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 guys this dearth of assocation was clear (aOR = 0.94 95 % CI 0.59-1.48); among HIV-positive men there was a nonsignificant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Female escorts near me Glen Waverley, Victoria. Only among men who suggested they were not aware of their HIV status (a small group in this study), UAI was more common with on-line than offline associates.
The amount of sex partners in the preceding 6months of the index was likewise 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). Glen Waverley female escorts. 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 just one sex act). Other factors significantly associated with UAI were group sex within the venture, and sex-connected multiple drug use within venture.
In multivariate model 3 (Tables 4 and 5 ), additionally including variants concerning sexual behaviour in the partnership (sex-related multiple drug use, sex frequency and partner type), the separate effect of online dating location on UAI became somewhat more powerful (though not significant) for the HIV positive men (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 men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more likely to occur 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 effect 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 groups, one for each HIV status. Among HIV positive men, UAI was more common in online in comparison to offline ventures (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative men no association was evident between UAI and on-line ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware men, UAI was more common in online in comparison 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 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 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 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 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 version 1, we adjusted the association between online/offline dating place 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 version 3, we adapted additionally for partnership sexual risk behavior (i.e., sex-related drug use and sex frequency) and partnership 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 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-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. No adjustments for multiple comparisons were made, in order not to lose potentially significant associations. As a rather big number of statistical tests were done and reported, this approach does lead to an increased danger of one or more false positive organizations. 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. 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 assumed 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; venture kind; sex frequency within partnership; group sex with partner; sex-related material use in venture).
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 features of participants, partners, and partnership sexual behaviour by online or offline venture, and computed P values predicated on logistic regression with robust standard errors, accounting for related 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. Odds ratio tests were used to gauge the importance of a variable in a model.
As a way to explore possible disclosure of HIV status we also asked the participant whether the casual sex partner knew the HIV status of the participant, together with the response choices: (1) no, (2) potentially, (3) yes. Sexual behavior with each partner was dichotomised as: (1) no anal intercourse or just protected 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, alternate, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if not one of these features were appropriate, 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.
HIV status of the participant was obtained by asking the question 'Do you understand whether you're HIV infected?', with five answer alternatives: (1) I am definitely not HIV-contaminated; (2) I think that I am not HIV-infected; (3) I don't understand; (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. Female escorts in Glen Waverley VIC. The questionnaire enquired about the HIV status of each sex partner with the question: 'Do you understand whether this partner is HIV-infected?' 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 didn't know 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 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 data on sexual behaviour with those partners. A comprehensive description of the study design and also the survey is supplied elsewhere 15 , 18 Our main determinant of interest, dating location (e.g., the name of a pub, park, club, or the name of a site) was obtained for every partner, and categorised into online (websites), and offline (physical sites) dating locations. To simplify the terminology 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 may understand written Dutch or English. Individuals could participate more than once, if subsequent visits to the practice 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 investigation were guys 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 odds for UAI increase as well 14 - 16 We compared the incidence of UAI in online got 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 online, and that this effect is partly clarified through better knowledge of partner characteristics, including HIV status.
Glen Waverley, VIC female escorts. A meta-evaluation in 2006 found limited evidence that acquiring a sex partner online increases the risk of unprotected anal intercourse (UAI) 3 Many previous studies compared men with internet partners to men with offline partners. Yet, men preferring online dating might differ in several unmeasured respects from men favoring 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 internet partners, which might 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 shown that MSM are more likely to engage 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 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 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). Fixed for demographic characteristics, online dating had no significant effect on UAI among HIV-negative and HIV status-unaware guys, but HIV-positive men were more likely to have UAI with online 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. Female Escorts near me Glen Waverley Victoria Australia.
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