AMC, Academic Medical Center; aOR, adjusted odds ratio; CI, confidence interval; CINIMA, Center for Infection and Immunology Amsterdam; DAG, directed acyclic graph; HIV, human immuno-deficiency virus; i.e., id est, it's, for example; IQR, interquartile range; MEC, Medical Ethics Committee; MSM, men who have sex with men; OR, odds ratio; RIVM, National Institute of Public Health and the Environment, Centre for Infectious Disease Control; STI, sexually transmitted infection; UAI, unprotected anal intercourse; UMCU, University Medical Center Utrecht
New research should remain up to date in regards to fast changing dating processes and sero-adaptive behaviours (like viral sorting and pre exposure prophylaxis). With every new way of dating and preventative opportunities, the rules of battles will be different. Our data are 8years old and net-based dating has developed since then. However these results are useful, as they show how internet-based partner acquisition may lead to more information on the sex partner, and this might impact on the frequency of UAI.
Dating online may offer other opportunities for communication on HIV status than dating in physical surroundings. Adult Hookups nearby Glen Waverley, Victoria. Adult Hookups Near Me Campbelltown Victoria. Easing more online HIV status disclosure during partner seeking makes serosorting simpler. Yet, serosorting may raise the load of other STI and will not prevent HIV infection entirely. Interventions to prevent HIV transmission should particularly be directed at HIV-negative and oblivious MSM and stimulate timely HIV testing (i.e., after danger occasions or when experiencing symptoms of seroconversion illness) as well as regular testing when sexually active.
Because conclusions on UAI seem to be partially based on perceived HIV concordance, accurate knowledge of one's own and the partner's HIV status is essential. In HIV-negative men and HIV status-oblivious men, judgements on UAI WOn't only be based on perceived HIV status of the partner but in addition on one's own negative status. HIV serosorting is challenged by the frequency of HIV testing as well as the HIV window phase during which individuals can transmit HIV but cannot be diagnosed with the commonly used HIV tests. So serosorting can't be regarded as a very powerful way of avoiding HIV transmission 22 Besides interventions to trigger the uptake of HIV and STI testing in sexually active men, interventions to caution against UAI based on sensed HIV negative concordant status are in order, irrespective of whether this concerns online or offline dating.
For HIV-unaware men the impact of dating place on UAI did not change by adding partner features, but it increased when adding lifestyle and drug use. It's difficult to assess the real risk for HIV for these men: do they act as HIV negative men that are trying to protect themselves from HIV infection, or as HIV positive guys attempting to guard their HIV negative partner from HIV infection? A study by Horvath et al. reported that 72% of men who were never tested for HIV, profiled themselves online as being HIV-negative, which might be debatable if they are HIV positive and participate in UAI with HIV negative partners 12 Formerly Matser et al. reported that 1.7% of the unaware and sensed HIV negative MSM were tested HIV-positive. The study population included the MSM reported in this study 15
Online dating was not correlated with UAI among HIV negative men, a finding in agreement with some previous studies, largely among young men 21 , but in contrast with other studies 1 - 5 This may be because of the fact that most earlier studies compared sexual behavior of two groups of MSM rather than comparing two sexual behavior patterns within one group of men. Yet it could also reflect lay changes; possibly in the beginning of online dating a more high risk group of men used the Internet, and over time online dating normalized and not as high-risk MSM today also make use of the Web for dating.
A vital strength of this study was that it explored the relationship between online dating and UAI among MSM who had recent sexual contact with both online and also offline casual partners. This averted bias caused by potential differences between guys only dating online and those simply dating offline, a weakness of several previous studies. By recruiting participants at the largest STI outpatient clinic in the Netherlands we could comprise a large number of MSM, and avoid potential differences in guys tried through Internet or face-to-face interviewing, weaknesses in certain previous studies 3 , 11
Among HIV positive guys, in univariate analysis UAI was reported significantly more often with on-line associates than with offline associates. When adjusting for partner characteristics, the effect of online/offline dating on UAI among HIV-positive MSM became somewhat smaller and became non-significant; this indicates that differences in partnership factors between online and also offline partnerships are accountable for the increased UAI in online established partnerships. This might be due to a mediating effect of more info on partners, (including perceived HIV status) on UAI, or to other factors. Among HIV-negative men no effect of online dating on UAI was discovered, either in univariate or in some of the multivariate models. Adult Hookups in Glen Waverley Victoria. Among HIV-oblivious guys, online dating was connected with UAI but only significant when adding partner and partnership variants to the model.
In this large study among MSM attending the STI clinic in Amsterdam, we found no evidence that online dating was independently associated with a higher danger of UAI than offline dating. Adult Hookups nearest Glen Waverley, Victoria. For HIV-negative guys this dearth of assocation was clear (aOR = 0.94 95 % CI 0.59-1.48); among HIV positive men there was a non-significant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Simply among guys who indicated they weren't conscious of their HIV status (a small group in this study), UAI was more common with on-line than offline associates.
The number of sex partners in the preceding 6months of the index was likewise associated with UAI (OR = 6.79 95 % CI 2.86-16.13 for those with 50 or more recent sex partners compared to those with fewer than 5 recent sex partners). UAI was significantly more likely if more sex acts had happened in the partnership (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the partnership compared to just one sex act). Other factors significantly associated with UAI were group sex within the venture, and sex-related multiple drug use within venture.
