Effectiveness of chronic obstructive pulmonary disease-management programs: systematic review and meta-analysis

BACKGROUND: Disease-management programs may enhance the quality of care provided to patients with chronic diseases, such as chronic obstructive pulmonary disease (COPD). The aim of this systematic review was to assess the effectiveness of COPD disease-management programs. METHODS: We conducted a computerized search of MEDLINE, EMBASE, CINAHL, PsychINFO, and the Cochrane Library (CENTRAL) for studies evaluating interventions meeting our operational definition of disease management: patient education, 2 or more different intervention components, 2 or more health care professionals actively involved in patients' care, and intervention lasting 12 months or more. Programs conducted in hospital only and those targeting patients receiving palliative care were excluded. Two reviewers evaluated 12,749 titles and fully reviewed 139 articles; among these, data from 13 studies were included and extracted. Clinical outcomes considered were all-cause mortality, lung function, exercise capacity (walking distance), health-related quality of life, symptoms, COPD exacerbations, and health care use. A meta-analysis of exercise capacity and all-cause mortality was performed using random-effects models. RESULTS: The studies included were 9 randomized controlled trials, 1 controlled trial, and 3 uncontrolled before-after trials. Results indicate that the disease-management programs studied significantly improved exercise capacity (32.2 m, 95% confidence interval [CI], 4.1-60.3), decreased risk of hospitalization, and moderately improved health-related quality of life. All-cause mortality did not differ between groups (pooled odds ratio 0.84, 95% CI, 0.54-1.40). CONCLUSION: COPD disease-management programs modestly improved exercise capacity, health-related quality of life, and hospital admissions, but not all-cause mortality. Future studies should explore the specific elements or characteristics of these programs that bring the greatest benefit.

G-1 Appeal Activity in the Public Assistance Programs, May 2003

Monthly report from the Iowa Department of Human Services

G-1 Appeal Activity in the Public Assistance Programs, June 2003

Monthly report from the Iowa Department of Human Services

G-1 Appeal Activity in the Public Assistance Programs, July 2003

Monthly report from the Iowa Department of Human Services

G-1 Appeal Activity in the Public Assistance Programs, August 2003

Monthly report from the Iowa Department of Human Services

G-1 Appeal Activity in the Public Assistance Programs, September 2003

Monthly report from the Iowa Department of Human Services

G-1 Appeal Activity in the Public Assistance Programs, October 2003

Monthly report from the Iowa Department of Human Services

G-1 Appeal Activity in the Public Assistance Programs, November 2003

Monthly report from the Iowa Department of Human Services

G-1 Appeal Activity in the Public Assistance Programs, August 2006

G-1 Appeal Activity in the Public Assistance Programs

G-1 Appeal Activity in the Public Assistance Programs, September 2006

G-1 Appeal Activity in the Public Assistance Programs

G-1 Appeal Activity in the Public Assistance Programs - October 2006

G-1 Appeal Activity in the Public Assistance Programs - October 2006

G-1 Appeal Activity in the Public Assistance Programs, November 2006

G-1 Appeal Activity in the Public Assistance Programs

G-1 Appeal Activity in the Public Assistance Programs, December 2006

G-1 Appeal Activity in the Public Assistance Programs

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

G-1 Appeal Activity in the Public Assistance Programs, January 2007

G-1 Appeal Activity in the Public Assistance Programs for January 2007