## Will You Die From Being Infected With Coronavirus? (Version 4, April 6, 2020)

I created this online risk calculator taking into account age, sex and the presence of chronic diseases to demonstrate the additive risk of dying with a COVID-19 infection. This model is based on:

Limitations:

• The assumption of this model, relies on the hypothesis that as you increase the number of diseases you have, you will increase your risk of dying from COVID-19 in a direct additive fashion (i.e. 1+1=2). However, adding more disease processes may not be directly additive. In fact, the risks of adding more than one disease could lead to an even higher risk (1+1=3). Alternatively, there may not be any additive risk (1+1=1).
• Importantly, these data are from a population of individuals with numerous confounding risk factors (i.e. tobacco use, air pollution, health, socioeconomic, lifestyle, limited access to care, etc.) that can only be accounted for in multivariate analysis.
• Additionally, individuals who are immunocompromised for reasons not accounted for in the conditions listed in the risk calculator also have a higher probability of infection-related complications and death (not able to estimate these risks in the model as they were not included in the published Chinese data).
• This model also can not account for the overwhelming of the healthcare system and inability to access intensive care services, which can lead to a higher mortality rate.

Case Mortality Rate:

Why Do The Calculated Risks Of Dying Look Lower Than What Is Reported On The News?

• The reason is that only 6% (estimated) of the total number of infections have been detected. This leads to a much larger number of undetected cases, which dilutes the case mortality rate.

## Probability of Death from COVID-19 Infection (Version 3, April 1, 2020)

I created this online risk calculator taking into account age, sex and the presence of chronic diseases to demonstrate the additive risk of dying with a COVID-19 infection. This model is based on recently updated (Lancet 3/30/20) Chinese epidemiological data.

Limitations:

• The assumption of this model, relies on the hypothesis that as you increase the number of diseases you have, you will increase your risk of dying from COVID-19 in a direct additive fashion (i.e. 1+1=2). However, adding more disease processes may not be directly additive. In fact, the risks of adding more than one disease could lead to an even higher risk (1+1=3). Alternatively, there may not be any additive risk (1+1=1).
• Importantly, these data are from a population of individuals with numerous confounding risk factors (i.e. tobacco use, air pollution, health, socioeconomic, lifestyle, limited access to care, etc.) that can only be accounted for in multivariate analysis.
• Additionally, individuals who are immunocompromised for reasons not accounted for in the conditions listed in the risk calculator also have a higher probability of infection-related complications and death (not able to estimate these risks in the model as they were not included in the published Chinese data).
• This model also can not account for the overwhelming of the healthcare system and inability to access intensive care services, which can lead to a higher mortality rate.

## Updated: Probability of Death from COVID-19 Infection

### Death Rates Are Hard to Calculate:

One of the points that has been noted by many astute epidemiologists is that we can never know the true death rate (total # dead divided by the total # of individuals infected) from any infectious epidemic or pandemic. The problem is that unless we were to test the entire population for the infectious disease of concern, we will not be able to know the true total number of individuals who are infected.

In the case of COVID-19, we only know the total number of confirmed cases of infection and total number of dead attributed to the virus (see the Johns Hopkins site).

When we calculate the death rate, we will get an artificially high number. If everyone was tested, we would likely see a much higher number of infected individuals than we know now due to the limited testing that has been done to date.

Additionally, each city, state and country will have different age populations, varying degrees of health and lifestyle, environmental issues, genetic and epigenetic differences, access to high quality health care, etc. Each of these variables factor into the probability of dying from diseases, including COVID-19.

Another factor that is very important is the ability of the local healthcare system to treat the most severely ill patients. Under the best of circumstances (no hospital overcrowding, adequate ICU beds and ventilators, etc.), many of the severely ill patients will be able to be treated and survive. If however, the growth rate of the cases shoots up to much the number of severely ill patients will overwhelm the capacity of the healthcare system to treat them and many who would normally survive will die. This is why we are trying to flatten the curve of infections, so we can minimize the numbers of cases presenting to hospitals at once.

Recognizing these variables exist, we can only roughly estimate death rates in a population.

### Calculator to Determine Your Risk of Dying From a COVID-19 Infection (Version 2):

See version one and the prior post on this topic for more information

I have taken the published data from China and built this into a model to help predict the probability of dying from COVID-19.

To calculate your probability of dying from a COVID-19 infection, you will need to know the total # of confirmed infections and the total # of deaths in your locale. I recommend using your country #’s (see the Johns Hopkins Site) instead of your city or state, to increase the statistical power of the model.

