key: cord-0146051-6dzb52t7 authors: Badillo-Goicoechea, Elena; Chang, Ting-Hsuan; Kim, Esther; LaRocca, Sarah; Morris, Katherine; Deng, Xiaoyi; Chiu, Samantha; Bradford, Adrianne; Garcia, Andres; Kern, Christoph; Cobb, Curtiss; Kreuter, Frauke; Stuart, Elizabeth A. title: Global Trends and Predictors of Face Mask Usage During the COVID-19 Pandemic date: 2020-12-21 journal: nan DOI: nan sha: 50ac82b617454f4235785ee737339fba0c36e6bd doc_id: 146051 cord_uid: 6dzb52t7 Background: Guidelines and recommendations from public health authorities related to face masks have been essential in containing the COVID-19 pandemic. We assessed the prevalence and correlates of mask usage during the pandemic. Methods: We examined a total of 13,723,810 responses to a daily cross-sectional representative online survey in 38 countries who completed from April 23, 2020 to October 31, 2020 and reported having been in public at least once during the last seven days. The outcome was individual face mask usage in public settings, and the predictors were country fixed effects, country-level mask policy stringency, calendar time, individual sociodemographic factors, and health prevention behaviors. Associations were modelled using survey-weighted multivariable logistic regression. Findings: Mask-wearing varied over time and across the 38 countries. While some countries consistently showed high prevalence throughout, in other countries mask usage increased gradually, and a few other countries remained at low prevalence. Controlling for time and country fixed effects, sociodemographic factors (older age, female gender, education, urbanicity) and stricter mask-related policies were significantly associated with higher mask usage in public settings, while social behaviors considered risky in the context of the pandemic (going out to large events, restaurants, shopping centers, and socializing outside of the household) were associated with lower mask use. Interpretation: The decision to wear a face mask in public settings is significantly associated with sociodemographic factors, risky social behaviors, and mask policies. This has important implications for health prevention policies and messaging, including the potential need for more targeted policy and messaging design. In an effort to control and prevent the spread of the novel coronavirus disease 2019 , health organizations have recommended the use of a face covering or mask in public settings, and an increasing number of studies suggest that face masks may be effective in reducing the transmission of COVID-19. [1] [2] [3] [4] [5] [6] Yet, despite growing evidence of the effectiveness of using face masks, 7 there is still a lack of data and formal studies examining mask-wearing behavior on a global scale. First, it is still unclear how mask-wearing behavior has changed over time across the globe. Second, it is unknown whether individual-or country-level factors are associated with mask-wearing. These questions are critical to better understand and target risky behavior patterns across individuals and places, clarify ongoing public health messaging around face mask usage, and, more generally, help better design prevention campaigns in future public health emergencies. Very few studies have examined the trends and predictors of face mask usage during the COVID-19 pandemic. Increased usage of face masks has been reported in a few countries since Spring 2020, 6, 8, 9 and some studies have examined sociodemographic factors and individual beliefs and attitudes as predictors of mask-wearing, but mostly in the context of past outbreaks such, as SARS-Cov-1 and H1N1. [10] [11] [12] [13] [14] Additional limitations of previous work are that many used small non-random samples (e.g., ~300-5000 self-selected participants); most had a limited time frame (e.g., one or two months) and/or narrow geographical coverage (e.g., one country); and many studies could not simultaneously examine individual-and country-level factors that may explain differences in mask-wearing behavior. The main objective of this study was, therefore, to examine the evolution of mask usage across different countries over time during the COVID-19 pandemic and assess whether individual and country-level factors were associated with the decision to wear a mask. We leveraged a novel dataset from the COVID-19 Symptom Survey, 15 which since April 2020 has daily tracked mask usage, sociodemographic characteristics, and other health prevention behaviors. We used data from respondents who were randomly selected to take a survey between April 23, 2020 to October 31, 2020 and who reported having been in public at least once during the last seven days, which included approximately 13 million adults in 38 countries. To the best of our knowledge, the COVID-19 Symptom Survey is currently the largest data collection effort systematically monitoring mask usage and other social responses to the ongoing COVID-19 pandemic at a global scale with representativeness at a country-level. No other study has used its data to formally assess trends and predictors of mask usage worldwide. The COVID-19 Symptom Survey is an ongoing repeated daily cross-sectional survey conducted by the University of Maryland and Carnegie Mellon University in partnership with Facebook, Inc., and asks various questions related to symptoms, testing, preventive behaviors, mental health, and more. The international version of the survey was launched