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Opinion When should the government lift pandemic restrictions? These four metrics can provide the answer.

A wastewater operator collects a sample at the Ballenger-McKinney Wastewater Treatment Plant in Frederick County, Md., on Dec. 18, 2020, to be screened for the coronavirus. (Katherine Frey/The Washington Post)
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Joseph G. Allen is an associate professor and director of the Healthy Buildings program at Harvard University’s T.H. Chan School of Public Health. He co-wrote “Healthy Buildings: How Indoor Spaces Drive Performance and Productivity.”

Decision-makers are increasingly embracing the reality that we will have to “learn to live with” the coronavirus. But how will they know when to impose or lift restrictions?

Fortunately, they don’t have to guess. There are four underutilized data metrics they can use to guide decisions — not only for this surge, but for the rest of the pandemic.

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First, if we want to know whether covid-19 is getting worse or better in an area, turn to the sewers. That’s right — levels of the virus in wastewater is one of the best early indicators of where the virus is spreading.

Take Boston, for example. I’ve been tracking wastewater reports closely for more than a year and found that it predicted last winter’s surge in Boston ahead of official case counts. Same goes for the omicron variant this winter. While the world focused on what was happening in South Africa and Europe, where omicron first hit, wastewater was already telling us omicron was here and spreading fast. We knew this in November.

Just as importantly, wastewater data told us not only when omicron was rising, but also when the surge was peaking. On Jan. 3, the data showed just the tiniest curvature after its sharp ascent, foreshadowing Boston’s imminent collapse of cases a full week before news reports covered it.

Moreover, wastewater data are unbiased. They’re not influenced at all by the amount of testing being done, or who is doing the testing. This is in stark contrast to the two metrics the Centers for Disease Control and Prevention uses — cases and test positive metrics — which are deeply flawed and getting worse. Consider that several hundred million at-home rapid tests are just not being captured in these metrics.

That doesn’t mean we stop counting cases, but it does mean they should no longer be the focal point for setting policy. Instead, every city should be monitoring wastewater in a way that is standardized across regions. It could be one of our country’s best tools for tracking diseases — and not just covid-19.

Second, keep an eye on health-care capacity. The biggest societal threat during the omicron surge was its impact on the health-care system, including on health-care workers. The Department of Health and Human Services does this already with its “Protect Public Health” data hub, and colleagues of mine have created their own “circuit breaker” tool. But these data tools don’t receive enough attention.

Health-care capacity can help us understand how much of a threat any coming surge will be and if additional strategies, such as canceling elective surgeries or activating the National Guard, should be implemented.

Third, track how many hospitalizations are actually due to covid. This is different from people who arrived at the hospital for something else but happened to test positive. Massachusetts just started reporting on this and the data are illuminating. About half of all hospitalizations there are for people being treated for covid. This means others who tested positive were not there for a covid infection; they were just captured in the routine surveillance testing.

This approach runs the risk of being perceived as minimizing covid, but it’s not. The health-care system is impacted by the total number of hospitalizations, so the total capacity metric is more important than the specific number of hospitalizations for covid. Still, the distinction is critical for several reasons, as my colleague Shira Doron has discussed. It provides transparency to the public, and helps clarify the severity of different variants and the efficacy of vaccines. Most importantly, it helps hospitals to plan and forecast capacity, and decision-makers to decide what controls are necessary and when.

Fourth, we must incorporate risk into our decision-making. The two biggest determinants of risk have stayed constant over the past year — age and vaccination status. The latest CDC data show that the hospitalization rate for unvaccinated adults is 67 per 100,000. For a vaccinated adults and unvaccinated teenagers, it is approximately 5 per 100,000. The rate for a vaccinated teen is 1 per million.

We have largely ignored this massive risk differential in setting policy. In fact, we have imposed more strict restrictions on those with the lowest risk — kids. Consider that adults in many areas are allowed to be in restaurants and bars, unmasked, but so many schools force children to eat lunch outdoors or in silence in cafeterias. This is not only inconsistent but also goes against what the data are telling us about risk.

Your covid questions, answered by Dr. Leana Wen

These four metrics can help decision-makers determine when to lift restrictions. And when they do, it doesn’t mean our work is done. The big problem remains the same — the risks for those adults who are unvaccinated is extraordinarily high. We should be spending more energy on vaccinating everyone — both in the United States and around the world — and less on imposing restrictions on those already vaccinated. Just as important as knowing when to put in controls is knowing when to pull them back.

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