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The number of sex partners in the preceding 6months of the index was also associated 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). Sex partner nearest Blaxland, New South Wales. UAI was significantly more likely if more sex acts had occurred in the partnership (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the venture compared to only one sex act). Sex Partner Near Me Asquith New South Wales. 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 variables concerning sexual behaviour in the venture (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 stronger (and critical) for HIV-unaware men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).

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In univariate analysis, UAI was significantly more likely to happen in online than in offline ventures (OR = 1.36 95 % CI 1.03-1.81) (Table 4 ). Sex Partner nearest New South Wales, Australia. The self-perceived HIV status of the participant was firmly correlated 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 association of online dating using three distinct reference categories, 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 evident between UAI and internet partnerships (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware guys, UAI was more common in online compared to offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

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 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 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 on-line partners more often 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 internet partners (50.9% vs. 41.3%; P 0.001). Blaxland, NSW, Australia sex partner. Sex-related material use, alcohol use, and group sex were less frequently reported with on-line partners.

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To be able to analyze the possible mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three multivariable 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 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 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 fresh six-category variable. For clarity, the effects of online/offline dating on UAI are also presented individually for HIV-negative, HIV positive, and HIV-unaware guys. We performed a sensitivity analysis restricted to partnerships in which only one sexual contact occurred. Statistical significance was defined as P 0.05. Sex partner near Blaxland Australia. 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 an elevated risk of one or more false positive organizations. Evaluations were done utilizing 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. 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 primary 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-associated 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 features of participants, partners, and venture sexual behavior by online or offline partnership, and computed 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 place (online versus offline) and UAI. Odds ratio tests were used to measure the importance of a variable in a model.

In order to explore possible disclosure of HIV status we additionally asked the participant whether the casual sex partner knew the HIV status of the participant, together with the response alternatives: (1) no, (2) perhaps, (3) yes. Sexual conduct with each partner was dichotomised as: (1) no anal intercourse or merely shielded anal intercourse, and (2) unprotected anal intercourse. To determine 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, 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 sort was categorised by the participants into (1) known traceable and (2) anonymous partners.

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HIV status of the participant was got by asking the question 'Do you know whether you are HIV infected?', with five answer alternatives: (1) I am certainly not HIV-contaminated; (2) I think that I am not HIV-infected; (3) I don't know; (4) I think I may be HIV-infected; (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 survey enquired about the HIV status of each sex partner with all the question: 'Do you know whether this partner is HIV-infected?' with similar response choices as previously. 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 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.

Participants completed a standardised anonymous survey during their visit to the STI outpatient clinic while waiting for preliminary test 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 information on sexual behavior with those partners. A thorough description of the study design and the survey is provided elsewhere 15 , 18 Our primary determinant of interest, dating location (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 distinguishing the partners per dating place, we refer to them as on-line or offline partners.

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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 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 evaluation were men 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 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 on-line and offline casual partners in the preceding six months. Sex partner closest to Blaxland, NSW Australia. 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 understanding of partner characteristics, including HIV status.

A meta-analysis in 2006 found limited evidence that getting a sex partner online increases the risk of unprotected anal intercourse (UAI) 3 Many previous studies compared guys with internet partners to men with offline partners. Sex Partner Near Me Dapto New South Wales. Yet, guys favoring online dating might differ in various unmeasured regards from men favoring offline dating, causing 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 online partners, which might indicate a mediating effect of more information on partners, (including perceived HIV status) on UAI 13

Men who have sex with men (MSM) frequently use the Net to locate sex partners. Blaxland, NSW sex partner. Several studies have revealed that MSM are prone to participate in unprotected anal intercourse with sex partners they meet through the Internet (online) 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 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 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 partnerships). 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 associate 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 awful about myself---just smarter about the way gay men (or perhaps men in general) place way too much emphasis on ridiculous characteristics like beards and ballcaps (hint: that is why you are all still cranky and single). And really, I actually don't think having long hair itself is the big hang up; it is what my hair implies. Sex Partner nearest Blaxland. Having long hair (particularly for a black man) means you're probably a bitchy dramatic queen that nobody wants to date. Even if the assumption is not that extreme, the underlying fear is you spent too much time on your appearance and that is not manly." That is frustrating, obviously, since stereotypical masculinity requires just as much work---we just don't think of it that way. I recall chatting with this scruffy, pretty muscular man with tattoos and torso hair and an Instagram full of masc pics; once we got to talking, he revealed his obsession with Beyonc and said yasss!" every other paragraph. But no matter---his image is butch, so his dating life is constantly full.