HIV statistics and do people actually understand them.

[Ts RedVeX]

Since I work in the sex industry and I often hear people saying bad things about barebacking, yand then I also see many other people not being concerned about contracting HIV too much, I find it interesting what makes all those people think so differently. This thread is by any means not meant to encourage anyone to bareback or conclude whether barebacking is good or bad. It is supposed to be more about understanding information provided by media correctly as well as how much the way of looking at the info changes one's personal the feeling about it.



Since nobody actually concluded how to calculate the probability of ending up HIV positive in the other thread..


Let's assume that the probability g of getting infected during intercourse with an HIV positive person is in accordance with the data in the table https://www.aidsmap.com/Estimated-risk-per-exposure/page/1324038/#item1324093


g=1/123


then every time I have sex with an unknown person, so I do not know whether or not they are infected, my probability of getting HIV is


P=g*x


where x is the probability of that person being HIV-positive.

To calculate x lets look at https://www.hiv.gov/hiv-basics/overview/data-and-trends/global-statistics and take the worst case scenario - that 51.8M people have HIV. Lets assume global population of 7.6G people in 2017- as wikipedia reads: https://en.wikipedia.org/wiki/World_population.


x=51.8M/7.6G
x=6.76m


therefore:


P=1/123*6.76m
P=54.9μ


which means that the probability of not getting infected during a single intercourse is:


N=1-P


Now we have a series of independent events of tossing a coin only that the coin is more likely to give the face that corresponds to not getting HIV.
The probability N(n) of not getting HIV during a series of n intercourses is


N(n)=N^n


So the probability P(n) of getting HIV in a series of n intercourses with different random people is:


P(n)=1-N(n)


P(n) is practically a linear function untill you get to the point of planning to see thousands of different random people. This means that if you are HIV negative and plan to see n different random people the probability of having caught HIV at the end of your punting can be calculated with

1043563

f(n)=0.000054*n


as long as you are not planning to see more than too many different random people.


Lemme know what you think, especially if you reckon that this approach is wrong. Not sure why the old thumbnail remains... Crappy GUI I guess ];>... The error in the thumbnail is that there should be "1 client per day" wherever it says more than 1.

[GroobySteven]

Nobody has sex with random people. We have sex within a very predefined group.
I'm not likely to have sex with people of certain ages, genders, locations, preferences. So that would affect your numbers massively.
Also the type of sex I'm likely to have would affect it. As a top, I've a different risk factor than a bottom. As someone who is overall healthy, then I've a different risk factor.

[Ts RedVeX]

How do you mean? I have all sorts of clients visit me. Married single, homo, hetero... They are mostly males who are UK citizens. Which only lowers the risk further because the UK is said to be a low-risk country.

If you want to say that clients are usually male then that is irrelevant because if you diminish the global population by the number of women, you also need to diminish the number of HIV positive people in the world accordingly and x as well as P remain are not affected. They are also not affected by age looks etc as the stats are not not taking those into consideration. Let's stick to the stats we have here and only discuss their interpretation - at least for now. Otherwise we will get lost again.

The assumption that I always bottom is made at the point of g=1/123

[GroobySteven]

Let's stick to the stats we have here and only discuss their interpretation - at least for now. Otherwise we will get lost again.

That's why they don't work. They're irrelevant.
You've worked out nice numbers but based on a global population, without any other factors. If the whole world was at our fingertips, then they might be relevant but we just don't have those sexual partners available.

So yes, your numbers are right if I was going to have the ability or want to sleep with any random person in the world.

[Ts RedVeX]

That basically means the data provided practically cannot be understood without doing what sounds like a hell of a lot of other research. In that case, I am wondering how many people who say that it is safe or unsafe to bareback would actually be able to give a reasonable explanation for their opinions.

[GroobySteven]

I am wondering how many people who say that it is safe or unsafe to bareback would actually be able to give a reasonable explanation for their opinions.

Yep.

[Devlishsaint]

That basically means the data provided practically cannot be understood without doing what sounds like a hell of a lot of other research. In that case, I am wondering how many people who say that it is safe or unsafe to bareback would actually be able to give a reasonable explanation for their opinions.

But in that case doesn't it make sense to be safe rather than sorry?

[trish]

Were I to make the calculation I’d use the same method; i.e. search the reputable data for the value of g and x and calculate the probability that I won’t contract the infection given a single encounter as N = 1-gx. Then the probability of remaining uninfected after n independent encounters would be N^n. The probability of winding up infected after n independent encounter would then be P(n) = 1-N^n.

