John Doe and his ilk: pitfalls of pseudonymity

8)

A little while ago, I remarked on the anonymity of commentator “Fulano de Tal”, who later insisted he was a genuine “de Tal”, namely, Mengano de Tal; and in private e-mails ventured the promise (so far unrequited) to tell me his real identity. So I was amused when a friend sent me a link to the following story:

“When it’s time for gossip, here are some handy names: Fulano, Mengano y Sutano”
http://www.amcostarica.com/101606.htm, accessed 30 July 2008

“No, these are not the names of the Three Stooges. They are monikers that are used to refer to people without using their given names. Many Costa Ricans use one of these, for example, when they can’t think of a person’s real name. It’s sort of like ‘what’s-his-name’ in English, or, as the Germans say, ‘dinksbumps.’

A story in which these three ‘characters’ appear might go something like this:

‘You know that Fulano, son of old Don Mengano— the guy who owns the general store —anyhow, this Fulano bought a used piece-of-junk motorcycle off some Sutano who lives in the first house after you cross the old bridge . . . .’

The interesting thing is that these three characters also have the same apellido, or last name, which is De Tal meaning ‘so-and-so.’

…
When you refer to a person as Fulano De Tal it means he is someone you don’t really care much about one way or the other. A Mengano De Tal, or Menganito, is someone you might feel sorry for. And Sutano de Tal is someone you don’t know at all, a perfect stranger.

…
Fulano, Mengano and Sutano also have wives. They are Fulanita, Menganita and Sutanita. So, when you are annoyed with the woman who does your shirts you can tell your friend about that little Fulanita down at the laundry who parades around like some kind of movie queen, but all she’s really after is poor Menganita’s husband!

Another way to use these pseudonyms is to make reference to somebody who is just too amazingly stupid. For example you might say to a friend whom you wish to dissuade from making a tremendous blunder: ‘¡No seas tan Fulano!’ Meaning: ‘Don’t be such an idiot!’ Or you could comment on a person who is acting foolishly by saying: ‘Ese Fulano si es menso’ or ‘That imbecile is really stupid.’ But the general principle is always the same, to camouflage, albeit at times rather thinly, someone’s real name. . . .”

HIV demographics are predictable; HIV is not a contagious infection

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conquering HIV/AIDS without antiretrovirals

There are innumerable anecdotes and personal testimonies and a few mentions in published scientific articles of people who overcame HIV-positive diagnoses, or AIDS diagnoses, without antiretroviral drugs. Roberto Giraldo is seeking testimonies from such indviduals for a book he is working on.

“If you are a person diagnosed as seropositive (HIV-positive) and live a normal life without taking antiretrovirals, you can help me with a book that I am writing, a book that will feature the testimonies of those who have survived this diagnosis. This book will be in support of and will be of great help to those who suffer the social Calvary of seropositivity and AIDS. History will recognize the courage and bravery of each witness and your testimony will support and bring hope to those affected, demonstrating that it is possible to escape the incorrect forecasts of the official view on AIDS.”

Please assist him of you can.   THANKS!

ABUSED WOMEN and “HIV”

“HIV” is the worst evil in the world, according to HIV/AIDS believers:
— They want to give drug addicts fresh needles, because cocaine and heroin are so much better, so much more healthy than “HIV”; see COCAINE AND HEROIN AREN’T GOOD FOR YOU! [a Golden Fleece Award, 13 June 2008];
— They will treat Africans for worm infestation only if that makes antiretroviral treatment more efficient; see PARASITIC WORMS are *NOT* GOOD for you!, 24 July 2008; ARE INTESTINAL WORMS GOOD FOR US? ARE THEY GOOD FOR AFRICANS? FOR AFRICAN CHILDREN?, 30 December 2007;
— They will provide food to malnourished Africans only if that helps with antiretroviral treatment ; see DRUGS OR FOOD?, 25 December 2007; FOOD IS GOOD FOR CHILDREN, 8 January 2008;
— “HIV” does ALL SORTS of dreadful things, like instigating bone fractures; see TALKING OF HIV’S MAGICAL POWERS…, 29 DECEMBER 2007.

And so on. No doubt about it, “HIV” — or, of course, “HIV/AIDS” — is the worst evil in the world.

Therefore it makes sense to study — and to acquire research grants to study — whether abused women are at greater risk of “HIV” than non-abused women are. If one finds that they are at greater risk, that would provide a compelling reason to regard the abusing and battering of women as a bad thing and perhaps even to look for ways of helping abused women and of trying to prevent such abuse.

