Traffic was down because of CNY. It shot back up on day 3 of CNY.
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AH SAM, big Supporter of Mask and Shutdown, Just have Heart Attack! NZ Just LOCKDOWN!
- Thread starter Kraken
- Start date
Traffic was down because of CNY. It shot back up on day 3 of CNY.
When the data cannot be disputed name calling is the only retort.
Chinese screwed u
I actually think you are insane.
Show me where my data is wrong and I'll gladly amend it along with the conclusion.
Make sure you wear a mask while biking..We don't want you dead from covid..
During the last countrywide lockdown I actually spent more time on my bike and less time on my computer.
Masks make things worse. It's the last thing on earth I'd do.
Please listen to your govt and official medical expert for your own good...
That's exactly what I am doing... listening to my director-general of health the hon Dr Ashley Bloomfield and my real gold standard Prime Minister.
*****************************
tvnz.co.nz
Public not required to wear masks when country moves to Level 2 on Thursday
5-7 minutes
Mon, May 11 • Source: 1 NEWS
Masks will not be required to be worn by members of the public when the country moves to Level 2 on Thursday.
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The Director-General of Health today said it was not a requirement but people may wear them if they wish. Source: 1 NEWS
"For the general public, what I can say is we're neither recommending nor requiring masks," Dr Ashley Bloomfield, the Director-General of Health, said today in an announcement.
"However, if you do choose to use a mask, that's fine, and just make sure you know how to use it safely so you reduce the risk to yourself and others."
Dr Bloomfield added that it was "not a supply issue" but an issue of evidence.
"We will keep watching, but at the moment, we're not requiring or recommending that the general public use masks."
Prime Minister Jacinda Ardern added that people should take precautions if they choose to wear masks, noting that surgical masks should be changed up to three times an hour.
"When you're changing it, you need to be very careful that you're not making any contact with the mask and ultimately, that you're not allowing it to become damp through the entire period you're wearing it," Ms Ardern said.
"Making sure that it's worn properly, I think, is one of the reasons evidence really can often fall on either side when you're asking those outside of the health profession to wear them."
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Herein lies the problem when it comes to masks. In theory the should work because they reduce droplet spray thereby decreasing the viral load spewing out when people talk, sneeze or cough.
HOWEVER... unless the mask is worn just once and then discarded it becomes a vehicle of transmission for the very viruses it is supposed to protect you from.
The latest case in NZ that has sparked panic was a lady who worked in a company that provides laundry and catering services to international airlines. She caught the UK variant which prior to this incident had no been detected in NZ. The only explanation that currently makes sense is that she caught it from the pillow cases, blankets or crockery that had been used by a transit passenger because her role does not involve face to face contact with any arriving passengers.
This is the mode of transmission that makes the use of masks by the general public so dangerous because very, very few actually discard their masks after a single use or after 3 hours or when they become damp. They are handled with unwashed hands, pulled down thereby coming into contact with the hands, chin, neck and other unsanitary parts of the body. People put them in their handbags and pockets along with items that have been handled by many people.
The theory behind mask use is sound. I agree with it wholeheartedly. The lab studies, the measurement of the amount of saliva spewing out without vs with a mask. All are perfectly sound experiments which show that masks offer protection. Sadly as is the case with all things in life the devil is in the details. We don't live in a perfect world where everyone follows guidelines religiously. It is the lapses in hygiene control that cause masks to make things worse.
This has got nothing to do with Trump or QAanon or Biden. I just don't see any conclusive data that shows masks work in the real world given the fact that people are not following all the instructions that accompany mask use.
HOWEVER... unless the mask is worn just once and then discarded it becomes a vehicle of transmission for the very viruses it is supposed to protect you from.
The latest case in NZ that has sparked panic was a lady who worked in a company that provides laundry and catering services to international airlines. She caught the UK variant which prior to this incident had no been detected in NZ. The only explanation that currently makes sense is that she caught it from the pillow cases, blankets or crockery that had been used by a transit passenger because her role does not involve face to face contact with any arriving passengers.
This is the mode of transmission that makes the use of masks by the general public so dangerous because very, very few actually discard their masks after a single use or after 3 hours or when they become damp. They are handled with unwashed hands, pulled down thereby coming into contact with the hands, chin, neck and other unsanitary parts of the body. People put them in their handbags and pockets along with items that have been handled by many people.
The theory behind mask use is sound. I agree with it wholeheartedly. The lab studies, the measurement of the amount of saliva spewing out without vs with a mask. All are perfectly sound experiments which show that masks offer protection. Sadly as is the case with all things in life the devil is in the details. We don't live in a perfect world where everyone follows guidelines religiously. It is the lapses in hygiene control that cause masks to make things worse.
This has got nothing to do with Trump or QAanon or Biden. I just don't see any conclusive data that shows masks work in the real world given the fact that people are not following all the instructions that accompany mask use.
You are big supporter of Q enon, Donald Trump, fake news, etc
We all laugh at you
Twitter, facebook ban your fake news
I support the advice that comes from my PM Miss Arden who is the gold standard when it comes to Covid.
She does not recommend the use of masks unless they are worn properly and discarded immediately after use. It is her advice that reveals the reason why masks are currently making things worse not better.
And with people like this, Marjorie Taylor Greene, you now know what is wrong with the world
That is last year .. You need to get updated!
Nothing has changed as far as the science is concerned and masks are still not compulsory in NZ.
liar liar pants on fire
AMA Insights
February 10, 2021
Effectiveness of Mask Wearing to Control Community Spread of SARS-CoV-2
John T. Brooks, MD1; Jay C. Butler, MD1Author Affiliations Article InformationJAMA. Published online February 10, 2021. doi:10.1001/jama.2021.1505
COVID-19 Resource Center
editorial comment icon
https://jamanetwork.com/channels/health-forum/fullarticle/2773247
https://jamanetwork.com/journals/jama/fullarticle/2772459
Audio Clinical Review (29:37)
Mask Wearing for COVID-19 Prevention—Summary of CDC Data
1x
0:00 / 0:00
Get CME Subscribe to Podcast
Prior to the coronavirus disease 2019 (COVID-19) pandemic, the efficacy of community mask wearing to reduce the spread of respiratory infections was controversial because there were solid relevant data to support their use. During the pandemic, the scientific evidence has increased. Compelling data now demonstrate that community mask wearing is an effective nonpharmacologic intervention to reduce the spread of this infection, especially as source control to prevent spread from infected persons, but also as protection to reduce wearers’ exposure to infection.
COVID-19 spreads primarily through respiratory droplets exhaled when infected people breathe, talk, cough, sneeze, or sing. Most of these droplets are smaller than 10 μm in diameter, often referred to as aerosols. The amount of small droplets and particles increases with the rate and force of airflow during exhalation (eg, shouting, vigorous exercise). Exposure is greater the closer a person is to the source of exhalations. Larger droplets fall out of the air rapidly, but small droplets and the dried particles formed from them (ie, droplet nuclei) can remain suspended in the air. In circumstances with poor ventilation, typically indoor enclosed spaces where an infected person is present for an extended period, the concentrations of these small droplets and particles can build sufficiently to transmit infection.
