Evaluation of the benefits and harms of alternative strategies for cervical screening and treatment in women living with HIV: modelling to support WHO 2021 cervical screening and treatment guidelines to prevent cervical cancer
Notice bibliographique
Résumé
Abstract The WHO strategy for the elimination of cervical cancer sets 2030 targets for the scale-up of HPV vaccination, cervical screening, and pre-cancer and invasive cancer treatment. Screening and treatment of precancerous disease will be critical to reduce mortality in the short term, particularly in high HIV burden settings, due to the increased risk of cervical cancer among women living with HIV. To inform the development of new screening and treatment guidelines for women living with HIV, we modelled alternate scenarios for the screening test, frequency, and interval, and assessed the benefits and potential harms of cervical screening and precancer treatment in Tanzania, a country with a high burden of both HIV and of cervical cancer. We used a dynamic model of HPV and HIV co-infection, Policy1-Cervix-HIV, parametrised to Tanzanian demographic and epidemiological data, which captured HIV and HPV interactions. With the support of the WHO Guidelines Development Group for Screening and Treatment to Prevent Cervical Cancer (GDG) we assessed the impact of seven screening algorithms including primary visual inspection with acetic acid (‘VIA’), primary cytology, and primary HPV DNA (‘primary HPV’) with no triage, or triage using HPV16/18 genotyping, colposcopy, cytology, or VIA for women aged 25-50. Screening intervals of 3 years were considered for primary VIA and cytology, and intervals of 3, 5 or 10 years were considered for primary HPV. Screening and triage test performance was informed by updated systematic review evidence. We assumed 70% of women attended each routine screen, and 90% complied with follow-up or treatment. Outcomes include reduction in cervical cancer incidence and mortality as a measure of benefits, and number of precancer treatments (NNT) needed to prevent a death and preterm delivery events directly due to precancer treatment (‘additional preterm delivery events’) as a measure of the potential harms. A range of assumptions were considered in sensitivity and supplementary analyses. We found that, without screening, 5,263 cervical cancer cases (age-standardised incidence rate [ASIR] 104.0/100,000/year) and 4,467 cervical cancer deaths (age-standardised mortality rate [ASMR] 99.8/100,000) are predicted over the lifetime of 100,000 Tanzanian WLHIV. Primary HPV testing without triage every 3-years for women aged 25-50 years was effective in terms of cancer reduction, resulting in a 63.6% reduction in ASIR and 71.7% reduction in ASMR, and an NNT of 38.7 to prevent one cervical cancer death. However, triaging HPV positive women before treatment resulted in minimal loss of effectiveness with overall reduction in ASIR of 57.3-62.2% (range depends on triaging test used) and ASMR of 66.4-70.5% and was associated with more favourable NNTs (19.7-33.0). Primary screening with VIA or cytology at 3-yearly intervals resulted in lesser reductions in cervical cancer incidence and mortality and less favourable NNTs: for VIA, the overall reduction in ASIR was 50.6% and the reduction in ASMR was 54.2%, with an NNT of 107.5 to prevent a death. Extending the HPV screening interval from 3 to 5 years slightly decreased screening program effectiveness but increased efficiency. In conclusion, primary HPV testing approaches were the most effective, and optimised benefits-to-harms compared to primary VIA or cytology. Triaging HPV positive women before treatment reduces precancer treatments and improves efficiencies with minimal loss in effectiveness. However, uncertainties in the primary data exist and further empirical data on the effectiveness of screening in WLHIV will strengthen the evidence base for screening in this population. These findings informed updated WHO cervical screening and treatment guidelines; WHO now recommends that women living with HIV have primary HPV screening with triage before treatment for ages 25-50 every 3 or 5 years.
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 | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,000 |
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.
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 ».