Background
Disease surveillance is a primary duty of public health agencies, to monitor the spread, prevalence, and seriousness of diseases in different geographical regions and population groups. This task includes gathering and disseminating basic information about incidence, hospitalizations, mortality, infection-fatality rates, sero-prevalence/antibodies, T-cell immunity, vaccinations, vaccine efficacy, vaccine adverse events, variants, and other parameters. Such knowledge lays the foundation for public health recommendations. Without reliable disease surveillance data, public health agencies, politicians, scientists and the public are operating blindly. For influenza, salmonella, e.coli and dozens of other infectious diseases, the CDC has reliable disease surveillance systems in place. For COVID-19, there was a profound lack of reliable and unbiased data, even after the first few confusing months of the pandemic. The lack of accurate data persists to this day.
Incidence and Hospitalizations
Incidence refers to the number of new cases of a disease in a specified time period.
For COVID-19, the CDC relied on its influenza-like illness surveillance system as a main data source for respiratory illness identification. This led to underestimation of SARS-CoV-2 transmission because it didn't count asymptomatic or mildly symptomatic individuals. Why were COVID-specific surveillance systems not quickly put in place by the CDC to monitor spread?
Why was the CDC unable to accurately record hospitalizations due to COVID-19? Why is there still no consistent system in place to separate actual COVID-19 hospitalizations, due to COVID-19, from incidental COVID-19 hospitalizations that are due to some other condition in people who also happened to have either asymptomatic or mildly symptomatic COVID-19?
Seroprevalence
To understand transmission and severity of COVID-19, we must know how many people have already been infected. If 100 people were infected, 100 sought health care and 10 died, mortality is high and contact tracing is both feasible and important. If 100,000 people were infected, 100 sought health care and 10 died, mortality is low and contact tracing is futile. A seroprevalence survey tests a selection of representative people to determine how many people have developed antibodies to the virus, by age-group and geographical regions over time. Public health agencies in other countries, such as Spain and Sweden, quickly conducted such surveys. The United States had to rely on small local surveys such as one done by Stanford University in Santa Clara County, California.
In early 2020, it was critical to quickly estimate disease prevalence. Why did the CDC fail to conduct seroprevalence surveys in key communities?
Why did the CDC not conduct a national seroprevalence survey using a random sample from different regions and age-groups, continuously updated by week or month?
The CDC did conduct a national seroprevalence study in February 2022. Why was it not done earlier?
COVID-19 Case Definitions
COVID-19 hospitalizations and deaths and associated comorbidities are important statistics for policy considerations. However, throughout the pandemic, these statistics were not consistently reported by the CDC. For a virus who clinical manifestation ranges from asymptomatic or mildly symptomatic to fatal, the percent of reported COVID-19 hospitalizations and deaths that were due to COVID-19 versus with COVID-19 should be separated out, i.e., when a patient was hospitalized or died due to another cause after testing positive for COVID-19. Over time, incidental COVID-19 positive cases were magnified by PCR testing, which is highly sensitive for the presence of viral genome, and by increasingly contagious variants. The more contagious and ubiquitous the variant, the more likely a COVID positive patient was hospitalized for an unrelated reason. By mid-late 2021, some U.S. hospitals reported that the majority of COVID-19 patients in their hospitals were hospitalized with COVID-19 as an incidental diagnosis. One audit of death data in Alameda County, CA, found that 25% of COVID-19 deaths reported were not due to COVID. Most concerning, the CDC has not reported accurate data on COVID-19 deaths in young people. A review of the WONDER database for Underlying Cause of Death (UCod) and Multiple Cause of Death (MCoD) through December 2021 indicates that the vast majority of reported pediatric COVID-19 deaths were in children with other serious conditions.
Why did the CDC or other federal agencies not conduct random surveys to determine the proportion of reported COVID-19 deaths that were due to COVID-19 as the primary cause of death versus deaths with COVID-19 that were unrelated to the virus?
COVID-19 mortality is very low in children. While every pediatric death from any cause is a unique tragedy, collecting data on which children are at risk would have been invaluable to parents and policy makers. Why did the CDC not conduct a complete evaluation of every child with a reported COVID-19 death, to determine how many were actually due to COVID-19 and what comorbidities those children had? Why did they ignore suggestions to do so?
FluNet data analysis indicates that COVID-19 presents a lower level of risk than influenza does for children under 12. Why was this information not incorporated into recommendations and policies?
COVID-19 Comorbidities
While age is the most important risk factor for COVID-19 hospitalization and death, it is important to know about other risk factors in order to more precisely define the vulnerable population and provide advice about modifiable risk factors. This is true for both adults and children.
Why did the CDC or NIH not immediately conduct or fund large studies to evaluate the effects of comorbidities on COVID-19 mortality?
Knowing that general health is important to fight off infections, and with obesity as a major risk-factor, why did the CDC and state health officials not encourage healthier eating and more exercise, instead of closing both outdoor and indoor recreational spaces?
