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Record W2004781678 · doi:10.1097/qad.0000000000000566

Sexual networks, HIV, race and bacterial vaginosis

2015· letter· en· W2004781678 on OpenAlexaboutno aff
Chris Kenyon, Kara Osbak

Bibliographic record

VenueAIDS · 2015
Typeletter
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsnot available
FundersMSD K.K.Astellas PharmaTorii PharmaceuticalNational Center for Global Health and MedicineViiV HealthcareGilead SciencesPfizer
KeywordsTyphoid feverBacterial vaginosisCholeraTransmission (telecommunications)Sexual transmissionDemographyRace (biology)Environmental healthMedicineHuman immunodeficiency virus (HIV)GeographyImmunologyVirologySociologyGender studiesMicrobicideObstetrics

Abstract

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In their recent article, Buvé et al.[1] argue that the higher HIV prevalence in ‘black populations’ is due, in large part, to racial differences in the vaginal microbiome. They base this argument on two findings. Firstly, bacterial vaginosis (BV) prevalence tends to be higher in black populations. Secondly, a number of studies have found an increased incidence of HIV following the diagnosis of BV. We would like to advance a sexual network based explanation that we believe provides a better fit to the observed patterning of BV, sexually transmitted infections (STIs), sexual behaviour and race. We illustrate our argument by way of an analogy with John Snow's insights into the forces underpinning cholera transmission in London in 1854. Snow [2] showed that persons whose houses were supplied by the water-pipes from the Southward water company had nine times the cholera-related mortality of those supplied by the Lambeth company. He argued that this was likely due to Lambeth, unlike Southward, drawing its water upstream from the sewage contamination. Faecal contamination of Southward's water network put the Southward supplied houses at a high risk for exposure to entero-pathogens, including cholera and other faecal-oral transmitted illnesses such as typhoid. It is also possible that there could have been an association between typhoid and cholera infections at an individual level. However, the higher prevalence of cholera in Southward, together with the association between typhoid and cholera, would have best been explained by their common source (a contaminated water network) rather than by typhoid potentiating the transmission of cholera. In a similar vein, black as opposed to white populations in the USA, UK and South Africa have been found to have higher prevalences of BV, HIV and other STIs [3–5]. The most parsimonious explanation in each case is that the black populations in these countries have more connected sexual networks [3,4,6]. This results from a number of factors, including the higher prevalence of partner-concurrency observed in black populations in each of these countries [3–5]. A more connected sexual network represents a higher risk network for all STIs entering the network [3]. These network-level properties could in turn explain a part of the observed higher prevalence of STIs in black populations in these countries. Network factors could also explain the observed association between different STIs at an individual level. Persons who contract one STI are by virtue of this more likely to be connected to a high-risk part of the sexual network and therefore more likely to contract other STIs. This effect is very difficult to control for in individual-level analyses [3]. Just as in the case of entero-pathogens in the Lambeth versus Southward populations, it would not be appropriate to assume that an association between STIs represented a causal relationship. This sexual network-level explanation is supported by a variety of types of evidence. Studies at both individual [7,8] and ecological levels [9] have found an association between partner-concurrency and BV prevalence. BV prevalence is not just increased in blacks but in a range of nonblack populations such as Greenland, Aboriginals in Canada and Aymara speakers in Peru [10–12]. In each of these cases, the populations with a high BV prevalence had markers of higher-risk sexual behaviour such as a high prevalence of other STIs [10–12]. This is commensurate with the findings of a systematic review and meta-analysis of the relationship between sex and BV that found that various forms of multiple partnering were associated with an increased incidence of BV [13]. A further crucial problem for hypothesis by Buvé et al.[1] is that the available data suggest that black populations with low-risk behaviour and low prevalence of other STIs have a low BV prevalence. In our systematic review of the global epidemiology of BV, we found that Burkina Faso, which has a relatively low prevalence of HIV and other STIs [14], has a very low prevalence of BV, 6.4% and 7.9% according to two large, high-quality studies [15]. This fits with findings from studies from other sub-Saharan African countries that find considerable differences between HIV and STI prevalence between different black ethnic groups that is strongly associated with differences in sexual behaviour [5,6,16]. We have enough evidence to conclude that the vaginal microbiome varies considerably according to a myriad of factors considered at the levels of individuals, couples and sex networks. Longitudinal studies that sample the genital microbiomes of women and their partners from the time of sexual-debut and in multiracial communities are required to assess whether any of these variations can be attributed to race. Acknowledgements Conflicts of interest There are no conflicts of interest.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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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Citations8
Published2015
Admission routes1
Has abstractyes

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