An Overview of Ethics and Public Health Data Collection
Abstract and Keywords
Collection of data is essential to the practice of public health. This chapter provides a brief introduction to ethics and public health data collection, as well as an overview of chapters in the related section of The Oxford Handbook on Public Health Ethics. A key ethics challenge has been, and will remain, how best to balance the health of the community with the respect owed to individual citizens. The four chapters in this section examine various aspects of those ethics challenges, including those related to the scope of public health surveillance activities, the distinction between public health practice and public health research, community-based participatory research (CBPR), and the use of big data to answer public health research questions.
The recorded history of public health surveillance in Europe begins with the Venetian Republic counting the victims of the pneumonic plague in the 1300s and the republic’s implementation of a quarantine of travelers from plague-endemic regions (Declich and Carter, 1994). The measurement of morbidity and mortality has remained the core of everyday practice of public health. Before the 1950s, surveillance efforts in the United States focused on the observation of individuals exposed to a communicable disease (Declich and Carter, 1994). Alexander Langmuir, who directed the US Epidemic Intelligence Service for almost twenty years, is credited with promoting the principles of surveillance for observation of disease in populations (Brachman, 1996). With this shift in focus, the practice of surveillance allowed for the detection of patterns and potential control of disease.
A key ethical challenge with the collection of public health data has been, and will remain, how best to balance the health of the community with the respect owed to individual citizens. The early use of public surveillance techniques led to the quarantine of travelers who had been exposed, or were assumed to be exposed, to a communicable disease. As we know from more modern examples, labeling an individual or population at risk of disease can have consequences beyond quarantine, such as stigma (Mahajan et al., 2008). Stigma can result in discrimination that unfairly restricts access to basic social goods such as employment and housing.
As the methods of public health data collection, analysis, interpretation, and dissemination have advanced, so has the need to consider and adopt robust ethical principles to (p. 316) guide the handling and use of such data. For example, as the ability to store and process large data sets compiled from multiple data sources advances, the more information those who have access to and are responsible for stewarding this data will know about the health and welfare of individuals and communities. In addition, aggregated data from individuals that are maintained electronically, such as electronic health record data, may be valuable for public health research. Entities and researchers who collect, hold, aggregate, and use those data have stewardship obligations, including the need to inform individuals how their data may be used for research purposes, including whether and how it will be linked with data from other sources, such as social media (Vayena et al., 2015).
Those who hold, aggregate, or use data also must commit to transparency, a responsibility that may not have been as relevant in the past. Similarly, an aggregator of data from across multiple sources must now think carefully about the use and release of findings and their implications for individuals and communities, including avoiding harms such as those that may result from unintentional breaches in confidentiality (Allen et al., 2013).
This section of The Oxford Handbook of Public Health Ethics is dedicated to the examination of ethics and public health data collection. Public health data is generally collected in two ways: through the public health surveillance system, and through a variety of public health research methods. The four chapters in the section each address the collection of a particular type of public health data and examine related ethical challenges.
In “Public Health Surveillance: Ethical Considerations,” Lisa M. Lee explains that the public health surveillance system relies on health care providers and public health laboratories to report test results for particular diseases and health conditions to local or state health departments. Some of these data are also shared with national authorities. This collection of data is relatively routine and constant, and as such it can be used to monitor community health. The goal is to identify and track potential risks to the health and well-being of the community. Fueling the surveillance system must therefore be balanced with a commitment to protect data confidentiality. A public health surveillance system must be built in a way that protects the privacy of those whose data is captured in the system to serve the goal of the community accruing the benefits of the system.
In some instances, the routine reporting of information has implications for individuals other than those who were tested (e.g., contact tracing). The handling and disclosure of this information require a commitment to limit the potential for a breach of confidentiality to maximize the likelihood that such efforts can achieve various public health goals, such as working to stem the tide of disease. Lee notes that as our understanding of disease, risk of disease, and biomedical markers has advanced, so too have public health surveillance systems. She explains how the core values and tensions in public health ethics are in play in the ethics of public health surveillance.
(p. 317) The bulk of the chapter is devoted to a description of the core components of a robust public health surveillance system—from creating the infrastructure to manage the data collected to the use and dissemination of data—presenting both key ethical considerations and best practices along the way. Lee lastly identifies and considers the ethical implications of recent advances in information systems that have allowed for the aggregation of public health surveillance data along with other data sources to advance knowledge beyond the traditional limits of public health.
The work of public health relies on more than the collection of surveillance data, including carrying out research whose data are used in service of public health. Public health research, conducted by government agencies, nongovernmental agencies, academics, and other stakeholders can take a variety of shapes and forms. As is the case with human subject research more generally, the goal of public health research is to produce generalizable knowledge to promote health and prevent disease. As the promotion of health and prevention of disease are also key goals of public health practice, the boundary between public health research and public health practice can be blurred. This blurring can lead to confusion about how best to balance the benefit of the production of new knowledge with whether and how the individuals from whom the data collected ought to be informed about their participation in the production of that knowledge.
In “Framing Public Health Research Ethics,” Holly A. Taylor examines the distinctions and boundaries between public health practice and research, focusing on two criteria that public health investigators and practitioners ought to consider: intent and experimentation. She then compares public health research with biomedical research, identifying a critical distinction that deserves ethical attention: communities, rather than individuals, are often the population of interest. Because communities are made up of individuals, the traditional concerns of respect for persons, minimizing risk to subjects, and the equitable distribution of risks and benefits apply, but other considerations are relevant as well. These include respect for the community, minimizing risks to the community, and considerations as to whether the planned research further disadvantages an already disadvantaged group.
