An important societal
level factor in cross-cultural research is tightness-looseness 24. Tightness is dened by having strong norms, strict
rules, and low tolerance of deviating behavior, oen considered characteristic of places such as Singapore or
Japan. Loose cultures, such as Italy or the United States, are characterized by weaker social norms and rules and
being generally more permissive about deviation 25. Since people in tighter cultures tend to follow rules more
strictly and are more accepting toward authoritarian leadership, cultural tightness should predict compliance
with public health measures. Importantly, a stronger preference for tighter structures aer the onset of the pan-
demic would be indicative of greater perception that societal circumstances demand for more control and rule
enforcement during these times 26,27.
In sum, prior research oers some evidence that subjective perceptions at the personal-, social-, or societal
level may predict various pandemic-related attitudes and behaviors. Yet, the existing literature has several limi-
tations. Firstly, no study has comprehensively investigated predictors at all three levels of analysis (personal-,
social-, and societal level). However, to design eective interventions, it would be useful to know whether certain
subjective perceptions are generally predictive, or whether a given subjective perception is most predictive of
a relevant outcome when considered at the same level of construal—be it at the personal-level, the social, or a
more generalized societal level 28. Such an analysis can also oer preliminary indication of whether and how
future research and policy should tailor interventions on subjective perception to the specic outcome of interest.
Second, most studies have examined these factors in the context of infectious diseases other than COVID-
19 29. e COVID-19 pandemic required instantaneous behavioral change at a global level. is mass nature of
the COVID-19 pandemic requires an understanding that cannot necessarily be translated from other infectious
diseases. For instance, the novel coronavirus (2019-nCoV) has a higher transmissibility than SARS (SARS-CoV)
and more patients with mild symptoms that fail to be isolated 2. As this occurred in a period when global travel
was much more accessible than in 2002, it meant a potentially large number of individual carriers were potentially
unknowingly spreading the disease before public health ocials had a chance to react.
Lastly, the studies that did address COVID-19 have been primarily of cross-sectional nature only 6,18. is
means previous research has not provided any information on temporal precedence of the eects. For example,
we do not know whether those subjective perceptions are indeed antecedents even though social psychology
has long recognized that subjective perceptions can change to match their prior behavior (e.g., self-perception
theory) 30. e present research seeks to overcome these limitations by studying predictors at all three levels of
analysis over time in the context of COVID-19 to establish temporal precedence. In doing so, we aim to identify
factors that predict compliance with preventative interventions that would allow policy makers to cra eective
interventions to mitigate the COVID-19 pandemic through behaviors such as hand washing, quarantining, and
social distancing.
Results
We tested whether the hypothesized predictors were reliably associated with changes in health behavior and
support for public health recommendations, while controlling for stability of the dependent variable and the
inuence of age, gender, employment status, education, religion, political view, date survey taken, time interval
between measurements, as well as subjective proximity to COVID-19 cases.
We standardized all predictors with respect to the sample mean and standard deviation. is was considered
optimal as standardized coecients are not currently available for multilevel models with random slopes esti-
mated in Mplus. Such grand-mean centering is typically used when within-cluster eects are not the primary
target of inference 31. e regression coecients of the standardized predictors can be interpreted as approximate
indicators of relative variable importance, albeit disregarding dierences across levels and random eects (i.e.,
dierences in variable importance across countries). For each hypothesized eect, we report whether the average
eect was signicant across countries and whether there was signicant between-country variance in the eect.
e multilevel design was hence employed to address the country-level hypotheses.
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Adherence to recommended health behaviors. Table1 shows the theoretically-derived variables that
reliably predicted adherence to health behaviors recommended by public health guidelines. With regards to per-
sonal hygiene, hand washing (LL = −3950.25, AIC = 7984.50, BIC = 8241.23) showed a signicant autoregressive
eect (B = 0.80; p < 0.001; CI [0.54, 1.06]), indicating stability across the waves. is stability varied signicantly
across countries (B = 0.19; p < 0.001; CI [0.07, 0.31]). Aer controlling for this autoregressive eect, changes in
hand washing were predicted by perceived risk to becoming infected (B = 0.05; p < 0.001; CI [0.02, 0.09]), belief
in conspiracy theories (B = 0.04; p < 0.001; CI [0.02, 0.07]), and perceptions that one receives clear and unam-
biguous messages about what to do about the coronavirus (B = 0.04; p > 0.001; CI [0.01, 0.08]). is suggests that,
over time, individuals who perceive greater risk to becoming infected, greater conspiracies, and greater message
clarity washed their hands more frequently. ese eects did not dier between countries, ps > 0.40.
Willingness to wear a face mask (LL = -1806.83, AIC = 3669.67, BIC = 3809.73) showed high stability across
waves (B = 2.02; p < 0.001; CI [1.38, 2.21]). is stability did not vary between countries, p > 0.79. When control-
ling for the autoregressive eect, changes in willingness to wear a face mask were predicted by subjective proxim-
ity to COVID-19 cases (B = 0.30; p = 0.05; CI [0.00, 0.59]). is eect did not dier between countries, p > 0.99.
