SOME OBSERVATIONS ON HIV/AIDS EPIDEMIOLOGY

In my post of 19 March, “HIV DISEASE” IS NOT AN ILLNESS, I treated changes over time in an over-simplified way to make the qualitatively sound point. Darin Brown sent a long comment that I thought too important, too instructive, to be hidden among “comments”, so I prevailed on him to let me use it as a direct post. It follows below (revised late 22nd); it’s also available as a pdf here: darinpdf0804232.

—————————–

Darin Brown, “SOME OBSERVATIONS ON HIV/AIDS EPIDEMIOLOGY”:

The mighty wall, atop which sits what I and a few other “insurgents” call the Humpty Dumpty of all biomedical hypotheses, was made from two kinds of bricks — fashioned from the stuff of virology and epidemiology, and held by what we contend is scientific cement of the most dangerously thin consistency. — Harvey Bialy

Epidemiology is like a bikini: What is revealed is interesting; what is concealed is crucial. — Peter Duesberg

We thought that we had the answers; It was the questions we had wrong. — U2, “11 O’Clock Tick Tock”

It’s no secret that the strongest evidence, if not the only evidence, in favor of the HIV hypothesis is epidemiological.

In his 1991 book Virus Hunting, Robert Gallo gave 7 reasons why he and his group of researchers in early 1984 concluded “HIV is the sole primary cause of the epidemic called AIDS” (1). All but one (#6) of Gallo’s 7 reasons are based purely on epidemiology and correlations:

1. “Finding of a new virus in AIDS patients…”
2. “The virus was also found in…’pre-AIDS cases’ and [in groups] at high risk…but only rarely in healthy heterosexuals…”
3. “[HIV] was a new…virus. AIDS as an epidemic was clearly new.”
4. “Wherever the HIV was found, AIDS was present…Conversely, no HIV — no AIDS.”
5. “Studies of blood donors showed…[a] perfect correlation.”
6. “The virus infected T4 lymphocytes.”
7. “We commonly found HIV in the brains of people who had died of AIDS.”

When challenged by Peter Duesberg in the pages of Science in 1988, William Blattner, Robert Gallo, and Howard Temin flatly admitted, “The strongest evidence that HIV causes AIDS comes from prospective epidemiological studies that document the absolute requirement for HIV infection for the development of AIDS.” (2) Note that this quote was made a full four years after HIV was announced as the cause of AIDS.

“The Evidence That HIV Causes AIDS”, an anonymous document produced by the National Institutes of Health, relies almost entirely on epidemiological arguments (3). The only direct claim of virological evidence is the following vague plea:

CD4+ T cell dysfunction and depletion are hallmarks of HIV disease. The recognition that HIV infects and destroys CD4+ T cells in vitro strongly suggests a direct link between HIV infection, CD4+ T cell depletion, and development of AIDS. A variety of mechanisms, both directly and indirectly related to HIV infection of CD4+ T cells, are likely responsible for the defects in CD4+ T cell function observed in HIV-infected people. Not only can HIV enter and kill CD4+ T cells directly, but several HIV gene products may interfere with the function of uninfected cells.

Recent investigators have not been as sanguine about our knowledge of HIV’s pathogenic mechanisms:

We still do not know how, in vivo, the virus destroys CD4+ T cells…. Several hypotheses have been proposed to explain the loss of CD4+ T cells, some of which seem to be diametrically opposed. (4)

Despite considerable advances in HIV science in the past 20 years, the reason why HIV-1 infection is pathogenic is still debated…. There is a general misconception that more is known about HIV-1 than about any other virus and that all of the important issues regarding HIV-1 biology and pathogenesis have been resolved. On the contrary, what we know represents only a thin veneer on the surface of what needs to be known. (5)

Twenty-five years into the HIV epidemic, a complete understanding of what drives the decay of CD4 cells — the essential event of HIV disease — is still lacking…. The puzzle of HIV pathogenesis keeps getting more pieces added to it. (6)

It is thus necessary to confront epidemiological arguments directly. One immediately faces a problem — all the evidence is presented in the context of a web of assumptions concerning the ontological status of HIV and the meaning of antibody, viral load and lymphocyte count tests. Consequently, this web of assumptions itself frames epidemiological data collection and questions.

