The structure of quality systems is important to the process and outcome, an empirical study of 386 hospital departments in Sweden

<p>Abstract</p> <p>Background</p> <p>Clinicians, nurses, and managers in hospitals are continuously confronted by new technologies and methods that require changes to working practice. Quality systems can help to manage change while maintaining a high quality of care. A new model of quality systems inspired by the works of Donabedian has three factors: structure (resources and administration), process (culture and professional co-operation), and outcome (competence development and goal achievement). The objectives of this study were to analyse whether structure, process, and outcome can be used to describe quality systems, to analyse whether these components are related, and to discuss implications.</p> <p>Methods</p> <p>A questionnaire was developed and sent to a random sample of 600 hospital departments in Sweden. The adjusted response rate was 75%. The data were analysed with confirmatory factor analysis and structural equation modeling in LISREL. This is to our knowledge the first large quantitative study that applies Donabedian's model to quality systems.</p> <p>Results</p> <p>The model with relationships between structure, process, and outcome was found to be a reasonable representation of quality systems at hospital departments (p = 0.095, indicating no significant differences between the model and the data set). Structure correlated strongly with process (0.72) and outcome (0.60). Given structure, process also correlated with outcome (0.20).</p> <p>Conclusion</p> <p>The model could be used to describe and evaluate single quality systems or to compare different quality systems. It could also be an aid to implement a systematic and evidence-based system for working with quality improvements in hospital departments.</p

Cause-specific mortality time series analysis: a general method to detect and correct for abrupt data production changes

<p>Abstract</p> <p>Background</p> <p>Monitoring the time course of mortality by cause is a key public health issue. However, several mortality data production changes may affect cause-specific time trends, thus altering the interpretation. This paper proposes a statistical method that detects abrupt changes ("jumps") and estimates correction factors that may be used for further analysis.</p> <p>Methods</p> <p>The method was applied to a subset of the AMIEHS (Avoidable Mortality in the European Union, toward better Indicators for the Effectiveness of Health Systems) project mortality database and considered for six European countries and 13 selected causes of deaths. For each country and cause of death, an automated jump detection method called Polydect was applied to the log mortality rate time series. The plausibility of a data production change associated with each detected jump was evaluated through literature search or feedback obtained from the national data producers.</p> <p>For each plausible jump position, the statistical significance of the between-age and between-gender jump amplitude heterogeneity was evaluated by means of a generalized additive regression model, and correction factors were deduced from the results.</p> <p>Results</p> <p>Forty-nine jumps were detected by the Polydect method from 1970 to 2005. Most of the detected jumps were found to be plausible. The age- and gender-specific amplitudes of the jumps were estimated when they were statistically heterogeneous, and they showed greater by-age heterogeneity than by-gender heterogeneity.</p> <p>Conclusion</p> <p>The method presented in this paper was successfully applied to a large set of causes of death and countries. The method appears to be an alternative to bridge coding methods when the latter are not systematically implemented because they are time- and resource-consuming.</p

Flyktingskap och hälsa - initiativ och engagemang för att främja hälsan.

Ledare av Ragnar Westerlin

Samarbete i Europa - specialiteten i socialmedicin gäller i hela EU!

Socialmedicin är den svenska specialitet som ingår i EU:s förteckning över erkända medicinska specialiteter i folkhälsa. EU:s direktiv innebär att en specialist som utbildas inom ett EU-land med automatik erkänns som en specialist i andra EU-länder. UEMS (European Union of Medical Specialists) organiserar de europeiska läkarförbundens arbete med specialitetsfrågor inom EU-området. UEMS sektion för folkhälsa (Public Health) har utformat en lista över de kompetenser som är gemensamma för medicinska specialister i folkhälsa i Europa och riktlinjer för hur specialistutbildningen skall organiseras, som överensstämmer med de svenska kraven. För att uppnå europeisk standard skulle dock antalet specialister i socialmedicin behöva utökas och specialiteten behöver få tydligare uppdrag och organisation i den svenska hälso- och sjukvården.The Swedish medical specialty Social Medicine is listed in the EU list of recognized medical specialties in Public Health. According to EU directives a specialist in Public Health from one EU country is automatically recognized as a specialist in another country. UEMS (European Union of Medical Specialists) organizes the work of the medical associations with speciality issues in the EU countries. UEMS section for Public Health has developed a list of competencies that would be applied for medical specialists in Public Health in Europe and directives for the organization of the specialist training, which are in accordance with Swedish rules. However, in order to achieve European standard the number of specialists must be increased and the mandate and organization of the specialty in Swedish health care must be clearer

Self-rated health in relation to employment status during periods of high and low levels of unemployment

BACKGROUND: There is a need for more research on the health impact of changes in the national unemployment rate. Therefore, the present study was carried out to compare levels of self-rated health during periods of high and low levels of unemployment. METHODS: Data included cross-sectional interviews from the Swedish Survey of Living Conditions, which were based on random samples of inhabitants between 16 and 64 years of age living in Sweden. Data were collected for the period 1983-89, when unemployment levels were low (n = 35 562; 2.5%) and for the period 1992-97 when unemployment was high (n = 24 019; 7.1%). RESULTS: After adjusting for sociodemographic variables as well as long-term disease or handicap, the differences in self-rated health between the unemployed and employed were larger when unemployment levels were high in the 1990s, than when they were low in the 1980s. More groups of the unemployed were afflicted with poor health when unemployment was high, compared with when it was low. In 1992-97, being married, living in larger cities, or not having a long-term disease or handicap no longer buffered the negative effects on health among the unemployed. CONCLUSIONS: Poorer self-rated health among the unemployed seems to be an increasing public health problem during high levels of unemployment