BackgroundLong term oxygen therapy (LTOT) has a strong evidence base in COPD patients with respiratory failure, but prescribing practices are recognized to need reform to ensure appropriate use and minimize costs. In the UK, since February 2006, all Home Oxygen prescription is issued by hospitals, making respiratory specialists totally in charge of home oxygen prescription. It has been widely noted that inappropriate home oxygen, often for intermittent use (“short burst”), is frequently prescribed in patients with COPD and related conditions with the intention to prevent hospital admissions outside of evidence based LTOT guidelines. We participated in a national Lung Improvement Project aimed at making LTOT use more evidence based. We utilised this unique opportunity of studying the effect of removal of oxygen from COPD patients (who did not meet LTOT criteria) on hospital admission rates.MethodsPrimary and secondary care data sources were used to identify patients with COPD in a single primary care trust who were admitted to hospital at least once due to COPD between April 2007 and November 2010. Admission rates were compared between LTOT users and non-users, adjusted for age and COPD severity. LTOT users were further studied for predictors of admission in those appropriately or inappropriately given oxygen according to NICE guidance, and for admissions before and after oxygen receipt, adjusting further for co-morbidity. Mortality and economic analyses were also conducted.ResultsReadmission was more likely in LTOT users (3.18 v 1.67 per patient, p < 0.001) after adjustment for FEV1 and age by multiple regression. When stratifying by appropriateness of LTOT prescription, adjusting also for Charlson index and other covariates, FEV1 predicted admission in appropriate users but there were no predictors in inappropriate users. In longitudinal analyses admission rates did not differ either side of oxygen prescription in appropriate or inappropriate LTOT users. Specialist assessment resulted in cost savings due to reduced use of oxygen.ConclusionsAdmission to hospital is more likely in LTOT users, independent of COPD severity. Oxygen use outside NICE guidance does not appear to prevent admissions.

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This chapter describes a GP procedure for modelling and solving academic resource planning problems in university management system with interval data uncertainty. In the proposed approach, the interval goals are first converted into the standard goals by using interval arithmetic technique. Certain objectives having the characteristics of fractional programming are transformed into linear goals by using linearization approach to solve the problem by employing linear GP methodology. In the model formulation of the problem, both the aspects of GP, minsum and minmax approaches, are addressed to construct the goal achievement function for minimising the possible regret towards achieving the goal values within the target intervals specified by the DM in the decision making environment. The potential use of the approach is illustrated by a case example. The model solution is compared with the solutions of the models studied previously.

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This article presents a goal programming (GP) procedure for solving interval valued multiobjective fractional programming problems (MOFPPs) with interval objective functions in an inexact environment.In the proposed approach, the interval objective functions are first converted into the standard objective goals in the fractional GP formulation by using the interval arithmetic technique. Then, in the decision process, the fractional goals are transformed into the linear goals by linearization approach [31] studied previously.To overcome the above situation, interval programming approaches [3,6,35,34] to mathematical programming problems in inexact environments have been studied [6,7,10,36] in the past. In interval programming, certain interval for the model parameters of the problems are introduced instead of assigning the estimated values (crisp or fuzzy) to them in a decision making situations. GP approaches [14] to interval programming problem have also been investigated by M. Inuiguchi and Y. Kume in the past.In solution process, the executable GP model of the problem is formulated with the objective to minimize the regret with the view to achieve the goals in their specified ranges and thereby arriving at a most satisfactory solution in the decision making environment.Two numerical examples are solved to illustrate the proposed approach and the model solution of one problem is compared with the solution of a fuzzy programming approach [28] studied previously.

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