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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 happened in the venture (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the venture compared to just one sex act). Casual Sex near me Carlingford New South Wales. Other variables significantly associated with UAI were group sex within the venture, and sex-connected multiple drug use within partnership.

In multivariate model 3 (Tables 4 and 5 ), also including variants concerning sexual behaviour in the venture (sex-associated multiple drug use, sex frequency and partner kind), the separate effect of online dating location on UAI became somewhat more powerful (though not critical) for the HIV-positive men (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 effect of online dating on UAI became more powerful (and important) for HIV-unaware 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 impact 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 categories, 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 men no association was evident between UAI and on-line ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-oblivious guys, UAI was more common in online compared 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 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 on-line partners was more often reported as known (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 on-line partners more frequently understood 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 internet partners (50.9% vs. 41.3%; P 0.001). Sex-related substance use, alcohol use, and group sex were less frequently reported with on-line partners.

To be able to analyze the possible mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three variant models. In model 1, we adjusted the organization 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 venture features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adapted also for partnership sexual risk behavior (i.e., sex-related drug use and sex frequency) and partnership kind (i.e., casual or anonymous). As we assumed a differential effect of dating location 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-unaware guys. We performed a sensitivity analysis limited 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 miss potentially significant associations. As a fairly big number of statistical tests were done and reported, this approach does lead to a heightened risk of one or more false-positive associations. Analyses 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. Casual sex in Carlingford, New South Wales. 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. 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; partnership kind; 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 conduct by on-line or offline venture, and calculated P values predicated on logistic regression with robust standard errors, accounting for correlated 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 location (online versus offline) and UAI. Likelihood ratio tests were used to gauge the importance of a variable in a model.

In order 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 answer alternatives: (1) no, (2) maybe, (3) yes. Sexual behaviour 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 one or more of the subsequent subcultures/lifestyles: casual, formal, alternate, drag, leather, military, sports, fashionable, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if not one of these characteristics were appropriate, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Accidental 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 options: (1) I am certainly not HIV-contaminated; (2) I believe that I am not HIV-infected; (3) I do not 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 questionnaire enquired about the HIV status of every sex partner together with the question: 'Do you understand whether this partner is HIV-infected?' with similar answer options as previously. Casual sex near Carlingford New South Wales, Australia. Perceived concordance in HIV status within ventures was categorised as; (1) concordant; (2) discordant; (3) unknown. The last category 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 survey during their visit to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation using 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 and the survey is provided elsewhere 15 , 18 Our chief determinant of interest, dating place (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 terminology of distinguishing 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 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 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. Included 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 acquaintance in sexual partnerships, for example by concordant ethnicity, age, lifestyle, HIV status, and raising sex frequency, the odds for UAI increase as well 14 - 16 We compared the incidence of UAI in online got casual partnerships to that in offline got casual partnerships among MSM who reported both online 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 explained through better knowledge of partner characteristics, including HIV status.

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A meta-analysis in 2006 found limited evidence that acquiring a sex partner online increases the risk of unprotected anal intercourse (UAI) 3 Many previous studies compared men with internet partners to men with offline partners. Yet, men preferring online dating might differ in a variety of unmeasured respects from guys preferring offline dating, leading to incomparable behavioural profiles. A more recent meta-analysis contained several studies analyzing MSM with both online and offline acquired sex partners and found evidence for an association between UAI and on-line partners, which would indicate a mediating effect of more information on partners, (including perceived HIV status) on UAI 13 Casual Sex near me Carlingford, NSW.

Men who have sex with men (MSM) frequently use the Net to locate sex partners. Several studies have shown that MSM are more inclined to participate in unprotected anal intercourse with sex partners they meet through the Internet (on-line) than with partners they meet at social venues (offline) 1 - 3 This implies that men 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 internet partners, the danger of HIV transmission also depends upon accurate 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 guys, but HIV positive men were more likely to have UAI with on-line associates (aOR = 1.65 95 % CI 1.05-2.57). After correction for associate 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 believe it, I did not come out of this experiment feeling lousy about myself---simply smarter about the way gay men (or maybe men in general) area way too much emphasis on daft features like beards and ballcaps (hint: that is why you're all still cranky and single). And actually, I do not believe having long hair itself is the big hang up; it's what my hair implies. Having long hair (particularly for a black man) means you're likely a bitchy remarkable queen that nobody needs to date. Casual sex near me Carlingford NSW. Even in the event the premise isn't that extreme, the underlying anxiety is you spent too much time on your look and that's not manly." That's frustrating, of course, since stereotypical masculinity requires only as much work---we simply don't think of it that way. I recall chatting with this scruffy, pretty muscular guy with tattoos and chest hair and an Instagram full of masc pics; after we got to talking, he shown his fixation with Beyonc and said yasss!" every other paragraph. But no matter---his picture is butch, so his dating life is constantly full.

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