on April 23, 2020 in >200 countries and territories (Supplementary Table 1 ) and the US version of the survey was launched on April 6, 2020. The survey instrument, sampling design, and weighting methodology are described in more detail below. The Symptom Survey was reviewed and approved by the Institutional Review Boards of both the University of Maryland and Carnegie Mellon University. The survey instrument was developed by public health and survey experts, 15 and included the following sections: COVID-19 related symptoms, testing, contact history, preventive behavior (e.g., face mask usage, hand washing, social distancing, etc.), mental health, economic security, and basic demographics. The questionnaire is publicly available online, 16, 17 and is translated into 56 locales (listed in Supplementary Table 1) . The sampling methodology for the Symptom Survey has been described previously. 18 Briefly, the sampling frame is composed of daily active Facebook users who are >=18 years, living within 200+ countries or territories, and using one of the supported languages. This coverage ensures that >95% of Facebook users are eligible. Every day, the Facebook app invites a stratified random sample to take the survey with an invitation at the top of their News Feed, with the sampling strata defined as the administrative boundaries within countries or territories. 19 Those who view the invitation and are interested in taking the survey are redirected to an off-Facebook survey administered by the academic institutions. Facebook does not share or receive data from the academic partners other than a list of random identification numbers of those who completed the survey to calculate and share survey weights. The details of the weighting methodology have been described previously. 18 Briefly, Facebook employs a two-stage weighting process to minimize bias related to non-response and coverage. In the first step, inverse propensity score weighting is used to adjust for non-response bias by making the sample more representative of the sampling frame of Facebook users. As stated above, Facebook only receives a list of identification numbers that indicate who completed the survey; therefore, the covariates used in this step are obtained from internal Facebook data, which consist of self-reported age, gender, geographical variables, and other attributes that have been found internally to correlate well with survey response. 20 At the second stage poststratification or raking is used to equate the distribution of age and gender among the Facebook population to benchmarks from the United Nations Population Division 2019 World Population Projections, and first administrative level region benchmarks from publicly available population density maps. 21 This analysis included adult participants who responded to the international COVID-19 Symptom Survey from April 23rd, 2020 until October 31st, 2020. We did not include responses from the US Symptom Survey in this analysis, as the question on face mask usage was not incorporated into the US questionnaire until September 2020. Since some of the 200+ countries and territories have relatively small sample sizes, with high variability in responses, we focused on 38 countries based on the following criteria: countries that are considered either members, candidates, or key partners of the Organisation for Economic Co-operation and Development (OECD) convention, 22 or countries with a sample size >600,000 during our study period ( Table 1 ). Over the course of field collection in the selected 38 countries, 741,496,298 Facebook users saw the survey invitation; 36,525,312 opened the survey invitation; and 18,730,575 responded to the survey. Of those, 1,020,188 reported being in public in the past 7 days. Missingness on the predictors ranged from 2-13% per variable, which overall resulted in 27% of the survey respondents being excluded, leading to a final analysis sample of 13,723,810. Our outcome was face mask usage, based on the survey question: "In the last 7 days, how often did you wear a mask when in public?" The response options were "All of the time", "Most of the time", "Some of the time", "A little of the time", "None of the time", or "I have not been in public during the past 7 days". We defined face mask usage as a binary variable: 1 if the respondent reported wearing a mask all or most of the time, and 0 otherwise. We included several individual and country-level factors that could be associated with face mask usage based on a priori hypotheses and existing literature. Individual-level predictors included age, gender, standardized years of education, urbanicity (defined as living in a city versus town, village, or rural area), and the following reported social behaviors from the last 24 hours: working outside the household, going to a market/grocery store/pharmacy, going to a restaurant/ cafe/shopping center, spending time with someone outside their household, and attending a public event with more than 10 people. We also included whether the respondent reported ever being tested for COVID-19, and two variables capturing individual economic aspects: worried about household finances and worked in the last seven days. The three variables on years of education, financial worry, and employment status in the last seven days were added to the survey on June 27, 2020; therefore, data on these items were not available earlier than this date. Country-level predictors were country fixed effects, the (time-varying) presence of official policies related to face masks, and the (time-varying) incidence of COVID-19 disease. The country-level mask usage policies were obtained from the University of Oxford Our World in Data's COVID-19 dataset, which contains daily country-level policies on the use of face coverings outside-of-the-home. The policies are graded from 0-5 and reflect the strength of the policy (i.e., no policy, recommended, required in some specified places, required in all shared/ public spaces, required at all times) for each country. 