Cloth Masks Do Protect the Wearer - Breathing in Less Coronavirus Means You Get Less Sick
By Monica Gandhi
Masks slow the spread of SARS-CoV-2 by reducing how much infected people spray the virus into the environment around them when they cough or talk. Evidence from laboratory experiments, hospitals and whole countries show that masks work, and the Centers for Disease Control and Prevention recommends face coverings for the U.S. public. With all this evidence, mask wearing has become the norm in many places.
I am an infectious disease doctor and a professor of medicine at the University of California, San Francisco. As governments and workplaces began to recommend or mandate mask wearing, my colleagues and I noticed an interesting trend. In places where most people wore masks, those who did get infected seemed dramatically less likely to get severely ill compared to places with less mask-wearing.
It seems people get less sick if they wear a mask.
When you wear a mask – even a cloth mask – you typically are exposed to a lower dose of the coronavirus than if you didn't. Both recent experiments in animal models using coronavirus and nearly a hundred years of viral research show that lower viral doses usually means less severe disease.
No mask is perfect, and wearing one might not prevent you from getting infected. But it might be the difference between a case of COVID-19 that sends you to the hospital and a case so mild you don't even realize you're infected.
Exposure Dose Determines Severity of Disease
When you breathe in a respiratory virus, it immediately begins hijacking any cells it lands near to turn them into virus production machines. The immune system tries to stop this process to halt the spread of the virus.
The amount of virus that you're exposed to – called the viral inoculum, or dose – has a lot to do with how sick you get. If the exposure dose is very high, the immune response can become overwhelmed. Between the virus taking over huge numbers of cells and the immune system's drastic efforts to contain the infection, a lot of damage is done to the body and a person can become very sick.
On the other hand, if the initial dose of the virus is small, the immune system is able to contain the virus with less drastic measures. If this happens, the person experiences fewer symptoms, if any.
This concept of viral dose being related to disease severity has been around for almost a century. Many animal studies have shown that the higher the dose of a virus you give an animal, the more sick it becomes. In 2015, researchers tested this concept in human volunteers using a nonlethal flu virus and found the same result. The higher the flu virus dose given to the volunteers, the sicker they became.
In July, researchers published a paper showing that viral dose was related to disease severity in hamsters exposed to the coronavirus. Hamsters who were given a higher viral dose got more sick than hamsters given a lower dose.
Based on this body of research, it seems very likely that if you are exposed to SARS-CoV-2, the lower the dose, the less sick you will get.
So what can a person do to lower the exposure dose?
Masks Reduce Viral Dose
Most infectious disease researchers and epidemiologists believe that the coronavirus is mostly spread by airborne droplets and, to a lesser extent, tiny aerosols. Research shows that both cloth and surgical masks can block the majority of particles that could contain SARS-CoV-2. While no mask is perfect, the goal is not to block all of the virus, but simply reduce the amount that you might inhale. Almost any mask will successfully block some amount.
Laboratory experiments have shown that good cloth masks and surgical masks could block at least 80% of viral particles from entering your nose and mouth. Those particles and other contaminants will get trapped in the fibers of the mask, so the CDC recommends washing your cloth mask after each use if possible.
The final piece of experimental evidence showing that masks reduce viral dose comes from another hamster experiment. Hamsters were divided into an unmasked group and a masked group by placing surgical mask material over the pipes that brought air into the cages of the masked group. Hamsters infected with the coronavirus were placed in cages next to the masked and unmasked hamsters, and air was pumped from the infected cages into the cages with uninfected hamsters.
As expected, the masked hamsters were less likely to get infected with COVID-19. But when some of the masked hamsters did get infected, they had more mild disease than the unmasked hamsters.
Masks Increase Rate of Asymptomatic Cases
In July, the CDC estimated that around 40% of people infected with SARS-CoV-2 are asymptomatic, and a number of other studies have confirmed this number.
