Artificial neural network approaches to identify maternal and infant risk and asset factors using Peridata.Net: a WI-MIOS study.

Objective: To analyze PeriData.Net, a clinical registry with linked maternal-infant hospital data of Milwaukee County residents, to demonstrate a predictive analytic approach to perinatal infant risk assessment. Materials and Methods: Using unsupervised learning, we identified infant birth clusters with similar multivariate health indicator patterns, measured using perinatal variables from 2008 to 2019 from n = 43 969 clinical registry records in Milwaukee County, WI, followed by supervised learning risk-propagation modeling to identify key maternal factors. To understand the relationship between socioeconomic status (SES) and birth outcome cluster assignment, we recoded zip codes in Peridata.Net according to SES level. Results: Three self-organizing map clusters describe infant birth outcome patterns that are similar in the multivariate space. Birth outcome clusters showed higher hazard birth outcome patterns in cluster 3 than clusters 1 and 2. Cluster 3 was associated with lower Apgar scores at 1 and 5 min after birth, shorter infant length, and premature birth. Prediction profiles of birth clusters indicate the most sensitivity to pregnancy weight loss and prenatal visits. Majority of infants assigned to cluster 3 were in the 2 lowest SES levels. Discussion: Using an extensive perinatal clinical registry, we found that the strongest predictive performance, when considering cluster membership using supervised learning, was achieved by incorporating social and behavioral risk factors. There were inequalities in infant birth outcomes based on SES. Conclusion: Identifying infant risk hazard profiles can contribute to knowledge discovery and guide future research directions. Additionally, presenting the results to community members can build consensus for community-identified health and risk indicator prioritization for intervention development.

A Blueprint to Evaluate One Health.

One Health (OH) positions health professionals as agents for change and provides a platform to manage determinants of health that are often not comprehensively captured in medicine or public health alone. However, due to the organization of societies and disciplines, and the sectoral allocation of resources, the development of transdisciplinary approaches requires effort and perseverance. Therefore, there is a need to provide evidence on the added value of OH for governments, researchers, funding bodies, and stakeholders. This paper outlines a conceptual framework of what OH approaches can encompass and the added values they can provide. The framework was developed during a workshop conducted by the "Network for Evaluation of One Health," an Action funded by the European Cooperation in Science and Technology. By systematically describing the various aspects of OH, we provide the basis for measuring and monitoring the integration of disciplines, sectors, and stakeholders in health initiatives. The framework identifies the social, economic, and environmental drivers leading to integrated approaches to health and illustrates how these evoke characteristic OH operations, i.e., thinking, planning, and working, and require supporting infrastructures to allow learning, sharing, and systemic organization. It also describes the OH outcomes (i.e., sustainability, health and welfare, interspecies equity and stewardship, effectiveness, and efficiency), which are not possible to obtain through sectoral approaches alone, and their alignment with aspects of sustainable development based on society, environment, and economy.