Community mask wearing substantially reduces transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2 ways. First, masks prevent infected persons from exposing others to SARS-CoV-2 by blocking exhalation of virus-containing droplets into the air (termed source control). This aspect of mask wearing is especially important because it is estimated that at least 50% or more of transmissions are from persons who never develop symptoms or those who are in the presymptomatic phase of COVID-19 illness.1 In recent laboratory experiments, multilayer cloth masks were more effective than single-layer masks, blocking as much as 50% to 70% of exhaled small droplets and particles.2,3 In some cases, cloth masks have performed similar to surgical or procedure masks for source control. Second, masks protect uninfected wearers. Masks form a barrier to large respiratory droplets that could land on exposed mucous membranes of the eye, nose, and mouth. Masks can also partially filter out small droplets and particles from inhaled air. Multiple layers of fabric and fabrics with higher thread counts improve filtration. However, the observed effectiveness of cloth masks to protect the wearer is lower than their effectiveness for source control,3 and the filtration capacity of cloth masks can be highly dependent on design, fit, and materials used. Standards for cloth masks are needed to help consumers select marketed products.
Epidemiological investigations have helped quantify the benefit of mask wearing to prevent the spread of SARS-CoV-2 (Table; Supplement). At a hair salon in which all staff and clients were required to wear a mask under local ordinance and company policy, 2 symptomatic, infected stylists attended to 139 clients and no infections were observed in the 67 clients who were reached for interviewing and testing. During a COVID-19 outbreak on the USS Theodore Roosevelt, persons who wore masks experienced a 70% lower risk of testing positive for SARS-CoV-2 infection.4 Similar reductions have been reported in case contact investigations when contacts were masked5 and in household clusters in which household members were masked.6
Table. Studies of the Effect of Mask Wearing on SARS-CoV-2 Infection Riska
View LargeDownload
An increasing number of ecological studies have also provided persuasive evidence that universal mandatory mask wearing policies have been associated with reductions in the number or rate of infections and deaths (Table). These studies did not distinguish the types of masks (cloth, surgical, or N95) used in the community. This association is strengthened because, in many cases, other mitigation strategies (eg, school and workplace closures, recommendations for social distancing, hand hygiene) had already been deployed before enactment of mask wearing policies, after which the reductions were observed. A study that examined changes in growth rates for infections in 15 states and the District of Columbia before and after mask mandates showed that rates were growing before the mandates were enacted and slowed significantly after, with greater benefit the longer the mandates had been in place.7
Wearing a mask can become uncomfortable, particularly for long periods in warm environments, and covering the nose and mouth may inhibit verbal and nonverbal communication, particularly for children and deaf individuals. However, children aged 7 to 13 years have been shown to be able to make accurate inferences about the emotions of others with partially covered faces,8 and the US Food and Drug Administration recently approved a transparent surgical mask that may be useful in such circumstances. Concerns about reduced oxygen saturation and carbon dioxide retention when wearing a mask have not been supported by available data.9
The overall community benefit of wearing masks derives from their combined ability to limit both exhalation and inhalation of infectious virus. Similar to the principle of herd immunity for vaccination, the greater the extent to which the intervention—mask wearing in this case—is adopted by the community, the larger the benefit to each individual member. The prevalence of mask use in the community may be of greater importance than the type of mask worn. It merits noting that a recent study has been improperly characterized by some sources as showing that cloth or surgical masks offer no benefit. This randomized trial in Denmark was designed to detect at least a 50% reduction in risk for persons wearing surgical masks. Findings were inconclusive,10 most likely because the actual reduction in exposure these masks provided for the wearer was lower. More importantly, the study was far too small (ie, enrolled about 0.1% of the population) to assess the community benefit achieved when wearer protection is combined with reduced source transmission from mask wearers to others.
During past national crises, persons in the US have willingly united and endured temporary sacrifices for the common good. Recovery of the nation from the COVID-19 pandemic requires the combined efforts of families, friends, and neighbors working together in unified public health action. When masks are worn and combined with other recommended mitigation measures, they protect not only the wearer but also the greater community. Recommendations for masks will likely change as more is learned about various mask types and as the pandemic evolves. With the emergence of more transmissible SARS-CoV-2 variants, it is even more important to adopt widespread mask wearing as well as to redouble efforts with use of all other nonpharmaceutical prevention measures until effective levels of vaccination are achieved nationally.
Back to top
Article Information
Corresponding Author: John T. Brooks, MD, Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, 1600 Clifton Rd, NE, Mailstop D-21, Atlanta, GA 30333 ([email protected]).
Published Online: February 10, 2021. doi:10.1001/jama.2021.1505
Conflict of Interest Disclosures: None reported.
Additional Information: The science summarized in this article is reviewed in greater detail with a full set of references on the Centers for Disease Control and Prevention’s COVID-19 website Scientific Brief: Community Use of Cloth Masks to Control the Spread of SARS-CoV-2 (https://www.cdc.gov/coronavirus/2019-ncov/more/masking-science-sars-cov2.html). This website and a public slide deck will be updated periodically.
References
1.
Johansson MA, Quandelacy TM, Kada S, et al. SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Netw Open. 2021;4(1):e2035057.PubMedGoogle Scholar
2.
Lindsley WG, Blachere FM, Law BF, Beezhold DH, Noti JD. Efficacy of face masks, neck gaiters and face shields for reducing the expulsion of simulated cough-generated aerosols. Aerosol Sci Technol. Published online January 7, 2021. doi:10.1080/02786826.2020.1862409Google Scholar
3.
Ueki H, Furusawa Y, Iwatsuki-Horimoto K, et al. Effectiveness of face masks in preventing airborne transmission of SARS-CoV-2. mSphere. 2020;5(5):e00637-20. doi:10.1128/mSphere.00637-20PubMedGoogle Scholar
4.
Payne DC, Smith-Jeffcoat SE, Nowak G, et al; CDC COVID-19 Surge Laboratory Group. SARS-CoV-2 infections and serologic responses from a sample of U.S. Navy Service Members: USS Theodore Roosevelt, April 2020. MMWR Morb Mortal Wkly Rep. 2020;69(23):714-721.PubMedGoogle ScholarCrossref
liar liar pants on fire
AMA Insights
February 10, 2021
Effectiveness of Mask Wearing to Control Community Spread of SARS-CoV-2
John T. Brooks, MD1; Jay C. Butler, MD1Author Affiliations Article InformationJAMA. Published online February 10, 2021. doi:10.1001/jama.2021.1505
COVID-19 Resource Center
editorial comment icon
https://jamanetwork.com/channels/health-forum/fullarticle/2773247
https://jamanetwork.com/journals/jama/fullarticle/2772459
Audio Clinical Review (29:37)
Mask Wearing for COVID-19 Prevention—Summary of CDC Data
1x
0:00 / 0:00
Get CME Subscribe to Podcast
Prior to the coronavirus disease 2019 (COVID-19) pandemic, the efficacy of community mask wearing to reduce the spread of respiratory infections was controversial because there were solid relevant data to support their use. During the pandemic, the scientific evidence has increased. Compelling data now demonstrate that community mask wearing is an effective nonpharmacologic intervention to reduce the spread of this infection, especially as source control to prevent spread from infected persons, but also as protection to reduce wearers’ exposure to infection.
