Precision, Equity, and Public Health and Epidemiology Informatics – A Scoping Review

Summary

Objectives: This scoping review synthesizes the recent literature on precision public health and the influence of predictive models on health equity with the intent to highlight central concepts for each topic and identify research opportunities for the biomedical informatics community.

Methods: Searches were conducted using PubMed for publications between 2017-01-01 and 2019-12-31.

Results: Precision public health is defined as the use of data and evidence to tailor interventions to the characteristics of a single population. It differs from precision medicine in terms of its focus on populations and the limited role of human genomics. High-resolution spatial analysis in a global health context and application of genomics to infectious organisms are areas of progress. Opportunities for informatics research include (i) the development of frameworks for measuring non-clinical concepts, such as social position, (ii) the development of methods for learning from similar populations, and (iii) the evaluation of precision public health implementations. Just as the effects of interventions can differ across populations, predictive models can perform systematically differently across subpopulations due to information bias, sampling bias, random error, and the choice of the output. Algorithm developers, professional societies, and governments can take steps to prevent and mitigate these biases. However, even if the steps to avoid bias are clear in theory, they can be very challenging to accomplish in practice.

Conclusions: Both precision public health and predictive modelling require careful consideration in how subpopulations are defined and access to data on subpopulations can be challenging. While the theory for both topics has advanced considerably, there is much work to be done in understanding how to implement and evaluate these approaches in practice.

Keywords

Public health - precision - health equity - prediction - bias

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