One could perhaps take more care in assigning values to g and x so that they better reflect the pool of people with whom you (the reader) expects to have sexual encounters, and the type sexual encounter you expect to be having. Some pools have greater or lesser infection rates, some kinds of sex of greater or lesser probability of spreading HIV.

In your example you take g, the probability of acquiring the HIV virus through one bareback encounter (I assume receptive anal) with an HIV infected person to be g=1/123 = 0.00813 . I didn’t double check this value with the link you gave; I assume you looked it up correctly. You also take the value of x to be x=0.00676. Again, the value one would want to use would depend upon the pool of people one is having encounter with. But lets go with your value. So N = 1-gx = 1-0.0000549 = 0.999945 .

You mention an interest in finding the value of P(n) if you’re seeing one client a day. If if you’re seeing one client a day, and the values you chose for g and x are suitable for your specific practices, then (in agreement with your calculations) we find...

the probability of acquiring HIV by the end of a year = 1-(0.999945)^365 = 0.0198 .

the probability of acquiring HIV by the end of 2 years = 1-(0.999945)^(2*365) = 0.039 .

the probability of acquiring HIV by the end of the decade = 1-(0.999945)^(10*365) = 0.182 .

The reservation that stands out most is the value chosen for x is probably too small for a transgender escort who’s seeing men on a daily basis. The value of x can have a dramatic effect on the rate at which P(n) climbs with n.

For an example, suppose you find out that for the last year you’ve been having weekly unprotected receptive anal intercourse with the same HIV inflected client. To make the calculations simple let’s just suppose he was your only client. Then x = 1. So N = 1-gx = 1-g = 0.99187 . We now find

the probability that you’ve been infected = 1-(0.99187)^52 = 0.346 .

Of course if you’ve been seeing other clients the probability is even higher.

[Ts RedVeX]

Tthe amount of sense it makes to use protection to an individual depends on the individual. I don't do bareback with clients, if that is what you wanted to know. The point of this thread is just to show how many different approaches people may have to statistics they encounter and how various interpretations of that information may be.

As to being picking other g and x, the value of g=1/123 is the value already representing the worst case scenario of being anally fucked bareback by a HIV-positive guy. Using the value already means we engage plan to in this most risky sexual activity every time.

As to x, I may have not mentioned it at the beginning of my original post, but I assume that every customer is a different guy and I stated that in the conclusion the original post's conclusion. This is justified for if an escort has a regular client who wants to penetrate her without protection then she can simply ask him to bring a certificate next time. - Considering that they trust each-other, of course. Still, it seems like having multiple encounters with an HIV-infected person can be easily avoided and is therefore unjustified to consider it here. - Nonetheless, it indeed is a very good example of how a way looking at data can dramatically affect the results of analysis:

P(52)=0.346 (same infected client seen 52 times; no other clients)

vs

P(52)=0.0028 (original conditions)

I don't think that being a transgender escort is much different to being a gay escort, so that difference is negligible too.

[trish]

I wasn't really asking anything about your case in particular. Just demonstrating how a change in the value of g or x can have a dramatic effect on the end result.

If one always uses protection then one can get a more accurate estimate of his or her risk if a more appropriate (in that case a lesser) value for g is used. For an escort in who deals with clients more likely to carry the HIV virus than other client pools, it may be a higher value of x (higher than the 0.00676 cited above) would give a more accurate estimation of the risks of business. The escort would have to know the stats for her particular sort of client base.

[broncofan]

Without knowing much about statistics but as someone who has tried to look at these numbers, I prefer looking at statistics that reflect the risk that inheres in the act itself. So I will look at numbers for serodiscordant couples (where one partner has hiv and the other does not), and know that it is providing a high water mark. Therefore, if I get a number for bareback or condom sex, I am getting a number that is based on the assumption that the other person has hiv.

Even with these statistics, there is obviously variation within the samples. Some people have rough sex, some people don't. Some people who use condoms use them well, some people don't put them on well. But it makes it less likely I'm deluding myself about my partner's status and just have a number that pertains to the relative risks of each act.

The ultimate question though is, how risky is the act and how likely is your partner to have hiv. The latter question really does depend a lot and you have to use your judgment. But it helps to get numbers overall to be able to anchor some sort of estimate. If you are a man sleeping with a man, what are the percentages for men who sleep with men. If you are sleeping with a ts, then what are those overall. Then you make adjustments from there for your own use and edification. But with more specific cohorts you can get more relevant data but eventually you get so specific you run out of data.

