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Background: The patterns of benzodiazepine prescriptions in older adults are of general and scientific interest as they are not yet well understood. The aim of this study was to compare the prescription patterns of benzodiazepines in elderly people in Germany to determine the share or proportion treated by general practitioners (GP) and neuropsychiatrists (NP). Methods: This study included 31,268 and 6,603 patients between the ages of 65 and 100 with at least one benzodiazepine prescription in 2014 from GP and NP, respectively. Demographic data included age, gender, and type of health insurance coverage. The share of elderly people with benzodiazepine prescriptions was estimated in different age and disease groups for both GP and NP patients. The share of the six most commonly prescribed drugs was also calculated for each type of practice. Results: The share of people taking benzodiazepines prescribed by GP increased from 3.2% in patients aged between 65 and 69 years to 8.6% in patients aged between 90 and 100 years, whereas this share increased from 5.4% to 7.1% in those seen by NP. Benzodiazepines were frequently used by patients suffering from sleep disorders (GP: 33.9%; NP: 5.5%), depression (GP: 17.9%; NP: 29.8%), and anxiety disorders (GP: 14.5%; NP: 22.8%). Lorazepam (30.3%), oxazepam (24.7%), and bromazepam (24.3%) were the three most commonly prescribed drugs for GP patients. In contrast, lorazepam (60.4%), diazepam (14.8%), and oxazepam (11.2%) were those more frequently prescribed to NP patients. Conclusion: Prescription patterns of benzodiazepine in the elderly varied widely between GP and NP.
Substance-dependent individuals often lack the ability to adjust decisions flexibly in response to the changes in reward contingencies. Prediction errors (PEs) are thought to mediate flexible decision-making by updating the reward values associated with available actions. In this study, we explored whether the neurobiological correlates of PEs are altered in alcohol dependence. Behavioral, and functional magnetic resonance imaging (fMRI) data were simultaneously acquired from 34 abstinent alcohol-dependent patients (ADP) and 26 healthy controls (HC) during a probabilistic reward-guided decision-making task with dynamically changing reinforcement contingencies. A hierarchical Bayesian inference method was used to fit and compare learning models with different assumptions about the amount of task-related information subjects may have inferred during the experiment. Here, we observed that the best-fitting model was a modified Rescorla-Wagner type model, the “double-update” model, which assumes that subjects infer the knowledge that reward contingencies are anti-correlated, and integrate both actual and hypothetical outcomes into their decisions. Moreover, comparison of the best-fitting model's parameters showed that ADP were less sensitive to punishments compared to HC. Hence, decisions of ADP after punishments were loosely coupled with the expected reward values assigned to them. A correlation analysis between the model-generated PEs and the fMRI data revealed a reduced association between these PEs and the BOLD activity in the dorsolateral prefrontal cortex (DLPFC) of ADP. A hemispheric asymmetry was observed in the DLPFC when positive and negative PE signals were analyzed separately. The right DLPFC activity in ADP showed a reduced correlation with positive PEs. On the other hand, ADP, particularly the patients with high dependence severity, recruited the left DLPFC to a lesser extent than HC for processing negative PE signals. These results suggest that the DLPFC, which has been linked to adaptive control of action selection, may play an important role in cognitive inflexibility observed in alcohol dependence when reinforcement contingencies change. Particularly, the left DLPFC may contribute to this impaired behavioral adaptation, possibly by impeding the extinction of the actions that no longer lead to a reward.
Cities and Mental Health
(2017)
Background: More than half of the global population currently lives in cities, with an increasing trend for further urbanization. Living in cities is associated with increased population density, traffic noise and pollution, but also with better access to health care and other commodities. Methods: This review is based on a selective literature search, providing an overview of the risk factors for mental illness in urban centers. Results: Studies have shown that the risk for serious mental illness is generally higher in cities compared to rural areas. Epidemiological studies have associated growing up and living in cities with a considerably higher risk for schizophrenia. However, correlation is not causation and living in poverty can both contribute to and result from impairments associated with poor mental health. Social isolation and discrimination as well as poverty in the neighborhood contribute to the mental health burden while little is known about specific inter actions between such factors and the built environment. Conclusion: Further insights on the interaction between spatial heterogeneity of neighborhood resources and socio-ecological factors is warranted and requires interdisciplinary research.