Avoiding crowds (LL = −3719.47, AIC = 7522.94, BIC = 7779.68) showed a signicant autoregressive eect
(B = 0.59; p < 0.001; CI [0.44, 0.73]). is stability varied signicantly across countries, B = 0.17; p < 0.001; CI [0.09,
0.25]). Changes in avoiding crowds were signicantly predicted by social distancing norms (B = 0.16; p < 0.001;
CI [0.09, 0.23]), risk perception to becoming infected (B = 0.06; p < 0.001; CI [0.03, 0.10]), people’s preference
for tight cultural structures (B = 0.04; p < 0.001; CI [0.02, 0.06]), and their belief in conspiracy theories (B = 0.02;
p = 0.01; CI [0.01, 0.04]). Hence, stronger norms regarding social distancing, higher perceived risk to becoming
infected, and a stronger preference for tight cultural structures predicted over-time increase in the tendency
to avoid crowds. Only the eect for social norms varied across countries (B = 0.06; p = 0.01; CI [0.02, 0.10]).
Further, authoritarianism (B = 0.04; p < 0.001; CI [0.01, 0.06]) and participants’ age (B = 0.05; p = 0.01; CI [0.01,
0.08]) positively predicted change over time in people’s tendency to avoid crowds. is suggests that, over time,
older and more authoritarian participants avoided crowds more. ese eects did not vary signicantly across
countries, ps > 0.82. Gender (B = −0.05; p = 0.04; CI [0.10, −0.00] was a signicant predictor, with women showing
greater change in the tendency to avoid crowds. Date of survey participation was also predictive; participants
who enrolled later showed decreases in the tendency to avoid crowds over-time (B = −0.01; p = 0.01; CI [−0.02,
−0.01]). ese eects did not dier between countries, ps > 0.09.
Quarantining (LL = −5320.38, AIC = 10,724.75, BIC = 10,981.48) showed a signicant autoregressive eect
(B = 1.06; p < 0.001; CI [0.96, 1.17]), which varied across countries (B = 0.28; p < 0.001; CI [0.20, 0.35]). Changes
in quarantining were predicted by social distancing norms (B = 0.09; p = 0.05; CI [0.00, 0.18]), time between
measurements (B = −0.13; p < 0.001; CI [−0.20, −0.06]), employment status (B = −0.09; p = 0.01; CI [−0.16, −0.03]),
age (B = −0.07; p < 0.001; CI [−0.11, −0.04]), and the date the survey was taken (B = −0.02; p < 0.001; CI [−0.03,
−0.01]). None of these eects did vary signicantly across countries, ps > 0.05.
In-person (face-to-face) contact with friends and family (LL = −7422.56, AIC = 14,929.13, BIC = 15,190.06)
showed signicant stability over time (B = 1.04; p < 0.001; CI [0.92, 1.17]), which varied across countries (B = 0.75;
p < 0.001; CI [0.60, 0.90]). Changes in social face-to-face contact with friends and family were predicted by social
norms on distancing (B = −0.17; p < 0.001; CI [−0.24, −0.09]), participants’ age (B = −0.19; p < 0.001; CI [−0.28,
−0.09]) and gender (B = 0.16; p = 0.02; CI [0.03, 0.29]). Overall, social distancing norms were the best predictor
for changes in face-to-face contact. Older participants and men had less in-person contact with friends and
family. ese eects did not dier between countries, ps > 0.24.
Social face to face contact with other people in general (“others”, LL = −7272.44, AIC = 14,628.88,
BIC = 14,889.61) showed signicant stability over time (B = 1.12; p < 0.001; CI [1.04, 1.21]). is autoregressive
eect varied across countries (B = 0.45; p < 0.001; CI [0.34, 0.57]). Controlling for this, changes in social face
to face contact with others were predicted by social distancing norms (B = −0.14; p < 0.001; CI [−0.22, −0.07])
Table 1. Predictors of health behaviors.
Health behavior Predictor B p CI
Hand washing
Perceived risk to becoming infected 0.05 < 0.001 [0.02, 0.09]
Belief in conspiracy theories 0.04 < 0.001 [0.02, 0.07]
Getting clear and unambiguous messages about what
to do 0.04 < 0.001 [0.01, 0.08]
Avoiding crowds
Social norms 0.16 < 0.001 [0.09, 0.23]
Perceived risk to becoming infected 0.06 < 0.001 [0.03, 0.10]
Preference for cultural tightness 0.04 < 0.001 [0.02, 0.06]
Belief in conspiracy theories 0.02 0.01 [0.01, 0.04]
Self-isolation/quarantine Social norms 0.09 0.05 [0.00, 0.18]
Face-to-face contact with friends and family Social norms −0.17 < 0.001 [−.24, −0.09]
Face to face contact with other people Social norms −0.14 < 0.001 [−0.22, −0.07]
Preference for cultural tightness −0.06 0.04 [−0.11, −0.00]
Days per week people le their house Social norms −0.06 < 0.001 [−0.08, −0.04]
Cultural tightness −0.04 0.04 [−0.07, −0.00]
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and preference for tightness (B = −0.06; p = 0.04; CI [−0.11, −0.00]. Another predictor was employment status
(B = 0.48; p < 0.001; CI [0.29, 0.66]), which diered across countries (B = 0.85; p > 0.001; CI [0.39, 1.31]), all other
ps > 0.10.
