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In this large study among MSM attending the STI clinic in Amsterdam, we found no evidence that online dating was independently related to a higher danger of UAI than offline dating. For HIV-negative guys this dearth of assocation was clear (aOR = 0.94 95 % CI 0.59-1.48); among HIV-positive guys there was a non significant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Casual Sex nearest Bateau Bay New South Wales. Simply among guys who suggested they weren't informed of their HIV status (a small group in this study), UAI was more common with on-line than offline associates.

The number 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). Bateau Bay casual sex. 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). Other factors significantly associated with UAI were group sex within the partnership, and sex-connected multiple drug use within venture.

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 critical) 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 ).

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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 ). 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 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 apparent between UAI and online partnerships (OR = 1.07 95 % CI 0.71-1.62). Among HIV-oblivious guys, UAI was more common in online when compared with offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

Characteristics of online 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 often reported as understood (61.4% vs. 49.4%; P 0.001), and in online 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 online partners (50.9% vs. 41.3%; P 0.001). Sex-associated substance use, alcohol use, and group sex were less often reported with online partners.

To be able to analyze the possible 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 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 characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adjusted additionally for venture sexual risk behaviour (i.e., sex-associated drug use and sex frequency) and venture type (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 brand new six-class variable. For clarity, the effects of online/offline dating on UAI are also presented individually for HIV negative, HIV positive, and HIV-oblivious men. We performed a sensitivity analysis restricted 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 organizations. 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 associations. Evaluations were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).

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Before the analyses 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 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; venture kind; sex frequency within venture; group sex with partner; sex-associated substance use in venture).

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 behaviour by online or offline partnership, and calculated 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 analyze the association between dating location (online versus offline) and UAI. Likelihood 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 additionally asked the participant whether the casual sex partner understood the HIV status of the participant, with the response alternatives: (1) no, (2) maybe, (3) yes. Sexual conduct with each partner was dichotomised as: (1) no anal intercourse or only protected 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 subsequent subcultures/lifestyles: casual, formal, alternate, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these characteristics were applicable, 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.

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HIV status of the participant was obtained 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 do not know; (4) I believe I may be HIV-infected; (5) I know for sure that I am HIV-infected. We categorised this into HIV negative (1,2), unknown (3), and HIV positive (4,5) status. Casual sex near me Bateau Bay NSW. The questionnaire enquired about the HIV status of every sex partner with the question: 'Do you understand whether this partner is HIV-infected?' with similar response options as above. 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 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 throughout 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 data on sexual conduct with those partners. A comprehensive 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 pub, park, club, or the name of a web site) was obtained for every partner, and categorised into online (websites), and offline (physical sites) dating places. To simplify the language 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. Individuals could participate more than once, if subsequent visits to the clinic were related to a potential new STI episode. Participants were regularly 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 evaluation were men who reported sexual contact with at least one casual partner dated online as well one casual partner dated offline.

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With increased familiarity in sexual partnerships, for example by concordant ethnicity, age, lifestyle, HIV status, and increasing 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 acquired 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 partly explained through better knowledge of partner features, including HIV status.

Bateau Bay, NSW casual sex. A meta-evaluation in 2006 found limited evidence that getting a sex partner online increases the danger 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 respects from men preferring offline dating, resulting in incomparable behavioural profiles. A more recent meta-analysis included 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 may indicate a mediating effect of more info on partners, (including perceived HIV status) on UAI 13

Men who have sex with men (MSM) often use the Internet to discover sex partners. Several studies have revealed 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 indicates 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 on accurate 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 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). Adjusted for demographic features, online dating had no major effect on UAI among HIV-negative and HIV status-unaware men, 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 characteristics the effect of online/offline dating on UAI among HIV-positive MSM was reduced and no longer critical. Casual sex near me Bateau Bay New South Wales Australia.

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