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Worldwide efforts to identify individuals infected with the hepatitis C virus (HCV) focus almost exclusively on community healthcare systems, thereby failing to reach high-risk populations and those with poor access to primary care. In the USA, community-based HCV testing policies and guidelines overlook correctional facilities, where HCV rates are believed to be as high as 40 percent. This is a missed opportunity: more than ten million Americans move through correctional facilities each year. Herein, the purpose of this paper is to examine HCV testing practices in the US correctional system, California and describe how universal opt-out HCV testing could expand early HCV detection, improve public health in correctional facilities and communities, and prove cost-effective over time.

A commentary on the value of standardizing screening programs across facilities by mandating all facilities (universal) to implement opt-out testing policies for all prisoners upon entry to the correctional facilities.

Current variability in facility-level testing programs results in inconsistent testing levels across correctional facilities, and therefore makes estimating the actual number of HCV-infected adults in the USA difficult. The authors argue that universal opt-out testing policies ensure earlier diagnosis of HCV among a population most affected by the disease and is more cost-effective than selective testing policies.

The commentary explores the current limitations of selective testing policies in correctional systems and provides recommendations and implications for public health and correctional organizations.

The introduction of “should cost” in 2011 required all Major Defense Acquisition Programs (MDAP) to create efficiencies and improvements to reduce a program’s “will-cost” estimate. Realistic “will-cost” estimates are a necessary condition for the “should cost” analysis to be effectively implemented. Owing to the inherent difficulties in establishing a program’s will-cost estimate, this paper aims to propose a new model to infuse realism into this estimate.

Using historical data from 73 Departments of Defense programs as recorded in the selected acquisition reports (SARs), the analysis uses mixed stepwise regression to predict a program’s cost from Milestone B (MS B) to initial operational capability (IOC).

The presented model explains 83 per cent of the variation in the program acquisition cost. Significant predictor variables include: projected duration (months from MS B to IOC); the amount of research development test and evaluation (RDT&E) funding spent at the start of MS B; whether the program is considered a fixed-wing aircraft; whether a program is considered an electronic system program; whether a program is considered ACAT I at MS B; and the program size relative to the total program’s projected acquisition costs at MS B.

The model supports the “will-cost and should-cost” requirement levied in 2011 by providing an objective and defensible cost for what a program should actually cost based on what has been achieved in the past. A quality will-cost estimate provides a starting point for program managers to examine processes and find efficiencies that lead to reduced program costs.

The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost.

In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period.

The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time.

Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight.

This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits.