The evolution of health status and chronic conditions in Catalonia, 1994-2006: the paradox of health revisited using the Blinder - Oaxaca decomposition

[EN] Background: The paradox of health refers to the improvement in objective measures of health and the increase in the reported prevalence of chronic conditions. The objective of this paper is to test the paradox of health in Catalonia from 1994 to 2006. Methods: Longitudinal cross-sectional study using the Catalonia Health Interview Survey of 1994 and 2006. The approach used was the three-fold Blinder - Oaxaca decomposition, separating the part of the differential in mean visual analogue scale value (VAS) due to group differences in the predictors (prevalence effect), due to differences in the coefficients (severity effect), and an interaction term. Variables included were the VAS value, education level, labour status, marital status, all common chronic conditions over the two cross-sections, and a variable for non-common chronic conditions and other conditions. Sample weights have been applied. Results: Results show that there is an increase in mean VAS for men aged 15-44, and a decrease in mean VAS for women aged 65-74 and 75 and more. The increase in mean VAS for men aged 15-44 could be explained by a decrease in the severity effect, which offsets the increase in the prevalence effect. The decrease in mean VAS for women aged 65-74 and 75 and more could be explained by an increase in the prevalence effect, which does not offset the decrease in the severity effect. Conclusions: The results of the present analysis corroborate the paradox of health hypothesis for the population of Catalonia, and highlight the need to be careful when measuring population health over time, as well as their usefulness to detect population's perceptions.

Efecto de la ingesta de cinc en los niveles séricos-plasmáticos, en el crecimiento y en el desarrollo neurológico en los lactantes : metaanálisis

Programa de doctorado: Salud pública: epidemiología, planificación y nutrición

A REGIONAL ASSESSMENT OF GROUNDWATER AVAILABILITY AS CONSTRAINED BY LOCAL HYDROGEOLOGY AND ENVIRONMENTAL LIMITS TO STREAMFLOW DEPLETION

Groundwater pumping from aquifers in hydraulic connection with nearby streams is known to cause adverse impacts by decreasing flows to levels below those necessary to maintain aquatic ecosystems. The recent passage of the Great Lakes--St. Lawrence River Basin Water Resources Compact has brought attention to this issue in the Great Lakes region. In particular, the legislation requires the Great Lakes states to enact measures for limiting water withdrawals that can cause adverse ecosystem impacts. This study explores how both hydrogeologic and environmental flow limitations constrain groundwater availability in the Great Lakes Basin. A methodology for calculating maximum allowable pumping rates is presented. Groundwater availability across the basin is shown to be constrained by a combination of hydrogeologic yield and environmental flow limitations varying over both local and regional scales. The results are sensitive to factors such as pumping time and streamflow depletion limits as well as streambed conductance. Understanding how these restrictions constrain groundwater usage and which hydrogeologic characteristics and spatial variables have the most influence on potential streamflow depletions has important water resources policy and management implications.

Investigating Peatland Stratigraphy and Hydrogeology Using Integrated Electrical Geophysics

Hydrology has been suggested as the mechanism controlling vegetation and related surficial pore-water chemistry in large peatlands. Peatland hydrology influences the carbon dynamics within these large carbon reservoirs and will influence their response to global warming. A geophysical survey was completed in Caribou Bog, a large peatland in Maine, to evaluate peatland stratigraphy and hydrology. Geophysical measurements were integrated with direct measurements of peat stratigraphy from probing, fluid chemistry, and vegetation patterns in the peatland. Consistent with previous field studies, ground-penetrating radar (GPR) was an excellent method for delineating peatland stratigraphy. Prominent reflectors from the peat-lake sediment and lake sediment-mineral soil contacts were precisely recorded up to 8 m deep. Two-dimensional resistivity and induced polarization imaging were used to investigate stratigraphy beneath the mineral soil, beyond the range of GPR. We observe that the peat is chargeable, and that IP imaging is an alternative method for defining peat thickness. The chargeability of peat is attributed to the high surface-charge density on partially decomposed organic matter. The electrical conductivity imaging resolved glaciomarine sediment thickness (a confining layer) and its variability across the basin. Comparison of the bulk conductivity images with peatland vegetation revealed a correlation between confining layer thickness and dominant vegetation type, suggesting that stratigraphy exerts a control on hydrogeology and vegetation distribution within this peatland. Terrain conductivity measured with a Geonics EM31 meter correlated with confining glaciomarine sediment thickness and was an effective method for estimating variability in glaciomarine sediment thickness over approximately 18 km(2). Our understanding of the hydrogeology, stratigraphy, and controls on vegetation growth in this peatland was much enhanced from the geophysical study.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Ms. germ. oct. 3 - Gebetbuch des Claus Humbracht

Vorbesitzer: Claus Humbracht; Joseph von Humbracht; Stadtarchiv Frankfurt am Main

Zur Biologie und Morphologie von Cephalobus elongatus de Man und Rhadbitis brevispina Claus, nebst Bemerkungen über einige andere Nematodenarten

Von Kati Marcinowski

Lillie Claus auf "Grammophon", die Stimme seines Herrn

Signatur des Originals: S 36/F08381

Mathilde Fränkel-Claus, Szenenfoto

Signatur des Originals: S 36/F08576