COVID-19 spreads primarily through respiratory droplets exhaled when infected people breathe, talk, cough, sneeze, or sing. Most of these droplets are smaller than 10 μm in diameter, often referred to as aerosols. The amount of small droplets and particles increases with the rate and force of airflow during exhalation (eg, shouting, vigorous exercise). Exposure is greater the closer a person is to the source of exhalations. Larger droplets fall out of the air rapidly, but small droplets and the dried particles formed from them (ie, droplet nuclei) can remain suspended in the air. In circumstances with poor ventilation, typically indoor enclosed spaces where an infected person is present for an extended period, the concentrations of these small droplets and particles can build sufficiently to transmit infection.
Community mask wearing substantially reduces transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2 ways. First, masks prevent infected persons from exposing others to SARS-CoV-2 by blocking exhalation of virus-containing droplets into the air (termed source control). This aspect of mask wearing is especially important because it is estimated that at least 50% or more of transmissions are from persons who never develop symptoms or those who are in the presymptomatic phase of COVID-19 illness.1 In recent laboratory experiments, multilayer cloth masks were more effective than single-layer masks, blocking as much as 50% to 70% of exhaled small droplets and particles.2,3 In some cases, cloth masks have performed similar to surgical or procedure masks for source control. Second, masks protect uninfected wearers. Masks form a barrier to large respiratory droplets that could land on exposed mucous membranes of the eye, nose, and mouth. Masks can also partially filter out small droplets and particles from inhaled air. Multiple layers of fabric and fabrics with higher thread counts improve filtration. However, the observed effectiveness of cloth masks to protect the wearer is lower than their effectiveness for source control,3 and the filtration capacity of cloth masks can be highly dependent on design, fit, and materials used. Standards for cloth masks are needed to help consumers select marketed products.
Epidemiological investigations have helped quantify the benefit of mask wearing to prevent the spread of SARS-CoV-2 (Table; Supplement). At a hair salon in which all staff and clients were required to wear a mask under local ordinance and company policy, 2 symptomatic, infected stylists attended to 139 clients and no infections were observed in the 67 clients who were reached for interviewing and testing. During a COVID-19 outbreak on the USS Theodore Roosevelt, persons who wore masks experienced a 70% lower risk of testing positive for SARS-CoV-2 infection.4 Similar reductions have been reported in case contact investigations when contacts were masked5 and in household clusters in which household members were masked.6
Table. Studies of the Effect of Mask Wearing on SARS-CoV-2 Infection Riska
View LargeDownload
An increasing number of ecological studies have also provided persuasive evidence that universal mandatory mask wearing policies have been associated with reductions in the number or rate of infections and deaths (Table). These studies did not distinguish the types of masks (cloth, surgical, or N95) used in the community. This association is strengthened because, in many cases, other mitigation strategies (eg, school and workplace closures, recommendations for social distancing, hand hygiene) had already been deployed before enactment of mask wearing policies, after which the reductions were observed. A study that examined changes in growth rates for infections in 15 states and the District of Columbia before and after mask mandates showed that rates were growing before the mandates were enacted and slowed significantly after, with greater benefit the longer the mandates had been in place.7
Wearing a mask can become uncomfortable, particularly for long periods in warm environments, and covering the nose and mouth may inhibit verbal and nonverbal communication, particularly for children and deaf individuals. However, children aged 7 to 13 years have been shown to be able to make accurate inferences about the emotions of others with partially covered faces,8 and the US Food and Drug Administration recently approved a transparent surgical mask that may be useful in such circumstances. Concerns about reduced oxygen saturation and carbon dioxide retention when wearing a mask have not been supported by available data.9
The overall community benefit of wearing masks derives from their combined ability to limit both exhalation and inhalation of infectious virus. Similar to the principle of herd immunity for vaccination, the greater the extent to which the intervention—mask wearing in this case—is adopted by the community, the larger the benefit to each individual member. The prevalence of mask use in the community may be of greater importance than the type of mask worn. It merits noting that a recent study has been improperly characterized by some sources as showing that cloth or surgical masks offer no benefit. This randomized trial in Denmark was designed to detect at least a 50% reduction in risk for persons wearing surgical masks. Findings were inconclusive,10 most likely because the actual reduction in exposure these masks provided for the wearer was lower. More importantly, the study was far too small (ie, enrolled about 0.1% of the population) to assess the community benefit achieved when wearer protection is combined with reduced source transmission from mask wearers to others.
During past national crises, persons in the US have willingly united and endured temporary sacrifices for the common good. Recovery of the nation from the COVID-19 pandemic requires the combined efforts of families, friends, and neighbors working together in unified public health action. When masks are worn and combined with other recommended mitigation measures, they protect not only the wearer but also the greater community. Recommendations for masks will likely change as more is learned about various mask types and as the pandemic evolves. With the emergence of more transmissible SARS-CoV-2 variants, it is even more important to adopt widespread mask wearing as well as to redouble efforts with use of all other nonpharmaceutical prevention measures until effective levels of vaccination are achieved nationally.
Back to top
Article Information
Corresponding Author: John T. Brooks, MD, Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, 1600 Clifton Rd, NE, Mailstop D-21, Atlanta, GA 30333 ([email protected]).
Published Online: February 10, 2021. doi:10.1001/jama.2021.1505
Conflict of Interest Disclosures: None reported.
Additional Information: The science summarized in this article is reviewed in greater detail with a full set of references on the Centers for Disease Control and Prevention’s COVID-19 website Scientific Brief: Community Use of Cloth Masks to Control the Spread of SARS-CoV-2 (https://www.cdc.gov/coronavirus/2019-ncov/more/masking-science-sars-cov2.html). This website and a public slide deck will be updated periodically.
References
1.
Johansson MA, Quandelacy TM, Kada S, et al. SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Netw Open. 2021;4(1):e2035057.PubMedGoogle Scholar
2.
Lindsley WG, Blachere FM, Law BF, Beezhold DH, Noti JD. Efficacy of face masks, neck gaiters and face shields for reducing the expulsion of simulated cough-generated aerosols. Aerosol Sci Technol. Published online January 7, 2021. doi:10.1080/02786826.2020.1862409Google Scholar
3.
Ueki H, Furusawa Y, Iwatsuki-Horimoto K, et al. Effectiveness of face masks in preventing airborne transmission of SARS-CoV-2. mSphere. 2020;5(5):e00637-20. doi:10.1128/mSphere.00637-20PubMedGoogle Scholar
4.