For example, it is now impossible to answer, “What is the distribution of AIDS-indicator diseases among different risk groups?” because the data needed to answer this question are no longer routinely tallied. Similarly, it is impossible to answer, “What is the relationship between specific protein band patterns on the Western blot test and HIV/AIDS demographics?” because data on such patterns are not routinely tallied, let alone related to other demographic data.

The philosopher of science Paul Feyerabend posited that sometimes the only way to demonstrate the irreparable inadequacy of a theory is to collect and interpret data within the context of a completely incompatible set of assumptions regarding the most fundamental ontological and epistemological issues (7). My own opinion is that this is the current case with regard to HIV — the hypothesis will never be rejected until a comprehensive, substantial theory giving a positive explanation for the data gains widespread acceptance. (See footnote 1.)

Nevertheless, some of the epidemiological arguments put forward in favor of HIV can be dismissed by a few thought exercises. Here, I want to address what is perhaps the most common epidemiological argument one hears: “The drugs are working. Death rates have fallen. People are living longer.” (See footnote 2.)

First it should be noted that epidemiological evidence by its very nature is indirect and weak, particularly when evaluating drug therapies. Epidemiology establishes associations which require additional criteria to be met to demonstrate causation (8). It is strongest when combined with other forms of evidence. In this case, the form of evidence is that much weaker, since it is not based on the proposed pathogen itself but on therapies given. One has to be especially vigilant of not committing the classic post hoc fallacy: B follows A; therefore, A causes B.

I expounded much the same argument in my previous article, “AIDS Case Fatality Rates” (9), and here I want to extrapolate my observations to a few hypothetical thought experiments, in order to expose the essence of the faulty logic.

Consider the following hypothetical disease, for which I will give the incidence for each calendar year, and deaths/survival for those year-specific cases. In Table 1, “INC” means annual incidence, the total number of cases diagnosed that year, “Dn” means the total number of deaths occurring in year n out of all cases diagnosed that year, and “D15+” means the total number of AIDS patients remaining alive at the end of year 15 out of all cases diagnosed that year. I am also assuming, without loss of generality, that all diagnoses occur on 1 January.

Suppose that two therapies are given for our hypothetical disease: Therapy X, which is introduced and available to all on 1 January of year 6; and Therapy Y, which is introduced and available to all on 1 January of year 9.

First let’s look at the annual mortality rates of this hypothetical disease, assuming 1,000,000 people in the susceptible population, constant over all years. (This would not be true in practice, due to population growth and other factors, but this would not affect the overall trend much.) The units are “deaths per hundred thousand”:

From the data in Tables 1 and 2, it would appear the epidemic “peaked” in year 8. Proponents of the therapies would point to the fact that both annual incidence and mortality fell dramatically immediately following introduction of therapy Y, and that although mortality increased after therapy X, it might have been much greater than it turned out, and anyway we shouldn’t be too hard on therapy X, because it was our “first attempt” and its toxicities were much greater than therapy Y.

But wait a minute. Go back and look at the data in Table 1. The annual incidence increased 15-fold and then decreased 3-fold. Does it therefore really make sense to consider absolute mortality rates?

Now let’s look at one-year “case-fatality rates”. By “case-fatality rate” in this instance, I mean 100% minus the 1-year survival rate, in other words, out of all cases diagnosed in a given year, the proportion of deaths in that cohort after a single year times 100%. For example, in year 10, there were 8000 cases diagnosed, and of those, 2000 died within one year, so the one-year case-fatality rate for year 10 is 2000/8000 = 25%. (See footnote 3.)

