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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 associated with a higher risk of UAI than offline dating. For HIV negative men this lack of assocation was clear (aOR = 0.94 95 % CI 0.59-1.48); among HIV positive guys there was a nonsignificant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Casual Encounters near me Epping, New South Wales. Just among men who suggested they were not 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 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). Epping 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 venture compared to only one sex act). Other variables significantly associated with UAI were group sex within the venture, and sex-related multiple drug use within venture.

In multivariate model 3 (Tables 4 and 5 ), also including variants concerning sexual behavior in the venture (sex-related multiple drug use, sex frequency and partner type), the independent effect of online dating place 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 men (aOR = 0.94 95 % CI 0.59-1.48). The effect of online dating on UAI became more powerful (and important) for HIV-unaware guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).

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In univariate analysis, UAI was significantly more inclined to occur 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 effect of dating location 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 groups, one for each HIV status. Among HIV-positive men, UAI was more common in online in comparison to offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative guys no association was apparent between UAI and internet partnerships (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware guys, UAI was more common in online when compared with offline ventures, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

Features of on-line and offline partners and ventures 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 online partners was more often reported as known (61.4% vs. 49.4%; P 0.001), and in online ventures, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their on-line partners more frequently knew 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 internet partners.

In order to analyze the possible mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three variant models. In version 1, we adjusted the organization 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 adapted also for partnership sexual risk behaviour (i.e., sex-related drug use and sex frequency) and venture 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 place was included 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-unaware men. 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 lose potentially important associations. As a fairly big number of statistical evaluations were done and reported, this approach does lead to a higher risk of one or more false positive organizations. Investigations were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).

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Prior to the investigations 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 presumed to be on the causal pathway between the principal exposure of interest and outcome (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership kind; sex frequency within venture; group sex with partner; sex-related material 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 characteristics of participants, partners, and partnership sexual behaviour by online or offline partnership, and computed 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 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 answer options: (1) no, (2) potentially, (3) yes. Sexual conduct with each partner was dichotomised as: (1) no anal intercourse or just shielded anal intercourse, and (2) unprotected anal intercourse. To discover 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, substitute, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these features were applicable, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Chance partner kind 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 know whether you're HIV infected?', with five answer choices: (1) I am definitely not HIV-contaminated; (2) I believe that I am not HIV-contaminated; (3) I don't understand; (4) I think I may be HIV-infected; (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 closest to Epping NSW. The questionnaire enquired about the HIV status of every sex partner with the question: 'Do you know whether this partner is HIV-contaminated?' with similar answer options 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 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 questionnaire throughout their trip to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation with 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 also the questionnaire is supplied elsewhere 15 , 18 Our main determinant of interest, dating location (e.g., the name of a bar, 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 language of recognizing 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 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 acquaintance 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 incidence of UAI in online acquired casual partnerships to that in offline acquired 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 online, and that this effect is partly described through better knowledge of partner features, including HIV status.

Epping NSW Casual Encounters. A meta-evaluation in 2006 found limited evidence that acquiring a sex partner online raises the danger of unprotected anal intercourse (UAI) 3 Many previous studies compared men with internet partners to guys with offline partners. Nonetheless, men preferring online dating might differ in several unmeasured respects from men preferring offline dating, causing 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 on-line partners, which would suggest a mediating effect of more information on partners, (including perceived HIV status) on UAI 13

Men who have sex with men (MSM) often utilize the Internet to locate sex partners. Several studies have revealed that MSM are more 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 on-line partners, the risk 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-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 important 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 features the effect of online/offline dating on UAI among HIV-positive MSM was reduced and no longer essential. Casual Encounters near me Epping New South Wales Australia.

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