Payne DC, Smith-Jeffcoat SE, Nowak G, et al; CDC COVID-19 Surge Laboratory Group. SARS-CoV-2 infections and serologic responses from a sample of U.S. Navy Service Members: USS Theodore Roosevelt, April 2020. MMWR Morb Mortal Wkly Rep. 2020;69(23):714-721.PubMedGoogle ScholarCrossref
I've read all these studies and I kept an open mind regarding masks at the beginning of the mask mandates.
However all these cited studies have one major flaw as that they confuse correlation with causation. If you're interested in the details you can read https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation
The most blatant example of using correlation to imply causation is the first item on the list above regarding the hairdresser. The fact that nobody caught the virus from the hairdresser is attributed to mask use but that is done by correlation and does not imply causation.
Let's take the example of the woman in NZ who recently caught the SA strain of Covid which is reported to be far more transmissible. After all the contact tracing was done it was found that the woman went all over the place but did not pass it to a SINGLE OTHER PERSON despite not wearing a mask even when she had symptoms. Even her husband was not infected. Can we conclude from this one case that the SA strain of covid is not infectious?
The answer of course is "no" because correlation is not causation. It is far more likely that this lady is just one of the 70% of covid cases that simply don't spread the virus.
Article here : https://arstechnica.com/science/202...es-behind-80-of-transmission-studies-suggest/
So in order to see whether masks work we need to look at very large sample sizes eg a whole country or a whole state with similar demographics and population densities
Based upon the data from these large population samples none of them show masks to make any difference whatsoever. If masks worked LA county would be performing better than Stockholm. A big difference would be observed between North Dakota and South Dakota. Florida would have a shockingly high death toll per million compared to other states given that it's full of retirees in the 70s and 80s.
By all means wear a mask if you think it helps but make sure you use it just once and discard it. You should also discard it after 3 hours of continuous use. When you put it on don't touch the inner surface and when you store it make sure it is in a sterile plastic bag. Sanitise your hands before you touch your mask. Don't put your mask down on dirty surfaces eg table which hundreds of other people have touched and then put it on. Don't stuff it in your unsanitised pocket etc etc etc.
Masks would make a difference if everyone wore them with the same discipline that medical professionals do in high risk situations. However the problem is that the vast majority of mask wearers do it because it is now the law and this is what is causing masks to make things worse not better.
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liar liar
https://www.cdc.gov/mmwr/volumes/70/wr/mm7006e2.htm?s_cid=mm7006e2_w
Decline in COVID-19 Hospitalization Growth Rates Associated with Statewide Mask Mandates — 10 States, March–October 2020
Weekly / February 12, 2021 / 70(6);212–216
On February 5, 2021, this report was posted online as an MMWR Early Release.
Please note:. This report has been corrected. An erratum will be published.
Heesoo Joo, PhD1; Gabrielle F. Miller, PhD1; Gregory Sunshine, JD1; Maxim Gakh, JD2; Jamison Pike, PhD1; Fiona P. Havers, MD1; Lindsay Kim, MD1; Regen Weber1; Sebnem Dugmeoglu, MPH1; Christina Watson, DrPH1; Fátima Coronado, MD1 (View author affiliations)
View suggested citation
Summary
What is already known about this topic?
Wearing masks is recommended to mitigate the spread of COVID-19.
What is added by this report?
During March 22–October 17, 2020, 10 sites participating in the COVID-19–Associated Hospitalization Surveillance Network in states with statewide mask mandates reported a decline in weekly COVID-19–associated hospitalization growth rates by up to start highlight5.6end highlight percentage points for adults aged 18–64 years after mandate implementation, compared with growth rates during the 4 weeks preceding implementation of the mandate.
What are the implications for public health practice?
Mask-wearing is a component of a multipronged strategy to decrease exposure to and transmission of SARS-CoV-2 and reduce strain on the health care system, with likely direct effects on COVID-19 morbidity and associated mortality.
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SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is transmitted predominantly by respiratory droplets generated when infected persons cough, sneeze, spit, sing, talk, or breathe. CDC recommends community use of face masks to prevent transmission of SARS-CoV-2 (1). As of October 22, 2020, statewide mask mandates were in effect in 33 states and the District of Columbia (2). This study examined whether implementation of statewide mask mandates was associated with COVID-19–associated hospitalization growth rates among different age groups in 10 sites participating in the COVID-19–Associated Hospitalization Surveillance Network (COVID-NET) in states that issued statewide mask mandates during March 1–October 17, 2020. Regression analysis demonstrated that weekly hospitalization growth rates declined by 2.9 percentage points (95% confidence interval [CI] = 0.3–5.5) among adults aged 40–64 years during the first 2 weeks after implementing statewide mask mandates. After mask mandates had been implemented for ≥3 weeks, hospitalization growth rates declined by start highlight5.6end highlight percentage points among persons aged 18–39 years (95% CI = start highlight0.9end highlight–10.4) and those aged 40–64 years (95% CI = start highlight1.0end highlight–10.2). Statewide mask mandates might be associated with reductions in SARS-CoV-2 transmission and might contribute to reductions in COVID-19 hospitalization growth rates, compared with growth rates during <4 weeks before implementation of the mandate and the implementation week. Mask-wearing is a component of a multipronged strategy to decrease exposure to and transmission of SARS-CoV-2 and reduce strain on the health care system, with likely direct effects on COVID-19 morbidity and associated mortality.
Data on statewide mask mandates during March 1–October 22, 2020, were obtained by CDC and the University of Nevada, Las Vegas, from state government websites containing executive or administrative orders, which were analyzed and coded to extract effective dates of statewide mask mandates. A statewide mask mandate was defined as the requirement that persons operating in a personal capacity (i.e., not limited to specific professions or employees) wear a mask 1) anywhere outside their home or 2) in retail businesses and in restaurants or food establishments. All coding and analyses underwent secondary review and quality assurance checks by two or more raters; upon agreement among all raters, coding and analyses were published in a freely available data set (2).
Cumulative COVID-19–associated hospitalization rates for each week during March 1–October 17, 2020, (33 weeks) were obtained from COVID-NET, a population-based surveillance system (3). COVID-NET provides laboratory-confirmed, COVID-19–associated hospitalization rates (hospitalizations per 100,000 persons) in 99 counties located in 14 states, commencing the week of March 1, 2020* (4). Certain counties in each state participate in COVID-NET, except Maryland, where all counties participate. A group of counties participating in COVID-NET within a state is termed a site. Sites in states that did not have statewide mask mandates during March 1–October 17, 2020, were excluded from the analyses. For analyses, cumulative hospitalization rates for each week of the study period for seven age cohorts (adults aged 18–29, 30–39, 40–49, 50–64, 65–74, 75–84, and ≥85 years) were aggregated into three age groups (18–39, 40–64, and ≥65 years)†; sites with a cumulative hospitalization rate of zero per 100,000 persons were imputed to 0.1 per 100,000. Hospitalizations among children and adolescents aged <18 years were not included because few hospitalizations were reported among this age group during the study period.
