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The amount of sex partners in the preceding 6months of the index was also connected 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 venture compared to just one sex act). Casual encounters near me Mango Hill, Queensland. Other factors significantly associated with UAI were group sex within the venture, and sex-related multiple drug use within partnership.

In multivariate model 3 (Tables 4 and 5 ), additionally including variants concerning sexual behaviour in the partnership (sex-related multiple drug use, sex frequency and partner kind), the separate effect of online dating place 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 guys (aOR = 0.94 95 % CI 0.59-1.48). The result of online dating on UAI became stronger (and significant) for HIV-unaware guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).

In univariate analysis, UAI was significantly more prone to occur 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 connected with UAI (OR = 11.70 95 % CI 7.40-18.45). The impact of dating place 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 categories, one for each HIV status. Among HIV positive men, UAI was more common in online compared to offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative guys no association was apparent between UAI and internet ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware guys, UAI was more common in online in comparison to offline ventures, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

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Features 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 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 on-line partners was more frequently 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 often 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 material use, alcohol use, and group sex were less often reported with online partners.

In order to examine the potential mediating effect of more info on partners (including perceived HIV status) on UAI, we developed three variant models. In version 1, we adapted the association between online/offline dating place 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 features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adapted additionally for partnership 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 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 guys. We performed a sensitivity analysis confined to partnerships in which just 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 large number of statistical evaluations were done and reported, this approach does lead to a heightened 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. Casual Encounters near Mango Hill, 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 presumed 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 type; sex frequency within partnership; group sex with partner; sex-related material use in venture).

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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 venture sexual behavior by on-line or offline venture, and computed P values predicated 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 examine the association between dating place (online versus offline) and UAI. Odds ratio tests were used to gauge the value of a variable in a model.

To be able to investigate possible disclosure of HIV status we also asked the participant whether the casual sex partner knew the HIV status of the participant, with the response options: (1) no, (2) maybe, (3) yes. Sexual behavior with each partner was dichotomised as: (1) no anal intercourse or simply shielded 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, alternate, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if not one of these characteristics were applicable, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Casual 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 understand whether you are HIV infected?', with five response choices: (1) I 'm definitely not HIV-contaminated; (2) I think that I'm not HIV-infected; (3) I don't know; (4) I believe I may be HIV-contaminated; (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 together with the question: 'Do you understand whether this partner is HIV-infected?' with similar answer choices as previously. Casual Encounters nearest Mango Hill Queensland Australia. Perceived concordance in HIV status within ventures was categorised as; (1) concordant; (2) discordant; (3) unknown. The final class represents all partnerships where the participant didn't 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 during their visit to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation using a nurse or doctor. The questionnaire 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 conduct with those partners. A detailed description of the study design as well as the survey is supplied elsewhere 15 , 18 Our primary 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 on-line (websites), and offline (physical sites) dating locations. To simplify the language of distinguishing the partners per dating place, 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 could comprehend written Dutch or English. Individuals could participate more than once, if following visits to the clinic 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 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 incidence of UAI in online got 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 on the internet, and that this effect is partly clarified through better understanding of partner characteristics, including HIV status.

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A meta-analysis in 2006 found limited evidence that getting a sex partner online raises the danger of unprotected anal intercourse (UAI) 3 Many previous studies compared men with internet partners to guys with offline partners. Nonetheless, men favoring online dating might differ in a variety of unmeasured respects from men favoring offline dating, resulting in 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 on-line partners, which might suggest a mediating effect of more information on partners, (including perceived HIV status) on UAI 13 Casual encounters nearby Mango Hill, QLD.

Men who have sex with men (MSM) frequently use the Net to find sex partners. Several research have revealed that MSM are prone to engage in unprotected anal intercourse with sex partners they meet through the Internet (on-line) than with partners they meet at social sites (offline) 1 - 3 This indicates that guys who acquire 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 threat of HIV transmission also depends upon exact knowledge of one's own and the sex partners' HIV status 7 - 10

Five hundred seventy-seven men (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). Adjusted for demographic characteristics, online dating had no major effect on UAI among HIV negative and HIV status-oblivious 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 features the effect of online/offline dating on UAI among HIV-positive MSM was reduced and no longer essential.

Believe it or not, I did not come out of this experiment feeling lousy about myself---just smarter about the way gay men (or maybe guys in general) area way too much emphasis on foolish characteristics like beards and ballcaps (hint: that is why you're all still cranky and single). And really, I don't think having long hair itself is the huge hang-up; it's what my hair implies. Having long hair (particularly for a black man) means you're probably a bitchy striking queen that nobody wants to date. Casual Encounters near Mango Hill, QLD. Even in the event the assumption isn't that extreme, the underlying fear is you spent too much time on your appearance and that is not masculine." That's frustrating, of course, since stereotypical masculinity requires only as much work---we simply do not think of it that way. I remember chatting with this scruffy, pretty muscular guy with tattoos and torso hair and an Instagram full of masc pics; after we got to speaking, he shown 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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