23 We generated standardized weekly averages of this mask-wearing policy stringency index for each country, and included the index as a continuous variable in the model. Country-day-level COVID-19 cases were obtained from the Johns Hopkins University Center for Systems Science and Engineering's repository, 24 which we used as a standardized seven-day lagged average to measure the association between the rate of COVID-19 cases during the last seven days and the individual's decision to wear a mask. In addition to examining descriptive statistics, a survey-weighted multivariable logistic regression model was used to formally assess whether individual and country-level factors were associated with mask-wearing. All statistical analyses were performed in R (version 4.0.3), using the R survey package (version 4.0) to account for the sampling design. We estimated two separate models to accommodate the fact that the three questions capturing socioeconomic factors (financial worry, years of education, and employment status in the last 7 days) were added later in field collection. The primary model included the entire sample from April 23, 2020, through October 31, 2020, with all predictors described above except for the three not available before June 27, 2020. A secondary model was fit with a narrower time period spanning from June 27, 2020 until October 31, 2020, to include the additional three socioeconomic factors. We included month as a categorical variable in all models. Facebook provided a gift that partially supported the collection of survey data and time for data analysis. Facebook provided feedback on the manuscript, but all analyses were conducted independently by the researchers at Johns Hopkins University. Table 1 documents the countries represented in the analysis sample and their corresponding sample sizes. The countries with the largest sample sizes were Brazil, Mexico, Japan, Italy, and India. Table 2 provides characteristics of the respondents used in analyses. Throughout the data collection period, most indicated that they had not gone to their workplace outside of home (64%), restaurant/cafe/shopping center (75%), or attended a public event with more than 10 people (89%) in the last 24 hours but had gone to a market, grocery store, or pharmacy in the last 24 hours (66%). The majority indicated that they wore a face mask most of the time or all of the time when in public (84%). Trends over time across the 38 countries (Figure 1) suggested considerable heterogeneity in selfreported mask-wearing in public across countries. Some countries had consistently high mask usage (>75%) from April until October (ex: Chile, Italy, Japan, Argentina, Colombia, Turkey, Romania, etc.) ( Figure 1A) . In some other countries, mask usage was relatively low in April, but eventually increased and remained at higher levels (ex: Brazil, Portugal, South Africa, Germany, France, Belgium, Greece, Canada, etc.) ( Figure 1B) . Mask usage was consistently low (<25%) in certain countries (ex: Denmark, Sweden, and Norway) (Figure 1C) , and was more irregular in others (ex: Austria, Czech Republic, Slovenia, etc.) ( Figure 1D ). Results from the logistic regression model confirmed the observed cross-country mask-wearing trends over time, with individuals from the vast majority of countries -particularly of Northern Europe -being significantly less likely to wear a mask when in public than individuals in Japan (the referent country). Individuals were more likely to wear a mask in later months (May: OR In the secondary model, which included data from late-June onwards and the three additional socioeconomic variables (financial worry, years of education, and employment status in the last seven days), the aforementioned demographic, behavioral, and policy-related factors remained significantly associated with face mask usage. The three additional socioeconomic variables were significantly associated with mask-wearing: higher years of education was associated with higher use ( Figure 2 depicts the predicted probabilities of wearing a face mask for a few covariates. The results demonstrate that overall, the probabilities of mask-wearing increased over time from April until November but the extent to which the probabilities increased over time varied considerably depending on country (ranging from ~1% increase in Sweden to 50% increase in the United Kingdom; Figure 2A ). The probability of mask-wearing was also higher among individuals who identify as females ( Figure 2B ) or are living in cities (Figure 2C) , while it was lower among those who have gone out to a restaurant/shopping center (Figure 2D) , socialized outside of the household (Figure 2E ), or attended a large public event ( Figure 2F) . The probabilities varied depending on the country. In this multi-national sample of over 13 million adults from 38 