The outcome was the hospitalization growth rate, defined as the weekly percentage change in cumulative COVID-19 hospitalizations per 100,000 persons. The weekly percentage change was calculated as the difference of logarithms in cumulative COVID-19 hospitalization rates by week.§ The association between mask mandates and COVID-19–associated hospitalization growth rates was measured using a time-based categorical variable with four mutually exclusive categories based on the week (Sunday through Saturday), with the effective date of the mask mandate (“implementation week”) characterized as follows: ≥4 weeks before the implementation week; <4 weeks before the implementation week (reference); <3 weeks after the implementation week; and ≥3 weeks after the implementation week.¶ Week zero (implementation week) was defined as the week that included the date the mask mandate went into effect and was included in the reference period. The hospitalization rate ≥4 weeks before implementation of the mask mandate was compared with that during the reference period to test whether sites with mask mandates had differential trends in COVID-19–associated hospitalization rates before issuance of mask mandates
This study used a regression model with panel data to compare COVID-19–associated hospitalization growth rates at COVID-NET sites with mandates before and after the dates that statewide mask mandates became effective (5). Using hospitalization growth rates before mask mandates were implemented (i.e., the reference period: <4 weeks before the implementation week and the implementation week), the model predicted hospitalization growth rates after mask mandates, assuming mandates had not been implemented. Then the model compared the predicted values with the observed hospitalization growth rates after mask mandates were implemented. The study controlled for mask mandates, state, age group, and time (i.e., week of the year).** The study also controlled for statewide closing and reopening as determined by the date of stay-at-home orders and business closures (Supplementary Table, https://stacks.cdc.gov/view/cdc/101127).†† P-values <0.05 were considered statistically significant. Analyses were conducted separately for three age groups (18–39, 40–64, and ≥65 years) and for all adults aged ≥18 years using Stata software (version 16.1; StataCorp). This study was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy.§§
Ten of the 14 COVID-NET participating sites were in states that had issued statewide mask mandates since March 2020 (Table 1). The overall COVID-19–associated hospitalization growth rates among all adults declined 2.4 percentage points (p-value = 0.04) <3 weeks after the implementation week and declined start highlight5.0end highlight percentage points (p-value <0.01) during the period ≥3 weeks after the implementation week (Table 2). The declines were statistically significant.
Among persons aged 18–39 years, the hospitalization growth rates <3 weeks after the implementation week were lower than were those during the <4 weeks before the implementation week and the implementation week (reference period) when no mask mandate existed, but the estimated percentage point difference (start highlight–2.2end highlight) was not statistically significant (p-value = start highlight0.30end highlight) (Figure) (Table 2). However, in this population, mask mandates were associated with a statistically significant start highlight5.6end highlight percentage-point decline in COVID-19 hospitalization growth rates (p-value = start highlight0.02end highlight) ≥3 weeks after the implementation week. Among adults aged 40–64 years, mask mandates were associated with a 2.9 percentage-point reduction in COVID-19 hospitalization growth rates (p-value = 0.03) <3 weeks after the implementation week. Hospitalization growth rates declined by start highlight5.6end highlight percentage points (p-value = 0.02) during ≥3 weeks after the implementation week. Among adults aged ≥65 years, COVID-19 hospitalization growth rates declined <3 weeks after the implementation week (start highlight1.2end highlight percentage points) and ≥3 weeks after the implementation week (start highlight0.7end highlight percentage points); however, the declines were not statistically significant.
In the ≥4 weeks before the implementation week, COVID-19–associated hospitalization growth rates were lower than were those <4 weeks before the implementation week and during the implementation week (reference). However, the percentage point differences were not statistically significant.
Top
Discussion
Masks are intended to reduce emission of virus-laden respiratory droplets, which is especially relevant for persons who are infected with SARS-CoV-2 but are asymptomatic or presymptomatic; masks also help reduce inhalation of respiratory droplets by the wearer (1). Findings from this study suggest that statewide mask mandates were associated with statistically significant declines in weekly COVID-19 hospitalization growth rates for adults aged 40–64 years <3 weeks after the week that the mandate was implemented, and for adults aged 18–64 years ≥3 weeks after the implementation week. The declines in hospitalization growth rates <3 weeks after the implementation week are consistent with the incubation period of SARS-CoV-2; in a report based on an analysis of publicly reported confirmed COVID-19 cases, the median estimated incubation period was 5.1 days, and most symptomatic patients reported symptoms within 11.5 days after exposure (6). Therefore, <3 weeks after the implementation of mask mandate would be long enough to identify an association between mask mandates and COVID-19–associated hospitalization growth rates. Previous studies have shown that the various physical distancing measures, including mask mandates, were associated with immediate declines in COVID-19 case growth rates (5,7).
This study did not demonstrate a statistically significant decline in COVID-19–associated hospitalization growth rates for adults aged ≥65 years, suggesting that there might have been less of a decline in this age group, compared with that of other adults, although CIs were wide. A study conducted during May 2020 indicated that approximately 70% of U.S. adults aged ≥65 years reported always wearing a mask in public, compared with only 44% of those aged 18–24 years (8). As a result, statewide mask mandates might have had a lesser impact on the masking behaviors of adults aged ≥65 years, compared with behaviors among other adults because of relatively high baseline level of mask use among this age group during the reference period (i.e., <4 weeks before the implementation week and the implementation week).
Declines in hospitalization growth rates during March 1–October 17, 2020, might also have resulted in a substantial decrease in health care costs associated with COVID-19. CDC has determined that COVID-19–related hospital costs per adult hospitalization varied from $8,400 in a general ward to >$50,000 in an intensive care unit with a ventilator (9). Because COVID-19 can lead to prolonged illness and require long-term treatment (10), the expected savings associated with the decline in hospitalization rates could be much higher than these reduced hospital costs associated with COVID-19.
The findings in this report are subject to at least four limitations. First, the model did not control for other policies that might affect hospitalization growth rates, including school closing and physical distancing recommendations; however, it did control for the dates of statewide closing and reopening, based on statewide stay-at-home orders and business closures. Second, these findings are limited to state-issued statewide mask mandates and do not account for local variability, such as county-level mask mandates.¶¶ Third, the findings are based on sites participating in COVID-NET and are limited to persons aged ≥18 years and therefore might not be generalizable to the entire U.S. population. Finally, it was assumed that the estimated effect in hospitalization growth rates after mask mandate implementation week did not depend on the issuance dates (e.g., Monday versus Friday), although number of days after the issuance of mask mandates in week zero varied by issuance date. Also, it was assumed that the mask mandates could not affect the hospitalization growth rates during the implementation week.
