Evidence map›Paper›PMID 38916675›Full record

ReviewCurrent HIV/AIDS reports2024

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.

Katherine Rucinski, Jesse Knight, Kalai Willis, Linwei Wang, Amrita Rao, Mary Anne Roach, Refilwe Phaswana-Mafuya, Le Bao, Safiatou Thiam, Peter Arimi and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current HIV/AIDS reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

12 authors.

Katherine RucinskiDepartment of International Health, Johns Hopkins School of Public Health, Baltimore, MD, USA. rucinski@jhu.edu.
Jesse KnightMAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada.
Kalai WillisDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.
Linwei WangMAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada.
Amrita RaoDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.
Mary Anne RoachDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.
Refilwe Phaswana-MafuyaSouth African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research (PACER) Extramural Unit, Johannesburg, South Africa.
Le BaoDepartment of Statistics, Pennsylvania State University, University Park, PA, USA.
Safiatou ThiamConseil National de Lutte Contre Le Sida, Dakar, Senegal.
Peter ArimiPartners for Health and Development in Africa, Nairobi, Kenya.
Sharmistha MishraMAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada.
Stefan BaralDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.

Funding

Vaccine Response and Immunotherapeutics SWGP30AI094189 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Anna Palmer Durbin · 2012 to 2026
$67.0M
Statistical Models for Estimating and Projecting HIV/AIDS EpidemicsR01AI136664 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI BAO, LE · 2017 to 2021
$3.6M
Leveraging Big Data Science to Focus the HIV Response in Countries with Generalized HIV EpidemicsR01AI170249 · NIAID · JOHNS HOPKINS UNIVERSITY · PI BARAL, STEFAN DAVID · 2022 to 2025
$2.9M
Implementation Research to Optimize ART Delivery for Adolescents and Young Adults Living with HIVK01MH129226 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Katherine B Rucinski · 2022 to 2026
$792k
Program and implementation context of a housing support initiative to address homelessness and improve HIV service uptake among youth in the United StatesK01MD020002 · NIMHD · JOHNS HOPKINS UNIVERSITY · PI Amrita Rao · 2024 to 2026
$381k
Canada Research Chairs 950-232643CIHR FN-13455NIAID NIH HHS P30 AI094189NIAID NIH HHS R01 AI136664NIAID NIH HHS R01 AI170249NIH HHS K01MH129226NIH HHS R01AI170249NIMHD NIH HHS K01 MD020002NIMH NIH HHS K01 MH129226South African Medical Research Council 57035
6 · The paper itself

Abstract

purpose of reviewBig Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. RECENT

findingsSocial, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.

Indexed as

Big DataData ScienceHIV InfectionsHealth InequitiesHumansSocial JusticeBig Data ScienceCommunity HIV responseExplanatory modelingHealth equityHIV transmission dynamicsKey populationsPredictive modeling

Identifiers

PMID38916675
PMCPMC11283392

What OpenQuestion holds

Textmetadata
LicenceTDM
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.