There’s an Epidemic That’s a Bigger Threat Than the Coronavirus
And we’re largely ignoring it
by Dr. David L. Katz
You are, probably, worrying about coronavirus.
For most of us, the anxious questions are:
- Am I going to get the coronavirus?
- Is someone I love going to get it?
- If we do, is it going to kill us?
For starters, let’s be clear that no one ever gets a health guarantee. You might still have a heart attack even if you do everything advisable to avoid one. If you eat optimally, exercise, don’t smoke, and so on- you make heart disease or cancer vastly less probable, but you don’t get a guarantee. Human health simply does not come with those. And, of course, you can do everything right to be fit and healthy and keep your coronaries pristine, reliably avoid heart disease, and still get hit by a bus, or a falling tree, or lightning. Or get a brain tumor, for reasons we don’t know.
One thing you learn in medicine is that we control ship and sail, but never wind and wave. We don’t control everything, ever. Bad things happen to good people doing everything right all the time. But they do happen much less often to those doing everything right than to everyone else, so what we do matters enormously. It shifts probability.
So, the questions about coronavirus revert to questions about probability. And those we can answer, or at least establish the basis for answers.
The ultimate questions — will I get this disease, and will it kill me if I do? — can be broken into component parts.
What is my risk of exposure?
Right now, unless you are in one of the rarefied populations around the world where the disease is concentrated, the answer is: probably very, very, very low. There are, as I write this (2/28/20) just under 84,000 global cases out of a population of nearly 8 billion humans. That is one case per 100,000. For comparison, the lifetime risk of being struck by lightning in the United States is roughly one in 3,000. The coronavirus numbers could change, of course, and likely will, but for now- total cases are of a “one in many, many thousands” magnitude, making exposure for any one of us highly improbable.
Being exposed is necessary, but not sufficient, to get infected.
If I am exposed, how probable is it I get the disease?
This is the infection rate. If we use the most concentrated outbreak in Wuhan, China, as our model, with the assumption (obviously not entirely true) that everyone there was “exposed,” then the answer at the moment is just under 79,000 cases in a population of 11 million. That is an infection rate of roughly 7 per thousand, or 0.7 percent.
If I get infected, how probable is it the disease will kill me?
This is the fatality rate. Once again, the most dire numbers come from Wuhan, where there have been just under 2,800 deaths among the just under 79,000 infected. That ratio yields a fatality rate of less than 4 per hundred, or just under 4 percent.
I hasten to apologize for any semblance here that these numbers are adequate messengers. Every number in this mix is a real person just like you and me, with a family just like yours or mine. One of the great liabilities of public health is the capacity to lose the human reality of it in a sea of anonymizing statistics. As I use numbers to make my point, I point to the people behind the veil of those numbers, those families, and invite us both to direct the full measure of our condolence, our compassion, and the solidarity of our human kinship there. Among the messages of this, and any, pandemic is that however good we may be at accentuating our superficial differences, we are one, great, global human family- the same kind of animal, with just the same vulnerabilities. COVID-19 does not care at all who issued our passport.
OK, back to numbers. Here’s an important reality check: We are much, much more likely to overlook the mildest cases of any disease than death from that disease. Death is hard to miss.
What would it mean if this common scenario pertains to COVID-19? It means many more people than we know are getting the infection, but with mild symptoms passing for a cold, or maybe even no symptoms at all. The “bad news” here is that the infection rate might be much higher than we think. But does that increase your risk of getting the disease (yes!), and dying from it (no!)? I’ll illustrate.
Let’s say you are a member of a hypothetical population of 2,000 people. We believe this population was exposed to coronavirus, that 200 people got infected, and that 8 died.
The infection rate here is (200/2000) or 10 percent (much higher than the reality in Wuhan), and the fatality rate is (8/200), or 4 percent (about what has been seen to date in Wuhan). If you are a typical member of this population, your risk of both getting the infection and dying from it is {(200/2000) X (8/200)}, or 0.4 percent. We can see this directly from the total population numbers: 8 deaths out of 2000 is, just as our calculations showed, 4 deaths per thousand, or 0.4 percent. And to flip this around, it means your chances of dodging the coronavirus bullet are 99.6 percent. Those are good odds!
But what if we were wrong — not a little, but a lot — about the number of infections, because we had overlooked many that were too mild to attract anyone’s attention? Well, then, maybe 4 times as many actually got infected- 800, rather than 200. This does mean you are much more likely to get the virus yourself, but does that make it more likely you will die from it? Not at all. The simple math shows why.
We now have an infection rate of (800/2000), or a very alarming 40 percent. But we now also have a fatality rate of only (8/800), or 1 percent. If we repeat the prior calculation for your personal risk of getting the virus and dying from it, we have: {(800/2000) X (8/800)}, or…the exact same 0.4 percent as before.
