For years I have been writing about how HIV/AIDS was deliberately seeded into
the American gay community via the government-sponsored contaminated
hepatitis B experiments (1978-1981) ---- and how this was covered-up by the
AIDS establishment.
Now we have statistical proof to show that HIV was indeed planted in the gay
community in those "pre-AIDS epidemic" years.
Please read Tom Keske's report in this file attachment. Much of it is
statistical data which proves his case. However, there is also much in his
report that is very easily read --- and which provides you with
historic/scientific facts that show, without doubt, that HIV/AIDS is
connected to government experiments using gay men as guinea pigs.
Please pass on to other interested parties who might be interested in the
origin of HIV/AIDS as it explains why AIDS was originally a "gay disease" in
the United States. This report is also if interest to groups interested in
vaccine dangers and contamination problems connected with vaccines.
Author Tom Keske can be reached at :
tkeske@mediaone.net
Personally, I believe Tom has done an amazing job with his research.
For those of us who have been saying for many years that AIDS is a man-made
disease, Tom Keske's hepatitis B vaccine report is required reading.
Regards,
Alan Cantwell Jr MD
-------------------------------------------------
Statistical Analysis Linking U.S. AIDS Outbreak to Hepatitis Experiments---Thomas R.
Keske
Version 4.2
Author:
Thomas R. Keske
-------------------------------------------------
TABLE OF CONTENTS ------------------------------------------------- Page 2
TABLE OF CONTENTS
1 Abstract
4
2 Introduction
5
3 HIV Correlation to Vaccine Trial
7
3.1 Methodology Overview
7
3.2 Lemp 1990 Study
8
3.3 Estimation of High Risk Population
9
3.4 Vaccine Correlation Analysis
12
3.5 Lemp Study, Odds Per Year
14
4 HIV Correlation to Non-vaccine Participation
15
4.1 Over-representation in earliest AIDS cases
15
4.2 High Rate of Seroconversion During Recruitment Years
20
4.3 Unreasonable Study Size/Selection
22
5 Epidemiological Anomalies
24
5.1 Unreasonable Delay of HIV in IV Drug Community
24
5.2 Anomalies Revealed by Computer Modeling
24
6 Unreasonable Approval of the Vaccine
26
7 Historical Context
27
8 Conclusions
30
9 Refuting Counter-Arguments
33
10 About the Author
37
11 References
38
12 Acknowledgments
41
13 Document Reproduction
41
Appendix A Demonstrating the Validity of the Statistical Approach 42
Appendix B Letter From Dr. George Lemp
44
Appendix C Error Analysis for Lemp Data Calculations
46
C.1 Effect of Variation in High Risk Population Estimate
46
C.2 Effect of Errors in HIV Infection Rate Figures
47
Appendix D Letter from Case Western Reserve Statistics Department 48
Table of Contents ------------------------------------------------- Page 3
Appendix E Software Epidemic Modeling Analysis
50
E.1 Per-Contact Infection Rates
51
E.2 Evidence of Program Accuracy
52
E.2.1 Consistency with Independent Mathematical Test
52
E.2.2 Consistency with Real-Life Experimental Results
52
E.3 First Year, SFHBVCS
54
E.4 Patient Zero Scenario
56
E.5 Estimated Seed Size in SF
57
E.6 From Where Comes the Seed?
58
E.7 Variable Infectivity Per Stage
59
Appendix F General Statistical Primer
61
Section 1 Abstract
------------------------------------------------ Page 4
1 Abstract
This statistical study concerns what is probably one of the most
significant and overlooked issues of our time. It demonstrates
proof of
a strong link between the U.S. outbreak of AIDS, and hepatitis studies
that were performed on gay males, starting in the late 1970s.
The
analysis refutes explanations that attribute the connection simply to
sexual risk behavior on the part of the study participants.
The analysis also presents evidence suggesting that HIV infections
occurring in the studies were more likely to have been intentional rather
than accidental. This raises the question of whether the men in
these
studies might have been used as guinea pigs for covert experimentation, or
whether a sexually-transmitted epidemic might have been deliberately
induced, as a means to rid society of "undesirables".
Regardless of
whether the virus itself came into existence naturally, its initial spread
was clearly unnatural.
The methodology used in this document is highly similar to that which is
typically used to evaluate the effectiveness and safety of vaccines.
The
analysis evaluates differences in infection rates between a suitable
control group, versus a vaccine test group.
In the first two years of the epidemic in San Francisco, between 50 and 60
percent of the earliest known AIDS cases were from persons involved in the
hepatitis studies. A goal of this analysis is to calculate specific
probabilities for these and other similar figures. It demonstrates that
such figures cannot credibly be attributed merely to chance, or to
differences in risk behaviors.
Odds of the disproportionate levels of HIV infection among men in the
vaccine trial, relative to other men of similar risk behaviors, are shown
to be as little as 1 in a trillion.
A statistical link exists not only to experimental vaccines, but also
simply to the fact of participation in the hepatitis studies, such as
simply to have blood drawn for purposes of monitoring hepatitis
prevalence. Few logical or benign possibilities exist to explain
why
there should be such a connection, yet it exists. Odds against the
higher
initial rate of AIDS among study participants was as little as 1 in 300,
000, when compared to men of equal or higher risk.
Various epidemiological anomalies also suggest that an artificial,
simultaneous, mass-infection would have been necessary in order to produce
the type of explosion in HIV that was observed in the early 1980s.
Full-
blown AIDS should have been evident many years earlier, before HIV was
nearly so widespread Thousands of infections would have been
necessary
to fuel the levels of HIV growth that were observed, during years in
which no retroactive evidence of HIV exists.
These anomalies are analyzed using computer modeling software.
Section
2 Introduction --------------------------------------------- Page 5
2 Introduction
Recently, there has been renewed interest as to whether massive polio and
smallpox vaccine programs in Africa may have initiated, or at least
accelerated, the spread of AIDS on that continent [1]. The possibility
that a similar vaccine phenomenon may have occurred in the United States
should heighten concern.
For two decades, there has been some concern about a possible connection
between the outbreak of AIDS in America and a government-sponsored
experimental hepatitis B vaccine which was injected into gay men in New
York City, San Francisco, Los Angeles, Chicago, St. Louis, and Denver,
between the years 1978-1982.
Several reports in the medical literature attest to the safety of the
experimental vaccine. Thus, the connection between the gay experiments
and AIDS has been largely dismissed as unworthy of investigation. There is
a possible element of bias in this hasty dismissal. The question
of
possible vaccine contamination is too important to allow political
concerns or emotional reactions to interfere with an objective
investigation and discussion.
The purpose of this document is to reinvestigate the connection of the
original AIDS outbreak in San Francisco gay men not only with the
experimental hepatitis vaccine, but also simply with the act of
participation in the government-sponsored hepatitis study. This is a
new
analysis of data collected from various published studies.
This document will demonstrate how gay men who volunteered for government
hepatitis experiments were far more likely to become infected with HIV
than those who did not take part in such experiments, to a degree not
credibly explainable by chance or by life-style.
This document does not speculate whether HIV is an old or a new virus, nor
does it explore the outbreak in Africa. It does not focus on whether
HIV
is a natural virus or a genetically engineered virus that could have
resulted from laboratory experimentation.
None of these scenarios preclude the possibility of HIV contamination of
the hepatitis vaccines.
The analysis will attempt to show that there is a significant statistical
correlation between vaccine volunteers and HIV infection, that is not
merely the result of "high-risk" sexual behaviors.
This document is being distributed to AIDS activists, virologists,
biostaticians, journalists and others, in an effort to promote further
research and dialog into the proposed connection of AIDS to the government
hepatitis studies.
Section
2 Introduction --------------------------------------------- Page 6
This document is intended for a broad target audience, including persons
of varying backgrounds and levels of knowledge about statistics. For
those who have more questions about the statistical computation, a primer
and a more extended discussion is provided in Appendix F. Persons who
have background in statistics can simply skip this section.
As will be explained, there is good reason to believe that HIV was in the
vaccines. There is also reason to believe that the presence of HIV was
not likely to have been accidental.
Furthermore, there is an even more peculiar connection of HIV infection
simply with participation in the government studies, even for gays who
received no experimental vaccine. This connection is not credibly
explained simply by the "risk" status of the men involved.
This
connection is even more disturbing, because there are no vaccines involved
for which a possible accident could have occurred in the production.
During World War II, a U.S. State Department official once dismissed
allegations about the Holocaust as being of too "fantastic" of a
nature to
be worthy of forwarding. It may be fashionable in current times to make
caricatures of every allegation concerning cover-ups, conspiracies, or
secret experiments. However, this fashion does not represent
wisdom
today, any more than it did in World War II.
There are already more than 33 million people estimated to be infected
with HIV/AIDS.
It is perhaps the single most significant incident that has occurred in
human history. The number of lives claimed can be expected to exceed
the
6 million killed in the Holocaust, or even to exceed the total global
battle deaths of World War II. If there is any chance of human
agency
involved in the genesis of the AIDS epidemic, it is perhaps the most
important question of our time.
If there is even the slightest chance of negligence or malfeasance, it
would deserve to be investigated. This document will put a quantitative
number on that chance, and show that it is more than slight.
The purpose of raising this question is not simply to cast accusation or
blame. If a vaccine accident occurred, it is important to determine
why,
so that such accidents do not happen again. If there is any chance that
vaccines could have been contaminated through carelessness or intention,
then there must be accountability. If there is a plausible chance
that
there were further factors causing HIV infection, in addition to the use
of vaccines, then those factors must also be identified.
Section
3: HIV Correlation to Vaccine Trial ------------------------ Page 7
3 HIV Correlation to Vaccine Trial
3.1 Methodology Overview
The primary focus for the statistical analysis of vaccine involvement is
based on two distinct groups (cohorts) of San Francisco gay men. One
group received the experimental hepatitis B vaccine; the other did not.
In 1978, a research group from the San Francisco Department of Public
Health began epidemiological studies of gay and bisexual men attending the
City Clinic, a public health clinic for treatment of sexually transmitted
diseases.
The San Francisco City Clinic Cohort Study (SFCCC) involved over 6700 gay
men. Most of these men participated by donating blood samples for
hepatitis study, but did not receive experimental vaccines.
A smaller cohort of 359 homosexual and bisexual men, selected from the
larger group of 6700+ men in the San Francisco City Clinic, participated
in a clinical trial of a vaccine to prevent Hepatitis B [2]. In this
document, this group is known as the San Francisco Hepatitis B Vaccine
Cohort Study (SFHBVCS).
The second group referenced in this document for purposes of
retrospectively analyzing HIV incidence is the San Francisco Men's Health
Study (SFMHS), a cohort of 799 homosexual and bisexual men sampled from 19
high-risk census tracts in San Francisco [2]. The SFMHS began in June
1984. It also included 204 HIV-negative heterosexual men, who are
not
relevant to this analysis.
Different studies sometimes refer to slightly differing numbers of men in
the SFCCC and SFMHS, depending on the date of the study. For example,
some cite 6875 for SFCCC and 809 for SFMHS. This document will use
values
as cited in the context of specific, referenced studies, or will otherwise
prefer the higher values.
The SFHBVCS group is composed of 359 San Francisco gay men who received
the experimental vaccine. The SFMHS group consists of the 799 high-risk
San Francisco gay/bisexual men who did not receive the vaccine. Both
groups were at equal risk of acquiring HIV. Further details on this
point
are addressed in section 9.
If "equal risk" can be adequately demonstrated, then any
differences in
the HIV rates between the two groups should be attributable merely to
random chance. The analysis will demonstrate that the hypothesis
of
"random chance" can be rejected.
Section 3: HIV Correlation to Vaccine Trial ------------------------ Page 8
The statistical comparison of the two groups is a very commonplace type of
problem, that is easily computed.
3.2 Lemp 1990 Study
One of the sources of data for this analysis is a study headed by Dr.
George Lemp [3]: "Projections of AIDS morbidity and mortality in San
Francisco".
Dr. Lemp, formerly with the AIDS Office of The City's Department of Public
Health, now serves as director of the University-wide AIDS Research
Program at the University of California (starting 1997) [4]
The purpose of Lemp's study was not to evaluate the hepatitis vaccine or
to compare the men in the trial with men who did not receive the
experimental vaccine. Its purpose was to develop a model for predicting
the growth over time of the AIDS epidemic. These projections required
tracking of HIV seroconversion in high-risk men. Stored blood samples,
taken from the men in the San Francisco Clinic studies, were useful for
this purpose. Testing of the blood samples determined exactly when the
men showed their first indications of exposure to HIV.
For these purposes of his own, it happened that Lemp collected data which
compares the SFHBVCS vaccine group with the SFMHS non-vaccine group. This
same data is also useful for the further analysis in this document.
This document does not claim any endorsement from Dr. Lemp. It merely
uses the Lemp data for a different purpose. I contacted Dr. Lemp
to
verify that his data was correct as quoted (see Appendix B).
The data in question, which I verified with Dr. Lemp, was derived from
charts contained in the study. It was previously posted to
sci.med.aids
by Billie Goldberg, a San Francisco lay scientist and AIDS researcher:
SFHBVCS: 1978 - 0.3%, 1979 - 4%, 1980 - 15%, 1981- 28%, 1982 - 40%,
1983 - 46%, 1984 - 47%, 1985 - 48%, 1986 - 48%, 1987 - 49.3%
SFMHS: 1978 - 0%, 1979 - 2%, 1980 - 4%, 1981- 10%, 1982 - 23%,
1983 - 42%, 1984 - 48%, 1985 - 49%, 1986 - 49.3%, 1987 - 49.3%
The above two lines show the total percent of HIV infection, in each year,
for the SFHBVCS (vaccine) group, and the SFMHS (high-risk gay men who did
not receive vaccine).
Section
3: HIV Correlation to Vaccine Trial ------------------------ Page 9
The SFHBVCS group had blood samples taken in each year shown, used to
estimate HIV prevalence. The SFMHS had an estimate derived from a
subgroup in 1982, and complete samples from 1984, on. The Lemp study
used
curve-fitting to fill in values for years that did not have actual samples.
The analysis in this document will focus primarily on the year 1982, since
that is the first figure based on actual measurement, and is therefore
likely to be the most accurate.
The rate of HIV infection in the SFMHS group will be taken as a measure of
the "expected" rate of HIV for the high-risk population of San
Francisco,
for this analysis.
The justification for this is based on the comparative patterns of HIV
growth in the SFMHS and the SFHBVCS, as shown in the Lemp data.
Both groups start out nearly the same. When each group hit a
level of 45+
percent infection, it abruptly ceased the high rate of growth.
Even though the SFHBVCS vaccine group had a slight "head start" in
infection, the SFMHS group "caught up" and actually hit the wall of
saturation at 49.3% infection, in 1986, a year ahead of SFCSS vaccine
group. This suggests that on average, the SFMHS, non-vaccine group may
have been even more promiscuous and high-risk than their vaccine
counterparts.
What most distinguishes the two groups is the large, initial level of HIV
seroconversion in the vaccine group, shortly after they received the
vaccine. Based on the fact that SFMHS appears to be at least at equal
risk for HIV infection, the two groups should have been roughly equal in
HIV prevalence during the earliest years, as well. What the statistical
analysis examines is the likelihood for this initial deviation.
3.3 Estimation of
High Risk Population
In order to compute probabilities for the vaccine group to have exhibited
their high rate
of HIV infection by random chance, it is necessary to have an estimate of
the total high-risk, gay male population in San Francisco, in 1978. It
is
important to note that this is an estimate of only the high-risk
population, not the total gay male population. The intent is to
eliminate
the high-risk status of the vaccine group as the postulated reason for
their exhibiting a higher rate of HIV infection.
