Precision Medicine for the Population-The Hope and Hype of Public Health Genomics

TL;DR

This paper critically examines the promises and risks of precision public health, emphasizing that over-reliance on genomic data can harm disadvantaged communities and advocating for integrated, multi-level data approaches.

Contribution

It compares precision public health with historical genotype-driven public health efforts, highlighting potential risks and proposing data integration across multiple levels to mitigate harm.

Findings

Overemphasis on genomics can harm underserved communities.

Historical parallels reveal risks of genotype-driven public health.

Multi-level data integration can improve public health outcomes.

Abstract

Public health is the most recent of the biomedical sciences to be seduced by the trendy moniker "precision." Advocates for "precision public health" (PPH) call for a data-driven, computational approach to public health, leveraging swaths of genomic "big data" to inform public health decision-making. Yet, like precision medicine, PPH oversells the value of genomic data to determine health outcomes, but on a population-level. A large historical literature has shown that over-emphasizing heredity tends to disproportionately harm underserved minorities and disadvantaged communities. By comparing and contrasting PPH with an earlier attempt at using big data and genetics, in the Progressive era (1890-1920), we highlight some potential risks of a genotype-driven preventive public health. We conclude by suggesting that such risks may be avoided by prioritizing data integration across many levels of analysis, from the molecular to the social.