When more detailed data appeared on COVID-19 comorbidities from other sources, why did the CDC not use these data to create better focused protection strategies for high-risk populations?
When CDC Director Rochelle Walensky was asked how many of the approximately 300 pediatric COVID-19 deaths in the U.S. at the time had a medical comorbidity, she was unable to answer. Why didn’t the CDC collect or provide comorbidity data for all 300 COVID-19 deaths in children? Did most of these deaths occur in children with severe comorbidities, such as leukemia or kidney disease?
COVID-19 comorbidity information can inform a targeted approach rather than subjecting healthy children to the mental and physical health consequences of educational loss, reduced physical activity, and profound social isolation. Why did the CDC recommend severe restrictions on the lives of more than 50 million children in the U.S., rather than collecting and utilizing data needed to craft appropriate recommendations to protect higher-risk children specifically?
Infection Fatality Rate
The infection fatality rate (IFR) is the risk that an infected person will die from a disease. Since not all infected persons are diagnosed, it is different from the case fatality rate (CFR), which is the risk of dying among those that have been diagnosed with the disease. The latter changes over time depending on the amount of testing done. During the beginning of the pandemic, public health officials and scientists conflated these two basic epidemiological concepts.
To accurately estimate an IFR, it is necessary to have accurate cause-of-death data but the CDC reports included deaths with an incidental COVID-19 infection. Why did the CDC consistently provide inaccurate IFR estimates?
The IFR is often given as a single number, even though there can be more than a thousandfold difference in IFR depending on age. Since different states and countries can have very different age structures, combined IFRs cannot be compared between different geographical regions. In light of this, why did scientists and the media continuously emphasize a single national number?
Risk Communication
Without accurate data, assessment of risk and perception of risk by the public was misleading, and national surveys showed that public perception of COVID-19 infection fatality rate was wildly inaccurate. Young people, particularly, thought that their risk of COVID-19 mortality was much higher than their actual risk, while some older people underestimated their mortality risk.
Why was public perception of hospitalization and mortality risk due to COVID-19 so different from the actual risk?
What actions, if any, did CDC take to help the public better and more accurately understand COVID-19 risk?
Why did public health officials not continuously update their risk figures as the population gained immunity, which caused risk to decrease over time?
How did the CDC and State Health Departments communicate about other risk factors for COVID-19 mortality, such as general health, obesity, and being immunocompromised?
One risk factor is obesity, especially in those under the age of 60. Would accurate and unapologetic communication of this risk have improved vaccine uptake before the Delta wave hit the Sun Belt in 2021? No other region has such high obesity rates and no other region suffered as large a Delta wave.
A long-established public health principle is to combat excess fear among the public. Yet, on March 29, 2021, after vaccines were widely available to vulnerable populations, CDC Director Rochelle Walensky spoke to the nation about her “feeling of impending doom”. Were the CDC and State Health Departments using fear to drive behavior change, in contradiction with most established public health principles?
As the experiences and observations of most Americans became dissonant with stated CDC statistics, there was an increasing loss of trust in CDC and public health officials. When parts of the public realize that the communicated risks are overblown, there can be a counter reaction where they dismiss any risk at all. Has this contributed to suboptimal vaccine uptake in high-risk individuals? Did some older high-risk Americans not take necessary precautions to avoid being infected? Will this affect how the public responds to future health crises?
Long COVID
For infectious diseases, there can be long term consequences lasting beyond the infection period. This phenomenon has received wide public attention during the pandemic, with widespread concerns about “long COVID”. It is important to understand potential long term effects after COVID-19 infection. So far, we lack robust scientific evidence that it is more common after COVID-19 than after other infectious diseases.
Why is long-COVID-19 of greater concern than e.g., “long influenza” or “long norovirus disease”? Is it a distinct clinical entity? In February 2021, NIH allocated 1.15 billion dollars in funding for long COVID-19 research over a four year period. Is this a reasonable amount? Historically, how much has NIH spent on research concerning long term effects after other infectious diseases?
Data Sharing
Federal and state agencies, including the CDC, failed to merge real-time Medicare and Medicaid data and state vaccination data. Failure to do so impeded population-wide analyses on natural immunity, comorbidity risk factors for COVID-19 death and hospitalization, and the study of vaccine adverse reactions.
Why were data not readily shared between different federal agencies such as CDC, FDA, Medicare and Medicaid?
While states had the most accurate vaccination data, Medicare and Medicaid had accurate clinical outcome data. Why were such data and collection strategies not shared between agencies to better evaluate vaccine uptake, efficacy, and safety? Combining such data could have saved lives and enabled a wiser vaccine rollout strategy between December 2020 and April 2021, when many Americans were dying each day because they could not get vaccinated in time.
Furthermore, ignoring population and large institutional data on infection acquired immunity also resulted in the redundant immunization of many people who were already protected from severe outcomes while high-risk unvaccinated seniors died waiting for a vaccine. How many Americans died because of this?