The importance of flu surveillance

In 2009, the H1N1 flu pandemic was an influenza pandemic that was first described in April 2009. The virus appeared to be a new strain of H1N1, which was responsible for the 1918 flu pandemic, that arose from a triple reassortment of bird, swine and human flu viruses that then further combined with a Eurasian pig flu virus, leading to the term “swine flu” to be used for this pandemic. Unlike other strains of flu, H1N1 does not disproportionately infect older adults (greater than 60 years), which makes this a characteristic feature of this H1N1 pandemic, and made it especially dangerous to children. The CDC has reported some sobering worldwide statistics for the 2009 H1N1 pandemic, including that between 151,700 and 575,400 people perished worldwide from 2009 H1N1 virus infection during the first year the virus circulated.  Continue reading “The importance of flu surveillance”

How pseudoscience makes its case. Part 3.

This is part of my ongoing discussion on how quacks use pseudoscience to push their myths and potions on the world.  Part 1 discussed the scientific method, which allows us to objectively analyze the natural world.  Part 2 discussed the best way for us to examine the difference between science and pseudoscience.

I just read an outstanding analysis, by Steven Novella, MD, a clinical neurologist at Yale University, of how pseudoscience (those who pretend to praise the scientific method, yet do it in a way that is not actually science) and anti-science (those who repudiate science outright, or even undermine science, with subjective analysis and untestable spirituality) to reject evidence-based medicine.

Dr. Novella clearly states how science in medicine works:

This leads us to the final continuum – the consensus of expert opinion based upon systematic reviews can either result in a solid and confident unanimous opinion, a reliable opinion with serious minority objections, a genuine controversy with no objective resolution, or simply the conclusion that we currently lack sufficient evidence and do not know the answer. It can also lead, of course, to a solid consensus of expert opinion combined with a fake controversy manufactured by a group driven by ideology or greed and not science. The tobacco industry’s campaign of doubt against the conclusion that smoking is a risk factor for lung cancer is one example. The anti-vaccine movement’s fear-mongering about vaccines and autism is another.

Basically, science evolves over time.  A conclusion that lacks sufficient evidence may eventually be supported by better analysis or groundbreaking research.  You’ll notice that anti-science and pseudoscience pushers do not allow themselves to participate in the this continuum of research–they state emphatically that they are right.

Science, by its very nature, must be falsifiable, meaning that any hypothesis or theory has the logical possibility that it can be contradicted by an observation or the outcome of a physical experiment. Just because a hypothesis or theory is “falsifiable,” we do not conclude that it is false.   To the contrary, we understand that if it is false, then some observation or experiment will provide a reproducible result that is in conflict with it.  Simply put, science assumes that it has it all wrong, and attempts to determine why a particular theory or hypothesis is wrong.  Of course, in these attempts, usually more evidence is found to support the original theory.  Just because science requires falsifiability, that does not mean that it will ever be falsified, but science is open to the possibility.  In other words, science evolves.

Pseudoscience, by its very nature, is not falsifiable.  It is mostly based on assertion rather than scientific observation, so it cannot be tested by experiment or observation.  Creationism is a perfect example.  It is based on a human text (the bible), so there is no experiment that could be designed to test the text, since it non-responsive in a natural sense.  It would be like trying to scientifically show that the muppets existed.

Two of the most misused and misunderstood terms in evaluating scientific evidence are correlation and causation, two powerful analytical tools that are critical to evidence based medicine.  Correlation is the grouping of variables that may occur together.  For example, smoking correlates with lung cancer in that those who smoke tend to develop lung cancer at a statistically significant rate.  It’s important to note that correlation does not prove causation.  However, once you have numerous well-designed studies that correlate lung cancer to smoking, along with adding in biological and physiological models that support the correlation, then we can arrive at a consensus that not only is smoking correlated with lung cancer, it causes it.

We observe correlations every day.  But they are subjective observations for which we cannot state a causal relationship without substantial research.  The anti-vaccination movement is rife with these observations which they use to “prove” a cause.  An anti-vaccine conspiracy website claims that pregnant women are miscarrying babies after getting the shot.  The fact is that there is a statistical chance that women will miscarry during any pregnancy.  This is random variability not a cause.  In fact, based on the rate of miscarriage, we could expect that thousands of women would miscarry within 24 hours of getting the H1N1 flu shot.  But it’s not correlation, unless significant studies show a causal relationship.  For example, I’m also sure that thousands of people broke a bone or had a desire to eat a burger after getting the shot, but that’s because in a large enough population of individuals, you can find literally millions of different actions after getting a shot.

So, the miscarriage rate after receiving the swine flu shot is not correlated.  It’s just a random observation.  And there is no biological cause that could be described.  Nevertheless, the “flu vaccine causes miscarriage” conspiracy has been thoroughly debunked by research, but still the internet meme continues.  Pseudoscience sometimes uses the same methodology (or lack of methodology) to make positive assertions.  Homeopaths will claim that their dilutions will cure whatever disease, yet they do not have scientific evidence supporting them, but there plenty of evidence that debunks what they practice.

As part of my analysis of medical claims of causation or “cures”, I often use this logic to test the possibility of the usefulness of any alternative medicine–is there any physical, chemical or biological mechanism that will allow the quack procedure to work?  If you cannot imagine it without violating some of the basic laws of science, then we should stand by Occam’s razor, which states often times the simplest solution is the best.  So, if there is no evidence of vaccinations being correlated, let alone causal, to autism, then that remains the simplest solution.  To explain a possible tie without any evidence would require us to suspend what we know of most biological processes.

As I’ve said in other posts, the internet gives us so much information, we tend to value it equally, as if every website provides accurate and logical data points.  Maybe you have a friend who had a miscarriage 24 hours after receiving the swine flu vaccine.  Maybe you’ve heard that many people have.  But that’s not science, that’s just a subjective observation.  Or even confirmation bias.

Once again, Dr. Novella says it perfectly:

In conclusion, correlation is an extremely valuable type of scientific evidence in medicine. But first correlations must be confirmed as real, and then every possible causational relationship must be systematically explored. In the end correlation can be used as powerful evidence for a cause and effect relationship between a treatment and benefit, or a risk factor and a disease. But it is also one of the most abused types of evidence, because it is easy and even tempting to come to premature conclusions based upon the preliminary appearance of a correlation.