Posts Tagged ‘Epidemiology’

Why are vaccines not shortening the length of the pandemic?

January 27, 2022

The Covid-19 virus was first encountered at the end of 2019 though the World Health Organization only declared the outbreak a Public Health Emergency of International Concern on 30 January 2020, and a pandemic on 11 March 2020. Total global deaths now exceed 5.6 million and after over 2 years, the pandemic continues. We received our first doses of vaccine in April 2021, the second dose in June 2021 and the third, booster shot in December 2021.

The major difference – for a layman – between the Spanish flu pandemic of 1918-1920 and this Covid pandemic is that there were no vaccines available 100 years ago. The Spanish flu hit in 4 major waves; one in March 1918, the second (the deadliest) in August 1918, a third, mainly in Australia, in January 1919 and the final fourth wave in early 1920. By March 1920 the Spanish flu was less deadly than common influenza and the pandemic was over. With no vaccines of any sort available, the Spanish influenza pandemic lasted just 2 years. It is estimated that the total number of deaths was somewhere between 17 and 50 million and that up to 500 million were infected.

With Covid-19, vaccines were available first about 11 months after the outbreak though most received vaccines in the second year of the outbreak. A remarkable achievement. The logistics of carrying out mass vaccinations has been equally impressive. So far over 5 billion of the 7.3 billion global population have received at least one dose. Around 4 billion have received two doses. Close to 60% of the global population has been vaccinated to some extent. Around 360 million are thought to have been infected and around 5.6 million have lost their lives.

There is little doubt that the quality of health care after being infected is orders of magnitude more effective than 100 years ago. It is also reasonable to conclude that the vaccines have prevented many deaths. Numbers infected are similar to 100 years ago (360 m / 500 m) but number of deaths are drastically lower (5.6m / 17 – 50 m). Yet the pandemic continues and the earliest it may recede – we think – is this autumn of 2022 which will be 3 years after it started.

It would seem that vaccines have not reduced the length of the pandemic at all. In spite of all the advances in health care and the huge medical/pharmaceutical efforts in understanding the virus and creating vaccines, we are entirely reactive in our response. Vaccine development is reactive. Getting vaccinated is proactive but defensive and does not harm the virus. Health care is reactive. We have no means, it would seem, of taking the initiative and attacking the virus. We are forced to rely on natural mutations eventually reducing its virulence. Our actions, being reactive, would seem to have no impact on the length of the pandemic. Epidemiology has not impressed me during this pandemic. Every so-called mathematical model (which depends finally upon human behaviour) was wrong. (Of course epidemiology is a discipline of clerks and statistics – a social “science” if it must be called a science). They have not been able to do more than regurgitate the same advice as from 700 years ago at the time of the Black Death. Avoid the infected, wash your hands, wear a mask, burn your dead!


700 years of epidemiology: “Avoid contact, wear a mask, wash your hands, burn your dead”

May 3, 2021

It was practised in 1350 during the Black Death. It was practised during the Great Plague in 1666. And it was still the best advice during the Spanish Flu in 1919. And it is no different today.

In 700 years the advice for the prevention of infection has not changed.

Like any other social “science”, epidemiology is a discipline and a field of study but it is no science.

“COVID-19 is a major acute crisis with unpredictable consequences. Many scientists have struggled to make forecasts about its impact. However, despite involving many excellent modelers, best intentions, and highly sophisticated tools, forecasting efforts have largely failed”.

1997: The Failure of Academic Epidemiology: Witness for the Prosecution, Carl Shy, American Journal of Epidemiology, Volume 145, Issue 6, 15 March 1997, Pages 479– 84.

Academic epidemiology has failed to develop the scientific methods and the knowledge base to support the fundamental public health mission of preventing disease and promoting health through organized community efforts. As a basic science of public health, epidemiology should attempt to understand health and disease from a community and ecologic perspective as a consequence of how society is organized and behaves, what impact social and economic forces have on disease incidence rates, and what community actions will be effective in altering incidence rates. However, as taught in most textbooks and as widely practiced by academicians, epidemiology has become a biomedical discipline focused on the distribution and determinants of disease in groups of individuals who happen to have some common characteristics, exposures, or diseases. The ecology of human health has not been addressed, and the societal context in which disease occurs has been either disregarded or deliberately abstracted from consideration.

And more recently:

2020: Forecasting for COVID-19 has failed, Ioannidis, Cripps and Tanner

Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned.

Of course, actual health care and the medications available have advanced immeasurably during this time. Medicine and the development of medicines and vaccines have come a very long way since the Spanish Flu. But the prediction of human behaviour – which is what epidemiology is – is as uncertain now as it was in the Middle Ages. Mathematical forecasts – whether for pandemics or for climate – are only as good as the most inaccurate assumption made. Very often assumptions made are to comply with some other agenda. Sometimes, the assumptions made are just downright stupid.

