Public health use of HIV phylogenetic data in sub-Saharan Africa: ethical issues

Jamrozik, Euzebiusz; Munung, Nchangwi Syntia; Abeler-Dörner, Lucie; Parker, Michael

doi:10.1136/bmjgh-2023-011884

Cited by 3 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While accuracy in calling transmission Genomics for accurate HIV transmission predictions pairs is improving, the study also shows that the analysis depends on several factors. High accuracy can be obtained on population level (Ratmann et al 2020;Hall et al 2021;Bbosa et al 2020), but studies need to be designed and conducted with care to ensure individuals and groups are not harmed, criminalised or stigmatised and their privacy is protected at all times (Jamrozik et al 2023;Mutenherwa et al 2019;Coltart et al 2018). On the individual level, sequence data alone can never be sufficient to demonstrate transmission in the absence of 100% sampling.…”

Section: Discussionmentioning

confidence: 99%

Directional predictions of HIV transmission with optimised genomics in cohorts of serodiscordant couples

Zhao,

Hall,

Wymant

et al. 2023

Preprint

View full text Add to dashboard Cite

Viral genetic information from people living with HIV can deepen our understanding of the infection’s epidemiology at many scales. To better understand the potentials and limits of tools that utilise such information, we show the performance of two representative tools (HIV-TRACE and phyloscanner) in describing HIV transmission dynamics, with different types of genetic data, and compare with previous findings. The samples were collected from three cohort studies in Sub-Saharan Africa and were deep sequenced to produce both short Illumina reads and long PacBio reads. By comparing phyloscanner’s performance with short and long reads, we show that long reads provide improved phylogenetic resolution for the classic transmission topology in joint within-host trees. Our pipeline accurately predicted the direction of transmission 88%-92% of the time. We also show that the timing of sample collection plays an important role in the reconstruction of directionality using deep sequencing data. Consensus sequences were also generated and used as HIV-TRACE input to show different patterns of clustering sensitivity and specificity for data from different genomic regions or the entire genome. Finally, we discuss adjusting expectations about sensitivity and specificity of different types of sequence data, considering rapid pathogen evolution, and highlight the potentials of high within-host phylogenetic resolution in HIV. In conclusion, viral genetic data collected and presented differently could greatly influence our ability to describe the underlying dynamics. Methods for source attribution analysis have reached levels of superior accuracy. However, residual uncertainty emphasizes sequence analysis alone cannot conclusively prove linkage at the individual level.

show abstract

Section: Discussionmentioning

confidence: 99%

Directional predictions of HIV transmission with optimised genomics in cohorts of serodiscordant couples

Zhao,

Hall,

Wymant

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Developers and designers of AI for HIV should be informed from previous cases of how AI systems have the risk of inadvertently stigmatizing populations who acquire certain diseases or conditions. The use of AI to identify COVID-19 hotspots, for example, was an epitome of how AI systems for certain diseases or conditions can impose stigmatization on particular populations based on the erroneous view that a specific variant of COVID-19 (e.g., Omicron) could be easily confined to those populations [27]. AI systems for HIV need to ensure such stigmatization is not exacerbated by the very systems that aim to be beneficial and useful for people living with HIV (PLWH).…”

Section: Ethical Considerations Regarding Fairnessmentioning

confidence: 99%

Ethical Considerations for Artificial Intelligence Applications for HIV

Garett,

Kim,

Young

2024

View full text Add to dashboard Cite

Human Immunodeficiency Virus (HIV) is a stigmatizing disease that disproportionately affects African Americans and Latinos among people living with HIV (PLWH). Researchers are increasingly utilizing artificial intelligence (AI) to analyze large amounts of data such as social media data and electronic health records (EHR) for various HIV-related tasks, from prevention and surveillance to treatment and counseling. This paper explores the ethical considerations surrounding the use of AI for HIV with a focus on acceptability, trust, fairness, and transparency. To improve acceptability and trust towards AI systems for HIV, informed consent and a Federated Learning (FL) approach are suggested. In regard to unfairness, stakeholders should be wary of AI systems for HIV further stigmatizing or even being used as grounds to criminalize PLWH. To prevent criminalization, in particular, the application of differential privacy on HIV data generated by data linkage should be studied. Participatory design is crucial in designing the AI systems for HIV to be more transparent and inclusive. To this end, the formation of a data ethics committee and the construction of relevant frameworks and principles may need to be concurrently implemented. Lastly, the question of whether the amount of transparency beyond a certain threshold may overwhelm patients, thereby unexpectedly triggering negative consequences, is posed.

show abstract

“…Further, attempts to scale-up NG-WGS and uses of pathogen sequence data in research and disease control have caused heated controversies in bioethics and health policy. Examples include the rollout of molecular HIV surveillance in the United States (US), HIV phylogenetics in Southern Africa, and global HIV molecular epidemiology [25][26][27][28][29]. TB is also a stigmatized condition often socially and epidemiologically linked to HIV/AIDS, making it critical to understand social dimensions and ethical issues related to implementation-particularly regarding how NG-WGS data are used in operations and communicated to patients and the public [19,20].…”

Section: Introductionmentioning

confidence: 99%

Botswana tuberculosis (TB) stakeholders broadly support scaling up next-generation whole genome sequencing: Ethical and practical considerations for Botswana and global health

Molldrem,

Bagani,

Subrahmanyam

et al. 2023

PLOS Glob Public Health

View full text Add to dashboard Cite

Global health agencies are increasingly promoting the scale-up of next-generation whole genome sequencing (NG-WGS) of pathogens into infectious disease control programs, including for tuberculosis (TB). However, little is known about how stakeholders in low-to-middle income countries (LMICs) understand the ethics, benefits, and risks of these proposals. We conducted a qualitative study in Greater Gaborone, Botswana to learn how TB stakeholders there viewed a potential scale-up of NG-WGS into Botswana’s TB program. We conducted 30 interviews and four deliberative dialogues with TB stakeholders based in Greater Gaborone, the country’s largest city and capital. We created and showed participants an animated video series about a fictional family that experienced TB diagnosis, treatment, contact tracing, and data uses that were informed by NG-WGS. We analyzed transcripts using reflexive thematic analysis. We found broad support for the scale-up of TB NG-WGS in Botswana, owing to perceived benefits. Support was qualified with statements about ensuring adequate planning, resource-allocation, community and stakeholder engagement, capacity-building, and assessing ethical norms around publishing data. Our results suggest that scaling up NG-WGS for TB in Botswana would be supported by stakeholders there, contingent upon the government and other entities adequately investing in the initiative. These findings are relevant to other LMICs considering scale-ups of NG-WGS and related technologies for infectious diseases and suggest the need for sustained research into the acceptability of pathogen sequencing in other contexts.

show abstract

Public health use of HIV phylogenetic data in sub-Saharan Africa: ethical issues

Cited by 3 publications

References 40 publications

Directional predictions of HIV transmission with optimised genomics in cohorts of serodiscordant couples

Directional predictions of HIV transmission with optimised genomics in cohorts of serodiscordant couples

Ethical Considerations for Artificial Intelligence Applications for HIV

Botswana tuberculosis (TB) stakeholders broadly support scaling up next-generation whole genome sequencing: Ethical and practical considerations for Botswana and global health

Contact Info

Product

Resources

About