A corollary that seems to me obvious, though apparently not to HIV/AIDS believers, is that if abused women are NOT at greater risk of “HIV”, then there’s no need to give further thought to the plight of abused women?

My e-mail friend Andy D. found this as absurd as I did, and drew my attention to the several news items in which the HIV status of abused women is treated as a matter of the highest newsworthiness:

“AIDS infection risk higher in abused Indian women, study says” (John Lauerman, Aug. 12, Bloomberg)
“Indian women who are physically and sexually abused by their husbands are four times more likely to have HIV than other wives . . . . AIDS prevention should focus more on mistreatment of women . . . .
India’s AIDS epidemic is the third largest of any country in the world, and infections among women are rising . . . . Health officials should target wives who are forced to have unsafe sex, along with their husbands, for preventive measures, said study author Jay Silverman, an associate professor of society, human development and health at the Harvard School of Public Health in Boston.
’Sexual abuse of adolescent girls and women is driving the HIV epidemic in India and around the world . . . . We need to make it a major priority for prevention.’
The findings echo a 2004 study of women in South Africa, . . . [where] abused women were 50 percent more likely to be HIV-infected than non-abused women, regardless of their own behavior.
’In many settings, women’s risk of HIV is largely driven by the behavior of their male partners,’ said Kristin Dunkle, an assistant professor of behavioral sciences and health education at the Emory Center for AIDS Research in Atlanta . . . .
About 0.73 percent of women who had been physically and sexually abused were infected, compared with 0.19 percent among non-abused women . . . . Almost all the women, 95 percent, reported that they had no extramarital sexual relations themselves . . . . That points to known patterns in the behavior of abusive husbands that puts their wives and children at higher risk of HIV infection . . . . Sexually abusive husbands may force their wives to have intercourse without condoms, or unprotected anal sex, both of which can significantly increase HIV infection risk . . . . The men may also be having risky sex with women outside the marriage, increasing their own chance of infection . . . . ‘We have to get to the men,’ Silverman said. ‘And we have to provide women with reasonable alternatives if they’re being abused, so they can maintain their children and not become destitute.’
. . . . ‘To be truly successful in addressing the spread of HIV in India, we must think of ways to address the all-too-widespread mistreatment of wives,’ said Donta Balaiah of the Indian Council of Medical Research, who helped write the study. The study was supported by the U.S. National Institute of Child Health and Human Development in Bethesda, Maryland, and the Indian Council of Medical Research in New Delhi, which funds and promotes research in the country.”

Another version was in CBC News: “Prevent abuse of women to stem rise of HIV: researchers” (August 12) :
“ . . . . despite a lower prevalence of infection among India’s general population, women account for a rising percentage of HIV cases. . . . ‘married Indian women who experienced both physical and sexual intimate partner violence demonstrated an HIV infection prevalence approximately four times greater than that of non-abused women,’ . . . . The risky sexual behaviour of husbands was the major source of women’s infection . . . . They suggested that doctors and public health officials focus on preventing intimate partner violence to help reduce the spread of HIV/AIDS.”

The scientific publication on which these stories are based is Silverman et al, JAMA 300 [2008] 703-710.

————————–

As I said at the outset: The prime reason for trying to do something about abuse of women is apparently to prevent the spread of HIV.

That’s a heartless HIV/AIDS cart-before-horse stupidities. To my mind, any abuse of human beings is a thoroughly despicable and detestable thing, and we should do everything we can think of to prevent it. Naturally enough, the more it can be prevented, the more beneficial COROLLARIES there will be — for the women’s emotional and mental as well as physical health, and that of their family members; and much more. How on earth does “HIV/AIDS” come to take priority over everything else? Perhaps because any mention of it brings the money flooding in?

Note also the HIV/AIDS-typical abuse of statistics and data in this:

“India’s AIDS epidemic is the third largest of any country in the world”
— only because India has so large a population. The HIV-positive rate in India is among the lowest in the world. Moreover, the HIV/AIDS guru at the World Health Organization admitted that there had not been and would not be a heterosexually spread epidemic there, see WHO SAYS that WE’VE BEEN VERY WRONG about HIV and AIDS? (Clue: WHO = World Health Organization), 10 June 2008. A year ago, it was conceded that there were about 2.5 million “HIV/AIDS” people in India rather than the 5.7 million estimated earlier (for example, REDIFF: India Abroad — “India’s HIV/AIDS affected reduced to half in revised figures” July 06, 2007; acknowledged in the Silverman et al. article). The earlier number had corresponded to a rate of 0.9%, so the newly estimated rate is 0.4% — as I said, among the very lowest in the world.

