BREAKING: Fake News Leads To Fake Science

A Deeper Look Into Fake News As It Relates To Epidemiology

In my recent studies, a huge topic of discussion among my colleagues is the importance of fake news versus real news. This is a huge issue in the field of epidemiology as it leads people to believe in misinformation. Commonly fake news is used by professionals to sell their product to the general public. This is done by the use of skewed news that does not display an accurate representation of its population. An example of this that I have studied is the concept of causal inference and ecological fallacy, and looked at the relationship of how the consumption of chocolate and winning the Nobel prize was “correlated”. Obviously this is not the case and is related to other factors such as their overall consumption of chocolate as well as their possibility of being a large manufacturer of chocolate (ex. Switzerland). This concept peaked my interest, and I wondered, how many other sources of fake news in current healthcare topics have we fed into? I will be using the COVID-19 pandemic to look deeper into this topic and the importance of discrete statistics for the proper identification of health risks.

Discrete Statistics In Epidemiology

Disease is not continuous but discrete. This means our typical statistical analysis (ex. mean, median and mode) would not suffice when looking at disease. This calls for a whole new set of statistical calculations to understand the behavior of disease. The main calculations I've focused on are odds, prevalence, cumulative incidence and incidence rate.

The Importance Of Proper Statistical Analysis

While I was researching the use of proper statistical analysis for the accurate prediction of COVID-19 outcomes and side effects, I came across a study that used incidence rate to analyze infection of COVID-19 in hemoglobinopathies (Haghpanah et al., 2021). Hemoglobinopathies are a collection of disorders that are of sickle cell disease. If an individual has this hereditary disease, then it means they produce too much hemoglobin. With the incidence rate calculation as well as a mortality rate observation, they could conclude that there is a slight correlation between people with this disease getting COVID-19 more readily when compared to the general population as well as getting the virus worse. This led them to conclude that receiving the vaccine was crucial for these individuals to keep them protected. Since they used incidence rate to analyze this information, an accurate correlation has been made. During the pandemic however, this was not always the case.

COVID-19 Fake News Example

One of the largest debates of recent times is that vaccines have a direct correlation to causing autism. This has been debated and argued for years and years with the development of modern medicine, and the issuing of more vaccines over the years. This “outbreak” of mass paranoia started in 1998, after a man named Andrew Wakefield published a paper which associated immunizations with autism (Carrion-Alvarez & Tijerina-Salina, 2020). Since this time parents have started to opt out and not immunize their children due to this fear, and the debate has come back around with the spreading of COVID-19. Parents have chosen not to give their children any number of doses of the COVID-19 vaccines, as they deem it unsafe. The fact of the matter is, they have been consumers of fake news which has led them astray.

One of the main contributors during the pandemic to the spreading of fake news was the internet. Everyone had the internet so readily available to them, and it was the only thing everyone was on for months. The problem with platforms like Youtube and Instagram is that anyone can post to them. There is no credential to post an article and or video on the topic, and anyone can view them. People on the internet tend to use statistics that are properly adjusted. This can include and is not limited to:

- Population adjustment

- Age adjustment

- Consideration of other health factors

- Consideration of environmental factors

This means that individuals used basic continuous statistics that should not be used when discussing disease, especially in the case of a pandemic. This led people from all around the world to become confused and not trust in science, as they didn't know who to believe. In Europe, during the pandemic a survey was conducted and 10% of France and Germany said they would not accept a vaccine. Later into the pandemic as it worsened, 28% of France said they were unsure about receiving the vaccine (Carrion-Alvarez & Tijerina-Salina, 2020). This uncertainty people felt was due to the mass spread of misinformation and the neglect of correct scientific analysis.

Too Many Graphs Equal Too Much Panic

The amount of statistics being conducted due to the pandemic caused an overflow of graphs being generated. When I searched “Highest Number of COVID-19 cases globally” into google, I was met with the following graph:

Table 1: Graph Depicting Number Of COVID-19 Tests

This graph was one of the first graphs to appear in my search. The general viewer of this graph who is overcome with panic from the pandemic may look at this graph and take it at face value (Kotsehub, 2020). This means they would correlate what they searched to this graph and believe this is the number of active cases being reported of COVID-19 in the specific case listed. By reading this graph you can see it says “number of COVID-19 tests per million of people”. This means this is the number of COVID-19 tests being administered, not the number of active cases. There are lots of factors and information missing from this graph. For example, how many cases are active? What is the morbidity rate? What is the incidence rate? None of these questions are answered and none of this information is readily available to the public. This is a prime example of the media using the general public's fear and lack of attention to elicit misinformation.

Final Thoughts And Takeaways

After lots of discussions on misinformation and incorrect statistical analysis in, I researched further to learn this is a much bigger problem than I thought. There is no real way to regulate the spreading of misinformation on social media platforms and this is causing a huge problem globally. People are using inaccurate analysis techniques to predict disease outcomes and sway people away from the truth. The use of discrete statistics is very commonly ignored on social media platforms and information is manipulated in favor of the individual's argument whether right or wrong.

References

Carrion-Alvarez, D., & Tijerina-Salina, P. X. (2020). Fake news in COVID-19: A perspective. Health Promotion Perspectives, 10(4), 290–291. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722992/

Haghpanah, S., Hosseini-Bensenjan, M., Sayadi, M., & Karimi, M. (2021). Incidence Rate of COVID-19 Infection in Hemoglobinopathies: A Systematic Review and Meta-analysis. Hemoglobin, 45(6), 371–379. https://www.tandfonline.com/doi/abs/10.1080/03630269.2021.1927751

Kotsehub, N. (2020, June 24). Stopping COVID-19 with Misleading Graphs. Medium. https://towardsdatascience.com/stopping-covid-19-with-misleading-graphs-6812a61a57c 9