Fw: Epidemiology Epedemic

["jjcohen" <jjcohen@PRODIGY.NET>]

----- Original Message -----

From: jjcohen

To: Jerry Cohen

Sent: Friday, July 02, 2004 4:38 PM

Subject: Fw: Epidemiology Epedemic







----- Original Message -----

From: jjcohen

To: radsafe@list.vanderbilt.edu

Sent: Friday, July 02, 2004 4:08 PM

Subject: Fw: Epidemiology Epedemic











In previous discussions on radsafe, concerns were expressed regarding how

epidemiology had been applied in determining effects of exposure to radon,

radioactivity , and 'hazardous" materials in general.

It appeared, at least to me, that certain epidemiological determinations

were being made that defied common sense. Recently, on another website, I

came across the following review  and thought the group might be interest.

Anyone have any thoughts on the subject??   Jerry Cohen

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An Epidemic of Epidemiology

by Rob Lyons



Fifty years ago, we discovered that smoking is bad for us. In 1954, Austin

Bradford Hill and Richard Doll published a preliminary report on a study

showing the very strong correlation between smoking and premature mortality

(1).





However, this classic study has in many ways sent medical science up a blind

alley. While the dangers of smoking have been demonstrated in numerous

subsequent studies, the attempts to find the New Smoking - another example

of an environmental or lifestyle factor that causes substantial health

problems - have largely failed. But the many pieces of junk science that

have been produced in the process have provided the ammunition for

unwarranted health scares too numerous to mention.





This state of affairs is well described in John Brignell's new book The

Epidemiologists. Hill and Doll were given the task of trying to find out why

cases of lung cancer had increased 15-fold in only 25 years. Their first

attempt was to ask 649 lung cancer patients, and 649 matched controls, about

their habits. What they found was a correlation between smoking and lung

cancer, albeit not a very strong one. However, it was strong enough to

warrant a fuller study, starting with a large group of healthy individuals,

assessing their smoking habits and then monitoring them to see what diseases

they developed.





This study began in 1951. Their method was to write to every doctor in the

country - around 35,000 doctors replied, of whom only 17 per cent were

lifelong non-smokers (how times change). The doctors were asked just a few

questions about their smoking habits. Three years later, Hill and Doll

published their first analysis of the results, and were already able to

indicate how strong the link was between smoking and lung cancer.





What they found was that persistent smokers were 24 times more likely to

develop lung cancer than non-smokers. Moreover, the risk of death from heart

disease in any particular year was roughly doubled. This study has been

followed up every few years, and these results have been confirmed time and

again.





Hill made it clear, however, that such a study had to comply with some

pretty strict criteria in order to be considered valid. These criteria are

worth restating, because they stand in sharp contrast to the bulk of

epidemiological research:



1. Strength



Is the association strong enough that we can rule out other factors?



2. Consistency



Have the results been replicated by different researchers, and under

different conditions?



3. Specificity



Is the exposure associated with a very specific disease as opposed to a wide

range of diseases?



4. Temporality



Did the exposure proceed the disease?



5. Biological gradient



Are increasing exposures associated with increasing risk of disease?



6. Plausibility



Is there a credible scientific mechanism that can explain the association?



7. Coherence



Is the association consistent with the natural history of the disease?



8. Experimental evidence



Does a physical intervention show results consistent with the association?



9. Analogy



Is there a similar result to which we can draw a relationship?

Above all, as Brignell emphasizes, correlation does not prove causation. He

draws an analogy with growing tomatoes and fertilizer. It can easily be

shown that increasing use of fertilizer will increase tomato yields. But

fertilizer does not cause tomatoes, it merely promotes the process of

growth. The same goes for smoking and lung cancer. Smoking may massively

promote the growth of lung cancer, but it does not cause the tumours. Hill

and Doll had nothing to say about why cancer occurs in the first place.





Nonetheless, it is an entirely reasonable conclusion to draw that smokers

will, on average, die younger than non-smokers, and we do not need to know

the precise mechanism to conclude that giving up smoking is prudent from a

health viewpoint.





What is not reasonable is the response to this one, classic study. First, it

has provided the justification for state intervention in lifestyle in a

previously unprecedented way. Secondly, it has encouraged the proliferation

of other studies, which make grand statements about disease based on

correlations far weaker than those found by Hill and Doll.





Brignell's book is a handy demolition of the science and statistics behind

this epidemic of epidemiology. He shows how statistical tests were

originally developed, based on certain assumptions. However, these

assumptions have long since been forgotten, so that meeting certain abstract

criteria has been elevated above whether the results are actually of any

real-world importance.





The most important of these is the test for statistical significance. The

idea behind this is that patterns can be found in any set of random results.

