Impact of practice, provider and patient characteristics on delivering screening and brief advice for heavy drinking in primary health care secondary analyses of data from the ODHIN five country cluster randomized factorial trial

BACKGROUND: The implementation of primary healthcare-based screening and advice that is effective in reducing heavy drinking can be enhanced with training. OBJECTIVES: Undertaking secondary analysis of the five-country ODHIN study, we test: the extent to which practice, provider and patient characteristics affect the likelihood of patients being screened and advised; the extent to which such characteristics moderate the impact of training in increasing screening and advice; and the extent to which training mitigates any differences due to such characteristics found at baseline. METHODS: A cluster randomized factorial trial involving 120 practices, 746 providers and 46 546 screened patients from Catalonia, England, the Netherlands, Poland, and Sweden. Practices were randomized to receive training or not to receive training. The primary outcome measures were the proportion of adult patients screened, and the proportion of screen-positive patients advised. RESULTS: Nurses tended to screen more patients than doctors (OR = 3.1; 95%CI: 1.9, 4.9). Screen-positive patients were more likely to be advised by doctors than by nurses (OR = 2.3; 95%CI: 1.4, 4.1), and more liable to be advised the higher their risk status (OR = 1.9; 95%CI: 1.3, 2.7). Training increased screening and advice giving, with its impact largely unrelated to practice, provider or patient characteristics. Training diminished the differences between doctors and nurses and between patients with low or high-risk status. CONCLUSIONS: Training primary healthcare providers diminishes the negative impacts that some practice, provider and patient characteristics have on the likelihood of patients being screened and advised. Trial registration ClinicalTrials.gov. Trial identifier: NCT01501552

Aggregation effects of surface heterogeneity in land surface processes

In order to investigate the aggregation effects of surface heterogeneity in land surface processes we have adapted a theory of aggregation. Two strategies have been adopted: 1) Aggregation of radiative fluxes. The aggregated radiative fluxes are used to derive input parameters that are then used to calculate the aerodynamic fluxes at different aggregation levels. This is equivalent to observing the same area at different resolutions using a certain remote sensor, and then calculating the aerodynamic fluxes correspondingly. 2) Aggregation of aerodynamic fluxes calculated at the original observation scale to different aggregation levels. A case study has been conducted to identify the effects of aggregation on areal estimates of sensible and latent heat fluxes. The length scales of surface variables in heterogeneous landscapes are estimated by means of wavelet analysis

Spatial aggregation of land surface characteristics: impact of resolution of remote sensing data on land surface modelling

Land surface models describe the exchange of heat, moisture and momentum between the land surface and the atmosphere. These models can be solved regionally using remote sensing measurements as input. Input variables which can be derived from remote sensing measurements are surface albedo, surface temperature and vegetation cover. A land surface model using those land surface characteristics is presented i.e. the Surface Energy Balance Index (SEBI) model. This model uses the observed temperature difference between the land surface and atmosphere as an indicator for evapotranspiration.Spatially distributed land surface model results can be used as a boundary condition for numerical weather predicton models. The results should therefore be aggregated from the remote sensing pixel scale to the atmospheric model scale. However aggregated values will differ when derived from remote sensing data with different resolutions. This difference, the error due to aggregation is caused by two different aspects: land surface heterogeneity and non-linearity of the land surface model. Two approaches are presented to quantify the error due to aggregation: the linearization approach, where the land surface model is approximated by a Taylor expansion and a geometrical approach where the range of valid results for the land surface model is derived using a convex hull.To measure the heterogeneity of land surfaces, the concept of length scale is introduced. The wavelet transform is being used to derive the length scale of the land surface characteristics. The wavelet variance derived from the Fast Wavelet Transform using the Haar wavelet is a good indicator for the variability of land surface characteristics at different spatial scales. For three different data sets the length scale of land surface characteristics have been derived: Barrax, Spain, the Jornada Experimental Range, USA and the Central Part of the Netherlands.The two approaches for quantifying the error due to aggregation have been verified using the three data sets. The results obtained by the linearization show that aggregation error can indeed be estimated. For the three test sites the large scale error did not exceed 10 %. However the results based on the convex hull analysis show that the large scale error due to aggregation can be much larger than observed for the three test cases. Therefore low resolution remote sensing data cannot be used a priori as input for land surface models.</p

An intercomparison of techniques to determine the area-averaged latent heat flux from individual in situ observations: a remote sensing approach using the European Field Experiment in a Desertification-Threatened Area data

Different procedures to obtain the area-averaged latent heat flux as a weighted average of ground-based observations of latent heat flux are described. Weighting coefficients are obtained from remote sensing data. A newly developed remote sensing algorithm, SEBAL, which solves the energy budget on a pixel-by-pixel basis, was successfully applied with EFEDA data. Two other methods for retrieving weighting coefficients were tested against SEBAL. The second method combines satellite images of surface temperature, surface albedo and Normalized Difference Vegetation Index (NDVI) into an index on a pixel-by-pixel basis. The third method uses a supervised classification

Aggregation effects of surface heterogeneity in land surface processes

International audienceIn order to investigate the aggregation effects of surface heterogeneity in land surface processes we have adapted a theory of aggregation. Two strategies have been adopted: 1) Aggregation of radiative fluxes. The aggregated radiative fluxes are used to derive input parameters that are then used to calculate the aerodynamic fluxes at different aggregation levels. This is equivalent to observing the same area at different resolutions using a certain remote sensor, and then calculating the aerodynamic fluxes correspondingly. 2) Aggregation of aerodynamic fluxes calculated at the original observation scale to different aggregation levels. A case study has been conducted to identify the effects of aggregation on areal estimates of sensible and latent heat fluxes. The length scales of surface variables in heterogeneous landscapes are estimated by means of wavelet analysis