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Enregistrement W2792117618 · doi:10.1097/qad.0000000000001769

Concerns about a study on sexually transmitted infections after initiation of HIV preexposure prophylaxis

2018· letter· en· W2792117618 sur OpenAlexaboutno aff
Julia L. Marcus, Jonathan E. Volk, Jonathan M. Snowden

Notice bibliographique

RevueAIDS · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueHIV/AIDS Research and Interventions
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on Minority Health and Health DisparitiesNational Institute of Allergy and Infectious DiseasesKaiser Permanente
Mots-clésPre-exposure prophylaxisGonorrheaMedicineChlamydiaRisk compensationHuman immunodeficiency virus (HIV)Incidence (geometry)Men who have sex with menDemographyGonococcal infectionGynecologySyphilisFamily medicineSexually transmitted diseaseImmunology

Résumé

récupéré en direct d'OpenAlex

An article in AIDS by Nguyen et al. [1] tests the hypothesis that use of HIV preexposure prophylaxis (PrEP) leads to risk compensation, and thus to increased rates of sexually transmitted infections (STIs). Nguyen et al.[1] use a Canadian clinic-based sample of MSM to make two comparisons: STI rates in PrEP users in the 12 months after PrEP initiation compared with 12 months before PrEP initiation, and STI rates in PrEP users in the 12 months after PrEP initiation compared with postexposure prophylaxis (PEP) users in the 12 months after PEP use. They conclude that their data support the risk compensation hypothesis. However, we have four concerns about this study, particularly with respect to study design and the degree to which the results support the conclusions of Nguyen et al.[1]. First, as Nguyen et al.[1] point out, gonorrhea and chlamydia incidence nearly doubled among men in Quebec during 2010–2015, so we would expect to see increases in STI rates over time regardless of PrEP initiation. Thus, any significant finding in the preinitiation/postinitiation comparison may be driven by these temporal changes rather than PrEP use. Analytic approaches exist to address secular changes over time; one is to compare PrEP users to a control group also experiencing the temporal change but not the exposure condition (i.e. PrEP), as in difference-in-differences analysis [2]. Second, PEP users are not a valid control group for PrEP users. Although PEP users have had at least one potential HIV exposure, those who have ongoing HIV risk would ideally transition from PEP to PrEP, while those with only a brief period of risk or a single exposure would not be indicated for PrEP [3]. Nguyen et al.[1] did not present data on transitions from PEP to PrEP or discuss how these were handled in the analyses. If PEP patients with ongoing risk transitioned to PrEP, the higher rates of STIs observed would likely reflect the appropriate prescribing of PrEP rather than risk compensation. Furthermore, most PEP users report a decrease in condomless sex in the 12 months post-PEP [4]; indeed, a comparison to STI rates in the 12 months prior to PEP use may have been more appropriate. Third, Nguyen et al.[1] do not acknowledge or discuss their null findings, and some conclusions are based on interpreting nonsignificant results as meaningful differences. For the post-PrEP vs. pre-PrEP comparison, there was no observed increase in rates of anal, oral, urethral, or any gonorrhea; syphilis; or anal, oral, or urethral chlamydia, and the conclusion that overall STI rates were higher post-PrEP vs. pre-PrEP was based on a result that was not statistically significant after adjustment for frequency of STI screening [adjusted incidence rate ratio (aIRR) 1.39, 95% CI 0.98–1.96]. For the post-PrEP vs. post-PEP comparison, there was no observed increase in rates of anal, oral, urethral, or any gonorrhea; syphilis; or oral chlamydia. Finally, we do not understand the conclusion that the highest rate ratios were for anal and oral gonorrhea, nor the argument of Nguyen et al.[1] that increases in asymptomatic STIs indicate risk compensation. The rate ratios for anal and oral gonorrhea in the post-PrEP/pre-PrEP comparison were above and below the null value of 1, respectively, and neither approached statistical significance (anal: aIRR 1.29, 95% CI 0.57–2.89; oral: aIRR 0.85, 95% CI 0.38–1.90). Even if Nguyen et al.[1] had in fact observed increases in asymptomatic STIs, it is not clear why this would be consistent with risk compensation. The higher frequency of screening among PrEP users will detect asymptomatic STIs that would otherwise have gone undiagnosed, a bias that is unlikely to be fully removed by adjusting for number of STI tests. Thus, an increase in symptomatic STIs would be a better marker of risk compensation because it would be independent of differences in screening. Most urethral gonorrhea infections in men are symptomatic [5], yet Nguyen et al.[1] observed no increased risk of urethral gonorrhea post-PrEP vs. pre-PrEP (aIRR 0.60, 95% CI 0.22–1.64) or post-PrEP vs. post-PEP (aIRR 2.42, 95% CI 0.47–12.55). There is a pressing need for further research on clinical and behavioral PrEP outcomes, given the rapidly shifting dialogue about PrEP uptake, the optimal delivery of this health service, and its potential effects on individuals’ sexual behaviors and the sexual health of populations. That said, our concerns about the study by Nguyen et al.[1], as well as similar concerns expressed about other recent studies on PrEP-associated risk compensation [6,7], highlight that there is an equally pressing need for rigorous research design and measured study interpretation that does not overreach the observed findings. Acknowledgements This work was supported by a Kaiser Permanente Northern California Community Benefit research grant, and by the National Institute of Allergy and Infectious Diseases (K01 122853). Conflicts of interest J.L.M. reports past research grant support from Merck. J.E.V. and J.M.S. report no potential conflicts. Sources of funding: This work was supported by a Kaiser Permanente Northern California Community Benefit research grant, and by the National Institute of Allergy and Infectious Diseases (K01 122853).

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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,469
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,029
Tête enseignante GPT0,335
Écart entre enseignants0,306 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

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

En bref

Citations5
Publié2018
Routes d'admission1
Résumé présentoui

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