As it happens, the conclusion of the statistical analysis is not highly
dependent on having more than a rough count of the "high risk"
population.
This question is analyzed further in section C.1.
Section
3: HIV Correlation to Vaccine Trial ------------------------ Page 10
The estimate used is based on statistics for the city of San Francisco,
for total HIV infections that occurred between 1978 and 1999. It
is
reasonable to imagine that the numbers of gay men who did in fact become
HIV+ in San Francisco, in the two decades to follow, would roughly reflect
the numbers who were at risk in 1978 (not accounting for immigration and
emigration).
Following is data from the city of San Francisco [5].
Reported AIDS cases since 1981: 26,398
AIDS deaths to date since 1981: 18,066
Persons currently living with AIDS: 8,332
Estimated HIV Infected to date (since 1981):
15,250
(approx. 1 in every 50 San
Franciscans (2.1%)
AIDS BY CATEGORY:
SAN FRANCISCO: 79% MSM, 11%
MSM+drug, 7% iv-drug, 1% heterosexual
CALIF:
71% MSM, 9% MSM+drug, 10% iv-drug,
4% heterosexual
US:
49% MSM, 6%
MSM+drug, 25% iv-drug, 9% heterosexual
("MSM"
= Men having sex with Men")
The total HIV infections in San Francisco is the total number of
cumulative AIDS cases, (26398) plus the total number of HIV infections
that are not yet progressed to AIDS (15250).
In San Francisco, 79% of the AIDS/HIV cases are gay men (MSM).
An additional 11% is combined "MSM plus IV drug" category.
For the sake
of this estimate, half of the 11% will be counted as attributable to MSM
(this is a small factor, anyway).
The total percent attributed to MSM is then .79 + (.5 * .11) = .84
The total estimate of high-risk gay men is then
(26398+15250) * (.84), or roughly 35000.
Section
3: HIV Correlation to Vaccine Trial ------------------------ Page 11
A factor that could possibly tend to make this estimate too high is that
the estimate does not account for possible immigration into the city.
Also, people who acquired HIV over a longer period of time may not have
been as high-risk as the people who acquired AIDS in the early years.
However, there are also significant factors that could tend to make the
estimate too low. It does not account for men who are HIV+, but simply
have not been tested and counted. It does not account for men who
adopted
"safe sex" practices after the AIDS epidemic became publicized.
This
would tend to significantly reduce the risk level of men who were
previously "high risk."
Thus, some factors would tend to make the estimate err on the side of
being too high, while other factors would make it err on the side of being
too low. This mix of factors has some tendency to cancel each
other out,
in overall effect.
The estimate of 35000 high-risk gay men was made independently of the Lemp
study. Interestingly, the Lemp study needed to make an estimate of the
entire gay male population of San Francisco. At a minimum, the
estimate
of 35000 "high risk" gay men should be less than the estimated
total of
gay males in the city.
In a random phone surveys, Lemp's study estimated 69,122 openly gay males.
In another, more conservative estimate, based on extrapolating
SFMHS
rates of AIDS to the entire city, produced a figure of 42,509. Lemp
compromised on a middle figure of 55,816 gay males in San Francisco.
Thus, the 35000 figure for "high risk" seems reasonable. Note
that Lemp's
phone estimate of 69000+ gay males does not account for persons who would
refuse to discuss their sexual orientation over the phone, which may have
been significant.
A case could be made for counting most of the gay males in the city as
being "high risk". Consider, for example two men who play
Russian
roulette, one using 4 of 6 empty chambers on each play, and another who
uses only 2 of 6 empty chambers. One has twice the risk of death, per
trial. After 25 trials, the "lower risk" man has a
99.996% chance of
being killed, versus a virtual 100% for the "higher risk" man.
A similar phenomenon appears to apply to the lion's share of men in San
Francisco- within less than a decade, infection rates were nearly 50% in
many areas. Thus, the distinction between "high risk"
and "low risk"
among gay males may be a false dichotomy.
As it turns out, the accuracy of the figure for "high risk" gay men
is
somewhat of a moot point. Later analysis will show that the net result
of
the statistical comparison is relatively insensitive to the effect of
varying the estimated number of high risk men (Section C.1)
Section 3: HIV Correlation to Vaccine Trial ------------------------ Page 12
3.4 Vaccine Correlation
Analysis
Referring again to the Lemp study data:
SFHBVCS: 1978 - 0.3%, 1979 - 4%, 1980 - 15%,
1981- 28%, 1982 - 40%,
1983 - 46%, 1984 - 47%, 1985 - 48%, 1986 - 48%,
1987 - 49.3%
SFMHS: 1978 - 0%, 1979 - 2%, 1980 - 4%, 1981-
10%, 1982 - 23%,
1983 - 42%, 1984 - 48%, 1985 - 49%, 1986 -
49.3%, 1987 - 49.3%
Both groups start out with zero or near-zero HIV exposure in 1978.
HIV
infection exploded in both groups from about 1980, onward.
By 1987, both groups are nearly identical once again, with a nearly 50%
infection level. New growth in both groups has slowed dramatically, to
little or no new increase.
The behavior is like an "explosion", starting from next to nothing,
spreading very quickly, and infecting a sizeable percentage of both groups.
It is not highly meaningful to examine either the very late period (1983-
1987), when both groups reached a near-saturation, or to examine the very
beginning (1979-1980), when hardly anyone was infected.
The early period from about 1980-1982 shows a more tell-tale difference.
In 1982, some 40% of the SFHBVCS shows HIV infection, while only 23% of
the SFMHS shows infection.
My question is: What is the exact statistical probability of this
difference occurring by "random chance" alone? This is
the essence of
the computation to follow.
We have estimated a pool of 35000, equally high-risk gay men. From
these,
we play "God" and draw a random sample of 23% (using the SFMHS
infection
rate), designating these as the men who will have become HIV+ by 1980.
This equals (23% x 35000) = 8050 men. Of these 8050, some (40% x
359) =
144 are from the SFHBVCS vaccine group (the actual result for that group).
What we would have normally expected, on average, would have been
(23% x 359) = 83 men, rather than our 144. The
probability for this
difference between the expected and actual results, by random chance alone,
is computed by a standard formula:
Let:
T = total population
size (=35000)
v = vaccine subgroup
size (=359)
s = sample size - no.
of HIV infected men drawn at random from total (=8050)
n = no. of men from the
vaccine group who were found in that sample (=144)
Section 3: HIV Correlation to Vaccine Trial ------------------------ Page 13
The odds for drawing exactly "n" men would be given by the formula
(vCn * (T-v)C(s-n)) / sCT
where the notation "xCy" is the computation for total combinations
in
choosing "y objects from a group of x objects":
xCy =
x!/((x-y)!y!)
where "!" is "factorial". E.g. x! = x *
(x-1) * (x-2) * (x-3) ... * 2 * 1
The odds for drawing "n" or more is computed iteratively.
After
computing the odds for "n", we then compute the odds for n+1, n+2,
... s,
and sum these probabilities.
This computation was performed using a computer program written by the
author, called "comb.c". The program source is not listed
here, due to
length, but is available upon request from the author. It is written in
C
programming language, for Unix or for Microsoft Visual C++.
The program output for this problem is as follows:.
Subgroup size = 359
Total group size = 35000
Sample size = 8050
n = 144
PROBABILITY IS: 2.72128e-13
This is 2.7 times 10 to the -13th power, which is a unimaginably small
probability (roughly 1 in 3,700,000,000,000).
The methodology for the above calculation is very analogous to what the
researchers themselves used in order to prove that their vaccine prevented
hepatitis (explained further in Appendix A). The validity of the
logical
and mathematical approach is easily demonstrated.
Accounting for margins of error does not alter the conclusion, as
discussed in Appendix C.
Section 3: HIV Correlation to Vaccine Trial ------------------------ Page 14
3.5 Lemp Study, Odds Per Year
Below are computations of the probabilities for all of the years from 1979-
1982, that the higher proportion of HIV infection in the vaccine group
might have been random chance.
In the earliest year of 1979, at the beginning of the vaccine trial, there
is not a statistically significant difference between the vaccine and non-
vaccine group. All of the other years show a significant difference.
* 1979: SFMHS =2% =700 ; SFHBVCS =4% =14
Subgroup size = 359
Total group size = 35000
Sample size = 700
n = 14
PROBABILITY IS: 0.014146 (1
in 71)
* 1980: SFMHS =4% =1400 , SFHBVCS =15% =54
Subgroup size = 359
Total group size = 35000
Sample size = 1400
n = 54
PROBABILITY IS: 5.62915e-17
( 1 in 18 quadrillion)
* 1981: SFMHS =10% =3500 , SFHBVCS =28% = 101
Subgroup size = 359
Total group size = 35000
Sample size = 3500
n = 101
PROBABILITY IS: 2.2762e-22 (1
in 4 billion-trillion)
* 1982: SFMHS =23% =8050 , SFHBVCS =40% = 144
PROBABILITY IS: 2.72128e-13 (this is the
previous example)
Section 4: HIV Correlation to Non-vaccine Participation ----------- Page 15
4 HIV Correlation to Non-vaccine Participation
4.1 Over-representation in earliest AIDS cases
Further analysis shows that there is peculiar, unexplained correlation of
early AIDS infection in the entire San Francisco City Clinic Cohort (SFCCC).
These 6875 men were studied by having blood drawn for purposes of
determining prevalence and transmissibility of hepatitis B. Only a much
smaller subgroup of 359 men, drawn from entire SFCCC, had received
experimental vaccine.
A number of studies suggest that the SFCCC was over-represented among the
earliest of AIDS cases, compared to men of equal risk. These
studies do
not identify if the early AIDS cases from the SFCCC also happened to be
the same men who had received the experimental vaccine. If they had in
fact received the vaccine, then the failure to mention this would have
been a gross omission on the part of the AIDS researchers.
If all or some of these disproportionate SFCCC AIDS cases were actually
attributable to the subset of men in the vaccine trial, then it suggests
an extremely strong link to the vaccines.
If the AIDS cases were merely men in the SFCCC who did not receive the
vaccine, then it raises the disturbing question of what other unknown
factor could explain the disproportionate HIV infection.
There is little in the study that should by rights have put men at
significantly higher risk for getting HIV. It is
unlikely, for example,
that unsterile or reused needles would have been involved in drawing blood.
It must be demonstrated that the men in the SFCCC did not have a higher
rate of early HIV infection simply because of such an obvious factor as
higher-risk behavior.
Consider how the shape of a graph should appear when a group at higher
risk for AIDS infection is compared to a lower risk group.
The higher-risk group will tend to produce AIDS cases earlier. The
numbers of cases will grow faster, thus producing a sharper, faster rising
curve.
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 16
Below is a chart showing the growth of clinical AIDS in the SFCCC, versus
the entire city of San Francisco, in the first years of the epidemic:
AIDS CASES
IN AIDS CASE IN ALL GAY MALES,
SFCCC
SF
STANDARD METROPOLITAN STATISTICAL AREA
--------------
-----------------------------------------
JUL-DEC 1981 6
12
JAN-JUN 1982 6
24
JUL-DEC 1982 12
58
JAN-JUN 1983 18
100
JUL-DEC 1983 20
146
JAN-JUN 1984 24
210
JUL-DEC 1984 38
280
The preceding figure is taken from Jaffe, et al [10].
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 17
We would of course expect to find a larger absolute number of AIDS cases
among gay men in the entire city than in the hepatitis trial, because
there were approximately 8 times more gay males in the general population,
than in the hepatitis trial. However, the growth rate was also
consistently higher for the city in general, even in relative terms,
during the early epidemic years. This fact is easily visible in the
sharper curve, represented in the chart. This suggests that these first
victims from the city as a whole were at even higher-risk for HIV
infection, than the men in the SFCCC. Between 1981 and 1984, the
numbers
of new cases in the city increased 20-fold, while the numbers of new cases
in the SFCCC increased by less than seven-fold.
The overall gay population of the entire city should have been lower risk
than the SFCCC, because the SFCCC was composed of men recruited at a VD
clinic. However, the very first of the AIDS cases in the city do not
merely reflect the overall population. They naturally tend to reflect a
subgroup of the very highest-risk men in the entire city. This
characterization is evidenced by the very fact of their becoming the first
to be infected, out of the entire population. Men who have large
numbers
of partners are far more likely to be infected first. Exceptions to the
rule would exist, but would be small in comparative numbers.
Thus, it is more than justified to treat the SFCCC and the total pool of
men who contracted AIDS in these first few years, from 1981-1984 as being
at least equal in risk for having acquired AIDS. This assumption is
similar to that made previously for the Lemp study, but is even more
conservative, because the "high-risk" population is defined using a
much
shorter period of time. The evidence of higher-risk in the general SF
group is even more pronounced.
The fact that the men in the city as a whole had a sharper growth curve
might at first seem to exonerate the hepatitis studies. However,
this is
contradicted by the significant over-representation of the SFCCC in the
very first years. This over-representation suggests that the AIDS
epidemic was somehow seeded initially among the SFCCC, and then spread
like wildfire to the rest of the city.
There is clear evidence that the men from the overall gay population who
became early AIDS cases were at least equal in risk for getting AIDS, in
comparison to the men in the SFCCC. Thus, we can estimate statistically
whether the higher representation of the SFCCC among the earliest AIDS
cases could be attributed to random chance.
By the end of 1984, 166 of the SFCCC were diagnosed with AIDS, including
19 who had moved to other cities (Jaffe, et al [10]). The
remaining
group of 147 men is our "subgroup size".
A total of 898 men in the San Francisco Standard Metropolitan Statistical
Area (SMSA), which includes the SFCCC, were diagnosed with AIDS by the end
of 1984. This represents our "total group size".
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 18
We are interested in the sample of men who were the first cases, in 1981
thru 1982,
so we can compute the odds for the higher SFCCC representation to be the
result of random chance.
The SFCC represented 60% of the first victims in 1981 [11] , then it
quickly plummeted to 38.5% by the last half of 1981, and to 14.6% in the
last half of 1984.
Six of the first 10 AIDS cases in San Francisco were members of the SFCCC
[11], and 11 of the first 24 cases were also members [10].
By Jan 1,
1983, there were 104 AIDS cases in the city [12]. Of these, 35
were from
the SFCCC [11] (estimated from previous chart).
* 6 of first 10 AIDS cases from SFCCC (1981, first half)
Subgroup size = 147
Total group size = 898
Sample size = 10
n = 6
PROBABILITY IS: 0.00207447 (1
in 480)
* 11 of first 24 from AIDS cases SFCCC (1981, last half):
Subgroup size = 147
Total group size = 898
Sample size = 24
n = 11
PROBABILITY IS: 0.00057291 (1 in 1700)
* 35 of first 104 AIDS cases from SFCCC (end of 1982):
Subgroup size = 147
Total group size = 898
Sample size = 104
n = 35
PROBABILITY IS: 2.76209e-06 (1 in
362,000)
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 19
For 1981, the sample sizes are small, so it becomes more difficult to
demonstrate highly dramatic differences (for very small samples, nearly
any outcome tends to be possible, within reason). However, the
probabilities are only in the range of a small fraction of a percent, so
they are revealing enough, in themselves.
As the years progress and sample sizes increase, the differences become
more evident, tending to confirm that the pattern is genuine and
significant.
By the end of 1982, it is the most revealing, with a probability of less
than 1 in 360,000 that the differences between the two groups were due
simply to chance.
