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 guys there was a nonsignificant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Lesbian Dating nearest Ashfield, Queensland. Only among guys who indicated they were not informed of their HIV status (a little group in this study), UAI was more common with on-line than offline partners.
The amount of sex partners in the preceding 6months of the index was likewise 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). Ashfield Lesbian Dating. 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 factors significantly associated with UAI were group sex within the partnership, and sex-related multiple drug use within partnership.
In multivariate model 3 (Tables 4 and 5 ), additionally including variants concerning sexual behavior in the venture (sex-associated 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 result 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 ).
In univariate analysis, UAI was significantly more likely to occur in on-line 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 effect of dating place on UAI differed by HIV status, as can be seen best in Table 5 Table 5 shows the organization 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 partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV negative guys no association was evident between UAI and online ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware men, 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 ventures, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their on-line partners more frequently understood the HIV status of the participant than offline partners (38.8% vs. 27.2%; P 0.001). Participants more often 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 often reported with internet partners.
To be able to examine the possible mediating effect of more info on partners (including perceived HIV status) on UAI, we developed three variant models. In version 1, we adjusted 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 venture characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In version 3, we adjusted also for venture sexual risk behavior (i.e., sex-related drug use and sex frequency) and partnership 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 place was contained in all three models by making a new 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 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 significant organizations. As a rather large number of statistical tests were done and reported, this approach does lead to an increased danger of one or more false positive organizations. Evaluations were done using the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).
Before 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; 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 supposed to be on the causal pathway between the principal exposure of interest and result (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership type; sex frequency within partnership; group sex with partner; sex-related substance 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 characteristics of participants, partners, and partnership sexual conduct by on-line or offline venture, and calculated P values predicated 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 examine the association between dating location (online versus offline) and UAI. Odds ratio tests were used to evaluate the significance of a variable in a model.
As a way to investigate potential disclosure of HIV status we also asked the participant whether the casual sex partner understood the HIV status of the participant, together with the answer options: (1) no, (2) possibly, (3) yes. Sexual behaviour with each partner was dichotomised as: (1) no anal intercourse or simply protected 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, alternate, drag, leather, military, sports, fashionable, 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. Chance partner sort 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 know whether you are HIV infected?', with five answer options: (1) I am definitely not HIV-infected; (2) I think that I'm not HIV-infected; (3) I don't understand; (4) I believe I may be HIV-contaminated; (5) I know for sure that I am HIV-contaminated. We categorised this into HIV-negative (1,2), unknown (3), and HIV positive (4,5) status. Lesbian dating near Ashfield QLD. The survey enquired about the HIV status of each sex partner together with the question: 'Do you understand whether this partner is HIV-contaminated?' with similar reply alternatives as previously. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. The final class 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 questionnaire throughout their trip to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation with 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 information on sexual behaviour with those partners. A detailed description of the study design as well as the survey is supplied elsewhere 15 , 18 Our main determinant of interest, dating place (e.g., the name of a bar, 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 language of distinguishing the partners per dating place, we refer to them as online 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. Individuals could participate more than once, if following 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 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. Contained in this investigation 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 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 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 clarified through better understanding of partner features, including HIV status.
Ashfield, QLD Lesbian Dating. 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 guys with online partners to guys with offline partners. Nonetheless, guys favoring online dating might differ in a variety of unmeasured respects from men preferring offline dating, leading to 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 online partners, which would indicate a mediating effect of more info on partners, (including perceived HIV status) on UAI 13
Men who have sex with men (MSM) frequently 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 sites (offline) 1 - 3 This implies that guys 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 online 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 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 on-line associates (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. Lesbian Dating in Ashfield Queensland Australia.
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