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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 partnership (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the partnership compared to just one sex act). College Sluts in Macleod Western Australia. Other variables 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 ), also including variants concerning sexual behavior in the partnership (sex-associated multiple drug use, sex frequency and partner type), the independent 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 guys (aOR = 0.94 95 % CI 0.59-1.48). The result of online dating on UAI became more powerful (and critical) for HIV-oblivious men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).

In univariate analysis, UAI was significantly more prone 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 strongly connected with UAI (OR = 11.70 95 % CI 7.40-18.45). The effect of dating place 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 classes, one for each HIV status. Among HIV positive guys, UAI was more common in online in comparison to offline ventures (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative guys no association was evident 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 in comparison to offline ventures, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

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Characteristics of online and offline partners and ventures 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 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 online partners more often 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 online partners (50.9% vs. 41.3%; P 0.001). Sex-related material 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 adapted the association 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 partnership features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adjusted additionally for partnership sexual risk behaviour (i.e., sex-associated drug use and sex frequency) and venture 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 place was contained in all three models by making a brand new six-category variable. For clarity, the effects of online/offline dating on UAI are also presented individually for HIV negative, HIV-positive, and HIV-oblivious guys. 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 miss potentially important associations. As a rather large number of statistical evaluations were done and reported, this approach does lead to a higher risk of one or more false positive associations. Analyses were done using 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. College Sluts near Macleod Western Australia. 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 assumed to be on the causal pathway between the main exposure of interest and results (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-associated 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 venture sexual conduct by online or offline venture, and calculated P values based on logistic regression with robust standard errors, accounting for linked data. Continuous variables (i.e., age, amount 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. Odds ratio tests were used to evaluate the value of a variable in a model.

In order to explore 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 reply options: (1) no, (2) perhaps, (3) yes. Sexual conduct 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, alternative, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these features 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 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 know; (4) I think I may be HIV-contaminated; (5) I know for sure that I 'm 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 together with the question: 'Do you understand whether this partner is HIV-contaminated?' with similar reply options as previously. College sluts closest to Macleod Western Australia, Australia. Perceived concordance in HIV status within ventures was categorised as; (1) concordant; (2) discordant; (3) unknown. The last group 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 throughout their visit to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation with a nurse or physician. 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 conduct with those partners. A detailed description of the study design and the questionnaire is supplied elsewhere 15 , 18 Our chief determinant of interest, dating place (e.g., the name of a bar, park, club, or the name of a website) was obtained for every partner, and categorised into on-line (websites), and offline (physical sites) dating places. To simplify the terminology of recognizing 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 may comprehend written Dutch or English. People 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 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 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 chances for UAI increase as well 14 - 16 We compared the occurrence of UAI in online acquired casual partnerships to that in offline obtained 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 on the internet, and that this effect is partially explained through better knowledge of partner features, including HIV status.

A meta-analysis 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 internet partners to guys with offline partners. Nevertheless, guys preferring online dating might differ in various unmeasured regards from guys preferring offline dating, resulting in 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 online partners, which might imply a mediating effect of more information on partners, (including perceived HIV status) on UAI 13 College sluts in Macleod WA.

Men who have sex with men (MSM) frequently use the Internet to discover sex partners. Several research have shown that MSM are more likely 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 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 internet partners, the threat of HIV transmission also depends upon precise 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-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). Corrected for demographic features, 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 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 critical.

Believe it or not believe it, I didn't come out of this experiment feeling bad about myself---only smarter about the way gay men (or maybe guys in general) area way too much emphasis on ridiculous characteristics like beards and ballcaps (hint: that's why you are all still cranky and single). And really, I really don't 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 are likely a bitchy stunning queen that nobody wants to date. College Sluts near Macleod WA. Even if the premise isn't that extreme, the underlying fear is you spent too much time on your look and that's not masculine." That is frustrating, of course, since stereotypical masculinity requires only as much work---we just 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 revealed 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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