Cost impact of prospective HLA-B*5701-screening prior to abacavir/lamivudine fixed dose combination use in Germany.

OBJECTIVE: Avoiding abacavir in HIV-infected patients tested positive for HLA-B*5701 reduces the risk of abacavir hypersensitivity reaction (ABC-HSR). Our aim was to assess the costs of clinically suspected HSR and to estimate potential cost savings of implementing prospective HLA-B*5701-screening for HIV-infected patients initiating abacavir/lamivudine fixed-dose combination (ABC/3TC FDC) compared to initiating respective treatment without screening. METHODS: Employing a decision tree model the expected HSR-related costs of screening vs. no screening were estimated from the societal and healthcare payer perspective (reference year 2007). A retrospective standardized assessment of all clinically suspected ABC-HSR cases without screening at 5 German HIV-centres was performed to measure resource consumption. In- and outpatient care, discarded ABC/3TC FDC and concomitant medication were considered. Direct resource utilization was valued using German fees (EBM, G-DRGs). Indirect costs were measured with the human capital approach. Estimates for the HLA-B*5701-prevalence, HSR-incidence, and hospitalization rate were based on clinical trials and cohorts and it was assumed that screening reduces the incidence of clinically suspected ABC-HSR from 10% to 0.5%. RESULTS: Thirty-two ABC-HSR cases were identified from 1998 to 2007. Mean direct and total costs per clinically suspected HSR case were Euro 1,362 and Euro 2,235, respectively. Hospital costs contributed 63.3% to direct costs. Potential cost savings when implementing genetic screening were estimated at Euro 44 and Euro 127 per screened patient, from a healthcare payer or societal perspective. CONCLUSION: HLA-B*5701 screening prior to ABC/3TC FDC initiation prevents significant HSR-related costs per screened patient and is likely to lead to overall net savings.

Effective models for medical and health response coordination: summary and action plan.

INTRODUCTION: To effectively respond to this relatively new, complex mandate, it is essential to find effective models of coordination to ensure that medical and health services can meet the standards now expected in a disaster situation. This theme explored various models, noting both the strengths that can be built on and the weaknesses that still need to be overcome. METHODS: Details of the methods used are provided in the preceding paper. The chairs moderated all presentations and produced a summary that was presented to an assembly of all of the delegates. Since the findings from the Theme 1 and Theme 4 groups were similar, the chairs of both groups presided over one workshop that resulted in the generation of a set of Action Plans that then were reported to the collective group of all delegates. RESULTS: The main points developed during the presentations and discussion included: (1) preplanning (predisaster goals), (2) information collection (assessment), (3) communication (materials and methods); and (4) response centres and personnel. There exists a need for institutionalization of processes for learning from experiences obtained from disasters. DISCUSSION: Action plans presented include: (1) creation of an information and data clearinghouse on disaster management, (2) identification of incentives and disincentives for readiness and develop strategies and interventions, and (3) action on lessons learned from evidence-based research and practical experience. CONCLUSIONS: There is an urgent need to proactively establish coordination and management procedures in advance of any crisis. A number of important insights for improvement in coordination and management during disasters emerged.

Observable operator models for discrete stochastic time series.

A widely used class of models for stochastic systems is hidden Markov models. Systems that can be modeled by hidden Markov models are a proper subclass of linearly dependent processes, a class of stochastic systems known from mathematical investigations carried out over the past four decades. This article provides a novel, simple characterization of linearly dependent processes, called observable operator models. The mathematical properties of observable operator models lead to a constructive learning algorithm for the identification of linearly dependent processes. The core of the algorithm has a time complexity of O(N + nm3), where N is the size of training data, n is the number of distinguishable outcomes of observations, and m is model state-space dimension.