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 men this lack 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). Casual encounter nearby Long Point, New South Wales. Only among men who suggested they weren't aware of their HIV status (a little 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). Long Point Casual Encounter. UAI was significantly more likely if more sex acts had occurred 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 variables significantly associated with UAI were group sex within the partnership, 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-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 guys (aOR = 0.94 95 % CI 0.59-1.48). The effect of online dating on UAI became more powerful (and critical) for HIV-oblivious guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more prone to occur 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 firmly correlated 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 different reference categories, 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 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).
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 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 frequently 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 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-related substance use, alcohol use, and group sex were less frequently reported with on-line partners.
To be able to examine the potential mediating effect of more information 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 partnership features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In version 3, we adjusted additionally for partnership 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 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 brand new six-category 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 confined 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 organizations. As a fairly large number of statistical tests were done and reported, this approach does lead to an increased risk of one or more false positive organizations. Analyses were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).
Prior to the evaluations 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; 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 primary exposure of interest and results (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; venture type; sex frequency within partnership; group sex with partner; sex-related substance use in partnership).
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 conduct by on-line or offline venture, and computed P values based on logistic regression with robust standard errors, accounting for correlated 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. Likelihood ratio tests were used to gauge the value 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 response alternatives: (1) no, (2) maybe, (3) yes. Sexual behaviour with each partner was dichotomised as: (1) no anal intercourse or simply shielded 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 following subcultures/lifestyles: casual, formal, alternative, 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 type was categorised by the participants into (1) known traceable and (2) anonymous partners.
HIV status of the participant was got by asking the question 'Do you know whether you are HIV infected?', with five answer options: (1) I am certainly not HIV-contaminated; (2) I think that I am 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 encounter nearest Long Point, NSW. The questionnaire enquired about the HIV status of each sex partner with all the question: 'Do you understand whether this partner is HIV-infected?' with similar answer choices as above. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. The final group represents all partnerships where the participant did not 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 visit to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation using 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 information on sexual behaviour with those partners. A detailed description of the study design and also the questionnaire is supplied elsewhere 15 , 18 Our primary determinant of interest, dating place (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 recognizing the partners per dating place, 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. People could participate more than once, if subsequent visits to the clinic 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. Included 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 chances for UAI increase as well 14 - 16 We compared the incidence of UAI in online acquired casual partnerships to that in offline got 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 partially explained through better understanding of partner features, including HIV status.
Long Point NSW casual encounter. A meta-analysis in 2006 found limited evidence that acquiring a sex partner online increases the risk of unprotected anal intercourse (UAI) 3 Many previous studies compared guys with online partners to guys with offline partners. Nevertheless, men preferring online dating might differ in various unmeasured regards from guys favoring offline dating, leading to incomparable behavioural profiles. A more recent meta-analysis contained several studies analyzing MSM with both online and also offline acquired sex partners and found evidence for an association between UAI and online partners, which would indicate 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 find sex partners. Several studies have shown 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 venues (offline) 1 - 3 This suggests 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 internet partners, the threat of HIV transmission also depends on precise 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-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 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 on-line associates (aOR = 1.65 95 % CI 1.05-2.57). After correction for associate and partnership characteristics the effect of online/offline dating on UAI among HIV-positive MSM was reduced and no longer essential. Casual encounter nearest Long Point New South Wales Australia.
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