In multivariate model 3 (Tables 4 and 5 ), additionally including variables concerning sexual behavior in the partnership (sex-related multiple drug use, sex frequency and partner kind), the separate effect of online dating location on UAI became somewhat stronger (though not critical) for the HIV positive guys (aOR = 1.62 95 % CI; 0.96-2.72), but remained similar for HIV-negative men (aOR = 0.94 95 % CI 0.59-1.48). The effect of online dating on UAI became more powerful (and essential) for HIV-oblivious guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more inclined to happen in online than in offline ventures (OR = 1.36 95 % CI 1.03-1.81) (Table 4 ). The self-perceived HIV status of the participant was strongly correlated with UAI (OR = 11.70 95 % CI 7.40-18.45). The effect of dating location on UAI differed by HIV status, as can be seen best in Table 5 Table 5 shows the organization of online dating using three different reference classes, one for each HIV status. Among HIV positive men, UAI was more common in online in comparison to offline ventures (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative men no association was evident between UAI and internet ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware guys, UAI was more common in online compared to offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).
Characteristics of on-line and offline partners and ventures are shown in Table 2 The median age of the partners was 34years (IQR 28-40). Compared to offline partners, more on-line partners were Dutch (61.3% vs. 54.0%; P 0.001) and were defined as a known partner (77.7% vs. 54.4%; P 0.001). The HIV status of on-line partners was more often reported as known (61.4% vs. 49.4%; P 0.001), and in on-line partnerships, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their online partners more frequently knew the HIV status of the participant than offline partners (38.8% vs. 27.2%; P 0.001). Participants more often reported multiple sexual contacts with internet partners (50.9% vs. 41.3%; P 0.001). Sex-associated substance use, alcohol use, and group sex were less frequently reported with online partners.
To be able to examine the potential mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three multivariable models. In version 1, we adjusted the organization between online/offline dating location and UAI for features of the participant: age, ethnicity, number of sex partners in the preceding 6months, and self-perceived HIV status. In model 2 we added the partnership characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In version 3, we adapted also for venture sexual risk behavior (i.e., sex-associated drug use and sex frequency) and partnership sort (i.e., casual or anonymous). As we assumed a differential effect of dating place for HIV-positive, HIV negative and HIV status unknown MSM, an interaction between HIV status of the participant and dating location was included in all three models by making a fresh six-class variable. For clarity, the effects of online/offline dating on UAI are also presented separately for HIV-negative, HIV-positive, and HIV-unaware men. We performed a sensitivity analysis restricted to partnerships in which only one sexual contact occurred. Statistical significance was defined as P 0.05. No adjustments for multiple comparisons were made, in order not to miss potentially important organizations. As a rather large number of statistical tests were done and reported, this strategy does lead to a higher danger of one or more false-positive associations. Analyses were done using the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).
Before the investigations we developed a directed acyclic graph (DAG) representing a causal model of UAI. In this model some variables were putative causes (self-reported HIV status; on-line partner acquisition), others were considered as confounders (participants' age, participants' ethnicity, and no. Glen Waverley Adult Hookups. of male sex partners in preceding 6months), and some were presumed to be on the causal pathway between the main exposure of interest and outcome (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; venture sort; sex frequency within venture; group sex with partner; sex-related material use in partnership).
We compared characteristics of participants by self-reported HIV status (using 2-tests for dichotomous and categorical variables and using rank sum test for continuous variables). Adult Hookups Near Me St Albans Victoria. We compared characteristics of participants, partners, and venture sexual conduct by on-line or offline partnership, and computed P values predicated on logistic regression with robust standard errors, accounting for correlated data. Continuous variables (i.e., age, number of sex partners) are reported as medians with an interquartile range (IQR), and were categorised for inclusion in multivariate models. Random effects logistic regression models were used to analyze the association between dating place (online versus offline) and UAI. Odds ratio tests were used to measure the significance of a variable in a model.
To be able to explore potential disclosure of HIV status we also asked the participant whether the casual sex partner knew the HIV status of the participant, with the response alternatives: (1) no, (2) maybe, (3) yes. Sexual conduct with each partner was dichotomised as: (1) no anal intercourse or simply protected anal intercourse, and (2) unprotected anal intercourse. To discover the subculture, we asked whether the participant characterised himself or his partners as belonging to at least one of the following subcultures/lifestyles: casual, formal, substitute, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if not one of these features were applicable, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Chance partner sort was categorised by the participants into (1) known traceable and (2) anonymous partners.
HIV status of the participant was got by asking the question 'Do you understand whether you're HIV infected?', with five answer choices: (1) I am certainly not HIV-infected; (2) I think that I'm not HIV-contaminated; (3) I don't know; (4) I think I may be HIV-contaminated; (5) I know for sure that I am HIV-infected. We categorised this into HIV-negative (1,2), unknown (3), and HIV positive (4,5) status. The questionnaire enquired about the HIV status of every sex partner with all the question: 'Do you know whether this partner is HIV-infected?' with similar response options as above. Perceived concordance in HIV status within ventures was categorised as; (1) concordant; (2) discordant; (3) unknown. Adult Hookups near Glen Waverley VIC. The final group represents all partnerships where the participant did not understand his own status, or the status of his partner, or both. In this study the HIV status of the participant is self-reported and self-perceived. The HIV status of the sexual partner is as perceived by the participant.