About this Research Topic
Precision Public Health aims to bring precision to disease prevention, health promotion, and the reduction of health disparities in populations. Advances in data-centric technology breathe life into Precision Public Health as a discipline and drive the evolution of its research. Recent years have seen the emerging role of data science-driven research as the driver of the development of precision public health as a young science and the maturation of small but emerging bodies of research centering on the application of data sciences to integrate and/or analyze real-world data from EHR, mobile devices, social media, and sensors for geospatial, surveillance, and health economic analysis. Meanwhile, an emerging body of data-centric precision public health research has also benefited from more empirical studies designed in accordance with the precision public health framework. However, there is a lack of any research demonstrating how data-driven precision public health research can be translated to inform decisions that can achieve "the right intervention at the right time, every time to the right population." Hence, there is a need for a data-driven approach to public health research.
Here, our objective is to introduce Precision Public Health research topics that are not only applications of data sciences that bring precision to the prevention of diseases, promotion of health, and the reduction of health disparities in populations but are also decision-centric, which enables the translation of findings to decisions at the level of care delivery, resource planning and allocation, and policymaking. Articles are also invited to address and quantify the socioeconomic benefits of implementing precision public health programs for risk identification, disease surveillance, and preventive intervention.
Notably, the effort to bring precision to medicine, and to public health in particular, also brings with it the issues of data privacy, security, consent, and self-determination, and the challenge of making the model that informs decisions be transparent, explainable, and free of bias. Hence, it is also our objective to include as our research topic perspectives on the ethical consideration of bringing precision to public health.
The research topics will be grouped under four areas. First, clinical and epidemiological studies that target subgroup(s) of a population whose rationale of selection was data-driven, and the finding of the studies possess substantiative implications in health policy decision-making. Second, studies on optimizing resource allocation pertaining to the delivery and planning of public health and primary care services use operations research and analytics methodology. Third, innovative data science and decision science methodology for achieving decision-centric precision public health. Finally, the ethical and moral examination of bringing precision to public health with the goal of making it decision-centric.
Topics of interest include but are not limited to the following aspects:
• Data-driven characterization of under-studied vulnerable subpopulations or groups and the potential implications for public health policy.
• Risk assessment and modeling studies that were either tailored to specific sub-populations or groups of individuals via analytical means or whose findings can demonstrate the unique characteristics of a subpopulation or a group of individuals.
• Implementation science studies whose focus is to develop or tailor instruments/tools for enhancing public health policy decision-making and evaluate the instruments’/tools’ adoption in, and/or impact on, analytically defined sub-populations or groups of individuals.
• Data-driven operations-research studies that optimize the allocation of resources among different groups of individuals at the service level or among different sub-populations at the policy level to inform public health planning.
• Methodological studies in data sciences or decision sciences that may enhance the precision in the prevention of diseases, promotion of health, and the reduction of health disparities in populations.
• Methodological studies that facilitate the translation of data into decisions in the context of precision public health.
• Bioethics research on the ethical and moral implications of the roles of data and artificial intelligent in public health decision-making that affects different subpopulations or vulnerable groups’ access to care or information to enhance health, or their rights to refuse (the provision of their personal data for) such care or information.
Please note: The authors should make an effort to include real examples in a critical way, considering data quality, unbiasedness, granularity and how data science contributed to decision making in real life.
Here, our objective is to introduce Precision Public Health research topics that are not only applications of data sciences that bring precision to the prevention of diseases, promotion of health, and the reduction of health disparities in populations but are also decision-centric, which enables the translation of findings to decisions at the level of care delivery, resource planning and allocation, and policymaking. Articles are also invited to address and quantify the socioeconomic benefits of implementing precision public health programs for risk identification, disease surveillance, and preventive intervention.
Notably, the effort to bring precision to medicine, and to public health in particular, also brings with it the issues of data privacy, security, consent, and self-determination, and the challenge of making the model that informs decisions be transparent, explainable, and free of bias. Hence, it is also our objective to include as our research topic perspectives on the ethical consideration of bringing precision to public health.
The research topics will be grouped under four areas. First, clinical and epidemiological studies that target subgroup(s) of a population whose rationale of selection was data-driven, and the finding of the studies possess substantiative implications in health policy decision-making. Second, studies on optimizing resource allocation pertaining to the delivery and planning of public health and primary care services use operations research and analytics methodology. Third, innovative data science and decision science methodology for achieving decision-centric precision public health. Finally, the ethical and moral examination of bringing precision to public health with the goal of making it decision-centric.
Topics of interest include but are not limited to the following aspects:
• Data-driven characterization of under-studied vulnerable subpopulations or groups and the potential implications for public health policy.
• Risk assessment and modeling studies that were either tailored to specific sub-populations or groups of individuals via analytical means or whose findings can demonstrate the unique characteristics of a subpopulation or a group of individuals.
• Implementation science studies whose focus is to develop or tailor instruments/tools for enhancing public health policy decision-making and evaluate the instruments’/tools’ adoption in, and/or impact on, analytically defined sub-populations or groups of individuals.
• Data-driven operations-research studies that optimize the allocation of resources among different groups of individuals at the service level or among different sub-populations at the policy level to inform public health planning.
• Methodological studies in data sciences or decision sciences that may enhance the precision in the prevention of diseases, promotion of health, and the reduction of health disparities in populations.
• Methodological studies that facilitate the translation of data into decisions in the context of precision public health.
• Bioethics research on the ethical and moral implications of the roles of data and artificial intelligent in public health decision-making that affects different subpopulations or vulnerable groups’ access to care or information to enhance health, or their rights to refuse (the provision of their personal data for) such care or information.
Please note: The authors should make an effort to include real examples in a critical way, considering data quality, unbiasedness, granularity and how data science contributed to decision making in real life.
Keywords: public health genomics, population health, big data, surveillance, targeted intervention, vaccination, population screening, genomics, chronic diseases, care integration, service optimization
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