It should be noted also that 19 of the SFCCC men who developed AIDS are
not reflected in the preceding statistics, because they had moved to other
cities. If any appreciable number of these men were dropped from the
charts before 1983, then it could significantly raise the levels of
improbability, to a degree as high as 10 to the -15
(1 in 1,000,000,000,000,000)
It is important to note that men in the SFCCC were not methodically
screened for AIDS in the early years of the epidemic. Thus, there was
not a disproportionately higher level of reporting that would distort the
comparison to other gay men. At the time of Jaffe's 6-year follow-up
study
[10], "No formal procedures were available to determine if patients who
were reported to their health departments were cohort members."
For the
earliest AIDS cases, membership in the SFCCC was determined after the fact
of diagnosis. These men had not received any experimental vaccine, so
therefore had no special reason to be concerned about possible health
effects. Epidemiological screening of the SFCCC for AIDS did not begin
until 1983 [14].
Men in the SFCCC had the right to refrain from testing for AIDS, as some
opted to do. None were forcibly tested.
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 20
4.2
High Rate of Seroconversion During Recruitment Years
This analysis hypothesizes that enrollment in the SFCCC seemed to spark a
sudden onset of new HIV infection during the time-frame of 1978-1980, when
men were recruited for this study.
Is this hypothesis born out by examination of the seroconversion dates for
SFCCC participants?
Estimated dates are provided by Rutherford, et al [14]. in the following,
reproduced table:
Table 1, Estimated Year of HIV-1 Seroconversion in San Francisco City Clinic
Cohort
________________________________________________________________
Estimated year
New
of seroconversion
Seroconversions
_______________________________________________________________
1977
3
1978
114
1979
143
1980
121
1981
42
1982
30
1983
7
1984
9
1985
4
1986
5
1987
3
1988
4
1989
4
The new seroconversion figures represent a sample of 489 SFCCC men who had
progressed to full-blown AIDS prior to the Rutherford study. These men
were used to study the incubation period for AIDS, from initial HIV
seroconversion.
The Rutherford study claims that 8% of a sample of 2877 men were HIV+ upon
entry into the SFCCC study. Of the above 489 subgroup, 64% were
supposedly seropositive upon entry, and 36% seroconverted within 24 months
of entry.
The Rutherford study's estimate of the AIDS incubation period (about 11
years) is in line with current thinking. This suggests that the
estimated dates of seroconversion are also accurate.
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 21
The fact that there were men who were claimed to be HIV-positive upon
entry into the study does not exonerate the study's possible connection to
the outbreak of AIDS.
Men were not recruited all at the same time for the SFCCC study.
Most
were recruited over a two year period, between 1978 and 1980. The first
831 participants were recruited over a 5 month period, between June and
May of 1978 [14]. If men were infected continuously as they were
recruited, they could also have been quickly spreading HIV through the
general population at the same time, also infecting new recruits.
Even if you subtracted entirely from the graph all 312 men who were
supposedly HIV+ upon entry, there would still be a noticeable bulge in the
graph of seroconversions, around the years of recruitment. As computed
from the table, this would still leave 69 seroconversions through the end
of the recruitment period in 1980, another 72 in the next two year period
of 81-82, and only 36 in the entire 7 year period following that,
from 83-89.
This is particularly suspicious because rate of HIV spread should have
continued to accelerate through much of the 1980s, because of the growing
pool of already-infected men. It is well documented, how there were
considerable difficulties in convincing the gay community to close the
baths, for example, and to change sexual habits. Given the
slowness and
marginal results of this process, it is not reasonable to see such drastic
fall-off in the rate of new infections in high-risk men, when the
critical mass of already-infected men is now so much larger.
Even if many of these men were supposedly seropositive upon entry into the
SFCCC, what would explain the extremely high clustering around the
recruitment years or 1978-1980? This peculiarity requires
explanation,
in any case.
Not only is the sharp drop-off of new HIV seroconversions in high-risk gay
men suspicious, but the paucity of seroconversions before 1978, when the
trials started, is also peculiar. For example, there is no evidence
that
HIV infection existed in San Francisco before 1977. This includes
no
retroactive evidence of transfusion-related AIDS, in the context of
totally unprotected blood banks, preceding 1977.
If there were accidental or intentional infection that had taken place in
during the hepatitis study, stored blood samples could also have been
contaminated at will, to obscure that fact.
Regardless of the necessarily-suspect claim of HIV upon entry into the
cohort, the huge burst of seroconversion matching precisely the
recruitment period is clear.
These statistical peculiarities suggest an unnatural, large-scale,
simultaneous mass-infection that seeded HIV into the gay community of San
Francisco.
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 22
4.3 Unreasonable Study
Size/Selection
The government's criteria for choosing its hepatitis study participants
and its sample sizes ought to seem questionable, in light of the basic
rules of sampling.
The 6875 men in the SFCCC represent between 1 in 8 and 1 in 10 all the gay
men in San Francisco, according to the Lemp estimates. Was such a large
percentage of the gay population really necessary for this study?
If the government had chosen a sample of 1000 gay men to study, its margin
of error in representing the total population would be 3.1% (95%
confidence interval).
If they had chosen a sample of 3438 men (half the size of the SFCCC), the
margin of error would be about 1.7% (95% confidence interval). By
doubling the size of the sample, to 6875 men, reduces the margin of error
only to 1.2%. This would represent a classic mistake in choosing sample
size, by doubling the size and expense of your study, in return for only a
slight extra return in accuracy.
This error would be compounded, however. One of the cardinal
rules of
sampling is that you must select a sample that is representative of the
larger population that you wish to study. The assumption for your
margin
of error, as calculated above, depends completely on this. You cannot
have "sampling bias", for example favoring young over old, rich
over poor,
urban over rural, etc, based on your method of picking study participants.
The SFCC was exclusively chosen from urban gay males, by way of a VD
clinic. For monitoring purposes, the study would not have
been
representative of the general public, or of the general gay community, or
even of the general gay urban male population. It included no
heterosexuals, no women, no lesbians, no rural or small town gay males,
and few urban gay males having more average sexual habits.
For "monitoring" purposes, what would be the point? So that
it could be
announced, with highest possible degree of accuracy, that in order to
avoid hepatitis, you should avoid the obvious factors of having thousands
of sex partners, and the extensive use of dangerous drugs? To
announce
that people who had already-known risk factors for hepatitis were indeed
getting lots of hepatitis?
Furthermore, these hepatitis studies involved not only the full 6.875 gay
men in San Francisco, but also roughly 9000 gay men in New York City
[13].
The most seemingly legitimate reason for the large screening program might
be to identify a subgroup of high-risk men that had never been exposed to
hepatitis B. Such men were needed as suitable subjects for the
hepatitis
vaccine trials.
Section 4 HIV Correlation to Non-vaccine Participation ----------- Page 23
However, even this does not quite make sense, because 3 to 5 times as many
gay men were screened and found to be negative for hepatitis, than were
actually used in the vaccine programs. In NY, about 10,000 men
were
screened, of which 3200 were HBV negative, of which only 1090 were
enrolled for the vaccine tests. In San Francisco, 6704 were screened,
of
which 1676 were HBV negative, of which only 359 were enrolled for vaccine
tests. Detailed questionnaires about sexual habits were issued
after the
vaccine selection was made [18]. It would have been
possible to screen
far fewer men in order to find the necessary vaccine trial participants.
It seems implausible that the government had an outpouring of concern for
the health of promiscuous urban gay males, relating to a common disease
(hepatitis) that involved comparatively few fatalities. When the fatal
AIDS epidemic began spreading like wildfire in the gay community, the
government appeared to be relatively unconcerned, for a long period of
time.
It also does not appear likely that subjects were chosen for hepatitis
studies merely to benefit those who were at highest risk for hepatitis.
Alaskan Eskimos, including the Dena' Ina Tribe, were also chosen for
hepatitis vaccine experiments [24]. They also alleged that they
suffered
health effects. Among their complaints was a charge that they were
among
the lowest risk for hepatitis, and were chosen merely for use as human
guinea pigs, as an expendable population.
The selection of such a large and biased population for the hepatitis
experiment was not consistent with sound rules of sampling, but would have
been consistent with an intent to infect an "undesirable"
population.
Section
5 Epidemiological Anomalies ------------------------------ Page 24
5 Epidemiological Anomalies
The following sections discuss various reasons why the early AIDS epidemic
does not fit a reasonable epidemiological model.
5.1
Unreasonable Delay of HIV in IV Drug Community
According to the city of San Francisco [5], the total number of AIDS cases
in the city, as compiled in 1999, showed about 79% gay males, 11% IV
drug-
using gay males, and 7% heterosexual IV drug users. Among
the IV drug
users, gay males were greater than half.
Because HIV is a blood-borne virus, crossover of HIV infections into the
gay and straight IV drug community should have been rapid. Lemp
estimates
that 1120 gay males in the city were infected as early as 1979. Yet
Lemp
claims a 0% rate of HIV infection among drug users as late as 1981.
After even 100 HIV infections, the probability would be 99.9998% that at
least one of those gay males would have been a drug user.
Furthermore, the infectivity rate of HIV due to a contaminated needle is
nearly as high as the infectivity rate of unprotected anal sex [16][17].
Once that crossover occurred into the IV community, it should have spread
quickly.
Lemp also estimated that 3% of the IV drug community would become infected,
per year. Thus, the delay of at least 3 years (from 1978 to 1981) for
crossover to have occurred in the IV drug users defies normal
epidemiological expectations.
5.2 Anomalies Revealed by Computer Modeling
It might make intuitive sense to some observers that there are peculiar
aspects to the manner in which the AIDS epidemic unfolded.
Throughout the 1970s there was no awareness of any such problem as AIDS,
in spite of a supposed presence of HIV in human beings, as early as 1930.
In the early 1980s, AIDS suddenly exploded with pronounced visible effect,
almost simultaneously in far-flung locations round the globe: in Africa,
in Haiti and the Caribbean, in Europe, in North America.
Retrospectively diagnosed AIDS cases from earlier in the 1970s exist at
best anecdotally, in small handfuls. In some cases, even the anecdotes
are subject to questions about reliability.
Epidemics cannot be modeled with great precision, because there are many
variables that are complicated, or unknown, or unpredictable. However,
computer modeling can determine if there are profound inconsistencies in
terms of parameters such as time, numbers of people infected, infectivity
rates per sexual contact, numbers of sexual contacts, etc.
Section 5 Epidemiological Anomalies ------------------------------ Page 25
Appendix E gives the details of an analysis using epidemic modeling
software that was developed by the author (a software engineer of more
than 25 years experience). Included in this discussion are
demonstrations
that the software accurately duplicates real-life experimental results, as
well as conforming with theoretical, mathematical testing.
The major conclusions of this analysis are as follows:
* The rate of HIV infection in the San Francisco Hepatitis B Vaccine
Cohort is far higher than what could be reasonably expected
* Full-blown AIDS cases should have been evident many years earlier in
San Francisco, based on the numbers of individuals infected in
the early
1980s.
* In order to produce the levels of new infections seen per year in
San Francisco, it would have required nearly 2000 infections to
have
existed as early as 1976- a time when virtually no HIV has been
retrospectively discovered in the city.
* The numbers of men that would be required to appear suddenly in the
late 1970s, in order to account for the subsequent level of HIV
growth,
is far more than can be reasonably accounted for by natural
explanations such as vacationing in high-risk areas.
* Models attempting to explain the HIV growth curve by postulating
variable rates of HIV infectivity are also inadequate.
The number of new HIV infections that can occur in a given year depends
heavily on the size of the existing pool of already-infected persons.
If
you live in a city where only a handful of persons are infected, your odds
of coming in contact with those few people is very small. If many
thousands of people are infected, then your odds of encountering an
infected person, and thus becoming infected yourself, are much greater.
This is why an epidemic "gathers steam" as it progresses, producing
new
cases per year at a faster pace, as more people become infected.
Similar to the adage that it "takes money to make money", you could
also
say that it "takes infections to make infections". The notion
that small
handfuls of infected people could spark a sudden explosion on the scale
that was seen in the early 1980s, is profoundly inconsistent with existing
knowledge about HIV infectivity rates. No matter how promiscuous
those
few individuals or their partners might have been, they could not produce
thousands of new cases within a span of a few years, even if they had
tried to do so.
Section 6 Unreasonable Approval of the Vaccine ------------------- Page 26
6 Unreasonable Approval of the Vaccine
The commercially-made hepatitis B vaccine was considered as "safe,
immunogenic, and efficacious" in a Sept. 1982 report by Dr. Don Francis
of
the CDC [15], without regard to the fact that gay men in the trials had
started to become infected with an unknown, new disease, starting in 1981.
Quick approval of the vaccine for general use was not prudent. Even if
the vaccine had no connection to AIDS, the researchers would have had no
way to be certain of that fact, until 1984, when HIV was discovered and
could be detected in blood products.
Researchers were already well aware by the early 1970s of the existence of
"slow" viruses [23].
For example, the visna virus, a sheep retrovirus with a long incubation
period, had been discovered as early as 1949 (Fields, Virology, Chapter 55).
Scientists were also well aware of the dangers for vaccine contamination.
In an earlier vaccine fiasco, a potentially cancer-causing monkey virus
(SV-40) had contaminated vaccines in the 1960s and was injected into
millions of people [22].
Alaskan Native-Americans also claim to have been harmed by hepatitis
vaccines[24]. The Yupik Eskimos and other Alaskan tribes were also used
in hepatitis vaccine experiments. In a 1990 Council meeting, the Dena'
Ina Tribe's Health Committee declared an "almost total loss of
confidence"
in the U.S. vaccine programs because of a wide variety of health problems,
including AIDS-like symptoms, following hepatitis vaccinations.
In 1985, Dr. Don Francis also painted a rosy picture of the outcome of the
hepatitis experiments on Alaskan natives, again calling them "safe,
immunogenic, and efficacious" [25].
If the approval of the vaccine was not simply motivated by profit, at the
expense of safety, then there is at least one other possible explanation.
Perhaps some scientists were not concerned that test subjects were
becoming infected, because they knew that the gay men had been infected
intentionally with HIV during the experiment. This may be harsh
speculation, but it is justified by the other wise inexplicable
irresponsibility entailed in the premature approval of the vaccine, in the
context of the circumstances and the state of knowledge that existed at
that time.
Section 7 Historical Context ------------------------------------- Page 27
7 Historical Context
The statistical analysis cannot be complete without reference to the
political atmosphere in the late 1960s and the 1970s.
In a 1969 Congressional appropriations hearing for the Department of
Defense, a Pentagon official named Donald MacArthur, a biological warfare
expert, stated, "Within the next 5 or 10 years, it would probably
be
possible to make a new infective micro-organism which could differ in
certain important aspects from any known disease-causing organisms.
Most
important of these is that it might be refractory to the immunological and
therapeutic processes upon which we depend to maintain our relative
freedom from infectious disease." [9] The proposed budget
implied that
this feat could be accomplished for the relatively modest sum of $10
million.
In the late 1960s, President Richard Nixon publicly renounced germ warfare,
except for "defensive research." In 1971 he ordered a large
part of the
army's biological warfare unit at Fort Detrick, Maryland, transferred over
to the nearby National Cancer Institute (NCI), where Dr. Robert Gallo
would later discover the AIDS virus (HIV) in 1984. With the transfer of
the biological war unit to the NCI, the army's DNA and genetic engineering
programs were coordinated into anti-cancer and molecular biology programs.
