Toward Consent in Molecular HIV Surveillance?: Perspectives of Critical Stakeholders

BACKGROUND

The emergence of molecular HIV surveillance (MHS) and cluster detection and response (CDR) programs as key features of the United States (US) HIV strategy since 2018 has caused major controversies. HIV surveillance programs that re-use individuals' routinely collected clinical HIV data do not require consent on the basis that the public benefit of these programs outweighs individuals' rights to opt out. However, criticisms of MHS/CDR have questioned whether expanded uses of HIV genetic sequence data for prevention reach beyond traditional public health ethics frameworks. This study aimed to explore views on consent within MHS/CDR among critical stakeholders.

METHODS

In 2021 we interviewed 26 US HIV stakeholders who identified as being critical or concerned about the rollout of MHS/CDR. Stakeholders included participants belonging to networks of people living with HIV, other advocates, academics, and public health professionals. This analysis focused on identifying the range of positions among critical and concerned stakeholders on consent affordances, opt-outs, how to best inform people living with HIV about how data about them are used in public health programs, and related ethical issues.

RESULTS

Participants were broadly supportive of introducing some forms of consent into MHS/CDR. However, they differed on the specifics of implementing consent. While some participants did not support introducing consent affordances, all supported the idea that people living with HIV should be informed about how HIV surveillance and prevention is conducted and how individuals' data are used.

CONCLUSIONS

MHS/CDR has caused sustained controversy. Among critical stakeholders, consent is generally desirable but contested, although the right for people living with HIV to be informed was centrally supported. In an era of big data-driven public health interventions and routine uses of HIV genetic sequence data in surveillance and prevention, CDC and other agencies should revisit public health ethics frameworks and consider the possibility of consent processes.

What's Missing from Data Modernization? A Focus on Structural Racism

Public health data modernization efforts frequently overlook the far-reaching effects of structural racism across the data life cycle. Modernizing data requires creating data ecosystems grounded in six principles: dismantling structural racism and building community power explicitly; centering justice in all stages of data collection and analysis; ensuring communities can govern their data; driving positive population-level change; engaging nonprofit organizations; and obtaining commitments from governments to make changes in policy and practice. As government agencies spearhead and finance data modernization initiatives, it is imperative that they address structural racism head-on and integrate these principles into all aspects of their work.

Community Insights in Phylogenetic HIV Research: The CIPHR Project Protocol

Inferring HIV transmission networks from HIV sequences is gaining popularity in the field of HIV molecular epidemiology. However, HIV sequences are often analyzed at distance from those affected by HIV epidemics, namely without the involvement of communities most affected by HIV. These remote analyses often mean that knowledge is generated in absence of lived experiences and socio-economic realities that could inform the ethical application of network-derived information in 'real world' programmes. Procedures to engage communities are noticeably absent from the HIV molecular epidemiology literature. Here we present our team's protocol for engaging community activists living in Nairobi, Kenya in a knowledge exchange process - The CIPHR Project (Community Insights in Phylogenetic HIV Research). Drawing upon a community-based participatory approach, our team will (1) explore the possibilities and limitations of HIV molecular epidemiology for key population programmes, (2) pilot a community-based HIV molecular study, and (3) co-develop policy guidelines on conducting ethically safe HIV molecular epidemiology. Critical dialogue with activist communities will offer insight into the potential uses and abuses of using such information to sharpen HIV prevention programmes. The outcome of this process holds importance to the development of policy frameworks that will guide the next generation of the global response.

Data justice and biodiversity conservation

Increases in data availability coupled with enhanced computational capacities are revolutionizing conservation. But in the excitement over the opportunities afforded by new data, there has been less discussion of the justice implications of data used in conservation, that is, how people and environments are represented through data, the conservation choices made based on data, and the distribution of benefits and harms arising from these choices. We propose a framework for understanding the justice dimensions of conservation data composed of five elements: data composition, data control, data access, data processing and use, and data consequences. For each element, we suggest a set of guiding questions that conservationists could use to think through their collection and use of data and to identify potential data injustices. The need for such a framework is illustrated by a synthesis of recent critiques of global conservation prioritization analyses. These critiques demonstrate the range of ways data could serve to produce social and ecological harms due to the choice of underlying data sets, assumptions made in the analysis, oversimplification of real-world conservation practice, and crowding out of other forms of knowledge. We conclude by arguing that there are ways to mitigate risks of conservation data injustices, through formal ethical and legal frameworks and by promoting a more inclusive and more reflexive conservation research ethos. These will help ensure that data contribute to conservation strategies that are both socially just and ecologically effective.

Advancing a Data Justice Framework for Public Health Surveillance

BACKGROUND

Bioethical debates about privacy, big data, and public health surveillance have not sufficiently engaged the perspectives of those being surveilled. The data justice framework suggests that big data applications have the potential to create disproportionate harm for socially marginalized groups. Using examples from our research on HIV surveillance for individuals incarcerated in jails, we analyze ethical issues in deploying big data in public health surveillance.

METHODS

We conducted qualitative, semi-structured interviews with 24 people living with HIV who had been previously incarcerated in county jails about their perspectives on and experiences with HIV surveillance, as part of a larger study to characterize ethical considerations in leveraging big data techniques to enhance continuity of care for incarcerated people living with HIV.

