Modelling the results of health promotion activities in Switzerland: development of the Swiss Model for Outcome Classification in Health Promotion and Prevention

This paper describes the Model for Outcome Classification in Health Promotion and Prevention adopted by Health Promotion Switzerland (SMOC, Swiss Model for Outcome Classification) and the process of its development. The context and method of model development, and the aim and objectives of the model are outlined. Preliminary experience with application of the model in evaluation planning and situation analysis is reported. On the basis of an extensive literature search, the model is situated within the wider international context of similar efforts to meet the challenge of developing tools to assess systematically the activities of health promotion and prevention.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

A prediction model for personal radio frequency electromagnetic field exposure

Radio frequency electromagnetic fields (RF-EMF) in our daily life are caused by numerous sources such as fixed site transmitters (e.g. mobile phone base stations) or indoor devices (e.g. cordless phones). The objective of this study was to develop a prediction model which can be used to predict mean RF-EMF exposure from different sources for a large study population in epidemiological research. We collected personal RF-EMF exposure measurements of 166 volunteers from Basel, Switzerland, by means of portable exposure meters, which were carried during one week. For a validation study we repeated exposure measurements of 31 study participants 21 weeks after the measurements of the first week on average. These second measurements were not used for the model development. We used two data sources as exposure predictors: 1) a questionnaire on potentially exposure relevant characteristics and behaviors and 2) modeled RF-EMF from fixed site transmitters (mobile phone base stations, broadcast transmitters) at the participants' place of residence using a geospatial propagation model. Relevant exposure predictors, which were identified by means of multiple regression analysis, were the modeled RF-EMF at the participants' home from the propagation model, housing characteristics, ownership of communication devices (wireless LAN, mobile and cordless phones) and behavioral aspects such as amount of time spent in public transports. The proportion of variance explained (R2) by the final model was 0.52. The analysis of the agreement between calculated and measured RF-EMF showed a sensitivity of 0.56 and a specificity of 0.95 (cut-off: 90th percentile). In the validation study, the sensitivity and specificity of the model were 0.67 and 0.96, respectively. We could demonstrate that it is feasible to model personal RF-EMF exposure. Most importantly, our validation study suggests that the model can be used to assess average exposure over several months.

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH4) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO2) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH4 fluxes from wetland systems. Even though modelling of wetland extent and CH4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

The Hamburg-Model of Integrated Care for Patients with Psychosis: Part 1

Objective: The "Hamburg model" designates an integrated care model for severely ill patients with psychotic disorders financed by the health insurance system in accordance with § 140 SGB V.Methods: It comprises comprehensive and long-term treatment within a regional network of the psychosis center of the University Medical Center Hamburg-Eppendorf (UKE) and private psychiatrists. The treatment model consists of therapeutic assertive community treatment (ACT) provided by a highly specialized treatment team and need-adapted in- and outpatient care.Results and conclusions: The present article summarizes the disease- and treatment-specific rationales for the model development as well as the model structure and treatment contents. The article further summarizes the effectiveness and efficiency results of a study comparing the Hamburg model and treatment as usual (without ACT) within a 12-month follow-up study (ACCESS trial).

Understanding the performance of the FLake model over two African Great Lakes

The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from similar to 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model

This study examines how different microphysical parameterization schemes influence orographically induced precipitation and the distributions of hydrometeors and water vapour for midlatitude summer conditions in the Weather Research and Forecasting (WRF) model. A high-resolution two-dimensional idealized simulation is used to assess the differences between the schemes in which a moist air flow is interacting with a bell-shaped 2 km high mountain. Periodic lateral boundary conditions are chosen to recirculate atmospheric water in the domain. It is found that the 13 selected microphysical schemes conserve the water in the model domain. The gain or loss of water is less than 0.81% over a simulation time interval of 61 days. The differences of the microphysical schemes in terms of the distributions of water vapour, hydrometeors and accumulated precipitation are presented and discussed. The Kessler scheme, the only scheme without ice-phase processes, shows final values of cloud liquid water 14 times greater than the other schemes. The differences among the other schemes are not as extreme, but still they differ up to 79% in water vapour, up to 10 times in hydrometeors and up to 64% in accumulated precipitation at the end of the simulation. The microphysical schemes also differ in the surface evaporation rate. The WRF single-moment 3-class scheme has the highest surface evaporation rate compensated by the highest precipitation rate. The different distributions of hydrometeors and water vapour of the microphysical schemes induce differences up to 49 W m−2 in the downwelling shortwave radiation and up to 33 W m−2 in the downwelling longwave radiation.