However, in places where everyone wears masks, the rate of asymptomatic infection seems to be much higher. In an outbreak on an Australian cruise ship called the Greg Mortimer in late March, the passengers were all given surgical masks and the staff were given N95 masks after the first case of COVID-19 was identified. Mask usage was apparently very high, and even though 128 of the 217 passengers and staff eventually tested positive for the coronavirus, 81% of the infected people remained asymptomatic.
Further evidence has come from two more recent outbreaks, the first at a seafood processing plant in Oregon and the second at a chicken processing plant in Arkansas. In both places, the workers were provided masks and required to wear them at all times. In the outbreaks from both plants, nearly 95% of infected people were asymptomatic.
There is no doubt that universal mask wearing slows the spread of the coronavirus. My colleagues and I believe that evidence from laboratory experiments, case studies like the cruise ship and food processing plant outbreaks and long-known biological principles make a strong case that masks protect the wearer too.
The goal of any tool to fight this pandemic is to slow the spread of the virus and save lives. Universal masking will do both.
Monica Gandh is a Professor of Medicine, Division of HIV, Infectious Diseases and Global Medicine at the University of California, San Francisco.
Disclosure statement: Monica Gandhi does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
Reposted with permission from The Conversation.
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By Eric Tate and Christopher Emrich
Disasters stemming from hazards like floods, wildfires, and disease often garner attention because of their extreme conditions and heavy societal impacts. Although the nature of the damage may vary, major disasters are alike in that socially vulnerable populations often experience the worst repercussions. For example, we saw this following Hurricanes Katrina and Harvey, each of which generated widespread physical damage and outsized impacts to low-income and minority survivors.
Mapping Social Vulnerability<p>Figure 1a is a typical map of social vulnerability across the United States at the census tract level based on the Social Vulnerability Index (SoVI) algorithm of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6237.8402002" target="_blank"><em>Cutter et al.</em></a> . Spatial representation of the index depicts high social vulnerability regionally in the Southwest, upper Great Plains, eastern Oklahoma, southern Texas, and southern Appalachia, among other places. With such a map, users can focus attention on select places and identify population characteristics associated with elevated vulnerabilities.</p>
Fig. 1. (a) Social vulnerability across the United States at the census tract scale is mapped here following the Social Vulnerability Index (SoVI). Red and pink hues indicate high social vulnerability. (b) This bivariate map depicts social vulnerability (blue hues) and annualized per capita hazard losses (pink hues) for U.S. counties from 2010 to 2019.<p>Many current indexes in the United States and abroad are direct or conceptual offshoots of SoVI, which has been widely replicated [e.g., <a href="https://link.springer.com/article/10.1007/s13753-016-0090-9" target="_blank"><em>de Loyola Hummell et al.</em></a>, 2016]. The U.S. Centers for Disease Control and Prevention (CDC) <a href="https://www.atsdr.cdc.gov/placeandhealth/svi/index.html" target="_blank">has also developed</a> a commonly used social vulnerability index intended to help local officials identify communities that may need support before, during, and after disasters.</p><p>The first modeling and mapping efforts, starting around the mid-2000s, largely focused on describing spatial distributions of social vulnerability at varying geographic scales. Over time, research in this area came to emphasize spatial comparisons between social vulnerability and physical hazards [<a href="https://doi.org/10.1007/s11069-009-9376-1" target="_blank"><em>Wood et al.</em></a>, 2010], modeling population dynamics following disasters [<a href="https://link.springer.com/article/10.1007%2Fs11111-008-0072-y" target="_blank" rel="noopener noreferrer"><em>Myers et al.</em></a>, 2008], and quantifying the robustness of social vulnerability measures [<a href="https://doi.org/10.1007/s11069-012-0152-2" target="_blank" rel="noopener noreferrer"><em>Tate</em></a>, 2012].</p><p>More recent work is beginning to dissolve barriers between social vulnerability and environmental justice scholarship [<a href="https://doi.org/10.2105/AJPH.2018.304846" target="_blank" rel="noopener noreferrer"><em>Chakraborty et al.</em></a>, 2019], which has traditionally focused on root causes of exposure to pollution hazards. Another prominent new research direction involves deeper interrogation of social vulnerability drivers in specific hazard contexts and disaster phases (e.g., before, during, after). Such work has revealed that interactions among drivers are important, but existing case studies are ill suited to guiding development of new indicators [<a href="https://doi.org/10.1016/j.ijdrr.2015.09.013" target="_blank" rel="noopener noreferrer"><em>Rufat et al.</em></a>, 2015].</p><p>Advances in geostatistical analyses have enabled researchers to characterize interactions more accurately among social vulnerability and hazard outcomes. Figure 1b depicts social vulnerability and annualized per capita hazard losses for U.S. counties from 2010 to 2019, facilitating visualization of the spatial coincidence of pre‑event susceptibilities and hazard impacts. Places ranked high in both dimensions may be priority locations for management interventions. Further, such analysis provides invaluable comparisons between places as well as information summarizing state and regional conditions.</p><p>In Figure 2, we take the analysis of interactions a step further, dividing counties into two categories: those experiencing annual per capita losses above or below the national average from 2010 to 2019. The differences among individual race, ethnicity, and poverty variables between the two county groups are small. But expressing race together with poverty (poverty attenuated by race) produces quite different results: Counties with high hazard losses have higher percentages of both impoverished Black populations and impoverished white populations than counties with low hazard losses. These county differences are most pronounced for impoverished Black populations.</p>
Fig. 2. Differences in population percentages between counties experiencing annual per capita losses above or below the national average from 2010 to 2019 for individual and compound social vulnerability indicators (race and poverty).<p>Our current work focuses on social vulnerability to floods using geostatistical modeling and mapping. The research directions are twofold. The first is to develop hazard-specific indicators of social vulnerability to aid in mitigation planning [<a href="https://doi.org/10.1007/s11069-020-04470-2" target="_blank" rel="noopener noreferrer"><em>Tate et al.</em></a>, 2021]. Because natural hazards differ in their innate characteristics (e.g., rate of onset, spatial extent), causal processes (e.g., urbanization, meteorology), and programmatic responses by government, manifestations of social vulnerability vary across hazards.</p><p>The second is to assess the degree to which socially vulnerable populations benefit from the leading disaster recovery programs [<a href="https://doi.org/10.1080/17477891.2019.1675578" target="_blank" rel="noopener noreferrer"><em>Emrich et al.</em></a>, 2020], such as the Federal Emergency Management Agency's (FEMA) <a href="https://www.fema.gov/individual-disaster-assistance" target="_blank" rel="noopener noreferrer">Individual Assistance</a> program and the U.S. Department of Housing and Urban Development's Community Development Block Grant (CDBG) <a href="https://www.hudexchange.info/programs/cdbg-dr/" target="_blank" rel="noopener noreferrer">Disaster Recovery</a> program. Both research directions posit social vulnerability indicators as potential measures of social equity.</p>
Social Vulnerability as a Measure of Equity<p>Given their focus on social marginalization and economic barriers, social vulnerability indicators are attracting growing scientific interest as measures of inequity resulting from disasters. Indeed, social vulnerability and inequity are related concepts. Social vulnerability research explores the differential susceptibilities and capacities of disaster-affected populations, whereas social equity analyses tend to focus on population disparities in the allocation of resources for hazard mitigation and disaster recovery. Interventions with an equity focus emphasize full and equal resource access for all people with unmet disaster needs.