e number of days in a week people le their house (LL = −3861.18, AIC = 7806.35, BIC = 8067.47) showed
signicant stability over time (B = 0.79; p < 0.001; CI [0.76, 0.82]). Changes in the number of days in a week people
leaving their house were predicted by social norms on distancing (B = −0.06; p < 0.001; CI [−0.08, −0.04]), cultural
tightness (B = −0.04; p = 0.04; CI [−0.07, −0.00]), being employed (B = 0.13; p < 0.001; CI [0.08, 0.18]), and being
politically on the right side of the spectrum (B = 0.02; p = 0.04; CI [0.00, 0.04]). ere were no between country
dierences in these eects, all ps > 0.26.
Attitudes toward behavioral mandates. Table2 shows the theoretically derived variables that pre-
dict attitudes toward behavioral mandates. People’s support for mandatory vaccination (LL = −5688.01,
AIC = 11,460.03, BIC = 11,719.13) showed signicant stability over time (B = 1.36; p < 0.001; CI [1.25, 1.47]). is
autoregressive eect varied across countries (B = 0.13; p = 0.02; CI [0.02, 0.24]). Controlling for it, changes in
support for mandatory vaccination were predicted by participants’ trust in the government to ght COVID-19
(B = 0.07; p = 0.01; CI [0.01, 0.13]). is shows that people support mandatory vaccination more when they have
greater trust in the government to ght COVID-19 eectively. Moreover, changes in support for mandatory vac-
cination were predicted by social distancing norms (B = 0.07; p = 0.01; CI [0.02, 0.13]), being politically on the
right side of the spectrum (B = −0.04; p = 0.03; CI [−0.08, −0.00]), and the date the survey was taken (B = −0.02;
p < 0.001; CI [−0.03, −0.01]; no between country dierences, all ps > 0.58).
Results further revealed that support for mandatory quarantine (LL = −4965.04, AIC = 10,014.08,
BIC = 10,273.18) showed signicant stability over time (B = 0.66; p < 0.001; CI [0.58, 0.74]). is autoregressive
eect varied signicantly across countries (B = 0.19; p > 0.001; CI [0.12, 0.26]). Controlling for it, individuals’
changes in support for mandatory quarantine were predicted by social distancing norms (B = 0.17; p < 0.001;
CI [0.09, 0.24]), trust in the government to ght COVID-19 (B = 0.08; p > 0.001; CI [0.02, 0.34]), preference for
cultural tightness (B = 0.08; p < 0.001; CI [0.02, 0.13]), being female (B = −0.08; p = 0.05; CI [−0.17, −0.00]), age
(B = 0.05; p = 0.01; CI [0.01, 0.10]), authoritarianism (B = 0.05; p = 0.02; CI [0.01, 0.09]), time interval between
initial and follow-up measurement (B = −0.04; p = 0.01; CI [−0.07, −0.01]), and the date the survey was taken
(B = −0.01; p < 0.001; CI [−0.02, −0.00]). ere were no between country dierences, all ps > 0.36.
People’s readiness to protest containment measures (LL = −1803.26, AIC = 3662.53, BIC = 3806.61) showed
a signicant autoregressive eect, indicating stability across the waves (B = 1.06; p > 0.001; CI [0.88, 1.25]). is
stability varied signicantly across countries (B = 0.33; p > 0.001; CI [0.19, 0.47]). Controlling for the autoregres-
sive eect, changes in the readiness to protest containment measures were predicted by participants’ perception
that they were getting clear and unambiguous messages about the coronavirus (B = −0.07; p = 0.04; CI [−0.13,
−0.00]). Lastly, changes in readiness to protest containment measures was predicted by whether participants were
religious or not (B = 0.15; p > 0.001; CI [0.07, 0.23]; no between country dierences, ps > 0.09).
Discussion
For behavioral science to successfully inform public health policy towards mitigating the pandemic, there is
a need to conduct holistic, cross-cultural, longitudinal research that identies the unique eects of various
candidate predictors on relevant outcomes of interests. Simultaneously testing multiple candidate predictors
can help to pinpoint the most important predictors for a given outcome or a set of outcomes. e global scale
of a pandemic may call for a unied global response, which means that a candidate predictor should be tested
across cultures to determine its generalizability. A longitudinal approach helps to establish temporal precedence
between predictors and outcomes, which gives early insight into potential causal inferences that can be tested
with intervention studies.