It is quite possible that military biowarfare research could have
continued under the guise of legitimate cancer research [9]
The Russians also signed the Biological and Toxin Weapons Convention in
1973, but immediately set up Biopreparat, a huge program for biowarfare
research. Only in the late 1990s did the Yeltsin government admit to
the
existence of this secret program, which astonished American scientists
with its scope, involving some 40 facilities. The Soviets embarked on
this program in large part because they had believed that the United
States had not ended its bioweapons program, but had simply hidden it away.
A British intelligence officer recalled, "The notion that the Americans
had
given up their biological weapons program was thought of as the Great
American Lie." [26].
As far back as the 1950s, the United States maintained the ability to kill
and incapacitate targeted people with biological weapons (see "In Search
for the Manchurian Candidate", John Marks [27]). The Technical
Services
Staff (TSS) of the CIA paid the Army Chemical Corp's Special Operations
$200,000 per year in return for operations systems to infect enemies with
disease (Chapter 5, [27]). Dozens of germs and toxins were
maintained
for killing purposes.
Specific instances of assassination efforts using poisons and diseases are
documented, targeting various figures including Fidel Castro and
Patrice
Lumumba of the Congo. A Newsday article reprinted in the Boston
Globe(1/
9/77) reports that CIA operatives received swine flu virus at a CIA
biological warfare training station, and then attempted to spread the
virus to Cuban pigs. Numerous other allegations of biological assaults
against Cuban crops, livestock, and civilian populations are recorded.
Section 7 Historical Context ------------------------------------- Page 28
Did the U.S. every really stop biowar activities, as Nixon claimed?
The
Congressional Church Committee hearings in the mid 1970s explored abuses
in the CIA, and revealed that millions had continued to be spent on
unauthorized biowar research.
Former CIA Director William Colby testified concerning a device called a
"non-discernible microbioinoculator", which was designed to
deliver fatal
injections of toxins, in such a way that could not easily be detected in
an autopsy [27]. The CIA's apparent goal was to develop various ways of
killing that would leave no trace. It was also revealed
that the CIA
had stored enough shellfish toxin to kill a half-million people, an amount
admitted to be far in excess of research needs.
Colby wrote in his memoirs that his admission about the
"microbioinoculator" had "blown off the roof", and led to
his immediate
dismissal. President Ford replaced Colby with future President George
Bush, who gained a reputation as a staunch defender of CIA secrecy.
Undertones of violence existed in the Nixon administration, proven by his
taped remarks urging the beating of anti-war protestors, and by G. Gordon
Liddy's admission in his autobiography of discussions contemplating the
murder of political columnist Jack Anderson [28].
The Chicago Tribune published transcripts of Nixon's Oval Office remarks
about gays and other minorities. Nixon openly defamed Mexicans,
blacks
and Jews, saying Mexicans were prone to steal; blacks lived like
"a bunch
of dogs"; and homosexuals "destroyed" strong societies.
Nixon spoke of
the historical need for the Catholic Church to "clean out" its
homosexuals.
He admired societies that tried to eliminate homosexuals, saying, "Let's
look at the strong societies. The Russians. Goddamn, they root `em out.
They don't let `em around at all." Deploring the alleged takeover
of San
Francisco by "fags", Nixon proclaimed, "...I can't shake hands
with anybody
from San Francisco."
The climate of hatred against gays intensified in the 1970s with thousands
of homosexuals coming out of the closet with unprecedented political
demands. It is easy to imagine that they might have been vulnerable to
covert government-sponsored medical experiments, similar to those secret
radiation experiments that had been conducted on unsuspecting citizens
during the Cold War years, up to the year 1974, when the government's
investigation of the records documenting these crimes ended.
During the 1970s, there was extensive animal retrovirus experimentation
undertaken as part of Nixon's "War on Cancer". Animal
viruses were
transferred between species and manipulated genetically. As a result,
new
cancerous and immunosuppressive diseases were produced experimentally.
Could the AIDS virus have arisen from this dangerous and unprecedented
experimentation? Is it possible that anecdotal cases of pre-1960s
AIDS
in Africa were misdiagnosed, or might have represented false positives, or
contaminated blood samples? Could such cases have been contrived as a
"cover-up" in order to discredit research pointing to a man-made
origin
of the AIDS epidemic, in vaccine programs of the 1960s and early 1970s in
Africa?
Section 7 Historical Context ------------------------------------- Page 29
In the 1970s Don Francis and Max Essex (later to become top scientists in
AIDS) experimented extensively with feline leukemia virus (FELV), an HIV-
like retrovirus that produced a disease in cats, similar to human AIDS.
Early in the AIDS epidemic, both scientists suspected that AIDS might be
caused by a retrovirus, because the new disease was so reminiscent of
these earlier studies. [30] .
No human retrovirus was known until 1978 when Robert Gallo discovered a
retrovirus that caused a rare type of human leukemia. In the early
1980s,
a second leukemia virus was discovered by Gallo. These human
"T-cell"
leukemia viruses were termed HTLV-1 and HTLV-2. When Gallo discovered
HIV
in 1984, he initially called it "human T-cell leukemia/lymphoma
virus", or
HTLV-3. Later, the term "human immunodeficiency virus".
(HIV) was
substituted, which represented only a change in name.
After Don Francis completed his work with the HIV-like cat retrovirus, he
joined the Centers for Disease Control and headed the hepatitis B vaccine
experiments, using gay men as guinea pigs in San Francisco and other
cities (the very same experiments discussed throughout this document) [31].
This document has demonstrated a statistical correlation between the
government-sponsored hepatitis experiments and the outbreak of HIV in the
gay male volunteers. The historical context reinforces the concern that
the correlation might have resulted from intentional infection of these
men. There is proof of extreme bigotry in high office.
There is a
proven record of intrigue, deception, political corruption, unauthorized
experimentation, and use of human guinea pigs. There was a great deal
of
relevant scientific research which suggests that it would have been
possible for our government to have either discovered or created HIV,
before the hepatitis trials began. This combination of circumstances
makes the requirement to reinvestigate the hepatitis experiments all the
more compelling.
Section 8 Conclusions ------------------------------------------- Page 30
8 Conclusions
In the early 1980s, gay men who were given experimental hepatitis B
vaccine showed significantly higher rates of HIV infection, compared to
other gay men of equally high risk for HIV infection, in the SFMHS group.
The odds for these differences being due to random chance alone are
extremely small, by some measures in a range of one-millionth of one-
millionth.
The justification for saying that the gay men in the SFMHS group were of
equally high risk is based not merely on their characterization as such,
in the Lemp study, or merely on the fact that the men were chosen from
high-risk tracts within San Francisco. It is based on comparison of HIV
growth patterns within the two groups, which were highly similar for many
years in the study period. It is also based on the fact that the SFMHS
group "caught up" and even surpassed the vaccine group in HIV
infection,
in spite of the "head start" that the vaccine group had in the
early years.
This suggests that the non-vaccine SFMHS group may have been even higher
risk. Therefore, the vaccine group should by rights have shown
noticeably
lower prevalence of HIV, much less the higher levels that they actually
showed, compared to the SFMHS.
The SFCCC group showed even stronger evidence of being lower risk in
comparison to the "control" group to which they were compared,
which
consisted of other early AIDS victims in the city. The higher initial
rate of AIDS in the SFCCC showed that mere participation in the hepatitis
studies, in any capacity, had a statistically significant association with
AIDS diagnosis, beyond what could be explained by risk alone.
In the past, the high rate of HIV/AIDS among men in the hepatitis study
has been attributed to their "high risk" status, or to random
chance, or
else it has been denied that the rate of HIV/AIDS was in fact higher than
that of the general population. This analysis has shown those rather
simplistic explanations to be inadequate.
With these significant possibilities effectively ruled out, it is still
conceivable that one might try to find alternative, benign explanations.
However, the search for benign explanations might begin to strain the
imagination, no less than the thought of contaminated vaccines, or
intentional infection by some covert, unknown means.
Because the production of the vaccine involved use of pooled blood from
high-risk gay men, the possibility of HIV-contaminated vaccines has
sometimes been imagined as a tragic accident.
However, vaccine (Heptavax-B produced by Merck Sharp & Dohme) was
inactivated using three steps: pepsin, urea, and formaldehyde (formalin)
(Francis et al., 1986) (see reference [6]):
"In this study, we demonstrate that each of the three
inactivation
steps used in the manufacture of Heptavax-B
independently will
inactivate the infectivity of high-titered
preparations of the
AIDS virus"
Section 8 Conclusions -------------------------------------------- Page 31
If this claim is correct, then not even the use of pooled blood from gay
men should have caused vaccine contamination.
Furthermore, samples of the vaccines were tested retroactively for HIV.
It was claimed that no HIV was detected in the vaccines.
An important question is why there appears to be unusually high rates of
HIV/AIDS associated with mere participation in the hepatitis studies, even
for men who received no vaccine. These differences clearly do not seem
attributable merely to a higher risk status.
The hepatitis trials in New York City would be worthy of similar analysis.
In 1980, the rates of HIV infection among vaccine trial participants were
in the range of 20%, even higher than the rate in San Francisco [9].
All possible explanations deserve consideration and investigation, even
the most politically sensitive explanations. Evidence
suggests that
accidental infection should have been unlikely, yet a nearly undeniable
statistical correlation remains to the vaccines and the hepatitis studies.
To the degree that accident can be ruled out, the possibility for
intentional infection is strengthened.
Significant numbers of people do not approve of homosexuals, and would not
object to their removal from society. Historically,
"undesirable"
populations, such as prisoners, mentally retarded and others were used for
unethical experimentation. Even if no such criminal malice
existed in
the hepatitis studies, the best way to establish that fact would be to
treat the possibility seriously, and investigate it thoroughly.
If HIV is an old virus, present long before the 1970s, then this would
only make it easier to suppose that the virus could have been discovered
without public announcement, and then tested on an undesirable population.
In the 1990s, horrific details of the government's Cold War
experimentation during the 1940s up to the 1970s came to light.
Thousands
of covert radiation experiments were performed on children, the mentally
ill, hospitalized patients, pregnant women, Native Americans, and other U.
S. citizens. Thus, during the 1970s, it would not have been
unprecedented
for government scientists to experiment on gay men, the most hated
minority in America.
Our government should long ago have made full disclosure as to the fates
of the men who volunteered for these experiments. Exactly how many men
who received the experimental vaccines died of AIDS, and in what years?
Section 8 Conclusions -------------------------------------------- Page 32
Why has the scientific community failed to notice these profound
statistical correlations? It is understandable that these realizations
might have escaped the notice of laymen, but they were well within the
ability of trained scientists to discern easily. The fact that
they did
not, even after allegations of a vaccine link, is evidence that these
allegations have not been taken seriously enough.
It is essential for the scientific community to explain the high rates of
HIV/AIDS in the hepatitis study members.
There is no suggestion being made here that starting a man-made epidemic
would have been anything other than an act of madness. It would
be
madness unprecedented in scale, but not historically unprecedented in its
recklessness or cruelty. It was little short of madness how the Reagan
administration essentially ignored the new disease, tried to slash the CDC
budget, and ordered the Surgeon General not to mention the word
"AIDS" in
public. If a government was capable of ignoring a new disease to such a
degree, it is only a short step further to infer that they might have been
foolish enough to precipitate the disease. It may have been an act of
irrational religious fervor. Perhaps it was imagined, or foreseen, that
the virus would be confined largely to risk groups. Perhaps the
perpetrators simply did not care about any citizens who did not live
according to strict Christian sexual morality. Perhaps the
perpetrators,
even if Americans, were infiltrated by foreign enemies who might have
wished the entire country's destruction.
It is not a necessity for this study to explain the exact nature of the
madness, but merely to document why an act of madness has very probably
occurred.
The connection between the origin of the AIDS epidemic and the government
experiments has been dismissed by the AIDS epidemic. However, the
statistical analysis presented here demonstrates a definite correlation.
A reopening of the entire matter is in order.
The purpose of this document is not to cast final judgement concerning the
origin of AIDS in the gay community. It is to demonstrate that
there is
a strong and suspicious link, which has no obvious explanation, between
the outbreak of AIDS and the government-sponsored hepatitis studies. The
purpose is to call for investigation of this important question.
Section 9 Refuting Counter-Arguments ---------------------------- Page 33
9 Refuting Counter-Arguments
This section will review and refute various attempted criticisms
concerning the link of AIDS and the vaccine studies.
* Perhaps the men in the vaccine trial had sex with each other, and
infected each other
It is true that the men signing up for a trial might be friends
who also
have sex with each other. However, this factor could be
equally true
for the SFMHS (non-vaccine group), as for the SFHBVCS (vaccine
group).
The sponsors of a vaccine trial would not publish lists of
everyone
involved in the trial. Fraternizing among participants
would likely be
limited to small groups of friends who might have known each
other prior
to the trial. It would not likely be intermingling among
the entire
cohorts.
The men were also chosen because of a high-risk profile, meaning
that
they were regular clients of institutions such as bars and baths.
They
would have been prone to sexual encounters with large number of
the
general gay male population, not merely with a small set of
friends.
* Perhaps the men in the vaccine group became complacent because of feeling
"protected" by the vaccine, and started practicing more
risky behavior
The analysis already demonstrated that risk behavior was if
anything,
even higher in the "control" (non-vaccine) group that
was used for
comparison.
The vaccine would have protected only against hepatitis, even if
it
worked. It would not have protected against a host of
other venereal
diseases, including herpes, syphilis, and gonorrhea.
The sponsors of
the trial would have been remiss if they had not explained this
to the
men involved.
* AIDS has a 10-year incubation period. Therefore, the men must have
been infected prior to the start of the trial, because most
became
infected in less than 10 years.
The Lemp study was specifically measuring the dates of initial
HIV
infection, and not the development of full-blown AIDS.
The incubation
period is not relevant, in this context.
The fact that we are looking at HIV seroconversion rather than
"AIDS"
also eliminates another argument used in the past against efforts
to
suggest a vaccine connection: that a reaction to the vaccine
challenged the immune system, and hastened the development of
AIDS.
Here, we are looking only at virus exposure, not disease symptom
development.
In the case of the SFCCC, where the analysis was examining
initial rates
of AIDS diagnosis, estimates of the HIV seroconversion dates were
Section 9 Refuting Counter-Arguments ---------------------------- Page 34
provided by the Rutherford study. The dates were heavily
clustered
around the dates of recruitment into the hepatitis study.
* Perhaps the men receiving the vaccine were simply monitored more
closely, and their HIV status was detected more quickly than for
men who
did not receive the vaccine
For the Lemp study, this line of argument clearly does not apply.
Both
groups, vaccine and non-vaccine, had stored blood samples that
were
taken annually and later examined for HIV.
* Perhaps the men in the vaccine group were actually higher risk than
the non-vaccine group
There are degrees of "high-risk". Simply because
the non-vaccine group
was characterized in the Lemp study as "high-risk", it
does not
necessarily mean that they were equally as high risk as the men
in the
vaccine group. However, this analysis is not relying merely
on
subjective descriptions. It assesses their risk level by
comparing
their actual patterns of HIV growth. The non-vaccine
control group, the
SMHS appeared, if anything, to be even higher risk for HIV
infection.
Furthermore, the SFHBVCS vaccine group was chosen only from men
who had
no previous exposure to HBV (hepatitis B virus), and were chosen
from
men who seemed in good health at the outset of the trial (else,
it would
have invalidated the vaccine trial results). HBV was
sexually spread
and epidemic among gay men, which was part of the reason that gay
men
were chosen for the vaccine trial, in the first place.
