HIV/AIDS Skepticism

Pointing to evidence that HIV is not the necessary and sufficient cause of AIDS

HIV demographics are predictable; HIV is not a contagious infection

Posted by Henry Bauer on 2008/08/27

The relative frequencies of HIV-positive tests in any group of people are predictable: the rate varies with race, sex, and age in a regular, reproducible, manner; and its geographic distribution reflects the racial compositions of the respective populations. The absolute magnitude of the rate of positive tests is determined by the degree and type of health challenge to which the tested group is or has been exposed.

Those regularities and trends are what I found astonishing, since they prove that those “HIV” tests do not track an infection; see the data in Part I of The Origin, Persistence and Failings of HIV/AIDS Theory. Because this is so crucial a point, I continue to draw attention to it as I come across more data that confirm the generality of the dependence of “HIV” on the variables of age, sex, race, and geography. For example:

— Married women test positive more often than prostitutes or widows, incongruous for an STD but obvious since positive HIV tests are most common in adults of young-middle-age and married women tend to be older than prostitutes and younger than widows; see TO AVOID HIV INFECTION, DON’T GET MARRIED 18 November 2007.
— Tuberculosis is very likely to produce a positive “HIV” test, its victims test positive as often as do the “high-risk” groups of gay men and drug abusers, see Figure 22, p. 83 in The Origin, Persistence and Failings of HIV/AIDS Theory; confirmed quite often in news reports about how TB must be treated if HIV/AIDS is to be defeated, see for example IS TUBERCULOSIS AN APHRODISIAC?, 4 January 2008;
TUBERCULOSIS AGAIN, 27 January 2008.
— The age-variation of positive tests, peaking in young-middle-age, is seen also in Rwanda, Kenya, Lesotho, and Tanzania; those data also confirm the suggestion in US data that black women are particularly prone to test positive; and the Kenyan data also show that females in their teens are more likely to test positive than teenaged males, just as in the USA (and, again, incongruous for an STD, especially one that is found most frequently among gay men); see HIV DEMOGRAPHICS FURTHER CONFIRMED: HIV IS NOT SEXUALLY TRANSMITTED, 26 February 2008.
— The same tendency to test HIV-positive in young-middle-age is seen also in death rates from “HIV disease”, which is truly odd if there’s a latent period and if antiretroviral treatment has a life-extending benefit; see “HIV DISEASE” IS NOT AN ILLNESS, 19 March 2008.
— The variations of positive HIV-tests with geography, population density, and race described in my book are replicated in a CDC publication on the geographic distribution of “AIDS” in rural areas, see REGULAR AS CLOCKWORK: HIV, THE TRULY UNIQUE “INFECTION”, 1 April 2008.
— The racial disparities in “HIV” are reproduced everywhere in the world, and they explain the geographic distribution of “HIV” globally as well as in the United States; see RACIAL DISPARITIES IN TESTING “HIV-positive”: IS THERE A NON-RACIST EXPLANATION?, 4 May 2008.
— One of the striking things about these racial disparities is that they subsist WITHIN HIGH-RISK GROUPS as well as in the general population. Not only in the United States, see The Origin, Persistence and Failings of HIV/AIDS Theory, but also in Britain:

“British men of South Asian origin who have sex with men have a significantly lower rate of HIV infection than other ethnic groups, including white men, the first survey of gay men from different ethnic groups in the United Kingdom has found”, according to Jonathan Elford in a presentation at the 27th international AIDS conference in Mexico City; BMJ 337 [2008] a1182.  Though this was described as the first such survey, an earlier publication had noted the same disparities: compared to gay white British men, gay black men in Britain are 2.06 times as likely to test HIV-positive while gay Asians in Britain are only 40% as likely to test positive, see Hickson et al., “HIV, sexual risk, and ethnicity among men in England who have sex with men”, Sexually Transmitted Infections 80 (2004) 443-45.