At the individual level, the prevention benefit of using a mask increases as more persons use masks consistently and correctly. Studies have confirmed the benefit of masking for SARS-CoV-2 control; each study demonstrated that, after implementation of directives from organizational or political leadership for universal masking, new infections decreased significantly (1). This study supports community masking to reduce the transmission of SARS-CoV-2. It also demonstrates that statewide mask mandates were associated with a reduction in COVID-19–associated hospitalization growth rates among adults aged 18–64 years and might affect age groups differently. Mask-wearing is part of a multipronged application of evidence-based strategies that prevent the transmission of SARS-CoV-2; wearing a mask reduces exposure, transmission, and strain on the health care system with likely direct effects on COVID-19 morbidity and associated mortality (1).
Top
Acknowledgments
COVID-19–Associated Hospitalization Surveillance Network; Angela Werner; Timmy Pierce; Nicholas Skaff; Matthew Penn.
Top
Corresponding author: Heesoo Joo, [email protected].
Top
1CDC COVID-19 Response Team; 2University of Nevada, Las Vegas.
Top
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.
Top
* Counties by state in COVID-NET surveillance: California (Alameda, Contra Costa, and San Francisco counties); Colorado (Adams, Arapahoe, Denver, Douglas, and Jefferson counties); Connecticut (New Haven and Middlesex counties); Georgia (Clayton, Cobb, DeKalb, Douglas, Fulton, Gwinnett, Newton, and Rockdale counties); Iowa (one county represented); Maryland (Allegany, Anne Arundel, Baltimore, Baltimore City, Calvert, Caroline, Carroll, Cecil, Charles, Dorchester, Frederick, Garrett, Harford, Howard, Kent, Montgomery, Prince George’s, Queen Anne’s, St. Mary’s, Somerset, Talbot, Washington, Wicomico, and Worcester counties); Michigan (Clinton, Eaton, Genesee, Ingham, and Washtenaw counties); Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties); New Mexico (Bernalillo, Chaves, Doña Ana, Grant, Luna, San Juan, and Santa Fe counties); New York (Albany, Columbia, Genesee, Greene, Livingston, Monroe, Montgomery, Ontario, Orleans, Rensselaer, Saratoga, Schenectady, Schoharie, Wayne, and Yates counties); Ohio (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway, and Union counties); Oregon (Clackamas, Multnomah, and Washington counties); Tennessee (Cheatham, Davidson, Dickson, Robertson, Rutherford, Sumner, Williamson, and Wilson counties); and Utah (Salt Lake County).
† The analysis for adults aged 18–39 years used observations of adults aged 18–29 and 30–39 years; the analysis for adults aged 40–64 years used observations of adults aged 40–49 and 50–64 years; the analysis for adults aged ≥65 years used observations of adults aged 65–74, 75–84, and ≥85 years.
§ Weekly cumulative hospitalization growth rate (HGrowthast) for age cohort a in site s during week t is defined as the weekly percentage change in COVID-19 hospitalizations per 100,000 persons, estimated by HGrowthast = ((log (HRast)-log (HRas(t-1)))×100, where HRast = cumulative hospitalization rate per 100,000 population for age cohort a in site s in week t. The log of the cumulative hospitalization growth rate is similar to the log of the cumulative cases per week, as the denominators are equivalent.
¶ Each period might include different numbers of weeks by site. For ≥4 weeks before the implementation week (i.e., –4 or before), the maximum number of weeks included was 17 (–20 through –4), and the minimum was 3 (–6 through –4). For the periods of <4 weeks before the implementation week (i.e., –3 through 0), all sites have 4 weeks. For <3 weeks after the implementation week (i.e., 1 through 2), all sites have 2 weeks. For ≥3 weeks after the implementation week (i.e., 3 or after), the maximum number of weeks included is 24 (3 through 26), and the minimum is 10 (3 through 12).
** The event study design was adopted from a previous study (https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.00818external icon) and modified for the current analyses. Regression models used National Center for Health Statistics vintage 2018 bridged-race population estimates (https://www.cdc.gov/nchs/nvss/bridged_race.htm) for each site as analytic weights. The model used was a weighted least squares regression which accounted for heteroskedasticity by estimating the standard errors using age cohort-state clusters.
†† The date of the statewide closing was the earlier of 1) the date persons were required to stay home or 2) the date that restaurants were required to cease on-premises dining and that nonessential retail businesses were ordered to close. The date of the statewide reopening was the earlier of 1) the date the stay-at-home order was lifted or 2) the date that restaurants were allowed to resume on-premises consumption and that nonessential retail businesses were permitted to reopen.
§§ 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 501 et seq.
¶¶ Some states issued orders that applied to certain counties, and others authorized counties to apply for and receive variances from mitigation measures if certain thresholds were met (e.g., COVID-19 percentage of positive test results below a specified level in that county). Cities and counties might have also issued local mask mandates.
liar liar
https://www.cdc.gov/mmwr/volumes/70/wr/mm7006e2.htm?s_cid=mm7006e2_w
Decline in COVID-19 Hospitalization Growth Rates Associated with Statewide Mask Mandates — 10 States, March–October 2020
Weekly / February 12, 2021 / 70(6);212–216
On February 5, 2021, this report was posted online as an MMWR Early Release.
Please note:. This report has been corrected. An erratum will be published.
Heesoo Joo, PhD1; Gabrielle F. Miller, PhD1; Gregory Sunshine, JD1; Maxim Gakh, JD2; Jamison Pike, PhD1; Fiona P. Havers, MD1; Lindsay Kim, MD1; Regen Weber1; Sebnem Dugmeoglu, MPH1; Christina Watson, DrPH1; Fátima Coronado, MD1 (View author affiliations)
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Summary
What is already known about this topic?
Wearing masks is recommended to mitigate the spread of COVID-19.
What is added by this report?
During March 22–October 17, 2020, 10 sites participating in the COVID-19–Associated Hospitalization Surveillance Network in states with statewide mask mandates reported a decline in weekly COVID-19–associated hospitalization growth rates by up to start highlight5.6end highlight percentage points for adults aged 18–64 years after mandate implementation, compared with growth rates during the 4 weeks preceding implementation of the mandate.
What are the implications for public health practice?
Mask-wearing is a component of a multipronged strategy to decrease exposure to and transmission of SARS-CoV-2 and reduce strain on the health care system, with likely direct effects on COVID-19 morbidity and associated mortality.
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SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is transmitted predominantly by respiratory droplets generated when infected persons cough, sneeze, spit, sing, talk, or breathe. CDC recommends community use of face masks to prevent transmission of SARS-CoV-2 (1). As of October 22, 2020, statewide mask mandates were in effect in 33 states and the District of Columbia (2). This study examined whether implementation of statewide mask mandates was associated with COVID-19–associated hospitalization growth rates among different age groups in 10 sites participating in the COVID-19–Associated Hospitalization Surveillance Network (COVID-NET) in states that issued statewide mask mandates during March 1–October 17, 2020. Regression analysis demonstrated that weekly hospitalization growth rates declined by 2.9 percentage points (95% confidence interval [CI] = 0.3–5.5) among adults aged 40–64 years during the first 2 weeks after implementing statewide mask mandates. After mask mandates had been implemented for ≥3 weeks, hospitalization growth rates declined by start highlight5.6end highlight percentage points among persons aged 18–39 years (95% CI = start highlight0.9end highlight–10.4) and those aged 40–64 years (95% CI = start highlight1.0end highlight–10.2). Statewide mask mandates might be associated with reductions in SARS-CoV-2 transmission and might contribute to reductions in COVID-19 hospitalization growth rates, compared with growth rates during <4 weeks before implementation of the mandate and the implementation week. Mask-wearing is a component of a multipronged strategy to decrease exposure to and transmission of SARS-CoV-2 and reduce strain on the health care system, with likely direct effects on COVID-19 morbidity and associated mortality.
