DC drops indoor masking as Montgomery County reinstates it — why the mandate confusion?

Cooler weather and the upcoming holiday season means that people will be spending more time indoors and in spaces that are more crowded. The issue of indoor masking immediately jumps to the forefront of the COVID-19 pandemic debate. 

Wearing masks indoor is a layer of protection against transmitting the virus. With over 70 percent of the adult population now fully vaccinated, and booster shots now widely available, some communities are rethinking their indoor mask mandate.  

Washington, D.C. announced that effective Nov. 22 masks will no longer be required in a number of indoor venues. The basis for this decision is widespread vaccination in the area and a drop in the number of new case, now resting at around 80 cases per 100,000 people. 

In contrast, Montgomery County in Maryland, which borders D.C. to the north, dropped their mask mandate on Oct. 28, but will reinstate it on Nov. 20, since their new case rate is around 60 per 100,000 people. 

How can two geographically adjacent communities come to antithetically different conclusions? What makes these decisions even more confusing to outside observers, the recent new case rate in D.C. is actually higher than in Montgomery County, yet D.C. is loosening their mask requirement while Montgomery County is tightening theirs. 

The rationale for each decision is metaphorically like the parable of the blind men and an elephant. Each community is using some piece of their data to enable them to support their decision.

The CDC guidelines on indoor masking is a function of the number of new case per 100,000 people, with 50 cases per 100,000 the threshold for indoor masking.  

The problem with such a one-size-fits-all metric is that it is too crude to be uniformly applicable. A rural county spread over a large area has inherently lower virus transmission risk than a densely populated urban community, even if each has the same population. Virus transmission risk is dependent on not only population, but population density over an area and how people congregate in this area. 

Even incorporating positivity rates into the masking requirement decision can be misleading. Positivity rates are dependent not just on the number of people tested, but the frequency at which the same people choose to get tested. If a highly cautious group of people get tested frequently, with their results all negative, they will artificially push the positivity rate down and potentially obfuscate the actual risk in a community. 

Top-down mask mandates are useful in targeted indoor venues with a high concentration of people, like grocery stores and shopping malls. This will be particularly true during the upcoming holiday season, with holiday shopping that occurs in brick-and-mortar retail outlets. However, demanding such mandates uniformly across all indoor venues makes little sense. 

The most effective policy would allow each indoor venue to make their own mask rules, since they are then more likely to enforce them. This also allows shoppers and patrons to decide whether they wish to visit venues without a masking requirement. Giving people such choices will likely lead to better enforcement and compliance than blanket mandates across highly variable venue types. 

As the holiday season nears, and the cooler weather blankets across the country, more activities will move indoors, creating more opportunities for virus transmission. The decisions by D.C. and Montgomery County in Maryland are symbolic of the mask confusion that permeates are nation.  

CDC guidelines are useful and necessary, but each venue must take ownership of their mask policy, and most importantly, enforce it.  

Indeed, mask policies that will be most effective are those that percolate from the ground up, not from the top down.     

Sheldon H. Jacobson, Ph.D., is a founder professor of Computer Science and the Carle Illinois College of Medicine at the University of Illinois at Urbana-Champaign. He applies his expertise in data-driven risk-based decision-making to evaluate and inform public health policy.