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The amount 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 occurred 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). Naughty date near Regents Park, Queensland. Other variables 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 ), also including variants 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 more powerful (though not significant) 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 significant) for HIV-unaware men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).

In univariate analysis, UAI was significantly more likely to occur 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 strongly associated 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 different reference types, one for each HIV status. Among HIV positive men, UAI was more common in online when compared with offline ventures (OR = 1.61 95 % CI 1.03-2.50). Among HIV negative men 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 in comparison to offline partnerships, 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 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 on-line ventures, 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 online partners (50.9% vs. 41.3%; P 0.001). Sex-related substance use, alcohol use, and group sex were less often reported with internet partners.

To be able to analyze the possible 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 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 venture features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adapted additionally for partnership sexual risk behavior (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 place 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-unaware men. We performed a sensitivity analysis confined 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 rather large number of statistical evaluations were done and reported, this strategy does lead to a higher danger of one or more false-positive associations. Evaluations were done using the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).

Before the evaluations we developed a directed acyclic graph (DAG) representing a causal model of UAI. Naughty date nearby Regents Park, Queensland. 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. of male sex partners in preceding 6months), and some were assumed to be on the causal pathway between the main exposure of interest and result (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership sort; sex frequency within venture; 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-tests for dichotomous and categorical variables and using rank sum test for continuous variables). We compared characteristics of participants, partners, and partnership sexual behaviour by online or offline venture, and computed P values based on logistic regression with robust standard errors, accounting for related 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. Odds ratio tests were used to gauge the value of a variable in a model.

As a way to investigate potential disclosure of HIV status we also asked the participant whether the casual sex partner knew the HIV status of the participant, together with the response options: (1) no, (2) maybe, (3) yes. Sexual behavior with each partner was dichotomised as: (1) no anal intercourse or merely 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 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. Accidental 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 know whether you're HIV infected?', with five answer choices: (1) I 'm certainly not HIV-contaminated; (2) I think that I'm not HIV-infected; (3) I do not understand; (4) I think I may be HIV-contaminated; (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 each sex partner with the question: 'Do you understand whether this partner is HIV-contaminated?' with similar response alternatives as previously. Naughty date nearby Regents Park Queensland, Australia. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. The last 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.

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Participants completed a standardised anonymous questionnaire throughout their visit to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation using a nurse or physician. The questionnaire elicited information on socio-demographics and HIV status of the participant, the three most recent partners in the preceding six months, and data on sexual conduct with those partners. A detailed description of the study design as well as the questionnaire is provided elsewhere 15 , 18 Our main determinant of interest, dating location (e.g., the name of a bar, park, club, or the name of a site) was obtained for every partner, and categorised into online (websites), and offline (physical sites) dating places. To simplify the terminology 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. People could participate more than once, if subsequent visits to the practice were related to a potential 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 approved by the medical ethics committee of the Academic Medical Center of Amsterdam (MEC 07/181), and written informed consent was obtained from each participant. Contained in this investigation were guys 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 chances for UAI increase as well 14 - 16 We compared the occurrence of UAI in online acquired casual partnerships to that in offline acquired 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 described through better knowledge of partner characteristics, including HIV status.

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A meta-evaluation in 2006 found limited evidence that getting a sex partner online raises the risk of unprotected anal intercourse (UAI) 3 Many previous studies compared men with online partners to guys with offline partners. However, men favoring online dating might differ in several unmeasured respects from men favoring offline dating, leading to incomparable behavioural profiles. A more recent meta-analysis contained several studies analyzing MSM with both online and also offline acquired sex partners and found evidence for an association between UAI and internet partners, which may indicate a mediating effect of more info on partners, (including perceived HIV status) on UAI 13 Naughty date in Regents Park QLD.

Men who have sex with men (MSM) often make use of the Web to find sex partners. Several research have shown that MSM are more inclined to participate in unprotected anal intercourse with sex partners they meet through the Internet (online) than with partners they meet at social sites (offline) 1 - 3 This suggests 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 on-line partners, the risk of HIV transmission also depends on exact 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-oblivious) reported UAI in 26% of 878 online, 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). Adjusted for demographic characteristics, online dating had no significant effect on UAI among HIV negative and HIV status-unaware men, but HIV-positive men were more likely to have UAI with online associates (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 essential.

Believe it or not, I didn't come out of this experiment feeling awful about myself---simply smarter about the way gay men (or maybe men in general) place way too much emphasis on silly characteristics like beards and ballcaps (hint: that is why you are all still cranky and single). And actually, I do not think having long hair itself is the big hang-up; it's what my hair implies. Having long hair (especially for a black man) means you're likely a bitchy remarkable queen that nobody needs to date. Naughty Date in Regents Park QLD. Even if the premise is not that extreme, the inherent fear is you spent too much time on your appearance and that is not manly." That's frustrating, obviously, since stereotypical masculinity requires just as much work---we simply don't think of it that way. I recall chatting with this scruffy, fairly muscular guy with tattoos and chest hair and an Instagram full of masc pics; once we got to talking, he shown his fixation with Beyonc and said yasss!" every other paragraph. But no matter---his image is butch, so his dating life is constantly full.

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