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Ethical challenges loom over digital epidemiology, data collection
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Overcoming the ethical hurdles of digital disease detection is a necessary step in the development of this emerging public health resource, according to an article recently published in PLoS Computational Biology.
“At the crux of the debate on the ethics of big data lies a familiar, but formidably complex, question,” Effy Vayena, PhD, of the biomedical ethics institute at the University of Zurich, and colleagues wrote. “How can big data be utilized for the common good whilst respecting individual rights and liberties, such as the right to privacy? What are the acceptable trade-offs between individual rights and the common good, and how do we determine the thresholds for such trade-offs? These ethical concerns and the tensions between them are not new to public health research and practice, but now they must be addressed in a new context, with the result that appropriate standards may vary.”
Vayena and colleagues discussed various moral issues that come with using online services such as Google, Twitter or Wikipedia to access private data, as well as the responsibilities of public health organizations to use these data responsibly. Although these may span a variety of scenarios, the investigators placed their main concerns into three major categories:
- context sensitivity, which includes privacy and consent in the context of public health;
- nexus of ethics and methodology, which includes developing a reliable algorithm to reduce the harm of false identification; and
- legitimacy requirements, which include best practice standards, accountability and transparency with the public.
Many of these issues are common challenges in any public health program, they wrote, yet the scale of digital technology and the unique sets of data available augment ethical concerns. In addition, the low costs and near-global availability of these methods could come with moral obligations to hold up detection and rights standards universally.
“By utilizing global real-time data, digital disease detection promises accelerated disease outbreak detection, and examples of this enhanced timeliness in detection have already been reported in the literature,” Vayena and colleagues wrote. “While changes in the ways in which epidemiologic information is obtained, analyzed and disseminated are likely to result in great social benefits, it is important to recognize and anticipate potential risks and unintended consequences.”
Disclosure: The researchers report no relevant financial disclosures.
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