Evidence map›Paper›PMID 30839863›Full record

ArticleInfectious Disease Modelling2018

The influence of mobility among high-risk populations on HIV transmission in Western Kenya.

Anna Bershteyn, Kennedy K Mutai, Adam N Akullian, Daniel J Klein, Britta L Jewell, Samuel M Mwalili

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Loss to Follow-Up and Visit Nonadherence in an HIV Incidence Cohort in Kisumu, Kenya.Journal of acquired immune deficiency syndromes (1999) · 2026
    Article
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  17. Review
  18. Flower sharing and pollinator health: a behavioural perspective.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Review
  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

6 authors.

Anna BershteynInstitute for Disease Modeling, 3150 139 Ave. SE, Bellevue, WA 98005, USA.
Kennedy K MutaiNational Aids Control Council, P.O. Box 61307-00200, Argwings Kodhek Rd, Nairobi, Kenya.
Adam N AkullianInstitute for Disease Modeling, 3150 139 Ave. SE, Bellevue, WA 98005, USA.
Daniel J KleinInstitute for Disease Modeling, 3150 139 Ave. SE, Bellevue, WA 98005, USA.
Britta L JewellInstitute for Disease Modeling, 3150 139 Ave. SE, Bellevue, WA 98005, USA.
Samuel M MwaliliCenters for Disease Control and Prevention, PO Box 606, Village Market, Nairobi, 00621, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Western Kenya suffers a highly endemic and also very heterogeneous epidemic of human immunodeficiency virus (HIV). Although female sex workers (FSW) and their male clients are known to be at high risk for HIV, HIV prevalence across regions in Western Kenya is not strongly correlated with the fraction of women engaged in commercial sex. An agent-based network model of HIV transmission, geographically stratified at the county level, was fit to the HIV epidemic, scale-up of interventions, and populations of FSW in Western Kenya under two assumptions about the potential mobility of FSW clients. In the first, all clients were assumed to be resident in the same geographies as their interactions with FSW. In the second, some clients were considered non-resident and engaged only in interactions with FSW, but not in longer-term non-FSW partnerships in these geographies. Under both assumptions, the model successfully reconciled disparate geographic patterns of FSW and HIV prevalence. Transmission patterns in the model suggest a greater role for FSW in local transmission when clients were resident to the counties, with 30.0% of local HIV transmissions attributable to current and former FSW and clients, compared to 21.9% when mobility of clients was included. Nonetheless, the overall epidemic drivers remained similar, with risky behavior in the general population dominating transmission in high-prevalence counties. Our modeling suggests that co-location of high-risk populations and generalized epidemics can further amplify the spread of HIV, but that large numbers of formal FSW and clients are not required to observe or mechanistically explain high HIV prevalence in the general population.

Identifiers

PMID30839863
PMCPMC6326217

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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.