One would
expect that there would be a significant correlation between HBV
exposure and HIV exposure. The exact extent of this
factor is
difficult to measure, but it is nonetheless an additional,
qualitative
reason to believe that the men in the vaccine trial might have
been
actually lower in overall risk for previous HIV exposure, at the
start
of the vaccine trial.
Dr. Lemp's study itself states, "Although these vaccine
cohort members
were recruited from sexually transmitted disease clinics, they
represent
lower-risk since none of the cohort members were seropositive for
hepatitis B virus at time of recruitment." Dr.
Paul M O'Malley,
Project Director of the SF Dept of Health AIDS research study,
also
concurred, "Their blood had not in 1980 shown signs of
infection with
hepatitis B, which can be spread through sexual activity.
The subjects
were therefore assumed to be less sexually active than other SF
clinic
visitors." If it is necessarily true that the men were
less active,
perhaps it would be more accurate to say that they had at least
beaten the odds, in terms of encountering infected partners.
* HIV growth is an exponential function. This can magnify differences
over time between groups, compared to what you would see with a
flat
rate of infection, such as by exposure to carcinogens.
It has been demonstrated that the hepatitis/vaccine study groups
had, if
anything, even less risk for HIV than the "control"
groups used for this
Section 9 Refuting Counter-Arguments ---------------------------- Page 35
comparative analysis. All of the men in both groups are from the
same
geographical area, visiting the same limited number of bars/
baths in
the city, showing similar behaviors. It is therefore
justified to treat
the hepatitis/vaccine study groups as being at least equal in
risk,
with any error in that assessment being weighted against a
conclusion
that implicates a correlation to the hepatitis study.
As long as the characterization of equal risk levels is accurate,
then
the earliest AIDS cases, which represent an essentially random
sample
taken from the total pool of men, should not disproportionately
reflect
either of the subgroups.
* Perhaps the statistical sample size is too small in order to draw
conclusions
This objection is not applicable to the probability calculations
of "n
or more" in this document. Similarly. if a coin is
flipped with 30
heads in a row, it is not "too small" of a number of
trials in order to
draw a confident conclusion. This point is discussed
further in the
general statistical primer, Appendix F.
Where "sample size" becomes an issue is in the question
of whether the
SFMHS group is sufficiently large in order to use as an estimate
of
the HIV rate for all high-risk men.
The Lemp study in fact uses SFMHS for the whole gay male
population of
the city, not distinguishing between "higher/lower"
risk. The study
states, "Since the SFMHS is a population-based probability
sample, its
seroprevalence estimates are likely to be representative of HIV
seroprevalence for homosexual and bisexual men in San
Francisco."
As a double-check, we can take the year of 1982, and do another
calculation based on a reversed hypothesis: suppose that
the 40% rate
of HIV infection in the SFHBVCS group, rather than the 23% HIV
infection
rate of the SFMHS group, was the "real" rate of HIV for
high-risk men.
What would the probability then be that the 799 men in the SFMHS
might
show their 23% rate of HIV infection, by random chance alone?
Might
this be within the bounds of normal possibility?
In this case, our subgroup size is 799 and the total group size
is still
35000. The sample size (the number of men that we expect to
have HIV in
1982) is 40% of 35000 =14000 . Of these 14000, the
SFMHS group
represents (23% x 799) = 184 men. What we want is the
probability
that 184 or fewer would be in the SFMHS group. Our program
calculates
"or greater", so we can simply calculate the odds for
"185 or greater",
and subtract this from one.
Using the program, this produces an answer of zero, or
essentially no
chance (too small of a value to represent). Thus, it
does not matter
which end of the spectrum that we choose as representing HIV
prevalence in the overall gay population. The difference
between the
vaccine and control groups in either case is more than what we
could
expect by random chance.
Section 9 Refuting Counter-Arguments ---------------------------- Page 36
* Perhaps the data concerning HIV infection rates are not reliable
HIV antibody tests are more than 99% accurate [7].
The false-positive
rate for the Elisa HIV antibody test is only 1 to 5 per 100,000
assays.
The false negative rate is only 1 in 450,000 to 1 in
660,000 [8] . It
can be assured that these tests must be reliable, because they
have had
long use in protecting our nation's blood supply.
These error rates have no significant impact on the computed
probabilities.
Furthermore, any false-positives or false-negatives would have
tended to
affect both the vaccine and non-vaccine cohorts in equal
proportions,
thereby tending to cancel out, in any case.
In the absence of better data for the period prior to 1982, it is
justified to use the Lemp data as representing the best of what is
currently available. The conclusion derived from this data would
also
represent the best conclusion that could be drawn at this time.
Section 10 About the Author -------------------------------------- Page 37
10 About the Author
I (Tom Keske) am a gay/AIDS activist in the Boston area, originally from
Ohio. I have been a data communications software engineer for 25 years,
B.
S.(with honors) in Computer Engineering, Case Western Reserve University,
`74. Education includes modest background in statistics, strong in math
and programming. I am an internet activist, writing frequently about
AIDS
and its origins. I am age 48, and in a committed relationship with a
lifetime partner of 28 years, Daniel.
Academic honors include National Merit Scholar and National Honor Society.
I was "survey statistician / computer programmer" responsible for
telecommunication software at the Bureau of Census, in Washington, D.C.,
from 1974 - 1979, where I received an "outstanding employee" award.
I was
President of the Board of Directors, Bradbury Park Condominiums, in the
Maryland suburbs of Washington. In 1979, I relocated to the Boston
area,
where I am currently employed as a senior staff engineer for a Fortune 500
vendor of communications equipment. Between 1980 and the present, I
have
worked on development of statistical multiplexors, intelligent matrix
switches, a data PABX, Ethernet bridges, multiprotocol routers and
cable
routers.
I have also worked in the area of data encryption, and have performed
statistical analysis of encryption methods. In the late 1990's, I
successfully defended a self-designed encryption program in the face of a
$1000 reward posted to break the code. During college, I had faced
discrimination at the National Security Agency during a job interview at
Fort Meade, with polygraph questions about homosexuality.
I am a supporter of progressive causes, such as in past participation as
an Amnesty International "Freedom Writer".
My activities instilled a keen awareness of political corruption and abuse,
which led to an interest in research and criticism of the CIA and
intelligence establishment.
As a gay activist, I engaged in public vigil/hunger strike in front of the
Massachusetts State House in support of the gay civil rights bill, which
passed immediately afterward, after more than 15 years of effort. There
was modest coverage in smaller, local papers. A state legislator said
that
the protest was lending moral weight to the cause. I have also engaged
in
civil disobedience protests, outside the Supreme Court, during the first
March on Washington, and in various cities including Raleigh, North
Carolina and Atlanta, Georgia.
I am health-conscious, and for hobbies enjoy swimming, hiking, bicycling,
and chess.
Thomas R. Keske, 205 Warren St., Randolph, Mass.
02368
email: tkeske@mediaone.net
(781) 961-1571
Section 11 References -------------------------------------------- Page 38
11 References
[1] The River: a Journey to the Source of HIV and AIDS, by Edward
Hooper (former BBC Africa correspondent)
(Penguin; Little, Brown,
New York, 1999)
[2] HIV RESEARCH SECTION, SAN FRANCISCO DEPARTMENT OF PUBLIC HEALTH,
http://www.dph.sf.ca.us/php/hivresearch.htm
[3] Lemp GF, Payne SF, Rutherford GW, Hessol NA, et al:
Projections of AIDS morbidity and mortality in San
Francisco,
JAMA 1990 Mar 16;263(11):1497-501
PMID: 2407871; UI: 90172481
The abstract of the Lemp 1990 study reads as follows:
Abstract: To develop a model for predicting acquired
immunodeficiency
syndrome (AIDS) morbidity in San Francisco, Calif, through
June 1993, we
combined annual human immunodeficiency virus seroconversion
rates for
homosexual and bisexual men and for heterosexual
intravenous drug users
with estimates of the cumulative proportion of the
population with AIDS
by duration of human immunodeficiency virus infection and
with estimates
of the size of the at-risk populations. We projected AIDS
mortality by
applying Kaplan-Meier estimates of survival time following
diagnosis to
the projected number of cases. The median incubation period
for AIDS
among homosexual and bisexual men infected with the human
immunodeficiency virus was estimated to be 11.0 years
(mean, 11.8 years;
95% confidence interval, 10.6 to 13.0 years). The model
projects 12,349
to 17,022 cumulative cases of AIDS in San Francisco through
June 1993,
with 9,966 to 12,767 cumulative deaths.
[4] University of San Francisco (home page, includes links to faculty):
http://www.ucop.edu/srphome/uarp/
[5] San Francisco HIV/AIDS Statistics as of Nov. 28, 1999,
http://www.sfaf.org/aboutaids/statistics/index.html
[6] Francis DP, Feorino PM, McDougal S, et al. The safety of the hepatitis
B vaccine. Inactivation of the AIDS virus
during routine vaccine
manufacture, JAMA 1986 Aug 15;256(7):869-72.
[7] Centers of Disease Control NAC, from Guide to Information and
Resources on HIV Testing, 1997
[8] CDC FAQ: http://www.cdc.gov/nchstp/hiv_aids/pubs/faq/faq15.htm
Section 11 References -------------------------------------------- Page 39
[9] AIDS and the Doctors of Death: An Inquiry into the Origin of the
AIDS Epidemic (1988), and Queer Blood: The Secret
AIDS Genocide Plot
(1990), by Dr. Alan Cantwell, Jr., Aries Rising
Press, PO Box 29532,
Los Angeles, Calif. 90029 (AlanRCan@aol.com)
[10] Jaffe HW, Darrow WW, Echenberg DF, et al.: The Acquired
Immunodeficiency Syndrome in a Cohort of
Homosexual Men, A Six-Year
Follow-up Study, Annals of Internal Medicine.
1985;103:210-214
[11] Centers For Disease Control, Morbidity and Mortality Weekly Report,
Sept. 27, 1985 / Vol 34 / No. 38
[12] Moss AR, Bacchetti P, Osmond D et al: Incidence of the Acquired
Immunodeficiency Syndrome in San Francisco,
1980-1983, Journal of
Infectious Diseases, Vol 152, No. 1, July 1985
[13] Koblin BA, Morrison JM, Taylor PE, et al.: Mortality Trends in a
Cohort of Homosexual Men in New York City,
1978-1988, American Journal
of Epidemiology, Vol 136, No. 6
[14] Rutherford GW, Lifson AR, Hessol NA et al: Course of HIV-1
infection
in cohort study of homosexual and bisexual men:
an 11-year follow up
study, Br Med, Vol 301, Nov 24, 1990
[15] Francis DP, Hadler SC, Thomppson SE, et al: The prevention of
hepatitis
B with vaccine. Report of the Centers for
Disease Control multi-center
trial among homosexual men,, Ann Intern Med
1982 Sep;97(3):362-6
PMID: 6810736, UI: 82282328
[16] Kaplan EH, Heimer R, A model-based estimate of HIV infectivity via
needle sharing, J Acquir Immune Defic Syndr
1992;5(11):1116-8, Yale,
PMID: 1403641, UI: 93020182
[17] Vittinghoff E, Douglas J, Judson F, et al: Per-contact risk of human
immunodeficiency virus transmission between
male sexual partners,
Am J Epidemiol 1999 Aug 1;150(3):306-11,
PMID: 10430236, UI: 99357305
[18] Padian NS, Shiboski SC, Glass SO, Vittinghoff E:
Heterosexual transmission of human
immunodeficiency virus
(HIV) in northern California: results from a
ten-year study,
Am J Epidemiol 1997 Aug 15;146(4):350-7
PMID: 9270414, UI: 97416464
[19] Godfried JP, Hessol NA, Koblin BA, et al: Epidemiology of Human
Immunodeficiency Virus Type 1, Infection among
Homosexual Men
Participating in Hepatitis B Vaccine Trials in
Amsterdam, New York
City, and San Francisco, 1978 - 1990, Amer
Journal of Epidemiology,
Vol 137, No .8, 1993.
Section 11 References -------------------------------------------- Page 40
[20] University of Southern California,
http://hivinsite.ucsf.edu/akb/1997/01txbld/index.html#Ba
[21] Jacques JA, Koopman JS, Simon CP, Longini IM: Role of primary
infection in epidemics of HIV infection in gay
cohorts, J Acquir
Immune Defic Syndr 1994 Nov, PMID: 7932084, UI:
95017548
[22] "The Polio Vaccine and Simian Virus 40", by By T.J. Moriarty,
http://www.chronicillnet.org/online/bensweet.html
[23] "Retroviruses- An Introduction", JAMA HIV/AIDS Information
Center,
http://www.ama-assn.org/special/hiv/newsline/briefing/retro.htm
[24] "Alaska Health Issues and Indigenous Peoples" (video), Mary
Ann Mills,
Bernadine Atchison, Delice Calcote, July
1991 Arctic Village Health
Conference. These women are Activists
against medical experimentation
on Alaska Native communities.
[25] Heyward WL, Bender TR, Francis, DP et al: The control of hepatitis B
virus infection with vaccine in Yupik Eskimos
Demonstration of safety,
immunogenicity, and efficacy under field
conditions, Am J Epidemiol 1985
PMID: 3160233, UI: 85248405
[26] "The Bioweaponeers", the New Yorker, March 1998, by Richard
Preston
[27] In Search of the Manchurian Candidate, by John Marks, 1988,
Times Books, ISBN: 0-440-20137-3 Senator Edward
Kennedy said of this
expose,"John Marks has accomplished
what two U.S. Senate committees
could not".
[28] Secret Agenda, by Jim Hougan, Random House, 1984, ISBN: 0-394-51428-9.
The Los Angeles Times called this book "a
monument of research and
fact-finding". Hougan was Washington
Editor of Harper's magazine, and
helped produce the Emmy Award winning
documentary, "Confessions of a
Dangerous Man"
[29] "The Role of Robert Gallo in the Origin of AIDS", Kwame
Ingemar
Ljungqvist, http://homepage.calypso.net/~ci-15476/toa/gallo.html.
Mr. Ljungqvist is editor the Swedish scientific
journal, "Science of
the 21st Century"
[30] 11/96 "1 in 10 Talk Show" interview with Max Essex
[31] Harvard Public Health Review, "The Gathering Storm", by Sarah
Abrams
http://www.hsph.harvard.edu/review/the_gathering.shtml
(describes the career of researcher Don
Francis)
Section 11 References -------------------------------------------- Page 41
12 Acknowledgments
Much thanks to Dr. Alan Cantwell, Jr., author of "Queer Blood (1993)" and
"AIDS and the Doctors of Death" (1988) [9] for help in editing this
document and reviewing the facts. Dr. Cantwell has spent some 14 years
investigating the hepatitis vaccine experiments.
Thanks also to Billi Goldberg, San Francisco AIDS researcher/activist,
whose discussions of the 1990 Lemp study inspired this further statistical
analysis.
13 Document Reproduction
This document may be freely reproduced and distributed, with attribution.
Appendix A Demonstrating the Validity of the Statistical Approach- Page 42
Appendix A Demonstrating the Validity of the Statistical Approach
The logical approach used in this document is to compare groups of gay men
who received vaccines, or who otherwise participated in hepatitis studies,
with other groups of similar gay men who did not engage in these
activities. This type of statistical analysis is virtually identical to
how researchers compare their own test vaccine group to a control/placebo
group.
Researchers are typically trying to prove that their vaccine group showed
statistically lower incidence of the targeted disease, compared to the
control group. Or, perhaps, they might try to show that the vaccine
group
showed statistically higher levels of protective antibody response,
compared to the control group.