Data on statewide mask mandates during March 1–October 22, 2020, were obtained by CDC and the University of Nevada, Las Vegas, from state government websites containing executive or administrative orders, which were analyzed and coded to extract effective dates of statewide mask mandates. A statewide mask mandate was defined as the requirement that persons operating in a personal capacity (i.e., not limited to specific professions or employees) wear a mask 1) anywhere outside their home or 2) in retail businesses and in restaurants or food establishments. All coding and analyses underwent secondary review and quality assurance checks by two or more raters; upon agreement among all raters, coding and analyses were published in a freely available data set (2).
Cumulative COVID-19–associated hospitalization rates for each week during March 1–October 17, 2020, (33 weeks) were obtained from COVID-NET, a population-based surveillance system (3). COVID-NET provides laboratory-confirmed, COVID-19–associated hospitalization rates (hospitalizations per 100,000 persons) in 99 counties located in 14 states, commencing the week of March 1, 2020* (4). Certain counties in each state participate in COVID-NET, except Maryland, where all counties participate. A group of counties participating in COVID-NET within a state is termed a site. Sites in states that did not have statewide mask mandates during March 1–October 17, 2020, were excluded from the analyses. For analyses, cumulative hospitalization rates for each week of the study period for seven age cohorts (adults aged 18–29, 30–39, 40–49, 50–64, 65–74, 75–84, and ≥85 years) were aggregated into three age groups (18–39, 40–64, and ≥65 years)†; sites with a cumulative hospitalization rate of zero per 100,000 persons were imputed to 0.1 per 100,000. Hospitalizations among children and adolescents aged <18 years were not included because few hospitalizations were reported among this age group during the study period.
The outcome was the hospitalization growth rate, defined as the weekly percentage change in cumulative COVID-19 hospitalizations per 100,000 persons. The weekly percentage change was calculated as the difference of logarithms in cumulative COVID-19 hospitalization rates by week.§ The association between mask mandates and COVID-19–associated hospitalization growth rates was measured using a time-based categorical variable with four mutually exclusive categories based on the week (Sunday through Saturday), with the effective date of the mask mandate (“implementation week”) characterized as follows: ≥4 weeks before the implementation week; <4 weeks before the implementation week (reference); <3 weeks after the implementation week; and ≥3 weeks after the implementation week.¶ Week zero (implementation week) was defined as the week that included the date the mask mandate went into effect and was included in the reference period. The hospitalization rate ≥4 weeks before implementation of the mask mandate was compared with that during the reference period to test whether sites with mask mandates had differential trends in COVID-19–associated hospitalization rates before issuance of mask mandates
This study used a regression model with panel data to compare COVID-19–associated hospitalization growth rates at COVID-NET sites with mandates before and after the dates that statewide mask mandates became effective (5). Using hospitalization growth rates before mask mandates were implemented (i.e., the reference period: <4 weeks before the implementation week and the implementation week), the model predicted hospitalization growth rates after mask mandates, assuming mandates had not been implemented. Then the model compared the predicted values with the observed hospitalization growth rates after mask mandates were implemented. The study controlled for mask mandates, state, age group, and time (i.e., week of the year).** The study also controlled for statewide closing and reopening as determined by the date of stay-at-home orders and business closures (Supplementary Table, https://stacks.cdc.gov/view/cdc/101127).†† P-values <0.05 were considered statistically significant. Analyses were conducted separately for three age groups (18–39, 40–64, and ≥65 years) and for all adults aged ≥18 years using Stata software (version 16.1; StataCorp). This study was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy.§§
Ten of the 14 COVID-NET participating sites were in states that had issued statewide mask mandates since March 2020 (Table 1). The overall COVID-19–associated hospitalization growth rates among all adults declined 2.4 percentage points (p-value = 0.04) <3 weeks after the implementation week and declined start highlight5.0end highlight percentage points (p-value <0.01) during the period ≥3 weeks after the implementation week (Table 2). The declines were statistically significant.
Among persons aged 18–39 years, the hospitalization growth rates <3 weeks after the implementation week were lower than were those during the <4 weeks before the implementation week and the implementation week (reference period) when no mask mandate existed, but the estimated percentage point difference (start highlight–2.2end highlight) was not statistically significant (p-value = start highlight0.30end highlight) (Figure) (Table 2). However, in this population, mask mandates were associated with a statistically significant start highlight5.6end highlight percentage-point decline in COVID-19 hospitalization growth rates (p-value = start highlight0.02end highlight) ≥3 weeks after the implementation week. Among adults aged 40–64 years, mask mandates were associated with a 2.9 percentage-point reduction in COVID-19 hospitalization growth rates (p-value = 0.03) <3 weeks after the implementation week. Hospitalization growth rates declined by start highlight5.6end highlight percentage points (p-value = 0.02) during ≥3 weeks after the implementation week. Among adults aged ≥65 years, COVID-19 hospitalization growth rates declined <3 weeks after the implementation week (start highlight1.2end highlight percentage points) and ≥3 weeks after the implementation week (start highlight0.7end highlight percentage points); however, the declines were not statistically significant.
In the ≥4 weeks before the implementation week, COVID-19–associated hospitalization growth rates were lower than were those <4 weeks before the implementation week and during the implementation week (reference). However, the percentage point differences were not statistically significant.
Top
Discussion
Masks are intended to reduce emission of virus-laden respiratory droplets, which is especially relevant for persons who are infected with SARS-CoV-2 but are asymptomatic or presymptomatic; masks also help reduce inhalation of respiratory droplets by the wearer (1). Findings from this study suggest that statewide mask mandates were associated with statistically significant declines in weekly COVID-19 hospitalization growth rates for adults aged 40–64 years <3 weeks after the week that the mandate was implemented, and for adults aged 18–64 years ≥3 weeks after the implementation week. The declines in hospitalization growth rates <3 weeks after the implementation week are consistent with the incubation period of SARS-CoV-2; in a report based on an analysis of publicly reported confirmed COVID-19 cases, the median estimated incubation period was 5.1 days, and most symptomatic patients reported symptoms within 11.5 days after exposure (6). Therefore, <3 weeks after the implementation of mask mandate would be long enough to identify an association between mask mandates and COVID-19–associated hospitalization growth rates. Previous studies have shown that the various physical distancing measures, including mask mandates, were associated with immediate declines in COVID-19 case growth rates (5,7).