</p><p>Yet newer studies of inequity in disaster programs have documented troubling disparities in income, race, and home ownership among those who <a href="https://eos.org/articles/equity-concerns-raised-in-federal-flood-property-buyouts" target="_blank">participate in flood buyout programs</a>, are <a href="https://www.eenews.net/stories/1063477407" target="_blank" rel="noopener noreferrer">eligible for postdisaster loans</a>, receive short-term recovery assistance [<a href="https://doi.org/10.1016/j.ijdrr.2020.102010" target="_blank" rel="noopener noreferrer"><em>Drakes et al.</em></a>, 2021], and have <a href="https://www.texastribune.org/2020/08/25/texas-natural-disasters--mental-health/" target="_blank" rel="noopener noreferrer">access to mental health services</a>. For example, a recent analysis of federal flood buyouts found racial privilege to be infused at multiple program stages and geographic scales, resulting in resources that disproportionately benefit whiter and more urban counties and neighborhoods [<a href="https://doi.org/10.1177/2378023120905439" target="_blank" rel="noopener noreferrer"><em>Elliott et al.</em></a>, 2020].</p><p>Investments in disaster risk reduction are largely prioritized on the basis of hazard modeling, historical impacts, and economic risk. Social equity, meanwhile, has been far less integrated into the considerations of public agencies for hazard and disaster management. But this situation may be beginning to shift. Following the adage of "what gets measured gets managed," social equity metrics are increasingly being inserted into disaster management.</p><p>At the national level, FEMA has <a href="https://www.fema.gov/news-release/20200220/fema-releases-affordability-framework-national-flood-insurance-program" target="_blank">developed options</a> to increase the affordability of flood insurance [Federal Emergency Management Agency, 2018]. At the subnational scale, Puerto Rico has integrated social vulnerability into its CDBG Mitigation Action Plan, expanding its considerations of risk beyond only economic factors. At the local level, Harris County, Texas, has begun using social vulnerability indicators alongside traditional measures of flood risk to introduce equity into the prioritization of flood mitigation projects [<a href="https://www.hcfcd.org/Portals/62/Resilience/Bond-Program/Prioritization-Framework/final_prioritization-framework-report_20190827.pdf?ver=2019-09-19-092535-743" target="_blank" rel="noopener noreferrer"><em>Harris County Flood Control District</em></a>, 2019].</p><p>Unfortunately, many existing measures of disaster equity fall short. They may be unidimensional, using single indicators such as income in places where underlying vulnerability processes suggest that a multidimensional measure like racialized poverty (Figure 2) would be more valid. And criteria presumed to be objective and neutral for determining resource allocation, such as economic loss and cost-benefit ratios, prioritize asset value over social equity. For example, following the <a href="http://www.cedar-rapids.org/discover_cedar_rapids/flood_of_2008/2008_flood_facts.php" target="_blank" rel="noopener noreferrer">2008 flooding</a> in Cedar Rapids, Iowa, cost-benefit criteria supported new flood protections for the city's central business district on the east side of the Cedar River but not for vulnerable populations and workforce housing on the west side.</p><p>Furthermore, many equity measures are aspatial or ahistorical, even though the roots of marginalization may lie in systemic and spatially explicit processes that originated long ago like redlining and urban renewal. More research is thus needed to understand which measures are most suitable for which social equity analyses.</p>
Challenges for Disaster Equity Analysis<p>Across studies that quantify, map, and analyze social vulnerability to natural hazards, modelers have faced recurrent measurement challenges, many of which also apply in measuring disaster equity (Table 1). The first is clearly establishing the purpose of an equity analysis by defining characteristics such as the end user and intended use, the type of hazard, and the disaster stage (i.e., mitigation, response, or recovery). Analyses using generalized indicators like the CDC Social Vulnerability Index may be appropriate for identifying broad areas of concern, whereas more detailed analyses are ideal for high-stakes decisions about budget allocations and project prioritization.</p>
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Sen. Bernie Sanders on Tuesday was the lone progressive to vote against Tom Vilsack reprising his role as secretary of agriculture, citing concerns that progressive advocacy groups have been raising since even before President Joe Biden officially nominated the former Obama administration appointee.