Quantifying Personal COVID-19 Risk

The ability to help a patient answer, “How likely is it?” can reduce the likelihood for all.

By Laurie Gelb, MPH, BCPA

Data relative to the pandemic’s impact are not difficult to obtain. With a click of the mouse, patients with Internet access can see new and cumulative Co-V cases and deaths in numeric and linear form, and by zip code, town, county, and state.

The ready availability of epidemiological information notwithstanding, patients are still gathering unsafely, from dinner parties to large rallies. Many are refusing to wear masks as a matter of misguided principle. Though education and income are correlated with mask-wearing, across demographies, many patients report learning about face coverings from a health care source [3].

Quantifying personal risk, which for many people is more compelling information than population data, offers an opportunity to further modify health behaviors [2], i.e. to increase new mask-wearing and distancing, and to reinforce the rationale for doing so among those who are already compliant with the recommendations.

Given the propensity to take personalized information more seriously, clinicians have many opportunities to help reduce viral transmission one patient at a time. These opportunities include clinical encounters, secure interactions, and less formal advice, e.g. to family and friends.

Understandably, physicians are generally averse to individual estimates of future disease risk that may prove inaccurate, or difficult for a patient to place in perspective. However, the need to reduce behaviors that endanger public health arguably takes precedence, so long as information can be placed in proper context.

For example, patients should be informed that Covid-19 risk estimates and calculators should be considered as an opportunity to consider modifiable risk factors, such as obesity and unnecessary public exposures, as much as generating any “magic number.” The differences between interval and cumulative risk of infection and death should also be clear.

In addition to 1x1 interaction, physicians and organizations can encourage realistic risk assessment using their Web sites, blogs, Facebook pages, Twitter feeds and other social networks as educational channels.

To at worst reinforce the concept that personal risk > 0 and that some of the factors that mediate it are controllable by patients, online calculators allow patients to enter variables such as location, age and gender, as well as key underlying conditions. The two ongoing risk calculators below also ask the patient to specify their own and household members’ behaviors relative to masking, exposures, and hygiene.

Some examples of estimation tools that can be shared in context for asymptomatic patients include:

Mathematica/23andMe risk calculator

Oxford/Nexoid risk calculator

Georgia Tech event risk calculator [inputs are the location and extent of proximal exposure to others]

For those who learn visually

Another way to communicate the extent of transmission risk that is personally controllable is through a diagram [4], showing that area ventilation, masking, exposure length, and proximity to other people all affect transmission risk. As colder weather approaches, ventilation and the extent of air filtration (e.g. the use of MERV-13 filters, availability and furnace capacity permitting) will be important considerations.

References

1. Centers for Disease Control and Prevention, Management of Patients with Confirmed 2019-NCoV. Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). 6 Apr. 2020
2. Ferrer R, Klein WM. Risk perceptions and health behavior. Curr Opin Psychol. 2015;5:85–89. doi:10.1016/j.copsyc.2015.03.012
3. Fisher KA, Barile JP, Guerin RJ, et al. Factors Associated with Cloth Face Covering Use Among Adults During the COVID-19 Pandemic — United States, April and May 2020. MMWR Morb Mortal Wkly Rep 2020;69:933–937. DOI: http://dx.doi.org/10.15585/mmwr.mm6928e3external icon.
4. Jones NR, Qureshi ZU, Temple RJ, Larwood JPJ, Greenhalgh T, Bourouiba L et al. Two metres or one: what is the evidence for physical distancing in covid-19? BMJ 2020; 370 :m3223
5. Liang W, Liang H, Ou L, et al. Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID–19. JAMA Intern Med. Published online May 12, 2020. doi:10.1001/jamainternmed.2020.2033