[Ts RedVeX]

One thing that might be worth mentioning here is that statistics are only numbers and it does not matter whether your probability is 0.99 or 1^-100, you may still be unlucky and get HIV during your first encounter, or have bareback sex with an HIV positive person 99999 and still remain healthy. So keep in mind that whether you are taking the statistic for an "average homosexual man" or "average heterosexual woman" you are talking about an abstract entity and neither of those "average persons" exist in reality.

[broncofan]

And I do appreciate that the above method doesn't have the rigor of the calculations on the first page. But for someone who knows his status and is concerned about a single act, I look at these numbers afterwards and this is the sort of thing I think about. I personally like to keep separate the risk in the act and the risk that the partner is infected, but what you two are discussing on the first page is obviously the way to go about it rigorously.

[trish]

Without knowing much about statistics but as someone who has tried to look at these numbers, I prefer looking at statistics that reflect the risk that inheres in the act itself. So I will look at numbers for serodiscordant couples (where one partner has hiv and the other does not), and know that it is providing a high water mark. Therefore, if I get a number for bareback or condom sex, I am getting a number that is based on the assumption that the other person has hiv.

I think what RedVex refers to as g (the probability of acquiring the HIV virus upon having a single bareback receptive encounter with an HIV infected partner) is an example of the sort of numbers you’re talking about. These are probabilities of HIV transmission per exposure with the type of exposure specified (e.g. insertive anal with condom, or receptive oral without condom etc.). The chart she linked, I presume, gives a list of such stats. These probabilities tend to look small to the inexperienced eye (usually below a tenth of a percent). Which is why some people like to look at what happens to the probability of transmission as the number of exposures increases. That’s all we’re doing on the first page. The formula we’re using is practically as old as probability theory itself. Nothing fancy. Everything depends on the inputs - the transmission rate per exposure (which is the number I think you were referring to) and the probability that your partner carries the virus (which is the number RedVex denoted by the letter x).

An aside: I’m not sure the value that RedVex uses for x is the correct one, but let’s go ahead and use it. Suppose Mr.K sees an escort, say MissMystery, a few times every year. Suppose MissMystery has had receptive anal once daily since she started her escorting business. Given the initial inputs for g and x suggested in Post#1, RedVex and I both calculate that the probability that MissMystery has acquired the HIV by the end of her first year in business is 0.02 (rounding up). If Mr.K started seeing MissMystery just after her first year in business, then this should be the value he gives to the number x; i.e. it’s his probability of having sex with an HIV infected partner. Notice it’s considerably larger than the value of 0.00676 that MissMystery assigned to x. The moral here is that all these values (of x and g) change with time, location, and from person to person.

[Ts RedVeX]

Yeah. They depend on a load of factors. However, your x should only have x=0.02 as Trish suggested if MissMystery has not gotten tested recently and proven to be HIV-negative, in which case you would be able to safely use the original statistics provided those have not changed over her 1 year of work.

This is can be explained with the "gambler's fallacy" where the gambler thinks that if he has a "lucky strike" then he is more likely to lose in the following go or vice versa. In this case, if an escort gambling with her health for 1 year gets tested and proven HIV-negative, then your x=0.00681 again.

As to the g, you should probably just look up some UK speciffic stats and determine whether you actually want to get fucked bareback, as that way her using the condom does not make much of a difference in your sensations, and you might use a lower probability factor here.

[trish]

Yeah. They depend on a load of factors. However, your x should only have x=0.02 as Trish suggested if MissMystery has not gotten tested recently and proven to be HIV-negative, in which case you would be able to safely use the original statistics provided those have not changed over her 1 year of work.Very true. Indeed, if MissMystery is the only person MrK's having sex with then the value of his x is zero. At least it starts out being equal to zero. Note too, that if MissMystery gets checked once per year, then by the end of the year MrK's value of x is back to 0.02 again. Actually MrK's x increases with each of MissMysteries exposures until she has her next 'all clear' whenever that is.

[filghy2]

Since I work in the sex industry and I often hear people saying bad things about barebacking, yand then I also see many other people not being concerned about contracting HIV too much, I find it interesting what makes all those people think so differently. This thread is by any means not meant to encourage anyone to bareback or conclude whether barebacking is good or bad. It is supposed to be more about understanding information provided by media correctly as well as how much the way of looking at the info changes one's personal the feeling about it.

Lemme know what you think, especially if you reckon that this approach is wrong.

But why are you trying to reinvent the wheel when it has already been invented? The probability you are trying to calculate is for unprotected receptive anal sex, partner unknown status, which is given in the same table as 0.27% (1/370). The probability of remaining uninfected after N sexual encounters is therefore (1-0.0027)^N.

