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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 risk of UAI than offline dating. For HIV negative guys this lack 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 encounters near Calamvale, Queensland. Just among guys who suggested they weren't aware of their HIV status (a small group in this study), UAI was more common with online than offline associates.

The amount of sex partners in the preceding 6months of the index was likewise 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). Calamvale Casual Encounters. 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 only one sex act). 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 variables concerning sexual behavior in the partnership (sex-associated multiple drug use, sex frequency and partner kind), the independent effect of online dating location on UAI became somewhat stronger (though not essential) 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 result of online dating on UAI became more powerful (and significant) for HIV-oblivious 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 on-line 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 associated 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 types, one for each HIV status. Among HIV-positive guys, UAI was more common in online compared to offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV negative men no association was evident between UAI and on-line partnerships (OR = 1.07 95 % CI 0.71-1.62). Among HIV-oblivious men, UAI was more common in online compared to offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

Characteristics of online 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 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 online partners was more often reported as understood (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 frequently reported multiple sexual contacts with internet partners (50.9% vs. 41.3%; P 0.001). Sex-associated substance use, alcohol use, and group sex were less frequently reported with internet partners.

In order to analyze the potential mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three variant models. In model 1, we adapted the organization 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 partnership features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In version 3, we adapted additionally for venture 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 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 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-oblivious guys. We performed a sensitivity analysis limited 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 organizations. As a rather large number of statistical evaluations were done and reported, this approach does lead to an increased risk of one or more false-positive associations. Analyses were done using the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).

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Prior to the evaluations we developed a directed acyclic graph (DAG) representing a causal model of UAI. In this model some variants 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 supposed to be on the causal pathway between the principal exposure of interest and results (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; venture sort; sex frequency within partnership; group sex with partner; sex-associated substance use in partnership).

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 partnership sexual behaviour by on-line or offline partnership, and computed P values based on logistic regression with robust standard errors, accounting for related 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 analyze the association between dating place (online versus offline) and UAI. Likelihood ratio tests were used to assess the significance of a variable in a model.

To be able to investigate possible disclosure of HIV status we additionally asked the participant whether the casual sex partner understood the HIV status of the participant, together with the reply choices: (1) no, (2) potentially, (3) yes. Sexual behaviour with each partner was dichotomised as: (1) no anal intercourse or merely shielded anal intercourse, and (2) unprotected anal intercourse. To ascertain 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, fashionable, 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. Accidental 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 obtained by asking the question 'Do you understand whether you're HIV infected?', with five answer options: (1) I 'm definitely not HIV-contaminated; (2) I believe that I am not HIV-contaminated; (3) I do not understand; (4) I think 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. Casual Encounters near me Calamvale QLD. The questionnaire 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 above. Perceived concordance in HIV status within partnerships 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.

Participants completed a standardised anonymous survey throughout their trip to the STI outpatient clinic while waiting for preliminary test results after their consultation with 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 data on sexual conduct with those partners. A thorough description of the study design and also the questionnaire is supplied elsewhere 15 , 18 Our chief 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 on-line (websites), and offline (physical sites) dating places. To simplify the terminology of differentiating 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 comprehend written Dutch or English. People could participate more than once, if subsequent 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 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 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 incidence 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. 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.

Calamvale, QLD casual encounters. 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 online partners to guys with offline partners. Nevertheless, men favoring online dating might differ in several 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 offline acquired sex partners and found evidence for an association between UAI and internet partners, which may indicate a mediating effect of more information on partners, (including perceived HIV status) on UAI 13

Men who have sex with men (MSM) often make use of the Net to locate sex partners. Several studies have shown 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 men who get 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 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). Fixed for demographic features, online dating had no significant effect on UAI among HIV negative and HIV status-oblivious men, but HIV-positive men were more likely to have UAI with on-line 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 significant. Casual encounters closest to Calamvale Queensland, Australia.

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