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 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). Lesbian dating nearest Bairnsdale, Victoria. Simply among men 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 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). Bairnsdale lesbian dating. 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 partnership compared to only one sex act). Other factors significantly associated with UAI were group sex within the partnership, and sex-connected multiple drug use within partnership.
In multivariate model 3 (Tables 4 and 5 ), also including variables concerning sexual behaviour in the venture (sex-related multiple drug use, sex frequency and partner type), the independent effect of online dating location on UAI became somewhat more powerful (though not significant) 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 result of online dating on UAI became more powerful (and significant) for HIV-unaware guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more prone 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 connected 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 organization of online dating using three distinct 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 apparent between UAI and on-line ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware guys, UAI was more common in online compared to offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).
Characteristics of online 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 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 partnerships, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their online 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 online 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 analyze the possible mediating effect of more information 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 partnership characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adapted also for venture sexual risk behaviour (i.e., sex-related drug use and sex frequency) and venture 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 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 lose potentially significant associations. As a rather big number of statistical evaluations were done and reported, this approach does lead to an increased danger of one or more false-positive organizations. Evaluations were done utilizing 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 assumed to be on the causal pathway between the principal exposure of interest and outcome (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership kind; sex frequency within partnership; group sex with partner; sex-related material use in venture).
We compared characteristics of participants by self-reported HIV status (using 2-evaluations for dichotomous and categorical variables and using rank sum test for continuous variables). We compared characteristics of participants, partners, and venture sexual conduct by online or offline partnership, and computed P values based on logistic regression with robust standard errors, accounting for linked 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 analyze 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.
In order to explore possible 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 alternatives: (1) no, (2) perhaps, (3) yes. Sexual behaviour 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 one or more 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 features were related, 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 got by asking the question 'Do you understand whether you're HIV infected?', with five answer choices: (1) I 'm certainly not HIV-contaminated; (2) I think that I'm not HIV-contaminated; (3) I do not understand; (4) I think 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. Lesbian dating in Bairnsdale, VIC. The survey enquired about the HIV status of every sex partner with all the question: 'Do you know whether this partner is HIV-infected?' with similar response choices as previously. 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 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 throughout their visit to the STI outpatient clinic while waiting for preliminary evaluation 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 behavior with those partners. A comprehensive description of the study design as well as the questionnaire is provided elsewhere 15 , 18 Our primary determinant of interest, dating location (e.g., the name of a bar, 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 recognizing 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. People could participate more than once, if following visits to the clinic 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 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 investigation were guys 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 occurrence 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 online, and that this effect is partially explained through better knowledge of partner features, including HIV status.
Bairnsdale, VIC Lesbian Dating. 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. However, guys favoring online dating might differ in several unmeasured respects from men preferring 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 would imply a mediating effect of more information on partners, (including perceived HIV status) on UAI 13
Men who have sex with men (MSM) often make use of the Internet to discover 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 venues (offline) 1 - 3 This implies that men 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 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-unaware) 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 ventures). Corrected for demographic characteristics, 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 online 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 nearby Bairnsdale Victoria, Australia.
Lesbian Dating Near Me Wendouree Victoria | Lesbian Dating Near Me Alphington Victoria