One approach to public health research fully embraces the principle of respect for community. Community-based participatory research (CPBR) is a relatively recent addition to the research methods used by public health researchers. As its name implies, CPBR is a method that expects and relies on the community to have an essential role in the research process as a beneficiary of the research and a partner in its conduct. In general, community refers to members of the population who are a part of the community to be studied—individuals who have a unique perspective as members of the community who partner with a study team. The community members and study team collaborate from beginning to end, from identifying the research question(s) to disseminating results. Engagement in CBPR requires commitment from the study team and community members. This relationship between the research subjects and study team is unique to CPBR and has resulted in a number of ethical challenges.
The ethical “promise” of CBPR is that its inclusive and deliberative approach has the potential to minimize community harm and avoid exploitation, and in so doing better (p. 318) serve the interests of the community and its members. In “Community-Based Participatory Research: Ethical Considerations,” David R. Buchanan explores the nature of community harm and how adopting a CBPR approach can lead researchers to act in ways that respect communities through researching in partnership with them. Buchanan identifies several unresolved ethical questions related to CBPR: Who is the “community” with which researchers ought to engage? What are the hallmarks of a successful partnership? What is the scope of responsibility a “community” has when engaged in CBPR? How do researchers make clear to communities that their participation in research may not lead to direct benefit to the community? The collaboration between Arizona State University (ASU) and the Havasupai Indian tribe is a cautionary tale. It began as a promising example of the use of CBPR to address health concerns prioritized by the tribe. Later, however, data were used to answer research questions without input from, or even knowledge of, the tribe, leading to what the tribe considered community harms and a breach of trust (Mello and Wolf, 2010).
An even more recent addition to public health research methods is the use of “big data” to answer questions about the promotion of health and prevention of disease. While the term has been defined in a number of ways, here “big data” refers to the aggregation and analysis of data from large data sources, including data from sources as diverse as routine public health surveillance activities and social media activity. Advances in computational capacity have made big data approaches possible. As with the other examples of public health research mentioned above, big data research puts the potential benefit to the health and well-being on the community in tension with the privacy rights of the individual.
In “Navigating the Ethics of Big Data in Public Health,” Effy Vayena and Lawrence Madoff scrutinize ethical issues arising out of the increasing use of big data as a novel tool in public health research and practice. Big data’s two core components are the variety of sources of data that can be brought together in a big data set, and the variety of techniques available to find meaning in the data once aggregated. The techniques developed to bring multiple data sources together allow for the blending of data collected for research purposes along with diverse sources of data, such as that collected for clinical or commercial purposes, social media postings, and behavior and activity trackers. This boundary-crossing raises ethical issues related to what is owed to those individuals who have, knowingly or unknowingly, become a data point (or many data points) in one or many data sets, introducing potentially novel risks to privacy and confidentiality.
Vayena and Madoff review the current technical challenges that limit the current potential of big data initiatives and highlight the importance of considering privacy concerns and protections as these technical challenges are overcome. The main sources of data currently being brought together under the banner of big data in an effort to improve public health include electronic health record systems, social media data, and electronic self-reporting, such as those designed for real-time tracking of infectious disease outbreaks like the flu. The authors posit that the ethical challenges in the establishment of big data resources that can benefit public health revolve around the blurring of once clear lines along three axes: “personal health data and nonhealth data; . . . private and the public (p. 319) sphere in the online world; and, . . . the powers and responsibilities of state and nonstate actors” (Vayena and Madoff, this volume).
Data collection is a core function of public health. So too are the obligations of the collectors, aggregators, and users of that data to ensure that individual interests are respected in the pursuit of knowledge benefiting the health and well-being of populations. The value and promise of public health data must be balanced against the potential harm it can bring to individuals and communities through its collection, handling, and dissemination.
Allen, J., Hulman, D., Meslin, E. M., and Stanley, F. 2013. “Privacy Protectionism and Harms to Health: Is There Any Redress?” Journal of Law and Medicine 21: 473–485.Find this resource:
Brachman, P. S. 1996. “Epilogue: Alexander Duncan Langmuir.” American Journal of Epidemiology 144 (Suppl. 8): S74–S75.Find this resource:
Declich, S., and Carter, A. O. 1994. “Public Health Surveillance: Historical Origins, Methods and Evaluation.” Bulletin of the World Health Organization 72(2): 285–304.Find this resource:
Mahajan, A. P., Sayles, J. N., Patel, V. A., Remien, R. H., Ortiz, D., Szekeres, G., et al. 2008. “Stigma in the HIV/AIDS Epidemic: A Review of the Literature and Recommendations for the Way Forward.” AIDS 22(Suppl. 2): S67–S79.Find this resource:
Mello, M. M., and Wolf, L. E. 2010. “The Havasupai Indian Tribe Case—Lessons for Research Involving Stored Biologic Samples.” New England Journal of Medicine 363(3): 204–207. https://www.nejm.org/doi/full/10.1056/NEJMp1005203.Find this resource:
Vayena, E., Salathé, M., Madoff, L. C., and Brownstein, J. S. 2015. “Ethical Challenges of Big Data in Public Health.” PLoS Computational Biology 11(2): e1003904. doi:10.1371/journal.pcbi.1003904.Find this resource: