Clinical decision making and outcome in the routine care of people with severe mental illness across Europe (CEDAR)

Aims. There is a lack of knowledge on clinical decision making and its relation to outcome in the routine treatment of people with severe mental illness. This study examined preferred and experienced clinical decision making from the perspectives of patients and staff, and how these affect treatment outcome. Methods. CEDAR (ISRCTN75841675) is a naturalistic prospective observational study with bimonthly assessments during a 12-month observation period. 588 adults with severe mental illness were consecutively recruited from caseloads of community mental health services at the six study sites (Germany, UK, Italy, Hungary, Denmark, and Switzerland). Clinical decision making was measured using two instruments (Clinical Decision Making Style Scale. CDMS;Clinical Decision Making Involvement and Satisfaction Scale, CDIS) from patient and staff perspectives. Outcomes assessed were unmet needs (Camberwell Assessment of Need Short Appraisal Schedule, CANSAS). Mixed-effects multinomial regression was used to examine differences in involvement in and satisfaction with actual decision making. The effect of clinical decision making on outcome was examined using hierarchical linear modelling controlling for covariates. Results. Shared decision making was preferred by patients (2=135.08; p<0.001) and staff (2=368.17; p<0.001). Decision making style of staff significantly affected unmet needs over time, with unmet needs decreasing more in patients whose clinicians preferred active to passive (-0.406 unmet needs per two months, p=0.007) or shared (-0.303 unmet needs per two months, p=0.015) decision making. Conclusions. A shift from shared to active involvement of patients is indicated, including the development and rigorous test of targeted interventions

Brief communication: The hidden labyrinth: deep groundwater in Wright Valley, Antarctica

Since the 1960s, a deep groundwater system in Wright Valley, Antarctica, has been the hypothesized source of brines to hypersaline Don Juan Pond and Lake Vanda, both of which are rich in calcium and chloride. Modeling studies do not support other possible mechanisms, such as evaporative processes, that could have led to the current suite of ions present in both waterbodies. In 2011 and 2018, an airborne electromagnetic survey was flown over Wright Valley to map subsurface resistivity (down to 600 m) in exploration of liquid water. The surveys revealed widespread unfrozen brine in the subsurface near Lake Vanda, Don Juan Pond, and the North Fork of Wright Valley. While our geophysical survey can neither confirm nor deny deep groundwater connectivity between Lake Vanda and Don Juan Pond, it does point to the potential for deep valley-wide brine, likely within the Ferrar Dolerite formation.</p

Participation in medical decision-making across Europe: an international longitudinal multicenter study

Background: The purpose of this paper was to examine national differences in the desire to participate in decision-making of people with severe mental illness in six European countries. Methods: The data was taken from a European longitudinal observational study (CEDAR; ISRCTN75841675). A sample of 514 patients with severe mental illness from the study centers in Ulm, Germany, London, England, Naples, Italy, Debrecen, Hungary, Aalborg, Denmark and Zurich, Switzerland were assessed as to desire to participate in medical decision-making. Associations between desire for participation in decision-making and center location were analyzed with generalized estimating equations. Results: We found large cross-national differences in patients’ desire to participate in decision-making, with the center explaining 40% of total variance in the desire for participation (p<0.001). Averaged over time and independent of patient characteristics, London (mean=2.27), Ulm (mean=2.13) and Zurich (mean=2.14) showed significantly higher scores in desire for participation, followed by Aalborg (mean=1.97), where scores were in turn significantly higher than in Debrecen (mean=1.56). The lowest scores were reported in Naples (mean=1.14). Over time, desire for participation in decision-making increased significantly in Zurich (b=0.23) and decreased in Naples (b=-0.14). In all other centers, values remained stable. Conclusions: This study demonstrates that patients’ desire for participation in decisionmaking varies by location. We suggest that more research attention be focused on identifying specific cultural and social factors in each country to further explain observed differences across Europe

Stochastic inversion of time-lapse electrical resistivity tomography data by means of an adaptive ensemble-based approach

Inversion of time-lapse electrical resistivity tomography is an extension of the conventional electrical resistivity tomography inversion that aims to reconstruct resistivity variations in time. This method is widely used in monitoring subsurface processes such as groundwater evolution. The inverse problem is usually solved through deterministic algorithms, which usually guarantee a fast solution convergence. However, the electrical resistivity tomography inverse problem is ill-posed and non-linear, and it could exist more than one resistivity model that explains the observed data. This paper explores a Bayesian approach based on data assimilation, the ensemble smoother multiple data assimilation. In particular, we apply an adaptive approach in which the inflation coefficient is chosen based on the error function, that is the ensemble smoother multiple data assimilation restricted step. Our inversion approach aims to invert the data acquired at two different times simultaneously, estimating the resistivity model and its variation. In addition, the Bayesian approach allows for the assessment of the posterior probability density function needed for quantifying the uncertainties associated with the results. To test the method, we first apply the algorithm to synthetic data generated from realistic resistivity models; then, we invert field data from the Pillemark landfill monitoring station (Samsø, Denmark). Inversion results show that the ensemble smoother multiple data assimilation restricted step can correctly detect the resistivity variation both in the synthetic and in the field case, with an affordable computational burden. In addition, assessing the uncertainties allows us to interpret the reconstructed resistivity model correctly. This paper demonstrates the potential of the data assimilation approach in Bayesian time-lapse electrical resistivity tomography inversion