Here are the one-year case-fatality rates:

This certainly paints a different picture of the epidemic. The severity of the epidemic reached its peak around year 2 to year 3, when survival was lowest and case-fatality was highest. Ever since then, except for some minor blips, severity has been decreasing steadily. Note that the peak severity of the epidemic was reached long before either therapy X or therapy Y was put on market, so any appeal to “falling death rates” as support for these therapies is shaky.

Now let’s put on our magic-hats and pretend we work for a hypothetical government agency, and we want to really give the impression that therapy Y is a life-saver (or at the least, a life-extender). Let’s conjure up 4 survival rates. Here “survival rate” means 100% minus the case-fatality rate. In other words the one-year survival rate is the proportion of patients surviving one year after diagnosis, the 2-year survival rate is the proportion of patients surviving 2 years after diagnosis, and so on.

Group 1: Diagnosed “pre-therapy Y”, say, years 1-7.

Group 2: Diagnosed immediately around and following therapy Y, say, years 8-11.

Group 3: Diagnosed after therapy Y was available for a substantial period, say years 12-13.

Group 4: Diagnosed during years 14-15.

We get the following numbers:

Group 1 Survival Rates after 1, 2, and 3 years respectively: 42%, 27%, 14%

Group 2 Survival Rates after 1, 2, and 3 years respectively: 62%, 51%, 45%

Group 3 Survival Rates after 1, 2, and 3 years respectively: 88%, 80%, 76%

Group 4 Survival Rates after 1, 2, and 3 years respectively: 90%, N/A, N/A

Based on these data, as well as the annual incidence and mortality rates above, it certainly seems like therapy Y has been a smashing success: just look at those plummetting incidence and mortality rates and steadily increasing survival rates, all of which occurred immediately following introduction of therapy Y.. Anyone who denies therapy Y is working must be a “therapy Y denialist” who thinks the earth is flat and the moon landings were faked. The only data which cast doubt on therapy Y are the case-fatality rates.

So which should we believe, the mortality rates and survival rates above, or the case-fatality rates?

I’ve already discussed why the absolute mortality rates are not too meaningful: the incidence of the disease has varied by more than an order of magnitude over the years, increasing and then decreasing, so comparing absolute mortality rates over a period of several years is like comparing apples to oranges to kumquats. The problem with the survival rates is clear once you look closely at the case-fatality rates: by lumping the first 7 years together into a single group,

(1) it obscures the fact that survival reached a low point around year 2 to year 3; and
(2) it disproportionately favors those diagnoses made in year 6 and year 7, because there were simply many more of them.

Together, the annual incidence and absolute mortality rates and the group-generated survival rates above give the false impression that therapy Y is working. Or to be more precise, they give the false impression that they are valid evidence that therapy Y is working.

POSTSCRIPT:

There is one other way around considering absolute mortality rates, and that is to scale the mortality rates to the population consisting only of AIDS patients. This is essentially the approach I took in my article a year ago (9), when I scaled death rates to AIDS prevalence, and it is the same measure that appears in some papers examining changes in death rate from the mid-1990s, with the units being “deaths per person-year” and the populations being cohorts of AIDS patients, not the total population. Again, in these papers, they note that death rate declined dramatically while HAART was rolled out and conclude HAART caused the decline. They fail to consider that the decline may have begun far earlier, even before AZT monotherapy was available. My analysis a year ago concluded that this is in fact the case: the AIDS epidemic, as measured by mortality rate among AIDS patients, peaked in the US around 1984-1986. It is instructive to see what a similar analysis yields for my hypothetical disease above.

To compute mortality rate scaled by AIDS prevalence, look at Table 1. Consider year 6. The total of all the numbers in the columns D7 to D15+ at or above the row of year 6 represent those patients who were alive at the beginning of year 6 and remained alive throughout year 6. They are thus each weighted with a single person-year. There are 100 + 1000 + 100 + 500 + 500 = 2200 such patients, so they contribute 2200 person-years. The total of all the numbers in column D6 represent those patients who were alive at the beginning of year 6 but died during year 6. Assuming that deaths are uniformly distributed throughout the calendar year, these patients are thus each weighted with half a person-year. There are 100 + 400 + 3000 = 3500 such patients, so they contribute 3500/2 = 1750 person-years. The numbers in columns D1 to D5 represent patients who died before the beginning of year 6 and are thus do not contribute to either deaths or person-years. For year 6, therefore, we get 3500 deaths divided by 2200 + 1750 = 3950 person-years, yielding 3500/3950 = 89% deaths/person-year. Calculating this for each year gives the following table:

Again, for our hypothetical disease, it is clear the severity of the epidemic peaked around year 2 or year 3. And because of the wide variation in incidence over the years, either this measure of mortality rate scaled to disease prevalence, or of survival rates among patients diagnosed in a given year, must be used rather than absolute mortality rates. It is true that mortality among patients declined and survival among patients increased after therapy Y was introduced, but in both cases, these trends were already occurring long before therapy Y was put on market.

The numbers for my hypothetical disease are not much different in terms of general trends from US AIDS data. The time period is stretched out over a few more years, and mortality isn’t quite as high as my hypothetical example, but the general pattern is qualitatively the same. And so the same argument applies to AIDS mortality throughout the years: the severity of the epidemic peaked around 1984-1986, before AZT and HAART, in other words, the trends in declining mortality, as measured by mortality scaled to prevalence or by survival rates, began long before HAART was introduced and even before AZT was introduced, so these cannot be used as even indirect evidence for the HIV hypothesis.

The general conclusion is quite sobering: Without the expansions of the definition of AIDS and the introduction of AZT in the late-1980s and early-1990s, the AIDS “epidemic” in the US clearly would have petered out on its own by the mid-1990s. For the past 15 years, “AIDS” has largely been prolonged by iatrogenic factors, in other words, the viral hypothesis itself has prolonged the epidemic.

FOOTNOTES:

1. An extended passage from Feyerabend is especially relevant here: “The concentration upon the theory will now be reinforced, the attitude towards alternatives will become less tolerant. Now if it is true…that many facts become available only with the help of such alternatives, then the refusal to consider them will result in the elimination of potentially refuting facts. More especially, it will eliminate facts whose discovery would show the complete and irreparable inadequacy of the theory. Such facts having been made inaccessible, the theory will appear to be free from blemish… By now the success of the theory has become public news. Popular science books…will spread the basic postulates of the theory… More than ever the theory will appear to possess tremendous empirical support. The chances for the consideration of alternatives are now very slight indeed. At the same point it is evident…that this appearance of success cannot in the least be regarded as a sign of truth and correspondence with nature. Quite the contrary, the suspicion arises that the absence of major difficulties is a result of the decrease of empirical content brought about by the elimination of alternatives, and of facts that can be discovered with the help of these alternatives only. In other words, the suspicion arises that this alleged success is due to the fact that in the process of application to new domains the theory has been turned into a metaphysical system. Such a system will of course be very ‘successful’, not, however, because it agrees so well with the facts, but because no facts have been specified that would constitute a test and because some such facts have even been removed. Its ‘success’ is entirely man-made. It was decided to stick to some ideas and the result was, quite naturally, the success of these ideas. If now the original decision is forgotten, or made only implicitly, then the survival will seem to constitute independent support, it will reinforce the decision, or turn it into an explicit one, and in this way close the circle. This is how empirical ‘evidence’ may be created by a procedure which quotes as its justification the very same evidence it has produced in the first place.” [italics as in original] (7)

2. I will not address here in detail the clinical evidence proposed in favor of combination therapy. There are many shortcomings of this evidence (inadequate trial duration, inadequate study size, reliance on dubious surrogate markers), but the most important point to remember is that a particular combination therapy is always tested against another “standard care” therapy, most often by simply adding another drug to an existing therapy. For example, a combination of 3 drugs is tested against a standard care combination of 2 drugs. The combination of 2 drugs is presumed to be standard care because it, in turn, has been previously tested against a standard care of monotherapy, which itself has been previously tested against a “true placebo” (inert drug). The point is that the claim that a combination therapy of 3 drugs is better than true placebo is dependent upon a chain of clinical comparisons, just as the proof of a mathematical theorem is dependent upon a chain of logical implications. So if even a single clinical comparison is invalid, the entire chain falls apart, as a mathematical proof falls apart if even a single logical implication is invalid. What this means for HIV therapy is that we must look very closely at the original studies comparing AZT monotherapy to true placebo. If we find these original studies to be unsound or fraudulent, then we have no basis for claiming combination therapy is better than true placebo.