countries, we found that mask usage has evolved differently across countries during the COVID-19 pandemic. In 13 countries, mask usage prevalence stayed at 70% or higher throughout our study period (April through October), while in Denmark, Sweden, and Norway, mask usage has consistently remained below 15%. In most other countries, mask usage was low in April and eventually reached higher levels, although the pace at which this happened varied widely across these countries. A few other countries have shown more irregular trends over time. Certain demographics (i.e., female gender, older age, higher education, living in an urban area) were associated with higher mask use, while more optional or risky behaviors, such as attending a large public event, socializing outside of the household, and going to a restaurant, cafe, or shopping center in the last 24 hours were associated with lower mask use. Examining the strength of country-level face covering policies suggested that stricter mask-wearing policies were associated with higher mask usage. To the best of our knowledge, this is the first study that describes global longitudinal trends of face mask-wearing in a public setting using large nationally representative samples of this scale. Past studies have been limited by narrow geographical coverage and time windows, and most included smaller and non-random samples, with which it is difficult to estimate population representative trends of mask use. 6, 8, 9 Consequently, it is challenging to compare and contrast our mask use estimates or trends to those from other data sources; however, we found that the trends of mask use for some countries in our study (ex: France, Germany, United Kingdom, and Sweden) are similar to reports provided by other online survey platforms such as YouGov's There was considerable heterogeneity in mask use across countries, and some cross-country differences were statistically significant even after adjusting for individual-and country-level factors, such as time-varying mask-wearing policy stringency. These differences in face mask usage across countries suggest that there may be unobserved underlying cultural phenomena across countries that contribute to the adoption of mask-wearing. Pre-existing social norms related to mask-wearing within countries should be taken into consideration when shaping maskrelated policy guidelines. Our findings that certain demographic factors (older age, female gender, and higher education levels) are associated with mask use corroborate findings from past studies reporting that age, gender, and education are significant predictors of face mask usage in the context of other outbreaks, such as SARS-Cov-1 and H1N1. [10] [11] [12] [13] [14] One study conducted in Australia reported that those living in rural areas as opposed to urban areas are more likely to wear a face mask; 11 however, in this current, global study, we observed that those living in urban areas are more likely to wear a mask. Notably, the previous study was conducted in the context of an anticipated outbreak scenario, not during an actual pandemic. Taken together, these findings provide a better understanding of who are more or less likely to wear face masks in public during an outbreak and suggest that public health messaging should better target individuals who do not wear face masks in public as frequently. Interestingly, we found that social behaviors were differentially associated with wearing a mask in that social behaviors that may be deemed more optional and risky in the context of the current pandemic 26 were associated with lower face mask usage, whereas other behaviors that take place outdoors but may be less optional were associated with higher mask use. For example, going out to a large public event, restaurant, cafe, shopping center, or socializing outside of the household in the last 24 hours were associated with lower mask use, whereas going to a market, grocery store, or pharmacy was associated with higher mask use. These results suggest that those who engage in risky social activities during the pandemic are also less likely to wear a mask, and highlight a critical target for public health intervention, as this may contribute to higher risks of COVID-19 spread. 27 Accordingly, our study found that stricter country-level policies around mask-wearing were associated with higher mask use. These results altogether suggest that policies should specifically highlight or put greater emphasis on wearing a face mask in settings where individuals are less likely to do so. The study has some limitations. First, years of education, financial worry, and employment status were not collected throughout the full field collection period, even though these may be important covariates to examine in association with mask use. To address this, however, we fit a secondary model that included these variables during the narrower time period and found that the results for most associations remained very similar. Second, given our non-experimental study design, we cannot infer any causation from our findings. Despite the limitations, there are many strengths to this study. This analysis leveraged the largest ongoing, representative data collection related to COVID-19, which allowed us to examine and compare the trends across many countries and include a long time period spanning seven months. We also simultaneously examined individual-and country-level characteristics