The only difference in this document's use of the same technique is that
the aim is to show the statistical presence of another disease, instead of
the absence of the disease that the vaccine tries to prevent. The
"control" groups are defined retrospectively, by identifying groups
of men
who were of demonstrably equal or higher risk for HIV/AIDS.
The following is an abstract of a Swiss study involving hepatitis B
vaccine. This will demonstrate how the vaccine researchers are using
very
similar statistical calculations:
"Evaluation of tolerability and antibody response after
recombinant human
granulocyte-macrophage colony-stimulating factor (rhGM-CSF)
and a single
dose of recombinant hepatitis B vaccine.
Tarr PE, Lin R, Mueller EA, Kovarik JM, Guillaume M, Jones
TC
Sandoz Pharma Ltd, Basel, Switzerland.
Recombinant human granulocyte-macrophage colony stimulating
factor
(rhGM- CSF) has been shown to augment antigen presentation
by
macrophages and dendritic cells in vitro, and to increase
antibody
responses to injected antigens in experimental animals.
To evaluate
the usefulness of rhGM- CSF as a vaccine adjuvant, 108
healthy
volunteers were randomly assigned to receive an injection
of rhGM-CSF
(n = 81) or placebo (control group; n = 27), followed by an
injection
with recombinant hepatitis B vaccine into the same site.
During the
study period of 28 days, protective antibody titers to
hepatitis
surface antigen (anti-HBs10 mIU ml-1) were observed in 11
of 81
subjects receiving rhGM- CSF, but in none of the controls
(P = 0.035).
Injections were well tolerated. A single i.m. or s.c.
injection of 20-
40 micrograms of rhGM- CSF significantly enhances antibody
responses
when given at the same site as recombinant hepatitis B
vaccination.
Publication Types: Clinical trial Randomized controlled
trial
Appendix A Demonstrating the Validity of the Statistical Approach- Page 43
PMID: 8961505, UI: 97120835"
The study involved 81 people in a vaccine group, and 27 in a placebo
(control) group, for a total of 108. In the vaccine group, 11 showed
protective antibodies, but none did in the placebo group. When the
researchers say (P = 0.035), they mean that the probability of this
outcome is 3.5%.
Using the same program in [27], we get the same result:
Subgroup size = 81
Total group size = 108
Sample size = 11
n = 11
PROBABILITY IS: 0.035144
Note that the researchers must make assumptions that are virtually
identical to what we must make in evaluating whether the vaccine was
causing HIV infection. When they try to prove that the vaccine
prevented
hepatitis B infection, they must assure that the vaccine group is at equal
risk for hepatitis, compared to their placebo group. There cannot
be
differences in age, general health, risk of exposure, etc, that might
account for the different outcomes between the two groups. The
researchers must compute statistically that the differences in
hepatitis
rates between the two groups are not merely a result of "random
chance".
There is no such thing two absolutely identical groups, but the process of
"averaging out" can mitigate the effects of small differences.
The Francis, et al study evaluated the gay hepatitis B vaccine using the
same type of analysis [15]. It estimated the effect of the
vaccine based
on about 907 men in a vaccine group , and 495 in a placebo group (this is
including other cities as well, not just San Francisco). It had
to assume
that these men were roughly equal in risk for acquiring hepatitis, just as
we have had to demonstrate equal risk of men for acquiring HIV.
The Francis study concluded that the vaccine was beneficial in preventing
hepatitis B on the strength of 56 new hepatitis infections in the control
group, versus only 11 in the vaccine group (probability = .0004, or 1 in
25000). These results are not nearly as compelling as the figures
cited
in this document linking HIV infection to the hepatitis studies and
vaccines.
Yet, the Francis figures were used to justify dispensing the vaccine to
millions of people, whose health and life would be in the balance.
It
is therefore difficult to argue that the figures in this document are not
on as solid of a scientific basis, and adequate justification to draw a
conclusion.
Appendix B Letter From Dr. George Lemp --------------------------- Page 44
Appendix B Letter From Dr. George Lemp
>Dear Mr. Keske:
Thank you for your interest in my research. The data cited appear
reasonably accurate. I assume the author looked at the published graphs
and guessed the approximate data points. My time doesn't allow me to try
to dig up the original data points, but I took another look at the graph
and the author's guesses seem reasonable (perhaps off by 1% at a few
points). Who was the author and where were these data cited? The JAMA
article would be on file at any University or major hospital medical
library in your area. JAMA is widely held by libraries and should be
available. If you have trouble finding it, please email your address and
we'll mail you a reprint.
Sincerely,
George Lemp
X-Sender: uarp@popserv.ucop.edu
>X-Mailer: QUALCOMM Windows Eudora Pro Version 4.2.0.58
>Date: Mon, 31 Jan 2000 09:38:08 -0800
>To: george.lemp@ucop.edu
>From: Universitywide AIDS Research Program <uarp@ucop.edu>
>Subject: Fwd: 1990 Study Data
>
>
>>From: "Thomas Keske" <TKeske@mediaone.net>
>>To: <uarp@ucop.edu>
>>Subject: 1990 Study Data
>>Date: Fri, 28 Jan 2000 23:24:48 -0500
>>X-Mailer: Microsoft Outlook Express 5.00.2919.6600
>>
>> Jan. 28, 2000
>>Dr. George Lemp
>>University of California
>>uarp@ucop.edu
>>
Appendix B Letter From Dr. George Lemp --------------------------- Page 45
>>Dear Dr. Lemp,
>>
>>I hope that my emailing will not impose on your time. I much
>>appreciate all the work that you have done for AIDS research.
>>I have a very brief question, and would much appreciate if you
>>could reply.
>>
>>I am trying to follow a thread on sci.med.aids, which quoted data
>>from your 1990 study, showing rates of HIV in the early 1980's:
>>
>> SFHBVCS: 1978 - 0.3%, 1979 - 4%, 1980 - 15%, 1981- 28%, 1982 - 40%,
>> 1983 - 46%, 1984 - 47%, 1985 - 48%, 1986 - 48%, 1987 - 49.3%
>>
>> SFMHS: 1978 - 0%, 1979 - 2%, 1980 - 4%, 1981- 10%, 1982 - 23%,
>> 1983 - 42%, 1984 - 48%, 1985 - 49%, 1986 - 49.3%, 1987 - 49.3%
>>
>> SFHBVCS = San Francisco City Clinic Cohort Study
>> SFMHS = San Francisco Men's Health Study
>>
>>The author said that this data was extracted from charts in the 1990
>>study, but I have been unable to locate the full text of the study:
>>
>> Lemp GF, Payne SF, Rutherford GW, Hessol NA, Winkelstein W Jr, Wiley
JA,
>> Moss AR, Chaisson RE, Chen RT, Feigal DW Jr, Thomas PA, Werdegar D.
>> Projections of AIDS morbidity and mortality in San Francisco. JAMA
1990
>> Mar 16;263(11):1497-1501
>>
>>Could you please tell me if the data above appears to be
>>reasonably accurate, or how I could obtain the full study?
>>I am asking only as an interested layman.
>>
>>Thanks very much for your time.
>>
>>Regards, Tom Keske
>>
Appendix C Error Analysis for Lemp Data Calculations ------------- Page 46
Appendix C Error Analysis for Lemp Data Calculations
C.1 Effect of Variation in High Risk Population Estimate
It might seem at first glance that the calculation could be in error if
the estimate of the size of the "high risk" gay population is too
high.
In actuality, it turns out that lowering the estimate of the total high
risk population size will work to decrease the probability that the result
could be attributed to random chance. The is because reducing the
estimate of the high risk population size also lowers the "sample
size" of
HIV+ men that we expect to draw in any one year (23 percent of the total
high-risk men, for the example year of 1982).
Below is a listing of the computed probabilities for different values of
the estimated number of high-risk gay men in San Francisco, ranging from
as many as 100000, to as few as 10000.
As it can be seen, these variations matter little in the resulting
probability:
Subgroup size = 359, Total group size = 100000, Sample size = 23000,
n=144
PROBABILITY IS: 3.3273e-13
Subgroup size = 359, Total group size = 50000, Sample size = 11500,
n=144
PROBABILITY IS: 2.98706e-13
Subgroup size = 359, Total group size = 30000. Sample size = 6900,
n=144
PROBABILITY IS: 2.58323e-13
Subgroup size = 359. Total group size = 25000, Sample size = 5750,
n=144
PROBABILITY IS: 2.40079e-13
Subgroup size = 359, Total group size = 20000, Sample size = 4600,
n=144
PROBABILITY IS: 2.14932e-13
Subgroup size = 359, Total group size = 15000, Sample size = 3450,
n=144
PROBABILITY IS: 1.78359e-13
Subgroup size = 359, Total group size = 10000, Sample size = 2300,
n=144
PROBABILITY IS: 1.21827e-13
The largest of these probabilities is roughly 1 in 3,000,000,000,000.
Appendix C Error Analysis for Lemp Data Calculations ------------- Page 47
C.2 Effect of Errors in HIV Infection Rate Figures
There could have been errors in reading the chart data from the Lemp study.
Dr. Lemp had suggested that this might have amounted to a percent or so
(Appendix B).
The following computations test the effect of errors in the probability
calculation for 1982, by reducing the number of HIV+ men in the vaccine
group, and increasing the number of HIV+ in the non-vaccine group, in
increments of 1%. Both of these adjustments work to increase the
probability that the outcome might be attributable to random chance.
The
computations range from 1% adjustments, to 3% adjustments. This was
only
an informally suggested error rate, so it is being tripled for safety,
with worst case assumed jointly for each affected variable:
* Adjusting sample size +1% and vaccine group size -1%
non-vaccine = 8400 (24% of 35000)
vaccine = 140
(39% of 359)
Subgroup size = 359, Total group size = 35000, Sample size =
8400, n = 140
PROBABILITY IS: 1.63957e-10
* Adjusting sample size +2% and vaccine group size -2%
non-vaccine = 8750 (25% of 35000)
vaccine = 136
(38% of 359)
Subgroup size = 359, Total group size = 35000, Sample size =
8750, n = 136
PROBABILITY IS: 4.00199e-08
* Adjusting sample size +3% and vaccine group size -3%
non-vaccine = 9100 (26% of 35000)
vaccine = 133
(37% of 359)
Subgroup size = 359, Total group size = 35000, Sample size =
9100, n = 133
PROBABILITY IS: 2.41677e-06
Allowing for errors of +3% in the HIV+ sample size and -3% in the
number
of HIV+ vaccine group men (jointly) has the effect of improving the
odds
that the correlation could be a product of random chance, but not to a
significant degree (worst case of roughly 1 in 400,000)
Appendix D Letter from Case Western Reserve Statistics Department Page 48
Appendix D Letter from Case Western Reserve Statistics Department
The following note from the CWRU Statistics Dept was in response to a
query that I made (appended), trying to validate the general reasoning
behind the analysis. In this query I had rephrased the question, to
avoid
biasing, as one of evaluating the safety of a "food additive"
(instead of
a vaccine) that was being tested as a possible "carcinogen"
(cancer-
causing, instead of AIDS-causing). I posed the question using the
identical numbers from the vaccine analysis, for the year 1980 in the Lemp
study, for group size, sample, size, etc. Below is the email
exchange:
From: Joe Sedransk
Department of Statistics, CWRU
Cleveland, OH 44106-7054
Mr. Keske: Your reasoning is mostly correct. The main assumption is that in
the absence of the food additive the mortality rate would be 4%; that is,
that the lab animals are "similar" to the general population (with
a mortality
rate of 4%). [I'd ask how the mortality rate of 4% was determined.] Then you
would find the probability of 54 or more deaths out of the 359 lab animals,
assuming a mortality rate for each of 4%. (There is also an assumption that
the events (life/death) are independent among the 359 animals. This would
usually be true, but should be verified.) I did a crude calculation using a
normal distribution approximation (approximating what you did) and found that
the probability of 54 or more deaths is extraordinarily small. The only
problem with your formulation is that it is 54 or more out of 359 rather than
out of 1400. I hope that this helps.
Sincerely,
Joe Sedransk
At 11:29 PM 2/4/00 -0500, you wrote:
> Jan. 31, 2000
>Dear Mr. Sedransk,
> I am an alumnus of the CWRU class of `74, in Computer
> Engineering.
> I was wondering if it would be too much trouble if you could help
> to clarify my understanding of a simple type of statistics problem,
Appendix D Letter from Case Western Reserve Statistics Department Page 49
> or if you could direct me to another resource. I am trying to
> understand how to evaluate the following type of
>problem:
> A group of 359 lab animals using a food
additive showed
> a 15% rate of cancer in a year (=54
animals). The normal rate
> of cancer, measured in a total population
of 35000 such animals,
> was 4% (=1400 animals)
>
> QUESTION: Is this a normal statistical
variation, or should
> it be judged that the food additive is
unsafe?
> It seems to me that this is similar to a problem where you
> have 35000 marbles in a bag, 359 are black and the rest white.
> If you draw a random sample of 1400 marbles, what is the probability
> of getting 54 or more black marbles, by random chance alone?
> This can be computed from the binomial distribution curve. I've computed
> the probability as 5.6 x (10 to the -17). Therefore, my conclusion is
> that the food additive should almost certainly be suspected as
> carcinogenic, and should not be approved for mass consumption. This
> question came up only as part of a newsgroup discussion (nothing related
> to business and school). We were (embarrassingly) unable to agree on the
> answer. Could you please help us to settle it?
>
> Regards,
>Tom Keske (class of `74)
NOTE: the issue of how the 4% figure was derived is explained earlier in
the document (basically, taking the HIV rate of the high-risk SFMHS group
of gay men as being representative for other high-risk gay men in San
Francisco).
The 54 men of the 359 in the SFHBVCS (vaccine) group of gay men is the
number that actually acquired HIV by 1980 (15% of the group, per the Lemp
study, as opposed to the roughly expected value of 4%). The
"random
sample" of all HIV positive men for 1980 is 1400 (4% of 3500). In
this
1400 is included the 54 from the group of 359 SFHBVCS men. Later, I
chose
1982 rather than 1980 as the main focus, because the data was measured
rather than extrapolated.
Appendix E Software Epidemic Modeling Analysis ------------------- Page 50
Appendix E Software Epidemic Modeling Analysis
Various epidemiological anomalies concerning the origin and spread of HIV
can be demonstrated through the use of computer modeling software.
The modeling software referenced in this section was developed by the
author. The sources are not listed here because of length (more than
1500
lines, for two programs), but are available on request from the author.
The programs can run on a PC with Microsoft Visual C++, or any ANSI
standard C compiler.
The software is general-purpose and flexible, capable of using any
modeling assumptions that the user might to make, and allowing various
different models to be tested.
The programs do not contain built-in assumptions about parameters such as
infectivity rates or degrees of risk behavior. These parameters
are
defined by means of an interactive dialogue, when the program is run.
The vepid.c software is capable of specifying any number of risk subgroups
that engage in particular mixes of activities, at different frequencies.
Infectivity rates may be time-varying, to account for factors such as the
stages of HIV infection, where the first few weeks might involve higher
infectiousness.
The program also ask for initial rates of HIV prevalence, when the
modeling period begins.
The program keeps track of each individual member of the modeled
population, and whether they are currently infected, or not. The
members
of the population are randomly paired for sexual contacts, according to
their risk group's quota for the year, evenly spread throughout the months
of each year. Partners are chosen based on their
willingness for a
compatible activity.
When an uninfected person is paired with an infected person, the program
decides whether the uninfected person will become infected or not.
