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This study investigated death anxiety in patients with primary brain tumor (PBT). We examined the psychometric properties of two validated death anxiety measures and determined the prevalence and possible determinants of death anxiety in this often-overlooked population.
Two cross-sectional studies in neuro-oncology were conducted. In Study 1, 81 patients with PBT completed psychological questionnaires, including the Templer Death Anxiety Scale (DAS). In Study 2, 109 patients with PBT completed similar questionnaires, including the Death and Dying Distress Scale (DADDS). Medical and disease-specific variables were collected across participants in both studies. Psychometric properties, including construct validity, internal consistency, and concurrent validity, were investigated. Levels of distress were analyzed using frequencies, and determinants of death anxiety were identified using logistic regression.
The DADDS was more psychometrically sound than the DAS in patients with PBT. Overall, 66% of PBT patients endorsed at least one symptom of distress about death and dying, with 48% experiencing moderate-severe death anxiety. Generalized anxiety symptoms and the fear of recurrence significantly predicted death anxiety.
Significance of results
The DADDS is a more appropriate instrument than the DAS to assess death anxiety in neuro-oncology. The proportion of patients with PBT who experience death anxiety appears to be higher than in other advanced cancer populations. Death anxiety is a highly distressing symptom, especially when coupled with generalized anxiety and fears of disease progression, which appears to be the case in patients with PBT. Our findings call for routine monitoring and the treatment of death anxiety in neuro-oncology.
We investigated the associations between dietary patterns and chronic disease mortality in Switzerland using an ecological design and explored their spatial dependence, i.e. the tendency of near locations to present more similar and distant locations to present more different values than randomly expected. Data of the National Nutrition Survey menuCH (n 2057) were used to compute hypothesis- (Alternate Healthy Eating Index (AHEI)) and data-driven dietary patterns. District-level standardised mortality ratios (SMR) were calculated using the Swiss Federal Statistical Office mortality data and linked to dietary data geographically. Quasipoisson regression models were fitted to investigate the associations between dietary patterns and chronic disease mortality; Moran’s I statistics were used to explore spatial dependence. Compared with the first, the fifth AHEI quintile (highest diet quality) was associated with district-level SMR of 0·95 (95 % CI 0·93, 0·97) for CVD, 0·91 (95 % CI 0·88, 0·95) for ischaemic heart disease (IHD), 0·97 (95 % CI 0·95, 0·99) for stroke, 0·99 (95 % CI 0·98, 1·00) for all-cancer, 0·98 (95 % CI 0·96, 0·99) for colorectal cancer and 0·93 (95 % CI 0·89, 0·96) for diabetes. The Swiss traditional and Western-like patterns were associated with significantly higher district-level SMR for CVD, IHD, stroke and diabetes (ranging from 1·02 to 1·08) compared with the Prudent pattern. Significant global and local spatial dependence was identified, with similar results across hypothesis- and data-driven dietary patterns. Our study suggests that dietary patterns partly contribute to the explanation of geographic disparities in chronic disease mortality in Switzerland. Further analyses including spatial components in regression models would allow identifying regions where nutritional interventions are particularly needed.
Most Icelandic glaciers show high-accumulation rates during winter and strong surface melting during summer. Although it is difficult to establish and maintain mass-balance programs on these glaciers, mass-balance series do exist for several of the ice caps (Björnsson and others, 2013). We make use of the frequent volcanic eruptions in Iceland, which cause widespread internal tephra layers in the ice caps, to reconstruct the surface mass balance (SMB) in the ablation zone. This method requires information about surface geometry and ice velocity, derived from remote-sensing information. In addition, the emergence angle of the tephra layer needs to be known. As a proof-of-concept, we utilize a prominent tephra layer of the Mýrdalsjökull Ice Cap to infer local SMB estimates in the ablation area back to 1988. Using tephra-layer outcrop locations across the glacier at different points in time it is possible to determine local mass changes (loss and redistribution) for a large part of the ablation zone, without the use of historic elevation models, which often are not available.
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