# See the risk calculator for COVID-19 estimating mortality.

## Estimate Risk of Dying From COVID-19 Once Infected

Greater than 80% of infected individuals will not have serious symptoms or complications from COVID-19. However, the remaining infected population will require intensive treatment. Deaths are most common in patients who are older and have other chronic medical conditions.

I created this online risk calculator taking into account both age and other chronic diseases to demonstrate the additive risk of dying with a COVID-19 infection. This model is based on recently published Chinese epidemiological data.

Limitations: The assumption of this model, relies on the hypothesis that as you increase the number of diseases you have, you will increase your risk of dying from COVID-19 in a direct additive fashion (i.e. 1+1=2). However, adding more disease processes may not be directly additive. In fact, the risks of adding more than one disease could lead to an even higher risk (1+1=3). Alternatively, there may not be any additive risk (1+1=1). Importantly, these data are from a population of individuals with numerous confounding risk factors (i.e. tobacco use, air pollution, health, socioeconomic, lifestyle, limited access to care, etc.) that can only be accounted for in multivariate analysis. Additionally, individuals who are immunocompromised for reasons not accounted for in the conditions listed in the risk calculator also have a higher probability of infection-related complications and death (not able to estimate these risks in the model as they were not included in the published Chinese data).

## Medical Conditions Associated With Immunocompromised State:

As mentioned, above, individuals who are immunocompromised for reasons not accounted for in the conditions listed in the risk calculator also have a higher probability of infection-related complications and death (not able to estimate these risks in the model as they were not included in the published Chinese data).

Severely immunocompromised people include those who have active leukemia or lymphoma, generalized malignancy, aplastic anemia, graft-versus-host disease, or congenital immunodeficiency; others in this category include people who have received recent radiation therapy (typically only for those treated to large areas) or checkpoint inhibitor treatment (therapy of autoimmune complications of treatment is immunosuppressive), those who have had solid-organ transplants and who are on active immunosuppression, and both CAR-T cell and hematopoietic stem cell transplant recipients (within 2 years of transplantation or still taking immunosuppressive drugs). See Table 5-02 for list of immunosuppressive drugs.

## IOE AntiCancer Glucose Score

While most cancers can fuel themselves on glucose, fatty acids, ketones and amino acids, their preferred energy source is glucose (due to the Warburg effect). Calculate your IOE AntiCancer Glucose Score.

Use this app to learn how changes in various lifestyle factors, functional medicine lab results, and your use of off-label drugs and supplements theoretically impact your cancer outcomes.

The basis of IOE AntiCancer Glucose Score is built on a synthesis of evidence-based preclinical, epidemiological and clinical published data.

• If you have not had these lab tests done recently, leave the defaulted selected values (normal values) and complete the remainder of the assessment.
• Proceed to the IOE AntiCancer Composite Score (only available to IOE Program enrollees). This combines the above IOE AntiCancer Glucose Score with lifestyle factors and functional medicine test results that are known to be associated with cancer outcomes.
• Next, calculate your IOE AntiCancer Glutamine & Fatty Acid Score (only available to IOE Program enrollees). This score examines the impact of combining various off-label drugs and supplements that may exert anticancer properties on the metabolism of glutamine and fatty acids. You can skip this assessment if you are not interested in using these compounds.
• Finally, calculate your IOE AntiCancer Signaling Pathways Score (only available to IOE Program enrollees). Many off-label drugs and supplements have reported anticancer properties by altering the pathways that promote cancer. You can also skip this assessment if you are not interested in using these compounds.

## Targeting Cancer Metabolism and Signaling With Off-Label Drugs and Supplements

Targeting cancer cell metabolism, growth signaling pathways and enhancing anti-cancer immunity are among the hottest topics being explored in oncology, today. It has become increasingly clear that many natural compounds, supplements and FDA-approved medications possess these anticancer properties and look quite promising in both preclinical and clinical studies. In fact, over 200 non-cancer drugs have shown some evidence of anticancer effects. Of these, 50% are supported by relevant human data and 16% are supported by data from at least one positive clinical trial. Some of these drugs include: mebendazole, cimetidine, nitroglycerin, diclofenac, itraconazole, clarithromycin, metformin, aspirin and hydroxychloroquine – all common, generic drugs with excellent safety records and a wide range of data sources showing potent anticancer effects.

Read Dr. Lawenda’s Article: Use of Off-Label Drugs and Supplements in Cancer in 2020

## Schedule A Consult With Dr. Lawenda

If you are confused about the use of off-label drugs and supplements, you are not alone. Dr. Lawenda offers educational consulting services to patients internationally (phone and video options).

## Functional Medicine Lab Testing

Dr. Lawenda often recommends functional medicine lab testing for patients to get a better understanding of their underlying health and variables associated with cancer growth and recurrence.