For example, in the spiked office, there are a number of people who were

born in May or June, but none were born in July. It would be possible to

draw the conclusion that there is something special about being born in May

or June that predisposes people to become journalists. This would be a

bizarre conclusion to draw from just a handful of people. In fact, the

spread of birthdays is completely coincidental.





In research it is therefore useful to have a preliminary statistical test of

results, to see how likely it is that they could be due to blind chance. The

usual benchmark is that if the chances of a set of results being

coincidental are less than five per cent, it is reasonable to go on to

assess whether the results are actually meaningful.





First, just because a study passes this test does not mean its results

aren't a complete coincidence. In fact, by definition, five per cent of

studies could pass this test even though the results are meaningless.





Secondly, just because the results are statistically significant doesn't

mean they are practically significant. Brignell gives the example of a book

called The Causes of Cancer, written by Richard Doll and Richard Peto.

Illustrating Doll's fall from previous high standards, the book describes

some deaths of people in their 80s and 90s as 'premature'.





  The public health agenda is justified by research that is often completely

worthless

These days, however, it seems that any result that passes this 'p-test' is

increasingly regarded as significant. Five per cent sounds like a low risk

of results being meaningless, until you realise that researchers often

plough through many, potential risk factors (what Brignell calls a 'data

dredge'), look for an apparently significant result, then try to speculate

some kind of mechanism to explain it, no matter how bizarre. So a test

designed as an initial filter to weed out spurious results is used to give

credence to them.





Thus he provides a huge list of different factors that have, at one time or

other, been accused of causing cancer: abortion, acetaldehyde, acrylamide,

acrylontiril, agent orange, alar, alcohol, air pollution, aldrin, alfatoxin,

arsenic, asbestos, asphalt fumes, atrazine, AZT.and that's just the letter

'A'.





There are also a number of techniques in epidemiology for imposing

assumptions on to data. The best of these is trend fitting. No set of data

will exactly fit a pattern but often a clear trend can be found nonetheless.

However, many studies appear to resort to drawing a line through an

apparently unconnected series of measurements to demonstrate an underlying

effect.





Epidemiology can be an effective tool when applied to the spread of

infectious disease. Unfortunately, there really isn't anything like enough

infectious disease in the developed world to justify the existence of so

many departments and researchers. In fact, the overwhelming cause of death

in the developed world is old age - a factor that is, incredibly, frequently

ignored by researchers. A person in their eighties is a thousand times more

likely to develop cancer than someone in their thirties. This factor is so

powerful that for most of the causes of disease studied, a very minor

underestimation of the effect of age can wipe out any putative effect from

the factor in hand.





Age is obvious - but many other confounding factors are not. Therefore, we

return to Hill's first criterion: to be sure there is actually something

going on, the effect must be strong. Otherwise, any apparent effect may

prove to be entirely illusory.





A topical example of this is passive smoking, and in particular what

Brignell calls 'the greatest scientific fraud ever'. In 1992, the US

Environmental Protection Agency published a meta-study, bringing together

many other studies on passive smoking. Unfortunately, the results were

negative. It appeared that passive smoking was not a health risk at all.

Mere facts could not be allowed to get in the way of a health scare, so some

imagination was applied to the problem. One negative study was removed - but

the meta-study still produced no statistically significant result.





So the goalposts were not so much moved as widened. The organisation found

that there was a greater than five per cent chance that the results were

coincidental, but less than 10 per cent - so they accepted them anyway. In

other words, the EPA accepted a bigger risk that the effect they found was

purely due to chance, quite at odds with standard practice.





The increased risk of lung cancer they found - 19 per cent - was frankly too

small to have been conceivably detected given the methods they used. There

are lots of ways in which inaccuracy could have crept into this final

result. For example, is it really possible to merge the results of many

different studies, all with different methodologies and subjects,

accurately? How could someone's actual exposure to environmental smoke be

measured over the course of years? Were all the people who said that they

were non-smokers absolutely honest? As indicated above, were other possible

contributory factors such as age, gender and income controlled for

accurately?





We can be pretty confident about Hill and Doll's conclusions about lung

cancer because the effect they found is massive - an increased risk of 2400

percent. To suggest that such a small effect as 19 per cent could be

accurately measured in this way is like trying to time a race with a

sundial.





That has not prevented smoking being banned in public places on the grounds

that thousands of people might die from inhaling second-hand smoke. The

public health agenda is therefore driven and justified by research that is

more often than not completely worthless.





It is undoubtedly the case that Hill and Doll's study has caused people to

give up smoking and extended many lives as a result. But it has also

inspired a heap of unnecessary panics based on dodgy research, and public

health campaigners only too willing to tell us how to live our lives.







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