The variation of HIV-positive tests with age is seen also in women in India: the rates were 0.21% up to age 29, 0.36% in the age range 30-34, 0.18% at ages 35-39 and 0.13% above age 40: once again, rising from the teens into young middle-age and then decreasing again (Silverman et al., “Intimate partner violence and HIV infection among married Indian women”, JAMA 300 [2008] 703-710). As noted in my book, the exact age at which the tendency to test positive peaks does vary somewhat with sex, race, and state of health, but it seems to be no later than the lower 40s and rarely before the 30s.

A bonanza of supporting demographic facts is in the South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey, 2005.

The usual variation with age is shown in the Survey’s Table 3.10, reflecting the standard generalities: the HIV-positive rate decreases from birth into the lower teens, then increases into young adulthood and decreases again after young-middle-age. There is the often-seen difference between the sexes in the ages where the frequency of HIV-positive peaks, in this case lower for females, in the range 25-29. The male-to-female ratio also decreases from birth, increases from young adulthood to later ages and possibly declines again — compare the results from public testing sites in the United States, Table 25, p. 98 in The Origin, Persistence and Failings of HIV/AIDS Theory.

Table 3-17 of the South African Survey shows the same decline from birth into the young teens followed by an increase again also for a survey carried out in 2002.

Pregnancy is one of the conditions that can bring about a positive HIV-test: at every age, women at pre-natal clinics tested positive more often than non-pregnant women in the same age group; and after pregnancy the rate of positives declined again (except among the teenagers), see Table 3.14 in the Survey:

The same racial disparities are seen as in other regions of the world, black >> white (from the Survey’s Table 3.17):However, something is wrong with at least some of these numbers. As it stands, 5.6% of whites  and 4.2% of coloreds must have died between 2002 and 2005 in order to bring the rates down to those extents.

Table 3.18 shows the same racial disparities in annual incidence of “HIV infection”: 3.4% among Africans, 0.3-0.5% among whites, coloreds, and Indians.

The annual incidence among adults is, just like the overall prevalence, highest at ages 25-34: 3.3% among 15-24-years olds, 7.1% for 25-34, 4.0% for 35-44, 1.7% at 45-54, 0.4% at ≥55. It is lowest among 10-14-year olds at 0.4% and higher among younger children, 0.8% at 2-4 years and 1.5% at 5-9 years. One might have thought that such high rates among children below the age of sexual activity would have brought at least some people to question whether sexual transmission of a virus is involved, since this phenomenon is seen in other countries as well. Thus the prevalence (not incidence, now) of HIV-positive children in 2004 in Botswana was 6.0% among males and 6.8% among females aged 18 months to 4 years; 5.9% among males and 6.2% among females aged 5-9; and 3.6% among males and 3.9% among females aged 10-14. In Zimbabwe, the prevalence was 5.8% among children aged 6-8.

For every age group, these South African data confirm the tentative suggestion (see pp. 74, 217, 246 in The Origin, Persistence and Failings of HIV/AIDS Theory) that black women are much more prone than others to test HIV-positive: male-to-female rates are lower among blacks than among other racial groups,. The following Table uses data from the Centers for Disease Control and Prevention reports on tests at public sites for 1995, 1996, 1997-98, and 1999-2004.

Unfortunately but predictably, the South African Survey takes for granted that relative rates of testing HIV-positive reflect sexual behavior, and nigh on 2/3 of the whole Report discusses behavioral issues. As I’ve pointed out often, if one accepts the sexual-transmission view, then one must also believe that black people actually behave as described in the most extreme racist stereotypes — see ANTHONY FAUCI EXPLAINS RACIAL DISPARITIES IN “HIV/AIDS”, 3 June 2008 and HIV/AIDS THEORY IS INESCAPABLY RACIST, 19 May 2008, and chapters 5-7 in The Origin, Persistence and Failings of HIV/AIDS Theory.

