[Applying robust management approaches to transit to value-based healthcare]

Health systems require continuous improvement. Along with that, despite the pace of the society development, they face a number of global problems, which can be solved by the transition to value-based healthcare through robust management. The purpose of the study is to analyze the relationship between sustainable healthcare management and life expectancy in the regions of the Russian Federation, with a special focus on the impact of robust management factors on this indicator. The study used theoretical methods: observation, measurement, comparison, analysis, modeling, synthesis, study and generalization, as well as practical ones: modeling using Pandas, Numpy, Scipy, Geopandas, Sklearn, Matplotlib and Seaborn packages for the Python programming language. The study resulted in the construction of a tree-like model of the relationship of indicators, which made it possible to obtain a multi-level list with the mutual influence of factors and thresholds. By examining the dynamics in the relationship between health management strategies and life expectancy across regions, we sought to identify key control factors that can be used to improve health outcomes across the country and assess which ones have more and less impact. The result of the analysis was the determination of the threshold values of indicators, the change in which leads to a significant change in life expectancy.

Identifying target reference points for harvesting assessment-limited wildlife populations: a case study

Identifying appropriate strategies for sustainable harvest is a challenge for many terrestrial vertebrate species because of uncertain system dynamics, limited data to inform population models, and potentially conflicting objectives that seek to harvest and maintain populations at desirable levels. The absence of monitoring and assessment infrastructure needed to regularly estimate abundance accentuates this challenge for many species, and limits application of rigorous state-dependent frameworks for decision making that are commonly advocated in natural resource management. Reference points, which define management targets or triggers for changing management, are often used to guide decision-making, but suffer from ambiguity when developed without explicit consideration of uncertainty or trade-offs among competing objectives. We describe an approach for developing unambiguous target reference points for assessment-limited species using structured decision making, and demonstrate the approach to develop target harvest rates for management of fall Wild Turkey (Meleagris gallopavo) harvests in the face of uncertain population and harvest dynamics. We use simulation and decision analyses to identify harvest rates that are optimal for accomplishing explicit management objectives in the face of uncertainty, and harvest rates with robust performance over broad regions of the demographic and harvest model parameter space. We demonstrate that population and harvest parameters commonly uncertain to wildlife managers interact to determine appropriate target harvest rates for Wild Turkeys, and that formally acknowledging a range of plausible values for structurally uncertain parameters results in more conservative target reference points than suggested by previously published studies. The structured decision making framework described here provides a natural conceptual and quantitative framework for extending our approach to develop unambiguous harvest targets for other assessment-limited wildlife populations while formally acknowledging structural uncertainty in system dynamics.