We read the piece by Rob Williams and feel compelled to respond. We are physicians who have spent over 30 years in both academic and primary care medicine and have learned to read the medical literature. Right now we are in perilous times, not only because we face a life-threatening new virus but also because of the pandemic of misinformation, disinformation and mistrust of all institutions.
Do all viruses follow a predictable pattern, which ends with their disappearance? Viruses are different and although similar types follow similar patterns, new viruses such as SARS-CoV-2 are continuously appearing. As humans push into the habitat of various animals, viruses contained within animal populations push into human populations and result in novel diseases. Ebola does not have the same pattern as rhinovirus (the common cold). Measles, despite immunity resulting from immunization, is very much still around. In fact, few viruses have really disappeared; modern medicine has controlled them.
Herd immunity occurs when the majority (50 to 80%) of the population is immune to a particular virus. Because of this immunity, few people get infected. Those who are not immune will have a reduced chance to be exposed and become infected. This works best when the majority of the population has been immunized. When the disease to which we are becoming immune has a high lethality, the surviving herd becomes very small. For example, if COVID-19 kills 1% of the population, 3.8 million people in the U.S. would die very quickly and although many of the remaining who were infected may be immune (we don’t know yet whether exposure to this virus confers lasting immunity), it will take a long time and more deaths to get to herd immunity. Not a future we look forward to. Herd immunity in modern times and especially with a virus as contagious and lethal as COVID-19 is anchored by a broadly used, effective and safe immunization, not a widespread exposure to the wild virus.
MODELS AND RESEARCH
Let’s talk about models and research. Models are mathematical algorithms that incorporate best evidence and data and to try to make predictions. They are intrinsically variable and always evolve as we learn more about the disease: how it spreads, what treatments we introduce to control it and how lethal it is. Something similar is true for medical research. We know that latching on to a great new study early may be risky, as further research using different techniques or more subjects may result in new conclusions. This is a good process. That’s the nature of scientific progress, not the result of fake news by nefarious actors being paid off by big business.
One word about money and medical research. For decades, there has been reduction of funding of research by neutral federal government entities (such as the NIH or the CDC), so the responsibility shifted to the private sector. That doesn’t mean that all this research is bad, just that there are no longer public funds for research. We would love to see more public funding for unbiased research. It is now one of the many variables that must be considered when evaluating new research.
COVID-19 transmission is a good example of a perfect storm. Scientists tried to predict risk based on early communication with China, as well as experience with other coronaviruses. It took a while to determine that person-to-person spread took place, then further research to understand whether it spread by droplets (virus in larger particles) or aerosol (virus suspended in tiny particles). Prevention works best, the more we know, so as a result it is expected that public health recommendations will evolve in the situation of a new virus. We have adopted older, tried and true practices (isolation, quarantine, contact tracing) combined with the more modern techniques of testing for the virus. These techniques have a long track record of success and, as long ago as 1918 during the influenza pandemic, resulted in dramatic reduction of deaths when followed.
It is important to remain skeptical and carefully analyze the quality of recommendations and the quality of the underlying science. Regarding hydroxychloroquine, there were several tiny, anecdotal studies (“someone told me it worked for them”) suggestive of benefit. But when higher standards were used (a randomized controlled trial, same type of population, the investigators don’t know which patients got which treatments, enough patients in the study to conclude that the results were cause and effect), it was shown not to be of benefit and was associated with more deaths. This experience is not uncommon in medical research. Malaria (the disease for which the drug is commonly prescribed) is a parasitic disease. COVID-19 is viral and has different pathophysiology (mechanisms of disease), so simply because it was used successfully before doesn’t mean it will be safe or effective in this new disease.
DO WHAT IS RECOMMENDED
An example of how we test new research is seen in a recent article in The Lancet (6/1/20). It is called a meta-analysis. It reviewed hundreds of papers on distancing and face masks, selected 172 that were of high enough quality to discuss, analyzed the statistics and made some suggestions. Bottom line is there is still not enough data out there to be certain of what to do. However, it showed that cloth and surgical masks help somewhat (prevent spread and acquisition), respirator masks are better and distancing of 3 feet or more helps. But there are not yet enough data to be certain or unequivocal. Keep doing what is recommended as best evidence: Wear a mask and social distance. Stay tuned.
This is a bad disease. Herd immunity without widespread immunizations, rather than costing “no one a single cent,” will cost millions of lives and trillions of dollars in lost income. We need to continue to practice social isolation and frequent handwashing and wearing masks, until the prevalence of the virus is lower and we have an effective vaccine. We should be proud of our state and how we have dealt with this crisis.
Dr. Consenstein and Dr. Madden live in Fayston.