Association between COVID-19 Outcomes and Mask Mandates, Adherence, and Attitudes

Adjodah, Dhaval; Dinakar, Karthik; Chinazzi, Matteo; Fraiberger, Samuel P.; Pentland, Alex; Bates, Samantha; Staller, Kyle; Vespignani, Alessandro; Bhatt, Deepak L.

doi:10.1101/2021.01.19.21250132

Cited by 16 publications

(11 citation statements)

References 85 publications

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“…Regulations around daily life, as seen in the pandemic response can be considered environmental cues to action. Mandatory mask-wearing had been implemented in the majority of European countries ( 32 ) included in this study and is a strong predictor for their use ( 43 ). Consequently, non-adherence can result in fines of up to €1,000 ( 44 ), illustrating a motivator that cannot be readily applied to preventive hygiene.…”

Section: Discussionmentioning

confidence: 99%

Behavioral Risk Factors and Adherence to Preventive Measures: Evidence From the Early Stages of the COVID-19 Pandemic

Mendoza-Jiménez

Hannemann

Atzendorf

2021

Front. Public Health

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Behavioral risk factors, such as smoking, excessive alcohol consumption, physical inactivity, obesity, and unhealthy food intake are added risk factors for severe outcomes of COVID-19 infections. Preventive measures to avoid infections are therefore particularly important for individuals engaging in behavioral risk factors. We seek to determine whether behavioral risk factors (BRFs) play a significant role in the adherence to preventive COVID-19 measures in a population aged 50 and above. The SHARE wave 8 (Survey of Health, Ageing and Retirement in Europe) and SHARE COVID-19 Survey served as the database, resulting in an analytical sample of 17,588 respondents from 23 European countries plus Israel. Of these 36.04% engaged in at least one BRF and 16.68% engaged in 3 or more BRFs. Multilevel logistic regressions revealed that engagement in one BRF was significantly associated with less adherence to hygiene preventive measures, i.e., hand-sanitizing, hand-washing and covering coughs and sneezes (OR: 0.86; 95% CI: 0.78; 0.94), as was engagement in two BRFs (OR: 0.85; 95% CI: 0.74; 0.97) and three or more BRFs (OR: 0.72; 95% CI: 0.59; 0.88). No such association was found between engagement in BRFs and adherences to social isolation preventive measures, i.e., avoiding meeting more than five people, visiting others or going shopping, or regulated preventive measures, i.e., wearing a mask and keeping physical distance. The found association was also stronger when three or more BRFs were engaged in (1 vs. 3 BRFs: χ2 = 3.43, p = 0.06; 2 vs. 3 BRFs: χ2 = 6.05; p = 0.01). The study gives insight into the protective behavior of a population with inherent vulnerability during a global health emergency. It lays the foundation for follow-up research about the evolution of adherence to preventive measures as the pandemic progresses and about long-term behavioral changes. In addition, it can aide efforts in increasing preventive compliance by raising awareness of the added risk behavioral risk factors pose.

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Section: Discussionmentioning

confidence: 99%

Behavioral Risk Factors and Adherence to Preventive Measures: Evidence From the Early Stages of the COVID-19 Pandemic

Mendoza-Jiménez

Hannemann

Atzendorf

2021

Front. Public Health

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“…The indicators that are available in the COVIDcast API have been used in dashboards produced by COVID Act Now [32], COVID Exit Strategy [33], and others; to inform the Delphi, DeepCOVID [34], and the Institute for Health Metrics and Evaluation (IHME) [35] COVID forecasting models; in various federal and state government reports and analyses; and in a range of news stories. Aside from operational use in decision-making and forecasting, they have also facilitated numerous analyses studying the impacts of COVID-19 on the public, the effectiveness of policy interventions, and factors that influenced the spread of the pandemic [17,18,[36][37][38]. The API currently serves hundreds of thousands of requests to thousands of users every day.…”

Section: Resultsmentioning

confidence: 99%

“…For example, in the context of other infectious diseases: syndromic surveillance in ambulatory clinics and emergency rooms improves the accuracy of outbreak detection for emerging pathogens such as H1N1 [4]; and digital surveillance (based on, e.g., search and social media trends) enables more accurate "nowcasts" and forecasts of traditional disease surveillance streams such as the CDC's ILINet [5,6], as do publication formats providing access to historical versions of a given data set [7,8].Several other examples exist that span a wide variety of data platforms and diseases [9][10][11][12]. During the COVID-19 pandemic, digital data streams have permitted faster prediction of case increases [13,14], while enabling analyses of the impact of public health policies on public behavior, the economy, and disease spread [15][16][17][18].The Delphi Group works with partner organizations and public data sets to build a massive database of indicators tracking COVID-19 activity and other relevant phenomena in the United States, which has been publicly available and continuously updated since April 2 All rights reserved. No reuse allowed without permission.…”

mentioning

confidence: 99%

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An Open Repository of Real-Time COVID-19 Indicators

Reinhart

Brooks

Jahja

et al. 2021

Preprint

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The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID- 19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

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“…In their study of US mandates, Rader et al (2021) [25] did not find a statistically significant relationship between mandates and subsequent wearing. In their study of 4 US states, Adjodah et al (2021) [26] find an average 23% post-mandate increase in wearing. Maloney (2020) [27] finds a 13% post-mandate increase, in US states (in the proportion 'frequently' or 'always' wearing masks).…”

Section: Mandates Are a Poor Proxy For Wearingmentioning

confidence: 97%

Mass mask-wearing notably reduces COVID-19 transmission

Leech

Rogers-Smith²,

Sandbrink

et al. 2021

Preprint

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Mask-wearing has been a controversial measure to control the COVID-19 pandemic. While masks are known to substantially reduce disease transmission in healthcare settings (Howard et al 2021), studies in community settings report inconsistent results (Brainard et al 2020). Investigating the inconsistency within epidemiological studies, we find that a commonly used proxy, government mask mandates, does not correlate with large increases in mask-wearing in our window of analysis. We thus analyse the effect of mask-wearing on transmission instead, drawing on several datasets covering 92 regions on 6 continents, including the largest survey of individual-level wearing behaviour (n=20 million) (Kreuter et al 2020). Using a hierarchical Bayesian model, we estimate the effect of both mask-wearing and mask-mandates on transmission by linking wearing levels (or mandates) to reported cases in each region, adjusting for mobility and non-pharmaceutical interventions. We assess the robustness of our results in 123 experiments across 22 sensitivity analyses. Across these analyses, we find that an entire population wearing masks in public leads to a median reduction in the reproduction number R of 25.8%, with 95% of the medians between 22.2% and 30.9%. In our window of analysis, the median reduction in $R$ associated with the wearing level observed in each region was 20.4% [2.0%, 23.3%]. We do not find evidence that mandating mask-wearing reduces transmission. Our results suggest that mask-wearing is strongly affected by factors other than mandates. We establish the effectiveness of mass mask-wearing, and highlight that wearing data, not mandate data, are necessary to infer this effect.

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Association between COVID-19 Outcomes and Mask Mandates, Adherence, and Attitudes

Cited by 16 publications

References 85 publications

Behavioral Risk Factors and Adherence to Preventive Measures: Evidence From the Early Stages of the COVID-19 Pandemic

Behavioral Risk Factors and Adherence to Preventive Measures: Evidence From the Early Stages of the COVID-19 Pandemic

An Open Repository of Real-Time COVID-19 Indicators

Mass mask-wearing notably reduces COVID-19 transmission

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