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The amount 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 occurred in the venture (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the partnership compared to only one sex act). Casual Encounter near Rockdale, New South Wales. Other factors significantly associated with UAI were group sex within the partnership, and sex-related multiple drug use within venture.

In multivariate model 3 (Tables 4 and 5 ), additionally including variants concerning sexual behavior in the venture (sex-associated multiple drug use, sex frequency and partner kind), the separate effect of online dating location on UAI became somewhat stronger (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 result of online dating on UAI became more powerful (and important) for HIV-oblivious men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).

In univariate analysis, UAI was significantly more likely to happen in online 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 connected with UAI (OR = 11.70 95 % CI 7.40-18.45). The result of dating location on UAI differed by HIV status, as can be seen best in Table 5 Table 5 shows the association of online dating using three distinct reference types, one for each HIV status. Among HIV positive guys, UAI was more common in online compared to offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative men no association was apparent between UAI and on-line ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware guys, UAI was more common in online when compared with offline ventures, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

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Features of online 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 online 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 ventures, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their online partners more frequently 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 online partners (50.9% vs. 41.3%; P 0.001). Sex-related material use, alcohol use, and group sex were less often reported with online partners.

In order to examine the possible mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three variant models. In model 1, we adapted 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 model 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 contained in all three models by making a brand 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-oblivious guys. We performed a sensitivity analysis limited 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 significant associations. As a fairly big number of statistical evaluations were done and reported, this strategy does lead to a heightened risk of one or more false-positive organizations. Evaluations were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).

Prior to the analyses we developed a directed acyclic graph (DAG) representing a causal model of UAI. Casual Encounter near Rockdale, New South Wales. 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. of male sex partners in preceding 6months), and some were supposed to be on the causal pathway between the principal exposure of interest and outcome (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership kind; sex frequency within partnership; group sex with partner; sex-related material use in partnership).

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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). We compared characteristics of participants, partners, and partnership 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 place (online versus offline) and UAI. Likelihood ratio tests were used to measure the value of a variable in a model.

To be able to explore potential disclosure of HIV status we additionally asked the participant whether the casual sex partner understood the HIV status of the participant, together with the reply alternatives: (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 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 kind 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 know whether you are HIV infected?', with five response options: (1) I am definitely not HIV-infected; (2) I think that I am not HIV-contaminated; (3) I don't understand; (4) I believe I may be HIV-infected; (5) I know for sure that I 'm 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 each sex partner with the question: 'Do you know whether this partner is HIV-infected?' with similar answer choices as previously. Casual Encounter nearest Rockdale New South Wales Australia. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. The final category represents all partnerships where the participant didn't know 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.

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Participants completed a standardised anonymous questionnaire during their visit to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation with a nurse or doctor. The survey elicited information on socio-demographics and HIV status of the participant, the three most recent partners in the preceding six months, and information on sexual behavior with those partners. A thorough description of the study design as well as the survey is supplied elsewhere 15 , 18 Our main determinant of interest, dating location (e.g., the name of a bar, park, club, or the name of a web site) was obtained for every partner, and categorised into online (websites), and offline (physical sites) dating locations. To simplify the language of differentiating the partners per dating location, we refer to them as on-line or offline partners.

We used data from a cross-sectional study focusing on spread of STI via sexual networks 15 Between July 2008 and August 2009 MSM were recruited from the STI outpatient clinic of the Public Health Service of Amsterdam, the Netherlands. Men were eligible for participation if they reported sexual contact with men during the six months preceding the STI consultation, they were at least 18years old, and might understand written Dutch or English. Individuals could participate more than once, if following visits to the practice were related to a possible new STI episode. Participants were routinely screened for STI/HIV according to the standard procedures of the STI outpatient clinic 15 , 17 The study was accepted by the medical ethics committee of the Academic Medical Center of Amsterdam (MEC 07/181), and written informed consent was obtained from each participant. Included in this analysis were men who reported sexual contact with at least one casual partner dated online as well one casual partner dated offline.

With increased familiarity in sexual partnerships, for example by concordant ethnicity, age, lifestyle, HIV status, and increasing sex frequency, the likelihood for UAI increase as well 14 - 16 We compared the occurrence of UAI in online acquired casual partnerships to that in offline got casual partnerships among MSM who reported both on-line and offline casual partners in the preceding six months. We hypothesised that MSM who date sex partners both online and offline, report more UAI with the casual partners they date online, and that this effect is partially clarified through better understanding of partner features, including HIV status.

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A meta-evaluation in 2006 found limited evidence that acquiring a sex partner online increases the danger of unprotected anal intercourse (UAI) 3 Many previous studies compared men with online partners to men with offline partners. Yet, guys preferring online dating might differ in various unmeasured regards from men preferring offline dating, causing incomparable behavioural profiles. A more recent meta-analysis included several studies examining MSM with both online and also offline acquired sex partners and found evidence for an association between UAI and on-line partners, which may imply a mediating effect of more information on partners, (including perceived HIV status) on UAI 13 Casual encounter nearby Rockdale, NSW.

Men who have sex with men (MSM) frequently make use of the Net to discover sex partners. Several studies have revealed that MSM are prone to engage in unprotected anal intercourse with sex partners they meet through the Internet (online) than with partners they meet at social places (offline) 1 - 3 This implies that men who get partners online may be at a higher risk for sexually transmitted infections (STI) and HIV 4 - 6 Although higher rates of UAI are reported with internet partners, the danger of HIV transmission also depends on precise knowledge of one's own and the sex partners' HIV status 7 - 10

Five hundred seventy-seven guys (351 HIV-negative, 153 HIV positive, and 73 HIV-unaware) reported UAI in 26% of 878 on-line, and 23% of 903 offline casual partnerships. The crude OR of online dating for UAI was 1.36 (95 % CI 1.03-1.81). HIV positive men were more likely to report UAI than HIV-negative men (49% vs. 28% of ventures). Corrected for demographic characteristics, online dating had no major effect on UAI among HIV-negative and HIV status-unaware guys, but HIV-positive men were more likely to have UAI with online partners (aOR = 1.65 95 % CI 1.05-2.57). After correction for partner and partnership characteristics the effect of online/offline dating on UAI among HIV positive MSM was reduced and no longer significant.

Believe it or not believe it, I did not come out of this experiment feeling bad about myself---simply smarter about the way gay men (or perhaps guys in general) place way too much emphasis on daft characteristics like beards and ballcaps (hint: that is why you are all still cranky and single). And actually, I don't think having long hair itself is the huge hang up; it's what my hair implies. Having long hair (especially for a black man) means you are likely a bitchy striking queen that nobody needs to date. Casual encounter in Rockdale, NSW. Even if the assumption is not that extreme, the inherent anxiety is you spent too much time on your look and that's not masculine." That is frustrating, of course, since stereotypical masculinity takes just as much work---we just do not think of it that way. I remember chatting with this scruffy, pretty muscular man with tattoos and chest hair and an Instagram full of masc pics; once we got to speaking, he revealed his obsession with Beyonc and said yasss!" every other paragraph. But no matter---his graphic is butch, so his dating life is always full.

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