This
is random, but is kept strictly within the bounds of the average
probability for infection, based that person's role in the current contact.
The program prints totals of new and cumulative infections, for each year.
The epid.c program is a simpler and faster epidemic modeling package that
models a single population group, with up to two specified active/passive
activities.
Appendix E Software Epidemic Modeling Analysis ------------------- Page 51
The modeling examples that follow will all take a number of measures to
produce "worst case" projections for HIV growth, when looking at
the
general gay population:
* Both receptive and insertive sexual roles are treated as having the same
infectivity as the more risky "receptive" role.
* No account is made for monogamous or partially monogamous partners.
All
members of the target population are treated as if being
promiscuous.
* No account is made for safe practices, such mutual masturbation, dildos,
condoms, etc.
* The rate of sexual activity is assumed for the entire population is
assumed to be as high as for the "high risk" men
in the vaccine trials.
E.1 Per-Contact Infection Rates
Published figures exist for per-contact probabilities of infection for
various sexual acts/roles with infected partners[16][17][18]. This
makes
it possible to do computer modeling to examine the spread of HIV.
Average rates of infectivity, per-contact with an HIV+ partner, are:
* Anal receptive: .0082
* Anal insertive: .0067
* Oral receptive: .0006
* Oral insertive: 0
(slight, theoretical only)
* Vaginal, male-to-female: .0009
* Vaginal, female-to-male: .0001125
In order to model, you also need data as to the sexual practices and
frequencies in the target populations. For the hepatitis B
vaccine trial
participants in San Francisco, this is listed as 67 contacts with
different partners per year [18]. For New York gay men in the vaccine
trials, the rate was lower, at about 40 partners per year.
Virtually all
men reported a mix of both anal and oral sex, with lower-risk oral sex
being somewhat more prevalent.
Since these figures are for high-risk men, it would be a generous over-
estimate to apply the same rate to all gay men in the city.
Appendix E Evidence of Program Accuracy -------------------------- Page 52
E.2 Evidence of Program Accuracy
E.2.1 Consistency with Independent Mathematical Test
As a check whether the program is working correctly, we can try a
simple
case that approximates a coin-flipping problem, where we can compute the
expected answer by another means. Say that you have a population of
100000 where half are infected. Say that the probability of
"infection" is
50% (= .5), and that these people pair up for a single sex act (50000
pairings). How many should be infected?
The program says:
% epid
Enter Population Size ( <= 100000): 100000
Enter number initially infected: 50000
Probability, infection per ACTIVE contact, type #1: .5
Probability, infection per PASSIVE contact, type #1: .5
Probability, infection per ACTIVE contact, type #2: .5
Probability, infection per PASSIVE contact, type #2: .5
Enter average number of contacts per year: 1
Enter number of years: 1
Enter random seed (any number between 1 and 4294967295): 987987347
New infections in year #1 = 12442, GRAND TOTAL = 62442
You might suppose that the expected value of new infections is (50000 * .5) =
2500. However, you must take into account that the pairings are random,
not simply pairings of infected persons with uninfected persons. When
an
infected person is paired with another infected person, or an uninfected
person is paired with another uninfected person, nothing changes. The
question is, how many pairings of uninfected and infected persons should
there be? The answer that we really expect is about half of the
number
of pairings of infected + uninfected partners.
This is computed using the "combinations" function, described
earlier.
The total pairings are
(100000 C 2) = 5e+09. Pairings of two infected or two uninfected
partners would each amount to ((50000 C 2) / 5e+09) = 25%.
The mixed
pairings would constitute the remaining 50%.
Thus, we should expect roughly (50000 * 0.5 * 0.5) = 12500 new infections,
versus the program's projected 12442, which is very close to expected
(within bounds of expected, random variation).
E.2.2 Consistency with Real-Life Experimental Results
A California study of heterosexuals [18] followed 360 heterosexual
woman
who were HIV negative, but had regular male partners who were HIV+.
These
women continued to have unprotected sex with their male partners. In a
ten-year period, the study reported 68 new HIV infections among the 360
women.
Appendix E Evidence of Program Accuracy -------------------------- Page 53
The vepid.c modeling software comes reasonably close in attempting to
duplicate the results of the California study. Starting with 360
infected
men, the program reported 73 new infections among the women- very close to
the reported value of 68.
Below is the output of the "vepid.c" epidemic modeling software,
for this
experiment.
Subgroup #1 represents the 360 males (all initially infected). Subgroup #2
represents the infected men's 360 female partners (0 initial infections).
The abstract of the study did not list a number of sexual contacts per
year, so I made a conservative estimate of one intercourse every other
week (26 contacts per year).
% vepid
Enter Total Population Size ( <= 100000): 720
Enter no. of activities to model: 1
DO ANY INFECTIVITY RATES VARY WITH TIME (y or n)? n
Does activity #1 involve exactly 2 partners (y or n)? y
Enter av probability of infection,
activity #1, ACTIVE
role: .0001125
Enter av probability of infection,
activity #1, PASSIVE
role: .0009
DO YOU WISH TO DEFINE POPULATION RISK SUBGROUPS (y or n)? y
ENTER NUMBER OF POPULATION RISK SUBGROUPS: 2
Enter size of subgroup #1: 360
Enter activity #1, ACTIVE , average no. contacts per year
for subgroup #1:
26
Enter activity #1, PASSIVE, average no. contacts per year
for subgroup #1:
0
Enter number initially infected for subgroup #1: 360
Enter size of subgroup #2: 360
Enter activity #1, ACTIVE , average no. contacts per year
for subgroup #2:
0
Enter activity #1, PASSIVE, average no. contacts per year
for subgroup #2:
26
Enter number initially infected for subgroup #2: 0
Appendix E Evidence of Program Accuracy -------------------------- Page 54
Enter number of years to model: 10
Enter random seed (any number between 1 and 4294967295):
987987987
NUM ACTIVITIES: 1
ACTIVITY #1, ONE_PARTNER
AV prob infection, ACTIVE ,
0.0001125 num_adjust, = 0
AV prob infection, PASSIVE,
0.0009 num_adjust, = 0
POP SIZE: 720
NUM SUBGROUPS = 2
TOTAL FOR SUBGROUP 0 = 360
TOTAL INFECTED IN SUBGROUP: 360
Contacts/yr for activity #1, ACTIVE : 26
TOTAL FOR SUBGROUP 1 = 360
TOTAL INFECTED IN SUBGROUP: 0
Contacts/yr for activity #1, PASSIVE: 26
New infections in year #1 = 13, GRAND TOTAL = 373
New infections in year #2 = 9, GRAND TOTAL = 382
New infections in year #3 = 8, GRAND TOTAL = 390
New infections in year #4 = 9, GRAND TOTAL = 399
New infections in year #5 = 7, GRAND TOTAL = 406
New infections in year #6 = 6, GRAND TOTAL = 412
New infections in year #7 = 6, GRAND TOTAL = 418
New infections in year #8 = 3, GRAND TOTAL = 421
New infections in year #9 = 7, GRAND TOTAL = 428
New infections in year #10 = 5, GRAND TOTAL = 433
TOTAL CONTACTS: 187200
TOTAL DUMMY CONTACTS, NO PARTNER: 0
REDUNDANT INFECTIONS: 12
Subgroup #1 infections: initial = 360, new = 0, total = 360
Subgroup #2 infections: initial = 0, new = 73, total = 73
E.3 First Year, SFHBVCS
The first modeling is of the 359 men in the vaccine trial, who went from .
3% infection (1 man) in 1978 to 4% infection (14 men) in a single year.
Is this rate suspiciously high? The following treats the 360 men as a
"closed" population, having sex with each other (which should make
cases
rise even faster). The average number of partners was increased
from 67
to 104, to make it even more conservative. The program output
follows:
Appendix E Evidence of Program Accuracy -------------------------- Page 55
% epid
Enter Population Size ( <= 100000): 360
Enter number initially infected: 1
Probability, infection per ACTIVE contact, type #1: .0082
Probability, infection per PASSIVE contact, type #1: .0082
Probability, infection per ACTIVE contact, type #2: .0006
Probability, infection per PASSIVE contact, type #2: .0006
Enter average number of contacts per year: 104
Enter number of years: 1
Enter random seed (any number between 1 and 4294967295): 24525
New infections in year #1 = 2, GRAND TOTAL = 3
The observed number of infections was nearly 5 times higher than expected
by a generous modeling estimate.
To double check that the program's modeling is not simply too low, we can
try another set of years, with a larger initial pool of infected men.
In
1982, 40% of the 359 were infected (144 men). By 1983, 46% were
infected
(165 men). The percent of the total population becoming newly infected
is
higher (6% versus 3.7%) and the absolute numbers of men newly infected is
higher (21 versus 14). The only difference is the pool of men
initially
infected.
For this, the program shows:
% epid
Enter Population Size ( <= 100000): 360
Enter number initially infected: 144
Probability, infection per ACTIVE contact, type #1: .0082
Probability, infection per PASSIVE contact, type #1: .0082
Probability, infection per ACTIVE contact, type #2: .0006
Probability, infection per PASSIVE contact, type #2: .0006
Enter average number of contacts per year: 104
Enter number of years: 1
Enter random seed (any number between 1 and 4294967295): 24525
New infections in year #1 = 42, GRAND TOTAL = 186
The program in this case shows more men being infected that actually
observed, demonstrating that it is not simply a matter of the infectivity/
frequency estimates that causes our previous low value. What makes the
difference is the number initially infected.
The computer model is saying that in order to have extremely high rates of
new HIV growth, it is necessary to have a significantly large initial pool
of infected men. High rates of HIV growth are not feasible in a
scenario
where only a small handful of men are initially infected. When such an
unreasonably high rate of HIV growth is observed, it suggests that some
mechanism exists to spread the virus that is above and beyond simply a
high rate of sexual contact.
Appendix E Evidence of Program Accuracy -------------------------- Page 56
E.4 Patient Zero Scenario
For this test, the program estimated the course of HIV growth over a 20
year period, starting with a single, infected person (a "Patient
Zero"
type of scenario), for a gay population of 100,000, having behaviors
similar to high-risk San Francisco men.
% epid
Enter Population Size ( <= 100000): 100000
Enter number initially infected: 1
Probability, infection per ACTIVE contact, type #1: .0082
Probability, infection per PASSIVE contact, type #1: .0082
Probability, infection per ACTIVE contact, type #2: .0006
Probability, infection per PASSIVE contact, type #2: .0006
Enter average number of contacts per year: 67
Enter number of years: 20
Enter random seed (any number between 1 and 4294967295): 4536356356
New infections in year #1 = 2, GRAND TOTAL = 3
New infections in year #2 = 5, GRAND TOTAL = 8
New infections in year #3 = 4, GRAND TOTAL = 12
New infections in year #4 = 5, GRAND TOTAL = 17
New infections in year #5 = 4, GRAND TOTAL = 21
New infections in year #6 = 7, GRAND TOTAL = 28
New infections in year #7 = 12, GRAND TOTAL = 40
New infections in year #8 = 10, GRAND TOTAL = 50
New infections in year #9 = 12, GRAND TOTAL = 62
New infections in year #10 = 19, GRAND TOTAL = 81
New infections in year #11 = 37, GRAND TOTAL = 118
New infections in year #12 = 39, GRAND TOTAL = 157
New infections in year #13 = 55, GRAND TOTAL = 212
New infections in year #14 = 64, GRAND TOTAL = 276
New infections in year #15 = 81, GRAND TOTAL = 357
New infections in year #16 = 144, GRAND TOTAL = 501
New infections in year #17 = 168, GRAND TOTAL = 669
New infections in year #18 = 242, GRAND TOTAL = 911
New infections in year #19 = 351, GRAND TOTAL = 1262
New infections in year #20 = 433, GRAND TOTAL = 1695
By the end of the 4th year of the 20-year period, there would have been
about 50 infections. In 10 more years, most of these cases would have
progressed to full-blown AIDS. At that time, there were still only
about
250-300 infections, total.
When AIDS broke out, it took only a few dozen usual cases of Kaposi's
Sarcoma before it was apparent to the medical establishment that there was
an unusual problem. Thus, a realistic model of HIV growth says
that the
AIDS epidemic should have become apparent, at a time when HIV prevalence
was still quite low. The rapid saturation of HIV in the gay
community,
subsequent to the initial outbreak of AIDS, points to the fact that there
Appendix E Evidence of Program Accuracy -------------------------- Page 57
was a mass, simultaneous infection of a larger number of men.
E.5 Estimated Seed Size in SF
How many initial, simultaneous, mass infections would have to suddenly
appear in the late 1970s, in order to account for the rates of explosive
growth that followed? For this discussion, this is what is meant by the
"seed size".
Lemp's data shows near zero infection in the San Francisco gay population
in 1978, rising to 49.3% for the entire gay male population of the city,
by 1987. This would be approximately (56000 * .493) = 26708 HIV
infections in 9 years.
The first reported case of transfusion AIDS in San Francisco was in 1982,
4 years after the start of the hepatitis study recruitment. Blood
supply
screening began in 1985. The first retroactively estimated case of
transfusion related HIV infection was 7 years earlier, in 1978, also
coinciding with the start of the hepatitis study [20].
To be generous, we can push the date of essentially-zero HIV prevalence to
1976, the year before the first back-dated projections of gay HIV
seroconversions listed for high-risk men, cited by Rutherford [14].
Approximately how many men would it take for a seed size, in order to get
26708 HIV infections by 1987 (11 years)?
This can be estimated by running the modeling program repeatedly, taking
an initial guess, and then working up or down, in iterative attempts.
As it turns out, the necessary seed size in 1976, as a conservative
estimate, would need to be between 1900 and 2000 men:
% epid
Enter Population Size ( <= 100000): 56000
Enter number initially infected: 1900
Probability, infection per ACTIVE contact, type #1: .0082
Probability, infection per PASSIVE contact, type #1: .0082
Probability, infection per ACTIVE contact, type #2: .0006
Probability, infection per PASSIVE contact, type #2: .0006
Enter average number of contacts per year: 67
Enter number of years: 11
Enter random seed (any number between 1 and 4294967295): 11324234
New infections in year #1 = 625, GRAND TOTAL = 2525
Appendix E Evidence of Program Accuracy -------------------------- Page 58
New infections in year #2 = 845, GRAND TOTAL = 3370
New infections in year #3 = 1070, GRAND TOTAL = 4440
New infections in year #4 = 1351, GRAND TOTAL = 5791
New infections in year #5 = 1732, GRAND TOTAL = 7523
New infections in year #6 = 2097, GRAND TOTAL = 9620
New infections in year #7 = 2616, GRAND TOTAL = 12236
New infections in year #8 = 3046, GRAND TOTAL = 15282
New infections in year #9 = 3487, GRAND TOTAL = 18769
New infections in year #10 = 3906, GRAND TOTAL = 22675
New infections in year #11 = 3949, GRAND TOTAL = 26624
E.6 From Where Comes the Seed?
There is no recorded evidence of extensive HIV infection in the gay
community, anywhere in America, in the mid-1970s. Traveling and
vacation
within the U.S. borders could not be a sufficient factor to account for
simultaneous mass infection of 2000 men in San Francisco, merely in the
space of a few years.
Immigration from other U.S. cities in time period also could not explain
the number of men simultaneously mass-infected, given the lack of
evidence for any appreciable degree of HIV elsewhere in the country.
The fact of a few anecdotal cases of supposed HIV infection from earlier
years, such as a case claimed in 1959 in St. Louis, do not alter this fact.
