The amount of sex partners in the preceding 6months of the index was likewise correlated 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). UAI was significantly more likely if more sex acts had happened 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). Casual Sex near me Northbridge, Western Australia. Other factors significantly associated with UAI were group sex within the partnership, and sex-related multiple drug use within partnership.
In multivariate model 3 (Tables 4 and 5 ), also including variables 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 more powerful (though not essential) for the HIV positive guys (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 guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more likely 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 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 groups, one for each HIV status. Among HIV-positive men, UAI was more common in online compared to offline ventures (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-oblivious men, 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 on-line and offline partners and partnerships 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 on-line partners was more frequently reported as understood (61.4% vs. 49.4%; P 0.001), and in on-line 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 often reported with internet partners.
To be able to analyze the potential mediating effect of more info on partners (including perceived HIV status) on UAI, we developed three variant models. In version 1, we adapted 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 venture features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adjusted also for venture sexual risk behaviour (i.e., sex-associated drug use and sex frequency) and partnership kind (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 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-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 significant associations. As a rather large number of statistical tests were done and reported, this approach does lead to a higher risk of one or more false positive associations. 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. Casual sex near Northbridge, Western Australia. 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 main exposure of interest and result (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership sort; 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-tests for dichotomous and categorical variables and using rank sum test for continuous variables). We compared characteristics of participants, partners, and partnership sexual conduct by online or offline venture, and calculated P values predicated 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 analyze the association between dating location (online versus offline) and UAI. Likelihood ratio tests were used to measure 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, with the answer options: (1) no, (2) potentially, (3) yes. Sexual behaviour with each partner was dichotomised as: (1) no anal intercourse or just shielded anal intercourse, and (2) unprotected anal intercourse. To ascertain 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, 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 got by asking the question 'Do you know whether you are HIV infected?', with five answer choices: (1) I am certainly not HIV-contaminated; (2) I believe that I am not HIV-infected; (3) I do not know; (4) I believe I may be HIV-contaminated; (5) I know for sure that I 'm HIV-contaminated. We categorised this into HIV-negative (1,2), unknown (3), and HIV-positive (4,5) status. The survey enquired about the HIV status of every sex partner with all the question: 'Do you know whether this partner is HIV-contaminated?' with similar answer choices as previously. Casual Sex closest to Northbridge Western Australia Australia. Perceived concordance in HIV status within ventures was categorised as; (1) concordant; (2) discordant; (3) unknown. The last category 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 questionnaire throughout their visit 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 information on sexual behavior with those partners. A thorough description of the study design and the questionnaire 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 website) 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 subsequent 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 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 familiarity 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 occurrence of UAI in online acquired casual partnerships to that in offline obtained 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 partially clarified through better knowledge of partner characteristics, including HIV status.
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 men with internet partners to guys with offline partners. Yet, men preferring online dating might differ in a variety of unmeasured regards from men preferring offline dating, leading to incomparable behavioural profiles. A more recent meta-analysis contained several studies examining MSM with both online and offline acquired sex partners and found evidence for an association between UAI and on-line partners, which would imply a mediating effect of more information on partners, (including perceived HIV status) on UAI 13 Casual Sex nearby Northbridge, WA.
Men who have sex with men (MSM) often make use of the Net to locate sex partners. Several studies have shown that MSM are prone to participate 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 implies that guys 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 danger of HIV transmission also depends on exact 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 features, 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 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.
Believe it or not believe it, I didn't come out of this experiment feeling lousy about myself---only smarter about the way gay men (or perhaps men in general) place way too much emphasis on foolish characteristics like beards and ballcaps (hint: that is why you're all still cranky and single). And actually, I don't think having long hair itself is the big hang-up; it's what my hair implies. Having long hair (especially for a black man) means you're likely a bitchy striking queen that nobody wants to date. Casual Sex nearby Northbridge, WA. Even if the premise is not that extreme, the underlying anxiety is you spent too much time on your appearance and that's not manly." That's frustrating, obviously, since stereotypical masculinity requires just as much work---we just don't think of it that way. I remember chatting with this scruffy, pretty muscular man with tattoos and torso hair and an Instagram full of masc pics; once we got to speaking, he revealed his obsession with Beyonc and said yasss!" every other paragraph. But no matter---his image is butch, so his dating life is always full.
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