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Background:
In recent years, there has been an increasing interest in psychosocial workplace risk assessments in Germany. One of the questionnaires commonly employed for this purpose is the Short Questionnaire for Workplace Analysis (KFZA). Originally, the KFZA was developed and validated for office workers. The aim of the present study was to examine the factorial validity of the KFZA when applied to hospital settings. Therefore, we examined the factorial structure of a questionnaire that contained all the original items plus an extension adding 11 questions specific to hospital workplaces and analyzed both, the original version and the extended version.
Methods:
We analyzed questionnaire data of a total of 1731 physicians and nurses obtained over a 10-year period. Listwise exclusion of data sets was applied to account for variations in questionnaire versions and yielded 1163 questionnaires (1095 for the extended version) remaining for factor analysis. To examine the factor structure, we conducted a principal component factor analysis. The number of factors was determined using the Kaiser criterion and scree-plot methods. Factor interpretation was based on orthogonal Varimax rotation as well as oblique rotation.
Results:
The Kaiser criterion revealed a 7-factor solution for the 26 items of the KFZA, accounting for 62.0% of variance. The seven factors were named: “Social Relationships”, “Job Control”, “Opportunities for Participation and Professional Development”, “Quantitative Work Demands”, “Workplace Environment”, “Variability” and “Qualitative Work Demands”. The factor analysis of the 37 items of the extended version yielded a 9-factor solution. The two additional factors were named “Consequences of Strain” and “Emotional Demands”. Cronbach’s α ranged from 0.63 to 0.87 for these scales.
Conclusions:
Overall, the KFZA turned out to be applicable to hospital workers, and its content-related structure was replicated well with some limitations. However, instead of the 11 factors originally proposed for office workers, a 7-factor solution appeared to be more suitable when employed in hospitals. In particular, the items of the KFZA factor “Completeness of Task” might need adaptation for the use in hospitals. Our study contributes to the assessment of the validity of this popular instrument and should stimulate further psychometric testing.
The success of diagnostic knowledge systems has been proved over the last decades. Nowadays, intelligent systems are embedded in machines within various domains or are used in interaction with a user for solving problems. However, although such systems have been applied very successfully the development of a knowledge system is still a critical issue. Similarly to projects dealing with customized software at a highly innovative level a precise specification often cannot be given in advance. Moreover, necessary requirements of the knowledge system can be defined not until the project has been started or are changing during the development phase. Many success factors depend on the feedback given by users, which can be provided if preliminary demonstrations of the system can be delivered as soon as possible, e.g., for interactive systems validation the duration of the system dialog. This thesis motivates that classical, document-centered approaches cannot be applied in such a setting. We cope with this problem by introducing an agile process model for developing diagnostic knowledge systems, mainly inspired by the ideas of the eXtreme Programming methodology known in software engineering. The main aim of the presented work is to simplify the engineering process for domain specialists formalizing the knowledge themselves. The engineering process is supported at a primary level by the introduction of knowledge containers, that define an organized view of knowledge contained in the system. Consequently, we provide structured procedures as a recommendation for filling these containers. The actual knowledge is acquired and formalized right from start, and the integration to runnable knowledge systems is done continuously in order to allow for an early and concrete feedback. In contrast to related prototyping approaches the validity and maintainability of the collected knowledge is ensured by appropriate test methods and restructuring techniques, respectively. Additionally, we propose learning methods to support the knowledge acquisition process sufficiently. The practical significance of the process model strongly depends on the available tools supporting the application of the process model. We present the system family d3web and especially the system d3web.KnowME as a highly integrated development environment for diagnostic knowledge systems. The process model and its activities, respectively, are evaluated in two real life applications: in a medical and in an environmental project the benefits of the agile development are clearly demonstrated.