Given these aims, the present research used a longitudinal design to identify the subjective perceptions that
predict individuals’ changes, over time, in adherence to behaviors recommended by public health guidance and
support of general public health policies in the context of COVID-19. We specically tested several predictors
that have been found potentially relevant by prior literature, that is, risk perception, social norms, punishments,
trust in the government, clear and unambiguous messages, cultural tightness, and belief in conspiracy theories.
Several attitudes and behaviors related to public health (e.g., quarantining, wearing a face mask, etc.) were
assessed as critical outcome variables at numerous points in time. is approach helps to determine whether
certain factors are generally predictive, across multiple outcomes of interest, or whether dierent subjective
Table 2. Predictors of attitudes toward behavioral mandates.
Attitudes toward mandates Predictor B p CI
Mandatory vaccination Trust in the government to ght COVID-19 0.07 0.01 [0.01, 0.13]
Social norms 0.07 0.01 [0.02, 0.13]
Mandatory quarantine
Social norms 0.17 < 0.001 [0.09, 0.24]
Trust in the government to ght COVID-19 0.08 < 0.001 [0.02, 0.34]
Preference for cultural tightness 0.08 < 0.001 [0.02, 0.13]
Protest containment measures Getting clear and unambiguous messages about what to do −0.07 0.04 [−0.13, −0.00]
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perceptions predict changes in specic virus prevention behaviors and attitudes over time. In this vein, the
most generalizable predictor was social and community-level norms, which reliably predicted outcomes across
conceptual levels, such as personal hygiene, public contact, and attitudes towards behavioral mandates. In prac-
tices, this means public health ocials should strongly consider existing and developing social norms not only
in what measures are needed to mitigate a pandemic, but also precisely how to communicate those messages.
However, social norms did not predict all outcomes and, ultimately, each outcome had its own idiosyncratic set
of predictors.
First-order perceptions of perceived individual risk of becoming infected predicted changes in adherence to
important health behaviors such as frequent hand washing. A possible implication of these ndings could be to
install nudges (small changes in choice architecture that encourage optimal decisions without force or genuine
changes in the circumstance 32) in public bathrooms that are related to concepts of risk infection. At a minimum,
our ndings suggest a potential utility for nudges that would increase the salience regarding the risk of becoming
infected (e.g., pictures of hands in which germs made visible through U.V. light could trigger disgust 33.
None of the theoretically-relevant variables predicted a change in willingness to engage in another health
behavior. However, particularly for wearing face masks, participants in proximity to infected individuals showed
an increased tendency to use face masks. is proximity and familiarity eect may have been even stronger
when people know someone in their social network who is infected. Indeed, people make judgments about the
frequency or likelihood of events based on how available information is to them 34. Given this proximity eect,
there is reason for policymakers to consider encouraging individuals to share positive diagnoses, or at least aim
to reduce stigma about positive results. If the eect is accurate, being made aware of proximal infections could
directly reduce negative attitudes toward healthy behaviors, if not directly increase likelihood of engaging in
those behaviors recommended by public health ocials. Such a nding can therefore directly inform public
health messaging.
Second-order social distancing norms predict changes in social distancing behavior. Public health messages
could aim at changing social norms or making existing ones more salient. For instance, common approaches
to modifying social norms are to change possible misperceptions of social norms 35 or to make certain group
memberships more salient 36.
Testing third-order eects showed clearly that trust in the government to eectively ght the COVID-19
pandemic predicts increased support for mandatory measures. In other words, as may seem obvious but is criti-
cal to state outright: where there is trust in the government to be eective, there is substantially greater support
for initiatives to combat a pandemic. Some considerations of this nding mean that, for instance, it is important
for governments to have frequent and informative briengs, in which leaders address the nation and give clear
instructions on what to do.To build trust from these, there would ideally be information that shows how past
guidelines have been eective, and what to expect out of the newest recommendations.
Also, preference for cultural tightness predicted over-time increases in the tendency to support behavioral
mandates. is backs the idea that countries with tighter cultures are better prepared to handle the outbreak 37.
Hence, policy makers should sensitize the public for a temporary need for tighter structures.
To conclude, the present ndings specify the subjective perceptions that policy makers should use as levers
of intervention for containment of the coronavirus pandemic. Despite the surge in behavioral research related
directly to the COVID-19 pandemic, longitudinal analyses are still lacking. Compared to cross-sectional studies,
autoregressive longitudinal research can make a stronger claim to causality, because it meets the requirements
of Granger causality 38.
