Evidence map›Paper›PMID 24704778›Full record

ArticlePloS one2014

Indices to measure risk of HIV acquisition in Rakai, Uganda.

Joseph Kagaayi, Ronald H Gray, Christopher Whalen, Pingfu Fu, Duncan Neuhauser, Janet W McGrath, Nelson K Sewankambo, David Serwadda, Godfrey Kigozi, Fred Nalugoda and 3 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
1.3field-weighted citation impact, top 21% 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  12. Predicting the Risk of Human Immunodeficiency Virus Type 1 (HIV-1) Acquisition in Rural South Africa Using Geospatial Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Machine Learning to Identify Persons at High-Risk of Human Immunodeficiency Virus Acquisition in Rural Kenya and Uganda.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020
    Article
  17. Article
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  19. Article
  20. Narrating the Transition to Adulthood for Youth in Uganda: Leaving School, Mobility, Risky Occupations, and HIV.Health education & behavior : the official publication of the Society for Public Health Education · 2019
    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

13 authors at 6 institutions in 2 countries.

Joseph KagaayiRakai Health Sciences Program, Entebbe, Uganda; Deparment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Ronald H GrayDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Christopher WhalenDepartment of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, United States of America.
Pingfu FuDeparment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Duncan NeuhauserDeparment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Janet W McGrathDepartment of Anthropology, Case Western Reserve University, Cleveland, Ohio, United States of America.
Nelson K SewankamboMakerere University College of Health Sciences, Kampala, Uganda.
David SerwaddaMakerere University College of Health Sciences, Kampala, Uganda.
Godfrey KigoziDeparment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Fred NalugodaDeparment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Steven J ReynoldsDivision of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America; Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America.
Maria J WawerDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Mendel E SingerDeparment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Case Western Reserve University · USJohns Hopkins University · USMakerere University · UGNational Institute of Allergy and Infectious Diseases · USRakai Health Sciences Program · UGUniversity of Georgia · US

Funding

International Center for Excellence in Research (ICER) in Uganda: Impact of ARVsZIAAI001040 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI REYNOLDS, STEVEN · 2009 to 2025
$23.5M
Circumcision: HIV/STIs and behaviors in a RCT and post-RCT surveillance; RakaiU01AI075115 · NIAID · JOHNS HOPKINS UNIVERSITY · PI GRAY, RONALD H · 2007 to 2011
$9.3M
ARV effects on HIV epidemiology &behaviors Rakai UgandaR01HD050180 · NICHD · JOHNS HOPKINS UNIVERSITY · PI WAWER, MARIA J · 2005 to 2009
$7.8M
Intramural NIH HHSNIAID NIH HHS U01 AI075115NIAID NIH HHS U01AI075115NICHD NIH HHS R01 HD050180NICHD NIH HHS R01HD050180
6 · The paper itself

Abstract

introductionTargeting most-at-risk individuals with HIV preventive interventions is cost-effective. We developed gender-specific indices to measure risk of HIV among sexually active individuals in Rakai, Uganda.

methodsWe used multivariable Cox proportional hazards models to estimate time-to-HIV infection associated with candidate predictors. Reduced models were determined using backward selection procedures with Akaike's information criterion (AIC) as the stopping rule. Model discrimination was determined using Harrell's concordance index (c index). Model calibration was determined graphically. Nomograms were used to present the final prediction models.

resultsWe used samples of 7,497 women and 5,783 men. 342 new infections occurred among females (incidence 1.11/100 person years,) and 225 among the males (incidence 1.00/100 person years). The final model for men included age, education, circumcision status, number of sexual partners, genital ulcer disease symptoms, alcohol use before sex, partner in high risk employment, community type, being unaware of a partner's HIV status and community HIV prevalence. The Model's optimism-corrected c index was 69.1 percent (95% CI = 0.66, 0.73). The final women's model included age, marital status, education, number of sex partners, new sex partner, alcohol consumption by self or partner before sex, concurrent sexual partners, being employed in a high-risk occupation, having genital ulcer disease symptoms, community HIV prevalence, and perceiving oneself or partner to be exposed to HIV. The models optimism-corrected c index was 0.67 (95% CI = 0.64, 0.70). Both models were well calibrated.

conclusionThese indices were discriminative and well calibrated. This provides proof-of-concept that population-based HIV risk indices can be developed. Further research to validate these indices for other populations is needed.

Indexed as

Health Status IndicatorsAdolescentAdultCohort StudiesFemaleHIV-1HIV InfectionsHumansMaleMiddle AgedRisk FactorsSex FactorsSexual BehaviorUgandaYoung Adult

Identifiers

PMID24704778
PMCPMC3976261
OpenAlexW2081575353

What OpenQuestion holds

Textmetadata
LicenceCC0
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.