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 danger 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). College Sluts nearest Wentworthville New South Wales. Just among men who suggested 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 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). Wentworthville college sluts. UAI was significantly more likely if more sex acts had happened in the venture (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 partnership (sex-related multiple drug use, sex frequency and partner kind), the separate effect of online dating location on UAI became somewhat stronger (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 result of online dating on UAI became more powerful (and critical) for HIV-oblivious men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more inclined 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 correlated 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 types, one for each HIV status. Among HIV positive guys, UAI was more common in online compared to offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative guys no association was evident between UAI and internet ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware men, UAI was more common in online compared to offline ventures, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).
Features of on-line 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 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 online partners more often understood 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 internet partners (50.9% vs. 41.3%; P 0.001). Sex-associated substance use, alcohol use, and group sex were less often reported with on-line partners.
To be able to examine the potential mediating effect of more info on partners (including perceived HIV status) on UAI, we developed three variant models. In model 1, we adapted the association 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 venture features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In version 3, we adapted additionally for venture sexual risk behaviour (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 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 brand 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-oblivious men. We performed a sensitivity analysis confined to partnerships in which just 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 large number of statistical evaluations were done and reported, this strategy does lead to an elevated risk of one or more false-positive organizations. Investigations 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 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 kind; sex frequency within venture; group sex with partner; sex-associated material 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 venture sexual behaviour by on-line or offline venture, and calculated P values based on logistic regression with robust standard errors, accounting for linked 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 evaluate the value of a variable in a model.
To be able to explore possible disclosure of HIV status we additionally asked the participant whether the casual sex partner understood the HIV status of the participant, with the answer options: (1) no, (2) perhaps, (3) yes. Sexual behaviour with each partner was dichotomised as: (1) no anal intercourse or only 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 subsequent subcultures/lifestyles: casual, formal, substitute, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these characteristics were related, 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 got by asking the question 'Do you know whether you're HIV infected?', with five answer choices: (1) I am certainly not HIV-infected; (2) I believe that I am not HIV-infected; (3) I don't know; (4) I believe 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. College Sluts nearest Wentworthville, NSW. The questionnaire enquired about the HIV status of every sex partner with the question: 'Do you understand whether this partner is HIV-contaminated?' with similar reply options as above. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. The last class 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 trip to the STI outpatient clinic while waiting for preliminary test results after their consultation using 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 behaviour with those partners. A detailed description of the study design and also 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 differentiating 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 could understand written Dutch or English. Individuals could participate more than once, if subsequent visits to the practice were related to a potential 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 got 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 online, and that this effect is partly clarified through better knowledge of partner features, including HIV status.
Wentworthville NSW college sluts. A meta-analysis in 2006 found limited evidence that acquiring a sex partner online raises the risk of unprotected anal intercourse (UAI) 3 Many previous studies compared guys with internet partners to guys with offline partners. Nevertheless, guys preferring online dating might differ in a variety of unmeasured respects from guys 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 on-line partners, which might imply a mediating effect of more info on partners, (including perceived HIV status) on UAI 13
Men who have sex with men (MSM) often use the Internet to find sex partners. Several research have revealed that MSM are more prone to participate in unprotected anal intercourse with sex partners they meet through the Internet (online) 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 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 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 partnerships). Adjusted 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 features the effect of online/offline dating on UAI among HIV positive MSM was reduced and no longer significant. College Sluts nearest Wentworthville New South Wales Australia.
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