The point your calculation misses is the one I already explained in the other thread, which is that if a person is willing to have unprotected sex then that tells you something important about them - namely, that they are more likely to have engaged in risky behaviour in the past. So it is not like a random draw from the gay population - the risk will be significantly higher. That means it is not possible to derive the probability in the way you have tried to do. In technical terms, this is a conditional probability, , where the probability of having unprotected sex with someone and the probability of them being infected are not independent. https://en.wikipedia.org/wiki/Conditional_probability Multiplying probabilities is only valid when the events are independent.

I haven't closely examined the studies underpinning the published probability estimates, but I understand they are based on the sexual experiences of a sample of the population that is designed to be representative. That means they should take account of the more risky behaviour of people having unprotected sex. There are lots of challenges in getting this right, but it is still likely to be more accurate than any 'back of the envelope' calculation. Also, the published probabilities are derived from a range of studies, so they don't depend on one particular study.

[davejones65]

Anything from the government, wikisleezia and the cdc cant be trusted and is a lie.

[buttslinger]

I don't do statistics, and I've said it here before, that I recall a report that in Washington DC, something like 98% of AIDs cases were amongst the class of people making less than 10,000 dollars a year.
One time I was completely smashed and following a prostitute down the hall of an abandoned hotel where all the nice old wooden doors had been removed, so that each room I gazed into was like a level towards Dante's inferno, people engaging in every conceivable variation of IV drug or sexual risky behavior. It was like a movie. The height of the crack epidemic.
I remember she stole my wallet.
I missed AIDs but I did run into some Hep-C that my body fought off.
My crack dealer had AIDs, her name was Dianne and her only talent was knowing where the best rock in town was and getting me to pay for it.
She got murdered before she was scheduled to die in jail.
Down and Out people have fantastic sex lives if they're young.

[Post Op Preferred]

The CDC recently published a summary of a meta study based on 9 million HIV tests that it funded, and analyzed. 2.7% of self identified MtF's were HIV positive (one tenth of previously published estimates, as noted in the briefiing). 0.09% of men were HIV+. https://www.cdc.gov/hiv/pdf/policies/cdc-hiv-transgender-brief.pdf. the underlying meta study, https://www.cdc.gov/mmwr/volumes/66/wr/mm6633a3.htm, takes note of its sample size, bias and geographic limitations, but it provides a useful data point for the missing "x."

[BostonBad]

What's the real risk for swallowing working girl ts cum? I love escorts and I love to pleasure them orally. Semen can be very yummy.

[filghy2]

The risk from oral sex seems to be very low, though there is a degree of uncertainty. https://www.aidsmap.com/Estimating-the-risk-per-exposure/page/1323967/

[DaphneCruz]

HIV is a big lie. Keep yourself healthy and a good immune system, and you're fine.

[filghy2]

Another idiot conspiracy theorist - just what the world needs.

[DaphneCruz]

Another dope who just swallows everything he's fed. eat up, buddy.

[broncofan]

Another idiot conspiracy theorist - just what the world needs.
They're floating around out there and there are more of them than some might think. Occasionally you will come across one who has actually read one of the conspiracy sites and thinks he knows something only to find there are professionals who have been studying the virus for decades.

But I've read a bunch of this person's tweets. Definite moron.

[broncofan]

But I've read a bunch of this person's tweets.
Sorry, wrong site. But right person. :tongue:

[DaphneCruz]

Do you guys believe vaccines eradicated polio, too? lol

[broncofan]

Do you guys believe vaccines eradicated polio, too? lol
Yeah and do you guys believe the germ theory of disease and that the earth revolves around the sun? hilarious kneeslapper lmao

But in all seriousness, I just read that there were 37 cases of polio worldwide in 2016 so I wouldn't call it a failure. No what's the deal with the polio vaccine? What am I missing? I'm sure it involves aliens, the new world order, and a bunch of other stuff.

[filghy2]

Another dope who just swallows everything he's fed. eat up, buddy.

Lol, I think you've confused me with the person I was responding to.