And so, for over 700 years the advice for the prevention of infection has been and remains “Avoid contact, wear a mask, wash your hands and burn your dead”.


No mask needed, just don’t breathe

August 25, 2020

There are some who believe that epidemiology is a science. Others believe it is an art and yet others that it is the study of social behaviour based on gut-feel and science.

Whatever it is, it is not settled science.

An enveloped virus such as the Wuhan virus can survive for up to 2 months in a refrigerated corpse.

In studies looking at how bird flu (avian influenza virus, an enveloped virus) survives in chicken carcasses, 90% of the virus was inactivated in around 15 days when the carcass was left at room temperature. But if the carcass was held at refrigerator temperatures (4C/40F), the virus lasted 4.5 times longer—that’s more than two months.


 

Epidemiology is still more art than science and sometimes just speculation

July 24, 2020

The Wuhan virus, after 6 months, is still not under control.

I have grown a little tired of being told by all kinds of people that they are just following the science in the fight against the Wuhan virus. What science? There is a widespread delusion that epidemiology is a “settled science”. Epidemiology is, in reality, a mix of science and art and of “social science” (which is always a politicized view of behaviour). It is about “the frequency and pattern of health events in a population”. With a little known virus, as in this case, epidemiology relies on models and speculation. When the speculation is garbage, the model results are also, necessarily, garbage. The model results have ranged from the ridiculously complacent to the grotesquely alarmist, but what they all have in common is that they are/were wrong. Nothing surprising in that. That is the nature of modelling. A mathematical model is nothing more than a crystal ball and model results are always forecasts of the future. The problem lies in the delusion that epidemiology is an exact science and that model results give a sound and certain basis for public policy.

In the absence of a vaccine we are being led (or misled) by politicians blindly following the epidemiologists’ speculations about both the characteristics of the unknown virus and about social behaviour. In the space of 4 months the “best” epidemiologists at the WHO have changed their view of the Wuhan virus from being “non communicable between humans”, to “communicable by liquid droplets between humans”, to now be of “air borne transmission”. The experts have been divided whether transmission is from the symptomatic or from the asymptomatic. There are as many speculative views about when herd immunity can be achieved as there are epidemiologists. No one really knows. Art not science. Herds are always moving and herd immunity depends upon leaving the weak behind. Public policy is floundering as it staggers from lockdowns to no lockdowns to social distancing, from masks to no masks to some masks to masks for some, and from testing those with symptoms to restricted testing to mass testing. There is no certainty about whether testing is to be for the virus or for antibodies to the virus.

The Center for Disease Control has this definition of epidemiology:

Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems.

But then they go on:

…. the practice of epidemiology is both a science and an art.

The reliance on speculation and the resulting weaknesses of epidemiology are well known and there are many scientific articles about spurious but statistically significant epidemiological forecasts. This article in the BMJ from 2004 is just an example.

The scandal of poor epidemiological research

Something surely must be wrong with epidemiology when the new editors of a leading journal in the field entitle their inaugural offering, “Epidemiology—is it time to call it a day?” Observational epidemiology has not had a good press in recent years. Conflicting results from epidemiological studies of the risks of daily life, such as coffee, hair dye, or hormones, are frequently and eagerly reported in the popular press, providing a constant source of anxiety for the public.  In many cases deeply held beliefs, given credibility by numerous observational studies over long periods of time, are challenged only when contradicted by randomised trials. In the most recent example, a Cochrane review of randomised trials shows that antioxidant vitamins do not prevent gastrointestinal cancer and may even increase all cause mortality. 
Now Pocock et al describe the quality and the litany of problems of 73 epidemiological studies published in January 2001 in general medical and specialist journals. …… Worryingly, Pocock et al find that the rationale behind the choice of confounders is usually unclear, and that the extent of adjustment varies greatly. They also confirm that observational studies often consider several exposures, outcomes, and subgroups. This results in multiple statistical tests of hypotheses and a high probability of finding associations that are statistically significant but spurious. 

Modern epidemiology starting from – say – the 1854 London cholera outbreak has vastly improved public health. But it is not just a science and it is certainly not a “settled science”. The Wuhan virus is not under control. The various public policy interventions (lockdowns of various kinds and the deselection of the old for treatment) have prolonged, rather than shortened, the outbreak. The lockdowns may have protected health systems while maximizing the number of deaths. In fact, politicians have often abdicated responsibility for public policy to epidemiologists and bureaucrats who have not been best-suited to make political decisions. In other cases public policy has exploited epidemiology to protect the system rather than protecting people.

This is not so much to criticize epidemiology as to criticize the manner in which public policy has misused epidemiology. Epidemiology can only be an input for determining public policy. It cannot replace common sense. And it is not a convenient shelter for politicians to hide behind.


 


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