How can precision prevention be approached from a general population perspective within the field of cancer epidemiology?

Abstract

Precision medicine has been facilitated by increased data availability on patients, including biomarkers at the molecular level and comprehensive information from clinical investigations, while developments in big data analysis have helped generate insights from such extensive data. Sweden’s innovation agency Vinnova has recently launched a widened area for research, development and innovation: sustainable precision health [1]. Sustainable precision health is about creating more accurate and precise methods for use in preventing, diagnosing and treating adverse health conditions, such as cancer, and promoting health and well-being. Big data analyses in cancer research have mainly addressed precision medicine, aiming at improving diagnostics and prognostics in order to come up with more individualised and efficient treatments for cancer patients. Precision cancer prevention integration has emerged as a research concept for approaching individualised screening; early detection innovations have moved towards the integration of molecular knowledge and risk stratification profiles to allow for a more reliable representation of at-risk individuals [2]. In a commentary published in 2017, Paolo Vineis and Christopher Wild state that the ‘precision’ in precision prevention should refer to the individuals who are the targets of the intervention [3]. They also emphasise that without careful consideration being given to equitable access, more sophisticated medical interventions for prevention (or treatment) pose the unintended risk of exacerbating social inequalities in health, rather than reducing them [3]. Herein, I put focus on precision prevention from a general population perspective. I distinguish clinical cancer epidemiology from cancer epidemiology with a public health focus. The above-mentioned references to precision prevention connect to clinical cancer epidemiology; that is, a research field where precision prevention/medicine is approached by using epidemiological tools to study questions related to diagnosis, prognosis and treatment in clinical settings. Undoubtedly, big data analyses have been, and will be, of crucial importance for precision prevention/medicine relating to clinical care. In this Letter, however, I consider cancer epidemiology with a public health focus, which addresses questions relating to the cancer burden in the general population. I take the view that cancer epidemiology addresses cancer in populations, rather than in individuals or patients [4]. Cancer epidemiologists build their studies on various types of data, not only comprehensive biomedical data but also data about lifestyle, environmental exposures, what people consume, where people live, how people move, which people participate in screening programs, etc. In public health research, precision prevention has been put forward as a concept that accounts for the social determinants of people’s health, with interventions based on relevant lifestyle and environmental factors [5]. Usage of data on individuals of a general population is regulated by data protection and privacy laws. Investigators may collect individual data from population-based research studies with voluntary participation. However, extensive data on vulnerable individuals may be particularly hard to collect in population-based research studies. Follow-ups of the general population have revealed higher cancer incidences and mortality among nonparticipants compared to participants in population-based cohort studies [6]. Data may be accessible from population registers, including also information on non-participants. However, registry data on typical non-participants may be too sensitive, or too limited, for implementing individually tailored interventions in the general population. The purpose of this Letter is to suggest a strategy of precision prevention that may help to make cancer prevention in the general population more efficient and describe how cancer epidemiology can contribute to this strategy.