Despite several strengths, this study also has some limitations. Like most social scientic research, our
approach assumed linearity of associations between variables. Unfortunately, this assumption is dicult to check
in the structural equation modeling context—but no strong theoretical evidence exists to presume a dierent
shape for the associations presented here. Furthermore, the predictors were chosen based on their theoretical
merit. is is not to say that there are no other potential determining factors for the behaviors investigated
here such as cultural ones. Although our data comprised multiple countries, we did not consider the potential
inuence of cultural moderators. It should be noted, however, that 18 countries is a very small sample size for
detecting between-country dierences. Moreover, almost all random slopes in the study were non-signicant,
indicating that there was little to no variance to be explained at the between-country level.
e current research can directly inform ocial public health messaging and intervention eorts. First, the
potential eectiveness for nudges to encourage hand washing is appropriate for eld-testing. Concurrently, poli-
cymakers may wish to focus eorts on raising awareness of proximal infections, which appears to increase the
likelihood of engaging in healthy behaviors. In doing so, public health messages should either aim at promoting
positive social norms or making existing ones more salient. Finally, transparency in the process for all of these
has clear importance and governments should be applied this to maximize eectiveness and build trust for the
well-being of the communities they serve.
Methods
Participants and design. Our international survey (https:// www. psyco rona. org) conducted in April 2020
served as the baseline. Participants could opt-in for weekly follow-ups lasting until June 2020. is longitudinal
study is the focus of the present analysis. Tables3 and 4 describe the participants per wave and country in our
nal analysis (N = 3040). Full methodological details, including exact dates of the measurements and description
of items/questionnaires in all languages, are provided in the survey codebook (https:// osf. io/ qhyue). Informed
consent was obtained and all methods were performed in accordance with the relevant guidelines and regula-
tions.
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Measures
Predictor variables. Personal risk perception (rst-order eects). Risk perception infection. We asked:
“How likely is it that the following will happen to you in the next few months? You will get infected with corona-
virus” (1 = Exceptionally unlikely; 8 = Already happened).
Risk perception economic. We also ask how likely they thought their personal situation will get worse due to
economic consequences of coronavirus (1 = Exceptionally unlikely; 8 = Already happened).
Subjective social and community norms (second-order eects). Social norms. Participants indicated their
agreement with: “Right now, people in my area should self-isolate and engage in social distancing” (-3 = Strongly
disagree, 3 = Strongly agree).
Community punishment. “To what extent is your community punishing people who deviate from the rules that
have been put in place in response to the coronavirus?” (1 = Not at all; 6 = very much).
Political and governmental perceptions (third-order eects). Perceived government’s ecacy to ght
COVID-19. We asked participants how much they trusted the government of their country to take the right
measures to deal with the coronavirus pandemic (1 = Not at all; 5 = A great deal).
Clarity of communication. We measured the extent to which participants believe they are “getting clear, unam-
biguous messages about what to do about the coronavirus?” (1 = messages are completely unclear/ambiguous,
6 = messages are very clear/unambiguous).
Generic conspiracy beliefs. We used three items to assess participants’ perceptions of societal-level conspira-
cies (e.g., “I think that government agencies closely monitor all citizens; I think that politicians usually do not
tell us the true motives for their decisions” (0 = Certainly not 0%, 10 = Certainly 100%).
Cultural tightness. We used country-level scores for a country’s position on the looseness-tightness spectrum.
Additionally, we asked people to indicate to what extent they thought that the country they currently live in
should have the following characteristics right now? (1 = Have exible social norms, 9 = Have rigid social norms;
1 = Be loose, 9 = Be tight; 1 = Treat people who don’t conform to norms kindly, 9 = Treat people who don’t conform
to norms harsh).
Table 3. Observations per country for health behaviors and attitudes toward behavioral mandates.
