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Igure 5(b) shows the distinction among the decentralized optimization model composite measure AE along with the result in the E2SFCA approach working with exactly the same scale. In comparison towards the optimization strategy, the E2SFCA process tends to show larger accessibility in locations with numerous centers (e.g., near Los Angeles and around New York). Additionally, it shows higher accessibility in lots of areas that lie in overlapping service places for centers (e.g., northern South Carolina, eastern Arkansas, and New Mexico). A pairwise t-test (1-tail) shows that for counties with more than 50 CF sufferers (127 “large” counties) or less than 5 CF individuals (1289 “small” counties), the measure from the E2SFCA system is significantly larger than measures in the optimization approach (respectively, with p-values 0.20 ?10-6 and 2.00 ?10-2); forLi et al. BMC Wellness Services Research (2015) 15:Web page eight ofFig. 4 Optimization outcomes for patient cost of prospective access. (a) Distance, and (b) Congestioncounties of other sizes (“medium” counties), the test is inconclusive. The F-test shows that for all groups of counties, the variance in the E2SFCA measure is larger (with p-value 1.88 ?10-4 for small counties, value significantly less than 10-6 for medium counties, and 3.90 ?10-2 for large counties. The Mann hitney-Wilcoxon test shows that the E2SFCA measure is greater in median than the optimization composite measure with p-values much less than 10-6 for tiny and medium counties, and 2.02 ?10-2 for big counties. The obtaining is constant together with the analytical outcomes in More file 1 section four displaying that with overlapping catchment areas, E2SFCA quantifies higher access when distances are S28463 web comparatively compact. The comparison involving the composite measure AM and theM2SFCA method is comparable but the magnitude of variations is smaller. The number of visits captured inside the E2SFCA method is shown in Fig. 6 in comparison for the visits required by the population. It truly is highest about facilities, and specifically with many facilities including around New York. For the optimization model, the realized visits per facility are estimated to be 0 to 3000. In contrast, the variety for the E2SFCA result is 0 to 10,540 per facility. This can be constant with all the analytical outcome that the amount of visits is higher in the E2SFCA strategy. The F test indicates that the variance in the facility congestion is substantially larger for the E2SFCA method, having a p-value significantly less than 10-6. This is comparable for the analyticalLi et al. BMC Well being Services Analysis (2015) 15:Page 9 ofFig. 5 Final results comparing optimization model with E2SFCA and M2SFCA for CF care in US. (a) Decentralized model composite measure AE, and (b) E2SFCA-AEresult that the optimization model usually features a reduce facility congestion. The results displaying access over the network indicate quite a few places which have uncovered populations, higher congestion, and/or high travel distances. Figure 7 shows the outcomes in several local regions after network interventions. 1 new facility was added for the network in locations with uncovered populations (Springfield, MO), plus the capacity of existing facilities was doubled in two 164027512453468 places (Columbus, OH; and Pittsburgh, PA). For the E2SFCA approach, the gain in access is centered over the interventions journal.pone.0169185 and decays with distance inside 150 miles.