Levels of depression and satisfaction with life as indicators of health services consumption

Objective: To evaluate the correlation between depression, satisfaction with life, and primary healthcare services consumption.

Methods: A random sample of primary healthcare clinic patients agreed to complete self-report questionnaires on demographics and physical activity, the Geriatric Depression Scale (GDS), Satisfaction with Life Scale and the Visual Analog Scale for Happiness. Treating physicians completed the Cumulative Illness Rating Scale (CIRS) for each patient. The relationships among psychometric, medical, the number of visits to health maintenance organization (HMO)-physicians during the previous year was assessed.

Results: Positive correlation was found between visits to HMO-physicians and depression severity, as assessed by GDS (p?=?.049), and between visits/year and illness severity, as measured by CIRS (p?p?=?.005). Physical activity correlated inversely with depression severity (p?=?.014). Gender and income had no impact on frequency of visits to HMO-physicians, depression, or satisfaction with life.

Conclusions: The results indicate that there is a correlation between depression and healthcare service consumption, as represented by number of HMO-physician visits and medication use. Thus, early detection of depression, using tools such as GDS, and early initiation of antidepressive treatment may help to lower the burden on the health system.     

Brain laterality,depression and anxiety disorders: New findings for emotional and verbal dichotic listening in individuals at risk for depression

ABSTRACT

Studies using dichotic listening tests and electroencephalographic (EEG) measures of hemispheric asymmetry have reported evidence of abnormal brain laterality in patients having depressive disorders. We present new findings from a multigenerational study of risk for depression, in which perceptual asymmetry was measured in dichotic listening tests of emotional and verbal processing. Biological offspring and grandchildren of probands with a major depressive disorder (MDD) who were at high risk and those of nondepressed controls who were at low risk were tested on dichotic emotional recognition and consonant–vowel syllable tests. In the emotion test, individuals with a lifetime diagnosis of MDD had a smaller right hemisphere advantage than those without a MDD, but there was no difference between high- and low-risk groups or between those with or without an anxiety disorder. In the syllable test, a smaller left hemisphere advantage was found in individuals with an anxiety disorder compared to those without an anxiety disorder, but there was no difference between high- and low-risk groups or between those with or without a MDD. This double dissociation indicates that lifetime diagnosis of MDD and anxiety disorders have a differential impact on lateralized hemispheric processing of emotional and verbal information.     

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.     

Quality Measures at the Interface of Behavioral Health and Primary Care

The development of quality measures has gained increasing attention as health care reimbursements transition from fee-for-service to value-based payment models. As behavioral health care moves towards integration of services with primary care, specific measures and payment incentives will be needed to successfully expand access. This study uses a keyword search to identify 730 quality indicators that are relevant to behavioral health and general medical health. Measures identified have been coded and grouped into domains based on a taxonomy developed by the authors. The analysis reveals that quality measures focusing on general medical conditions exceed those focused on behavioral health diagnoses for evidence-based treatments, patient safety, and outcomes. Furthermore, measures predominantly concentrate on care during or following hospitalizations, which represents a minority of behavioral health care and does not characterize the outpatient settings that are the focus of many models of integrated care. The authors offer recommendations for future steps to identify the quality measures that can best evaluate the evolving behavioral health care system.