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'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 accelerated changing dating procedures and sero-adaptive behaviours (such as viral sorting and pre exposure prophylaxis). With each new way of dating and preventative opportunities, the rules of battles will vary. Our data are 8years old and internet-based dating has developed since then. Nevertheless these results are useful, as they reveal how internet-based partner acquisition can lead to more info on the sex partner, and this might affect on the frequency of UAI.
Relationship online may offer other opportunities for communicating on HIV status than dating in physical surroundings. Adult hookups near Marrickville, New South Wales. Adult Hookups Near Me Collingwood New South Wales. 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 disease completely. Interventions to prevent HIV transmission should notably be directed at HIV negative and unaware MSM and spark timely HIV testing (i.e., after risk events 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, precise knowledge of one's own and the partner's HIV status is essential. In HIV negative men 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 period 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 successful 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 effect of dating place on UAI did not change by adding partner characteristics, but it improved when adding lifestyle and drug use. It's hard to assess the real risk for HIV for these guys: do they behave as HIV negative guys that want to protect themselves from HIV infection, or as HIV positive men trying to safeguard 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 problematic if they are HIV positive and participate in UAI with HIV negative partners 12 Previously Matser et al. reported that 1.7% of the oblivious and sensed HIV-negative MSM were analyzed HIV positive. The study population comprised the MSM reported in this study 15
Online dating was not associated 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 behaviour patterns within one group of men. Yet it could also reflect secular 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 now also utilize the Internet for dating.
A vital strength of the 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 avoided prejudice caused by potential differences between guys only dating online and those only dating offline, a weakness of numerous previous studies. By recruiting participants at the biggest STI outpatient clinic in the Netherlands we could include a high number of MSM, and prevent potential differences in guys tried through Internet or face-to-face interviewing, weaknesses in some previous studies 3 , 11
Among HIV-positive guys, in univariate analysis UAI was reported significantly more often with online 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 nonsignificant; this implies that differences in partnership factors between online and offline partnerships are in charge of the increased UAI in online established partnerships. This could be because of a mediating effect of more information on associates, (including perceived HIV status) on UAI, or to other variables. Among HIV negative guys no effect of online dating on UAI was found, either in univariate or in the multivariate models. Adult hookups nearest Marrickville, New South Wales. Among HIV-oblivious guys, online dating was connected with UAI but only critical when adding partner and venture variables to the model.
In this large study among MSM attending the STI clinic in Amsterdam, we found no signs that online dating was independently associated with a higher danger of UAI than offline dating. Adult Hookups in Marrickville New South Wales. For HIV negative men this lack of assocation was clear (aOR = 0.94 95 % CI 0.59-1.48); among HIV positive guys there was a nonsignificant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Simply among guys who suggested 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 also 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 venture (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the venture compared to only one sex act). Other factors significantly associated with UAI were group sex within the partnership, and sex-connected multiple drug use within partnership.
In multivariate model 3 (Tables 4 and 5 ), additionally including variants concerning sexual behavior in the venture (sex-related multiple drug use, sex frequency and partner type), 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 stronger (and important) for HIV-oblivious men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more inclined to occur in on-line than in offline ventures (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 impact 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 distinct reference groups, one for each HIV status. Among HIV-positive men, UAI was more common in online compared to offline ventures (OR = 1.61 95 % CI 1.03-2.50). Among HIV negative guys no association was apparent between UAI and online 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 partnerships 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 online partners was more often reported as understood (61.4% vs. 49.4%; P 0.001), and in online ventures, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their on-line partners more often understood 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 internet partners (50.9% vs. 41.3%; P 0.001). Sex-related substance use, alcohol use, and group sex were less frequently reported with online partners.
To be able to examine the possible mediating effect of more info on partners (including perceived HIV status) on UAI, we developed three multivariable models. In model 1, we adjusted the organization between online/offline dating place 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 venture characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In version 3, we adapted also for venture sexual risk behaviour (i.e., sex-associated 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 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 lose potentially important associations. As a rather big number of statistical evaluations were done and reported, this strategy does lead to an elevated danger of one or more false positive organizations. Evaluations 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 variants were putative causes (self-reported HIV status; on-line partner acquisition), others were considered as confounders (participants' age, participants' ethnicity, and no. Marrickville adult hookups. of male sex partners in preceding 6months), and some were presumed to be on the causal pathway between the primary exposure of interest and result (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; venture kind; sex frequency within partnership; group sex with partner; sex-associated substance use in venture).
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 Rockdale New South Wales. We compared features of participants, partners, and venture sexual behaviour by on-line or offline venture, and calculated P values based on logistic regression with robust standard errors, accounting for linked 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 location (online versus offline) and UAI. Likelihood ratio tests were used to measure the significance of a variable in a model.
In order to investigate potential disclosure of HIV status we also asked the participant whether the casual sex partner knew the HIV status of the participant, with the answer choices: (1) no, (2) potentially, (3) yes. Sexual conduct with each partner was dichotomised as: (1) no anal intercourse or only protected 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 subsequent subcultures/lifestyles: casual, formal, substitute, drag, leather, military, sports, fashionable, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these characteristics were related, 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 obtained by asking the question 'Do you understand whether you're HIV infected?', with five response alternatives: (1) I am certainly not HIV-infected; (2) I believe that I am not HIV-contaminated; (3) I don't understand; (4) I think I may be HIV-infected; (5) I know for sure that I am HIV-contaminated. 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 understand whether this partner is HIV-contaminated?' with similar answer options as previously. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. Adult hookups near me Marrickville, NSW. 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.