The coupled atmosphere–chemistry–ocean model SOCOL-MPIOM

The newly developed atmosphere–ocean-chemistry-climate model SOCOL-MPIOM is presented by demonstrating the influence of the interactive chemistry module on the climate state and the variability. Therefore, we compare pre-industrial control simulations with (CHEM) and without (NOCHEM) interactive chemistry. In general, the influence of the chemistry on the mean state and the variability is small and mainly restricted to the stratosphere and mesosphere. The largest differences are found for the atmospheric dynamics in the polar regions, with slightly stronger northern and southern winter polar vortices in CHEM. The strengthening of the vortex is related to larger stratospheric temperature gradients, which are attributed to a parametrization of the absorption of ozone and oxygen in the Lyman-alpha, Schumann–Runge, Hartley, and Higgins bands. This effect is parametrized in the version with interactive chemistry only. A second reason for the temperature differences between CHEM and NOCHEM is related to diurnal variations in the ozone concentrations in the higher atmosphere, which are missing in NOCHEM. Furthermore, stratospheric water vapour concentrations differ substantially between the two experiments, but their effect on the temperatures is small. In both setups, the simulated intensity and variability of the northern polar vortex is inside the range of present day observations. Sudden stratospheric warming events are well reproduced in terms of their frequency, but the distribution amongst the winter months is too uniform. Additionally, the performance of SOCOL-MPIOM under changing external forcings is assessed for the period 1600–2000 using an ensemble of simulations driven by a spectral solar forcing reconstruction. The amplitude of the reconstruction is large in comparison to other state-of-the-art reconstructions, providing an upper limit for the importance of the solar signal. In the pre-industrial period (1600–1850) the simulated surface temperature trends are in reasonable agreement with temperature reconstructions, although the multi-decadal variability is more pronounced. This enhanced variability can be attributed to the variability in the solar forcing. The simulated temperature reductions during the Maunder Minimum are in the lowest probability range of the proxy records. During the Dalton Minimum, when also volcanic forcing is an important driver of temperature variations, the agreement is better. In the industrial period from 1850 onward SOCOL-MPIOM overestimates the temperature increase in comparison to observational data sets. Sensitivity simulations show that this overestimation can be attributed to the increasing trend in the solar forcing reconstruction that is used in this study and an additional warming induced by the simulated ozone changes.

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a set-up of the Weather Research and Forecasting Model (WRF) model that minimises system- atic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable set-up: the horizontal resolution, the planetary boundary layer (PBL) parameterisation scheme and the way the WRF is nested to the driving data set. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model set-ups, but sharing the same driving data set. The results show that the lack of representation of the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis nudging, spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model performance is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave emissions of snowpacks in the frequency range 5–100 GHz. It is based on six-flux theory to describe radiative transfer in snow including absorption, multiple volume scattering, radiation trapping due to internal reflection and a combination of coherent and incoherent superposition of reflections between horizontal layer interfaces. Here we introduce MEMLS3&a, an extension of MEMLS, which includes a backscatter model for active microwave remote sensing of snow. The reflectivity is decomposed into diffuse and specular components. Slight undulations of the snow surface are taken into account. The treatment of like- and cross-polarization is accomplished by an empirical splitting parameter q. MEMLS3&a (as well as MEMLS) is set up in a way that snow input parameters can be derived by objective measurement methods which avoid fitting procedures of the scattering efficiency of snow, required by several other models. For the validation of the model we have used a combination of active and passive measurements from the NoSREx (Nordic Snow Radar Experiment) campaign in Sodankylä, Finland. We find a reasonable agreement between the measurements and simulations, subject to uncertainties in hitherto unmeasured input parameters of the backscatter model. The model is written in Matlab and the code is publicly available for download through the following website: http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html.

Climate forcing reconstructions for use in the PMIP simulations of the last millennium

Simulations of climate over the Last Millennium (850–1850 CE) have been incorporated into the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3). The drivers of climate over this period are chiefly orbital, solar, volcanic, changes in land use/land cover and some variation in greenhouse gas levels. While some of these effects can be easily defined, the reconstructions of solar, volcanic and land use-related forcing are more uncertain. We describe here the approach taken in defining the scenarios used in PMIP3, document the forcing reconstructions and discuss likely implications.

Climate forcing reconstructions for use in PMIP simulations of the Last Millennium (v1

We update the forcings for the PMIP3 experiments for the Last Millennium to include new assessments of historical land use changes and discuss new suggestions for calibrating solar activity proxies to total solar irradiance.

DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands

Simulating the spatio-temporal dynamics of inundation is key to understanding the role of wetlands under past and future climate change. Earlier modelling studies have mostly relied on fixed prescribed peatland maps and inundation time series of limited temporal coverage. Here, we describe and assess the the Dynamical Peatland Model Based on TOPMODEL (DYPTOP), which predicts the extent of inundation based on a computationally efficient TOPMODEL implementation. This approach rests on an empirical, grid-cell-specific relationship between the mean soil water balance and the flooded area. DYPTOP combines the simulated inundation extent and its temporal persistency with criteria for the ecosystem water balance and the modelled peatland-specific soil carbon balance to predict the global distribution of peatlands. We apply DYPTOP in combination with the LPX-Bern DGVM and benchmark the global-scale distribution, extent, and seasonality of inundation against satellite data. DYPTOP successfully predicts the spatial distribution and extent of wetlands and major boreal and tropical peatland complexes and reveals the governing limitations to peatland occurrence across the globe. Peatlands covering large boreal lowlands are reproduced only when accounting for a positive feedback induced by the enhanced mean soil water holding capacity in peatland-dominated regions. DYPTOP is designed to minimize input data requirements, optimizes computational efficiency and allows for a modular adoption in Earth system models.

Maternal mental health in the first 3-week postpartum: the impact of caregiver support and the subjective experience of childbirth - a longitudinal path model

Acute stress reactions (ASR) and postpartum depressive symptoms (PDS) are frequent after childbirth. The present study addresses the change and overlap of ASR and PDS from the 1- to 3-week postpartum and examines the interplay of caregiver support and subjective birth experience with regard to the development of ASR/PDS within a longitudinal path model.