This is true of coronavirus in the real world. If we are finding every case, then your risk of getting infected is, for now at least, very low, and your risk of dying if you do is also very low. If we are missing a lot of cases, your risk of infection may be much higher, but your risk of dying if infected is commensurately lower. It’s a zero-sum game, and each sum, for now, means a very low probability indeed that you or someone you love will die from this disease.
Before we wrap up, let’s examine our propensity for risk distortion whenever confronting the new, the seemingly exotic, and the uncertain — and let’s consider how epidemiologic familiarity clearly does breed contemptuous disregard.
Worries over the exotic coronavirus are roiling the world now in every way imaginable. Those not anxious about life, limb, and loved ones are fretting over their stock portfolios.
To date, there are a total of 60 cases in the United States — and zero deaths. In contrast, humble influenza thus far this year has infected as many as 40 million of us (about 1 in 9) and caused as many as 40,000 deaths (a fatality rate of 1 per thousand). We breathlessly await the rushed development of a vaccine for COVID-19, even as we balk ever more routinely at a flu vaccine which is in fact very safe, effective at reducing infection and transmission, and directed at a disease so far many orders of magnitude more dire than the coronavirus. Nor is our penchant for risk distortion limited to infectious diseases.
As I write this, I am mere days away from the release of my new book, co-authored with Mark Bittman, “How to Eat.” We wrote the book together not because we weren’t already busy enough, but because infusing the conversation about diet and health in America with science filtered through a generally missing lens of sense is that important.
Poor overall diet quality is the single leading cause of premature death in the United States today, causing an estimated 500,000 or so deaths each year. That is more than ten times worse than a fairly bad strain of influenza, monumentally worse than coronavirus thus far, and happens every year.
Diet — what should be a source of nourishment, sustenance, and vitality — is the reason for one death in six here. And that is just the tip of the epidemiologic iceberg, since diet causes much more morbidity than premature death. To borrow directly from Dariush Mozaffarian and Dan Glickman in The New York Times:
- More than 100 million adults — almost half the entire adult population — have pre-diabetes or diabetes.
- Cardiovascular disease afflicts about 122 million people and causes roughly 840,000 deaths each year, or about 2,300 deaths each day.
- Three in four adults are overweight or obese. More Americans are sick, in other words, than are healthy.
Photo by Tim Sloan/AFP/Getty Images
The exposure risk for diet is 100 percent; everyone eats. So for coronavirus to rival diet, every last one of us would need to be exposed.
Poor overall diet quality is the single leading cause of premature death in the United States today, causing an estimated 500,000 or so deaths each year. That is more than ten times worse than a fairly bad strain of influenza, monumentally worse than coronavirus thus far, and happens every year.
Let’s say that the ‘infection rate’ for diet is the probability of it harming you. Since less than 10 percent of Americans meet recommendations for fruits and vegetables, and since overall diet quality is poor on average, we can say that diet is harming — to one degree or another — at least 90 percent of us. So, for coronavirus to rival that, 90 out of 100 people exposed — almost everyone — would need to get infected.
What about mortality? The deaths attributed directly to diet don’t really tell the whole tale. Diet is the major contributor to diabetes, heart disease and stroke, and an important contributor to cancer, liver disease, dementia and more. At least 50 percent of all premature death can be traced to effects of diet in whole or part, so let’s call the fatality rate 50 percent. For coronavirus to match that, the virus would need to kill one out of every two of us infected.
Admittedly, coronavirus kills quickly when it kills, and diet tends to kill more slowly. This matters, but less than first meets the eye. Dying prematurely and abruptly is bad, but dying prematurely after a long chronic disease — losing life from years before losing years from life — is no bargain either. We have a native blind spot for any risk that plays out slowly rather than immediately — but climate change shows how calamitously costly that can prove to be. So, OK, coronavirus “wins” for speed, but really deserves far less preferential respect than it gets. Flu warrants far more. Diet, willfully engineered to put profit ahead of public health while evoking no apparent outrage, warrants far more still.
Courtesy of David Dees: https://www.ddees.com/
Back to COVID-19, sure it is scary, mostly because of the attendant uncertainties. The relatively unknown threat is always the scariest. But for the coronavirus to rival mundane but massively greater risks that hide in plain sight and go routinely neglected, it would need to be literal orders of magnitude worse than it has thus far shown itself to be. That might happen — but we might also be struck by a large asteroid while worrying about it.
I am not saying “don’t worry, be happy.” I am saying, if your worries relate to you or those you love getting sick and dying, that they could be far more productively directed than at COVID-19. I am saying get some perspective, get a grip, get a flu shot, drive a hybrid, go for a walk, and…eat a salad.
