Precision Public Health: Empowering Communities with Hyperlocal Data for Targeted Interventions and Improved Outcomes

Abstract

Background Precision public health is an effective strategy for reaching the last mile in service delivery, but is frequently hampered by its dependence on unattainable data standards and the non-transferability of the solutions designed. This paper proposes a five-part system involving 1) dynamic data governance, 2) hyperlocal community data, 3) data synthesis and analysis, 4) the design and implementation of precision interventions, and 5) correlation between community data and traditional outcome data. Recent studies of community network data have found the connectedness of communities to be positively correlated with community social and environmental outcomes. Taking advantage of hyperlocal community data is therefore a promising approach to improve community outcomes by characterizing and optimizing for greater connectivity. Methods Collection and governance of hyper-local data that is community-owned can be accomplished through such transferable systems as IRIS, a community-led referral network originally designed for multi-sector social and healthcare organizations. Using this data, communities can identify precise areas of intervention through descriptive and network analysis techniques, and design a responsive, community-led intervention. Immersive Innovation Labs, an applied learning approach, is an effective methodology for the adaptive design of innovative precision interventions. This combination of approaches can empower communities and public health professionals. Conclusion The COVID-19 pandemic revealed the impact of chronic understaffing and skills gaps, particularly at the local level. This paper aims to broaden the definition of precision public health as a response, beyond the traditional application that is dependent on big, non-contextual data sources. Reframing precision public health to a methodology dependent on community-owned, ongoing data collection allows the design of hyper-local solutions while shifting the burden of scalability to data collection technology. While challenges in implementation remain, precision is necessary to make public health and communities more responsive and effective in delivering equitable health outcomes and reaching the last mile.