Evidence map›Paper›PMID 32736626›Full record

ArticleBMC health services research2020

The current landscape of pre-exposure prophylaxis service delivery models for HIV prevention: a scoping review.

Jef Vanhamel, Anke Rotsaert, Thijs Reyniers, Christiana Nöstlinger, Marie Laga, Ella Van Landeghem, Bea Vuylsteke

Open access · goldAbstract readScoping Review
In one paragraph

Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 4 pooled it
1.3field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 4 syntheses or guidelines pooled it, 83 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Jef VanhamelDepartment of Public Health, Institute of Tropical Medicine, Nationalestraat 155, B-2000, Antwerp, Belgium. jvanhamel@itg.be.ORCID http://orcid.org/0000-0003-0020-1876
Anke RotsaertDepartment of Public Health, Institute of Tropical Medicine, Nationalestraat 155, B-2000, Antwerp, Belgium.
Thijs ReyniersDepartment of Public Health, Institute of Tropical Medicine, Nationalestraat 155, B-2000, Antwerp, Belgium.
Christiana NöstlingerDepartment of Public Health, Institute of Tropical Medicine, Nationalestraat 155, B-2000, Antwerp, Belgium.
Marie LagaDepartment of Public Health, Institute of Tropical Medicine, Nationalestraat 155, B-2000, Antwerp, Belgium.
Ella Van LandeghemDepartment of Public Health, Institute of Tropical Medicine, Nationalestraat 155, B-2000, Antwerp, Belgium.
Bea VuylstekeDepartment of Public Health, Institute of Tropical Medicine, Nationalestraat 155, B-2000, Antwerp, Belgium.
Instituut voor Tropische Geneeskunde · BE

Funding

Fonds Wetenschappelijk Onderzoek S004919N
6 · The paper itself

Abstract

backgroundStrengthening HIV prevention is imperative given the continued high HIV incidence worldwide. The introduction of oral PrEP as a new biomedical HIV prevention tool can be a potential game changer because of its high clinical efficacy and the feasibility of its provision to different key populations. Documenting the existing experience with PrEP service delivery in a variety of real-world settings will inform how its uptake and usage can be maximised.

methodsWe conducted a scoping review using the five-step framework provided by Arksey and O'Malley. We systematically searched the existing peer-reviewed international and grey literature describing the implementation of real-world PrEP service delivery models reporting on four key components: the target population of PrEP services, the setting where PrEP was delivered, PrEP providers' professionalisation and PrEP delivery channels. We restricted our search to English language articles. No geographical or time restrictions were set.

resultsThis review included 33 articles for charting and analysing of the results. The identified service delivery models showed that PrEP services mainly targeted people at high risk of HIV acquisition, with some models targeting specific key populations, mainly men who have sex with men. PrEP was often delivered centralised and in a clinical or hospital setting. Yet also community-based as well as home-based PrEP delivery models were reported. Providers of PrEP were mainly clinically trained health professionals, but in some rare cases community workers and lay providers also delivered PrEP. In general, in-person visits were used to deliver PrEP. More innovative digital options using mHealth and telemedicine approaches to deliver specific parts of PrEP services are currently being applied in a minority of the service delivery models in mainly high-resource settings.

conclusionsA range of possible combinations was found between all four components of PrEP service delivery models. This reflects differentiation of care according to different contextual settings. More research is needed on how integration of services in these contexts could be expanded and optimised to respond to key populations with unmet HIV prevention needs in different settings.

Indexed as

Anti-HIV AgentsDelivery of Health CareFemaleHIV InfectionsHumansMalePre-Exposure ProphylaxisSexual and Gender MinoritiesAnti-HIV AgentsDelivery of health careHealth care providersHIV preventionMSMPrEPSexual health

Identifiers

PMID32736626
PMCPMC7395423
OpenAlexW3046406812

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.