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 danger 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 guys there was a non significant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Female Escorts closest to Stafford, Queensland. Simply among guys who indicated they weren't aware 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). Stafford Female Escorts. 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 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 partnership.
In multivariate model 3 (Tables 4 and 5 ), also including variants concerning sexual behavior in the venture (sex-related multiple drug use, sex frequency and partner type), the independent effect of online dating place on UAI became somewhat stronger (though not critical) 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 stronger (and critical) for HIV-unaware men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more inclined to occur 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 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 classes, one for each HIV status. Among HIV positive men, UAI was more common in online compared to offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV negative men 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 partnerships, 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 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 online partners was more often reported as understood (61.4% vs. 49.4%; P 0.001), and in online partnerships, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their on-line 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 online partners (50.9% vs. 41.3%; P 0.001). Sex-related material use, alcohol use, and group sex were less frequently reported with internet partners.
In order to analyze the possible mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three variant models. In model 1, we adapted the organization between online/offline dating location 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 model 3, we adjusted additionally for partnership sexual risk behaviour (i.e., sex-associated 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 place was included in all three models by making a new six-class 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 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 miss potentially significant associations. As a rather big number of statistical tests were done and reported, this approach does lead to a heightened risk of one or more false positive organizations. Investigations were done using 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 result (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 behaviour by on-line or offline venture, and calculated P values based 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 analyze the association between dating location (online versus offline) and UAI. Odds ratio tests were used to assess the significance of a variable in a model.
As a way to explore 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 reply alternatives: (1) no, (2) potentially, (3) yes. Sexual conduct with each partner was dichotomised as: (1) no anal intercourse or merely 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 one or more of the following 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 applicable, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Casual partner kind 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 response alternatives: (1) I 'm definitely not HIV-contaminated; (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 'm HIV-infected. We categorised this into HIV-negative (1,2), unknown (3), and HIV-positive (4,5) status. Female escorts near me Stafford, QLD. The survey 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 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 questionnaire throughout their visit to the STI outpatient clinic while waiting for preliminary evaluation results after their consultation with a nurse or physician. 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 the survey is provided elsewhere 15 , 18 Our main 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 on-line (websites), and offline (physical sites) dating places. 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 may comprehend written Dutch or English. Individuals could participate more than once, if following visits to the practice were related to a potential 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 evaluation 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 raising sex frequency, the chances for UAI increase as well 14 - 16 We compared the incidence of UAI in online got casual partnerships to that in offline acquired 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 on the internet, and that this effect is partially described through better understanding of partner features, including HIV status.
Stafford, QLD female escorts. A meta-analysis in 2006 found limited evidence that getting a sex partner online raises the risk of unprotected anal intercourse (UAI) 3 Many previous studies compared guys with internet partners to men with offline partners. Nevertheless, men preferring online dating might differ in several unmeasured respects from guys favoring offline dating, causing 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 on-line partners, which may imply a mediating effect of more info on partners, (including perceived HIV status) on UAI 13
Men who have sex with men (MSM) often make use of the Web to find sex partners. Several research have revealed that MSM are more likely to participate 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 indicates that men 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 threat of HIV transmission also depends upon 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-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). Fixed 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 partners (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. Female escorts nearest Stafford Queensland Australia.
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