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P1-S4.10 The use of social network analysis to quantify the importance of sex partner meeting venues in an infectious syphilis outbreak in Alberta, Canada

2011· article· en· W2323496138 on OpenAlexaffabout
Rachel Lai Fat Fur, Elizabeth A. Henderson, Ron Read, Jenny Godley, Chloé Le Roy, Kathryn Bush

Bibliographic record

VenueSexually Transmitted Infections · 2011
Typearticle
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsSyphilisContact tracingMedicineMen who have sex with menOutbreakSocial network (sociolinguistics)Transmission (telecommunications)Sexually transmitted diseasePublic healthDemographyInfectious disease (medical specialty)Family medicineEnvironmental healthDiseaseTelecommunicationsVirologyHuman immunodeficiency virus (HIV)Social mediaComputer sciencePathologyWorld Wide Web

Abstract

fetched live from OpenAlex

Background Places where people meet sex partners may play an important role in the propagation of sexually transmitted infections. Social network analysis (SNA) has the potential to quantify the role that places of social aggregation play in syphilis transmission based on a relational approach. The primary objective of this study was to explore the use of SNA as both an epidemiological and methodological tool to determine the relative importance of sex partner meeting venues to the transmission of syphilis, in a sustained infectious syphilis outbreak. Methods In a network survey study, we identified and enrolled 52 cases and named contacts of infectious syphilis among individuals, aged 18−75 years at a Sexually Transmitted Disease clinic in Alberta Canada, during routine public health measures of infectious disease control between April and August, 2009. In addition to standard contact tracing information, participants were asked to list all venues attended in the last 6 months where sexual partnering may have occurred. We constructed a sexual affiliation network by linking together persons infected with syphilis, and their named sexual contacts, to sex partner meeting venues. By transposing the sexual affiliation matrix and applying matrix multiplication we created two separate networks; a network of persons connected by venues and a dual network of venues connected by persons. Hierarchal clustering was performed to model patterns of individual patronage of venues, and network algebraic measures of centrality and permutation statistical methods were used to determine what type of venue connected the most individuals infected with syphilis. Results 77% of participants reported meeting a sex partner at a social venue in the last 6 months. We identified a densely connected sexual affiliation network of 94 men who have sex with men (MSM), comprised of 18 cases of infectious syphilis and 76 named sexual contacts connected by 21 venues. In the network of sex partner meeting venues, Internet venues had higher degree centrality than non-internet venues (p<0.05). In the network of men connected by venues, hierarchal clustering detected a cluster of 35 men linked together by their patronage of three Internet venues see Abstract P1-S4.10 Figure 1. These three Internet venues had the highest degree centrality in the network of sex partner meeting venues and connected two thirds of all infectious syphilis cases. Abstract P1-S4.10 Figure 1 Number and range of projected HIV prevalence estimates for the PB population (from the model fits to KH and DD data). *Integrated biological and behavioural assessment (IBBA) 2009, collected within the monitoring and evaluation of Avahan, the India initiative. Conclusions To our knowledge, this is the first study to use SNA of a sexual affiliation network to quantify the importance of places in an outbreak of infectious syphilis. Network analysis allowed identification of three key venues that connected individuals who were infected with syphilis. These venues could provide public health officials with an epidemiologic target for primary and secondary prevention strategies to prevent further dissemination of disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.291
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2011
Admission routes2
Has abstractyes

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