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Sexual Health

Jan18

Chances are?

Monday, 18 January 2016 Written by // David Phillips Categories // Gay Men, Research, Health, Sexual Health, Living with HIV, Opinion Pieces, Population Specific , Sex and Sexuality , David Phillips

David Phillips says “January 4 marked for me one-third of a century living with HIV”; it’s time to ponder on how we communicate risk and for the active non condom using negative man “how long it is before his “luck runs out.”

Chances are?

I mused for several months that I would throw myself a big party on January 4, 2016. In reality, I was spent by 4pm despite it being a “work from home” day. My husband lured me from home to meet at our favorite neighborhood restaurant at 5:30pm for a quick bite at the bar. I walked our dog Chuck and was asleep by 9pm, in order to rise the next morning at 5am for the gym before a day in the office. 

The autobiographical musical production being staged in my mind struggled with the lyrics “Seventeen million five hundred thirty-two thousand minutes...” 

January 4 also marked for me one-third of a century living with HIV. Thirty-three years and four months as a less than satisfactory host for the virus. A fearful mess for the first half of that period, an educated, self-determined partner in my care for almost 18 years and counting. After losing hundreds of friends to HIV early in my adult life, I have come to the age which my grandparents foretold when I would begin marking the loss of my peers to cardiovascular diseases, cancer, and other non-HIV causes with regular and increasing frequency. 

Heterosexual friends have turned to me to “model grief” at these times, as if a common loss should affect a diverse group of individuals in the same manner; yet, I can only offer celebration for a life that was and thanks for having witnessed just a fraction of it. Four years ago I wrote to a friend from college days amid his struggle to snatch raison d'etre back from depression and substance abuse, unaware that he would pass quietly and unexpectedly a few weeks later: “PLEASE, remember how tenacious I have been, my zeal for what’s just, my want for good for everyone, and my comfort with a path of great resistance. Call out love when it’s been apparent; and where I’ve been an asshole, claim that, too!” Ian’s reply was simply “Co-signed.” 

While the return to a “normal” life expectancy despite HIV for the newly diagnosed and many long-term survivors has been the biomedical success story of the last twenty years, the persistent incidence of new infections among many of the populations most at-risk marks an abject failure to-date of policy, technology, and communication. Policy and technology are topics for another time, but when I name “communication” I’m inevitably referring to how we describe risk and help ourselves and others make sense of it. 

For almost thirty years following the identification of HIV, we clamored for information that quantified the risk of acquiring HIV associated with various activities, typically a type of sexual intercourse and one’s preferred role in it. A 2010 publication based on a study of HIV-seronegative homosexual men in Sydney, Australia provided such a metric that has been embraced by public health authorities such as the U.S. Centers for Disease Control and Prevention and widely discussed, particularly in the context of risk reduction protocols like pre-exposure prophylaxis: 

The estimated per-contact probability of HIV transmission for receptive UAI* was 1.43% (95% CI 0.48%-2.85%) if ejaculation occurred inside the rectum occurred, and it was 0.65% (95% CI 0.15%-1.53%) if withdrawal prior to ejaculation was involved.

*unprotected (read “condomless”) anal intercourse 

A 1.43% estimate of the probability of acquiring HIV as the bottom in barrier-free buggery with a random, unknown serostatus partner gave numbers people a fine figure with which to model a variety of scenarios. However, the assumptions inherent in the estimate and the general challenge of health numeracy--that is, the ability to understand and interpret numeric and mathematical concepts related to health--kept the estimate out of broad community discourse or social media until the last two years. Additionally, the estimate is not universal and pertains to a group of gay men studied in Sydney, Australia during a certain period of time amid a certain HIV prevalence and community viral load. Still, as additional studies provided estimates of risk associated with other practices, side-by-sidecomparisons of risk appeared. 

For the life of me, though, I can’t remember the last time a guy asked me to help decide if he should share needles, top condomless for vaginal sex, or bottom condomless for anal sex. Choices, choices!! 

1.43% also provides some context for comparing the cumulative effects of risk reduction technologies. For instance, one can easily model the likelihood of remaining uninfected over repeated risk events between:

  1. a technology that is a few decades old but easy to misuse in the heat of the moment, “top-driven,” and, thusly, reduces per-event risk by only 80% overall;
  2. a technology that is relatively new, bottom-driven and used outside of sexual act, reducing risk by at least 99%; and
  3. receiving penetration solely from known HIV-positives with an undetectable serum viral load and consistent ARV use.

 

The effect of the latter tactic (risk = 0%) is easy to conceptualize, but computing the cumulative effects over many like events of the first two options and the base exposure can seem like quantum theory to most people. 

It’s really not so difficult, though… really!  If the probability of not having been infected after an act is 1 minus the risk of being infected expressed as a decimal number (e.g. 1-r), then the risk of not having been infected after N like events is (1-r) to the N power or (1-r)N. Doing the math may still be a challenge, but charting the equation y = (1-x)N using a graphing calculator like desmos.com will assist in visualizing and interpreting the cumulative effects of risk and risk reduction over hundreds or thousands of events. 

The graph below models scenarios 1 and 2 above (blue and orange lines, respectively), along with the estimated base risk of acquiring HIV at 1.43% (in purple), in terms of the probability of remaining HIV-uninfected (y-axis) over thousands of events (x-axis).  As the curves are based on a crude estimate and arbitrary numbers, one should not fixate on any specific values. Instead, one should note the dramatic differences in the slopes of the lines and how those differences affect how many events must occur, roughly speaking, before the probability of still being HIV-negative dips below 0.5 (or 1:1 odds).  After all, isn’t that we’re often being asked, “How long until my luck runs out?”

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