Data Ethics in Epidemiology: Autonomy, Privacy, Confidentiality and Justice

Abstract

Epidemiology is an essential tool of public health, which largely relies on collection, collation, analysis and interpretation of data and actions based on the information gleaned from the data. Such data-intensive processes lead to unique ethical issues relating to autonomy of the data generator, privacy, confidentiality and justice. In this chapter, we attempt to describe the ethical issues in data handling in epidemiology using two examples, public health surveillance and Big Data analytics in digital epidemiology. One of the unique ethical issues that arises in public health surveillance is the difficulty in operationalizing autonomy, as any individual refusing to share surveillance data will compromise the effectiveness of the surveillance. The need to balance the benefits of health security with the risks of infringement into individual privacy and confidentiality lies as the undercurrent of the ethics of public health surveillance. Big Data analytics is becoming increasingly popular in public health. The rich variety of the data, while offering chances of multilevel, multivariate analysis of disease models, also raises concern of biases in data collection due to huge variations in the sources of the data. The high velocity of the data also throws open opportunities for rapid analysis and interventions to improve population health. The power of Big Data analytics needs to be harnessed with the important ethical principles of public health in mind.