[Ts RedVeX]

flighty2, I don't think there is any point in bringing theory of conditional probability into this, as whoever came up with the 0.27% obviously had no idea of it. Look at these values:


Receptive anal sex amongst gay men, partner unknown status: gu=0.27% (1:370)
Receptive anal sex amongst gay men, partner HIV positive g=0.82% (1:123)


Perhaps you have not noticed it, but g=3gu (more less) That means the probability of encountering an HIV-positive homosexual man (let's assume that is what they mean by "gay") is 1/3 (every third gay man has HIV). I remember that i took that 4% of the world population is homosexual. If half of the population are HIV positive males out of whom every third has HIV, then that leaves us with 50666666 HIV-positive homosexual men. Considering that the other stats from https://www.hiv.gov/hiv-basics/overview/data-and-trends/global-statistics (only 36mln people around the world know they are HIV-positive and in fact maybe there are 40% more), the 56,5 mln HIV- positive peeps is already a bit weird as it is the 36M+40% already. That means there are no women with HIV, or heterosexual men with HIV at all. Don't you think that is "a bit iffy" - to say the least? This is the reason why I think this whole table is some sort of anti-homosexual propaganda.

[trish]

Let P(C|R^G) denote one’s conditional probability of contracting HIV from a single anal receptive encounter with a gay partner. This then is what you’ve been calling gu. So

P(C|R^G) = 0.0027

Let P(C|R^G^H) denote one’s conditional probability of contracting HIV from a single anal receptive encounter with a gay partner with aids. This is your g. So

P(C|R^G^H) = 0.0082.

The point of using this standard notation from probability theory is that now we can readily use the definition of conditional probability in each of these cases, namely

P(C^R^G)/P(R^G) = 0.0027; and

P(C^R^G^H)/P(R^G^H) = 0.0082.

RedVex notes that the ratio is approximately 3; i.e.

[P(C^R^G^H)/P(R^G^H)]/[P(C^R^G)/P(R^G)] = 3.

However, this does NOT reduce to

P(H^G)/P(G) = 1/3;

i.e. the chart does NOT imply one gay person out of every three has the HIV virus. The bullshit artist here is not HIV.Gov.

[Ts RedVeX]

Frankly, I am not sure what you are writing about Trish. What are the R, G, H, C events?

[KelliBlueEyes]

Isn't a risk matrix constructed of not only the probability of a bad outcome, but also the severity of the outcome should a bad outcome be experienced?

I'd say in the ongoing example the worst possible outcome is cotracting hiv. Even with the mitigating factors such as recent medical developments it's still prudent to limit one's risk. Unless one is ok with the worst possible outcome.

[trish]

Frankly, I am not sure what you are writing about Trish. What are the R, G, H, C events?

Let P(C|R^G) denote one’s conditional probability of Contracting HIV from a single anal Receptive encounter with a Gay partner. This then is what you’ve been calling gu. So

P(C|R^G) = 0.0027

Let P(C|R^G^H) denote one’s conditional probability of contracting HIV from a single anal receptive encounter with a gay partner carrying the HIV virus. This is your g. So

P(C|R^G^H) = 0.0082.

Sorry, I hoped it was clear from the context. Above I highlighted the letters denoting the corresponding events.

[Ts RedVeX]

Since we are only considering cases of receptive anal sex with gay men, why do you even have R and G events in your equations? Those two are certain events.

[trish]

We are considering cases where one has anal receptive sex (that's R) with a partner who is gay (that's G). We're also considering cases where the partner is a carrier of the HIV versus (that's H) and cases where you (the generic you) contract the virus from the encounter (that's C). You pretend to have deduced that P(H|G) = 1/3 from the listed values of P(C|R^G) and P(C|R^G^H). However the value of P(H|G) cannot be determined simply by taking the ratio of the latter two quantities.

[filghy2]

flighty2, I don't think there is any point in bringing theory of conditional probability into this, as whoever came up with the 0.27% obviously had no idea of it. Look at these values:


Receptive anal sex amongst gay men, partner unknown status: gu=0.27% (1:370)
Receptive anal sex amongst gay men, partner HIV positive g=0.82% (1:123)


Perhaps you have not noticed it, but g=3gu (more less) That means the probability of encountering an HIV-positive homosexual man (let's assume that is what they mean by "gay") is 1/3 (every third gay man has HIV). I remember that i took that 4% of the world population is homosexual. If half of the population are HIV positive males out of whom every third has HIV, then that leaves us with 50666666 HIV-positive homosexual men. Considering that the other stats from https://www.hiv.gov/hiv-basics/overview/data-and-trends/global-statistics (only 36mln people around the world know they are HIV-positive and in fact maybe there are 40% more), the 56,5 mln HIV- positive peeps is already a bit weird as it is the 36M+40% already. That means there are no women with HIV, or heterosexual men with HIV at all. Don't you think that is "a bit iffy" - to say the least? This is the reason why I think this whole table is some sort of anti-homosexual propaganda.

The point you seem to be determined to miss is that you cannot have unprotected anal sex with a random client. You can only do so with those who are willing to have unprotected anal sex with a stranger (ie a risk-taker). That is my point about conditional probability. Your calculation assumes that the 0.27% probability is conditional only on the person being gay: in fact, it is conditional on the person being gay and willing to have unprotected sex with strangers.

[Ts RedVeX]

I am only analysing the data given in the tables I gave links to at the beginning of this thread. You can condition your probability on whether or not you take a massive dump or just a small one in the morning, if you know how that affects the probability of contracting HIV. Both values I used refer to unprotected receptive anal sex with a gay man, and that implicates we are both taking the risk. The only difference is that in one case he has HIV for sure, in the other - it is not certain that he has HIV. You are making a mountain out of a molehill. If only you had done that while your global warming research, then maybe we'd not have to worry about our CO2 emissions because it would turn out there are also other important causes for climate change...

If you want to bring some more data in, e.g. on how farting increases the probability, then go ahead. There is not much left to discuss here, really. People generally do not understand stats, they do not trust them, do not know how to read them and what they actually mean, in my opinion.

[Wendy Summers]

If you want to bring some more data in, e.g. on how farting increases the probability, then go ahead. There is not much left to discuss here, really. People generally do not understand stats, they do not trust them, do not know how to read them and what they actually mean, in my opinion.

While I generally agree with the idea that most folks don't get statistics, I don't think that's what's happening from most people in this thread.

From an abstract standpoint, your math may be a reasonable back of the envelope calculation of risk across the entire population. I don't think the push back you are getting here is driven by lack of understanding of your calculation, but instead driven by the debate of whether your calculation is actually useful. As others have noted, your inputs are too simplified for this to have a practical purpose. That's kind of the key here. While math oriented types love digging into numbers like this, the rest of the world evaluates results with the simple question: Does this help me make a better decision?

If your calculation doesn't help people make a better decision, they will reject your model. Many of the suggestions for missing inputs in this thread are aimed at making it a useful tool.

[Ts RedVeX]

I am happy to move on to more complex calculations taking into account more specific data. I just do not get what would be the point of that if people won't understand it anyway. Moreover, if a simple calculation shows that the likelihood of data found on-line being just some sort of homophobic propaganda suggesting that gay men are the only HIV carriers in the world is extremely high, then why bother even looking at the data? Never mind wasting time on analysing it and presenting in an way understandable by an average John Doe.

If you want a challenge, I can give you some probability tasks to solve as well. I cannot see the point, however, in making simple things like like 0.0027/0.0082 look complex because of factors we have no stats about anyway!

[trish]

I cannot see the point, however, in making simple things like like 0.0027/0.0082 look complex because of factors we have no stats about anyway!The point isn't that 0.0027/0.0082 is too simple; the point is that it is meaningless. It certainly doesn't mean what you claim it does.

[Ts RedVeX]

Why not?

[trish]

See post #32

[Jericho]

Bollox to all this for a laugh. I'm not getting my slide rule out before I fuck someone.
I'd rather just use a condom! :shrug

[trish]

Bollox to all this for a laugh. I'm not getting my slide rule out before I fuck someone.
I'd rather just use a condom! :shrugHeyy, I wouldn't object to you getting out your 'slide rule.' I'd love to unroll the condom down over it myself, before you start sliding?

[JerzeyBtm]

I think general based HIV contraction statistics can be thrown out the window when determining your risk in the sex industry (politely put... ie prostitution). You could take 100 random people and figure out a .001% chance of death by falling off a bridge but I am sure the statistics are quite different looking at 100 iron workers. Any way... If a study shows there's a 1 in 10,000 chance that most certainly doesn't mean that by sexual experience 10,000 you will be exposed. I'm willing to bet that most people who have contracted HIV have had wayyyyyyyyy less partners and experiences. Heck...one encounter can leave you exposed. Whether you are a customer seeing an Escort OR an Escort seeing a customer who has seen other Escorts the risk is GREAT. So use protection and your common sense!

[Ts RedVeX]

post #32 is irrelevant since R^G=W where W is the set of all possible events so in this particular case it can be left out

[trish]

Now you're just being silly. The conjunction two proper events cannot be the entire universe; or if you prefer think in terms of sets, the intersection of two proper subsets of W cannot be equal W.