This study did not demonstrate a statistically significant decline in COVID-19–associated hospitalization growth rates for adults aged ≥65 years, suggesting that there might have been less of a decline in this age group, compared with that of other adults, although CIs were wide. A study conducted during May 2020 indicated that approximately 70% of U.S. adults aged ≥65 years reported always wearing a mask in public, compared with only 44% of those aged 18–24 years (8). As a result, statewide mask mandates might have had a lesser impact on the masking behaviors of adults aged ≥65 years, compared with behaviors among other adults because of relatively high baseline level of mask use among this age group during the reference period (i.e., <4 weeks before the implementation week and the implementation week).
Declines in hospitalization growth rates during March 1–October 17, 2020, might also have resulted in a substantial decrease in health care costs associated with COVID-19. CDC has determined that COVID-19–related hospital costs per adult hospitalization varied from $8,400 in a general ward to >$50,000 in an intensive care unit with a ventilator (9). Because COVID-19 can lead to prolonged illness and require long-term treatment (10), the expected savings associated with the decline in hospitalization rates could be much higher than these reduced hospital costs associated with COVID-19.
The findings in this report are subject to at least four limitations. First, the model did not control for other policies that might affect hospitalization growth rates, including school closing and physical distancing recommendations; however, it did control for the dates of statewide closing and reopening, based on statewide stay-at-home orders and business closures. Second, these findings are limited to state-issued statewide mask mandates and do not account for local variability, such as county-level mask mandates.¶¶ Third, the findings are based on sites participating in COVID-NET and are limited to persons aged ≥18 years and therefore might not be generalizable to the entire U.S. population. Finally, it was assumed that the estimated effect in hospitalization growth rates after mask mandate implementation week did not depend on the issuance dates (e.g., Monday versus Friday), although number of days after the issuance of mask mandates in week zero varied by issuance date. Also, it was assumed that the mask mandates could not affect the hospitalization growth rates during the implementation week.
At the individual level, the prevention benefit of using a mask increases as more persons use masks consistently and correctly. Studies have confirmed the benefit of masking for SARS-CoV-2 control; each study demonstrated that, after implementation of directives from organizational or political leadership for universal masking, new infections decreased significantly (1). This study supports community masking to reduce the transmission of SARS-CoV-2. It also demonstrates that statewide mask mandates were associated with a reduction in COVID-19–associated hospitalization growth rates among adults aged 18–64 years and might affect age groups differently. Mask-wearing is part of a multipronged application of evidence-based strategies that prevent the transmission of SARS-CoV-2; wearing a mask reduces exposure, transmission, and strain on the health care system with likely direct effects on COVID-19 morbidity and associated mortality (1).
Top
Acknowledgments
COVID-19–Associated Hospitalization Surveillance Network; Angela Werner; Timmy Pierce; Nicholas Skaff; Matthew Penn.
Top
Corresponding author: Heesoo Joo, [email protected].
Top
1CDC COVID-19 Response Team; 2University of Nevada, Las Vegas.
Top
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.
Top
* Counties by state in COVID-NET surveillance: California (Alameda, Contra Costa, and San Francisco counties); Colorado (Adams, Arapahoe, Denver, Douglas, and Jefferson counties); Connecticut (New Haven and Middlesex counties); Georgia (Clayton, Cobb, DeKalb, Douglas, Fulton, Gwinnett, Newton, and Rockdale counties); Iowa (one county represented); Maryland (Allegany, Anne Arundel, Baltimore, Baltimore City, Calvert, Caroline, Carroll, Cecil, Charles, Dorchester, Frederick, Garrett, Harford, Howard, Kent, Montgomery, Prince George’s, Queen Anne’s, St. Mary’s, Somerset, Talbot, Washington, Wicomico, and Worcester counties); Michigan (Clinton, Eaton, Genesee, Ingham, and Washtenaw counties); Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties); New Mexico (Bernalillo, Chaves, Doña Ana, Grant, Luna, San Juan, and Santa Fe counties); New York (Albany, Columbia, Genesee, Greene, Livingston, Monroe, Montgomery, Ontario, Orleans, Rensselaer, Saratoga, Schenectady, Schoharie, Wayne, and Yates counties); Ohio (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway, and Union counties); Oregon (Clackamas, Multnomah, and Washington counties); Tennessee (Cheatham, Davidson, Dickson, Robertson, Rutherford, Sumner, Williamson, and Wilson counties); and Utah (Salt Lake County).
† The analysis for adults aged 18–39 years used observations of adults aged 18–29 and 30–39 years; the analysis for adults aged 40–64 years used observations of adults aged 40–49 and 50–64 years; the analysis for adults aged ≥65 years used observations of adults aged 65–74, 75–84, and ≥85 years.
§ Weekly cumulative hospitalization growth rate (HGrowthast) for age cohort a in site s during week t is defined as the weekly percentage change in COVID-19 hospitalizations per 100,000 persons, estimated by HGrowthast = ((log (HRast)-log (HRas(t-1)))×100, where HRast = cumulative hospitalization rate per 100,000 population for age cohort a in site s in week t. The log of the cumulative hospitalization growth rate is similar to the log of the cumulative cases per week, as the denominators are equivalent.
¶ Each period might include different numbers of weeks by site. For ≥4 weeks before the implementation week (i.e., –4 or before), the maximum number of weeks included was 17 (–20 through –4), and the minimum was 3 (–6 through –4). For the periods of <4 weeks before the implementation week (i.e., –3 through 0), all sites have 4 weeks. For <3 weeks after the implementation week (i.e., 1 through 2), all sites have 2 weeks. For ≥3 weeks after the implementation week (i.e., 3 or after), the maximum number of weeks included is 24 (3 through 26), and the minimum is 10 (3 through 12).
** The event study design was adopted from a previous study (https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.00818external icon) and modified for the current analyses. Regression models used National Center for Health Statistics vintage 2018 bridged-race population estimates (https://www.cdc.gov/nchs/nvss/bridged_race.htm) for each site as analytic weights. The model used was a weighted least squares regression which accounted for heteroskedasticity by estimating the standard errors using age cohort-state clusters.
†† The date of the statewide closing was the earlier of 1) the date persons were required to stay home or 2) the date that restaurants were required to cease on-premises dining and that nonessential retail businesses were ordered to close. The date of the statewide reopening was the earlier of 1) the date the stay-at-home order was lifted or 2) the date that restaurants were allowed to resume on-premises consumption and that nonessential retail businesses were permitted to reopen.
§§ 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 501 et seq.
¶¶ Some states issued orders that applied to certain counties, and others authorized counties to apply for and receive variances from mitigation measures if certain thresholds were met (e.g., COVID-19 percentage of positive test results below a specified level in that county). Cities and counties might have also issued local mask mandates.
Lots of words as usual but the data does not correlate with the conclusions drawn above. Don't be misled by journalists and organisations with agendas. Just look at the data and draw your own conclusions and you'll come up with will coincide with mine.
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