Rationale for the CDC's new guidance for masks

Last update: Thursday 3/13/22 

Before the development of highly successful vaccines in late 2020, our pandemic management efforts sought to reduce the speed with which the coronavirus spread throughout our communities, our goal being to prevent severe illness from overwhelming our health care systems. In September 2020, the CDC measured community transmission of the virus by a combination of two metrics: (1) total new cases per 100,000 persons in the past 7 days, and (2) the percentage of coronavirus test results that were positive during the previous 7 days. 

September 2020 metrics

Slide #3 in the PowerPoint PDF linked to the "Overview" section of the CDC's new "COVID-19 Community Levels" page makes the following points:

• The CDC used these metrics to classify communities into four transmission levels: Low, Moderate, Substantial, and High
  
  
• These levels were "Used by CDC to inform setting-specific guidance and layered prevention strategies (e.g., screening testing in schools, masking, etc.)"
  
  
• They were also used by "Public health practitioners, schools, businesses, and community organizations also rely on these metrics to inform decisions about prevention measures"

Regular readers of this blog know that its editor has repeatedly accused the CDC of producing the same guidance for all states and local communities, i.e., "one size fits all". Four levels of community transmission yielded striking multi-colored thematic maps, but the CDC's recent update to its guidances is an admission that the September 2020 levels yielded maps that displayed what lawyers sometimes call "distinctions without differences". In this case, the distinctions did not lead to more effective differences in the CDC's guidance that was based on these levels.

Why not? Because the specified levels were not based on the most important differences that distinguish one community's response to new infection from another's ==> the differences in their "background immunity" and in their available healthcare resources. 

In an interview for a NY Times podcast in January 2022, Dr. Fauci used the term "background immunity" to describe the sum of the immunities of all of the residents of a community which they received from vaccines, boosters, and recovery from infection. Communities with higher background immunity would be expected to be suffer less severe illness from new COVID infections. They would therefore use less of their healthcare systems's resources; communities with lower background immunity would require more resources.

(Note: the CDC's Website and PowerPoint presentation refer to this as "population immunity" which, to the editor of this blog, sounds uncomfortably like "herd immunity", a condition that's unattainable in the presence of variants that produce substantial numbers of "breakthrough cases", e.g, Delta and more emphatically Omicron)

Communities should strive to increase their vaccination and booster efforts until their background immunity could be expected to prevent new infections from producing levels of severe illness that would exceed the limits of their healthcare resources.

• The podcast audio for Dr. Fauci's interview with the NY Times, "We need to talk about Covid, Part 2", 1/31/22, can be found ==> Here
  Note: This page contains a link to the transcript of this interview.

February 2022 metrics

The bullets in slide #7 of the PowerPoint PDF identify the two metrics that underly the new levels proclaimed by the CDC on 2/25/22:

• "New hospital admissions with confirmed COVID-19/100,000 people and ... "
  
  
• "... percent of inpatient beds occupied with COVID-19 patient"

Combinations of these two metrics were used to specify three "COVID-19 Community Levels" ==> low, medium, and high, as specified in slide #12. A copy of this slide appears below:

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According to the "How CDC Measures the COVID-19 Community Levels" section of the "COVID-19 Community Levels" page:

• "The COVID-19 Community Level is based on the higher of the new admissions and inpatient beds metrics."

Break points for each metric were determined by testing a range of values for the metrics against historical data for admissions to determine which values best predicted subsequent low, medium, and high surges in hospitalizations. 

• First row of the table
  For example, average 7 day increases in COVID admissions of 5, 6,  7, 8, 9, 10,11, 12, etc, etc, etc percent might have been tested; the team must have found that 10 percent was the best predictor of low hospitalizations. Similar statistical explorations must have shown that 10.0 to 19.9 percent, and 20.0 percent or more were the best predictors for medium and high surges.
  
  
• Second row of the table
  Similar explorations of the percent staffed hospital beds occupied by COVID patients determined that the best predictors of low, medium, and high surges were less than 10 percent, 10.0 to 14.9 percent, and 15 percent or more.

It's important to note that if surges were coming, these breakpoints would not be encountered until the surges actually began. The breakpoints might be the best statistical predictors but, by themselves, they might not provide timely warnings to community residents, community leaders, and hospital administrators. 

Ideally, residents and community leaders would want warnings that arrived before the surges began so that they could adopt stronger mitigations for a few days or weeks  -- e.g., masks, distancing, limits on the size of group meetings -- in the hopes of slowing down the speed of the spreading virus, thereby reducing the number of hospital admissions that would be needed each week. Hospital administrators would want time to acquire more medical equipment and hire more staff. The appearance of "New Cases" in the first column on the left side of the table represents an effort to make the predictions more timely.

• Third row
  The third row of the table shows that whenever the total number of new infections exceeds 200 per 100,000 residents of the county in the previous seven days, the app will predict a medium surge, even if no one has been admitted to the hospitals yet. And if ten or more people have been admitted within seven days, the app will predict a high surge.
  
  
• Fourth row
  The fourth row shows that whenever the average number of new infections exceeds 200 per 100,000 county residents in the previous seven days and less than 10 percent of staffed hospital beds are occupied by COVID patients, the app will predict a medium surge; but if 10 percent or more of staffed hospital beds are occupied by COVID patients, the app will predict a high surge .

Interested readers can scroll through all of the slides in the CDC's entire PowerPoint presentation via the following frame.

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The untimely Devil is in the Omicron breakthroughs

When the pandemic reached the U.S. back in early 2020, it was quickly noted that surges in new cases were usually followed about three weeks later by surges in hospitalizations. In those days breakthrough infections were rare. Substantial breakthroughs occurred under Delta, but not so many as to substantially distort this relationship. When Omicron became dominant, breakthroughs became the norm. 

• "Omicron will infect ‘just about everybody,’ Fauci says", Andrew Jeong and Ellen Francis, Washington Post, 1/12/22   

Omicron can infect fully vaccinated persons, even boosted persons, and there are indications that Omicron can infect them more than once. That's the bad news. The good news is that the overwhelming majority of these breakthrough cases are asymptomatic or mild; severe illness among fully vaccinated persons is still rare. 

High breakthrough rates and high transmissibility imply that Omicron can keep sloshing around highly vaccinated counties; but very few residents will be hospitalized. Therefore the "new cases" trip wire that was supposed to provide early warnings before large surges in hospitalizations will more frequently provide false alarms in communities that have high background immunity. 

While it is increasingly unlikely that residents of those communities will heed calls for more masks, more social distancing, and more limits on group meetings, false alarms will cause further erosion of the CDC's credibility in these communities, a loss that could have catastrophic consequences if a new variant emerges that is more transmissible than Omicron and more lethal.

Why not include background immunity in the next group of metrics?

The CDC's new guidance is a gigantic advance way beyond its previous efforts.  The next big advance might be achieved by adding background immunity to the Community Level metrics, if only because this is the metric that most of us expected would measure the progress we were making in our return to normal living. Counties that achieve high background immunity will experience low instances of hospitalization per 100,000 residents. Counties that only achieve low background immunity will experience high hospitalizations per 100,000 that can be anticipated as well as they could have been anticipated before the emergence of Omicron. So the CDC needs to develop reliable break points for counties in the middle range.

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Links to related notes on this blog:  

• "The CDC's new guidance for masks concedes that one size does not fit all", Last update: 3/1/22