[Ts RedVeX]

Just read the table. we are not considering sex other than receptive and other than with a gay man. The only difference is that in one case he has HIV and the other is he is just a random guy who might have HIV

[trish]

But you wish claim to have proven one third of all gay men carry the HIV virus.

every third gay man has HIV
i.e. you claim to have shown P(H|G) = 1/3, not P(H|G^R) = 1/3. But if we examine your ‘reasoning’ we see you haven’t shown the latter either.

Let’s take the universe W to be R^G; i.e. W=R^G. Then you may replace P(C|R^G) with P(C) as it's already understood (it's given) by your choice of universe one is having an anal receptive encounter with a gay partner. You may also replace P(C|R^G^H) with P(C|H). So using your choice of universe,

P(C) = 0.0027 and P(C|H) = 0.0082.

What you wish to prove is P(H)=1/3. You claim to do this by taking the ratio of the above two quantities. The ratio is

P(C)/P(C|H) = 0.0027/0.0082 = 1/3 (approximately).

By definition of conditional probability P(C|H) = P(C^H)/P(H). So

P(C)/[P(C^H)/P(H)] = 0.0027/0.0082 = 1/3 (approximately).

Simplifying we see that

P(C)P(H)/P(C^H) = 1/3.

This does not reduce to the formula you require.

[trish]

Addendum: We can save your weaker assertion by ignoring the distinction between partners and stipulate C=H. I'm not certain of the legitimacy of such a stipulation, but the result is no longer as surprising as 'one third of all gay men carry the HIV virus.'

[filghy2]

I am only analysing the data given in the tables I gave links to at the beginning of this thread. Y

No, you are propagating a ridiculous conspiracy theory because it happens to fit in with your loopy anti-government fixation. It's nothing but a clever game of starting with the 'answer' and then reverse-engineering dubious calculations to 'prove' it.

You started this thread pretending to be seriously trying to understand these probabilities and people have taken you at your word and gone to some trouble to try and explain it. You are clearly not interested in any explanation that contradicts your conspiracy theory, so I don't intend to waste any more time on you.

[Ts RedVeX]

Can you really not see that in this case your P(C)=P(C^H)? Do you think you can contract HIV from a person that is not infected?! That's why it fucking is 1:3 and the table is a load of crap.

[trish]

But you already agreed that the probability of contracting HIV from a single anal receptive encounter with a gay partner is 0.0027; i.e. you already agreed that P(C) = 0.0027 - now you want it to be equal to 1/3. Yeah it must be the table's fault :D

You still are long way from proving your claim that: the table implies one out of every three gays has aids.

[trish]

Look, if P(C) = P(C^H), then (in the finite setting of our universe) C=H. In that case, the conditional probability of contracting HIV given a single anal receptive encounter with a gay partner who already carries the virus is P(C|H) = P(C^H)/P(H) = P(C)/P(C) = 1. Your assumption leads to a contradiction without even consulting the table!

[filghy2]

Moreover, if a simple calculation shows that the likelihood of data found on-line being just some sort of homophobic propaganda suggesting that gay men are the only HIV carriers in the world is extremely high, then why bother even looking at the data?

One last very simple question. If what you are suggesting is true then how is it that the probabilities for everything other than gay sex are greater than zero? https://www.aidsmap.com/Estimated-risk-per-exposure/page/1324038/#item1324093 They must be very incompetent propagandists, particularly if only a genius like yourself can see the supposed point.

[broncofan]

So she starts a thread titled do people understand hiv statistics. She shows over the course of the thread she doesn't. Then she asserts it's anti-gay propaganda.....apparently all of these organizations are vainly hoping that by making the same errors Redvex did that people will conclude that lots of gay men have hiv. That was the purpose of all that information in the tables. The data on vaginal sex, on oral sex, on anal. It's that if people make a false inference, they will overestimate the prevalence of hiv among gay men.

In the U.S. if you want to know who is homophobic, it's the people who fought to defend the constitutionality of anti-sodomy laws. It's the people who fought tooth and nail to make sure that gay men and women couldn't file joint tax returns. Could not get married. Now that they're married can get snubbed by businesses. You can't make this shit up. But Redvex believes it's the people trying to calculate hiv risk numbers.

[filghy2]

Exactly - if someone wanted to spread homophobic propaganda it is hard to think of a less effective way that relying on people going to this website and performing an obscure calculation. But of course common sense if never a barrier to the dedicated conspiracy theorist.

