The inaugural issue of the Online Journal of Public Health Informatics under the JMIR Publications platform will feature articles on precision public health, a technology-enhanced, data-driven targeted approach to public health practice and research. In the absence of location-specific information about the spread of diseases during an epidemic, traditional public health interventions employ strategies like mass testing, vaccination, and lockdowns in entire neighborhoods or communities. With access to massive amounts of data, information and communication technologies, informatics, data science, and predictive analytics, it is becoming increasingly cost effective to identify and target specific neighborhoods or subpopulations for public health interventions in order to prevent the spread of diseases to entire populations. This strategy was derived from precision medicine, which uses personalized information to diagnose and treat specific individuals.

In his presentation at the 2015 Precision Medicine Initiative, Dr MJ Khoury described precision public health as going beyond the individualized treatment of patients with illness. Precision medicine uses an individual’s genetic, lifestyle, and environmental data to design and implement individualized treatment plans. In their article “Will precision medicine improve population health?” Drs Khoury and Galea add that precision public health is “about increased accuracy and granularity in defining public cohorts and delivering target interventions.” A public informatics workforce is required for the effective implementation of precision public health programs. Some researchers have characterized precision public health as a hyperlocal public health practice aided by information and communication technologies, data analytics, and informatics.

The Evolution of Precision Public Health

The evolution of precision public health can be traced to the launch of PulseNet by the Centers for Disease Control and Prevention in 1996. This project employed DNA fingerprinting to identify bacteria that caused food poisoning. Armed with this knowledge, large outbreaks were halted earlier and more effectively.

The Australian researcher Dr TS Weeramanthri coined the term “precision public health” in 2013. The first precision public health conference was hosted by the University of California, San Francisco in 2016. In the same year, health officials in Miami used geographic information system (GIS) technology to target and spray insecticides on mosquitoes in select blocks to contain the spread of Zika to the wider population. During the COVID-19 pandemic, epidemiologists at the New York City Department of Health and Mental Hygiene used GIS maps to track outbreaks, in near real time, to distribute testing resources, masks, and gloves to appropriate locations, thereby avoiding mass testing. It is evident that the era of big data, informatics, and data analytics has made it possible to employ sophisticated technologies to hone in on specific clusters of diseases and risk factors for intervention. However, precision public health is not all about technologies, predictive analytics, and informatics. Human, social, ethical, legal, and environmental factors must also be considered if society is to avoid unintended consequences such as increased health inequalities and other negative impacts on marginalized populations.

In the near future, artificial intelligence (AI) could be used to select specific subpopulations for public health interventions. However, the quality of the data and the biases built into AI applications could have harmful impacts on marginalized populations.

The current special issue invites articles in the following as well as other interesting areas of precision public health:

Use of big data to predict public health risks such as smoking progression, gun violence, air or water pollution, emerging infectious diseases (including zoonotic diseases), antibiotic resistance, asthma, diabetes, implementation of precision public health programs
Integration of the social determinants of health and electronic health records in precision public health programs
Application of DHIS2 (District Health Information System 2), the world’s largest health information systems platform, in precision public health programs in middle- and low-income countries
Measurement of the multidimensional attributes of data quality used in precision public health programs, given the fact that data constitute the most important asset of precision public health
Application of effective interventions to homogeneous subpopulations within a larger heterogeneous population in order to contain the spread of diseases in higher-income countries and the Global South
Socioeconomic benefits of implementing precision public health programs for risk identification, disease surveillance, and preventive interventions
Innovations in the application of AI and big data to precision public health use cases such as the tracking of infectious disease pathways
Research in the areas of risk, legal and ethical issues, and governance problems arising from the implementation of AI and predictive analytics in precision public health programs
Risks and challenges of using big data in precision public health programs

Further Considerations

Precision public health cannot be successfully implemented without a well-trained informatics workforce, whose tasks include data collection, analysis, and information and knowledge generation using sophisticated data science methods. This workforce is lacking in resource-poor settings and the Global South. As emphasized by Drs Khoury and Galea, it is not enough to focus on “precision” by using data and technology to target small subpopulations for precise interventions. This strategy could result in the neglect of the well-being of the most marginalized populations, thereby negating a major aim of conventional public health. Striking a good balance between regular public health practices and precision public health will avoid exacerbation of health inequalities. Technology and informatics competencies alone cannot achieve this balance. Informatics must complement knowledge of the social determinants of health in communities, ethics, and privacy.

We invite authors to submit original research, literature reviews, and scholarly or well-argued viewpoints to this call for papers on precision public health. We welcome original, unpublished submissions from researchers and practitioners in public health, medicine, health care, computer science, and related fields.

Submission Guidelines

All submissions will undergo a rigorous peer-review process, and accepted articles will be published as part of the “Precision Public Health” special issue.

Submission Deadline: No deadline. This is an open call for submissions.

Theme Issue Editors

Nsikak Akpakpan MD, PhD

Digital Innovation Senior Principal, Accenture Applied Intelligence

Global Applied Intelligence Lead, Accenture Development Partnerships

nsikak.e.akpakpan@accenture.com

Edward K Mensah PhD, MPhil

Associate Professor Emeritus of Health Economics and Informatics, Health Policy and Administration Division, School of Public Health, University of Illinois Chicago

Submissions not reviewed or accepted for publication in this Online Journal of Public Health Informatics theme issue may be offered cascading peer review or transfer to other JMIR Publications journals, according to standard publisher policies. For example, highly technical papers may be transferred or submitted to JMIR Biomedical Engineering. Early-stage formative work that informs the design of future interventions or research may better fit the scope of JMIR Formative Research. Authors are encouraged to submit study protocols or grant proposals to JMIR Research Protocols before data acquisition to preregister the study (Registered Reports; subsequent acceptance in one of the JMIR Publications journals is then guaranteed).

Please direct questions regarding this theme issue to the Online Journal of Public Health Informatics editorial team at ed-support@jmir.org.

All articles submitted to this theme issue will be shared and published rapidly through the following mechanisms:

All peer-reviewed articles in this theme issue will be immediately and permanently made open access. This is the standard for all titles within the JMIR Publications portfolio.
Articles can be made immediately available via JMIR Preprints (with a DOI) if authors select the preprint option at submission to enable this service.

References

Weeramanthri TS, Dawkins HJS, Baynam G, Bellgard M, Gudes O, Semmens JB. Editorial: precision public health. Front Public Health. 2018;6. doi: 10.3389/fpubh.2018.00121

Arnold C. Is precision public health the future—or a contradiction? Nature. 2022;601:18-20. doi: 10.1038/d41586-021-03819-2

Khoury MJ, Galea S. Will precision medicine improve population health? JAMA. 2016;316(13):1357-8. doi:10.1001/jama.2016.12260

Khoury MJ, Iademarco MF, Riley WT. Precision public health for the era of precision medicine. Am J Prev Med. 2016:50(3):398. doi:10.1016/j.amepre.2015.08.031

Dowell SF, Blazes D, Desmond-Hellmann S. Four steps to precision public health. Nat News. 2016:540(7632):189. doi:10.1038/540189a

Arnett DK, Claas SA. Precision medicine, genomics, and public health. Diabetes Care. 2016: 39(11):1870-3. doi:10.2337/dc16-1763

This paper is in the following e-collection/theme issue:

Exploring the Value of Learned Representations for Automated Syndromic Definitions

Exploring the Value of Learned Representations for Automated Syndromic Definitions