Background: Persistent pain after inguinal herniorrhaphy is a disabling condition with a lack of evidence-based pharmacological treatment options. This randomized placebo-controlled trial investigated the efficacy of a capsaicin 8% cutaneous patch in the treatment of severe persistent inguinal postherniorrhaphy pain. Methods: Forty-six patients with persistent inguinal postherniorrhaphy pain were randomized to receive either a capsaicin 8% patch or a placebo patch. Pain intensity (Numerical Rating Scale [NRS 0-10]) was evaluated under standardized conditions (at rest, during movement, and during pressure) at baseline and at 1, 2 and 3 months after patch application. Skin punch biopsies for intraepidermal nerve fiber density (IENFD) measurements were taken at baseline and 1 month after patch application. Quantitative sensory testing was performed at baseline and at 1, 2, and 3 months after patch application. The primary outcome was comparisons of summed pain intensity differences (SPIDs) between capsaicin and placebo treatments at 1, 2 and 3 months after patch application (significance level P<0.01). Results: The maximum difference in SPID, between capsaicin and placebo treatments, was observed at 1 month after patch application, but the pain reduction was not significant (NRS, mean difference [95% CI]: 5.0 [0.09 to 9.9]; P=0.046). No differences in SPID between treatments were observed at 2 and 3 months after patch application. Changes in IENFD on the pain side, from baseline to 1 month after patch application, did not differ between capsaicin and placebo treatment: 1.9 [-0.1 to 3.9] and 0.6 [-1.2 to 2.5] fibers/mm, respectively (P=0.32). No significant changes in sensory function, sleep quality or psychological factors were associated with capsaicin patch treatment. Conclusions: The study did not demonstrate significant differences in pain relief between capsaicin and placebo treatment, although a trend toward pain improvement in capsaicin treated patients was observed 1 month after patch application.
Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of similar to 4 Mio km(2). The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.
Background: International disease management guidelines recommend the regular assessment of depression and anxiety in heart failure patients. Currently there is little data on the effect of screening for depression and anxiety on the quality of life and the prognosis of heart failure (HF). We will investigate the association between the recognition of current depression/anxiety by the general practitioner (GP) and the quality of life and the patients' prognosis.
Methods/Design: In this multicenter, prospective, observational study 3,950 patients with HF are recruited by general practices in Germany. The patients fill out questionnaires at baseline and 12-month follow-up. At baseline the GPs are interviewed regarding the somatic and psychological comorbidities of their patients. During the follow-up assessment, data on hospitalization and mortality are provided by the general practice. Based on baseline data, the patients are allocated into three observation groups: HF patients with depression and/or anxiety recognized by their GP (P+/+), those with depression and/or anxiety not recognized (P+/-) and patients without depression and/or anxiety (P-/-). We will perform multivariate regression models to investigate the influence of the recognition of depression and/or anxiety on quality of life at 12 month follow-up, as well as its influences on the prognosis (hospital admission, mortality).
Discussion: We will display the frequency of GP-acknowledged depression and anxiety and the frequency of installed therapeutic strategies. We will also describe the frequency of depression and anxiety missed by the GP and the resulting treatment gap. Effects of correctly acknowledged and missed depression/anxiety on outcome, also in comparison to the outcome of subjects without depression/anxiety will be addressed. In case results suggest a treatment gap of depression/anxiety in patients with HF, the results of this study will provide methodological advice for the efficient planning of further interventional research.
BACKGROUND: Climate change will probably alter the spread and transmission intensity of malaria in Africa. OBJECTIVES: In this study, we assessed potential changes in the malaria transmission via an integrated weather disease model.
METHODS: We simulated mosquito biting rates using the Liverpool Malaria Model (LMM). The input data for the LMM were bias-corrected temperature and precipitation data from the regional model (REMO) on a 0.5 degrees latitude longitude grid. A Plasmodium falciparum infection model expands the LMM simulations to incorporate information on the infection rate among children. Malaria projections were carried out with this integrated weather disease model for 2001 to 2050 according to two climate scenarios that include the effect of anthropogenic land-use and land-cover changes on climate.
RESULTS: Model-based estimates for the present climate (1960 to 2000) are consistent with observed data for the spread of malaria in Africa. In the model domain, the regions where malaria is epidemic are located in the Sahel as well as in various highland territories. A decreased spread of malaria over most parts of tropical Africa is projected because of simulated increased surface temperatures and a significant reduction in annual rainfall. However, the likelihood of malaria epidemics is projected to increase in the southern part of the Sahel. In most of East Africa, the intensity of malaria transmission is expected to increase. Projections indicate that highland areas that were formerly unsuitable for malaria will become epidemic, whereas in the lower-altitude regions of the East African highlands, epidemic risk will decrease.
CONCLUSIONS: We project that climate changes driven by greenhouse-gas and land-use changes will significantly affect the spread of malaria in tropical Africa well before 2050. The geographic distribution of areas where malaria is epidemic might have to be significantly altered in the coming decades.
