Informing HIV Prevention Programs for Adolescent Girls and Young Women: A Modified Approach to Programmatic Mapping and Key Population Size Estimation
Notice bibliographique
Résumé
Background: Standard programmatic mapping involves identifying locations where key populations meet, profiling of these locations (hotspots), and estimating the key population size. Information gained from this method has been used for HIV programming—resource allocation, program planning, service delivery, and monitoring and evaluation—for people who inject drugs, men who have sex with men, and female sex workers (FSWs). With an increasing focus on adolescent girls and young women (AGYW) as a priority population for HIV prevention, programs need to know the location of and how to effectively reach individuals who are at increased risk for HIV but were conventionally considered part of the general population. We hypothesize that AGYW who engage in transactional and casual sex also congregate at sex work hotspots to meet sex partners. Therefore, we adapted the standard programmatic mapping approach to understand the geographic distribution and population size of AGYW at increased HIV risk in Mombasa County, Kenya. Objectives: The objectives are several-fold: (1) detail and compare the modified programmatic mapping approach used in this study to the standard approach, (2) estimate the number of young FSWs, (3) estimate the number of AGYW who congregate in sex work hotspots to meet sex partners other than clients, (4) estimate the overlap in sexual network in hotspots, (5) describe the distribution of sex work hotspots across Mombasa and its four subcounties, and (6) compare the distribution of hotspots that were known to the local HIV prevention program prior to this study and those newly identified. Methods: The standard programmatic mapping approach was modified to estimate the population of young women aged 14 to 24 years who visit sex work hotspots in Mombasa to meet partners for commercial, transactional, and casual sex. Results: We estimated that there were 11,777 FSWs (range 9265 to 14,290) in Mombasa in 2014 among whom 6127 (52.02%) were 14 to 24 years old. The population estimates for women aged 14 to 24 years who engaged in transactional and casual sex and congregated at the hotspots were 5348 (range 4185 to 6510) and 4160 (range 3194 to 5125), respectively. Of the 1025 validated sex work hotspots, 870 (84.88%) were locations also visited by women engaged in transactional and casual sex. Only 47 (4.58%) hotspots were exclusive sex work locations. The geographic and typological distribution of hotspots were significantly different between the four subcounties (P<.001). Of the 1025 hotspots, 419 (40.88%) were already known to the local HIV prevention program and 606 (59.12%) were newly identified. Conclusions: Using the adapted programmatic mapping approach detailed in this study, our results show that HIV prevention programs tailored to AGYW can focus delivery of their interventions to sex work hotspots to reach subgroups that may be at increased risk for HIV.
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 | 0,007 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».