[trish]

The proper response to post #56 is that the assumption P(C) = P(C^H) only implies that C is a subset of H, and consequently under the assumption P(C|H) is not necessarily unity. I point this out because I like to admit my mistakes. So let’s grant the additional constraint that C is a subset of H within the context that the universe is W = R^G. Then the ratio you’re focused upon is

P(C)/P(C|H) = P(C)/{P(C^H)/P(H)} = P(C)/{P(C)/P(H)} = P(H).

But this is not the probability that a given gay man carries the aids virus as you claim.


That means the probability of encountering an HIV-positive homosexual man (let's assume that is what they mean by "gay") is 1/3 (every third gay man has HIV)....

This is because we’ve agreed at your suggestion to limit the universe to R^G. So P(H) is the conditional probability that a gay man carries the HIV virus given that he has anal receptive sex and is gay.

As you can see, restricting the universe to R^G can be done, but it makes interpretation problematic.

If you see this is the proper interpretation of the ration under the additional constraint, then fine. If not, then let’s go back and take the universe W to be the collection of all possible outcomes. The ratio of interest is then

ratio = P(C|R^G)/P(C|R^G^H).

This, according to the chart is 1/3, but let’s just forget about the chart for now. The denominator is

denominator = P(C|R^G^H).

Your assumption narrower universe was that C is a subset of H. In the broader context this means C^R^G is a subset of R^G^H. So C^R^G^H = C^R^G. Now using the definition of conditional probability


denominator = P(C|R^G^H) = P(C^R^G^H)/P(R^G^H) = P(C^R^G)/P(R^G^H).

Also let’s apply the definition of conditional probability to the numerator of the ratio. Then

numerator = P(C|R^G) = P(C^R^G)/P(R^G).

So now the ratio can be rewritten as

ratio = P(C^R^G)P(R^G^H)/P(R^G)P(C^R^G) = P(H^R^G)/P(R^G) = P(H|R^G).

This is not the promised P(H|G).

[BostonBad]

Can someone give me some real advice? If I'm swallowing loads from girls from Eros what's the risk of getting HIV? I love ts women and I don't feel the sex act is complete unless I swallow the load. Please give advice people. Thank you.

[Stavros]

Can someone give me some real advice? If I'm swallowing loads from girls from Eros what's the risk of getting HIV? I love ts women and I don't feel the sex act is complete unless I swallow the load. Please give advice people. Thank you.

This is a quote from the San Francisco Aids Foundation website linked below-

"Can I get HIV from a blowjob? From giving head? From getting a BJ? Or from swallowing semen?

Oral sex is "low risk" in terms of getting HIV. There is no transmission risks for receiving oral sex. You probably will not get HIV from giving oral sex either—but having cuts or sores in your mouth, gum disease, having an STI in your throat, or recent dental work increases your risk. If any of these applies, you may consider refraining from performing oral sex to reduce your of exposure to HIV. If you're taking PrEP every day as prescribed, there's very little that you'll get HIV by giving someone a blowjob, or otherwise.
Remember, while the chance of getting infected with HIV from oral sex is very low, you can easily get other STIs such as gonorrhea and chlamydia."
http://sfaf.org/hiv-info/basics/can-i-get-hiv-from-oral.html

[Ts RedVeX]

1) You take a group of men and ask them to have receptive anal sex with HIV-positive men and come up with probability of P1.
2) You take a group of men and ask them to have receptive anal sex with men who are not necessarily HIV-positive and you get P2=P1/3.
Does that not mean that every third man from the last group, where he did not necessarily have HIV, needs to have HIV? In other words, does that not mean there needs to be 3 times less HIV positive men in that group? (This is the bit that I don't get why you disagree with.) Again - provided that all conditions apart from the last group's people HIV status are the same. I also do not get what kind of people would agree to participate in such tests but I guess we had better skip that one. The way I see the universe is that you can only have C if you have H and you always have R and G because those two are the properties of the probabilities in question. So why use R and G at all if, being both of the tests' requirements, they are certain events?

[trish]

You have not proven your claim that :the table implies one of every three gays carries the HIV virus. I can only imagine your confusion lies in the fact that you've restricted the universe of discussion to only those gays who have anal-receptive sex and then you accidentally or deliberately ignore that restriction to make a claim about all gays.

[filghy2]

I can only imagine your confusion lies in the fact that you've restricted the universe of discussion to only those gays who have anal-receptive sex and then you accidentally or deliberately ignore that restriction to make a claim about all gays.

Actually, it's gays who have unprotected anal-receptive sex, so it's an even smaller subset. But this has been explained umpteen times already. And if RV was genuinely trying to understand the issue she would have looked at the source studies for the probability estimates, which are listed in the aidmap website.