Copyright 2020 by Dr. David L. Katz is a board-certified specialist in Preventive Medicine/Public Health and co-author with Mark Bittman of the forthcoming How to Eat
— First published on Linkedin.
PS Dr. David Katz on whether the fight against coronavirus is worse than the disease
https://video.foxnews.com/v/6150669450001
Links
PS 2 Statistics Reports
Coronavirus (COVID-19) Mortality Rate
Considering that a large number of cases are asymptomatic (or present with very mild symptoms) and that testing has not been performed on the entire population, only a fraction of the SARS-CoV-2 infected population is detected, confirmed through a laboratory test, and officially reported as a COVID-19 case. The number of actual cases is therefore estimated to be at several multiples above the number of reported cases. The number of deaths also tends to be underestimated, as some patients are not hospitalized and not tested.
If we base our calculation (deaths / cases) on the number of reported cases (rather than on the actual ones), we will greatly overestimate the fatality rate. […]
As of May 1, 23,430 people are estimated to have died out of a total population of 8,398,748 in New York City. This corresponds to a 0.28% crude mortality rate to date, or 279 deaths per 100,000 population, or 1 death every 358 people. […]
But we can calculate it for the entire population under 65 years old (both healthy and unhealthy): with 6,188 deaths (26% of the total deaths in all age groups) occurring in this age group, of which 5,498 deaths (89%) in patients with a known underlying condition, the crude mortality rate to date will correspond to 6,188 / 7,214,525 = 0.09% CMR, or 86 deaths per 100,000 population (compared to 0.28% and 279 deaths per 100,000 for the general population). [ This is nearly 0.1% – fatality rate of a seasonal flu ]
Comparison with other viruses
For comparison, the case fatality rate with seasonal flu in the United States is less than 0.1% (1 death per every 1,000 cases). Mortality rate for SARS was 10%, and for MERS 34%.
Source: https://www.worldometers.info/coronavirus/coronavirus-death-rate/
FluWatch Reports (by Canadian Government )
FluWatch annual report: 2018-19 influenza season
- The 2018-19 influenza season in Canada was longer than the previous five seasons and was characterized by two waves of influenza A activity and very little influenza B circulation. The national season started in week 43 (October 21-27, 2018), peaked in week 52 (December 23-29, 2018) and ended in week 21 (May 19-25, 2019).
- A(H1N1) was predominant in the earlier part of the season (October to February) followed by a smaller wave of A(H3N2) circulation (March to April). Overall, A(H1N1) was the predominant strain nationally this season.
- Two waves of activity were observed in the number of reported outbreaks. The predominant subtype for typed outbreaks was A(H1N1) in the early part of the season (October to January), and A(H3N2) in the latter part of the season (Feburary onward).
- The annual seasonal hospitalization rate was above average compared to the previous five seasons. Adults 65 years of age and older had the highest overall hospitalization rate; however, the highest cumulative hospitalization rate shifted during the season from children 0-4 years of age (November to March) to adults 65 years of age and older (March onward), likely due to the second wave of A(H3N2).
Nine provinces and territories report influenza associated hospitalizations and deaths for all ages to FluWatch each week – Alberta, Manitoba, Saskatchewan, Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Yukon and the Northwest Territories. The number of reporting provinces/territories varied over the course of the season. On average, seven provinces/territories reported each week.
A total of 3,657 influenza-associated hospitalizations were reported which corresponds to an annual seasonal hospitalization incidence of 45 hospitalizations per 100,000 population in the 2018-19 season (Table 1).
- 96% (3,525) were associated with influenza A.
- Among the 2,149 hospitalized cases for which the influenza subtype was available, the majority were associated with A(H1N1) (68%).
- Similar to the temporal pattern observed in laboratory detections, in the earlier part of the season (October to February) the majority (93%) of hospitalizations for which the influenza subtype was available were A(H1N1). In the latter part of the season (March to April) the majority (87%) of hospitalizations were A(H3N2).
- Overall, adults ? 65 years of age had the highest rate of hospitalizations (132 hospitalizations per 100,000 population); however, children 0-4 years of age had the highest cumulative hospitalization rate in the first part of the season from weeks 45 to 10, while adults ? 65 years of age had the highest cumulative rates in the second part of the season from weeks 10 to 29 which is likely due to the change in predominant subtype over time.
- This season’s hospitalization rate (45 per 100,000 population) was slightly above the average compared to the previous five seasons (40 per 100,000 population), and notably higher than the annual seasonal hospitalization rates of the previous two A(H1N1) predominant seasons (25 and 34 per 100,000 population). The higher observed hospitalization rate relative to previous A(H1N1)-predominant seasons is likely due to the circulation of A(H3N2) in the latter part of the season.
