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Disease management has emerged as a new strategy to enhance quality of care for patients suffering from chronic conditions, and to control healthcare costs. So far, however, the effects of this strategy remain unclear. Although current models define the concept of disease management, they do not provide a systematic development or an explanatory theory of how disease management affects the outcomes of care. The objective of this paper is to present a framework for valid evaluation of disease-management initiatives. The evaluation model is built on two pillars of disease management: patient-related and professional-directed interventions. The effectiveness of these interventions is thought to be affected by the organisational design of the healthcare system. Disease management requires a multifaceted approach; hence disease-management programme evaluations should focus on the effects of multiple interventions, namely patient-related, professional-directed and organisational interventions. The framework has been built upon the conceptualisation of these disease-management interventions. Analysis of the underlying mechanisms of these interventions revealed that learning and behavioural theories support the core assumptions of disease management. The evaluation model can be used to identify the components of disease-management programmes and the mechanisms behind them, making valid comparison feasible. In addition, this model links the programme interventions to indicators that can be used to evaluate the disease-management programme. Consistent use of this framework will enable comparisons among disease-management programmes and outcomes in evaluation research.

COPD is characterised by damage to small airways due to an inflammatory process as well as an imbalance between oxidants and antioxidants. Several cytokines and cell adhesion molecules enhancing a mainly neutrophilic inflammation have been associated with COPD. The aim of the study was to investigate whether inflammation or oxidative markers gave an indication of the course of COPD during an exacerbation. Fourteen patients with moderate to severe COPD admitted to the St. Antonius Hospital because of an exacerbation have been monitored during treatment with prednisolone 50 mg intravenously during 24 h at admission, reduced to 25 mg at day 3 and tapered off with oral prednisolone at day 7. On three separate occasions, day 1, 3 and 7, H2O2 in exhaled air, IL-8 and the soluble cell adhesion molecule sICAM and sE-selectin in serum were measured. We compared the patients at day 1 with healthy controls (in both non-smokers and smokers). Furthermore, we examined the changes from the study group in time during therapy. At admission all the markers were raised in comparison with the control groups. During treatment H2O2 concentrations in breath condensate declined significantly (P<0.001) as well as IL-8 and sICAM in serum (P=0.002, respectively, P<0.001). There was no significant change in sE-selectin (P=0.132). No significant improvement has been found in spirometry. These data suggest that the markers H2O2 in exhaled air, IL-8 and sICAM in serum are suitable markers in monitoring exacerbated COPD.

ABSTRACT:Here we present an exploratory statistical analysis aimed at the minimization of the 'screen bias' from affected ancient air temperature time series over the Western Mediterranean. Our approach lies in the statistical analysis of about 6 years of daily paired temperature observations taken using the ancient Montsouri shelter and the modern Stevenson screen for daily maximum (T x ) and minimum (T n ) temperature data recorded at two experimental sites: the meteorological gardens of La Coruña and Murcia, Spain (locations under the influence of the Oceanic/Atlantic/Galician and Mediterranean arid and semi-arid climate types, respectively), where ongoing field trials have been carried out. Descriptive statistical analysis of the paired series shows pre-sheltered temperatures tended to induce a strong warm bias in T x data (of about 1°C at the annual scale but with a clear seasonal cycle with higher values in summer and lower in winter), while T n readings have a small (∼0.2°C, and sustained all year round) cold bias compared to the modern period. Statistical relationships between the screen bias and other related meteorological variables show the highest correlation coefficients between the 'screen bias' and T x , T n and the diurnal temperature range (DTR) recorded under the replicated ancient shelters at both locations and point to the reliability of these variables as potential predictors of the T x . We generate a parsimonious regression model based on the data from both experimental sites, which takes into account polynomial terms of lower order for the predictor variables (T x and DTR recorded under the ancient shelter) and harmonic terms, in order to represent the seasonal cycle of the screen bias. The goodness-of-fit of the model is satisfactory, as it explains up to 51.7% of the additional T x variability.

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