Background: The Global initiative for chronic Obstructive Lung Disease (GOLD) defines COPD as a fixed postbronchodilator ratio of forced expiratory volume in 1 second and forced vital capacity (FEV1/FVC) below 0.7. Agedependent cut-off values below the lower fifth percentile (LLN) of this ratio derived from the general population have been proposed as an alternative. We wanted to assess the diagnostic accuracy and prognostic capability of the GOLD and LLN definition when compared to an expert-based diagnosis. Methods: In a prospective cohort study, 405 patients aged ≥ 65 years with a general practitioner’s diagnosis of COPD were recruited and followed up for 4.5 (median; quartiles 3.9; 5.1) years. Prevalence rates of COPD according to GOLD and three LLN definitions and diagnostic performance measurements were calculated. The reference standard was the diagnosis of COPD of an expert panel that used all available diagnostic information, including spirometry and bodyplethysmography. Results: Compared to the expert panel diagnosis, ‘GOLD-COPD’ misclassified 69 (28%) patients, and the three LLNs misclassified 114 (46%), 96 (39%), and 98 (40%) patients, respectively. The GOLD classification led to more false positives, the LLNs to more false negative diagnoses. The main predictors beyond the FEV1/FVC ratio for an expert diagnosis of COPD were the FEV1 % predicted, and the residual volume/total lung capacity ratio (RV/TLC). Adding FEV1 and RV/TLC to GOLD or LLN improved the diagnostic accuracy, resulting in a significant reduction of up to 50% of the number of misdiagnoses. The expert diagnosis of COPD better predicts exacerbations, hospitalizations and mortality than GOLD or LLN. Conclusions: GOLD criteria over-diagnose COPD, while LLN definitions under-diagnose COPD in elderly patients as compared to an expert panel diagnosis. Incorporating FEV1 and RV/TLC into the GOLD-COPD or LLN-based definition brings both definitions closer to expert panel diagnosis of COPD, and to daily clinical practice.
Introduction The fast, precise, and accurate measurement of the new generation of oral anticoagulants such as dabigatran and rivaroxaban in patients' plasma my provide important information in different clinical circumstances such as in the case of suspicion of overdose, when patients switch from existing oral anticoagulant, in patients with hepatic or renal impairment, by concomitant use of interaction drugs, or to assess anticoagulant concentration in patients' blood before major surgery. Methods Here, we describe a quick and precise method to measure the coagulation inhibitors dabigatran and rivaroxaban using ultra-performance liquid chromatography electrospray ionization-tandem mass spectrometry in multiple reactions monitoring (MRM) mode (UPLC-MRM MS). Internal standards (ISs) were added to the sample and after protein precipitation; the sample was separated on a reverse phase column. After ionization of the analytes the ions were detected using electrospray ionization-tandem mass spectrometry. Run time was 2.5 minutes per injection. Ion suppression was characterized by means of post-column infusion. Results The calibration curves of dabigatran and rivaroxaban were linear over the working range between 0.8 and 800 mu g/L (r > 0.99). Limits of detection (LOD) in the plasma matrix were 0.21 mu g/L for dabigatran and 0.34 mu g/L for rivaroxaban, and lower limits of quantification (LLOQ) in the plasma matrix were 0.46 mu g/L for dabigatran and 0.54 mu g/L for rivaroxaban. The intraassay coefficients of variation (CVs) for dabigatran and rivaroxaban were < 4% and 6%; respectively, the interassay CVs were < 6% for dabigatran and < 9% for rivaroxaban. Inaccuracy was < 5% for both substances. The mean recovery was 104.5% (range 83.8-113.0%) for dabigatran and 87.0%(range 73.6-105.4%) for rivaroxaban. No significant ion suppressions were detected at the elution times of dabigatran or rivaroxaban. Both coagulation inhibitors were stable in citrate plasma at -20 degrees C, 4 degrees C and even at RT for at least one week. A method comparison between our UPLC-MRM MS method, the commercially available automated Direct Thrombin Inhibitor assay (DTI assay) for dabigatran measurement from CoaChrom Diagnostica, as well as the automated anti-Xa assay for rivaroxaban measurement from Chromogenix both performed by ACL-TOP showed a high degree of correlation. However, UPLC-MRM MS measurement of dabigatran and rivaroxaban has a much better selectivity than classical functional assays measuring activities of various coagulation factors which are susceptible to interference by other coagulant drugs. Conclusions Overall, we developed and validated a sensitive and specific UPLC-MRM MS assay for the quick and specific measurement of dabigatran and rivaroxaban in human plasma.
Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community.
Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.