In this large study among MSM attending the STI clinic in Amsterdam, we found no signs that online dating was independently related to a higher risk of UAI than offline dating. For HIV-negative men 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). College sluts nearby Caulfield Victoria. Simply among guys who indicated they weren't informed of their HIV status (a small group in this study), UAI was more common with online than offline partners.
The amount 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). Caulfield college sluts. UAI was significantly more likely if more sex acts had occurred in the venture (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the partnership compared to only one sex act). Other variables significantly associated with UAI were group sex within the venture, and sex-connected multiple drug use within partnership.
In multivariate model 3 (Tables 4 and 5 ), also including variants concerning sexual behaviour in the partnership (sex-related multiple drug use, sex frequency and partner type), the separate effect of online dating place 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 effect of online dating on UAI became more powerful (and critical) for HIV-unaware guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more prone to happen 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 strongly 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 organization of online dating using three distinct reference categories, one for each HIV status. Among HIV-positive guys, UAI was more common in online when compared with offline ventures (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 in comparison to offline ventures, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).
Characteristics of on-line and offline partners and ventures are shown 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 on-line 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 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 often reported with online partners.
To be able to analyze the possible mediating effect of more info on partners (including perceived HIV status) on UAI, we developed three multivariable models. In model 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 characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adjusted additionally for partnership sexual risk behaviour (i.e., sex-related drug use and sex frequency) and partnership sort (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 location was included in all three models by making a 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-oblivious guys. 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 miss potentially significant associations. As a rather big number of statistical tests were done and reported, this strategy does lead to a higher danger of one or more false-positive associations. Analyses were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).
Before 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 assumed to be on the causal pathway between the main exposure of interest and results (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; venture type; sex frequency within venture; 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 venture sexual behavior by on-line or offline venture, and calculated P values based 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 analyze the association between dating place (online versus offline) and UAI. Likelihood ratio tests were used to evaluate the importance of a variable in a model.
As a way to investigate possible disclosure of HIV status we additionally asked the participant whether the casual sex partner knew the HIV status of the participant, together with the reply choices: (1) no, (2) possibly, (3) yes. Sexual conduct with each partner was dichotomised as: (1) no anal intercourse or merely 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, alternate, drag, leather, military, sports, fashionable, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these characteristics were appropriate, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Casual 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 know whether you're HIV infected?', with five response choices: (1) I am definitely not HIV-contaminated; (2) I believe that I am not HIV-infected; (3) I do not 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. College Sluts near me Caulfield VIC. The questionnaire enquired about the HIV status of each sex partner together with the question: 'Do you know whether this partner is HIV-contaminated?' with similar reply 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 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 survey during their visit to the STI outpatient clinic while waiting for preliminary test 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 data on sexual conduct with those partners. A thorough description of the study design and the survey 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 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 could comprehend written Dutch or English. People could participate more than once, if following 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 analysis were men 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 raising sex frequency, the odds 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 online, and that this effect is partially clarified through better knowledge of partner characteristics, including HIV status.
Caulfield, VIC college sluts. A meta-analysis in 2006 found limited evidence that acquiring a sex partner online increases the danger of unprotected anal intercourse (UAI) 3 Many previous studies compared guys with online partners to men with offline partners. Nevertheless, men preferring online dating might differ in a variety of unmeasured respects from guys favoring offline dating, leading to 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 may imply a mediating effect of more information on partners, (including perceived HIV status) on UAI 13
Men who have sex with men (MSM) often use the Web to find sex partners. Several research have shown 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 indicates 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 online partners, the threat 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 partnerships). Corrected for demographic characteristics, online dating had no important effect on UAI among HIV-negative and HIV status-oblivious 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 significant. College sluts near me Caulfield Victoria, Australia.
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