Evidence map›Paper›PMID 31764260›Full record

ReviewJournal of acquired immune deficiency syndromes (1999)2019

Using Social Networks to Understand and Overcome Implementation Barriers in the Global HIV Response.

Guy Harling, Alexander C Tsai

Abstract readReview
In one paragraph

Review in Journal of acquired immune deficiency syndromes (1999), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

2 authors.

Guy HarlingInstitute for Global Health, University College London, London, United Kingdom.
Alexander C TsaiHarvard Center for Population and Development Studies, Harvard University, Cambridge, MA.

Funding

Social Networks, HIV Stigma, and the HIV Care Cascade in Rural UgandaR01MH113494 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI TSAI, ALEXANDER C · 2017 to 2021
$3.2M
Causal Pathways to population health impact of HIV antiretroviral treatmentR01HD084233 · NICHD · UNIVERSITY OF KWAZULU-NATAL · PI BARNIGHAUSEN, TILL, TANSER, FRANK COURTENEY · 2015 to 2021
$2.3M
Maximizing combination HIV prevention, uptake and retention by adolescent girls and young womenR01MH114560 · NIMH · KWAZULU-NATAL RESEARCH INSTITUTE TB-HIV · PI SHAHMANESH, MARYAM · 2017 to 2023
$2.0M
NICHD NIH HHS R01 HD084233NIMH NIH HHS R01 MH113494NIMH NIH HHS R01 MH114560Wellcome Trust 210479
6 · The paper itself

Abstract

backgroundDespite the development of several efficacious HIV prevention and treatment methods in the past 2 decades, HIV continues to spread globally. Uptake of interventions is nonrandomly distributed across populations. Such inequality is socially patterned and reinforced by homophily arising from both social selection (becoming friends with similar people) and influence (becoming similar to friends).

methodsWe conducted a narrative review to describe how social network analysis methods-including egocentric, sociocentric, and respondent-driven sampling designs-provide tools to measure key populations, to understand how epidemics spread, and to evaluate intervention take-up.

resultsSocial network analysis-informed designs can improve intervention effectiveness by reaching otherwise inaccessible populations. They can also improve intervention efficiency by maximizing spillovers, through social ties, to at-risk but susceptible individuals. Social network analysis-informed designs thus have the potential to be both more effective and less unequal in their effects, compared with social network analysis-naïve approaches. Although social network analysis-informed designs are often resource-intensive, we believe they provide unique insights that can help reach those most in need of HIV prevention and treatment interventions.

conclusionIncreased collection of social network data during both research and implementation work would provide important information to improve the roll-out of existing studies in the present and to inform the design of more data-efficient, social network analysis-informed interventions in the future. Doing so will improve the reach of interventions, especially to key populations, and to maximize intervention impact once delivered.

Indexed as

Health Plan ImplementationHIV InfectionsSocial NetworkingGlobal HealthHumans

Identifiers

PMID31764260
PMCPMC6923140

What OpenQuestion holds

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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.