Re: HP Newsletter - Effects of Low Doses

[Muckerheide <muckerheide@mediaone.net>]

Bob Flood wrote:
> 
> My copy of the September Newsletter just arrived. It contains an
> interesting piece by Dr. Lars Perrson of Sweden defending the
> linear-no threshold model. I really don't want to restart the debate 
> on LNT again. I have a couple of questions about epidemiological 
> studies in general.

Without arguing the LNT...  :-)

> Dr. Perrson refers to the atomic bomb data, indicating that a
> "substantial" risk increase is evident at 20 rem and above. 

Not true. Its small above 200 rem.

> He also  says that a more recent study (Pierce et al., 1996) found 
> "substantial" risk at 5 to 10 rem.  

This has been substantially criticized and is nottaken seriously (significance
is above 35 rem, but the dose is substantially underestimated since the
neutron component is substantially in error, plus many other confounding factors.

>Later he says that the Oxford 
> Study of Childhood Cancer (1970) found increased cancer among 
> children up to age 15 following in utero exposure at dose between 
>1 and 2 rem.

Alice Stewart, dosimetry is poor and therefore estimates are from numbers of
films and patient recollections, but even accepting the results it applies to
in-utero at cell differentiation (2d trimester)  from high dose rate x-rays;
not what we usually consider conditions for LNT applications to low dose/low
dose rate. But...

> When I teach dosimetry techs about background subtraction for
> personnel dosimeters, I tell them that, because we subtract an 
> average background
> value from each individual TLD measurement, we get both positive and
> negative results. Since most of the people we monitor receive no
> measurable
> dose, the net readings for a large group of measurements (e.g., end of
> month or end of quarter) should look like a normal distribution
> centered
> about zero. There should be about as many small positive values as
> small negative values. The techs can look for this pattern as a "sanity
> check" on the data. Finding no negative values or too many negative 
> values would be
> indicative of a bias of some type in the measurement system.
> 
> Doesn't this idea apply to the aggregate of low-dose epidemiological
> studies? I have not (personally) compiled a summation of study
> results, but it seems to me that I've seen about as many negative 
> coreletion results as positive, and most can't find any corelation. 

This is a real fallacy. There are only a few studies that are used to
"support" the LNT, there are 100s that contradict it; most that support are
largely fallacious (like the IARC "study" that misrepresents its own data -
and speaking of making a case with one cancer in dozens and even then without
p<0.05, while other cancers were lower than expected - and ignored.)

In our lists of studies that contradict the LNT there are several hundred
sound studies, mostly human, some animal, and from biology, vs a handful that
stretch to claim any effect, and some that are stretched way beyond the
breaking point (like the Wing work that even justifies its misrepresentation
because the nuclear enterprize is immoral, paid by DOE and published in the JAMA).

> If no relationship existed
> between low doses and cancer risk, that's exactly what I would expect
> from a variety of studies where the ambient cancer rate is significant.
> 
> It seems to me that a citation of one particular study from the
> multitude
> of studies is selecting only the data that fit the hypothesis, and
> excludes the data that don't. Am I over-simplifying?

No. Although, the "multitude" that substantially support the LNT is very
limited, see BEIR V; and only a few confirmatory high quality studies can
contradict many poor, biased, results. 
 
> In general use, the definition of what constitutes a "substantial"
> risk of cancer depends on the listener (how afraid is the person of 
> dying at all, how afraid of cancer, etc). So, when one uses the term 
> "substantial" in desribing the findings of a study, does this have a 
> specific definition in the epidemiology world?

In epi "substantial" is usually Obs/Exp > 2, p<0.05; and for many epi's its
more like 3 (unless its 'politically correct' to find "substantial" for
radiation Obs/Exp <1.2, and p<0.8 --  in other words, junk science).

> Bob Flood
> Stanford Linear Accelerator Center
> (415) 926-3793     bflood@slac.stanford.edu
> Unless otherwise noted, all opinions are mine alone.

Thanks.

Regards, Jim Muckerheide
jmuckerheide@delphi.com 
Radiation, Science, and Health, Inc.