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Enregistrement W2004781678 · doi:10.1097/qad.0000000000000566

Sexual networks, HIV, race and bacterial vaginosis

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

Notice bibliographique

RevueAIDS · 2015
Typeletter
Langueen
DomaineImmunology and Microbiology
ThématiqueReproductive tract infections research
Établissements canadiensnon disponible
Organismes subventionnairesMSD K.K.Astellas PharmaTorii PharmaceuticalNational Center for Global Health and MedicineViiV HealthcareGilead SciencesPfizer
Mots-clésTyphoid feverBacterial vaginosisCholeraTransmission (telecommunications)Sexual transmissionDemographyRace (biology)Environmental healthMedicineHuman immunodeficiency virus (HIV)GeographyImmunologyVirologySociologyGender studiesMicrobicideObstetrics

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,614
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,279
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2015
Routes d'admission1
Résumé présentoui

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