However:
“and infections among women are rising”

This illustrates a common way in which statistics are abused for the purpose of misleading. If something starts at zero and then “increases” to barely noticeable, that’s an enormous increase if you express it in percentages!

This device is used pervasively in marketing medicines. “Take XXXXX”, we are assured, and “cut in half” our risk of YYYYY; where YYYYY might be heart attack, stroke, just about anything undesirable. If you are inclined to take this sort of thing at face value, then you should read Malignant Medical Myths by Joel Kauffman (read this for an excellent summary). If the risk of YYYYY is, say, 1%, does it make sense to try to reduce this to 0.5% when the “side” effects of prolonged dosing with XXXXX brings its own tangible risks? The only clinical trials worth attending to are those for which the important data are rarely published: namely, changes (if any) in ALL-CAUSE MORTALITY. If XXXX is good for you, then it should lower ALL-CAUSE mortality, not just the risk of YYYYY.

Silverman et al. further illustrate misleading via numbers with “Despite recent reductions in HIV prevalence among both the general population and many high-risk groups, the percentage of all infections occurring among Indian women (currently estimated at 39%) has continued to rise relative to that among men” [emphasis added].
How impressive that “39%” appears! An enormous “increase”!
But in India the overall rate for women is 0.22% and for men 0.36%, both extraordinarily low by any standards. Yet these trivially low rates allegedly cause India to be “recognized as the source of increasing HIV prevalence among its South Asian neighbors”!
I suppose a prevalence of even 0.4% poses a threat to neighbors like China, Laos, and Pakistan where the prevalence is estimated at 0.1%; let alone to those where it’s estimated at LESS than 0.1% (Afghanistan, Bhutan, Bangladesh, Sri Lanka); but surely the threat is the other way around from Myanmar (1.3%) or even Nepal (0.5%). This is worse than ludicrous.

Silverman et al. reported that “7.68%” (2161) of 28,139 women had been both physically and sexually abused; and “0.73%” (205) tested HIV-positive. A statistical test marked the difference between that 0.73% and the 0.19% among non-abused women as “statistically significant”. Maybe, although we lay people wonder why fewer than 1 in 10 of those “at risk” abused women were actually HIV-positive; but bear in mind that “statistically significant” is not the same as PROVEN. More important, what’s statistically significant is NOT that physical and sexual abuse are CAUSATIVE of testing HIV-positive, only that the two things are CORRELATED; and

CORRELATION NEVER PROVES CAUSATION

Note, too, the usual abundance of assertions about matters that are not known:
“women’s risk of HIV is largely driven by the behavior of their male partners”
— Were all the male partners investigated to arrive at that “largely”?
— And who established “known patterns in the behavior of abusive husbands”?

Where I would thoroughly agree — at least for the purpose of the published study — is that “’We have to get to the men,’ Silverman said”.

I wish the authors had done that, and had tested all the husbands of those abused women of whom 95% had not had extramarital relations, because an essential — but missing — part of the study is to discover, how many of those husbands are themselves HIV-positive. If they aren’t, then they didn’t infect their wives, after all.

My prediction is that very few of those husbands are HIV-positive, certainly many fewer than 95% of them.

Think about it. 0.73% of Indian women are physically and sexually abused and HIV-positive. Each has a husband, so those husbands represent about 0.73% of Indian men (only “about” because the ratio of males to females is not 1 and varies with age). In the overall population of India, however, only 0.36% of men are HIV-positive. Therefore physically and sexually abusive husbands must be twice as likely as other men to be HIV-positive AND ALL OF THOSE MUST HAVE INFECTED THEIR WIVES — a truly remarkable set of circumstances, especially given that the claimed average rate of sexual transmission of “HIV-positive” without use of condoms is about 1 per 1000. It gets only more remarkable when one takes into account that about ¾ of all “HIV transmission” in India is NOT owing to marital sex, if India is at all comparable to Asia as a whole, see HIV/AIDS ILLUSTRATES COGNITIVE DISSONANCE, 29 April 2008. Then the husbands of those poor HIV-positive abused women must themselves be not twice as likely but 8 times as likely as other Indian heterosexual men to be HIV-positive?