3. One important note for those who choose to read the previous article “AIDS Case Fatality Rates”: There I use a different definition of “case-fatality rate”. Part of the problem is that after reading some standard university epidemiology textbooks, I’m a bit mystified about the precise definition of “case-fatality rate”, as you can find out by reading the “Notes on Definitions and Computations” from that article. This confusion compels me to make my own computations, spelling out precisely what it is I’m computing. If my choice of using the term “case-fatality rate” in both that article and this one causes confusion, I don’t care to accept the charge it’s my fault. If you wish, just ignore the term “case-fatality rate” everywhere you see it, and go by my own explanations of my computations, which are not imprecise at all.

REFERENCES:

1. Robert Gallo, Virus Hunting: AIDS, Cancer, and the Human Retrovirus: A Story of Scientific Discovery, Basic Books, 1991.
2. William Blattner, Robert Gallo, and Howard Temin, “HIV Causes AIDS”, Science 241: 514-517, 29 July 1988.
3. National Institutes of Health, “The Evidence That HIV Causes AIDS”, updated 2003, http://www.niaid.nih.gov/Factsheets/evidhiv.htm
4. Joseph McCune, “The dynamics of CD4+ T-cell depletion in HIV disease”, Nature, 2001 Apr 19; 410 (6831): 974-9.
5. Mario Stevenson, “HIV-1 Pathogenesis”, Nature Medicine, HIV Special. July 2003. Vol.9, No. 7. 853-861.
6. WK Henry et al., “Explaining, Predicting, and Treating HIV-Associated CD4 Cell Loss: After 25 years Still a Puzzle”, JAMA, 27 September 2006; 296: 1523-1525.
7. Paul Feyerabend, Against Method: Outline of an Anarchistic Theory of Knowledge, Verso, 1975.
8. Austin Bradford Hill, “The Environment and Disease: Association or Causation?”, Proceedings of the Royal Society of Medicine, 58 (1965), 295-300, http://www.edwardtufte.com/tufte/hill
9. Darin Brown, “United States AIDS Case Fatality Rates 1981-2005”, 25 May 2007, http://www.reviewingaids.com/awiki/index.php/Document:AIDS_Case_Fatality_Rates

WHAT “HIV” IS NOT: IT’S NOT SEXUALLY TRANSMITTED

If one thing is certain about HIV/AIDS, it is that “HIV” is not a sexually transmitted agent.

I can be certain about that because I’ve examined the reported evidence for myself.

Not all of the evidence, of course, because no one could possibly do that; but I have gathered the published data about HIV tests in every HIV/AIDS Surveillance Report published by the Centers for Disease Control and Prevention (CDC); every pertinent article in the CDC’s Morbidity and Mortality Weekly Report; and hundreds of articles reporting HIV tests in JAMA, New England Journal of Medicine, and other medical-scientific journals. I used PubMed to find many relevant articles and to guide me from one article to related ones.

The data represent more than 50,000,000 tests. Several social groups have been tested routinely–applicants for military service, active-duty military personnel, blood donors, Job Corps members–and the results from those groups comprise an unparalleled resource for identifying trends: unparalleled because the tests were carried out on essentially all members of those groups, so that there are none of the uncertainties associated with sampling that often leave interpretation of statistical medical and social data less than certain.

