If I contract Covid-19, how likely is it that I will succumb to it? Age, gender, and preexisting condition suggest that my risk is some multiple of whatever counts for “average.” I wanted to explore those odds.
If you’ve been reading this blog for a while, you know that I don’t expect to partake in this state’s reopening. Writing in Wired, Maria Streshinksy examines what life is and will be like for those of us who will be remaining in lockdown as the world around us loosens the strictures. Her circumstances are more dramatic than mine — two people in her “pod” are at risk, her partner undergoing chemotherapy and her mother with a heart condition. Like Streshinksy, I will be waiting for science to come through with a solution before venturing very far from home or expanding my social circle.
A starting point for considering my risk is this NYTimes article from last month, which does a broad-stroke comparison of various populations’ Covid-19 risks with other dangerous endeavors: skydiving, climbing in the Himalayas above 26,000 feet, going on an RAF bombing run over Germany in WW-II.
The Times looks at the risk across populations. The number I am seeking is more personal.
Here’s a very rough stab at an answer, from the simple calculator on the data-scraping Covid tracking site created and run by 17-year-old high school student Avi Schiffmann. (It’s one of the more trustworthy dashboards for up-to-date data, but its calculator’s inner working are opaque.) Inputs to the calculator are age, gender, and the presence or absence of five pre-existing medical conditions. I am positive for one of those conditions and perhaps borderline for two more. Of course the calculator does not account for “borderline”; you declare that you have a condition or you don’t. Trying all the combinations gives odds of anywhere from 54% to 92% that I would die if I contracted Covid-19.
I don’t know what sort of algorithm Schiffmann implemented. Do the co-morbidity factors add together? Do they multiply? Something else? According to the calculator at QxMD, “There is no model for a single mortality risk that takes into account multiple variables.” So perhaps Schiffmann just made something up.
The best numbers we have for morbidity’s dependence on pre-existing conditions come from a published study out of Wuhan, China, summarized in the table above. It uses patient data up to February 11th of this year and represents 1023 deaths among 44,672 confirmed Covid-19 cases.
If it required 44,000 cases to discover single-condition dependencies, I expect that pinning down the effect of multiple pre-existing conditions would need a million cases. Knock wood we never get to those kinds of numbers.
For those wanting to understand the reasons why it is so hard to answer my simple question about personal risk, I commend to you this essay in Our World in Data: Mortality Risk of COVID-19. It explains the various metrics that are sometimes bandied about, and confused, in media coverage — the case fatality rate (CFR), crude mortality rate, and infection fatality rate. Popular accounts mostly use the CFR, but the essay details why this is not a single number, but rather a snapshot in time at a particular place.
So I’m not going to get a simple answer to my simple question. The best I can do is to continue avoiding risky behavior. Doing that.
[ Note added 2020-07-07: ] Here is a tool that purports to answer the question I asked. It is a calculator for one’s odds of contracting and succumbing to the virus. It asks detailed questions to get at levels of exposure, lifestyle and health pre-disposers, prescription drugs taken, and much more. (It can feel a little daunting to answer such a range of personal questions, but the survey’s originators, Nexoid in the UK, say that only your IP address, and thus your rough location, are stored with the data you supply.)
At the end of the survey you get an estimate of your chances of catching Covid-19 and your odds of dying from it if you do. The survey helpfully multiplies these probabilities together for you to arrive at your overall odds of succumbing. I come out at about 99.6% chance of surviving. But that figure incorporates a 19% chance of becoming infected, eventually.
Another bonus for completing the survey (as over 800,000 people have already done): you’re taken to a page from which you can download aggregated results. Here’s that download page.
[ Note added 2020-07-12: ] A large study out of Britain offers the finest resolution yet on how various risk factors affect one’s chances of dying from Covid. The study examined 17 million patient records from the national health service, which chowed almost 11,000 Covid-related deaths.
The baseline is a 50-60 year old woman, white, not overweight, no pre-existing conditions such as diabetes or heart issues, and in the upper level of income.
Age is by far the largest factor, making me over 8.6 times as likely as that hypothetical white woman to die after contracting Covid-19. All of my other risk factors together multiply out to 4.5 times the baseline risk.