The fact of the matter is that testing HIV-positive does not mark infection by a sexually transmitted agent, it is a sign of physiological stress. That physically and sexually abused women are 4 times as likely as untroubled women to be seriously stressed should be no surprise to anyone, not even to researchers who conjure up imaginative grant proposals.

CDC versus CDC: Which Data to Believe?

I’ve commented critically, on numerous occasions, in many connections, on the fallacy of accepting outputs from computer models as though they were reliable data. I’ve also noted on several occasions that the so-called “Surveillance Reports” published by the Centers for Disease Control and Prevention (CDC) have increasingly — since the late 1990s — featured estimates rather than reported numbers (for example, see Table 33, below, from The Origin, Persistence and Failings of HIV/AIDS Theory, and the following pages in the book).

Another egregious example of estimates taking the place of reported numbers turned up as I was looking into information about deaths from “AIDS” (= “HIV disease”). That led me to remember that bureaucracies are ill suited to doing, assessing, managing, or reporting matters scientific: bureaucracies are not good at self-criticism; internal disagreements are wherever possible hidden from outsiders and settled by political rather than scientifically substantive negotiations. That’s part of the reason why 21st-century science is becoming riddled with knowledge monopolies and research cartels.

The Centers for Disease Control and Prevention is a sizeable bureaucracy. Some 16 units report to the Director:

Within the Coordinating Center for Infectious Diseases reside four National Centers, for:
— Immunization and Respiratory Diseases (NCIRD)
— Zoonotic, Vector-Borne, and Enteric Diseases (NCZVED)
— HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP)
— Preparedness, Detection, and Control of Infectious Diseases (NCPDCID)

NCHHSTP houses a variety of programs under 6 “topics”:
— Sexually Transmitted Diseases
— HIV/AIDS
— Viral Hepatitis
— Tuberculosis
— Global AIDS
— BOTUSA (Botswana-USA).
[That “HIV/AIDS” and “Sexually Transmitted Diseases” are separate “topics” does not, regrettably, mean that the CDC has now acknowledged that HIV/AIDS is not sexually transmitted.]

Within (presumably) the “HIV/AIDS” topic is the Division of HIV/AIDS Prevention, which has published HIV/AIDS Surveillance Reports.

Within the Coordinating Center for Health Information and Service (CCHIS) reside three National Centers:
— Health Statistics (NCHS)
— Public Health Informatics (NCPHI) (has 5 divisions)
— Health Marketing (NCHM)
[For anyone who is not squeamish about bureaucratic and PR jargon, I recommend highly the explanation of what “health marketing” is (and if you can explain what the explanation means, please let me know)]

Evidently the publishers of the HIV/AIDS Surveillance Reports are quite a few bureaucratic steps away from the National Center for Health Statistics, which publishes the National Vital Statistics Reports (NVSR) and annual summaries of Health, United States (HUS). Perhaps that explains why the data in the Surveillance Reports differ so much from those in NVSR and HUS.

Take the instance of deaths in 2004 from “HIV disease”.

NVSR 56 #5, 20 November 2007, using “information from all death certificates filed in the 50 states and the District of Columbia”, lists by age group (in its Table 1) the numbers of recorded deaths, and the death rates per 100,000, for the ten leading causes of death in each group. “Human immunodeficiency virus (HIV) disease” appears as one of those ten leading causes only between ages 19 and 54. There are listed 160 deaths among 20-24-year-olds, 1468 deaths among ages 25-34, 4826 deaths among ages 35-44, and 4422 deaths among ages 45-54.

However, numbers for some of the other age groups can be calculated because the death rates for them are supplied in Health, United States, 2007 — With Chartbook on Trends in the Health of Americans (National Center for Health Statistics, Hyattsville, MD: 2007). Appendix I confirms what is said in NSVR: “Numbers of . . . deaths from the vital statistics system represent complete counts . . . . Therefore, they are not subject to sampling error”. Table 42 [also featured in an earlier post, HIV DISEASE” IS NOT AN ILLNESS, 19 March 2008] is for deaths from HIV disease:

* Rates based on fewer than 20 deaths are considered unreliable and are not shown.

(Note again, under the heading of Table 42, “Data are based on death certificates”.)

These rates allow calculation of actual numbers of HIV-disease deaths for age groups from 5 through 84 years of age (column F, Table I below), because the NVSR gives not only numbers but also the corresponding rates for each age group, allowing calculation of the factor connecting rate and number, see column D. (The factor is independent of the particular disease but varies with age: it reflects how many individuals are within that age group in the whole population.) Together with the numbers already given in NVSR, this yields numbers of deaths for the whole range from 5 to 84 years of age, column G.

