The ability of humanity to understand, intervene and hopefully minimise causes of premature death is perhaps one of our crowning achievements to date. Of course, it’s far from a complete and comprehensive achievement but, nonetheless, we have far outstripped any other organism’s ability (or even awareness) to challenge our mortality. And the change has been fairly swift. As recently as the Roman era, life expectancy was as low as 22 years. Yet now, in the UK at least, life expectancy is as high as 81.1 years for women.
A large part of this dramatic increase in longevity has been the development of our understanding of epidemiology as a methodology for studying disease. An early dramatic example of the direct impact of epidemiological study and subsequent intervention came from John Snow’s mapping of cholera cases in a London outbreak in 1854. This led to the identification of the specific water pump that was causing the outbreak and thus enabled intervention.
Epidemiology is now a massive part of healthcare science. From the study of environmental risk factors in heart disease to the hunt for genetic markers for complex disease traits, these are all, essentially, epidemiological in nature. Undeniably, this kind of study has been hugely beneficial for those who want to live to see beyond their 30th birthday.
And yet… Success always brings its own problems. One of the biggest problems that I see with the public perception of epidemiology is, basically, a lack of understanding about to handle the information. Take the H1N1 flu pandemic. And yes, pandemic is the correct word. A new variant of the influenza virus arose. There was no innate immunity to it. It very rapidly spread across the globe. Serious cases were also more demographically skewed to younger patients than those seen in seasonal flu.
Then there were the predictions as to the impact of the pandemic. And to my mind this is where public understanding and scientific intention (for want of a better phrase) really start to come apart.
People like numbers. Scientists like numbers. Numbers feel solid and real. The trouble is, that they’re often not. This is particularly true when we’re talking about predictive numbers. Predictions are made using models constructed using available data. The numbers generated for these predictions are qualified by all manner of statistical disclaimers such as p-values and confidence intervals. These are all, basically, measures of the likelihood of a real-world value occurring within a range of values. Yes, it is complicated, but that’s life for you.
However, most people don’t think like this. Even most scientists have to force themselves to do this (particularly biologists, who, almost as a breed, find statistics a bit distasteful). So when numbers started appearing in the newspapers predicting the end of humanity as a result of 2009’s H1N1 the picture was a bit confused. The papers invariably published the upper end of the prediction ranges, without any realistic explanation of where those numbers came from, because it’s more dramatic. And, guess what? I’m not criticising them for that. That’s what papers do.
The simple fact of the matter is that this pandemic could easily have gone beyond the upper estimates that were quoted. Everyone’s heard of the 1918 Spanish flu pandemic. This killed 50 million people and infected 500 million in total. By no means was this a conspiracy by big pharma to sell drugs, which is essentially what 2009’s H1N1 pandemic is touted to be by the usual suspects.
One of the major differences between 1918 and now is our understanding of infectious disease and the surrounding epidemiology. The very act of being able to make more rigorous predictions about the spread of the virus meant that more was able to be done to combat it. The World Health Organisation (WHO), and a host of scientists across the world very rapidly identified the virus and the potential of it to spread. Steps were immediately taken to minimise the impact. This is quite different to the 1918 outbreak. In 2009 action took place on the scale of days and weeks. In 1918 cohesive and wide-ranging responses took much longer to enact. This had a direct impact on the severity of the death and suffering.
It is impossible to say what would have happened if the WHO had not behaved as it did. Certainly the potential is there for all predictions to have been fulfilled, and more so. Equally, I accept that it is possible that the impact would have been much less than predicted. What I don’t understand is people’s resentment about the WHO hoping for the best but preparing for the worst. The simple fact is that the very act of recognising the dangers of the pandemic may well have led to the negating of those dangers, with the result that after the event it all looked like a fuss over nothing. To my mind, that’s the same logic as resenting your house because you’ve never died of exposure.
One thing that I am fairly certain about is that if the WHO had failed to take action on a pandemic which then went on to kill millions they would be rightly condemned. So it just seems a bit unfair if they’re condemned for erring on the side of caution and taking the threat of a highly transmissible variant of a respiratory virus seriously.
So it seems that our knowledge about the spread of infectious disease suffers from a massive PR hiccup. If disease can be acted against with speed and knowledge it is much less likely to have the impact of pandemics of the past. This in turn though means that people think that disease is not something to worry about. I hope that it isn’t the case that people will remember the importance of good epidemiological knowledge only when there is a failure and a pandemic does go on to kill millions.