It is well known that treatments – for example the likes of chemotherapy drugs to treat various cancers – can be successful for some patients and not for others.

Precision medicine acknowledges these shortcomings and represents a shift away from a one-size-fits-all approach towards treatments that are tailored toward individuals (genes, environments, the specific disease aetiology) and thus more targeted therapy. This movement is well underway, for example, patients with a range of cancers undergo routine molecular testing to identify treatments with the highest likelihood of success, or stratification of routine clinical data can delineate aetiological subtypes of diabetes that makes precision diabetes medicine a possibility.

Precision public health (PPH), however, is a relatively novel field. PPH takes the principles of precision medicine (tailoring interventions to suit the target individual/group) into the realm of public health and thus can be characterised as ‘providing the right intervention to the right population at the right time’. PPH enables the integration of new biology technologies such as genomics into public health strategies within the wider context of other determinants of health, such as socioeconomic, behavioural, and environmental factors, leading to more precise individual and population-based interventions. Potential areas include: screening, prevention and diagnosis; epidemiology and surveillance; and determinants of health and targeting of healthcare disparities.

THE ROLE OF DATA, DIGITALISATION AND GENOMICS IN PPH

Whether in a narrow, biology-oriented context (e.g. the “omics”) or in the broader health approach (e.g. social determinants of health), digitalisation and data can be leveraged to bring about a revolution in the field of public health. PPH is bound to live in a highly digitised context, with far reaching implications for top-down policy and intervention, as well as in individual decision-making and person-centredness.

The current practice of public health genomics revolves primarily around monogenic (rare) diseases (e.g. screening in newborns), diseases governed by high-penetrance genes (e.g. BRCA in breast cancer), and infectious disease surveillance (e.g. identification of pathogen variants).

Therefore, the next frontier of genomics in the field of public health is the risk assessment of polygenic diseases i.e. diseases that are governed by a plethora of low-penetrance genes which individually have low effect on the risk of disease. Researchers are currently seeking to identify such genes and aggregate them into so called polygenic risk scores.

‘Due to the low penetrance of the underlying genes, polygenic diseases are highly susceptible to environmental effects that either exacerbate or counterbalance the genetically determined risk of disease.

Therefore, a digital, data-driven integration of a polygenic risk profile with a complementary risk profile based on social determinants of health could, in theory, provide a very powerful tool to public health policymakers and individuals alike.

PRECISION PUBLIC HEALTH IN ACTION: TACKLING CHILDHOOD OBESITY

Obesity is a major public health problem worldwide. One study sought to predict early childhood obesity with machine learning and data from electronic health records. Insights from the machine learning tool, which effectively predicted childhood obesity at an early age, could be used as a clinical decision support tool. In particular, such a model could aid paediatricians who wish to identify children at heightened obesity risk in order to provide early interventions.