Now compare those numbers with the estimates published in Table 7 of HIV/AIDS Surveillance Report, volume 18, “Cases of HIV infection and AIDS in the United States and Dependent Areas, 2006”, presenting data “reported to CDC through June 2007”) :

For 2004, here is a comparison of the numbers from these two sources within CDC:

The estimates from the CDC are on average 21% greater than the actually recorded numbers. Moreover, the error varies with age group in a remarkably regular way; one that exaggerates the median age of death by more than 3 years.

Now, Table 7 in the Surveillance Report does have this caveat, in small print in a footnote to the Table: “These numbers do not represent reported case counts. Rather, these numbers are point estimates, which result from adjustments of reported case counts. The reported case counts have been adjusted for reporting delays and for redistribution of cases in persons initially reported without an identified risk factor, but not for incomplete reporting” [emphasis added]. Incomplete reporting for 2004 should hardly be a problem, however, in a publication that presents data “reported to CDC through June 2007”; nor would incomplete reporting vary with age group in this remarkable manner, it would be more random.

Such “adjustments” 3 and 4 years after the event are no rarity in these CDC HIV/AIDS publications. For example, deaths “reported” for the 1980s were “adjusted” downwards in wholesale fashion more than half-a-dozen years later, thereby altering the fact that the earlier data had shown deaths to have been leveling off, see Table 33, p. 221 in The Origin, Persistence and Failings of HIV/AIDS Theory:

Note how “reported” deaths for the years through 1986 somehow decreased dramatically between the 1988 report and the 1989 report. Such re-writing of historical facts will be familiar to students of the former Soviet Union, but it is not normally found in scientific publications.

At any rate, CDC unapologetically—indeed, without admitting it or drawing attention to it—routinely publishes considerably revised “estimates”; for example (Table III), for deaths in 2002 as given in the 2005 and 2006 Surveillance Reports. Table 7 in the 2006 Report does not warn that numbers for as far back as 2002 are different from those for the same years in the 2005 Report.

The Technical Notes do warn: “Tabulations of deaths of persons with AIDS (Table 7) do not reflect actual counts of deaths reported to the surveillance system. Rather, the estimates are based on numbers of reported deaths, which have been adjusted for delays in reporting”.

The estimates may be based on reported deaths; but if so, then they are very loosely based on them indeed, since they differ by as much as 38% in some age groups, see Table II above. That adjustments from one year to the next are so similar in percentage terms for the various age groups (Table III); that the differences between actual counts and “estimates” vary in such regular fashion with age (Table II); and that the numbers given are “point estimates” all indicate that the estimates are arrived at by means of some sort of overarching algorithm, computer model, or graphical representation, with—presumably—periodic adjustment of some of the assumptions or parameters defining the model. However, when estimates, no matter how derived, are claimed to be “based on numbers of reported deaths”, one expects that the mode of estimating will be progressively refined over the years to bring the estimates closer to the actual numbers. That has evidently not been the case here: estimated “data” for deaths for 2004 are shockingly different from the reports based on death certificates (Table II).

Once again—or rather, as usual—HIV/AIDS “researchers” imply greater accuracy than is warranted. The “point estimates” in Table II differ from year to year by a couple of percent, so the numbers should never be written to more than 3 significant figures. When they differ from actual numbers as much as in Table III, even two significant figures give a false impression.

The overall description at the beginning of the Surveillance Report is also misleading: “Data are presented for cases of HIV infection and AIDS reported to CDC through June 2007. All data are provisional.” Nothing here about “estimates”, and the reader who scans without careful attention to fine-print footnotes and Technical Notes could easily believe—given that numbers are given to four and five significant figures—that these really are “reported” “data”, not computer garbage-output emanating from invalid models. Nor are readers referred to NVSR or HUS; the only mention of either is in the Technical Notes and does not refer to Table 7: “The population denominators used to compute these rates for the 50 states and the District of Columbia were based on official postcensus estimates for 2006 from the U.S. Census Bureau [24] and bridged-race estimates for 2006 obtained from the National Center for Health Statistics [25].”

Why would one publish estimates when actual numbers are reported by a sibling unit in the same bureaucracy? After all, death certificates are a legal requirement, and information from them should be as trustworthy as demographic data ever can be. Is it coincidental that the HIV/AIDS specialists always overestimate?