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Try out PMC Labs and tell us what you think. Learn More. Baltimore, Maryland ranks among U. Current strategies for HIV prevention and control emphasize testing as many people as possible and linking infected individuals to care and treatment, to limit the complications of HIV and reduce the of people in the community with high viral lo White House Office, ; Marrazzo et al. Despite these efforts, in the United States, HIV incidence continues to persist at approximately 50, new infections each year and disproportionately affects certain populations such as men who have sex with men MSM Centers for Disease Control and Prevention, In light of these challenges, and given limited availability of resources for implementing HIV prevention and control programs, more efficient strategies are needed.

Current approaches may be enhanced by targeted control, which aims to identify and disrupt transmission in key networks, or groups of interconnected individuals who, through their sexual or needle-sharing connections, are actively transmitting HIV from one person to another and subsequently to others in the group. Targeted control is an approach that is grounded in network theory, which emerged in the s and s, when social scientists introduced the concept of a social network as a group of individuals with distinct patterns of relationships and interactions with other individuals Newman, ; Doherty et al.

The quantitative, visual analysis of social networks has extended to investigations of patterns of sexual contacts, emerging from historical efforts to prevent and control sexually transmitted infections STIs Doherty et al. STI control efforts such as contact tracing routinely incorporate network theory.

In contact tracing, STI infected individuals are asked during partner services interviews about their recent sexual partners. Attempts are then made to contact and inform the partners about their potential exposure. To initiate targeted control of HIV within a transmission network, one important consideration is the structure of the network.

For example, sexual networks can be analyzed to identify individuals who are more connected, directly or indirectly, to other individuals in a network. Moreover, their force of infectivity, i. Another important consideration for the force of infectivity of the network is the composition of the network, i.

The likelihood of transmission from an infected to a susceptible individual increases according to the amount of virus in the infected person. HIV infected individuals who are most likely to transmit are those with an unsuppressed viral load practicing transmission risk behaviors, particularly individuals newly infected within the past three months Hollingsworth et al.

Leveraging these two considerations e network structure and availability of high viral load individuals e may yield new and more effective targeted HIV control strategies to reduce the spread of HIV. When available, network and viral load data can be combined to identify the most likely high transmission networks, i. These networks can then be targeted by local outreach programs to implement HIV testing, and ultimately, link people living with HIV into care or introduce preexposure prophylaxis PrEP as a prevention tool for susceptible individuals.

An effective strategy for accessing transmission networks characterized by ificant density and high viral load is to focus targeted control activities on social venues Jennings et al. Social venues are places where individuals congregate and can include places where individuals meet sex partners and coalesce into sexual networks with specific structural characteristics, such as density of network connections.

These sex partner meeting places can include formal venues such as bars, nightclubs, hair salons, schools, and informal venues such as parks, abandoned houses, street corners, and alleys Wohl et al. More recently, the rapid rise of the use of social media has introduced new online platforms such as chat rooms and geosocial networking GSN applications for meeting sex partners Doherty et al. Through the formation of sexual networks, some of these places may generate a context of HIV transmission risk through dense network connections that include infected individuals with unsuppressed viral lo and those who are susceptible to infection.

Examining the density of network connections among the venues themselves i. Identifying venues with high HIV viral lo that are highly connected, or identifying high viral load venues connected to lower transmission venues would yield specific targets for HIV outreach programs.

One means to identify these transmission risk places or venues is to use a variation on sexual network analysis, venue affiliation network analysis. Compared to traditional sexual networks, venue affiliation network analysis connects individuals nominating sex partner meeting venues into a sexual network of venues Frost, ; Oster et al. The focus is thus on the network of venues and the connections between venues rather than the individual and the connections between individuals.

The information required for venue affiliation network analyses is often less resource intensive to obtain and has fewer biases compared to other forms of sexual network analyses, which are limited by recall and disclosure of individual sex partner information Frost, Moreover, venue affiliation network analyses can reveal tightly connected venues where HIV transmission may be occurring by evaluating different metrics related to network density and venue centrality Frost, ; Borgatti and Halgin, Measures of degree, betweenness, and closeness centrality, have been useful in studying HIV, STI, and tuberculosis transmission networks Oster et al.

In a venue affiliation network analysis conducted in Jackson, Mississippi, Oster et al. The social and sexual networks of both HIV-infected and HIV-uninfected men overlapped through a small cluster of venues, establishing a setting for heightened risk of new HIV infections Oster et al.

While this and other studies Frost, ; Oster et al.

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In addition, the distribution of epidemiologically ificant elements and their relationship to transmission risk and generalizability to different settings, populations, and geographies remains unknown. The objectives of this study were to evaluate the network structure e. Baltimore City, Maryland ranks among U. One goal of the partnership is to reduce new HIV infections in Baltimore City through the innovative use of surveillance data. Reporting of HIV to state and local health departments is legally mandated in Maryland. During routine partner services, in addition to collecting demographic, risk behavior, and sex partner information, BCHD routinely collects qualitative information on sex partner meeting places e.

These surveillance data allowed for the de of egocentric network analyses to understand the structure and composition of network ties between individual cases and the reported venues. To further inform prioritization of local targeted control programs, BCHD implemented a new viral load testing protocol in October The of the viral load assays are used strictly for epidemiological purposes and not for HIV diagnosis or patient management protocols.