11 Responses to “HIV demographics are predictable; HIV is not a contagious infection”

  1. clue said

    Your argument is that “HIV” can’t be a sexually transmitted disease because it infects ethnic groups consistently and predictably at the same rate. However, rates of other STD’s have the same predictable infection patterns among ethnic groups, are they not sexually transmitted diseases?

  2. Henry Bauer said


    Look at the relative rates of particular STDs over a range of years, and they vary quite considerably. Figures 4, 5, and 7, pp. 32-33 in my book, are CDC data showing a spike in black syphilis cases starting about 1985 without increase among whites or Hispanics; changing relative rates of gonorrhea among whites, blacks, Asians, Hispanics and Native Americans, 1999-2003.

    It’s not only the racial disparities in HIV+ that are uniform, also the geographic distribution and the age variation. HIV+ is always highest in young-middle-age adults, whereas it’s adolescents and young adults who are most at risk of STDs. HIV+ has remained distributed for more than two decades in the same way in the United States and globally, whereas STDs jump around, e.g. in my book Table 5 for gonorrhea and Figure 8 for syphilis, again from CDC.

  3. clue said

    What you’re saying is that ethnic groups consistently test HIV+ at unvarying rates? For example,this is a hypothetical number, blacks will always test HIV+ 8 times more than whites?

    It’s odd that “HIV” infection is highest among young-middle aged and not adolescents. What’s the CDC explanation for this?

  4. Henry Bauer said


    It’s not always exactly the same number; it couldn’t be, unless race were the only influential variable. Most groups tested also differ in composition by age and sex, both of which influence the tendency to test HIV+. I go into some detail on this in my book, that if there’s a “true” ratio it could only be found by multivariate analysis, which is rare in the published data.

    Moreover, none of us belongs to a “pure” race, there’s just a high probability that the race-determining genes (for skin color, hair color, eyes, etc.) are linked to whatever genes are associated with a tendency to give the “HIV+” response.

    What’s astonishing is that despite all that , the racial disparities are almost quantitative. The relative rankings apply in every report I’ve seen: black > Hispanic > Native American > white > Asian. Asians mostly between 1/4 and 2/3 of whites. Native Americans less than twice whites, not far from 1 1/2. Blacks usually >5 times whites, but the difference is even greater between black women and white women. It also varies with risk group. Among gay men, where the average positive rate may be 30% or even more, it would obviously be impossible for any sub-group to be more than 2 or 3 times greater than that. In the lowest-risk group of blood donors, the black-to-white ratio seems to be about 10 in the USA but much higher in South Africa. I’ve got a lot more about this sort of thing in my book; as well as that black Hispanics and white Hispanics differ in much the same way.

    Seems to me that the constant relative ranking in itself is astonishing; and that it’s so nearly quantitative seems to me to make it conclusive that there’s a genetically determined basis.


    As to “young middle age”, I’m not aware that the CDC, or any HIV/AIDS theorist or believer for that matter, has even acknowledged this fact, let alone offered an explanation. One still sees the shibboleth that AIDS struck “young, previously healthy, gay men”, even though Michelle Cochrane showed by examination of the original records that they weren’t previously healthy, and their average age was mid-to-late 30s; and many early reports that mention drugs indicate that drug abuse was more indicative of AIDS risk than being gay was. It’s quite remarkable that, to this very day, the prime age for testing HIV+, for being diagnosed with AIDS, and for dying of “HIV disease”, all continue to be in the 30s or low 40s. My tentative suggestion is this: The “HIV test” was invented as a way to detect things common in the sera of men aged mid-30s or so who where very ill. What the tests now pick up are proteins and RNA bits that are generated under some sort of health challenge, particularly strongly in men aged mid-30s or so. But anyone who tests HIV+ is said to have “HIV/AIDS”, and anyone HIV+ who dies — from just about anything — is said to die of AIDS. So the nature of the tests constitutes a self-fulfilling prophesy whereby the group most hit by “AIDS” originally set the criteria for testing HIV+ and for having AIDS even as the number of “AIDS-defining” conditions expanded, because the HIV+-test has become the central criterion.

