Evidence map›Paper›PMID 36287587›Full record

ArticleJMIR public health and surveillance2022

Key Population Size Estimation to Guide HIV Epidemic Responses in Nigeria: Bayesian Analysis of 3-Source Capture-Recapture Data.

Anne F McIntyre, Andrew Mitchell, Kristen A Stafford, Samuel Uchenna Nwafor, Julia Lo, Victor Sebastian, Amee Schwitters, Mahesh Swaminathan, Ibrahim Dalhatu, Man Charurat

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

9 citing papers in PubMed, 11 citations in OpenAlex.

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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

10 authors at 3 institutions in 2 countries.

Anne F McIntyreDivision of Global HIV and TB, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, United States.ORCID 0000-0001-6611-9002
Andrew MitchellInstitute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-2418-6626
Kristen A StaffordInstitute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0001-5620-6436
Samuel Uchenna NwaforCenter for International Health, Education, and Biosecurity, University of Maryland School of Medicine, Abuja, Nigeria.ORCID 0000-0002-4290-8581
Julia LoInstitute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-5765-7850
Victor SebastianDivision of Global HIV and TB, Center for Global Health, Centers for Disease Control and Prevention, Abuja, Nigeria.ORCID 0000-0001-5594-1201
Amee SchwittersDivision of Global HIV and TB, Center for Global Health, Centers for Disease Control and Prevention, Abuja, Nigeria.ORCID 0000-0003-0708-5353
Mahesh SwaminathanDivision of Global HIV and TB, Center for Global Health, Centers for Disease Control and Prevention, Abuja, Nigeria.ORCID 0000-0001-5827-3245
Ibrahim DalhatuDivision of Global HIV and TB, Center for Global Health, Centers for Disease Control and Prevention, Abuja, Nigeria.ORCID 0000-0002-3287-5173
Man CharuratInstitute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-1719-2796
University of Maryland, Baltimore · USNigeria Centre for Disease Control · NGCenters for Disease Control and Prevention · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNigeria has the fourth largest burden of HIV globally. Key populations, including female sex workers, men who have sex with men, and people who inject drugs, are more vulnerable to HIV than the general population due to stigmatized and criminalized behaviors. Reliable key population size estimates are needed to guide HIV epidemic response efforts.

objectiveThe objective of our study was to use empirical methods for sampling and analysis to improve the quality of population size estimates of female sex workers, men who have sex with men, and people who inject drugs in 7 states (Akwa Ibom, Benue, Cross River, Lagos, Nasarawa, Rivers, and the Federal Capital Territory) of Nigeria for program planning and to demonstrate improved statistical estimation methods.

methodsFrom October to December 2018, we used 3-source capture-recapture to produce population size estimates in 7 states in Nigeria. Hotspots were mapped before 3-source capture-recapture started. We sampled female sex workers, men who have sex with men, and people who inject drugs during 3 independent captures about one week apart. During hotspot encounters, key population members were offered inexpensive, memorable objects unique to each capture round. In subsequent rounds, key population members were offered an object and asked to identify objects received during previous rounds (if any). Correct responses were tallied and recorded on tablets. Data were aggregated by key population and state for analysis. Median population size estimates were derived using Bayesian nonparametric latent-class models with 80% highest density intervals.

resultsOverall, we sampled approximately 310,000 persons at 9015 hotspots during 3 independent captures. Population size estimates for female sex workers ranged from 14,500 to 64,300; population size estimates for men who have sex with men ranged from 3200 to 41,400; and population size estimates for people who inject drugs ranged from 3400 to 30,400.

conclusionsThis was the first implementation of these 3-source capture-recapture methods in Nigeria. Our population size estimates were larger than previously documented for each key population in all states. The Bayesian models account for factors, such as social visibility, that influence heterogeneous capture probabilities, resulting in more reliable population size estimates. The larger population size estimates suggest a need for programmatic scale-up to reach these populations, which are at highest risk for HIV.

Indexed as

HIV InfectionsSexual and Gender MinoritiesSex WorkersBayes TheoremFemaleHomosexuality, MaleHumansMaleNigeriaPopulation Densitydatadrug injectiondrugsepidemicfemaleHIVmenmen who have sex with menNigeriapeople who inject drugspopulationpopulation sizesex workers

Identifiers

PMID36287587
PMCPMC9647455
OpenAlexW4294781460

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.