RESULTS

Most participants expressed support for the state health department tracking HIV testing results and viral load data. Several viewed HIV surveillance as a violation of privacy, and several had actively avoided contact from state public health outreach workers. Participants were most likely to express reservations about surveillance when they viewed the state's motives as self-interested. Perspectives highlight the mistrust that structurally vulnerable people may have in the state's capacity to act as an agent of welfare. Findings suggest that adopting a nuanced, context-sensitive view on surveillance is essential.

CONCLUSIONS

Establishing trustworthiness through interpersonal interactions with public health personnel is important to reversing historical legacies of harm to racial minorities and structurally vulnerable groups. Empowering stakeholders to participate in the design and implementation of data infrastructure and governance is critical for advancing a data justice agenda, and can offset privacy concerns. The next steps in advancing the data justice framework in public health surveillance will be to innovate ways to represent the voices of structurally vulnerable groups in the design and governance of big data initiatives.

Citizen science as a data-based practice: A consideration of data justice

Citizen science has been motivated by several perspectives, including increased efficiency in data collection and distributed analysis, democratizing knowledge production, making science more responsive to community needs, and improving the representation of marginalized populations in public data. Despite the potential of citizen science to achieve social justice agendas through a data-intensive and data-driven participatory scientific enquiry, scholarship in critical data studies offers several problematizations of data-based practices, highlighting risks of exclusion and inequality. To understand the extent to which citizen science supports and challenges forms of injustice, this study used a “data justice” analytical framework to critically explore the assemblages of citizen science. We examined four citizen science cases with different levels of citizen engagement, intended outcomes, and data systems. The analysis suggests instances of injustice occurring throughout the data processes of the citizen science cases across the dimensions of procedural, instrumental, rights-based, structural, and distributive data justice.

•

A “data justice” analytical framework was used to study citizen science

•

Five dimensions of data justice were explored in citizen science cases

•

Some forms of injustice were found in citizen science cases under review

Citizen science has been regarded for its contribution to scientific research, inclusive science engagement, and addressing of social justice issues. Within citizen science, social justice is pursued through different approaches, including facilitating public participation in research and utilizing citizen science data in social justice advocacy. Although citizen science is a data-based practice, the structural dimensions of the data processes that support and hamper the pursuit of social justice in citizen science remain understudied. This article applies a “data justice” framework to unpack the elements and practices that constitute the generation, circulation, and use of data and data-related outcomes in citizen science. We demonstrate the relevance and limitations of the framework with regard to the domain of citizen science. This work thus contributes to the growing research interest in critical data studies, i.e., the study around equity issues in data science.

When Data Justice and Environmental Justice Meet: Formulating a Response to Extractive Logic through Environmental Data Justice

Environmental data justice (EDJ) emerges from conversations between data justice and environmental justice while identifying the limits and tensions of these lenses. Through a reflexive process of querying our entanglement in non-innocent relations, this paper develops and engages EDJ by examining how it informs the work of the Environmental Data & Governance Initiative (EDGI), a distributed, consensus-based organization that formed in response to the 2016 US presidential election. Through grassroots archiving of data sets, monitoring federal environmental and energy agency websites, and writing rapid-response reports about how federal agencies are being undermined, EDGI mobilises EDJ to challenge the 'extractive logic' of current federal environmental policy and data infrastructures. 'Extractive logic' disconnects data from provenance, privileges the matrix of domination, and whitewashes data to generate uncertainty. We use the dynamic EDJ framework to reflect on EDGI's public comment advising against the US Environmental Protection Agency's proposed rule for Transparent Science. Through EDJ, EDGI aspires to create new environmental data infrastructures and practices that are participatory and embody equitable, transparent data care.

Re-thinking Digital Health: Data, Appisation and the (im)possibility of 'Opting out'

Presented as providing cost-, time- and labour- effective tools for the (self)management of health, health apps are often celebrated as beneficial to all. However, their negative effects - commodification of user data and infringement on privacy - are rarely addressed. This article focuses on one particularly troubling aspect: the difficulty of opting out of data sharing and aggregation during app use or after unsubscribing/uninstalling the app. Working in the context of the new European General Data Protection Regulation and its implementation in the UK health services, our analysis reveals the discrepancy between the information presented to users, and the apps' actual handling of user data. We also point to the fundamental tension in the digitisation of health, between the neoliberal model where both health and data concerns are viewed as an individual's responsibility, and the digital-capitalist model, which puts forward, and capitalises on, collective ('Big') data. Pulled between the 'biopolitics of the self' and the 'biopolitics of the population' (concepts coined by Btihaj Ajana), opting out of health datafication therefore cannot be resolved as a matter of individual right alone. The article offers two contributions. Methodologically, we present a toolkit for a multi-level assessment of apps from the perspective of opting out, which can be adapted and used in future research. Conceptually, the article brings together critical digital health scholarship with the perspective of data justice, offering a new approach to health apps, which focuses on opt-out as a legal, social and technical possibility, and as a collective citizen and user right.