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Enregistrement W2057148814 · doi:10.1097/00002030-200010200-00030

Prevalence of HIV infection in a multi-site sample of injecting drug users not in contact with treatment services in England

2000· letter· en· W2057148814 sur OpenAlexaboutno aff
Ali Judd, Gerry V. Stimson, Matthew Hickman, Gillian Hunter, Steve Jones, John V. Parry, Peter W. Madden

Notice bibliographique

RevueAIDS · 2000
Typeletter
Langueen
DomaineMedicine
ThématiqueHIV, Drug Use, Sexual Risk
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePublic healthPsychological interventionOutbreakFamily medicineDemographyEnvironmental healthPsychiatryVirologyPathology

Résumé

récupéré en direct d'OpenAlex

Injecting drug use accounts for the largest proportion of AIDS cases in Europe, and this proportion is increasing [1]. In England, relatively low prevalence of HIV (between 1 and 10%) were recorded from injecting drug users (IDU) recruited from both drug agencies and community settings between 1991 and 1998 [2–4]. The swift introduction of preventive interventions in the UK when HIV prevalence among IDU was low has been hailed a public health success [5]. However, in the wake of recent reports of high prevalence and rapid outbreaks of HIV in other parts of Europe [1,6], has the UK's relatively fortunate position been sustained? We recruited the first ever multi-site sample of IDU not in contact with treatment services in England. The methodology of this study has been described elsewhere [7]. Briefly, 753 IDU were recruited from community-based settings in seven sites in England in 1997/1998, and completed a structured interview and provided oral fluid specimens using the EpiScreen device (Epitope Inc., Oregon, USA). All had injected drugs, and had not received treatment for their drug use, in the previous month. Respondents were recruited through social network sampling by trained interviewers [4]. Specimens were anonymously screened for anti-HIV by the Wellcozyme HIV 1+2 GACELISA (VK61; Murex Diagnostics Ltd., Dartford, UK), with confirmatory testing for positive specimens [8]. t-Tests, χ2 tests and exact binomial confidence intervals for proportions were used to describe the data and examine the association between risk factors and prevalence of anti-HIV. The majority of IDU were male (71.1%), and just under a third (30.2%) were under 25 years of age. Over a third (39.8%) had been injecting for less than 5 years, and a third were recruited in London. London injectors had a higher mean duration of injecting career than their counterparts in the rest of England (12 versus 7 years, P < 0.001). The overall prevalence of anti-HIV was 1.9% (95% confidence interval 1.0–3.1%, Table 1). No anti-HIV was detected among those aged 25 years and under, and those injecting for 5 years or less. However, prevalence rose with increasing age and increasing duration of injecting (Table 1). Anti-HIV prevalence was also raised among IDU in London and stimulant injectors. Because there were no cases of HIV among young injectors, and those with short injecting careers, it was not possible to conduct multivariate analysis.Table 1: Prevalence of anti-HIV by key demographic characteristics. These results provide new evidence to suggest that the prevalence of HIV infection among IDU in England continues to be low. Although there are limitations with using prevalence as a marker of disease burden, it is encouraging that in this study no HIV was detected in younger injectors and those with shorter injecting careers, potentially indicating that incidence is low or negligible in this group. This finding adds further support to evidence for the effectiveness of harm reduction measures in England [4,5], and should serve to reassure UK policy makers that prevention activities continue to have an impact on HIV transmission. These results also lead to optimism about the prevention of other blood-borne infections among IDU in the UK. All 15 member states of the European Union provide syringe exchange programmes and substitution therapy for drug users [6], although the scale, nature and year of introduction of these interventions varies. The sheer scale of interventions in the UK, combined with their early introduction, may be the key to continued success in preventing blood-borne virus transmission. For example, in the UK an estimated 28 million syringes were distributed in 1997, from over 2000 outlets (J. Parsons, personal communication). There were also an estimated 150 distinct drug services working in the London area alone, providing a range of harm reduction and specialist treatment interventions [9]. In contrast, the recent rise in HIV prevalence from a low and apparently stable base in Vancouver has been attributed at least partly to the inadequate access to drug treatment, methadone maintenance and counseling services in that city [10]. Recent changes in UK drug policy emphasize a shift in focus from public health approaches towards criminal justice interventions [11,12]. Clearly, policy makers should be warned that such a shift should not be made at the expense of public health interventions, especially as these interventions at the very least have been effective in reducing the harms associated with injecting drug use. Acknowledgements The authors are grateful to the fieldworkers who conducted interviews, those who gave up their time to be interviewed, and the drug agencies that assisted in the recruitment of fieldwork staff and allowed access to outreach clients. The authors would also like to thank Julie Newham at the Virus Reference Division for laboratory support. Ali Judda Gerry V. Stimsona Matthew Hickmana Gillian M. Hunterb Steve Jonesc John V. Parryd Peter Maddena

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)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,035
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)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,032
Tête enseignante GPT0,308
Écart entre enseignants0,276 · 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'étudeObservationnel
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

Citations8
Publié2000
Routes d'admission1
Résumé présentoui

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