AMC, Academic Medical Center; aOR, adjusted odds ratio; CI, confidence interval; CINIMA, Center for Infection and Immunology Amsterdam; DAG, directed acyclic graph; HIV, human immunodeficiency virus; i.e., id est, it is, 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 stay up to date when it comes to accelerated shifting dating strategies as well as sero-adaptive behaviours (like viral sorting and pre exposure prophylaxis). With every new way of dating and preventive chances, the rules of engagements will be different. Our data are 8years old and internet-based dating has developed since then. Yet these results are useful, as they demonstrate how web-based partner acquisition can result in more information on the sex partner, and this may impact on the frequency of UAI.
Relationship online may offer other chances for communication on HIV status than dating in physical surroundings. Free hook ups near me Caulfield, Victoria. Free Hook Ups Near Me Carlton North Victoria. Facilitating more on-line HIV status disclosure during partner seeking makes serosorting easier. However, serosorting may increase the burden of other STI and WOn't prevent HIV disease completely. Interventions to prevent HIV transmission should especially be directed at HIV negative and oblivious MSM and spark timely HIV testing (i.e., after risk occasions or when experiencing symptoms of seroconversion illness) as well as routine testing when sexually active.
Because determinations on UAI seem to be partially based on sensed HIV concordance, exact knowledge of one's own and the partner's HIV status is very important. In HIV-negative guys and HIV status-oblivious men, conclusions 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 and the HIV window phase during which individuals can transmit HIV but cannot be diagnosed with the commonly used HIV tests. Hence serosorting cannot be regarded as a very effective way of averting HIV transmission 22 Besides interventions to trigger the uptake of HIV and STI testing in sexually active men, interventions to warn 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 didn't change by adding partner characteristics, but it increased when adding lifestyle and drug use. It's hard to evaluate the actual risk for HIV for these men: do they behave as HIV negative guys who are attempting to protect themselves from HIV infection, or as HIV-positive men trying to shield their HIV-negative partner from HIV infection? A study by Horvath et al. reported that 72% of guys who were never tested for HIV, profiled themselves online as being HIV negative, which might be debatable if they're 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 examined HIV positive. The study population comprised the MSM reported in this study 15
Online dating wasn't correlated with UAI among HIV negative guys, a finding in agreement with some previous studies, largely among young men 21 , but in comparison with other studies 1 - 5 This may be due to the reality that most earlier studies compared sexual behavior of two groups of MSM rather than comparing two sexual behavior patterns within one group of guys. Nonetheless it can also represent lay changes; maybe in the beginning of online dating a more high-risk group of guys used the Internet, and over time online dating normalized and less high risk MSM nowadays additionally use the Web for dating.
An integral strength of the study was that it explored the connection between online dating and UAI among MSM who had recent sexual contact with both online and offline casual partners. This avoided prejudice due to potential differences between men only dating online and those only dating offline, a weakness of numerous previous studies. By recruiting participants at the greatest STI outpatient clinic in the Netherlands we could contain 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 frequently with on-line associates than with offline associates. When correcting for partner characteristics, the effect of online/offline dating on UAI among HIV positive MSM became somewhat smaller and became non significant; this suggests that differences in partnership factors between online and offline partnerships are liable for the increased UAI in online established partnerships. This might be because of a mediating effect of more info on associates, (including perceived HIV status) on UAI, or to other variables. Among HIV negative guys no effect of online dating on UAI was observed, either in univariate or in the multivariate models. Free Hook Ups near me Caulfield, Victoria. Among HIV-unaware men, online dating was connected with UAI but only significant when adding associate and venture 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. Free hook ups near Caulfield, Victoria. For HIV negative guys this lack 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). Only among men who indicated they were not informed of their HIV status (a small group in this study), UAI was more common with online than offline associates.
The number of sex partners in the preceding 6months of the index was likewise correlated 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 only one sex act). Other factors significantly associated with UAI were group sex within the partnership, and sex-related multiple drug use within partnership.
In multivariate model 3 (Tables 4 and 5 ), additionally including variables concerning sexual behavior in the partnership (sex-associated multiple drug use, sex frequency and partner kind), the independent effect of online dating place on UAI became somewhat more powerful (though not essential) 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 critical) for HIV-unaware guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more inclined to happen in on-line than in offline partnerships (OR = 1.36 95 % CI 1.03-1.81) (Table 4 ). The self-perceived HIV status of the participant was firmly correlated with UAI (OR = 11.70 95 % CI 7.40-18.45). The effect of dating place on UAI differed by HIV status, as can be seen best in Table 5 Table 5 shows the organization of online dating using three distinct reference groups, one for each HIV status. Among HIV-positive men, UAI was more common in online when compared with offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative men no association was apparent between UAI and internet ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-oblivious guys, UAI was more common in online when compared with offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).
Features of on-line and offline partners and partnerships are revealed 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 online partners was more frequently reported as understood (61.4% vs. 49.4%; P 0.001), and in online partnerships, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their on-line partners more often knew the HIV status of the participant than offline partners (38.8% vs. 27.2%; P 0.001). Participants more frequently reported multiple sexual contacts with online 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.
In order to analyze the potential mediating effect of more info on partners (including perceived HIV status) on UAI, we developed three multivariable models. In version 1, we adjusted the association between online/offline dating location and UAI for characteristics 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 partnership sexual risk behaviour (i.e., sex-related drug use and sex frequency) and venture kind (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 contained in all three models by making a new six-class variable. For clarity, the effects of online/offline dating on UAI are also presented individually for HIV-negative, HIV positive, and HIV-oblivious guys. 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 lose potentially significant organizations. As a fairly large number of statistical tests were done and reported, this approach does lead to an increased danger of one or more false positive associations. Investigations were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).
Before the analyses 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; online partner acquisition), others were considered as confounders (participants' age, participants' ethnicity, and no. Caulfield free hook ups. 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; partnership type; sex frequency within venture; group sex with partner; sex-associated substance use in partnership).
We compared characteristics of participants by self-reported HIV status (using 2-evaluations for dichotomous and categorical variables and using rank sum test for continuous variables). Free Hook Ups Near Me Campbellfield Victoria. We compared characteristics of participants, partners, and partnership sexual behavior by on-line or offline venture, and calculated P values predicated on logistic regression with robust standard errors, accounting for linked data. Continuous variables (i.e., age, amount 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 examine the association between dating location (online versus offline) and UAI. Odds ratio tests were used to measure the value of a variable in a model.
To be able to explore possible disclosure of HIV status we additionally asked the participant whether the casual sex partner knew the HIV status of the participant, together with the reply options: (1) no, (2) potentially, (3) yes. Sexual behavior with each partner was dichotomised as: (1) no anal intercourse or merely shielded anal intercourse, and (2) unprotected anal intercourse. To ascertain the subculture, we asked whether the participant characterised himself or his partners as belonging to one or more of the following subcultures/lifestyles: casual, formal, alternative, drag, leather, military, sports, fashionable, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these characteristics were appropriate, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Chance partner type 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 are HIV infected?', with five answer choices: (1) I am certainly not HIV-contaminated; (2) I think that I am not HIV-infected; (3) I don't know; (4) I think I may be HIV-infected; (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 survey enquired about the HIV status of every sex partner with the question: 'Do you know whether this partner is HIV-contaminated?' with similar reply alternatives as previously. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. Free hook ups near Caulfield, VIC. The final category 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.