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Q&A: Public Health Lessons from Historic (and Current) Pandemic Data

COVID-19 is just the most recent pandemic in a long history of health crises whose lessons could have helped prepare public health data infrastructure for COVID-19. What are those lessons and how can we ensure that this time they stick?

Joshua E. Porterfield, PhD
September 16, 2021

Pandemics are not uncommon throughout history, although they were not part of regular discourse prior to 2020. These historical public health crises should have yielded lessons to help prepare us for the COVID-19 pandemic. Dr. Alexandre White from the Departments of History of Medicine and Sociology provides perspective on historic public health data and advises on learning from the current pandemic’s data to provide for more equitable and effective public health.

How has public health data evolved over time?

We have more data, especially globally, around COVID-19 than any other prior pandemic, but we have had systems of international or even global infectious disease surveillance for over a century. In the late 19th and early 20th centuries, there was global reporting on bubonic plague, cholera, typhus, yellow fever, and several other diseases. The development of the telegraph made that reporting easier and more effective. International regulations on epidemic disease reporting and disease surveillance in general have been the central focus of international and national infectious disease response since the middle of the 20th century. There have long been global mechanisms for infectious disease control as well as national mechanisms in place for surveillance and reporting. The World Health Organization, through the International Health Regulations, has required reporting of all unusual, novel, or re-emergent cases of pathogens thought capable of producing epidemic spread. We have more data, especially globally, around COVID-19 than any other prior pandemic.

“We now have capacity to collect these data, but establishing how and when they are collected and released remains a problem.”

What lessons can we learn from historic and current pandemic data?

One of the pressing lessons is that high rates of cases or fatalities from an epidemic disease tend to have repercussions far beyond the field of medicine and the sites of the epidemic. Regions that tend to have high rates of disease transmission are often isolated from travel and trade in ways that invoke a double penalty, adding economic strain, trade and travel isolation, stigmatization, and ostracization on top of the human suffering directly caused by the disease. The risk of those penalties has at times become a justification to misreport or underreport cases and deaths. In this pandemic, nations and states have misrepresented case data and death rates to make it appear that the epidemic was not as severe.

Learning that lesson is further complicated by one of the most dangerous false narratives: that we must choose between free economic exchange and effective public health measures. Responses to epidemics require not only public health solutions, but social welfare solutions and actions that exist beyond public health. There is a relationship between disease and economic prosperity. Less disease equals more effective and secure economies. As we've seen throughout this pandemic, the United States especially, should be able to provide more welfare support, supplies, and resources to citizens to protect their own health while managing an epidemic situation.

“We can have effective public health support, while also supporting the needs of the economy.”

We also need to learn that the challenges of healthcare and public health service delivery did not just emerge during this pandemic. We've been divesting from public health infrastructure in the United States since at least the 1970s. Without that support, it becomes harder to collect data and provide care and resources to those with the least access. It makes any effective epidemic response far more difficult. Our current quandary is the result of flawed policy and declining investment over decades.

How do we prevent repeating historical public health mistakes and shift the conversation back to facts and data?

We pulled back the curtain on how data and scientific knowledge are produced, and it runs contrary to the popular understanding of how science works. In science, especially with emerging infectious diseases, we're constantly learning new things. The knowledge that we have around COVID-19 is rarely settled knowledge, which can be frustrating to people who don't understand the contested nature by which scientific knowledge is produced. Our understanding of COVID-19 is grounded in facts, research, and data, but the ways in which we interpret those data are constantly re-envisioned through academic discourse. Public understanding of how scientific knowledge is actually produced is critical.

The ways in which these data are presented to the public need to also be more conscientious of the public’s lack of background knowledge. I don't think much of the world was well-versed in public health concepts before this pandemic, but now people use these concepts, like viral replication, in daily parlance. We have not provided the foundation to have a sophisticated conversation around these things with the public. Complex scientific knowledge needs to be communicated to the public in a transparent way that allows for greater receptivity to new information.

Has the United States improved its previous data efforts with minority communities?

The fundamental problem, when it comes to data collection and the use of data with minority populations and epidemics, has historically not always been a lack of data (although with some illnesses such as cancer this has been a serious problem), but rather the ways in which these data have been collected and interpreted. What we see in data collections from the 19th and early 20th centuries is a problem of interpretation. It was a biased, racist interpretation that was intentionally so at the time. We could look at those same data today and recognize that disparities didn't emerge out of some fundamental biological difference we can attribute to a false concept of racial categories, but rather that these were the result of a host of factors we would now call social determinants of health.

When we look back at COVID-19 data, it will be very important to disentangle and analyze these data within a wider historical, social, political context that disabuses us of easy conclusions that suggest inherent inferiority of certain groups due to race, class, gender, sexuality, or a host of other factors that have been used to oppress people within society. The ways in which we're collecting data now have the capacity to be interpreted in profoundly better ways than previously, but we still need to be conscious of reducing people with all their varied complexities to clear identity labels. We can use our understanding of health disparities to advocate for more effective and equitable healthcare distribution to alleviate those disparities, or we can do what is anti-scientific and ahistorical and look at health disparities as indicators of innate biological difference.

“We must use complex data to humanize, rather than categorize differences.”

Joshua E. Porterfield, PhD

Dr. Joshua E. Porterfield, Pandemic Data Initiative content lead, is a writer with the Centers for Civic Impact. He is using his PhD in Chemical and Biomolecular Engineering to give an informed perspective on public health data issues.