Tuberculosis preventive treatment in people living with HIV—Is the glass half empty or half full?
Notice bibliographique
Résumé
The most recent World Health Organization Global Tuberculosis (TB) Report suggests that 50% of people living with HIV (PLHIV) newly enrolled in HIV care initiated tuberculosis preventive treatment (TPT) in 2019 [1].TPT is an essential intervention to prevent TB disease among people infected with Mycobacterium tuberculosis-some 25% of the world's population [2].Without TPT, it is estimated that up to 10% of individuals will progress to TB disease.Among PLHIV, the prognosis is worse.Of the approximately 1.4 million annual deaths from TB, 200,000 occur among PLHIV [1], who experience TB at rates more than 30 times [3] higher than people living without HIV.In 2018, governments at the United Nations High-Level Meeting (UNHLM) on TB committed to rapid expansion of testing for TB infection and provision of TPT [4].The goal was the provision of TPT to at least 24 million household contacts of people with TB disease and 6 million PLHIV between 2018 and 2022.However, by the end of 2019, fewer than half a million household contacts had initiated TPT, well short of the pace needed to achieve the 5-year target [1].On the other hand, approximately 5.3 million PLHIV have initiated TPT in the past 2 years [1], with particularly dramatic increases in countries supported by the President's Emergency Plan for AIDS Relief (PEPFARAU : PleasenotethatPEPFARhasbeendefinedasPresident 0 sEmergen ) [5].Globally, among PLHIV entering HIV care programs, TPT initiation rose from 36% in 2017 to 49% in 2018 and 50% in 2019 [6,7].To provide insight into scaling up TPT for PLHIV, it is important to consider each of the many steps involved in the "cascade of care" for TPT.A previous systematic review of studies in several populations receiving TPT concluded that nearly 70% of all people who may benefit from TPT were lost to follow-up at cascade of care steps prior to treatment initiation [8].To maximize the impact of TPT for TB prevention among PLHIV, the full TPT cascade of care must be assessed to identify problems and develop targeted solutions addressing barriers at each step.Until now, these data had not been synthesized for PLHIV.In order to address important research gaps related to TPT in PLHIV such as this one, we are now presenting a Collection in PLOS Medicine on TPT in PLHIV.In the first paper in this Collection, Bastos and colleagues performed a systematic review and meta-analysis of the TPT cascade of care in 71 cohorts with a total of 94,011 PLHIV [9].AU : Anabbreviationlisthasbeencompiled This analysis highlights key steps in the cascade where substantial attrition occurs and identifies individual-level and programmatic barriers and facilitators at each step.In stratified analyses, they found that losses during the TPT cascade were not different in high-income compared to low-or middleincome settings, nor were losses greater in centers performing tests for TB infection (tuberculin skin test [TST] or interferon gamma release assay [IGRA]) prior to TPT initiation.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,018 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,005 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».