  5. Clue,

    You’ve hit upon a common misunderstanding of Bauer’s argument, frequently batted around at Aetiology and other places as a supposed refutation.

    Bauer’s argument is not that for true STDs, all races should test positive with equal frequency. This is a strawman which is then followed by: “Well, blacks test positive much more frequently than any other race for gonorrhea, so by your logic gonorrhea isn’t sexually transmitted?”

    It is true, if racial disparities were sufficient to say something isn’t sexually transmitted, then most STDs wouldn’t be considered sexually transmitted. But it’s more than that. It’s that the racial disparities remain constant even while other factors are changed.

    In my review of Bauer’s book, I stated:

    “I find it astonishing that apparently no statistician has ever undertaken a rigorous multivariate statistical analysis to determine the full implications of these findings. Bauer himself performs a very crude form of multiple regression in Chapter 6, ‘What Is It About Race?’, demonstrating that racial and population density demographics are sufficient to obtain an extremely good prediction of F(HIV).”

    Some people may not be familiar with some of the terminology I used there.

    “Multivariate statistical analysis” is a form of statistics which involves analysis of more than one variable simultaneously, in other words, the analysis of possible relationships among variables. There are two types of variables — independent and dependent. The independent variables are the variables that we suspect could influence or affect the dependent variables. For our purposes here, the independent variables involved would be things like race, age, time, geographic location, gender, and other demographic variables. The dependent variable, of course, would be F(HIV), the prevalence (or frequency) of positive HIV test results.

    “Multiple regression” is in reference to regression analysis, in which basically we try to get an algebraic formula for the dependent variable in terms of the independent variable, some parameters, and an error term. The important thing to keep in mind is that we actually vary the parameters and then try to figure which values of the parameters give us the best formula. The simplest example of regression analysis is met by students in Stats 101 when they study correlation coefficients and the line of best fit for bivariate data (two variables, one independent, one dependent, the formula is just writing the dependent variable as a linear function of the independent variable.) The parameters in that case are just the correlation coefficient and a y-intercept.

    Now, what Bauer did in Chapter 6 was try to find a “formula” for F(HIV) in terms of just 2 dependent variables, race and population density. Strictly speaking, there were more than 2 dependent variables involved, because Bauer calculated race (“R”) and population density (“D”) themselves in terms of several other variables. (For example, race was based on a weighted formula in terms of racial demographics.) But there were just 2 variables in the sense only race and population density were used.

    Bauer took the product of R and D. I might have taken the square root of the product (their geometric mean). In any case, the result was that the product turned out to be a very good predictor of actual F(HIV). He didn’t actually attempt to mathematically estimate a parameter, and this is why I said “I find it astonishing that apparently no statistician has ever undertaken a rigorous multivariate statistical analysis to determine the full implications of these findings.” But he did do a crude type of shading by state, this is what appears on p. 70. And just by looking at the numbers for RD and the 2 graphs on p. 70, it’s clear RD is a good approximation.

    Whether it’s just (“just”) a very good approximation or a really great approximation I can’t say. What’s clear is that you would NEVER be able to get such a result for a real STD. Just 2 independent variables!! Suppose someone came up to you and said, “I bet you I can predict the prevalence of gonorrhea in your state or even your county, if you just tell me your racial and population density demographics.” You don’t have to be statistician, or even a scientist, to see how silly and absurd such a bet would be. And yet, with F(HIV), it can be done. So regardless of what the HIV tests “measure” or are “testing for”, we can definitely rule out the possibility that they’re testing for an STD.

