Evidence map›Paper›PMID 33857195›Full record

ArticlePloS one2021

Cost of pre-exposure prophylaxis delivery in family planning clinics to prevent HIV acquisition among adolescent girls and young women in Kisumu, Kenya.

Valentine Wanga, Kathryn Peebles, Alfred Obiero, Felix Mogaka, Victor Omollo, Josephine B Odoyo, Jennifer F Morton, Elizabeth A Bukusi, Connie Celum, Jared M Baeten and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
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  3. Pre-Exposure Prophylaxis Uptake, Implementation and Barriers in Africa: A Scoping Review Protocol.International journal of environmental research and public health · 2025
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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

11 authors at 2 institutions in 2 countries.

Valentine WangaDepartment of Epidemiology, University of Washington, Seattle, WA, United States of America.ORCID 0000-0002-9104-0125
Kathryn PeeblesDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
Alfred ObieroCentre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya.
Felix MogakaCentre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya.ORCID 0000-0002-7750-0404
Victor OmolloCentre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya.
Josephine B OdoyoCentre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya.
Jennifer F MortonDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
Elizabeth A BukusiDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
Connie CelumDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
Jared M BaetenDepartment of Epidemiology, University of Washington, Seattle, WA, United States of America.
Ruanne V BarnabasDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
University of Washington · USKenya Medical Research Institute · KE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOral pre-exposure prophylaxis (PrEP) is increasingly being implemented in sub-Saharan Africa. Adolescent girls and young women (AGYW) in Kenya contribute more than half of all new infections among young people aged 15-24 years, highlighting the need for evidence on the cost of PrEP in real-world implementation to inform the budget impact, cost-effectiveness, and financial sustainability of PrEP programs.

methodsWe estimated the cost of delivering PrEP to AGYW enrolled in a PrEP implementation study in two family planning clinics in Kisumu county, located in western Kenya. We derived total annual costs and the average cost per client-month of PrEP by input type (variable or fixed) and visit type (initiation or follow-up). We estimated all costs as implemented in the study, and under implementation by the Kenyan Ministry of Health (MoH), both at the program volume observed and if the facilities were delivering PrEP at full capacity (scaled-MoH).

resultsFor the costing period between March 2018 and March 2019, 615 HIV-negative women contributed 1,128 (502 initiation and 626 follow-up) visits. The average cost per client-month of PrEP dispensed per study protocol and per the MoH scenario was $28.92 and $14.52, respectively. If the MoH scaled the program so that facilities could see PrEP clients at capacity, the average cost per client-month of PrEP was $10.88. Medication costs accounted for the largest proportion of the total annual costs (48% in MoH scenario and 65% in the scaled-MoH scenario).

conclusionsUsing data from a PrEP implementation program, we found that the cost per client-month of PrEP dispensed is reduced by 62% if PrEP delivery at the two clinics is scaled up by the MoH. Our findings are valuable for informing local resource allocation and budgetary cost projections for scale-up of PrEP delivery to AGYW. Additionally, previous cost-effectiveness studies have been limited by the use of fixed assumptions of the cost of PrEP per person-month. Our study provides cost estimates from practical data which will better inform cost-effectiveness and budget impact analyses.

Indexed as

Administration, OralAdolescentAnti-HIV AgentsCost-Benefit AnalysisFamily Planning ServicesFemaleHIV InfectionsHumansKenyaPre-Exposure ProphylaxisProgram EvaluationYoung AdultAnti-HIV Agents

Identifiers

PMID33857195
PMCPMC8049260
OpenAlexW3154226193

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.