26 research outputs found
Properties of Healthcare Teaming Networks as a Function of Network Construction Algorithms
Network models of healthcare systems can be used to examine how providers
collaborate, communicate, refer patients to each other. Most healthcare service
network models have been constructed from patient claims data, using billing
claims to link patients with providers. The data sets can be quite large,
making standard methods for network construction computationally challenging
and thus requiring the use of alternate construction algorithms. While these
alternate methods have seen increasing use in generating healthcare networks,
there is little to no literature comparing the differences in the structural
properties of the generated networks. To address this issue, we compared the
properties of healthcare networks constructed using different algorithms and
the 2013 Medicare Part B outpatient claims data. Three different algorithms
were compared: binning, sliding frame, and trace-route. Unipartite networks
linking either providers or healthcare organizations by shared patients were
built using each method. We found that each algorithm produced networks with
substantially different topological properties. Provider networks adhered to a
power law, and organization networks to a power law with exponential cutoff.
Censoring networks to exclude edges with less than 11 shared patients, a common
de-identification practice for healthcare network data, markedly reduced edge
numbers and greatly altered measures of vertex prominence such as the
betweenness centrality. We identified patterns in the distance patients travel
between network providers, and most strikingly between providers in the
Northeast United States and Florida. We conclude that the choice of network
construction algorithm is critical for healthcare network analysis, and discuss
the implications for selecting the algorithm best suited to the type of
analysis to be performed.Comment: With links to comprehensive, high resolution figures and networks via
figshare.co
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Going beyond the Flood Insurance Rate Map: insights from flood hazard map co-production
Abstract. Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essential information for insurance purposes, but do not necessarily provide relevant information for non-insurance aspects of flood risk management (FRM) such as public education and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end-users. In this study, social scientists and engineers explore opportunities for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end-users' FRM needs. Specifically, two-dimensional flood modeling produced a set of baseline hazard maps for stakeholders of the Tijuana River Valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource managers, city planners, emergency managers, academia, non-profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end-user preferences emerged, such as (1) legends that frame flood intensity both qualitatively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired hazard map content, end-users' requests revealed general consistency with mapping needs reported in European studies and guidelines published in Australia. However, requested map content that is not commonly produced included: (1) standing water depths following the flood, (2) the erosive potential of flowing water, and (3) pluvial flood hazards, or flooding caused directly by rainfall. We conclude that the relevance and utility of commonly produced flood hazard maps can be most improved by illustrating pluvial flood hazards and by using concrete reference points to describe flooding scenarios rather than exceedance probabilities or frequencies
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Addressing Pluvial Flash Flooding through Community-Based Collaborative Research in Tijuana, Mexico
Pluvial flash flooding (PFF) is a growing hazard facing cities around the world as a result of rapid urbanization and more intense precipitation from global warming, particularly for low-resourced settings in developing countries. We present collaborative modeling (CM) as an iterative process to meet diverse decision-making needs related to PFF through the co-production of flood hazard models and maps. CM resulted in a set of flood hazard maps accessible through an online viewer that end-users found useful and useable for understanding PFF threats, including debris blockages and barriers to mobility and evacuation. End-users of information included individuals concerned with general flood awareness and preparedness, and involved in infrastructure and emergency management, planning, and policy. CM also showed that rain-on-grid hydrodynamic modeling is needed to depict PFF threats in ways that are intuitive to end-users. These outcomes evidence the importance and transferability of public health rationale for community-based research and principles used here including recognizing community as a unit of identity, building on strengths of the community, and integrating knowledge for the benefit of all partners