An “argument from incredulity” is a type of informal logical fallacy where it’s claimed that because a subject is not well understood - either by the speaker or by others - it cannot be true. Bauer’s writings are riddled with fallacies of this kind – for the simple reason that much of his argument is based on his readings of epidemiology, a discipline in which he has no training or experience (and manifestly no understanding, imagination or talent) – and that he evidently hasn’t taken the trouble to listen to anyone who does have a grasp of the field.
Here is an example, which Henry alludes to as the starting point of his bizarre and meandering journey into the intellectual wasteland of HIV/AIDS denialism:
My research into HIV-associated matters had been stimulated by the unbelievable assertion cited by Harvey Bialy [in his biography of Duesberg], that in the mid-1980s teen-aged females applying for military service tested HIV-positive as frequently as their male peers.
- The debilitating distraction of “HIV”
By “unbelievable” what he is saying is that it is beyond him to understand why the proportion of teenaged military applicants testing positive might have a different sex ratio from that of the overall US HIV+ population of the time (which was - to be more accurate - 1985 to 1989, not the “mid-1980s”).
In fact, the original argument of Duesberg’s that Bialy was citing was that the male to female ratio of AIDS diagnoses in teenagers during that period overall was four to one, while male and female teenage military applicants tested HIV positive at roughly equal per capita rates. However, the reason for both Bauer’s and Duesberg’s apparent puzzlement are the same: both have failed to realize that teenage military applicants c1985-9 did not form a sample representative of teenagers with AIDS or of the all-age population at that time, in regard to their risk of HIV infection.
Through 1989, there had been a total of 367 reported AIDS cases among adolescents aged 13-19 of both sexes in the US. The reported transmission risk factors for these 367 were as follows:
160 (43.6%) were blood product recipients (many from factor VIII used for treatment of the almost exclusively male disease hemophilia).
136 (37.1%) acquired HIV through male to male sex
26 (7.1%) were injecting drug users
23 (6.3%) were both homosexually active males and IDUs
5 (1.4%) came from Pattern II countries
4 (1.1%) acquired infection heterosexually
13 (3.5%) the HIV risk was listed as “other”.
Source: HG Miller, CF Turner, LE Moses (1990): AIDS: The Second Decade: National Research Council (U.S.). Committee on AIDS Research and the Behavioral, Social, and Statistical Sciences: National Academies Press.
ISBN 0309042879, 9780309042871 (p 162)
Now the distribution of incident AIDS diagnoses by infection category in the late 1980s was not the same as the distribution of new HIV infections at the time: heterosexual transmission of HIV had been accounting for an increasing proportion of cases both of HIV infection and AIDS ever since the start of the epidemic in the US (in 2006 it accounted for over 30% of HIV notifications, with women infected heterosexually at nearly twice the rate men are) and there were almost no incident cases of HIV infection through contaminated blood products after screening donations for HIV and heat treatment of clotting factors were introduced in the mid 1980s.
You really have to wonder at Duesberg’s and Bauer’s lack of imagination in not being able to work out why the male predominance of HIV among the US population as a whole or of AIDS among late 80s adolescents is not reflected among cohorts of teenage military applicants.
A career in the armed services is not usually the first choice for a young male with hemophilia for obvious reasons, and it is not hard to imagine why the overtly heterosexist culture of the military is less likely to appeal to the young male homosexually active teenagers most at risk of HIV infection than to their heterosexual brothers. Injecting drug use contributes moderately to HIV being a disproportionately male infection in the US (more males acquire HIV through IDU than females do) but military recruiters generally try to discourage illicit drug users from joining up. In other words, the applicants who were surprised to find out for the first time they had HIV were far more likely than the overall population to have acquired their infection by routes other than those that tend to affect males disproportionately.
Among heterosexually infected teenagers there is a female predominance, most marked at younger ages. Nearly twice as many females overall in the US are infected through high risk heterosexual sex as males, as noted above: the male partners who infect them may include some heterosexually infected men, but also include a substantial proportion who have been infected by other (non-heterosexual) routes such as male to male sex or injecting drug use. As well, HIV prevalence increases with age and sexual experience, and there is a general tendency for males to be slightly older on average in heterosexual couplings than their female partners, and for HIV to heterosexually transmit somewhat more efficiently from male to female than female to male: thus the relative female predominance of heterosexually acquired HIV is most marked at the ages closest to the onset of sexual activity.
In other words, in Burke et al’s study of teenage military recruits, the female bias in heterosexually acquired HIV was likely to have approximately balanced the male bias in other risk groups for HIV acquisition, given that military recruiting policies select against teenage males with those risks – hemophilia, homosexuality and injecting drug use.
For all of the above reasons, the relatively similar rates of first HIV diagnoses between male and female military recruits (which totalled only 393 diagnoses among 1.14 million teenage applicants over nearly 3½ years) is not surprising. What would be strange is if the HIV risk patterns among these youngsters were identical to the pattern of HIV prevalence in the US population as a whole.
ARGUMENTS FROM INCREDULITY (often presented as supposedly "unanswerable” questions) and similar fallacies are as much a staple of the rhetoric of AIDS denialists as they are for the proponents of other brands of pseudoscience such as Creationism.
It is difficult to believe that no one has tried to explain the relatively equal rates of new HIV diagnoses among teenage military applicants to Henry Bauer before. To persistently argue a point from incredulity even when explanations are available, plausible and even fairly obvious once you take the trouble to examine the epidemiology in a little detail takes more than mere ignorance. It takes a particular combination of ignorance with closed-minded arrogance.
4 comments:
This is a good reference if you want to understand the psychology of Bauer and other cranks.
Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments
That's a great study...and it is exactly why I stick to science and do not try to delve into Politics/Government, Physics, Religion or even Accounting!
Too bad others can not do as Jeri Blank from "Strangers with Candy" puts it: "Go with what you know!"
J. Todd DeShong
This article highlights what has always been one of the biggest inherent problems in epidemiology: how to select your study population. Some of the most commonly used populations, such as pregnant women attending prenatal clinics and, as described here, military recruits, although having no overt bias always have a pre-selection filter. Snout describes nicely some of these for the military recruit population and the pregnant women cohort is obviously totally biased towards sexually active women who have unprotected sex (but exludes women who use birth control pills).
Every epidemiological study must always be interpreted in the light of the criteria used to select the population under study. Extrapolating the data to give an estimate of incidence or prevalence in the overall population is a science in itself. Cranks such as Bauer like to jump on the inevitable differences between the outcomes of separate epidemiological studies as evidence that scientists don't know what they're talking about. This is either ignorant or dishonest, or both.
Steve
Steve wrote:Extrapolating the data to give an estimate of incidence or prevalence in the overall population is a science in itself.
A glaring example of not doing this correctly is provided by Bauer here.
Age variation of HIV prevalence
Bauer seems to be proud of this graph because he hs used it in several of his blog entries.
There are so many things wrong with it that it is hard to know where to start.
The data that Bauer uses/abuses comes from testing various disparate groups for HIV. For the age group 0-4 years the tests are on children at risk of HIV infection ie born to mothers who are or are suspected to be HIV+. It is ridiculous to extrapolate this to the overall population but this is exactly what Bauer does.
Bauer conveniently leaves the y-axis scale off the graph. From the tables in his articles it can be seen that Bauer is claiming that 3% of 0-4 year olds test HIV+.
Post a Comment