AMC, Academic Medical Center; aOR, adjusted odds ratio; CI, confidence interval; CINIMA, Center for Infection and Immunology Amsterdam; DAG, directed acyclic graph; HIV, human immuno-deficiency virus; i.e., id est, it's, for example; IQR, interquartile range; MEC, Medical Ethics Committee; MSM, men who have sex with men; OR, odds ratio; RIVM, National Institute of Public Health and the Environment, Centre for Infectious Disease Control; STI, sexually transmitted infection; UAI, unprotected anal intercourse; UMCU, University Medical Center Utrecht
New research should stay up to date when it comes to accelerated shifting dating methods as well as sero-adaptive behaviours (like viral sorting and pre exposure prophylaxis). With each new way of dating and preventative chances, the rules of engagements will be different. Our data are 8years old and web-based dating has developed since then. However these results are useful, as they demonstrate how web-based partner acquisition can result in more info on the sex partner, and this may impact on the frequency of UAI.
Relationship online may offer other chances for communicating on HIV status than dating in physical surroundings. Localsex nearest Caboolture Queensland. Localsex Near Me North Lakes Queensland. Facilitating more on-line HIV status disclosure during partner seeking makes serosorting easier. Yet, serosorting may increase the load of other STI and WOn't prevent HIV infection completely. Interventions to prevent HIV transmission should particularly be directed at HIV negative and unaware MSM and excite timely HIV testing (i.e., after risk events or when experiencing symptoms of seroconversion illness) as well as routine testing when sexually active.
Because determinations on UAI appear to be partially based on perceived HIV concordance, precise knowledge of one's own and the partner's HIV status is essential. In HIV-negative men and HIV status-oblivious guys, conclusions on UAI will not only be based on perceived HIV status of the partner but also on one's own negative status. HIV serosorting is challenged by the frequency of HIV testing and also the HIV window phase during which individuals can transmit HIV but cannot be diagnosed with the commonly used HIV tests. So serosorting cannot be regarded as a very effective method of averting HIV transmission 22 Besides interventions to trigger the uptake of HIV and STI testing in sexually active men, interventions to warn against UAI based on sensed HIV-negative concordant status are in order, irrespective of whether this concerns online or offline dating.
For HIV-oblivious guys the effect of dating place on UAI didn't change by adding partner features, but it increased when adding lifestyle and drug use. It's difficult to assess the real risk for HIV for these men: do they behave as HIV negative men who want to shield themselves from HIV infection, or as HIV positive guys attempting to protect their HIV negative partner from HIV infection? A study by Horvath et al. reported that 72% of guys who were never tested for HIV, profiled themselves online as being HIV-negative, which might be problematic if they're HIV-positive and engage in UAI with HIV-negative partners 12 Formerly Matser et al. reported that 1.7% of the oblivious and sensed HIV-negative MSM were examined HIV positive. The study population included the MSM reported in this study 15
Online dating wasn't connected with UAI among HIV-negative men, a finding in agreement with some previous studies, mainly among young men 21 , but in comparison with other studies 1 - 5 This may be because of the reality that most earlier studies compared sexual behaviour of two groups of MSM rather than comparing two sexual behavior patterns within one group of guys. Nevertheless it may also represent secular changes; possibly in the beginning of online dating a more high-risk group of guys used the Internet, and over time online dating normalized and less high-risk MSM now also make use of the Net for dating.
A key strength of this study was that it investigated the connection between online dating and UAI among MSM who had recent sexual contact with both online and also offline casual partners. This prevented prejudice caused by potential differences between guys only dating online and those simply dating offline, a weakness of several previous studies. By recruiting participants at the largest STI outpatient clinic in the Netherlands we could comprise a high number of MSM, and prevent potential differences in guys tried through Internet or face to face interviewing, weaknesses in some previous studies 3 , 11
Among HIV-positive guys, in univariate analysis UAI was reported significantly more frequently with on-line partners than with offline partners. When adjusting for partner features, the effect of online/offline dating on UAI among HIV-positive MSM became somewhat smaller and became nonsignificant; this implies that differences in partnership variables between online and offline partnerships are liable for the increased UAI in online established ventures. This may be because of a mediating effect of more info on partners, (including perceived HIV status) on UAI, or to other variables. Among HIV-negative guys no effect of online dating on UAI was discovered, either in univariate or in some of the multivariate models. Localsex closest to Caboolture, Queensland. Among HIV-oblivious men, online dating was correlated with UAI but just critical when adding partner and venture variables to the model.
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 risk of UAI than offline dating. Localsex closest to Caboolture, Queensland. For HIV negative guys this lack 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). Simply among guys who suggested they weren't aware of their HIV status (a little group in this study), UAI was more common with online than offline associates.
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). 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 variables significantly associated with UAI were group sex within the venture, 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 separate 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 men (aOR = 0.94 95 % CI 0.59-1.48). The result of online dating on UAI became more powerful (and important) for HIV-oblivious men (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).
In univariate analysis, UAI was significantly more inclined to happen 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 firmly correlated with UAI (OR = 11.70 95 % CI 7.40-18.45). The impact of dating place 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 types, one for each HIV status. Among HIV positive guys, UAI was more common in online when compared with offline partnerships (OR = 1.61 95 % CI 1.03-2.50). Among HIV negative guys no association was apparent between UAI and online ventures (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware men, UAI was more common in online when compared with offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).
Characteristics of online 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 often reported as known (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 often knew 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-associated material use, alcohol use, and group sex were less frequently reported with online partners.
In order to examine 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 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 characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adapted 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-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 restricted 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 miss potentially significant associations. As a rather large number of statistical tests were done and reported, this strategy does lead to an elevated danger of one or more false-positive organizations. Investigations were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).
Prior to the analyses 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. Caboolture Localsex. of male sex partners in preceding 6months), and some were supposed to be on the causal pathway between the primary exposure of interest and results (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership type; 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). Localsex Near Me Rochedale Queensland. We compared characteristics of participants, partners, and venture sexual behaviour by on-line or offline partnership, and computed P values based on logistic regression with robust standard errors, accounting for related 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 gauge the significance of a variable in a model.
In order 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) perhaps, (3) yes. Sexual behaviour with each partner was dichotomised as: (1) no anal intercourse or only 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, alternative, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if not one of these features 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 understand whether you are HIV infected?', with five response options: (1) I 'm definitely not HIV-infected; (2) I think that I am not HIV-infected; (3) I do not know; (4) I think I may be HIV-infected; (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 questionnaire enquired about the HIV status of each sex partner with the question: 'Do you understand whether this partner is HIV-contaminated?' with similar response options as above. Perceived concordance in HIV status within partnerships was categorised as; (1) concordant; (2) discordant; (3) unknown. Localsex near Caboolture QLD. The last class represents all partnerships where the participant didn't 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.