Rethinking Vaccination Policy: Insights from the Latest Data
As a point of clarification, I am not a so-called “anti-vaxxer.” Since the first inoculation of the smallpox vaccine by the English scientist Edward Jenner in 1796, vaccines and related medical technology have saved hundreds of millions of lives. With an eye toward the future, I am optimistic that vaccine technology will evolve to the point when there is a vaccine for every deadly disease known to science. However, vaccines are only as effective as their underlying science. Thus, it is in the interest of not only current generations, but also future generations to seriously examine and question the science behind the COVID-19 vaccines. This article seeks to provide an objective analysis of the efficacy of the COVID-19 vaccines and the methodology of vaccination data collection — a task that has unfortunately been diluted by political polarization and the politicization of science.
Interpreting the Data
As of July 1, 2021, there are an estimated 96 different COVID-19 vaccines in development, though very few are ready for widespread distribution. Naturally, we are inclined to examine the efficacy of these vaccines — particularly the six major vaccines on the market: Comirnaty BNT162b2/Pfizer and BioNTech, mRNA-1273/Moderna, Ad26.COV2.S/Johnson & Johnson, Vaxzevria AZD1222/Oxford and AstraZeneca, Sputnik V, and Sinopharm/BBIBP-CorV. Vaccine efficacy is typically calculated as the ratio of attack rates with and without a vaccine — this is referred to as a relative risk reduction (RRR). Fortunately, all the major COVID-19 vaccines have shown to be effective in clinical trials. For example, reports indicate that the Pfizer and BioNTech vaccine has 95% efficacy seven days after the second dose. However, vaccine efficacy ought not to be interpreted as a stand-alone statistic and should rather be paired with the risk of infection and illness from COVID-19. The latest publicly available cumulative data on average COVID-19 transmission rates reports anywhere from a low of 0.84 persons to a high of 1.11 persons as of January 2021. To clarify, this transmission rate data indicates the number of people an infected person is likely to infect. Note that the seven-day average of COVID-19 cases has drastically decreased since January 2021; therefore it is more than likely that the transmission rate of COVID-19 has consequently declined relative to the data from January. As of June 26, 2021, data from the Center for Disease Control (CDC) for the United States indicates 0.2 weekly hospitalizations per 100,000 among people ages 0 to 4 and 5 to 17, 1.1 per 100,000 among people ages 18 to 49, 1.7 per 100,000 among people ages 50 to 64, and 2.6 per 100,000 among people 65 or older.
Even if we were to take vaccine efficacy data paired with the background risk of infection and illness, that does not account for the reporting bias associated with efficacy data that solely relies on RRR. Absolute risk reduction (ARR) accounts for the entirety of a population as opposed to RRR, which does not. Since ARRs have a larger sample size than RRRs, they typically yield a lower efficacy. ARRs are particularly important as they are used to derive the number needed to vaccinate (NNV) — the number of vaccinations required to prevent one more case of COVID-19. It is very possible that vaccine efficacy data that only contain RRR data sets risk inflating the effectiveness of COVID-19 vaccines. Israel’s mass vaccination campaign is a notable example: despite clinical trials indicating an RRR of 94%, Israel’s ARR was 46%. Such a disparity between the RRR and ARR indicates that eight times more people may be required to be vaccinated in Israel.
Problems with Data Collection
One would think that given a global pandemic like COVID-19, as much data as possible should be collected to coordinate an appropriate response in health policy circles. However, as of May 2021, the CDC has declared that it is no longer collecting data on breakthrough COVID-19 infections and will only remain collecting data on breakthrough cases that result in hospitalization.
It does not take a health expert to understand the potentially disastrous consequences of the CDC’s reversal in data collection. In an interview with the Daily Mail, the director of the Center for Drug Discovery at Washington University, Dr. Michael Kinch, warned, “We have spent so much time worrying about different versions of the virus, the variants, but have missed why two to three people who get sick die . . . It’s about looking at not just the variation in the virus but the variation in the people.”
Another cause for concern is the possibility that the actual number of breakthrough cases of COVID-19 is an undercount. According to the deputy director of the Division of Viral Disease at the CDC Dr. Tom Clark, there may be an implicit incentive to underreport data considering that state health departments must report data on breakthrough COVID-19 cases voluntarily. Nevertheless, this is a matter of speculation.
More recently, Johns Hopkins’ Dr. Marty Makary openly criticized the CDC for refusing to release data on COVID-19, specifically data surrounding an uptick in cases of myocarditis in people under 30 who received the Pfizer and Moderna vaccines. Myocarditis is a rare but serious heart condition involving inflammation and damage of the heart muscle. Makary’s criticism comes in the wake of the CDC’s convening of an emergency meeting to address the concerns surrounding myocarditis.
Where Do We Go from Here?
Without a doubt, the distribution of the various COVID-19 vaccines has saved millions of lives. However, we must err on the side of caution concerning health policies which incentivize rapid vaccination before proper and cumulative data on vaccine efficacy can be collected. Although I am not a health expert, my skepticism urges me to worry that our government and health institutions are putting a vaccination agenda above public health and safety, when vaccines and public health are not necessarily homogeneous despite their correlation. To conflate vaccines with public health is an error of judgement that could open the door to serious issues of medical malpractice in the foreseeable future.