The convergence of racial and income disparities in health insurance coverage in the United States

Objective This study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US. Data source The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study. Study design Logistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively. Data collection/extraction methods We constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findings While income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health. Conclusion Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

The Convergence of Racial and Income Disparities in Health Insurance Coverage in the United States

ObjectiveThis study applies the vulnerability framework and examines the combined effect of race and income on health insurance coverage in the US. Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage.Data sourcesThe household component of the US Medical Expenditure Panel Survey (MEPS-HC) in 2017 was used for the study.Study designLogistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or partially insured, partially insured only, and uninsured only, respectively.Data collection/extraction methodsWe constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findingsWhile income was a significant predictor of health insurance coverage (a difference of 6.1%-7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 66% less odds of being insured instead of uninsured or partially insured than high-income Whites with good health.ConclusionsPolicymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and are racial/ethnic minorities.

Domination integrity of some graph classes

The stability of a communication network has a great importance in network design. There are several vulnerability measures used to determine the resistance of network to the disruption in this sense. Domination theory provides a model to measure the vulnerability of a graph network. A new vulnerability measure of domination integrity was introduced by Sundareswaran in his Ph.D. thesis (Parameters of vulnerability in graphs (2010)) and defined as DI(G) = min{|S| + m(G − S):S ∈ V(G)} where m(G − S) denotes the order of a largest component of graph G − S and S is a dominating set of G. The domination integrity of an undirected connected graph is such a measure that works on the whole graph and also the remaining components of graph after any break down. Here we determine the domination integrity of wheel graph W1,n, Ladder graph Ln, Sm,n, Friendship graph Fn, Thorn graph of Pn and Cn which are commonly used graph models in network design.

A Comparison Between Edge Neighbor Rupture Degree and Edge Scattering Number in Graphs

The vulnerability measure of a graph or a network depends on robustness of the remained graph, after being exposed to any intervention or attack. In this paper, we consider two edge vulnerability parameters that are the edge neighbor rupture degree and the edge scattering number. The values of these parameters of some specific graphs and their graph operations are calculated. Thus, we analyze and compare which parameter is distinctive for the different type of graphs by using tables.

A New Vulnerability Measure for Water Distribution Network

The main objective of a water distribution network is to provide water to users in compliance with quality and service standards under different conditions. The ability to meet the water demand at the nodes, under the required pressures head, depends on many characteristic factors of the water network, such as various infrastructural components. A water distribution network is a complex system consisting of numerous structural elements and dependent by several factors. Resilience, robustness and vulnerability are of great interest, for these systems, in relation to the possible failure conditions which may compromise the network’s ability to fulfill the project conditions. Vulnerability measures how much the network is fragile: a higher value of vulnerability means that the network is prone to fail in achieving the project conditions. In the present work, a new vulnerability measure based on a topological approach is proposed. A first application of the proposed vulnerability measure on two water networks known in the literature is described, and the obtained results are compared with other performance indices showing a significant correlation.

Link Vulnerability in Networks

Link residual closeness is reported as a new graph vulnerability measure, a graph-based approach to network vulnerability analysis, and more sensitive than some other existing vulnerability measures. Residual closeness is of great theoretical and practical significance to network design and optimization. In this paper, how some of the graph types perform when they suffer a link failure is discussed. Vulnerability of graphs to the failure of individual links is computed via link residual closeness which provides a much fuller characterization of the network.