Country
Health behaviors Attitudes toward behavioral mandates
Hand washing Avoiding
crowds Self-isolation/
quarantine Wearing face
mask
Face-to-face
contact
friends and
family
Face to face
contact other
people
Days per
week house
le Mandatory
vaccination Mandatory
quarantine
Protest
containment
measures
USA 94 94 94 34 109 109 109 94 94 41
UK 191 191 191 99 232 231 232 191 191 116
Ukraine 103 103 103 33 142 141 142 103 103 37
Tur key 103 103 103 9 114 113 116 103 103 12
Spain 191 191 191 82 227 227 229 190 190 94
South Korea 5 5 5 2 11 11 11 5 5 2
Saudi Arabia 34 34 34 6 49 48 49 34 34 11
Russia 159 159 159 24 163 164 164 159 159 26
Netherlands 132 132 132 134 194 192 194 132 132 147
Japan 63 63 63 32 69 70 70 63 63 35
Italy 331 331 331 128 355 352 355 331 331 143
Indonesia 69 69 69 11 85 85 85 69 69 14
Greece 101 101 101 60 171 171 171 101 101 68
Germany 189 189 189 114 223 224 225 189 189 127
Canada 166 166 166 52 183 183 184 166 166 63
Brazil 166 166 166 59 180 179 181 166 166 65
Australia 151 151 151 58 161 160 161 151 151 69
Argentina 159 159 159 43 189 188 190 159 159 47
Tot a l 2407 2407 2407 980 2857 2848 2868 2406 2406 1117
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NAge Gender Education Religious
USA 116
18–24 = 1%
25–34 = 4%
35–44 = 15%
45–54 = 22%
55–64 = 30%
65–75 = 24%
75–85 = 5%
85 + = 0%
Female = 71%
Male = 30%
Other = 0%
Primary education = 2%
General secondary education = 22%
Vocational education = 9%
Higher education = 33%
Bachelor’s degree = 21%
Master’s degree = 11%
PhD degree = 3%
No = 43%
Yes = 57%
UK 241
18–24 = 1%
25–34 = 5%
35–44 = 14%
45–54 = 21%
55–64 = 21%
65–75 = 33%
75–85 = 5%
85 + = 1%
Female = 51%
Male = 49%
Other = 0%
Primary education = 0%
General secondary education = 33%
Vocational education = 24%
Higher education = 14%
Bachelor’s degree = 22%
Master’s degree = 6%
PhD degree = 0%
No = 68%
Yes = 32%
Ukraine 154
18–24 = 4%
25–34 = 21%
35–44 = 11%
45–54 = 13%
55–64 = 38%
65–75 = 13%
75–85 = 0%
85 + = 0%
Female = 54%
Male = 46%
Other = 0%
Primary education = 0%
General secondary education = 6%
Vocational education = 15%
Higher education = 45%
Bachelor’s degree = 7%
Master’s degree = 24%
PhD degree = 3%
No = 38%
Yes = 62%
Tur key 121
18–24 = 3%
25–34 = 26%
35–44 = 27%
45–54 = 17%
55–64 = 22%
65–75 = 4%
75–85 = 0%
85 + = 0%
Female = 56%
Male = 44%
Other = 0%
Primary education = 1%
General secondary education = 1%
Vocational education = 19%
Higher education = 10%
Bachelor’s degree = 56%
Master’s degree = 10%
PhD degree = 3%
No = 34%
Yes = 66%
Spain 242
18–24 = 2%
25–34 = 6%
35–44 = 7%
45–54 = 19%
55–64 = 30%
65–75 = 32%
75–85 = 4%
85 + = 0%
Female = 47%
Male = 53%
Other = 0%
Primary education = 4%
General secondary education = 19%
Vocational education = 14%
Higher education = 21%
Bachelor’s degree = 30%
Master’s degree = 7%
PhD degree = 5%
No = 57%
Yes = 43%
South Korea 12
18–24 = 0%
25–34 = 25%
35–44 = 42%
45–54 = 17%
55–64 = 17%
65–75 = 0%
75–85 = 0%
85 + = 0%
Female = 50%
Male = 50%
Other = 0%
Primary education = 0%
General secondary education = 8%
Vocational education = 0%
Higher education = 25%
Bachelor’s degree = 50%
Master’s degree = 8%
PhD degree = 8%
No = 50%
Yes = 50%
Saudi Arabia 54
18–24 = 6%
25–34 = 37%
35–44 = 35%
45–54 = 13%
55–64 = 9%
65–75 = 0%
75–85 = 0%
85 + = 0%
Female = 52%
Male = 48%
Other = 0%
Primary education = 0%
General secondary education = 11%
Vocational education = 2%
Higher education = 4%
Bachelor’s degree = 74%
Master’s degree = 7%
PhD degree = 2%
No = 17%
Yes = 83%
Russia 166
18–24 = 1%
25–34 = 10%
35–44 = 16%
45–54 = 26%
55–64 = 27%
65–75 = 20%
75–85 = 1%
85 + = 0%
Female = 58%
Male = 42%
Other = 0%
Primary education = 0%
General secondary education = 5%
Vocational education = 26%
Higher education = 44%
Bachelor’s degree = 9%
Master’s degree = 13%
PhD degree = 2%
No = 36%
Yes = 64%
Netherlands 224
18–24 = 0%
25–34 = 0%
35–44 = 8%
45–54 = 19%
55–64 = 33%
65–75 = 30%
75–85 = 9%
85 + = 0%
Female = 55%
Male = 45%
Other = 0%
Primary education = 4%
General secondary education = 25%
Vocational education = 40%
Higher education = 22%
Bachelor’s degree = 4%
Master’s degree = 5%
PhD degree = 1%
No = 54%
Yes = 46%
Japan 74
18–24 = 11%
25–34 = 15%
35–44 = 7%
45–54 = 16%
55–64 = 23%
65–75 = 23%
75–85 = 4%
85 + = 1%
Female = 53%
Male = 47%
Other = 0%
Primary education = 0%
General secondary education = 12%
Vocational education = 3%
Higher education = 16%
Bachelor’s degree = 62%
Master’s degree = 5%
PhD degree = 1%
No = 80%
Yes = 20%
Continued
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Outcome variables. Health behavior. Respondents were asked how much they agreed with: “To mini-
mize my chances of getting coronavirus, I” “…wash my hands more oen”, “…avoid crowded spaces”, and “…put
myself in quarantine” (-3 = Strongly disagree, 3 = Strongly agree). We assessed face mask use: "In the past week, I
have covered my face in public places." (1 = (Almost) never, 2 = ((Almost) always).1 In addition, we asked: “In the
past 7days, how many days did you have in-person (face-to-face) contact with other people in general [friends
or relatives]” (0 = 0days, 7 = 7days). We also inquired: “In the past week, how oen did you leave your home?