Data were limited to cases with interview records and information on at least one sex partner meeting place. Descriptive analyses and network analyses were performed using R Version 3. As a preliminary step, individuals reporting a sex partner meeting place were compared to those not reporting using chi-squared tests or t-tests, as appropriate.

We described individuals included in the analysis by demographics e. Venues were classified into six types: bar or club, internet based site e. Data were used to create affiliation network graphics using two modes: newly diagnosed MSM and their reported sex partner meeting places. First, an affiliation network graph was generated to visualize the extent to which new diagnoses were connected to the entire set of all reported sex partner meeting venues. Then, focusing on the network of venues reported by at least two cases, an additional affiliation network graph was created and venues were evaluated by venue-case degree centrality, i.

To compare the relative prominence of venues, each venue-case degree centrality score was normalized by dividing the value by the maximum value possible for the network, i. To create the graphic, venues were connected if they shared at least one MSM case. Exploratory analysis of the co-occurrence network was conducted using three centrality metrics, which were evaluated at the venue-venue level degree, betweenness, and closeness.

Venue-venue degree centrality was calculated by linking venues via shared cases, and node size Sex chat network in Meredith adjusted according to of ties to other venues. Illustrating varying levels of venue-venue degree centrality may further refine the prioritization of venues for outreach to those venues that are highly central, while simultaneously identifying venues that are less central, i.

For this analysis, betweenness centrality was used to identify the location of certain venues within the broader network of venues and to begin to uncover potentially critical bridging venues. Venues characterized by high betweenness centrality are, for example, highly connected via bridging linkages to other venues, a characteristic which is fundamental to the persistence of HIV transmission in a given sub-population Doherty et al.

The third centrality metric, closeness, is used to specify how closely connected an individual is to all other individuals through mutual ties with cases, or in sexual affiliation networks, a venue to all other venues through mutual ties with venues Frost, ; Borgatti and Halgin, We used closeness to identify the venues that comprised the tightest-knit cluster. For Sex chat network in Meredith exploratory sub-analysis, we hypothesized that transmission risk was not equal across venues, and thus with the data available, we sought to explore variation in viral load, specifically venue viral load, as a potential marker of the force of infectivity of a venue.

Venues found to have higher viral load may al the existence of high transmission networks where prevention among susceptible individuals such as pre-exposure prophylaxis PrEP may be critical. In addition these venues may be useful for targeted linkage to and retention in care.

To determine variability in viral load, viral load data from individuals reporting the venue as a sex partner meeting venue were aggregated to create venue-level viral load. This approach is similar to approaches used for areas or subpopulations i.

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Specifically, among the network of venues limited to those nominated by at least two cases, venue viral load was calculated as the geometric mean viral load of cases linked to a particular venue Centers for Disease Control and Prevention, The geometric mean was used compared to, for example, an arithmetic mean because it is less sensitive to extreme outliers.

Venues were ranked by venue-case degree centrality and plotted on a histogram to depict the most central venues during the study period, specifically identifying venues that ed for a majority of all reports of sex partner meeting venues i. Venue viral load data were then layered onto the venuevenue co-occurrence network graph.

In addition to characterizing locations according to transmission riskvenues were differentiated by venue type to better understand how different types of venues were connected e. Cases who provided sex partner meeting place information were younger and reported more sex partners compared to those without meeting place reports; racial distributions and mean viral load were not ificantly different between the two groups data not shown. Cases with viral load information, compared to those without viral load, were not ificantly different by age, race, or of sex partners.

Cases reported an average of two range: 1e11 and a total of sex partner meeting place reports in the past year. The venue affiliation network of these venues revealed a large variation in the report of unique places Fig. In the network of venues, one main component emerged, consisting of many peripheral venues and a few centrally located venues.

Peripheral venues were connected to the main component via cases that reported more than one sex partner meeting place, creating linkages across multiple venues. Many of these cases also clustered around a set of venues located centrally within the main component.

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Separate from the main component were dy of venue-case pairs that were not otherwise linked to the venues in the main component. These isolated venue-case pairs were primarily streets, parks, and neighborhoods and other types of public venues e. The network graphic differentiates which cases reported meeting sex partners at multiple venues of the same or different types e.

The venue node size represents the venue-venue degree centrality metric and was used to visually locate venues with the most shared cases of newly diagnosed MSM, informing ways to maximize outreach coverage by targeting a select of venues. Overall, the network was characterized by a high degree of connectedness across venues.

Venues were tied to an average of 5. Venue WEB 11 demonstrated the highest venue-venue degree centrality i. Connections between different meeting place types were common. For example, bars or clubs were primarily connected to other bars or clubs but also internet-based sites, suggesting that newly-diagnosed MSM meet sex partners within a tight network of both physical and internet based places.

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Note: Venues are linked if they have at least one shared case. The size of the node reflects level of degree centrality i. The width of the lines indicates tie strength i. BAR 8 was a high transmission venue with the most connections to the cluster, including both bars and internet-based venues. WEB 24 exhibited the strongest ties i. The overall goal of this study was to inform targeted HIV control strategies through the evaluation of the structure of a network of sex partner meeting venues of newly diagnosed HIV-infected MSM, and in an exploratory sub-analysis, to describe the variability of viral load across the network of venues.

This study applied a novel methodology, namely venue affiliation network analysis and the findings have important implications for public health programs considering how best to allocate resources for targeted HIV control.

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