Having a few stray cases, even if these are not simply myths, does not
create a scenario to allow rapid infection of a large number of men within
a few years.
The same is true even of travel and vacation to other foreign locations,
such as Africa or Haiti. As an example, following is an estimate of
what
would happen if nearly the entire gay population of San Francisco
vacationed in Haiti for a couple weeks, mingling with 100% infected men,
and having an average of 3 sexual contacts during that vacation:
% epid
Enter Population Size ( <= 100000): 100000
Enter number initially infected: 50000
Probability, infection per ACTIVE contact, type #1: .0082
Probability, infection per PASSIVE contact, type #1: .0082
Probability, infection per ACTIVE contact, type #2: .0006
Probability, infection per PASSIVE contact, type #2: .0006
Enter average number of contacts per year: 3
Enter number of years: 1
Enter random seed (any number between 1 and 4294967295): 222345
New infections in year #1 = 352, GRAND TOTAL = 50352
Appendix E Evidence of Program Accuracy -------------------------- Page 59
In other words, a grand total of only 350+ infections. Of course,
nowhere
near the whole gay male population of SF men is going to vacation in Haiti
in the space of a couple years, nor will this number immigrate.
It is a "Catch-22" which forbids large, sudden simultaneous mass
infections: it requires a large pool of existing infections. To
make a
large pool of infections takes time, when you are starting from only a few
infections. In this necessary time, AIDS would reveal itself much
earlier.
The degree of apparent seeding shows an artificial nature, more consistent
with a hypothesis of unnaturally produced mass infection, such as in the
hepatitis experiments.
E.7 Variable Infectivity Per Stage
A 1994 study at University of Michigan (Jacquez, et al) [21] attempted to
explain the sharp rise and rapid fall-off of HIV infection, stating that
"Thousandfold differences in transmission probabilities by stage of
infection are needed to fit the epidemic curves". Their
hypothesis was
that the initial infection stage, characterized by flu-like symptoms,
would cause an infectivity rate 1000 to 3000 times higher than the rate of
infectivity in the "long, asymptomatic phase", lasting 10 years or
more.
The per-contact risk for anal sex is broken out in this study as
"0.1-0.3
per anal intercourse in the period of initial infection , 10(-4) to 10(-3)
in the long asymptomatic period, and 10(-3) to 10(-2) in the period
leading to AIDS."
When fed into the program, allowing a 1-month duration for an initial
infection period, and using the higher of the Jacquez figures in each case (=
0.3 for initial infection, .001 for asymptomatic) this still did not
appear to explain the pattern of HIV growth in the early epidemic years.
In fact, when starting with a single infection, over a span of 11 years,
the variable-rate infectivity figures actually came out to be lower
than
flat rate figures used in the previous examples (trying 56000 men, 200
initially infected, for 11 years). Only in the first year did the
Jacquez
figures produce a higher rate of infections (113 versus 59). In later
years, it fell off sharply (after 5 years, 699 total for variable
infectivity, 795 for flat rate; after 11 years, 1988 for variable rate and
3992 for flat rate).
This is probably because the high rate of .1-.3 only applies to an initial
infectivity period that is very short (2-8 weeks). After that, the
listed
infectivity figures for Jacquez are actually lower (.001 versus .0082),
for a much longer period of time (10 years).
There are additional, possible objections to the notion that a high rate
of infectivity in the "initial infection" stage could account for
the
early explosion of AIDS. This initial period is characterized by
symptoms
Appendix E Evidence of Program Accuracy -------------------------- Page 60
such as headache, vomiting and diarrhea This is not a scenario
where
even promiscuous gay men are likely to seek, or succeed in finding, a lot
of partners.
If the body were that overcome with huge amounts of virus, the symptoms
might be more severe than merely flu-like (one might imagine that the
person would be dying). The authors of the Michigan study acknowledged
that the questions of viral load during the different stages were a matter
of controversy.
If the body is that vulnerable to massive proliferation of virus, when
initially exposed, then it is more difficult to understand why the
asymptomatic phase, with lower viral load, should be all that less
infectious. It would seem only to require a small amount of virus to
cause infection, if the virus can duplicate that quickly and freely in an
unprepared host.
In the case of IV drug injection, the amount of virus is far less than
in
a typical amount of semen, yet the infectivity is very efficient.
This
also suggests that the amount of virus required for infection would not
necessarily need to be great.
It is possible also that the infectivity of HIV has changed over time.
It
is not in the best evolutionary interest of a virus to kill its only
natural host. It is a commonplace phenomenon for viruses to becomes
less
virulent over time. However, if such drastic changes have occurred
merely
within the last 20 years, then it might suggest that HIV was a relatively
"new" virus, and detract from the likelihood that it has been
infecting
humans since the 1950s or 1930s.
More studies concerning viral loads at different stages of infection, and
concerning the effect of viral load on infectivity of unprotected sex,
would be useful.
There is a risk of circular reasoning in the example of the University of
Michigan study- a "good" model must fit the observed curve.
Thus, the
model cannot test whether the observed curve is a natural phenomenon- that
is an implicit assumption .
At this point, it appears more likely that researchers are stretching to
find explanations for the high initial rates of HIV spread, and the sharp
drop-off that followed. Perhaps part of the reason for these
contortions
is the refusal to examine a more controversial hypothesis: an artificial,
mass seeding of HIV infections into the gay population.
Appendix F General Statistical Primer -------------------------- Page 61
Appendix F General Statistical Primer
There is sometimes a naive tendency to assume the cynical attitude that
"you can prove anything with statistics." However, you
do not live in a
world of truths that possess absolute certainties. You live in a world
that is probabilistic in nature. For better or worse, statistics is one
of your best tools for assessing truth and falsehood in the world around
you. Your ability to be deceived by statistics, or to be enlightened by
it, will depend in large part on how much effort you put into acquiring a
good command of the subject, so that you can critically evaluate
statistical claims.
The vaccine analysis is similar to a problem in drawing samples of marbles
of different colors from a jar. Suppose that you had a jar with
100
white marbles and 100 black marbles, evenly mixed. You close your
eyes,
and draw a sample of 20 marbles at random.
Because there are equal numbers of white and black marbles in the jar, you
would expect, on average, that the 20 that you draw would also be roughly,
evenly mixed- about 10 white marbles and 10 black. Of course,
this is
only an average, that you would expect to find over many similar trials.
In any one sample of 20 marbles, you might have a few more white, or a few
more black. This is a normal variation by random chance, or what you
would call "luck of the draw".
Take an extremely simple case- a jar has one white marble and one black
marble. You close your eyes and draw one marble. What is
the chance
that it will be white?
When this problem is fed into the computer program listed in the appendix,
the answer is:
* Chance of drawing a white marble, when pulling one marble at random from a
jar containing 1 white marble and 1 black marble:
Subgroup size = 1
Total group size = 2
Sample size = 1
n = 1
PROBABILITY IS: 0.5
A probability of .5 means 50%, or an even 50-50 chance, the same as the
chance of getting "heads" when flipping a coin.
When you flip a coin, or draw from two colored marbles, there is no
particular reason that one outcome would be favored over another, so you
tend over many trials to get half (50%) of each result. This same
logic
applies to any two events where there exist no logical reasons to favor
one outcome over the other. If you randomly painted an
"X" on half of
the people in a room, and "Y" on the other half, then blindly chose
a
random sample of people in the room, you would expect to get about half
"X"
and half "Y" on average, regardless of how the initial selection
had
happened to have been made.
Appendix F General Statistical Primer -------------------------- Page 62
The same is true of the "random" grouping of men who received a
vaccine,
versus those who didn't. It should be simply irrelevant as a factor
when
drawing samples of men "chosen" at random to become HIV infected,
as if
you had merely painted "X" or "Y" on them.
If you have a factor that does favor an outcome, such as having a coin
weighted on one side, then it is no longer a 50-50 chance. You have to
be
very careful not to have such hidden bias.
You also have the opportunity, if you see someone flipping a coin and
getting 30 heads in a row, to know that something is fishy. It is
probably a trick coin, because the probability would be only about 1 in a
billion, by random chance. It is similar with our vaccines-
something is
clearly fishy. What exactly it might be, we have to investigate, but
there is something that needs investigation.
The problem of drawing marbles from a jar is a bit different than flipping
a coin. If you keep flipping a coin, the odds of getting
"heads" is the
same on every flip. If we pull a second marble from our jar, we
are sure
to get the white, because it is the only marble left. Our program
should
show this: if we draw two marbles, we should have 100% percent chance of
getting a white marble:
* Chance of drawing a white marble, when pulling two marbles from a jar
containing 1 white marble and 1 black marble:
Subgroup size = 1
Total group size = 2
Sample size = 2
n = 1
PROBABILITY IS: 1
This is a case so obvious as to be nearly silly, but it demonstrates how
marble-drawing problems (hypergeometric distributions) sometimes differ
significantly from coin-flipping problems (binomial distributions).
It
also helps to confirm that our probability-computing program is working
correctly. The program comes in handy, because the computations
become
extremely lengthy, when the mixtures of marbles and the tested conditions
become more complicated.
What would be the odds of finding 10 or more white marbles, when drawing
20 marbles from a jar holding 100 white marbles and 100 black marbles?
This is the most-expected result.
* Chance of 10 or more white marbles, drawn in a sample of 20 marbles,
randomly pulled from a jar of 100 white marbles and 100 black
marbles:
Subgroup size = 100
Total group size = 200
Sample size = 20
n = 10
PROBABILITY IS: 0.592851
Appendix F General Statistical Primer -------------------------- Page 63
That is, there is about a .59, or a 59% chance of getting 10 or more white
marbles.
Since this is the most expected result, we should see a slightly smaller
probability for drawing 11 or more white marbles, and even less for
drawing 12 or more white marbles. The computer program output
below
shows that this is true:
* Chance of 11 or more white marbles, drawn in a sample of 20 marbles,
randomly pulled from a jar of 100 white marbles and 100 black
marbles:
Subgroup size = 100
Total group size = 200
Sample size = 20
n = 11
PROBABILITY IS: 0.407149
* Chance of 12 or more white marbles, drawn in a sample of 20 marbles,
randomly pulled from a jar of 100 white marbles and 100 black
marbles:
Subgroup size = 100
Total group size = 200
Sample size = 20
n = 12
PROBABILITY IS: 0.240184
We had a 59% chance of drawing 10 or more white marbles. To draw 11 or
more white marbles, the chances fall to 40%. To draw 12 or more
white
marbles, the chances fall to 24%.
What are the chances that the entire group of 20 marbles will be 100% all-
white?
* Chance of finding 20 all-white marbles, drawn in a sample of 20 marbles,
randomly pulled from a jar of 100 white marbles and 100 black
marbles:
Subgroup size = 100
Total group size = 200
Sample size = 20
n = 20
PROBABILITY IS: 3.32169e-07
This works out to about 1 chance in 3 million- an extremely unlikely
outcome. Even though we look at a small number of marbles- only 20 -
the
improbability becomes enormous. This shows why these types of problems
are sometimes not completely obvious, at first glance.
Appendix F General Statistical Primer -------------------------- Page 64
This is not to say that the outcome cannot ever happen. It does
indeed
happen. This would be almost exactly the rate at which you would expect
to find it happening, if you repeated the experiment an infinite number of
times- roughly one time out every 3 million attempts.
What if you are trying to analyze a situation where there are multiple
possible explanations for getting the result that you did, besides simply
one of random chance? Suppose, for example, that the experimenter
forgot
the importance of evenly mixing the marbles in the jar. Instead, 100
black
marbles were poured on the bottom, and 100 white marbles poured on top.
This might guarantee a near 100% chance that you could pull a handful of
all-white marbles, instead of the rightful probability of near-zero.
Most of the preceding problems have involved an equal number of marbles of
one color an another. You can also have imbalances in the numbers of
marbles of each type in the jar. If you have 9999 white marbles, and
only
1 black, then draw one marble, the chance of getting the black marble is
very small (1 in 10000).
Our program is geared for the general case: X objects of one type, Y
objects of another type, drawing a sample of Z objects, then computing the
probability of getting "n" or more of the "X" type in the
sample of Z.
You fill in the values of X, Y, Z, and n, for any problem of this sort.
Suppose that this situation were one where you were trying to evaluate the
safety of a food additive, to make sure that it was not a carcinogen
(cancer-causing). Say, that you have studied a large population of X
animals, and that in a given year, you normally see Y animals that get
cancer (maybe this would be a flat percentage, such as 1% of X).
Now,
you study a smaller group of Z animals, using the food additive. You
find
that out of these, you have "n" that get cancer in a year.
You would
expect for "n" to be roughly equal to Z * (Y/X). You
expect it to be
slightly different than this, because of random chance variation, but you
do not expect it to be greatly different. You can compute the
probability for the difference that you see, just as you can for a problem
in drawing marbles from a jar.
Say that the odds against a higher cancer rate being attributable to
random chance is only 1 in a million. Do you approve the food additive
(or in our case, the vaccine)? Of course not. There
is indeed still a
chance that the higher rate is only random, but you would not take that
kind of chance.
What constitutes "statistical significance" is not something that
has a
completely hard-and-fast rule. A common convention used in
many
evaluations is a level of "1 in 1000". Other, less
demanding problems
might test for significance at, say, the 1% level (1 in 100), or the
5%
level (1 in 20).
Appendix F General Statistical Primer -------------------------- Page 65
The desired level of significance is often chosen based on the needs of
the problem under consideration. Since our problem is a
critical one of
vaccine safety, we are more than meeting a reasonable definition of
"statistical significance".
If you draw a graph of probabilities for different outcomes, for these
types of problems, you tend to get a "bell-shaped" curve.
The center of
the curve is the most expected result (such as 50% white marbles drawn in
a sample from a jar with a 50-50 white and black mix). The edges of the
curve as the least likely results (such as all-white marbles).
As your population size and sample size grow (i.e., more marbles in the
jar and bigger handfuls taken out), the odds against getting any one
particular outcome (such as getting exactly 1,203 white marbles and 2,459
black marbles in a giant handful) becomes extremely small. This
type of
"improbable" result is nothing unusual or suspicious, because there
are
many, many such uninteresting, particular mixes that are equally possible.
Our program is not computing odds for single outcomes of this sort.
It
is computing a whole range of combined possible outcomes ("n or
more").
This is in effect an "area under the curve" for the bell-shaped
probability curve. That is why the results are meaningful,
regardless of
the population and sample sizes, or the shape of the curve.
As mentioned earlier, the extremely small probability values computed in
our analysis point to the fact that our sample sizes are large enough to
be significant. Smaller populations and samples tend in general to
yield
higher probability values.
For example, the odds of getting 5 heads out of 6 coin tosses is about 10%,
which is perfectly feasible. The odds of getting 500 heads out of 600
coins tosses is about 3 times (10 to the -65th power). A trillionth
would
be 10 to the -12th power, so this is an unimaginably small possibility.
The ratio of "head" outcomes to the total coin-toss trials is the
same in
both cases, at 5:6.
Yet the probabilities are vastly different. Six coins tosses are
clearly
not enough to guarantee that the outcome of 5 "heads" was not by
random
chance. Six hundred coins tosses are far more than enough.
The same phenomena apply to the 799 men in the SFMS and the 359 in the
SFHBVCS.
The computed probability is the real probability, and is fully meaningful,
so long as the data used to make the calculation are reliable.
If the formula or program seem complicated, that is the only real illusion.
It is enough to realize that this is a common type of problem, found in
nearly any statistics text. It would be pointless to attempt to
create a
mirage, in a document that will subject to the criticism of experts.