in our models. In summary, our study demonstrates various sociodemographic factors, such as older age, female gender, higher education, and urbanicity, are associated with higher face mask usage, while more risky social behaviors, such as going out to a large public event, restaurant, shopping center, and socializing outside of the household are associated with lower mask use. In addition, stronger face mask-related policies are associated with higher mask usage. Taken together, our findings have important implications for health prevention policies and messaging in the context of the ongoing and future public health emergencies, as they highlight the importance of better targeting specific populations and behaviors when designing policies and messaging campaigns. All data used in this study and from the COVID-19 Symptom Survey are available and publicly available on the University of Maryland's website (https://covidmap.umd.edu/api.html). This website also has the survey questionnaire and detailed documentation of the data that are aggregated and uploaded daily. Individual-level data are available for researchers upon request. For instructions, please visit the website at https://covidmap.umd.edu/. Other data used in this study, such as those from Johns Hopkins University's COVID-19 data repository and University of Oxford's COVID-19 dataset are also publicly available online. Years of educa3on Figure 1 : Weighted self-reported weekly mask usage prevalence by country , grouped by A) 2 countries with consistently high face mask usage, B) countries that transitioned from low to high face mask usage, C) countries that had consistently low face mask usage, D) countries that showed irregular trends over time Weights adjust each country sample to their corresponding national population. Effectiveness of Face Masks in Preventing Airborne Transmission of SARS-CoV-2. mSphere Homemade cloth face masks as a barrier against respiratory droplets -Systematic review Community Use Of Face Masks And COVID-19: Evidence From A Natural Experiment Of State Mandates In The US. Health Aff (Millwood) Mask or no mask for COVID-19: A public health and market study Sally Yaacoub HJS. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis Mask Wearing and Control of SARS-CoV-2 Transmission in the United States. medRxiv Preventing the Spread of SARS-CoV-2 with Masks and Other "low-tech" Interventions Public Awareness and Mask Usage during the COVID-19 Epidemic: A Survey by China CDC New Media Factors Associated with Cloth Face Covering Use Among Adults During the COVID-19 Pandemic -United States The use of facemasks to prevent respiratory infection: A literature review in the context of the Health Belief Model Public health measures during an anticipated influenza pandemic: Factors influencing willingness to comply. Risk Manag Healthc Policy Who is that masked person: The use of face masks on Mexico City public transportation during the Influenza A (H1N1) outbreak. Health Policy (New York) Prevalence of preventive behaviors and associated factors during early phase of the H1N1 influenza epidemic Factors influencing the wearing of facemasks to prevent the severe acute respiratory syndrome among adult Chinese in Hong Kong Partnering with Facebook on a university-based rapid turn-around global survey COVID Symptom Survey University of Maryland COVID-19 World Survey Data Weights and methodology brief for the COVID-19 Symptom Facebook Data Policy World population prospects 2019, standard projections Coronavirus Pandemic (COVID-19) Johns Hopkins University CSSE GIS and Data COVID-19 Public Monitor COVID-19) advice for the public Activities & Going Out 64%) 257,938 (1.38%) 254,406 (1.36%) Finland 92%) 398,395 (2.13%) .9%) 89%) 247,065 (1.32%) 238,697 (1.27%) Romania 52%) 518,427 (2.77%) 503,994 (2.69%) SUPPLEMENTARY MATERIALS Supplementary Table 1: List of countries, territories, and languages supported by the COVID-19 Symptom Survey Countries or territories Languages Andorra; United Arab Emirates; Afghanistan; Antigua and Barbuda; Anguilla; Albania; Armenia Democratic Republic of the Central African Republic; Congo; Switzerland Cook Islands; Chile; Cameroon; China; Colombia Faroe Islands; France United Kingdom of Great Britain and Northern Ireland; Grenada; Georgia; French Guiana; Guernsey; Ghana Equatorial Guinea; Greece; Guatemala; Guam Isle of Man; India; Iraq; Iran (Islamic Republic of); Iceland; Italy; Jersey; Jamaica Republic of; Kuwait; Cayman Islands Republic of; Montenegro Macedonia, the former Yugoslav Republic of; Mali; Myanmar; Mongolia Northern Mariana Islands; Martinique; Mauritania Malaysia; Mozambique; Namibia Norfolk Island; Nigeria; Nicaragua; Netherlands; Norway State of; Portugal; Palau; Paraguay; Qatar; Réunion; Romania Russian Federation; Rwanda; Saudi Arabia; Solomon Islands; Seychelles; Sudan; Sweden; Singapore; Saint Helena, Ascension and Tristan da Cunha; Slovenia Syrian Arab Republic United Republic of United States of America; Uruguay; Uzbekistan Virgin Islands (British) Virgin Islands South Africa; Zambia; Zimbabwe Arabic; Azerbaijani; Bulgarian; Bengali; Czech; Cebuano UK); NA; Spanish (Spain); Spanish; Persian; Finnish France); Gujarati; Hebrew; Hindi; Croatian; Hungarian; Indonesian; Italian; Japanese; Kannada; Korean Norwegian (bokmal); Dutch; Punjabi; Polish; Portuguese (Brazil) Portuguese (Portugal); Romanian; Russian; Slovak Simplified Chinese (China); Traditional Chinese