    What’s even more remarkable for F(HIV) is that when we hold certain independent variables constant, and then see how well one or more other independent variables predict F(HIV), the same parameters seem to work. For example, suppose we look at different groups like gay men, STD clinic applicants, applicants for military service, repeat blood donors, first-time blood donors, etc. Suppose I told you, “I can write down a number on a piece of paper representing the ratio of black to white testing positive, before you tell me which group you’re thinking of, and that number will be pretty close.” Or, “Suppose I can write down an age distribution for F(HIV), before you tell me which group you’re thinking of, and that age distribution will match pretty well.” This would be impossible for a true STD.

    You might be able to find disparities for gonorrhea or other STDs, but the disparities would not be so constant among different groups. So, these are the differences between F(HIV) and STDs:

    1. F(HIV) can be predicted well over huge, diverse populations, by a small number of independent variables.

    2. The disparities observed in F(HIV) (racial, age, etc.) hold pretty well no matter how we fix, or hold constant, other variables.

    This is what you expect when you’re testing something based on genetic and/or physiological factors. Behavioral factors may come into play for specific individuals, but behavior alone cannot account for these, as HIV is said to do.

  6. Martin said

    Dr. Bauer, Dr. Brown, I would not be surprised if a multivariate analysis has been performed — just out of curiosity — but never published because dissenting voices aren’t published.

  7. Henry Bauer said


    I wish there were some sign that HIV/AIDS “researchers” have curiosity beyond how to fit everything into their theory, no matter how farfetched and implausible they have to make their “explanations” — like an incredible rate and variety of mutation while remaining pathogenic and infectious.

    Some of the early publications had so many indications that HIV/AIDS is a wrong theory, yet those were ignored.

  8. Joe said

    I visited the ‘HIV clinic’ with my friend on numerous occasions. The area he lives in is a majority Asian area (I couldn’t find the exact ethnic breakdown, but I just checked and 65% of the local government representatives there are Asian). People with an African background are the minority (probably around 5%). Yet in the clinic, the majority were of African descent, and there were never any Asians. Step outside the clinic, and one is in a sea of Asian faces. How the staff could fail to notice that is beyond me.

    Another thing I find interesting is that the UK government keeps releasing figures for the large year-on-year increases in STDs. For the past 5 years or so, these figures don’t include ‘HIV/AIDS’ (probably because there is so little growth, and therefore less alarming than they would like the figures to be).

    When one does look at the government statistics for ‘HIV/AIDS’ the racial disparities are probably even more marked in the UK than in the US, as people of African descent are only about 5% of the UK population, yet (as was obvious at the clinic I mentioned) they make up the majority of non-MSM ‘infected’ people ( [Don’t you just love how the population has to be served statistics in ‘nuggets’?]

  9. Henry Bauer said


    Thanks for that link.

    When you say the racial disparities may be more marked in the UK than in US, I think you mean more obviously noticeable, in terms of observations like you describe. In most of what I’ve written, I’ve been describing disparities in terms of rates ; in other words, if in a given group, 10 whites in 1000 whites test positive, among every 1000 Asians in the group only about 3 will test positive, but in every 1000 blacks in the group 50 or more will test positive. I’ve seen differences in those relative numbers between UK and South Africa, but haven’t seen sufficiently detailed info about UK to compare those. It may well be that the ratios there are more like in Africa than in US, because in the US most “African Americans” have a sizeable proportion of “white” heritage, and (to a somewhat lesser degree) “white” Americans have on average an appreciable proportion of African genetic heritage.

  10. Joe said

    The inter-mixing of ‘Africans’ and ‘Caucasians’ in the UK is probably similar to that in the US. Most of the people here who would trace their recent genetic material back to Africa are from the Caribbean, so even though the UK black population has not been here as long as black people have been in the US, I think that since the majority came from the Caribbean they would have a more mixed genetic basis than people coming here recently from, say, Somalia.

  11. Henry Bauer said


    The link you gave led me to lots of UK info, not too much specific about race, but there’s sometimes a distinction between British-born people of African ancestry and recent arrivals. I’ll be posting a summary some time. Thanks again!

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