Clinical categories of patients and encounter rates in primary health care – a three-year study in defined populations

BACKGROUND: The objective was to estimate the proportion of inhabitants with a diagnosis-registered encounter with a general practitioner, and to elucidate annual variations of clinical categories of patients in terms of their individual comorbidity. METHODS: A three-year retrospective study of encounter data from electronic patient records, with an annual-based application of the Johns Hopkins Adjusted Clinical Groups (ACG) system. Data were retrieved from every patient with a diagnosis-registered encounter with a GP during the period 2001–2003 at 13 publicly managed primary health care centres in Blekinge county, southeastern Sweden, with about 150000 inhabitants. Main outcome measures: Proportions of inhabitants with a diagnosis-registered encounter, and ranges of the annual proportions of categories of patients according to ACGs. RESULTS: The proportion of inhabitants with a diagnosis-registered encounter ranged from about 64.0% to 90.6% for the primary health care centres, and averaged about 76.5% for all inhabitants. In a three-year perspective the average range of categories of patients was about 0.4% on the county level, and about 0.9% on the primary health care centre level. About one third of the patients each year had a constellation of two or more types of morbidity. CONCLUSION: About three fourths of all inhabitants had one or more diagnosis-registered encounters with a general practitioner during the three-year period. The annual variation of categories of patients according to ACGs was small on both the county and the primary health care centre level. The ACG system seems useful for demonstrating and predicting various aspects of clinical categories of patients in Swedish primary health care

The importance of comorbidity in analysing patient costs in Swedish primary care

BACKGROUND: The objective was to explore the usefulness of the morbidity risk adjustment system Adjusted Clinical Groups(® )(ACG), in comparison with age and gender, in explaining and estimating patient costs on an individual level in Swedish primary health care. Data were retrieved from two primary health care centres in southeastern Sweden. METHODS: A cross-sectional observational study. Data from electronic patient registers from the two centres were retrieved for 2001 and 2002, and patients were grouped into ACGs, expressing the individual combination of diagnoses and thus the comorbidity. Costs per patient were calculated for both years in both centres. Cost data from one centre were used to create ACG weights. These weights were then applied to patients at the other centre. Correlations between individual patient costs, age, gender and ACG weights were studied. Multiple linear regression analyses were performed in order to explain and estimate patient costs. RESULTS: The variation in individual patient costs was substantial within age groups as well as within ACG weight groups. About 37.7% of the individual patient costs could be explained by ACG weights, and age and gender added about 0.8%. The individual patient costs in 2001 estimated 22.0% of patient costs in 2002, whereas ACG weights estimated 14.3%. CONCLUSION: ACGs was an important factor in explaining and estimating individual patient costs in primary health care. Costs were explained to only a minor extent by age and gender. However, the usefulness of the ACG system appears to be sensitive to the accuracy of classification and coding of diagnoses by physicians

Health problems and disability in long-term sickness absence: ICF coding of medical certificates

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to test the feasibility of International Classification of Functioning, Disability and Health (ICF) and to explore the distribution, including gender differences, of health problems and disabilities as reflected in long-term sickness absence certificates.</p> <p>Methods</p> <p>A total of 433 patients with long sick-listing periods, 267 women and 166 men, were included in the study. All certificates exceeding 28 days of sick-listing sent to the local office of the Swedish Social Insurance Administration of a municipality in the Stockholm area were collected during four weeks in 2004-2005. ICD-10 medical diagnosis codes in the certificates were retrieved and free text information on disabilities in body function, body structure or activity and participation were coded according to ICF short version.</p> <p>Results</p> <p>In 89.8% of the certificates there were descriptions of disabilities that readily could be classified according to ICF. In a reliability test 123/131 (94%) items of randomly chosen free text information were identically classified by two of the authors. On average 2.4 disability categories (range 0-9) were found per patient; the most frequent were 'Sensation of pain' (35.1% of the patients), 'Emotional functions' (34.1%), 'Energy and drive functions' (22.4%), and 'Sleep functions' (16.9%). The dominating ICD-10 diagnostic groups were 'Mental and behavioural disorders' (34.4%) and 'Diseases of the musculoskeletal system and connective tissue' (32.8%). 'Reaction to severe stress and adjustment disorders' (14.7%), and 'Depressive episode' (11.5%) were the most frequent diagnostic codes. Disabilities in mental functions and activity/participation were more commonly described among women, while disabilities related to the musculoskeletal system were more frequent among men.</p> <p>Conclusions</p> <p>Both ICD-10 diagnoses and ICF categories were dominated by mental and musculoskeletal health problems, but there seems to be gender differences, and ICF classification as a complement to ICD-10 could provide a better understanding of the consequences of diseases and how individual patients can cope with their health problems. ICF is feasible for secondary classifying of free text descriptions of disabilities stated in sick-leave certificates and seems to be useful as a complement to ICD-10 for sick-listing management and research.</p

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

<p>Abstract</p> <p>Background</p> <p>Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community.</p> <p>Results</p> <p>This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment.</p> <p>Conclusions</p> <p>AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.</p

Luftföroreningar vid svetsning

PIXE-analyser kan utföras med utvecklad analysuppställning med noggrannhet och precision av c:a 10 % och med hög analyskapacitet. Ett dataprogram för evaluering av räntgenspektra presenteras. Inverkan av provtjocklek vid PIXE-analys av inhomogena prov har studerats och korrektioner föreslås. Fluorinnehållet i filterprov har bestämts, samtidigt med PIXE-analys, genom utnyttjande av en kärnfysikalisk reaktion som ger resultat med god noggrannhet och precision. Svetsaerosoler har karakteriserats m.h.a. PIXE, ESCA och TEM/EDAX. En uppställning för insamling av svetsaerosoler under utveckling och hittillsvarande resultat indikerar representativ provinsamling med god reproducerbarhet