Asthma prevalence is increasing and asthma-related costs are likely to increase, but few studies have analysed the relationship of asthma costs and severity. The impact of severity on costs was quantified in a cohort of 318 asthmatic patients followed up prospectively for 1 yr.Patients presenting with a broad range of severity of the disease (intermittent, mild persistent, moderate persistent, severe persistent) were recruited by chest physicians throughout France and treated for 1 yr according to customary clinical practice and following international guidelines. Severity, direct and indirect costs, and quality of life (QoL) were assessed. A multivariate analysis was conducted to relate factors contributing to the costs measured.Mean direct costs for goods and services excluding hospitalization, numbers of consultations, supplementary examinations, and the use and cost of bronchodilators and corticosteroids, indirect costs of days lost from work, and adverse QoL parameters all increased significantly with increasing severity. This also applied to mean age, body weight, asthma duration, depression of forced expiratory volume in one second, and inhaled corticosteroid posology in the 234 patients completing the study. There was a significant relationship (r=0.614, pv0.001) between direct costs (hospitalization and cures were excluded) and three domains of the QoL questionnaire (mobility, pain and energy).Overall costs of asthma (including individual direct costs, indirect costs, and intangible quality of life costs) are clearly related to severity. This is the first study in asthma to combine rigorous independent classification of grades of severity in statistically valid numbers of patients of grades receiving "real-world" treatment and followed-up prospectively for 1 yr. It allowed severity to be accurately related to direct, indirect and intangible costs of asthma. Quality of life explained a significant part of these costs.
Text mining refers to the discovery of previously unknown knowledge that can be found in text collections. In recent years, the text mining field has received great attention due to the abundance of textual data. A researcher in this area is requested to cope with issues originating from the natural language particularities. This survey discusses such semantic issues along with the approaches and methodologies proposed in the existing literature. It covers syntactic matters, tokenization concerns and it focuses on the different text representation techniques, categorisation tasks and similarity measures suggested.
This work suggests that the techniques used as a basis from which to calculate QALY values are flawed. In particular, the underlying assumptions of the multiattribute utility model do not correspond to behaviour patterns observed in a real population. It therefore appears that use of the QALY technique should be questioned in healthcare decision-making settings.
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