The regular trends in those data are nothing less than astonishing. Whether “HIV” tests concerned newborns or their mothers, or military personnel, or blood donors, or gay men or people injecting illegal drugs, several things are always the same:
—The geographic distribution of positive HIV tests is the same. Even though the average rate of testing HIV-positive varies by a factor of 100 or more between drug users and blood donors, within each group the geographic distribution is the same: highest in the North-East and South-East, lowest in the North Central regions, higher in the South than in the West.
So unvarying a geographic distribution across social groups is not found with syphilis, gonorrhea, or other known sexually transmitted diseases (STDs).
—This geographic distribution of positive HIV tests has remained the same throughout the AIDS era: it was the same in the early 1980s as in the late 1990s. That’s certainly not like a contagious disease, and certainly not like an STD that spread across the country from New York, Los Angeles, and San Francisco since the 1970s.
—Among the low-risk groups–excluding gay men and drug injectors, in other words–, the frequency of positive HIV tests varies with age and sex in the same manner in every tested group:

With genuine STDs, it is typically adolescents who are at greatest risk, not middle-aged people; and newborns and young children are not infected with STDs at rates comparable to those among adults; yet “HIV-positive” is as common among newborns as among the most highly “infected” middle-aged adults in low-risk groups.

* * * * * *

If you have unprotected sex with someone who has gonorrhea or syphilis, your chance of catching that infection yourself is something like 50:50 (anywhere from 10% to 90%).

If you have unprotected sex with an HIV-positive person, what are the odds that you will become HIV-positive yourself?

About 1 in a 1000.

* * * * * *

Why believe what I’ve just written, when the media are full of official statements warning that everyone is at risk, that condoms should always be used, that sex is the main way that “HIV” is transmitted?

You shouldn’t believe anything just because I say so. And you shouldn’t believe anything just because others say so, either, even if they are a Director of the National Institute for Allergy and Infectious Diseases, or because they have won a Nobel Prize or other prizes, or because they have been acclaimed for discovering something. You should believe something only if you have the good reason of having seen for yourself that the evidence supports the statements made.

One of the things I learned through doing science is that anyone can be wrong; and I learned the more difficult lesson that I myself can be wrong. I’ve been wrong through accepting what others said, and through misinterpreting data, and because there were totally unknown and unsuspected factors involved, and I’ve been wrong through just plain making mistakes because of muddle-headedness or tiredness or ignorance. So I’m wary of saying I’m certain about something, and especially wary when “everyone” knows something different.

It took me months to come to terms with the data showing that “HIV” is not sexually transmitted, and I reached the conclusion simply because there is no other way to explain the data. If you want to make up your mind about this, you may have to look at all the data for yourself. Ideally you should start from scratch, gather whatever data you can find about HIV tests, and tabulate the results by age and geography and sex and date and anything else that you think might be relevant. Then look to see whether there are any regularities to be explained.

A second-best way would be to look at my collection of the data and discussion about them, and to check my sources: make sure I haven’t misquoted or omitted, and search the literature for things I overlooked and that might contradict my analysis.

A not-very-good way to make up your mind would be to judge that I’m sincere and to trust that I’ve done what I say I’ve done. But that would be no worse than believing what you read in the newspapers, or believing that gurus in white coats are always right. In fact, believing the white coats or the media may be the worst possible way of making up your mind about anything important.

* * * * * *

From where did I get that “1 chance in 1000” for sexual transmission of HIV? I looked hard into the literature but found no study that claimed more than a few per 1000. Chapter 4 of my book, The Origins, Persistence and Failings of HIV/AIDS Theory, http://www.failingsofhivaidstheory.homestead.com/ cites a score of publications that all arrive at about the same 1-per-1000 odds, and it cites the doctors and biostatisticians who concluded that “the transmission probabilities presented are so low that it becomes difficult to understand the magnitude of the HIV-1 pandemic” (Chakraborty et al. AIDS 15 [2001] 621-6).

I found in Robert Gallo’s memoirs an acknowledgment that HIV is “distinctively difficult to transmit” (p. 131, “Virus Hunting”, 1991).

* * * * * *

Gonorrhea and syphilis, transmitted quite efficiently at about 1 chance in 2, cause local outbreaks periodically, but they don’t bring about worldwide epidemics. With HIV/AIDS, we are being asked to believe that something transmitted 100 times less efficiently than gonorrhea or syphilis is producing epidemics all over the world. Seems like a good time to offer some more Brooklyn Bridges for sale.
Gisselquist and colleagues have published a number of articles arguing, on the basis of observed sexual behavior as well as lack of transmission efficiency, that sexual transmission cannot explain the African epidemic of “AIDS” (“Not investigating HIV riddles puts lives at risk”, Business Day [Johannesburg], 4 October 2007; “How much do blood exposures contribute to HIV prevalence in female sex workers in sub-Saharan Africa, Thailand and India?” International Journal of STD & AIDS 18 [2007] 581-588; Gisselquist et al., “HIV infections in sub-Saharan Africa not explained by sexual or vertical transmission”, 13 [2002] 657-666; “Running on empty: sexual co-factors are insufficient to fuel Africa’s turbocharged HIV epidemic” ibid. 15 [2004] 442-452; Brewer et al., “Mounting anomalies in the epidemiology of HIV in Africa: cry the beloved paradigm”, ibid. 14 [2003] 144-147).

Pillars of the orthodoxy have offered specious arguments running about like this: “Sure, on average it’s only 1 per 1000, but there may be special circumstances when it’s much higher, say just after infection when the virus is replicating madly”. The sufficient but not only basis for calling that suggestion specious is that epidemics require an average, overall “reproduction ratio” appreciably greater than 1. You cannot have an epidemic unless, on the whole, on average, every infected person infects more than one other person within a rather short space of time. A score or more of specific studies, in Africa and Haiti as well as the United States, tells us that with “HIV” this does not happen.

* * * * * *

This was known long ago. Already in 1988, Anderson & May (Nature, 333: 514-9) guessed that there might be some special period of high infectiousness because the average apparent transmission rate is too low to bring about an epidemic. Reporters for the Wall Street Journal recognized in 1996, from CDC sources, that “for most heterosexuals, the risk from a single act of sex was smaller than the risk of ever getting hit by lightning” (Bennett and Sharpe, “AIDS fight is skewed by federal campaign exaggerating risks”, 1 May, pp. A1, 6). Fumento (“The Myth of Heterosexual AIDS”, 1990) among others pointed out that AIDS never spread into the general population outside Africa and the Caribbean. But the white-coated gurus who uphold the mistaken HIV/AIDS theory continue to do their best to obfuscate these facts. Take what Anthony Fauci said on the Diane Rehm show (“HIV/AIDS”, 17 August 2006, PBS Radio, transcript by Soft Scribe LLC).

Fauci admitted that “it is not a one to one ratio by any means. It’s not you have one sexual contact, and therefore you’ll get infected. It’s a relatively low efficiency”–but he failed to acknowledge that it’s about 1 per 1000, a vast and misleading difference from “not one to one”. And Fauci went on to venture this: “since there is so much sexual activity . . . , when you compound all of the sexual contacts among people, . . . , then you get the infection rates that we just spoke about where you windup getting five million new infections per year. There has to be a lot of sexual contact for that to occur. But, in fact, there is a lot of sexual contact going on everyday in the world”.

But that probability of 1 per 1000 applies only when one of the sex partners is already HIV-positive. UNAIDS puts the average global infection rate at about 1%: on average, if you choose your sexual partner at random, you have 1 chance in 100 of getting an HIV-positive one. So your overall risk is 1 in 100 multiplied by 1 in 1000, in other words 1 in 100,000. That, Fauci would have us believe, is capable of producing 5,000,000 new infections in the world each year.

And all that sexual activity Fauci conjures up somehow fails to spread gonorrhea or syphilis while disseminating something that is 100 times less infective.

So, I suggest, don’t believe everything that Dr. Anthony Fauci says, even about matters on which he is supposed to be expert.

But, of course, as I said, don’t believe what I say, either.

Just look at the evidence for yourself. That’s the smart thing to do.