Evidence map›Paper›PMID 34321310›Full record

ArticleBMJ open2021

Diverse experts' perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study.

Ariadne A Nichol, Eran Bendavid, Farirai Mutenherwa, Chirag Patel, Mildred K Cho

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In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. 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

5 authors.

Ariadne A NicholCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA ariadnen@stanford.edu.ORCID 0000-0001-9174-9671
Eran BendavidDepartment of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California, USA.
Farirai MutenherwaCollege of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.
Chirag PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
Mildred K ChoCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA.

Funding

Big Data Analysis of HIV Risk and Epidemiology in Sub-Saharan AfricaR01AI127250 · NIAID · STANFORD UNIVERSITY · PI BENDAVID, ERAN, PATEL, CHIRAG J. · 2017 to 2020
$2.7M
NIAID NIH HHS R01 AI127250
6 · The paper itself

Abstract

objectiveTo better understand diverse experts' views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data.

designThree rounds of semi-structured surveys in an online expert panel using a modified Delphi approach.

participantsExperts in informatics, African public health and HIV/AIDS and bioethics were invited to participate. MEASURES: Perceived importance of or agreement about relevance of ethical issues on 5-point unipolar Likert scales. Qualitative data analysis identified emergent themes related to ethical issues and development of an ethical framework and recommendations for open-ended questions.

resultsOf the 35 invited experts, 22 participated in the online expert panel (63%). Emergent themes were the inclusion of African researchers in all aspects of study design, analysis and dissemination to identify and address local contextual issues, as well as engagement of communities. Experts focused on engagement with health and science professionals to address risks, benefits and communication of findings. Respondents prioritised the mitigation of stigma to research participants but recognised trade-offs between privacy and the need to disseminate findings to realise public health benefits. Strategies for responsible communication of results were suggested, including careful word choice in presentation of results and limited dissemination to need-to-know stakeholders such as public health planners.

conclusionExperts identified ethical issues specific to the African context and to research on sensitive, publicly available data and strategies for addressing these issues. These findings can be used to inform an ethical implementation framework with research stage-specific recommendations on how to use publicly available data for machine learning-based predictive analytics to predict HIV/AIDS risk in SSA.

Indexed as

Acquired Immunodeficiency SyndromeAfrica South of the SaharaDelphi TechniqueEthics, ResearchHumansMachine Learningethics (see medical ethics)health informaticsmedical ethicspublic health

Identifiers

PMID34321310
PMCPMC8320245

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.