(1 = I did not leave my home, 4 = Four times or more).
Attitudes toward behavioral mandates. Participants indicated their agreement with: "I would sign a petition that
supports…mandatory vaccination once a vaccine has been developed for coronavirus…mandatory quarantine
NAge Gender Education Religious
Italy 370
18–24 = 4%
25–34 = 10%
35–44 = 20%
45–54 = 18%
55–64 = 16%
65–75 = 30%
75–85 = 2%
85 + = 1%
Female = 46%
Male = 54%
Other = 0%
Primary education = 1%
General secondary education = 9%
Vocational education = 8%
Higher education = 52%
Bachelor’s degree = 6%
Master’s degree = 21%
PhD degree = 4%
No = 35%
Yes = 65%
Indonesia 88
18–24 = 22%
25–34 = 41%
35–44 = 17%
45–54 = 12%
55–64 = 8%
65–75 = 0%
75–85 = 0%
85 + = 0%
Female = 42%
Male = 58%
Other = 0%
Primary education = 0%
General secondary education = 28%
Vocational education = 9%
Higher education = 6%
Bachelor’s degree = 53%
Master’s degree = 3%
PhD degree = 0%
No = 16%
Yes = 84%
Greece 188
18–24 = 4%
25–34 = 5%
35–44 = 9%
45–54 = 20%
55–64 = 41%
65–75 = 20%
75–85 = 1%
85 + = 0%
Female = 47%
Male = 53%
Other = 0%
Primary education = 1%
General secondary education = 3%
Vocational education = 6%
Higher education = 27%
Bachelor’s degree = 45%
Master’s degree = 15%
PhD degree = 4%
No = 28%
Yes = 72%
Germany 243
18–24 = 2%
25–34 = 3%
35–44 = 10%
45–54 = 19%
55–64 = 29%
65–75 = 34%
75–85 = 4%
85 + = 0%
Female = 52%
Male = 48%
Other = 0%
Primary education = 0%
General secondary education = 7%
Vocational education = 54%
Higher education = 14%
Bachelor’s degree = 9%
Master’s degree = 13%
PhD degree = 2%
No = 72%
Yes = 28%
Canada 189
18–24 = 2%
25–34 = 6%
35–44 = 14%
45–54 = 30%
55–64 = 18%
65–75 = 25%
75–85 = 4%
85 + = 0%
Female = 50%
Male = 50%
Other = 0%
Primary education = 1%
General secondary education = 22%
Vocational education = 20%
Higher education = 17%
Bachelor’s degree = 26%
Master’s degree = 12%
PhD degree = 2%
No = 65%
Yes = 35%
Brazil 190
18–24 = 6%
25–34 = 18%
35–44 = 17%
45–54 = 24%
55–64 = 19%
65–75 = 13%
75–85 = 2%
85 + = 0%
Female = 55%
Male = 45%
Other = 0%
Primary education = 1%
General secondary education = 21%
Vocational education = 14%
Higher education = 26%
Bachelor’s degree = 28%
Master’s degree = 7%
PhD degree = 2%
No = 26%
Yes = 74%
Australia 172
18–24 = 0%
25–34 = 6%
35–44 = 16%
45–54 = 17%
55–64 = 24%
65–75 = 30%
75–85 = 7%
85 + = 1%
Female = 55%
Male = 45%
Other = 0%
Primary education = 1%
General secondary education = 24%
Vocational education = 24%
Higher education = 17%
Bachelor’s degree = 26%
Master’s degree = 6%
PhD degree = 1%
No = 63%
Yes = 37%
Argentina 196
18–24 = 2%
25–34 = 15%
35–44 = 12%
45–54 = 24%
55–64 = 33%
65–75 = 12%
75–85 = 3%
85 + = 0%
Female = 62%
Male = 38%
Other = 0%
Primary education = 3%
General secondary education = 24%
Vocational education = 16%
Higher education = 23%
Bachelor’s degree = 26%
Master’s degree = 8%
PhD degree = 1%
No = 43%
Yes = 57%
Table 4. Demographics of longitudinal sample.