- A total of 613 ICU admissions and 224 deaths were reported this season.
- 97% (595) of reported ICU admissions and 98% (220) of reported deaths were associated with influenza A.
- Similar to the previous two A(H1N1)-predominant seasons, the highest proportion of ICU admissions was reported among adults 45-64 years of age (39%).
- The highest proportion of deaths was reported among adults ? 65 years of age (66%). This was approximately 20% higher than the number reported in the previous two A(H1N1)-predominant seasons and approximately 20% lower than the number reported in the previous three A(H3N2)-predominant seasons, likely as a result of the mixed A(H1N1) and A(H3N2) season.
Source: https://www.canada.ca/en/public-health/services/publications/diseases-conditions/fluwatch/2018-2019/annual-report.html#a5
Vaccine Effectiveness
The Canadian Sentinel Practitioner Surveillance Network (SPSN) provides estimates of the effectiveness of the seasonal influenza vaccine in preventing primary care visits for laboratory confirmed influenza among Canadians of all ages but primarily those from 20-64 years of age.
Based on data collected between November 1, 2018 and April 30, 2019, VE (Vaccine Effectiveness) against any influenza, foremost driven by A(H1N1) viruses, was 56% (95% CI: 47 to 64%), and for A(H1N1) alone was 67% (95% CI: 58 to 75%) (Table 5).
This substantial protection against A(H1N1) was observed in all age groups. Conversely, the SPSN reported little or no vaccine protection against A(H3N2) viruses, with an overall VE against medically-attended outpatient A(H3N2) illness of 17% (95% CI: -13 to 39). Overall, the A(H3N2) VE estimate for 2018-19 was lower than expected generally for A(H3N2) vaccines (~30%), and similar to that observed by SPSN in the 2017-18 A(H3N2)-dominant season where VE was estimated at 14% (95% CI: -8 to 31).
More information on the SPSN including study methodology and available publications can be viewed here.
The Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN-SOS) provides estimates of the effectiveness of the seasonal influenza vaccine in preventing hospitalization for laboratory-confirmed influenza in adults.
Based on data collected between November 1, 2018 and June 1, 2019 among adult (?16 years of age) hospitalized cases of influenza, VE against any influenza was 43% (95% CI: 28 to 55%), and for A(H1N1) alone was 68% (95% CI: 52 to 79%). CIRN-SOS also reported little or no vaccine protection against A(H3N2) viruses, with an overall VE against hospitalized cases of influenza A(H3N2) of 19% (95% CI: -35 to 51). The number of influenza B hospitalized cases was too low to calculate an adjusted VE estimate.
- FluWatch annual report: 2018-19 influenza season
- Source: https://www.canada.ca/en/public-health/services/diseases/flu-influenza/influenza-surveillance/weekly-reports-2019-2020-season.html
PS 3 Bill & Melinda Gates Foundation
The following table lists the top receiving organizations to which the Bill & Melinda Gates Foundation has committed funding, between 2009 and 2015. The table again only includes grants recorded in the Gates Foundation’s IATI publications. [ Source: Wikipedia]
One of the most recently funded projects: Project to support public confidence in immunization programs; to develop a global surveillance system to identify and track rumors/misinformation related to immunization, with a particular focus on GAVI-eligible countries
[ source: https://iatiregistry.org/dataset/bmgf-activitiesjl ]
Seth Berkley has been the CEO of GAVI since 2011, as of 2020. The Bill and Melinda Gates Foundation has donated $1.56 billion to the alliance’s 2016-2020 strategic period, as of March 2019.
CEO Seth Berkley commented that the global response to the COVID-19 pandemic had started off remarkably well. However he cautioned that there was a need for a co-ordination of production at a global level. He advocated that the pandemic needed a global response whereby the best global facilities for separate parts of the processes should then be integrated into a global process. He said he hoped that the G20 countries should work together with a budget of tens of billions of dollars, and that individual countries should be prepared for finished vaccines to be allocated according to greatest need.
Three manufacturers – GSK, Merck and Pfizer– have committed to continue providing countries that transition out of Gavi support with access to prices similar to those offered to Gavi-supported countries, or to maintain the prices that these countries are currently paying for certain vaccines, for a certain period of time, depending on commitment terms.
Gavi yearly shipment reports
This is very interesting material to study.
Historic data on shipments supported by Gavi, the Vaccine Alliance.
https://www.unicef.org/supply/documents/gavi-yearly-shipment-reports
Overview of UNICEF vaccine deliveries in 2019 by PO creation date, funded by Gavi.
https://www.unicef.org/supply/media/3126/file/Gavi-shipments-2019.pdf