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for those that have coronavirus and those that have been exposed to the virus” (-3 = Strongly disagree, 3 = Strongly
agree), and “I would join a protest against social distancing measures” (-2 = Strongly disagree, 2 = Strongly agree).
Also, we measured age, gender (0 = female; 1 = male), employment status, education, political view, religion,
the date the survey was taken, as well as whether participants knew of any COVID-19 cases among friends and
family.
Strategy of analyses. We conducted our analyses in R (https:// www.R- proje ct. org/), using the workow
for open reproducible code in science (WORCS) to make the analyses reproducible 39. All code is available on
GitHub. Mplus 8.0 was used to estimate the multilevel models with full information maximum likelihood esti-
mation, which is robust to non-normality of residuals and makes use of all available data without imputing miss-
ing values. We automated analyses and tabulated results using the MplusAutomation (https:// CRAN.R- proje ct.
org/ packa ge= Mplus Autom ation) and tidySEM R-packages (www. github. com/ cjvan lissa/ tidyS EM).
To examine over-time predictive eects, we restructured the data to long format, with one observation per
time point per participant. Table5 shows how many time points were available for each variable. For each time
point t, the dependent variable was taken at t + 1. An autoregressive eect was included for the dependent vari-
able, thus controlling for stability, and we included the time dierence Dt as a control variable. e results should
thus be interpreted in terms of change in the dependent variable. We present the most parsimonious model with
only the direct eects, as preliminary tests of interactions between all predictors and Dt indicated convergence
issues and few were reliable. Regarding predictor variables, we selected all available data that coincided with
the available waves of the dependent variable. We entered the predictors simultaneously to isolate their unique
eects, above and beyond any explanatory variance shared among predictors. When repeated measures were
available for a predictor variable, it was used as a within-participants factor. us, for predictors with repeated
measures, the predictor at each time point t was used to predict the dependent variable at t + 1. If only a single
assessment of a predictor variable was available, it was used as a between-participants factor.
Open practices statement. e study was not formally preregistered but all data will be made available
online upon publication. Full methodological details, including exact dates of the waves and questionnaires in
all languages, are provided in the survey codebook (https:// osf. io/ qhyue). We conducted our analyses in R, using
the workow for open reproducible code in science to make the analyses reproducible. All code is available on
GitHub (https:// github. com/ cjvan lissa/ schum pe).
Ethics statement. e study was approved by the Ethics Committees of the University of Groningen (PSY-
1920-S-0390)and New York University Abu Dhabi (HRPP-2020-42).
Received: 20 February 2021; Accepted: 13 December 2021
References
1. Cowling, B. J. & Aiello, A. E. Public health measures to slow community spread of coronavirus disease 2019. J. Infect. Dis. 221(11),
1749–1751. https:// doi. org/ 10. 1093/ infdis/ jiaa1 23 (2020).
2. Wilder-Smith, A., & Freedman, D. O. Isolation, quarantine, social distancing and community containment: Pivotal role for old-
style public health measures in the novel coronavirus (2019-nCoV) outbreak.J. Travel Med. 27(2), taaa020. https:// doi. org/ 10.
1093/ jtm/ taaa0 20 (2020).
3. Bavel, J. J. V. et al. Using social and behavioural science to support COVID-19 pandemic response. Nat. Hum. Behav. 4(5), 460–471.
https:// doi. org/ 10. 1038/ s41562- 020- 0884-z (2020).
Table 5. Observations per time point for health behaviors and attitudes toward behavioral mandates.
Wav e
Health behaviors Attitudes toward behavioral mandates
Hand washing Avoiding
crowds Self-isolation/
quarantine Wearing face
mask
Face-to-face
contact with
friends and
family
Face to face
contact with
other people
Days per week
people le
their house Mandatory
vaccination Mandatory
quarantine
Protest
containment
measures
Baseline 2403 2404 2404 – 2391 2381 2404 2403 2403 –
1 – – – – – – – – – –
2 – – – – – – – – – –
3 – – – – – – – – – –
4 933 933 932 – – – – 1127 1127 –
5 – – – – 1297 1288 1300 – – –
6 – – – 169 – – – – – 203
7 – – – – – – – – – –
8 – – – 930 – – – – – 1066
9 – – – – – – – – – –
10 – – – – – – – – – –
11 – – – – – – – – – –
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Author contributions
B.M.S. developed the study concept and wrote the paper with help by N.P.L. Data analysis were performed by
C.J.V.L. All other authors contributed to the study design, provided critical revisions, or contributed to data
collection. All authors approved the nal version of the manuscript for submission.
Funding
is research received support from the New York University Abu Dhabi (VCDSF/75-71015), the University of
Groningen (Sustainable Society & Ubbo Emmius Fund), and the Instituto de Salud Carlos III (COV20/00086),
co-funded by the EuropeanRegional Development Fund (ERDF) “A way to make Europe.”
Competing interest
e authors declare no competing interests.
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